Atlas of the player pool

How deep is a national player pool — and why

9.37 players in Europe's 9 strongest leagues per million inhabitants, 2025/26 rosters

Per head, Norway ranks 3rd of 9 peer countries for players in Europe’s strongest leagues. The reasons below are measured, not guessed: 1.6 regular under-21 starters per club at home against 2.5 in Denmark; and a first move abroad at 22.5.

Three minutes: this number, the five rungs below it, and the one-page brief. Everything else is the evidence.

In context

How one country’s professional pool compares with its peers, where the road abroad leaks, and what the national team gets from it — from public data, every number recomputed on each run. The largest cohort gap is in defenders aged 23-25: 1 Norwegian player in the top-9 leagues against a peer median of 2. A recent Norwegian export first reached a top-9 roster at a median age of 22; one from Czechia at 22. Built from FBref, Wikipedia and Wikidata. Norway is the worked example; the pipeline takes a nationality code and a peer set. Football people recognise these numbers player by player; there is no place where they are aggregated.

* 52 NOR players with ≥ 450 minutes in the UEFA top-9 leagues, 2025/26, per 5.5 M inhabitants; peers counted the same way. Methodology.

What to take from it

1

Norway's Big-5 presence: fall in 04/05 (×0.66), then rise in 21/22 (×1.32).

25 players with 450+ Big-5 minutes now, 26 at the 98/99 peak. 1 small peer (Czechia) ended in a fall.

2

One mechanism carries the gap to every peer it trails: youth minutes at home.

Against Denmark youth minutes at home carries 98 % of a 3.2-per-million gap.

3

The trend runs the right way: Norway's own under-21s went from 8 % to 11 % of home-league minutes in 6 seasons.

Over the same seasons Denmark 16 % → 15 % and +2.3 per million in the Big-5; Czechia 10 % → 6 % and -0.1 per million in the Big-5; Norway +2.7 per million. Across the 16 covered leagues the change in youth minutes and the change in Big-5 presence lean the same way — a weak signal with the right sign, not a law.

4

Where the mistake shows: Norway is out of line on how its exports fare once abroad.

Out of line: how its exports fare: a median 37 % of their club's minutes, 9 of 9.

Not the problem: minutes for its own under-21s at home: 11.5 % of league minutes and 1.6 regular under-21 starters per club, 4 of 8 among the peers (Denmark 15.3 %; Denmark 2.5 starters per club); the first move abroad at a median 22; the exporters move at 22–23; the home league itself: multiplier ×0.37, 3 of 9 among the peers; how many leave at all: 60 first moves in the covered seasons (Denmark 112); how many clubs the exports leave from: 22 % of the 54 players who went from the home league to a top-9 league since 20/21 left from Lillestrøm or Tromsø (Denmark: 31 % from its top two, 150 exports in all).

The four numbers to watch every summer: under-21 share, regular under-21 starters per club, age of the first move, first Big-5 seasons. None of this is a proven cause; it is where the nation is out of line with the peers that grew, on the mechanisms the gap decomposition weighs most.

5

Can a reform cause it? The data can follow 2 documented cases, and only as a sequence.

Germany after its DFB/DFL academy licensing (01/02): its own under-21s' share of Bundesliga minutes went from 5 % to 11 % by 09/10 but is 6 % now — the turn did not hold. England after its Elite Player Performance Plan (12/13): its own under-21s' share of Premier League minutes went from 3 % to 6 % by 19/20 but is 3 % now — the turn did not hold. A sequence in one country against none in another is the strongest thing this data can say; it is not a counterfactual. Read as a heuristic: minutes for the young at home are the lever a federation holds, the effect is counted in seasons, and a weaker league yields less from it.

Each statement is written from the numbers on this page and on the six-nation comparison; the evidence follows, one question at a time.

Everything below is the evidence for these numbers, one question at a time.

Terms used on this page are explained in the glossary.

Why the train left

Five numbers, in the order they build on each other, show where the pool of players available to the national team gets thin. Each one is shown for Norwegian football and for the two countries used as a comparison throughout this report; each links to the part of the report that shows the full evidence.

Five-stage funnel: Norwegian football next to Denmark and Czechia on the share of playing time young players get at home, the league's average age, the age of the first move abroad, the share of sideways moves, and players per million people in Europe's strongest leagues.
How to read it: one rung per stage of the argument, each on its own scale, so the dots show the distance between countries, not the size of the number. The filled acid dot is Norway; the open dots are the two comparison countries. Under each rung is which direction means a more open pathway. Hover a dot for the exact value.

  1. Young players do not get much of the playing time in the country's own top league.

    Share of league minutes that went to players aged 21 or under

    NOR 11.5 % DEN 15.3 % CZE 6.4 %

    Clubs that gave those young players more than a tenth of their own playing time

    NOR 12 of 16 clubs DEN 11 of 12 clubs CZE 9 of 16 clubs

    Under-21 nationals who were regular starters, per club in the league

    NOR 1.56 per club (25 players) DEN 2.50 per club (30 players) CZE 1.06 per club (17 players)

    The regular-starter line is drawn at ten starts; at five it is 2.4 per club, at fifteen 0.9 (ten: 1.6).

    The shortage is one of selection, not of late cameos: 11.2 % of the league’s starts went to them against 11.5 % of its minutes, so they are picked to start about as often as they are played at all. They are, though, taken off early — 75 minutes in a start against the league’s own 80.

  2. The league those minutes are missing from is, on average, an old one.

    Average age of a minute played in the league

    NOR 25.6 DEN 25.4 CZE 26.0

    Share of league minutes that went to players aged 30 or over

    NOR 15.9 % DEN 19.5 % CZE 21.1 %

  3. So the first move to a foreign league, when it happens, comes late.

    Age the first time a player has real playing time in a foreign league, over the players abroad today

    NOR 22.5 years (60 players) DEN 23 years (112 players) CZE 24 years (32 players)

  4. And the move, when it happens, is often not a step up.

    Share of moves abroad to a league no stronger than the player's own

    NOR 22 % DEN 10 % CZE —

  5. The layer of players in Europe's strongest leagues stays thin, and it fell sharply once.

    Players in Europe's strongest leagues, for every million people

    NOR 9.37 DEN 12.58 CZE 2.39

    Around the 2004/05 season, the model finds a possible shift in the count of Norwegian players in Europe's strongest leagues, with 27 percent probability that it is a genuine change rather than an ordinary season-to-season dip. Break & forecast.

If you take one thing from this: the single measured link that carries the most of the gap with both Denmark and Czechia is how strong the domestic league is. That is one decomposition over 8 countries — a description of the gap, not a weight to plan by.

What this does not show
  • Money: transfer fees, wages and academy budgets play no part in any number above.
  • How the academy and coaching set-up actually work day to day, which no public data source used here can see.
  • Agents, and how a move abroad actually gets arranged, which happens off any table this pipeline can read.
  • The direction of the arrow: a low share of playing time for young players at home could help cause a thin generation, or just as easily be a symptom of one — the five numbers above are associations measured the same way for every country.

How deep is the pool?

The next three questions count who is in the pool and where the count runs thin.

Is the Norwegian pool thin?

Norway ranks 3rd of 9 countries at 9.37 per million; Denmark leads at 12.58.

DEN Denmark
12.58
CRO Croatia
12.18
NOR Norway
9.37
SUI Switzerland
6.03
AUT Austria
4.80
SVK Slovakia
3.14
CZE Czechia
2.39
HUN Hungary
1.57
POL Poland
1.23
How we know

As an analytics question In numbers: distinct players with ≥ 450 minutes on 2025/26 rosters of the 9 strongest leagues, per million inhabitants, against 9 peers.

What we did We counted every player with the home nation's nationality who had real playing time in one of Europe's strongest leagues that season, then divided the count by the country's population.

Sources Distinct players with ≥ 450 minutes on 2025/26 rosters of the 9 strongest leagues ÷ population (Eurostat 2024); every country counted the same way.

A federation tracking this would watch the per-million rank move over seasons, not any one year's number.

Where exactly is it thin?

The largest cohort gap: Defenders aged 23-25, 1 Norwegian players vs a peer median of 2.

GroupCohortNORPeer median
Defenders 23-25 1 2
Defenders 30+ 2 2
Forwards 23-25 1 1
Forwards 30+ 1 0.5
Midfielders U22 2 1.5
How we know

As an analytics question In numbers: Norwegian player count against the peer-country median, by age cohort and position group, ≥ 450 minutes, 2025/26 season.

What we did We split players into position groups and age bands, counted how many the home nation had in each one, and compared that count with the middle value among the other countries.

Sources Age at season start; cohorts U22 / 23–25 / 26–29 / 30+; ≥ 450 minutes.

A federation tracking this would watch which cohort's gap narrows or widens season to season, not just today's snapshot.

Where does the path leak?

From here the questions follow a player through youth minutes at home, the move abroad, and how that move turns out.

Do young players get minutes at home?

Under-21s get 11.5 % of the home league’s minutes. In Denmark, the best of the peers, 15.3 %.

How sure is the link to the top? Across 8 countries, ten points more under-21 share go with +10.11 more top-9 players per million, but the interval (−1.69 to 23.26) includes zero; within countries, from one season to the next, the estimate is −0.16 (−2.53 to 2.17) — nothing. The share is a fact; its weight in the gap is not settled.

DEN Denmark
15.3 %
HUN Hungary
14.1 %
CRO Croatia
14.0 %
NOR Norway
11.5 %
AUT Austria
9.3 %
POL Poland
9.3 %
SUI Switzerland
7.7 %
CZE Czechia
6.4 %
SVK Slovakia
—
Across countries
Scatter of U21 share of domestic-league minutes against top-9 players per million, 8 countries, two seasons each connected by a line, Norway highlighted, with the fitted line and its 90 % band.
How we know

Across the 8 countries (country averages over both seasons), 10 points more U21 share go with +10.11 more top-9 players per million (−1.69–23.26).

As an analytics question In numbers: share of domestic-league minutes played by players aged ≤ 21 at season start, 2025/26, by country's top flight.

What we did We counted every minute played in the league and looked at how many of them went to players aged 21 or younger.

Sources Share of all league minutes played by players aged ≤ 21 at season start, 2025/26, per domestic top flight.

A federation tracking this would watch the U21 share season by season, not the export count.

How the youth share compares across the editions, and what it weighs →

Where do Norwegian players go when they leave?

59 of 236 play abroad; 66 % in the 9 strongest leagues, 22 % moved sideways (to a league no stronger than the Norwegian one).

39 top-9 median multiplier 0.666
66 %
17 peer country league median multiplier 0.371
29 %
2 stepping stone median multiplier 0.473
3 %
1 other median multiplier 0.434
2 %
How we know

As an analytics question In numbers: destination-league tier of every Norwegian-eligible player's 2025/26 row, split into top-9, sideways and other abroad moves.

What we did We looked at where every eligible player was actually playing that season and grouped each one by how strong that league is compared with the player's own home league.

Sources Destination league of every Norwegian-eligible player's 2025/26 row; sideways = destination multiplier ≤ Norwegian league multiplier (league strength: two estimates, § Methodology).

A federation tracking this would watch the sideways-move share over time, not the raw count of players abroad.

Does leaving later cost anything?

No. Players who reach the top-9 at 21 and at 24 produce the same league-adjusted G+A per 90 over their first two seasons (difference +0.001, an interval that includes zero, n = 115). Norwegian exports arrive at a median age of 21.

Line chart: the fitted age-at-export curve with its 90 % band, Norwegian exports as points against every other peer export, and a rug of every export's age along the axis.
How to read it: the horizontal axis is a player’s age in his first top-9 season; the vertical axis is his league-adjusted goals plus assists per 90 over the first two seasons there. The line is the model’s expected value at each age, the band its 90 % interval; a flat line means arriving later costs nothing measurable. Acid dots are Norwegian exports — hover for the name.
How we know

At 21: 0.14 (0.12–0.16); at 24: 0.14 (0.12–0.16); the difference +0.001 (−0.010–0.013).

As an analytics question In numbers: mean league-adjusted goals + assists per 90 over the first two top-9 seasons, as a function of age at the first one, given origin-league strength and position, 115 peer-nationality exports.

What we did We looked at the age a player first arrived in one of the strongest leagues and checked whether players who arrived earlier ended up producing more, once we accounted for the strength of the league they came from.

Sources Age curve (quadratic) on league-adjusted production, given origin-league strength (§ Methodology), position and a country effect, 115 peer-nationality exports (Gelman et al., 2013; Hoffman and Gelman, 2014); better players tend to leave earlier, so the curve mixes selection with development and this report does not separate them.

A federation tracking this would watch how the age-at-export curve shifts across cohorts, not any single player's outcome.

How do they fare there?

Norwegian exports keep 37 % of their club's minutes (9th of 9).

Dot plot: each country's median share of club minutes for players abroad, with a thin line spanning the other countries' values; Norway highlighted.
How to read it: one row per country, sorted; the dot is the median share of his club’s minutes that a country’s exported player keeps, the thin line the range across its exports. Norwegian exports are the acid row. Further right means exports who play, not sit.
Country-by-country figures
SVK Slovakia n = 17
76 %
HUN Hungary n = 15
57 %
CZE Czechia n = 26
50 %
SUI Switzerland n = 54
48 %
CRO Croatia n = 47
46 %
DEN Denmark n = 75
46 %
AUT Austria n = 44
43 %
POL Poland n = 45
41 %
NOR Norway n = 52
37 %
How we know

A Eliteserien season converts to 0.57 of a Premier League one by the transfer-graph model (0.51–0.64), against 0.37 by UEFA coefficient.

As an analytics question In numbers: median share of a club's 2025/26 minutes kept by players abroad, by country of origin.

What we did We worked out what share of each club's playing time the player actually got, and where that club stood in its own league's scoring table that season.

Sources Median share of club minutes for players abroad, per country of origin (league strength: two estimates, § Methodology).

A federation tracking this would watch whether an export's minutes share holds after the first season, not just the median.

What reaches the national team?

The last stretch looks at what actually gets picked, and when the door to the biggest leagues opened and shut.

What is the national-team squad built from?

65 % of the 2026 FIFA World Cup squad plays in the 9 strongest leagues; Switzerland 88 %.

  • Top-9 %
  • Stepping %
  • Domestic %
  • Other %
  • Erling Haaland
  • Sander Berge
  • Leo Østigård
  • Kristian Thorstvedt
  • Fredrik Aursnes
  • Julian Ryerson
  • Torbjørn Heggem
  • Antonio Nusa
  • Alexander Sørloth
  • Kristoffer Ajer
  • Andreas Schjelderup
  • Jørgen Strand Larsen
  • Martin Ødegaard
  • David Møller Wolfe
  • Morten Thorsby
  • Oscar Bobb
  • Ørjan Nyland
  • Sander Tangvik
  • Henrik Falchener
  • Fredrik André Bjørkan
  • Patrick Berg
  • Jens Petter Hauge
  • Egil Selvik
  • Marcus Holmgren Pedersen
  • Thelo Aasgaard
  • Sondre Langås
How the peers are sourced
  • Top-9 %
  • Stepping %
  • Domestic %
  • Other %
NOR Norway Squad 26
65 %
SUI Switzerland Squad 26
88 %
AUT Austria Squad 26
69 %
CZE Czechia Squad 26
35 %
How we know

As an analytics question In numbers: league tier of every 2026 FIFA World Cup squad member's most-minutes 2025/26 row, matched by name and birth year, per country.

What we did We matched every named squad player to his club season and recorded which level of league he was actually playing in.

Sources Wikipedia squad lists matched to 2025/26 league rows; tier = league of the most-minutes row.

A federation tracking this would watch the tier mix of future squads over cycles, not one tournament's snapshot.

When did the train leave?

Norwegian players with ≥ 450 Big-5 minutes: 26 at the 1998/99 peak, 4 at the 2007/08 low, 25 in 2025/26. The break is dated to 2004/05. The recovery after it is dated to 2021/22.

Line chart: Norwegian players with at least 450 minutes in the Big-5 leagues, 1995/96 to 2025/26, against eight peer countries; a lower panel shows per-million rates for Norway, Denmark and Czechia.
How to read it: each line is one country’s count of players with at least 450 minutes in the five biggest leagues, season by season since 1995/96. Norwegian players are the acid line; the dashed rules are the seasons the model dates the rise and the fall to, with their probabilities; the point past the last season is the forecast for next season with its 90 % interval. Toggle countries and switch to per million to compare fairly across sizes.
How we know

Break posterior 27 %; a level change of ×0.66 (0.29–1.34). Rise posterior 23 %; ×1.32.

As an analytics question In numbers: Norwegian players with ≥ 450 minutes in the Big-5 leagues each season since 2020/21, with the rise and the fall each dated by a change-point model.

What we did We counted, season by season, how many home-nation players had real playing time in Europe's five biggest leagues, then looked for the two seasons where that count changed level for good — once up, once down — and stayed there.

Sources FBref Big-5 player tables 1995/96 → 2025/26; peers on the same rule; the names are the most-minutes Norwegian players of each peak season, goalkeepers included — a lineup of presence, not a quality ranking: 1998/99: Thomas Myhre, Petter Rudi, Claus Lundekvam; 1999/00: Gunnar Halle, Steffen Iversen, Trond Andersen; 2025/26: Erling Haaland, Sander Berge, Leo Østigård. The break dates come from a Bayesian local-level model with two ordered change points, one for the rise and one for the fall (Adams and MacKay, 2007); detail in § Methodology.

A federation tracking this would watch whether the post-break level holds for another season, not treat one break as final.

The same series for 17 countries since 1995/96, with two dated steps each →

The nation by year of birth — who reached the Big-5, and where the exports come from →

How do Denmark and Czechia do it?

Slope chart: Denmark, Czechia and Norway on six pathway metrics, each rescaled 0 (worst of the three) to 1 (best of the three), one line per country.
How to read it: six measures, each rescaled so 0 is the worst of the three countries and 1 the best, one line per country. A line that stays high is a pathway that is open at every stage; where the acid line dips is where Norway loses ground.
The six numbers, unscaled
MetricNORDENCZE
Players per million 9.37 12.58 2.39
U21 share of domestic minutes 11.5 % 15.3 % 6.4 %
Export age (recent) 22 22 22
Sideways moves 22 % 10 % —
Exports' club-minutes share 37 % 46 % 50 %
National-team squad in the top-9 leagues 65 % — 35 %
Big-5 players now 25 38 10
How we know

As an analytics question In numbers: Denmark and Czechia against Norway on the same six 9-strongest-league pathway definitions, same seasons.

What we did We compared the home nation with the two comparison countries on six numbers, each one defined and measured in exactly the same way for all three.

Sources Same definitions, same seasons; a comparison, not a causal claim. Each metric is rescaled 0-1 across the three countries for the chart above, direction chosen so 1.0 is always the more open pathway (more players per million, more U21 minutes, an earlier export age, fewer sideways moves, a bigger minutes share abroad, more of the squad in the top-9); the table below keeps the raw numbers.

A federation tracking this would watch which of the six numbers moves first, not the overall picture alone.

Do goalkeepers follow a different path?

1 Norwegian goalkeepers play ≥ 450 minutes in the top-9 leagues — rank 7 of 9 per million — and they get there later than outfield exports.

Strip plot of age at first top-9-league appearance, Norwegian goalkeepers against outfield exports, one dot per player, medians marked.
How to read it: one dot per player at the age of his first season in a top-9 league; goalkeepers in the upper strip, outfield exports in the lower, medians marked. Further left is an earlier first appearance.
Club tier: Norwegian top-9 goalkeepers, 2025/26
PlayerClub LeagueMinutes Club goals percentile
Ørjan NylandSevillaESP-La Liga450 45 %
Goalkeeper production: Norwegian keepers, 2025/26
PlayerClub LeagueMinutes GA/90Saves/90 Save %Clean-sheet share GA/90, quality-adj.
Ørjan NylandSevillaESP-La Liga450 1.602.20 68.7 %20 % 1.79
Mathias DyngelandBrannNOR-Eliteserien2700 1.532.27 65.5 %17 % 4.01
Martin BørsheimFredrikstadNOR-Eliteserien1080 1.253.50 66.7 %25 % 3.53
Amund WichneHaugesundNOR-Eliteserien1440 2.503.94 66.1 %12 % 5.56
Einar FauskangerHaugesundNOR-Eliteserien990 2.913.73 65.5 %0 % 5.86
Emil ØdegaardKFUM OsloNOR-Eliteserien2610 1.281.55 65.3 %24 % 3.50
Adrian SætherKristiansundNOR-Eliteserien1956 1.933.04 65.7 %14 % 4.72
Knut-André SkjærsteinKristiansundNOR-Eliteserien540 2.173.83 66.1 %33 % 4.52
Jacob KarlstromMoldeNOR-Eliteserien2520 1.322.61 66.4 %32 % 3.59
Sander TangvikRosenborgNOR-Eliteserien2700 1.403.20 66.7 %37 % 3.75
Per Kristian BråtveitStrømsgodsetNOR-Eliteserien2218 2.154.34 66.3 %8 % 5.17
Arild ØstbøVikingNOR-Eliteserien1032 0.783.14 66.9 %42 % 2.87
Kristoffer KlaessonVikingNOR-Eliteserien1218 1.332.44 66.1 %29 % 3.64
Thomas KinnVikingNOR-Eliteserien450 1.801.20 65.8 %0 % 4.11
Jacob StorevikVålerengaNOR-Eliteserien630 1.864.00 66.3 %29 % 4.25
Magnus SjøengVålerengaNOR-Eliteserien810 1.561.56 65.7 %22 % 3.95
Viljar MyhraOdenseDEN-Superliga2008 1.752.82 63.8 %17 % 4.44

Peer median quality-adjusted GA/90: NOR 4.01 · DEN 3.69 · SUI 4.95 · AUT 4.76 · CZE 2.86 · CRO 5.19 · POL 2.74 · HUN 5.29 · SVK 2.90

Goalkeepers per million, by country
DEN Denmark
0.67
CRO Croatia
0.52
SUI Switzerland
0.45
SVK Slovakia
0.37
CZE Czechia
0.37
POL Poland
0.19
NOR Norway
0.18
AUT Austria
0.11
HUN Hungary
0.10
How we know

0.18 per million; first top-9 season at a median age of 32, against 22 for outfield exports.

As an analytics question In numbers: Norwegian goalkeepers with ≥ 450 minutes in the top-9 leagues, per million inhabitants, against outfield export age.

What we did We counted goalkeepers the same way we counted outfield players, then compared the age each group first reached one of the strongest leagues.

Sources 450-minute floor, 2025/26 rosters, same K = 900 shrinkage as the rest of the report; first season in a fetched top-9 table; players already there in 2020/21 are censored — 0 % of the goalkeepers, 30 % of the outfield exports; a comparison of two pathways inside one nation, not a causal claim.

A federation tracking this would watch whether the goalkeeper pathway keeps diverging from outfield export age, not one season's gap.

What is the gap made of?

Of the 3.21 players per million between Denmark and Norway, U21 minutes go with +3.15, league strength with −3.54, export age with 0.00.

One horizontal stacked bar per contrast country: the contribution of U21 minutes, league strength and export age to its per-capita gap with Norway, plus the residual; whiskers show each channel's bootstrap interval.
How to read it: the whole bar is the gap in players per million between the comparison country and Norway. Each segment is how much of that gap goes with one measured channel — youth minutes, league strength, export age — under the decomposition; the hatched remainder is what the three channels do not carry. A segment can be negative when the channel works the other way.
How we know

+3.60 is not carried by the three channels.

As an analytics question In numbers: a linear split of the per-capita gap into U21 minutes, league strength and export age across 8 peer countries.

What we did We used the players who changed leagues to work out what a season in one league is worth in another, then split the gap between countries into the parts that line up with young players' minutes, league strength and the age players move abroad.

Sources Ridge-regression linear split, Blinder-Oaxaca-style (Oaxaca, 1973; Blinder, 1973), fit on the 8 peer countries with data on all three channels; bootstrap 90 % intervals, 1000 resamples; a decomposition of a correlation, not a causal accounting.

A federation tracking this would watch which channel's contribution grows, not treat the split as fixed.

The decomposition for every edition, side by side →

Who are the players?

18 cards chosen by six rules.

How the 18 cards were chosen

Why these cards: one card per position group per rule, applied in this order — (a) highest goals + assists per 90, league-adjusted, (b) youngest national-team call-up, (c) most top-9 minutes among the 2026 FIFA World Cup squad, (d) most domestic-league minutes among the 2026 FIFA World Cup squad, (e) most top-9 minutes, (f) most domestic minutes under 23 without a top-9 season. A player already chosen by an earlier rule falls through to the next name, so a later row can show the second name by its measure. Rows group the six rules; the national-team core row holds two of them.

Highest quality-adjusted production

Erling HaalandManchester CityFW0.83G+A / 90 adj.↑ improving · +0.17 G+A / 90 adj.Career →
Manchester City

Erling Haaland

FW · 26 · Manchester City (2026/27) · NT 2025–26

2025/26 · Manchester City · ENG-Premier League

G+A / 90 adj.
0.83
Non-penalty goals / assists per 90
0.73 / 0.24
Minutes
2953 (86 %)
Style map Older forwards with moderate playing time Quality map High-card-rate forwards

Tactical read

Experienced forwards on managed minutes — median age 31, about 40 % of minutes, output at median. The impact or target forward used in rotation (Haaland, Sørloth, Marković).

Trajectory 2024/25 → 2025/26

improving +0.17 G+A / 90 adj. 2736 min → 2953 min

Historical analogs at age 26
  1. Alexander Isak SWE ENG-Premier League 2024/25 · 2756 min · 0.73 · d = 0.92
  2. Mateo Retegui ITA ITA-Serie A 2024/25 · 2383 min · 0.77 · d = 1.16
  3. Marcus Rashford ENG ENG-Premier League 2022/23 · 2879 min · 0.63 · d = 1.74

2026 FIFA World Cup

Selected as: highest goals + assists per 90, league-adjusted among FW.

Julian RyersonDortmundMF0.39G+A / 90 adj.—Career →
Dortmund

Julian Ryerson

MF · 29 · Dortmund (2026/27) · NT 2025–26

2025/26 · Dortmund · GER-Bundesliga

G+A / 90 adj.
0.39
Non-penalty goals / assists per 90
0.00 / 0.60
Minutes
2266 (74 %)
Style map High-card-rate midfielders Quality map Productive midfielders in top-five leagues

Tactical read

Ball-winning, duel-heavy midfielders — the card rate (about 2.5 times the midfield median) is the defining feature, production at the floor of the group. The destroyer profile, often in a double pivot (Berg, Elyounoussi, Ryerson).

Historical analogs at age 29
  1. Julian Brandt GER GER-Bundesliga 2024/25 · 2303 min · 0.38 · d = 0.06
  2. Jens Stage DEN GER-Bundesliga 2024/25 · 2204 min · 0.39 · d = 0.11
  3. Alassane Pléa FRA GER-Bundesliga 2021/22 · 2070 min · 0.36 · d = 0.33

2026 FIFA World Cup

Selected as: highest goals + assists per 90, league-adjusted among MF.

Joachim SoltvedtBrannDF0.15G+A / 90 adj.↑ improving · +0.09 G+A / 90 adj.Career →
Brann

Joachim Soltvedt

DF · 31 · Brann (2026/27)

2025/26 · Brann · NOR-Eliteserien

G+A / 90 adj.
0.15
Non-penalty goals / assists per 90
0.10 / 0.46
Minutes
1752 (65 %)
Style map Goal-scoring defenders Quality map Young low-minute defenders

Tactical read

Set-piece threats — defenders scoring at five times the DF median on 60 % of minutes. Aerial presence in both boxes (Braude, Sjøvold, Dahl).

Trajectory 2024/25 → 2025/26

improving +0.09 G+A / 90 adj. 1200 min → 1752 min

Historical analogs at age 31
  1. Viljar Vevatne NOR NOR-Eliteserien 2024/25 · 1727 min · 0.09 · d = 0.56
  2. Casper Højer Nielsen DEN TUR-Süper Lig 2024/25 · 2024 min · 0.11 · d = 0.72
  3. Bas Kuipers NED NED-Eredivisie 2024/25 · 1724 min · 0.12 · d = 0.72

Selected as: highest goals + assists per 90, league-adjusted among DF.

National-team core — 2026 FIFA World Cup squad: most top-9 minutes, most home-league minutes

Jørgen Strand LarsenCrystal PalaceFW0.19G+A / 90 adj.↓ declining · −0.34 G+A / 90 adj.Career →
Crystal Palace

Jørgen Strand Larsen

FW · 26 · Crystal Palace (2026/27) · NT 2025–26

2025/26 · Wolves · ENG-Premier League

G+A / 90 adj.
0.19
Non-penalty goals / assists per 90
0.00 / 0.06
Minutes
1404 (45 %)
Style map Primary scorers Quality map High-assist forwards in top-five leagues

Tactical read

Primary scorers — non-penalty goal rate nearly double the forward median on starter minutes (56 %). The finishing forward of a first-choice line (Larsen, Solbakken, Heggebø).

Trajectory 2024/25 → 2025/26

declining −0.34 G+A / 90 adj. 2587 min → 2307 min

Historical analogs at age 26
  1. Vitinha POR ITA-Serie A 2025/26 · 2226 min · 0.24 · d = 0.71
  2. David Okereke NGA ITA-Serie A 2022/23 · 2266 min · 0.23 · d = 0.71
  3. Walid Cheddira MAR ITA-Serie A 2023/24 · 2120 min · 0.27 · d = 0.78

2026 FIFA World Cup

Selected as: most top-9 minutes among 2026 FIFA World Cup squad FW.

Sander BergeFulhamMF0.08G+A / 90 adj.→ stable · +0.01 G+A / 90 adj.Career →
Fulham

Sander Berge

MF · 28 · Fulham (2026/27) · NT 2025–26

2025/26 · Fulham · ENG-Premier League

G+A / 90 adj.
0.08
Non-penalty goals / assists per 90
0.00 / 0.03
Minutes
2905 (85 %)
Style map Older rotation midfielders Quality map Older rotation midfielders

Tactical read

Veteran rotation midfielders — median age 30 on managed minutes (40 %), output at median, card rate above it. Experience kept in the squad rather than on the pitch every week (Berge, Ulvestad, Jenssen).

Trajectory 2024/25 → 2025/26

stable +0.01 G+A / 90 adj. 2224 min → 2905 min

Historical analogs at age 28
  1. Nélson Semedo POR ENG-Premier League 2020/21 · 2983 min · 0.08 · d = 0.12
  2. George Baldock GRE ENG-Premier League 2020/21 · 2787 min · 0.09 · d = 0.18
  3. Kyle Walker-Peters ENG ENG-Premier League 2024/25 · 2918 min · 0.10 · d = 0.21

2026 FIFA World Cup

Selected as: most top-9 minutes among 2026 FIFA World Cup squad MF.

Leo ØstigårdGenoaDF0.14G+A / 90 adj.↑ improving · +0.11 G+A / 90 adj.Career →
Genoa

Leo Østigård

DF · 27 · Genoa (2026/27) · NT 2025–26

2025/26 · Genoa · ITA-Serie A

G+A / 90 adj.
0.14
Non-penalty goals / assists per 90
0.17 / 0.03
Minutes
2622 (77 %)
Style map Everyday starting defenders Quality map High-assist defenders in top-five leagues

Tactical read

The defensive core — 85 % of minutes, output at the DF floor. Availability and continuity are the signal; production is not (Østigård, Cornic, Falchener).

Trajectory 2024/25 → 2025/26

improving +0.11 G+A / 90 adj. 2154 min → 2622 min

Historical analogs at age 27
  1. Jules Koundé FRA ESP-La Liga 2024/25 · 2605 min · 0.11 · d = 0.33
  2. Emanuele Valeri ITA ITA-Serie A 2024/25 · 2873 min · 0.14 · d = 0.34
  3. Benjamin Henrichs GER GER-Bundesliga 2023/24 · 2526 min · 0.13 · d = 0.36

2026 FIFA World Cup

Selected as: most top-9 minutes among 2026 FIFA World Cup squad DF.

Jens Petter HaugeBodø/GlimtFW0.18G+A / 90 adj.→ stable · +0.01 G+A / 90 adj.Career →
Bodø/Glimt

Jens Petter Hauge

FW · 27 · Bodø/Glimt (2026/27) · NT 2025–26

2025/26 · Bodø/Glimt · NOR-Eliteserien

G+A / 90 adj.
0.18
Non-penalty goals / assists per 90
0.32 / 0.16
Minutes
2216 (82 %)
Style map Young low-minute forwards Quality map High-minutes starting forwards

Tactical read

The development tier and the largest forward cluster — median age 23, under 30 % of minutes, output just below median. Where most of the home pool's young forwards sit (Lauritsen, Arnstad, Brunes).

Trajectory 2024/25 → 2025/26

stable +0.01 G+A / 90 adj. 2003 min → 2216 min

Historical analogs at age 27
  1. Lirim Qamili MKD DEN-Superliga 2024/25 · 2059 min · 0.21 · d = 0.31
  2. Peter Christiansen DEN NOR-Eliteserien 2025/26 · 2098 min · 0.22 · d = 0.38
  3. Sebastian Bergier POL POL-Ekstraklasa 2025/26 · 2278 min · 0.22 · d = 0.43

2026 FIFA World Cup

Selected as: most domestic minutes among 2026 FIFA World Cup squad FW.

Patrick BergBodø/GlimtMF0.21G+A / 90 adj.↑ improving · +0.08 G+A / 90 adj.Career →
Bodø/Glimt

Patrick Berg

MF · 29 · Bodø/Glimt (2026/27) · NT 2025–26

2025/26 · Bodø/Glimt · NOR-Eliteserien

G+A / 90 adj.
0.21
Non-penalty goals / assists per 90
0.20 / 0.47
Minutes
2292 (85 %)
Style map High-card-rate midfielders Quality map Older rotation midfielders

Tactical read

Ball-winning, duel-heavy midfielders — the card rate (about 2.5 times the midfield median) is the defining feature, production at the floor of the group. The destroyer profile, often in a double pivot (Berg, Elyounoussi, Ryerson).

Trajectory 2024/25 → 2025/26

improving +0.08 G+A / 90 adj. 2610 min → 2292 min

Historical analogs at age 29
  1. Jordan Larsson SWE DEN-Superliga 2025/26 · 2281 min · 0.20 · d = 0.03
  2. Emrah Başsan TUR TUR-Süper Lig 2020/21 · 2283 min · 0.21 · d = 0.54
  3. Miguel Cardoso POR TUR-Süper Lig 2022/23 · 2333 min · 0.21 · d = 0.54

2026 FIFA World Cup

Selected as: most domestic minutes among 2026 FIFA World Cup squad MF.

Fredrik André BjørkanBodø/GlimtDF0.06G+A / 90 adj.↓ declining · −0.06 G+A / 90 adj.Career →
Bodø/Glimt

Fredrik André Bjørkan

DF · 28 · Bodø/Glimt (2026/27) · NT 2025–26

2025/26 · Bodø/Glimt · NOR-Eliteserien

G+A / 90 adj.
0.06
Non-penalty goals / assists per 90
0.11 / 0.07
Minutes
2457 (91 %)
Style map Everyday starting defenders Quality map Everyday starting defenders

Tactical read

The defensive core — 85 % of minutes, output at the DF floor. Availability and continuity are the signal; production is not (Østigård, Cornic, Falchener).

Trajectory 2024/25 → 2025/26

declining −0.06 G+A / 90 adj. 1792 min → 2457 min

Historical analogs at age 28
  1. Eirik Saunes NOR NOR-Eliteserien 2025/26 · 2496 min · 0.05 · d = 0.10
  2. Jesper Tåje NOR NOR-Eliteserien 2024/25 · 2228 min · 0.04 · d = 0.34
  3. Jacob Rasmussen DEN DEN-Superliga 2024/25 · 2520 min · 0.02 · d = 0.35

2026 FIFA World Cup

Selected as: most domestic minutes among 2026 FIFA World Cup squad DF.

Youngest national-team call-up

Antonio NusaRB LeipzigFW0.29G+A / 90 adj.—Career →
RB Leipzig

Antonio Nusa

FW · 21 · RB Leipzig (2026/27) · NT 2025–26

2025/26 · RB Leipzig · GER-Bundesliga

G+A / 90 adj.
0.29
Non-penalty goals / assists per 90
0.18 / 0.13
Minutes
2027 (68 %)
Style map Young low-minute forwards Quality map High-minutes starting forwards

Tactical read

The development tier and the largest forward cluster — median age 23, under 30 % of minutes, output just below median. Where most of the home pool's young forwards sit (Lauritsen, Arnstad, Brunes).

Historical analogs at age 21
  1. Vinicius Júnior BRA ESP-La Liga 2020/21 · 1969 min · 0.25 · d = 0.35
  2. Jamie Leweling GER GER-Bundesliga 2021/22 · 1959 min · 0.24 · d = 0.37
  3. Emanuel Emegha NED FRA-Ligue 1 2023/24 · 2078 min · 0.27 · d = 0.61

2026 FIFA World Cup

Selected as: youngest national-team call-up among FW.

Andreas SchjelderupBenficaMF0.26G+A / 90 adj.—Career →
Benfica

Andreas Schjelderup

MF · 22 · Benfica (2026/27) · NT 2025–26

2025/26 · Benfica · POR-Primeira Liga

G+A / 90 adj.
0.26
Non-penalty goals / assists per 90
0.26 / 0.26
Minutes
1723 (58 %)
Style map High-card-rate midfielders Quality map Productive midfielders in top-five leagues

Tactical read

Ball-winning, duel-heavy midfielders — the card rate (about 2.5 times the midfield median) is the defining feature, production at the floor of the group. The destroyer profile, often in a double pivot (Berg, Elyounoussi, Ryerson).

Historical analogs at age 22
  1. Hákon Haraldsson ISL FRA-Ligue 1 2024/25 · 1755 min · 0.23 · d = 0.31
  2. Sofiane Diop MAR FRA-Ligue 1 2021/22 · 1948 min · 0.25 · d = 0.35
  3. Francisco Conceição POR POR-Primeira Liga 2023/24 · 1888 min · 0.22 · d = 0.37

2026 FIFA World Cup

Selected as: youngest national-team call-up among MF.

Henrik FalchenerVikingDF0.07G+A / 90 adj.—Career →
Viking

Henrik Falchener

DF · 23 · Viking (2026/27) · NT 2025–26

2025/26 · Viking · NOR-Eliteserien

G+A / 90 adj.
0.07
Non-penalty goals / assists per 90
0.21 / 0.00
Minutes
2576 (99 %)
Style map Everyday starting defenders Quality map High-assist defenders in top-five leagues

Tactical read

The defensive core — 85 % of minutes, output at the DF floor. Availability and continuity are the signal; production is not (Østigård, Cornic, Falchener).

Historical analogs at age 23
  1. Fredrik Sjøvold NOR NOR-Eliteserien 2025/26 · 2594 min · 0.10 · d = 0.24
  2. Mikkel Rakneberg NOR NOR-Eliteserien 2024/25 · 2311 min · 0.06 · d = 0.36
  3. Mathias Tønnessen NOR NOR-Eliteserien 2025/26 · 2422 min · 0.02 · d = 0.45

2026 FIFA World Cup

Selected as: youngest national-team call-up among DF.

Most top-9 minutes

Tobias LauritsenSampdoriaFW0.21G+A / 90 adj.→ stable · +0.03 G+A / 90 adj.Career →
Sampdoria

Tobias Lauritsen

FW · 29 · Sampdoria (latest known)

2025/26 · Sparta R. · NED-Eredivisie

G+A / 90 adj.
0.21
Non-penalty goals / assists per 90
0.27 / 0.15
Minutes
3039 (99 %)
Style map Young low-minute forwards Quality map High-minutes starting forwards

Tactical read

The development tier and the largest forward cluster — median age 23, under 30 % of minutes, output just below median. Where most of the home pool's young forwards sit (Lauritsen, Arnstad, Brunes).

Trajectory 2024/25 → 2025/26

stable +0.03 G+A / 90 adj. 2707 min → 3039 min

Historical analogs at age 29
  1. Riad Bajić BIH TUR-Süper Lig 2022/23 · 2868 min · 0.20 · d = 0.27
  2. Lennart Thy GER NED-Eredivisie 2020/21 · 2782 min · 0.22 · d = 0.34
  3. Victor Edvardsen SWE NED-Eredivisie 2024/25 · 2740 min · 0.23 · d = 0.42

Selected as: most top-9 league minutes among FW.

Sondre ØrjasæterTwenteMF0.18G+A / 90 adj.→ stable · +0.03 G+A / 90 adj.Career →
Twente

Sondre Ørjasæter

MF · 23 · Twente (2026/27)

2025/26 · Twente · NED-Eredivisie

G+A / 90 adj.
0.18
Non-penalty goals / assists per 90
0.13 / 0.25
Minutes
2130 (70 %)
Style map High-card-rate midfielders Quality map Older rotation midfielders

Tactical read

Ball-winning, duel-heavy midfielders — the card rate (about 2.5 times the midfield median) is the defining feature, production at the floor of the group. The destroyer profile, often in a double pivot (Berg, Elyounoussi, Ryerson).

Trajectory 2024/25 → 2025/26

stable +0.03 G+A / 90 adj. 2428 min → 3375 min

Historical analogs at age 23
  1. Kodai Sano JPN NED-Eredivisie 2025/26 · 3060 min · 0.15 · d = 0.42
  2. Fisayo Dele-Bashiru NGA TUR-Süper Lig 2023/24 · 3035 min · 0.15 · d = 0.47
  3. Jarne Steuckers BEL BEL-Pro League 2024/25 · 3097 min · 0.17 · d = 0.50

Selected as: most top-9 league minutes among MF.

Oliver BraudeHeerenveenDF0.06G+A / 90 adj.→ stable · +0.02 G+A / 90 adj.Career →
Heerenveen

Oliver Braude

DF · 22 · Heerenveen (2026/27)

2025/26 · Heerenveen · NED-Eredivisie

G+A / 90 adj.
0.06
Non-penalty goals / assists per 90
0.00 / 0.13
Minutes
2815 (92 %)
Style map Goal-scoring defenders Quality map Everyday starting defenders

Tactical read

Set-piece threats — defenders scoring at five times the DF median on 60 % of minutes. Aerial presence in both boxes (Braude, Sjøvold, Dahl).

Trajectory 2024/25 → 2025/26

stable +0.02 G+A / 90 adj. 2607 min → 2815 min

Historical analogs at age 22
  1. Bünyamin Balcı TUR TUR-Süper Lig 2021/22 · 2740 min · 0.06 · d = 0.17
  2. Yukinari Sugawara JPN NED-Eredivisie 2021/22 · 2635 min · 0.06 · d = 0.24
  3. Milan van Ewijk NED NED-Eredivisie 2021/22 · 2897 min · 0.03 · d = 0.25

Selected as: most top-9 league minutes among DF.

Most domestic minutes under 23, no top-9 season yet

Daniel KarlsbakkSarpsborg 08FW0.17G+A / 90 adj.—Career →
Sarpsborg 08

Daniel Karlsbakk

FW · 23 · Sarpsborg 08 (2026/27)

2025/26 · Sarpsborg 08 · NOR-Eliteserien

G+A / 90 adj.
0.17
Non-penalty goals / assists per 90
0.42 / 0.04
Minutes
2347 (90 %)
Style map Young low-minute forwards Quality map High-minutes starting forwards

Tactical read

The development tier and the largest forward cluster — median age 23, under 30 % of minutes, output just below median. Where most of the home pool's young forwards sit (Lauritsen, Arnstad, Brunes).

Historical analogs at age 23
  1. Christian Gammelgaard DEN DEN-Superliga 2025/26 · 2330 min · 0.16 · d = 0.11
  2. Tobias Bech DEN DEN-Superliga 2024/25 · 2262 min · 0.15 · d = 0.21
  3. Bohdan Viunnyk UKR POL-Ekstraklasa 2024/25 · 2329 min · 0.16 · d = 0.34

Selected as: most domestic-league minutes among under-23 FW without a top-9 season.

Jens Hjertø-DahlTromsøMF0.11G+A / 90 adj.↑ improving · +0.07 G+A / 90 adj.Career →
Tromsø

Jens Hjertø-Dahl

MF · 21 · Tromsø (2026/27)

2025/26 · Tromsø · NOR-Eliteserien

G+A / 90 adj.
0.11
Non-penalty goals / assists per 90
0.15 / 0.15
Minutes
2372 (88 %)
Style map Older rotation midfielders Quality map Older rotation midfielders

Tactical read

Veteran rotation midfielders — median age 30 on managed minutes (40 %), output at median, card rate above it. Experience kept in the squad rather than on the pitch every week (Berge, Ulvestad, Jenssen).

Trajectory 2024/25 → 2025/26

improving +0.07 G+A / 90 adj. 2016 min → 2372 min

Historical analogs at age 21
  1. Mario Dorgelès CIV DEN-Superliga 2024/25 · 2225 min · 0.09 · d = 0.27
  2. Thomas Jørgensen DEN DEN-Superliga 2025/26 · 2579 min · 0.13 · d = 0.33
  3. Mateusz Kowalczyk POL POL-Ekstraklasa 2024/25 · 2465 min · 0.12 · d = 0.34

Selected as: most domestic-league minutes among under-23 MF without a top-9 season.

Eivind HellandBolognaDF0.02G+A / 90 adj.→ stable · −0.01 G+A / 90 adj.Career →
Bologna

Eivind Helland

DF · 21 · Bologna (2026/27)

2025/26 · Brann · NOR-Eliteserien

G+A / 90 adj.
0.02
Non-penalty goals / assists per 90
0.00 / 0.04
Minutes
2502 (93 %)
Style map Young low-minute defenders Quality map Older defenders

Tactical read

Development defenders — median age 22, about a third of minutes, output at the floor. The tier the 23–25 cohort draws from (Ulvestad, Fredriksen, Valsvik).

Trajectory 2024/25 → 2025/26

stable −0.01 G+A / 90 adj. 914 min → 2986 min

Historical analogs at age 21
  1. Bung Meng Freimann SUI SUI-Super League 2025/26 · 2752 min · 0.03 · d = 0.51
  2. Filip Luberecki POL POL-Ekstraklasa 2025/26 · 2615 min · 0.02 · d = 0.58
  3. Bünyamin Balcı TUR TUR-Süper Lig 2020/21 · 3022 min · 0.06 · d = 0.64

Selected as: most domestic-league minutes among under-23 DF without a top-9 season.

Goalkeepers — most top-9 minutes · youngest in the top-9

Sevilla

Ørjan Nyland

GK · Sevilla (ESP-La Liga) · NT 2025–26 · Career, season by season →

2025/26 · 450 min

GA/90
1.60
Saves/90
2.20
Save %
68.7 %

Club tier

Sevilla (ESP-La Liga) · 45 % of the league's goals scored

2026 FIFA World Cup

Selected as: most top-9 minutes among home goalkeepers.

* Each card renders the existing dataset; no computation beyond the join. Age on the name line is the 2026/27 season-start age (start year − birth year); the club is from the 2026/27 tables, or — labelled "latest known" — from FBref's country page where the player has no 2026/27 row. Age on the analog line follows the analog finder's convention (season start year + 1 − birth year).

The atlas: every player-season of 2025/26, one dot each

Two-panel atlas of forwards 2025/26 in PCA projection. Left panel: style map without league multipliers; right panel: quality-adjusted map. Grey points are the whole corpus of 924 players; coloured points are the 44 Norwegian-eligible players by cluster; bright rings mark the 5 with a national-team call-up 2025–26.
How to read it: every dot is the 2025/26 season of one of the forwards in the leagues this report covers. The two axes are the first two principal components of his five per-90 numbers (goals, assists, minutes share, age, cards) — dots that sit close together had similar seasons. The style projection uses the raw numbers, the quality projection the league-adjusted ones, so switching shows who moves when the strength of his league is counted. Colours are the clusters named below; acid dots are Norwegian-eligible players, a white ring marks a national-team call-up. Hover a dot for the player, click to pin, scroll to zoom, type a name to find him.
Two-panel atlas of midfielders 2025/26 in PCA projection. Left panel: style map without league multipliers; right panel: quality-adjusted map. Grey points are the whole corpus of 2648 players; coloured points are the 103 Norwegian-eligible players by cluster; bright rings mark the 10 with a national-team call-up 2025–26.
How to read it: every dot is the 2025/26 season of one of the midfielders in the leagues this report covers. The two axes are the first two principal components of his five per-90 numbers (goals, assists, minutes share, age, cards) — dots that sit close together had similar seasons. The style projection uses the raw numbers, the quality projection the league-adjusted ones, so switching shows who moves when the strength of his league is counted. Colours are the clusters named below; acid dots are Norwegian-eligible players, a white ring marks a national-team call-up. Hover a dot for the player, click to pin, scroll to zoom, type a name to find him.
Two-panel atlas of defenders 2025/26 in PCA projection. Left panel: style map without league multipliers; right panel: quality-adjusted map. Grey points are the whole corpus of 1947 players; coloured points are the 89 Norwegian-eligible players by cluster; bright rings mark the 5 with a national-team call-up 2025–26.
How to read it: every dot is the 2025/26 season of one of the defenders in the leagues this report covers. The two axes are the first two principal components of his five per-90 numbers (goals, assists, minutes share, age, cards) — dots that sit close together had similar seasons. The style projection uses the raw numbers, the quality projection the league-adjusted ones, so switching shows who moves when the strength of his league is counted. Colours are the clusters named below; acid dots are Norwegian-eligible players, a white ring marks a national-team call-up. Hover a dot for the player, click to pin, scroll to zoom, type a name to find him.
Every player in the 2025/26 pool — all 258, searchable

The cards above pick by rule. This is everyone with a 2025/26 season in the data: 44 forwards, 103 midfielders, 89 defenders and 22 goalkeepers. Open a name for what the report measures about him, in plain words. Rank is within his own position group on quality-adjusted production; the two arrows mark a mid-season move.

258 of 258
    How we know

    As an analytics question In numbers: 18 showcase cards chosen from the 2025/26 pool by six selection rules, one per position group per rule.

    What we did We matched each player's season to his national-team call-ups and photo, then picked one player per position group under each of six rules.

    Sources 2025/26 FBref, Wikipedia and Wikidata rows joined by name and birth year; six selection rules, applied in order — see below.

    A federation tracking this would watch how the showcase set changes as a cohort ages, not any one card.

    What changed since last season?

    11 climbed a rung, 7 came down. The stepping-stone leagues hold 2 of the pool, from 1; the top nine hold 38, from 34.

    How to read it: the left column is last season’s rung for every player in the pool, the right column this season’s; each ribbon is the players who went from one to the other, its width their number. Acid ribbons climb, orange come down, mint are new to the pool, grey are no longer in a covered league. Hover a ribbon for the names.

    Eliteserien

    193 → 176

    293 797 → 259 396 minutes

    stepping-stone league

    1 → 2

    1 396 → 3 738 minutes

    top-9 league

    34 → 38

    60 395 → 63 393 minutes

    other covered league

    20 → 18

    27 585 → 27 570 minutes

    Players of the pool on each rung, 2024/25 → 2025/26, with the minutes they played there; 248 qualifying players last season, 234 this season.

    Climbed a rung 11

    • Sebastian Sebulonsen DEN-Superliga → GER-Bundesliga
    • Sondre Liseth NOR-Eliteserien → POL-Ekstraklasa
    • Jonas Therkelsen NOR-Eliteserien → GER-2. Bundesliga
    • Sondre Ørjasæter NOR-Eliteserien → NED-Eredivisie
    • Jakob Romsaas NOR-Eliteserien → BEL-Pro League
    • Runar Norheim NOR-Eliteserien → DEN-Superliga
    • and 5 more

    Came down a rung 7

    • Kristoffer Askildsen DEN-Superliga → NOR-Eliteserien
    • Magnus Knudsen GER-Bundesliga → DEN-Superliga
    • Ola Solbakken ITA-Serie A → DEN-Superliga
    • Gustav Wikheim DEN-Superliga → NOR-Eliteserien
    • Simen Bolkan Nordli DEN-Superliga → NOR-Eliteserien
    • Ivan Näsberg DEN-Superliga → NOR-Eliteserien
    • and 1 more

    New to the pool 58

    • Henrik Falchener NOR-Eliteserien
    • Eirik Saunes NOR-Eliteserien
    • Kristian Thorstvedt ITA-Serie A
    • Mathias Tønnessen NOR-Eliteserien
    • Daniel Karlsbakk NOR-Eliteserien
    • Aaron Olsen NOR-Eliteserien
    • and 52 more

    No longer in a covered league 72

    • Jonas Svensson TUR-Süper Lig
    • Ruben Gabrielsen NOR-Eliteserien
    • Sondre Langås NOR-Eliteserien
    • Vegard Kongsro NOR-Eliteserien
    • Filip Jørgensen NOR-Eliteserien
    • Ruben Kristiansen NOR-Eliteserien
    • and 66 more

    “No longer in a covered league” means exactly that: retired, injured for the season, or playing in a league this report does not fetch. The data cannot tell those apart and this page does not guess. “New to the pool” likewise mixes debutants with players returning from leagues outside the set.

    How we know

    As an analytics question In numbers: every Norwegian-eligible player with at least 450 minutes in 2024/25 or 2025/26, placed on the pathway’s tier ladder in each season, and the move between the two.

    What we did We took each player’s main league last season and this season, sorted the leagues into the four rungs the report uses everywhere — home league, other covered league, stepping stone, top nine — and counted who climbed, who came down, who appeared and who is no longer in any league we can see.

    Sources Season feature tables for both seasons; a player’s league is the one he played most minutes in; 450-minute floor in a season to count as present in it; rungs as defined in the pathways chapter.

    A federation tracking this would watch the two moving columns each summer, and the stepping-stone count above all.

    For a federation

    One-page brief

    The whole argument on one screen: the five numbers behind why the pool of players runs thin, the single link that carries most of the gap, two checks on the models behind the numbers, and what none of this shows.

    Show the one-page brief
    Number NORDENCZE
    Share of league minutes that went to players aged 21 or under11.5 %15.3 %6.4 %
    Average age of a minute played in the league25.625.426.0
    Age the first time a player has real playing time in a foreign league, over the players abroad today22.52324
    Share of moves abroad to a league no stronger than the player's own22 %10 %—
    Players in Europe's strongest leagues, for every million people9.3712.582.39

    If you take one thing from this: the single measured link that carries the most of the gap with both Denmark and Czechia is how strong the domestic league is. That is one decomposition over 8 countries — a description of the gap, not a weight to plan by.

    How strong is the home league
    A season in the home league is worth about 0.57 of a season in the Premier League (0.51–0.64).
    When the count of players in the strongest leagues turned
    Around the 2004/05 season, with 27 percent probability that the shift is genuine rather than an ordinary season-to-season dip.

    What this does not show

    • Money: transfer fees, wages and academy budgets play no part in any number above.
    • How the academy and coaching set-up actually work day to day, which no public data source used here can see.
    • Agents, and how a move abroad actually gets arranged, which happens off any table this pipeline can read.
    • The direction of the arrow: a low share of playing time for young players at home could help cause a thin generation, or just as easily be a symptom of one — the five numbers above are associations measured the same way for every country.

    Explore the data

    Benchmark vs peer countries

    Structural benchmark vs peer countries

    Football intuition recognises the Norwegian pool player by player. Its structural position among the peer countries needs an aggregation nobody holds in one place. Three numbers below that are usually not collected together.

    Cohort gaps — forwards

    Cohort U2223-2526-2930+
    Norway 1 1 4 1
    Denmark 1 7 2 —
    Switzerland — 1 3 —
    Austria 1 2 — 1
    Czechia — 1 2 2

    Cohort gaps — midfielders

    Cohort U2223-2526-2930+
    Norway 2 9 11 4
    Denmark 6 6 13 6
    Switzerland 4 6 10 3
    Austria 2 6 4 7
    Czechia 1 2 3 1

    Cohort gaps — defenders

    Cohort U2223-2526-2930+
    Norway 3 1 5 2
    Denmark 2 5 7 2
    Switzerland 2 5 4 6
    Austria 1 3 5 1
    Czechia — 1 3 3

    * Count and median npG+A per 90 of players with the country's nationality in a top-9 league, 2025/26, at least 450 minutes; cohort by age at the season's calendar turn (start year + 1 − birth year). 5 of the 9 countries are shown; the heatmap above carries all of them. Largest Norwegian shortfalls against the peer median count: defenders 23-25 (1 vs 2), defenders 30+ (2 vs 2), forwards 23-25 (1 vs 1).

    The full picture, all nine peer countries at once, as a heatmap:

    Heatmap of the international cohort benchmark: 9 countries by position group and age cohort, 2025/26 season; the Norwegian row is outlined.
    Median non-penalty goals + assists per 90 by country, position group and age cohort, 2025/26; the outlined row is Norway.

    Observations

    Per capita: rank 3 of 9 · The largest cohort gap: defenders 23-25 · Trajectories 2024/25 → 2025/26: mostly stable

    Per capita: rank 3 of 9

    52 Norwegian players on 2025/26 rosters of the nine strongest leagues give 9.37 per million inhabitants, rank 3 of 9. Denmark leads with 12.58, 1.3 times the Norwegian density; Croatia sits one place above with 12.18 from 47 players and a population 1.4 times smaller. Below Norway: Switzerland, Austria, Slovakia, Czechia, Hungary, Poland.

    The largest cohort gap: defenders 23-25

    Counting 2025/26 top-9 players by position group and age cohort and comparing the Norwegian count with the median of the other eight peers, the three largest shortfalls are defenders 23-25 — 1 Norwegian against a peer median of 2; defenders 30+ — 2 Norwegian against a peer median of 2; forwards 23-25 — 1 Norwegian against a peer median of 1. The cohort tables above show the medians behind the counts.

    Trajectories 2024/25 → 2025/26: mostly stable

    101 Norwegian-eligible players had at least 900 minutes in both 2024/25 and 2025/26: forwards 19 (7 up, 8 stable, 4 down); midfielders 42 (8 up, 30 stable, 4 down); defenders 40 (6 up, 31 stable, 3 down). A move counts as up or down when league-adjusted goals + assists per 90 changed by more than 0.05; 69 of 101 stayed within that band. These are season-over-season deltas, not projections.

    Cluster archetypes

    Cluster archetypes (style projection)

    Clusters are fitted on the whole corpus of 2025/26 player-seasons and read here through their Norwegian members, K chosen by silhouette score (Rousseeuw, 1987) with scikit-learn (Pedregosa et al., 2011). The label describes the cluster's median footprint; the count is Norwegian members of the corpus cluster; names are the Norwegian members with the most minutes.

    Two-panel atlas of forwards 2025/26 in PCA projection. Left panel: style map without league multipliers; right panel: quality-adjusted map. Grey points are the whole corpus of 924 players; coloured points are the 44 Norwegian-eligible players by cluster; bright rings mark the 5 with a national-team call-up 2025–26.
    Atlas of forwards 2025/26 in both projections: 44 Norwegian-eligible players in colour against a corpus of 924. Bright rings mark the national-team pool (call-up 2025–26, 5 players).
    Two-panel atlas of midfielders 2025/26 in PCA projection. Left panel: style map without league multipliers; right panel: quality-adjusted map. Grey points are the whole corpus of 2648 players; coloured points are the 103 Norwegian-eligible players by cluster; bright rings mark the 10 with a national-team call-up 2025–26.
    Atlas of midfielders 2025/26 in both projections: 103 Norwegian-eligible players in colour against a corpus of 2648. Bright rings mark the national-team pool (call-up 2025–26, 10 players).
    Two-panel atlas of defenders 2025/26 in PCA projection. Left panel: style map without league multipliers; right panel: quality-adjusted map. Grey points are the whole corpus of 1947 players; coloured points are the 89 Norwegian-eligible players by cluster; bright rings mark the 5 with a national-team call-up 2025–26.
    Atlas of defenders 2025/26 in both projections: 89 Norwegian-eligible players in colour against a corpus of 1947. Bright rings mark the national-team pool (call-up 2025–26, 5 players).

    Forwards

    High-assist forwards 2 Norwegian of 142 · NT pool 0 · median born 2000 Leander Alvheim
    Corpus medians: 0.30 non-penalty goals and 0.10 assists per 90, 33 % of the club's minutes, age 24, 0.29 cards per 90. Tactical readCreators from the front line — assist rate more than double the forward median on rotation minutes (38 %), scoring at median. Wide forwards and second strikers who feed the box rather than occupy it (Alvheim, Sæter).
    Duel-heavy rotation forwards 4 Norwegian of 142 · NT pool 0 · median born 1993 Moses MawaMagnus Wolff EikremFredrik Gulbrandsen
    Corpus medians: 0.28 non-penalty goals and 0.08 assists per 90, 46 % of the club's minutes, age 32, 0.17 cards per 90. Tactical readRotation forwards whose signature is physical engagement — card rate roughly three times the forward median, a third of minutes, output at median. Pressing and duel-heavy roles rather than finishing (Mawa, Eikrem, Gulbrandsen).
    High-minutes starting forwards 10 Norwegian of 115 · NT pool 0 · median born 2000 Sanel BojadzicUlrik MathisenOle Didrik BlombergNoah Holm
    Corpus medians: 0.31 non-penalty goals and 0.21 assists per 90, 32 % of the club's minutes, age 25, 0.15 cards per 90. Tactical readEvery-week starters — three quarters of the season's minutes, assist rate about 1.5 times the forward median, scoring at median. Mostly a top-five-league footprint: the first-choice forward who links play as much as he finishes (Bojadzic, Mathisen, Blomberg).
    Older forwards with moderate playing time 5 Norwegian of 121 · NT pool 2 · median born 1999 Erling HaalandAlexander SørlothEman MarkovićObilor Okeke
    Corpus medians: 0.50 non-penalty goals and 0.10 assists per 90, 53 % of the club's minutes, age 25, 0.16 cards per 90. Tactical readExperienced forwards on managed minutes — median age 31, about 40 % of minutes, output at median. The impact or target forward used in rotation (Haaland, Sørloth, Marković).
    Young low-minute forwards 13 Norwegian of 145 · NT pool 2 · median born 2001 Tobias LauritsenKristian ArnstadJonatan Braut BrunesDaniel Karlsbakk
    Corpus medians: 0.30 non-penalty goals and 0.13 assists per 90, 76 % of the club's minutes, age 24, 0.14 cards per 90. Tactical readThe development tier and the largest forward cluster — median age 23, under 30 % of minutes, output just below median. Where most of the home pool's young forwards sit (Lauritsen, Arnstad, Brunes).
    Primary scorers 10 Norwegian of 259 · NT pool 1 · median born 2001 Jørgen Strand LarsenOla SolbakkenAune HeggebøJulian Gonstad
    Corpus medians: 0.27 non-penalty goals and 0.09 assists per 90, 29 % of the club's minutes, age 23, 0.13 cards per 90. Tactical readPrimary scorers — non-penalty goal rate nearly double the forward median on starter minutes (56 %). The finishing forward of a first-choice line (Larsen, Solbakken, Heggebø).

    Midfielders

    High-card-rate midfielders 18 Norwegian of 293 · NT pool 4 · median born 1999 Patrick BergMohamed ElyounoussiJulian RyersonSondre Ørjasæter
    Corpus medians: 0.14 non-penalty goals and 0.22 assists per 90, 59 % of the club's minutes, age 25, 0.17 cards per 90. Tactical readBall-winning, duel-heavy midfielders — the card rate (about 2.5 times the midfield median) is the defining feature, production at the floor of the group. The destroyer profile, often in a double pivot (Berg, Elyounoussi, Ryerson).
    Older rotation midfielders 25 Norwegian of 592 · NT pool 3 · median born 1999 Sander BergeFredrik UlvestadRuben Yttergård JenssenJoshua Kitolano
    Corpus medians: 0.08 non-penalty goals and 0.09 assists per 90, 79 % of the club's minutes, age 25, 0.18 cards per 90. Tactical readVeteran rotation midfielders — median age 30 on managed minutes (40 %), output at median, card rate above it. Experience kept in the squad rather than on the pitch every week (Berge, Ulvestad, Jenssen).
    Young low-minute midfielders 18 Norwegian of 320 · NT pool 0 · median born 1999 Sander KilenEdvin AustbøSondre SørløkkUlrik Saltnes
    Corpus medians: 0.25 non-penalty goals and 0.13 assists per 90, 54 % of the club's minutes, age 24, 0.17 cards per 90. Tactical readDevelopment midfielders — the home pool's largest midfield group: median age 22, under a third of minutes, output at median. The pipeline's waiting room (Kilen, Austbø, Sørløkk).
    Everyday starting midfielders, low scoring output 10 Norwegian of 423 · NT pool 1 · median born 1995 Ole SelnæsMorten BjørloMorten KonradsenMarcus Mehnert
    Corpus medians: 0.08 non-penalty goals and 0.10 assists per 90, 39 % of the club's minutes, age 30, 0.21 cards per 90. Tactical readThe engine room — nearly 80 % of minutes with median output: holding and box-to-box midfielders whose value is continuity and structure, not numbers (Selnæs, Bjørlo, Konradsen).
    High-scoring attacking midfielders 27 Norwegian of 626 · NT pool 2 · median born 2003 Jakob RomsaasJakob HansenVictor HalvorsenSander Risan Mørk
    Corpus medians: 0.08 non-penalty goals and 0.09 assists per 90, 29 % of the club's minutes, age 22, 0.17 cards per 90. Tactical readGoal-scoring attacking midfielders — non-penalty goal rate three times the midfield median on starter minutes (56 %). The number 8/10 who arrives in the box (Romsaas, Hansen, Halvorsen).
    High-minutes creative midfielders 5 Norwegian of 392 · NT pool 0 · median born 2002 Sverre SandalSivert MannsverkMagnus RiisnæsOliver Edvardsen
    Corpus medians: 0.08 non-penalty goals and 0.07 assists per 90, 38 % of the club's minutes, age 24, 0.33 cards per 90. Tactical readStarting playmakers — assist rate three times the midfield median on 62 % of minutes, scoring nearly double. The creative hub of the middle third (Sandal, Mannsverk, Riisnæs).

    Defenders

    Everyday starting defenders 15 Norwegian of 218 · NT pool 3 · median born 1998 Leo ØstigårdLeo CornicHenrik FalchenerFredrik André Bjørkan
    Corpus medians: 0.10 non-penalty goals and 0.04 assists per 90, 55 % of the club's minutes, age 25, 0.20 cards per 90. Tactical readThe defensive core — 85 % of minutes, output at the DF floor. Availability and continuity are the signal; production is not (Østigård, Cornic, Falchener).
    High-card-rate defenders 17 Norwegian of 424 · NT pool 0 · median born 2004 Sebastian JarlHåkon SjåtilBirk RisaVetle Egeli
    Corpus medians: 0.02 non-penalty goals and 0.02 assists per 90, 34 % of the club's minutes, age 22, 0.18 cards per 90. Tactical readDuel-heavy defenders — card rate 2.5 times the DF median with rotation minutes (46 %). The physical stopper profile (Jarl, Sjåtil, Risa).
    Young low-minute defenders 22 Norwegian of 430 · NT pool 1 · median born 1999 Dan Peter UlvestadUlrik FredriksenGustav ValsvikTobias Guddal
    Corpus medians: 0.03 non-penalty goals and 0.03 assists per 90, 84 % of the club's minutes, age 25, 0.18 cards per 90. Tactical readDevelopment defenders — median age 22, about a third of minutes, output at the floor. The tier the 23–25 cohort draws from (Ulvestad, Fredriksen, Valsvik).
    Goal-scoring defenders 15 Norwegian of 227 · NT pool 0 · median born 1997 Oliver BraudeFredrik SjøvoldFredrik DahlDaniel Eid
    Corpus medians: 0.04 non-penalty goals and 0.12 assists per 90, 64 % of the club's minutes, age 25, 0.20 cards per 90. Tactical readSet-piece threats — defenders scoring at five times the DF median on 60 % of minutes. Aerial presence in both boxes (Braude, Sjøvold, Dahl).
    Older defenders 3 Norwegian of 284 · NT pool 0 · median born 2000 Jesper DalandHalldor Stenevik
    Corpus medians: 0.02 non-penalty goals and 0.02 assists per 90, 47 % of the club's minutes, age 24, 0.36 cards per 90. Tactical readExperienced defenders on managed minutes — median age 31, 44 % of minutes, output at the floor. Leadership and cover rather than a starting role (Daland, Stenevik, Meling).
    High-assist defenders with high playing time 17 Norwegian of 363 · NT pool 1 · median born 1996 Lars-Christopher VilsvikFredrik SjølstadKristoffer AjerAxel Kryger
    Corpus medians: 0.02 non-penalty goals and 0.02 assists per 90, 44 % of the club's minutes, age 31, 0.20 cards per 90. Tactical readAttacking full-backs — assist rate seven times the DF median on starter minutes (65 %). The wide defender whose job ends in the final third (Vilsvik, Sjølstad, Ajer).
    Trajectories

    Trajectories 2024/25 → 2025/26 (Norwegian-eligible, ≥ 900 minutes in both seasons)

    Season-over-season change in goals + assists per 90, league-adjusted. A move counts as up or down beyond ± 0.05; everything inside that band is stable and not listed.

    Forwards — 19 players: 7 up, 8 stable, 4 down

    Moving up · G+A / 90 adj.

    PlayerLeagueMin 2024/25 / 2025/26Change
    Erling Haaland ENG-Premier League 2736 / 2953 +0.174
    Kristian Strømland Lien NOR-Eliteserien 1072 / 2286 +0.120
    Eman Marković POL-Ekstraklasa 2518 / 1124 +0.110
    Ole Didrik Blomberg NOR-Eliteserien 1944 / 1196 +0.097
    Sondre Liseth POL-Ekstraklasa 1389 / 2341 +0.074

    Moving down · G+A / 90 adj.

    PlayerLeagueMin 2024/25 / 2025/26Change
    Jørgen Strand Larsen ENG-Premier League 2587 / 2307 −0.342
    Alexander Sørloth ESP-La Liga 1566 / 1980 −0.298
    Magnus Wolff Eikrem NOR-Eliteserien 1597 / 1623 −0.116
    Aune Heggebø NOR-Eliteserien 1282 / 1161 −0.059

    Midfielders — 42 players: 8 up, 30 stable, 4 down

    Moving up · G+A / 90 adj.

    PlayerLeagueMin 2024/25 / 2025/26Change
    Hugo Vetlesen BEL-Pro League 1500 / 1289 +0.153
    Morten Thorsby ITA-Serie A 1898 / 1374 +0.105
    Patrick Berg NOR-Eliteserien 2610 / 2292 +0.080
    Jens Hjertø-Dahl NOR-Eliteserien 2016 / 2372 +0.072
    Ruben Yttergård Jenssen NOR-Eliteserien 2375 / 2700 +0.066

    Moving down · G+A / 90 adj.

    PlayerLeagueMin 2024/25 / 2025/26Change
    Kristian Eriksen NOR-Eliteserien 2412 / 902 −0.111
    Victor Halvorsen NOR-Eliteserien 1056 / 1446 −0.089
    Morten Konradsen NOR-Eliteserien 1315 / 1296 −0.069
    Håkon Evjen NOR-Eliteserien 1974 / 2198 −0.062

    Defenders — 40 players: 6 up, 31 stable, 3 down

    Moving up · G+A / 90 adj.

    PlayerLeagueMin 2024/25 / 2025/26Change
    Leo Østigård ITA-Serie A 2154 / 2622 +0.110
    Joachim Soltvedt NOR-Eliteserien 1200 / 1752 +0.091
    Christopher Cheng NOR-Eliteserien 2060 / 2597 +0.082
    Odin Bjørtuft NOR-Eliteserien 1577 / 2266 +0.071
    Martin Ove Roseth NOR-Eliteserien 1281 / 1441 +0.059

    Moving down · G+A / 90 adj.

    PlayerLeagueMin 2024/25 / 2025/26Change
    Adrian Pereira NOR-Eliteserien 1354 / 1704 −0.063
    Fredrik André Bjørkan NOR-Eliteserien 1792 / 2457 −0.062
    Eirik Haugan NOR-Eliteserien 1382 / 2366 −0.060
    Why does the train leave?

    Why does the train leave?

    Four exhibits comparing Norway with 8 peer countries: youth exposure at home, export route, how exports fare, and who made it.

    Exhibit A — youth exposure at home

    Share of a domestic league's total minutes played by its own nationals aged 21 or under, 2025/26.

    * Σ minutes of players with the league country's nationality and age ≤ 21 at the season's start ÷ Σ minutes of all players in the league, 2025/26. Under-23 shares: DEN 21.2 %, HUN 22.6 %, CRO 22.7 %, NOR 19.8 %, AUT 16.5 %, POL 14.4 %, SUI 16.6 %, CZE 13.5 %. A league without a bar is not covered by FBref.

    Exhibit B — export route

    For every peer-country player on a 2026/27 top-9 roster: the age at the first top-9 season (full roster, and recent entrants only) and, for recent entrants, the league of the season before it. Norwegian exports: 37 players, median export age 21 (recent entrants 17, median 22), 53 % of the recent ones straight from the Eliteserien*.

    CountrynRecent Export age (all)Export age (recent) DomesticStepping stoneOther top-9Not covered Censored
    NOR Norway 37 17 21 22 53 % 0 % 0 % 47 % 19 %
    DEN Denmark 65 25 22 22 60 % 0 % 0 % 40 % 14 %
    SUI Switzerland 42 13 23 21 85 % 0 % 0 % 15 % 33 %
    AUT Austria 37 13 23 22 54 % 8 % 0 % 38 % 35 %
    CZE Czechia 15 7 23 22 0 % 0 % 0 % 100 % 20 %
    CRO Croatia 39 10 23 24.5 60 % 10 % 0 % 30 % 31 %
    POL Poland 30 13 23 24 38 % 23 % 0 % 38 % 20 %
    HUN Hungary 18 11 22 22 91 % 0 % 0 % 9 % 11 %
    SVK Slovakia 11 4 24 23 0 % 0 % 0 % 100 % 27 %

    * Export age = age at the first season in any headline league, history back to 2020/21; a first appearance already in 2020/21 is censored. Recent entrants: first top-9 season 2025/26 or 2026/27, the only ones whose previous season lies inside the fetched window (2024/25 onwards for peer domestic leagues). Stepping-stone leagues: NED-Eredivisie, BEL-Pro League, POR-Primeira Liga, TUR-Süper Lig, GER-2. Bundesliga. Not covered: no earlier row in the data.

    Exhibit C — how the exports fare

    Peer-country players at a top-9 club in 2025/26: median share of the club's minutes, and the club's strength within its league.

    HUN Hungary n = 15
    72 %
    CZE Czechia n = 26
    67 %
    SVK Slovakia n = 17
    60 %
    AUT Austria n = 44
    59 %
    NOR Norway n = 52
    56 %
    DEN Denmark n = 75
    56 %
    CRO Croatia n = 47
    55 %
    POL Poland n = 45
    55 %
    SUI Switzerland n = 54
    50 %

    * Minutes share = player minutes ÷ (club matches × 90), 2025/26, one row per player-season. Club strength proxy: goals-scored percentile within league — clubs ranked by the goals their own roster scored that season (ClubElo was unreachable at run time). Both are medians over the country's exports; n per country in the bars.

    Exhibit D — profile of those who made it

    Goals + assists per 90, league-adjusted, in 2025/26 by the tier of the player's own league: domestic, stepping stone, top-9, or another covered league. Norwegian count and median against the median of the peer countries' values.

    Profile table by tier and position group
    TierGroupNOR nNOR medianPeer median nPeer median
    domestic league FW 31 0.18 16.5 0.12
    domestic league MF 72 0.10 57 0.06
    domestic league DF 76 0.04 42.5 0.02
    stepping-stone league FW 0 — 2 0.28
    stepping-stone league MF 2 0.07 2 0.09
    stepping-stone league DF 1 0.01 2 0.05
    top-9 league FW 6 0.29 5 0.28
    top-9 league MF 23 0.13 16 0.14
    top-9 league DF 11 0.06 8 0.04
    other covered league FW 7 0.16 6 0.13
    other covered league MF 9 0.08 6.5 0.08
    other covered league DF 3 0.02 10.5 0.02

    * Tier = the league of the player's own 2025/26 season. Peer median n and peer median are medians across the peer countries present in that tier and group; a tier a country has no player in is absent, not zero.

    Exhibit E — where Norwegian exports go

    Of the 236 mapped Norwegian players, 59 play outside the Eliteserien.

    • top-9 Sondre Ørjasæter · Tobias Lauritsen · Erling Haaland
    • peer country league Fredrik Ulvestad · Kristian Arnstad · Robin Dahl Østrøm
    • stepping stone Jonas Therkelsen · Jesper Daland
    • other Sivert Mannsverk

    * Buckets by the league of the player's own 2025/26 season; a player is counted once per position group, like everywhere else on the page, so one with rows in two groups counts in each; the number in front of each bar is the player count, the median multiplier is the bucket's median league multiplier. Sideways: destination league multiplier <= the domestic league's multiplier.

    F · The 2026 FIFA World Cup squad by league tier

    Where the 26 players named to the 2026 FIFA World Cup squad played in 2025/26, next to Switzerland, Austria, Czechia. Tier = the league of the player's most-minutes 2025/26 row.

    Minutes, multipliers and age cohorts by country
    Country Median minutesMedian multiplier U2223–25 26–2930+
    NOR Norway 2164.5 0.832 2 6 14 4
    SUI Switzerland 2132 0.788 1 5 10 10
    AUT Austria 1711.5 0.788 1 6 10 9
    CZE Czechia 1826.5 0.434 1 6 10 9

    * 6 squad players have no 2025/26 row in the fetched leagues and are counted as unmatched.

    Historical analogs

    Historical analogs

    For each showcase player the finder takes the nearest 5 player-seasons at the same age across the whole corpus of every fetched league — the 9 headline leagues back to 2020/21, the rest from 2024/25 — all nationalities. Distance is computed on three standardised features: npG+A/90 (quality-adjusted), minutes, league multiplier. For every analog the following seasons are shown as they happened. Description, not prediction: the reader sees the spread of paths; the method imposes none.

    Target

    Erling Haaland

    FW · age 26 · ENG-Premier League 2025/26 · 2953 min · 0.83 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Alexander Isak SWE ENG-Premier League 2024/25 · 2756 min · 0.73 npG+A/90 · d = 0.92
      Followed by: 2025/26  ENG-Premier League · 699 min · 0.44
    2. 2 Mateo Retegui ITA ITA-Serie A 2024/25 · 2383 min · 0.77 npG+A/90 · d = 1.16
      No later season in the corpus.
    3. 3 Marcus Rashford ENG ENG-Premier League 2022/23 · 2879 min · 0.63 npG+A/90 · d = 1.74
      Followed by: 2023/24  ENG-Premier League · 2271 min · 0.26 2024/25  ENG-Premier League · 978 min · 0.35 2025/26  ESP-La Liga · 1763 min · 0.46
    4. 4 Lautaro Martínez ARG ITA-Serie A 2022/23 · 2576 min · 0.65 npG+A/90 · d = 1.77
      Followed by: 2023/24  ITA-Serie A · 2656 min · 0.62 2024/25  ITA-Serie A · 2564 min · 0.42 2025/26  ITA-Serie A · 2159 min · 0.67
    5. 5 Diogo Jota POR ENG-Premier League 2021/22 · 2364 min · 0.64 npG+A/90 · d = 1.85
      Followed by: 2022/23  ENG-Premier League · 1131 min · 0.69 2023/24  ENG-Premier League · 1145 min · 0.81 2024/25  ENG-Premier League · 1196 min · 0.59

    Target

    Antonio Nusa

    FW · age 21 · GER-Bundesliga 2025/26 · 2027 min · 0.29 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Vinicius Júnior BRA ESP-La Liga 2020/21 · 1969 min · 0.25 npG+A/90 · d = 0.35
      Followed by: 2021/22  ESP-La Liga · 2690 min · 0.63 2022/23  ESP-La Liga · 2823 min · 0.45 2023/24  ESP-La Liga · 1864 min · 0.61 2024/25  ESP-La Liga · 2253 min · 0.48
    2. 2 Jamie Leweling GER GER-Bundesliga 2021/22 · 1959 min · 0.24 npG+A/90 · d = 0.37
      Followed by: 2023/24  GER-Bundesliga · 1523 min · 0.30 2024/25  GER-Bundesliga · 1663 min · 0.17 2025/26  GER-Bundesliga · 2367 min · 0.40
    3. 3 Emanuel Emegha NED FRA-Ligue 1 2023/24 · 2078 min · 0.27 npG+A/90 · d = 0.61
      Followed by: 2024/25  FRA-Ligue 1 · 2293 min · 0.41
    4. 4 Arnaud Kalimuendo FRA FRA-Ligue 1 2022/23 · 1849 min · 0.31 npG+A/90 · d = 0.69
      Followed by: 2023/24  FRA-Ligue 1 · 2139 min · 0.26 2024/25  FRA-Ligue 1 · 2578 min · 0.35 2025/26  GER-Bundesliga · 1417 min · 0.36
    5. 5 Josh Sargent USA GER-Bundesliga 2020/21 · 2506 min · 0.25 npG+A/90 · d = 0.71
      Followed by: 2021/22  ENG-Premier League · 1572 min · 0.26

    Target

    Jørgen Strand Larsen

    FW · age 26 · ENG-Premier League 2025/26 · 2307 min · 0.24 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Vitinha POR ITA-Serie A 2025/26 · 2226 min · 0.24 npG+A/90 · d = 0.71
      No later season in the corpus.
    2. 2 David Okereke NGA ITA-Serie A 2022/23 · 2266 min · 0.23 npG+A/90 · d = 0.71
      Followed by: 2024/25  TUR-Süper Lig · 2073 min · 0.25
    3. 3 Walid Cheddira MAR ITA-Serie A 2023/24 · 2120 min · 0.27 npG+A/90 · d = 0.78
      Followed by: 2024/25  ESP-La Liga · 640 min · 0.28 2025/26  ITA-Serie A · 1000 min · 0.31
    4. 4 Moise Kean ITA ITA-Serie A 2025/26 · 2036 min · 0.28 npG+A/90 · d = 0.84
      No later season in the corpus.
    5. 5 Sam Lammers NED ITA-Serie A 2022/23 · 1983 min · 0.22 npG+A/90 · d = 0.84
      Followed by: 2023/24  NED-Eredivisie · 1605 min · 0.32 2024/25  NED-Eredivisie · 1501 min · 0.23 2025/26  NED-Eredivisie · 1545 min · 0.28

    Target

    Jens Petter Hauge

    FW · age 27 · NOR-Eliteserien 2025/26 · 2216 min · 0.18 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Lirim Qamili MKD DEN-Superliga 2024/25 · 2059 min · 0.21 npG+A/90 · d = 0.31
      Followed by: 2025/26  DEN-Superliga · 1265 min · 0.10
    2. 2 Peter Christiansen DEN NOR-Eliteserien 2025/26 · 2098 min · 0.22 npG+A/90 · d = 0.38
      Followed by: 2026/27  NOR-Eliteserien · 1629 min · 0.27
    3. 3 Sebastian Bergier POL POL-Ekstraklasa 2025/26 · 2278 min · 0.22 npG+A/90 · d = 0.43
      No later season in the corpus.
    4. 4 Afimico Pululu COD POL-Ekstraklasa 2025/26 · 2352 min · 0.21 npG+A/90 · d = 0.44
      No later season in the corpus.
    5. 5 Moussa Sylla MLI GER-2. Bundesliga 2025/26 · 2193 min · 0.19 npG+A/90 · d = 0.49
      No later season in the corpus.

    Target

    Tobias Lauritsen

    FW · age 29 · NED-Eredivisie 2025/26 · 3039 min · 0.21 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Riad Bajić BIH TUR-Süper Lig 2022/23 · 2868 min · 0.20 npG+A/90 · d = 0.27
      Followed by: 2023/24  TUR-Süper Lig · 815 min · 0.22
    2. 2 Lennart Thy GER NED-Eredivisie 2020/21 · 2782 min · 0.22 npG+A/90 · d = 0.34
      Followed by: 2021/22  NED-Eredivisie · 2386 min · 0.14 2023/24  NED-Eredivisie · 2674 min · 0.26
    3. 3 Victor Edvardsen SWE NED-Eredivisie 2024/25 · 2740 min · 0.23 npG+A/90 · d = 0.42
      Followed by: 2025/26  NED-Eredivisie · 1992 min · 0.24 2026/27  NED-Eredivisie · 493 min · 0.28
    4. 4 Krzysztof Piątek POL TUR-Süper Lig 2023/24 · 2694 min · 0.22 npG+A/90 · d = 0.49
      Followed by: 2024/25  TUR-Süper Lig · 2607 min · 0.28
    5. 5 Ali Sowe GAM TUR-Süper Lig 2022/23 · 2749 min · 0.25 npG+A/90 · d = 0.50
      Followed by: 2023/24  TUR-Süper Lig · 2123 min · 0.19 2024/25  TUR-Süper Lig · 2978 min · 0.29 2025/26  TUR-Süper Lig · 2284 min · 0.23

    Target

    Daniel Karlsbakk

    FW · age 23 · NOR-Eliteserien 2025/26 · 2347 min · 0.17 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Christian Gammelgaard DEN DEN-Superliga 2025/26 · 2330 min · 0.16 npG+A/90 · d = 0.11
      No later season in the corpus.
    2. 2 Tobias Bech DEN DEN-Superliga 2024/25 · 2262 min · 0.15 npG+A/90 · d = 0.21
      Followed by: 2025/26  DEN-Superliga · 2668 min · 0.19 2026/27  DEN-Superliga · 467 min · 0.05
    3. 3 Bohdan Viunnyk UKR POL-Ekstraklasa 2024/25 · 2329 min · 0.16 npG+A/90 · d = 0.34
      Followed by: 2025/26  POL-Ekstraklasa · 966 min · 0.11
    4. 4 Derry Scherhant GER GER-2. Bundesliga 2024/25 · 2415 min · 0.19 npG+A/90 · d = 0.51
      Followed by: 2025/26  GER-Bundesliga · 1301 min · 0.26
    5. 5 Kristian Arnstad NOR DEN-Superliga 2025/26 · 2611 min · 0.13 npG+A/90 · d = 0.51
      Followed by: 2026/27  DEN-Superliga · 657 min · 0.04

    Target

    Julian Ryerson

    MF · age 29 · GER-Bundesliga 2025/26 · 2266 min · 0.39 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Julian Brandt GER GER-Bundesliga 2024/25 · 2303 min · 0.38 npG+A/90 · d = 0.06
      Followed by: 2025/26  GER-Bundesliga · 1606 min · 0.37
    2. 2 Jens Stage DEN GER-Bundesliga 2024/25 · 2204 min · 0.39 npG+A/90 · d = 0.11
      Followed by: 2025/26  GER-Bundesliga · 2460 min · 0.30
    3. 3 Alassane Pléa FRA GER-Bundesliga 2021/22 · 2070 min · 0.36 npG+A/90 · d = 0.33
      Followed by: 2022/23  GER-Bundesliga · 1774 min · 0.33 2023/24  GER-Bundesliga · 1913 min · 0.37 2024/25  GER-Bundesliga · 1902 min · 0.41
    4. 4 Leon Goretzka GER GER-Bundesliga 2023/24 · 2241 min · 0.35 npG+A/90 · d = 0.33
      Followed by: 2024/25  GER-Bundesliga · 1325 min · 0.23 2025/26  GER-Bundesliga · 1947 min · 0.25
    5. 5 Filip Kostić SRB GER-Bundesliga 2020/21 · 2534 min · 0.41 npG+A/90 · d = 0.43
      Followed by: 2021/22  GER-Bundesliga · 2522 min · 0.31 2022/23  ITA-Serie A · 2564 min · 0.28 2023/24  ITA-Serie A · 1815 min · 0.16 2024/25  TUR-Süper Lig · 1959 min · 0.14

    Target

    Andreas Schjelderup

    MF · age 22 · POR-Primeira Liga 2025/26 · 1723 min · 0.26 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Hákon Haraldsson ISL FRA-Ligue 1 2024/25 · 1755 min · 0.23 npG+A/90 · d = 0.31
      Followed by: 2025/26  FRA-Ligue 1 · 2460 min · 0.21
    2. 2 Sofiane Diop MAR FRA-Ligue 1 2021/22 · 1948 min · 0.25 npG+A/90 · d = 0.35
      Followed by: 2022/23  FRA-Ligue 1 · 1228 min · 0.19 2023/24  FRA-Ligue 1 · 499 min · 0.19 2024/25  FRA-Ligue 1 · 1064 min · 0.37 2025/26  FRA-Ligue 1 · 2130 min · 0.19
    3. 3 Francisco Conceição POR POR-Primeira Liga 2023/24 · 1888 min · 0.22 npG+A/90 · d = 0.37
      Followed by: 2024/25  ITA-Serie A · 1340 min · 0.26 2025/26  ITA-Serie A · 2078 min · 0.25
    4. 4 Maghnes Akliouche FRA FRA-Ligue 1 2023/24 · 1613 min · 0.30 npG+A/90 · d = 0.44
      Followed by: 2024/25  FRA-Ligue 1 · 2406 min · 0.29 2025/26  FRA-Ligue 1 · 2400 min · 0.25
    5. 5 Timothy Weah USA FRA-Ligue 1 2021/22 · 1616 min · 0.21 npG+A/90 · d = 0.45
      Followed by: 2022/23  FRA-Ligue 1 · 1748 min · 0.05 2023/24  ITA-Serie A · 1258 min · 0.10 2024/25  ITA-Serie A · 1638 min · 0.26 2025/26  FRA-Ligue 1 · 2253 min · 0.11

    Target

    Sander Berge

    MF · age 28 · ENG-Premier League 2025/26 · 2905 min · 0.08 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Nélson Semedo POR ENG-Premier League 2020/21 · 2983 min · 0.08 npG+A/90 · d = 0.12
      Followed by: 2021/22  ENG-Premier League · 2131 min · 0.08 2022/23  ENG-Premier League · 2632 min · 0.03 2023/24  ENG-Premier League · 3084 min · 0.07 2024/25  ENG-Premier League · 2886 min · 0.15
    2. 2 George Baldock GRE ENG-Premier League 2020/21 · 2787 min · 0.09 npG+A/90 · d = 0.18
      Followed by: 2023/24  ENG-Premier League · 969 min · 0.09
    3. 3 Kyle Walker-Peters ENG ENG-Premier League 2024/25 · 2918 min · 0.10 npG+A/90 · d = 0.21
      Followed by: 2025/26  ENG-Premier League · 1383 min · 0.06
    4. 4 João Palhinha POR ENG-Premier League 2022/23 · 3108 min · 0.11 npG+A/90 · d = 0.42
      Followed by: 2023/24  ENG-Premier League · 2699 min · 0.18 2024/25  GER-Bundesliga · 673 min · 0.10 2025/26  ENG-Premier League · 2198 min · 0.27
    5. 5 Lewis Cook ENG ENG-Premier League 2024/25 · 2978 min · 0.15 npG+A/90 · d = 0.60
      Followed by: 2025/26  ENG-Premier League · 874 min · 0.16

    Target

    Patrick Berg

    MF · age 29 · NOR-Eliteserien 2025/26 · 2292 min · 0.21 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Jordan Larsson SWE DEN-Superliga 2025/26 · 2281 min · 0.20 npG+A/90 · d = 0.03
      No later season in the corpus.
    2. 2 Emrah Başsan TUR TUR-Süper Lig 2020/21 · 2283 min · 0.21 npG+A/90 · d = 0.54
      Followed by: 2021/22  TUR-Süper Lig · 1940 min · 0.19 2022/23  TUR-Süper Lig · 1220 min · 0.17 2023/24  TUR-Süper Lig · 1483 min · 0.09 2024/25  TUR-Süper Lig · 801 min · 0.13
    3. 3 Miguel Cardoso POR TUR-Süper Lig 2022/23 · 2333 min · 0.21 npG+A/90 · d = 0.54
      Followed by: 2023/24  TUR-Süper Lig · 2508 min · 0.13 2024/25  TUR-Süper Lig · 2857 min · 0.19 2025/26  TUR-Süper Lig · 2852 min · 0.11
    4. 4 Marco Richter GER GER-2. Bundesliga 2025/26 · 2208 min · 0.18 npG+A/90 · d = 0.55
      No later season in the corpus.
    5. 5 Tomáš Ladra CZE CZE-First League 2025/26 · 2020 min · 0.17 npG+A/90 · d = 0.56
      No later season in the corpus.

    Target

    Sondre Ørjasæter

    MF · age 23 · NED-Eredivisie 2025/26 · 3375 min · 0.15 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Kodai Sano JPN NED-Eredivisie 2025/26 · 3060 min · 0.15 npG+A/90 · d = 0.42
      No later season in the corpus.
    2. 2 Fisayo Dele-Bashiru NGA TUR-Süper Lig 2023/24 · 3035 min · 0.15 npG+A/90 · d = 0.47
      Followed by: 2024/25  ITA-Serie A · 947 min · 0.24 2025/26  ITA-Serie A · 1187 min · 0.10
    3. 3 Jarne Steuckers BEL BEL-Pro League 2024/25 · 3097 min · 0.17 npG+A/90 · d = 0.50
      Followed by: 2025/26  BEL-Pro League · 1439 min · 0.17
    4. 4 Isa Sakamoto JPN BEL-Pro League 2025/26 · 2999 min · 0.16 npG+A/90 · d = 0.59
      No later season in the corpus.
    5. 5 Matisse Samoise BEL BEL-Pro League 2023/24 · 3069 min · 0.10 npG+A/90 · d = 0.65
      Followed by: 2024/25  BEL-Pro League · 2029 min · 0.04 2025/26  BEL-Pro League · 1368 min · 0.13

    Target

    Jens Hjertø-Dahl

    MF · age 21 · NOR-Eliteserien 2025/26 · 2372 min · 0.11 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Mario Dorgelès CIV DEN-Superliga 2024/25 · 2225 min · 0.09 npG+A/90 · d = 0.27
      Followed by: 2025/26  POR-Primeira Liga · 996 min · 0.14
    2. 2 Thomas Jørgensen DEN DEN-Superliga 2025/26 · 2579 min · 0.13 npG+A/90 · d = 0.33
      No later season in the corpus.
    3. 3 Mateusz Kowalczyk POL POL-Ekstraklasa 2024/25 · 2465 min · 0.12 npG+A/90 · d = 0.34
      Followed by: 2025/26  POL-Ekstraklasa · 2188 min · 0.07
    4. 4 Tomasz Pieńko POL POL-Ekstraklasa 2024/25 · 2487 min · 0.10 npG+A/90 · d = 0.35
      Followed by: 2025/26  POL-Ekstraklasa · 1384 min · 0.11
    5. 5 Sander Kilen NOR NOR-Eliteserien 2025/26 · 2066 min · 0.12 npG+A/90 · d = 0.42
      Followed by: 2026/27  NOR-Eliteserien · 1264 min · 0.10

    Target

    Joachim Soltvedt

    DF · age 31 · NOR-Eliteserien 2025/26 · 1752 min · 0.15 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Viljar Vevatne NOR NOR-Eliteserien 2024/25 · 1727 min · 0.09 npG+A/90 · d = 0.56
      No later season in the corpus.
    2. 2 Casper Højer Nielsen DEN TUR-Süper Lig 2024/25 · 2024 min · 0.11 npG+A/90 · d = 0.72
      Followed by: 2025/26  TUR-Süper Lig · 2702 min · 0.02
    3. 3 Bas Kuipers NED NED-Eredivisie 2024/25 · 1724 min · 0.12 npG+A/90 · d = 0.72
      Followed by: 2025/26  POR-Primeira Liga · 1348 min · 0.09 2026/27  NED-Eredivisie · 540 min · 0.10
    4. 4 Kaan Ayhan TUR TUR-Süper Lig 2024/25 · 1726 min · 0.08 npG+A/90 · d = 0.83
      No later season in the corpus.
    5. 5 Uğur Çiftçi TUR TUR-Süper Lig 2022/23 · 1793 min · 0.08 npG+A/90 · d = 0.84
      Followed by: 2023/24  TUR-Süper Lig · 3206 min · 0.03 2024/25  TUR-Süper Lig · 2655 min · 0.07

    Target

    Henrik Falchener

    DF · age 23 · NOR-Eliteserien 2025/26 · 2576 min · 0.07 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Fredrik Sjøvold NOR NOR-Eliteserien 2025/26 · 2594 min · 0.10 npG+A/90 · d = 0.24
      Followed by: 2026/27  NOR-Eliteserien · 1646 min · 0.07
    2. 2 Mikkel Rakneberg NOR NOR-Eliteserien 2024/25 · 2311 min · 0.06 npG+A/90 · d = 0.36
      Followed by: 2025/26  NOR-Eliteserien · 2057 min · 0.02
    3. 3 Mathias Tønnessen NOR NOR-Eliteserien 2025/26 · 2422 min · 0.02 npG+A/90 · d = 0.45
      No later season in the corpus.
    4. 4 Zinedin Smajlovic SWE NOR-Eliteserien 2025/26 · 2274 min · 0.04 npG+A/90 · d = 0.45
      Followed by: 2026/27  NOR-Eliteserien · 896 min · 0.03
    5. 5 Mattia Zanotti ITA SUI-Super League 2025/26 · 2548 min · 0.04 npG+A/90 · d = 0.47
      No later season in the corpus.

    Target

    Leo Østigård

    DF · age 27 · ITA-Serie A 2025/26 · 2622 min · 0.14 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Jules Koundé FRA ESP-La Liga 2024/25 · 2605 min · 0.11 npG+A/90 · d = 0.33
      Followed by: 2025/26  ESP-La Liga · 2261 min · 0.11
    2. 2 Emanuele Valeri ITA ITA-Serie A 2024/25 · 2873 min · 0.14 npG+A/90 · d = 0.34
      Followed by: 2025/26  ITA-Serie A · 2643 min · 0.08
    3. 3 Benjamin Henrichs GER GER-Bundesliga 2023/24 · 2526 min · 0.13 npG+A/90 · d = 0.36
      Followed by: 2024/25  GER-Bundesliga · 935 min · 0.10
    4. 4 Nico Schlotterbeck GER GER-Bundesliga 2025/26 · 2520 min · 0.14 npG+A/90 · d = 0.36
      No later season in the corpus.
    5. 5 Theo Hernández FRA ITA-Serie A 2023/24 · 2791 min · 0.17 npG+A/90 · d = 0.38
      Followed by: 2024/25  ITA-Serie A · 2696 min · 0.15

    Target

    Fredrik André Bjørkan

    DF · age 28 · NOR-Eliteserien 2025/26 · 2457 min · 0.06 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Eirik Saunes NOR NOR-Eliteserien 2025/26 · 2496 min · 0.05 npG+A/90 · d = 0.10
      Followed by: 2026/27  NOR-Eliteserien · 1454 min · 0.01
    2. 2 Jesper Tåje NOR NOR-Eliteserien 2024/25 · 2228 min · 0.04 npG+A/90 · d = 0.34
      Followed by: 2025/26  NOR-Eliteserien · 2186 min · 0.05
    3. 3 Jacob Rasmussen DEN DEN-Superliga 2024/25 · 2520 min · 0.02 npG+A/90 · d = 0.35
      Followed by: 2025/26  AUT-Bundesliga · 2232 min · 0.02 2026/27  GER-2. Bundesliga · 450 min · 0.00
    4. 4 Odin Bjørtuft NOR NOR-Eliteserien 2025/26 · 2266 min · 0.10 npG+A/90 · d = 0.39
      Followed by: 2026/27  NOR-Eliteserien · 1676 min · 0.02
    5. 5 Stratos Svarnas GRE POL-Ekstraklasa 2024/25 · 2504 min · 0.03 npG+A/90 · d = 0.40
      Followed by: 2025/26  POL-Ekstraklasa · 2361 min · 0.03

    Target

    Oliver Braude

    DF · age 22 · NED-Eredivisie 2025/26 · 2815 min · 0.06 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Bünyamin Balcı TUR TUR-Süper Lig 2021/22 · 2740 min · 0.06 npG+A/90 · d = 0.17
      Followed by: 2022/23  TUR-Süper Lig · 2810 min · 0.04 2023/24  TUR-Süper Lig · 1858 min · 0.07 2024/25  TUR-Süper Lig · 1363 min · 0.03 2025/26  TUR-Süper Lig · 2349 min · 0.06
    2. 2 Yukinari Sugawara JPN NED-Eredivisie 2021/22 · 2635 min · 0.06 npG+A/90 · d = 0.24
      Followed by: 2022/23  NED-Eredivisie · 2398 min · 0.16 2023/24  NED-Eredivisie · 2557 min · 0.16 2024/25  ENG-Premier League · 1557 min · 0.16 2025/26  GER-Bundesliga · 2428 min · 0.15
    3. 3 Milan van Ewijk NED NED-Eredivisie 2021/22 · 2897 min · 0.03 npG+A/90 · d = 0.25
      Followed by: 2022/23  NED-Eredivisie · 3060 min · 0.09
    4. 4 Jurriën Timber NED NED-Eredivisie 2022/23 · 3029 min · 0.06 npG+A/90 · d = 0.28
      Followed by: 2024/25  ENG-Premier League · 2417 min · 0.12 2025/26  ENG-Premier League · 2454 min · 0.23
    5. 5 Melle Meulensteen NED NED-Eredivisie 2020/21 · 2945 min · 0.03 npG+A/90 · d = 0.31
      Followed by: 2021/22  NED-Eredivisie · 2880 min · 0.03 2022/23  NED-Eredivisie · 2699 min · 0.07 2023/24  NED-Eredivisie · 2039 min · 0.05 2025/26  NED-Eredivisie · 2826 min · 0.11

    Target

    Eivind Helland

    DF · age 21 · NOR-Eliteserien 2025/26 · 2986 min · 0.02 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Bung Meng Freimann SUI SUI-Super League 2025/26 · 2752 min · 0.03 npG+A/90 · d = 0.51
      Followed by: 2026/27  SUI-Super League · 450 min · 0.00
    2. 2 Filip Luberecki POL POL-Ekstraklasa 2025/26 · 2615 min · 0.02 npG+A/90 · d = 0.58
      Followed by: 2026/27  POL-Ekstraklasa · 716 min · 0.00
    3. 3 Bünyamin Balcı TUR TUR-Süper Lig 2020/21 · 3022 min · 0.06 npG+A/90 · d = 0.64
      Followed by: 2021/22  TUR-Süper Lig · 2740 min · 0.06 2022/23  TUR-Süper Lig · 2810 min · 0.04 2023/24  TUR-Süper Lig · 1858 min · 0.07 2024/25  TUR-Süper Lig · 1363 min · 0.03
    4. 4 Morrison Agyemang GHA CRO-HNL 2024/25 · 2788 min · 0.02 npG+A/90 · d = 0.66
      No later season in the corpus.
    5. 5 Marvin Young NED NED-Eredivisie 2025/26 · 2970 min · 0.02 npG+A/90 · d = 0.66
      Followed by: 2026/27  NED-Eredivisie · 528 min · 0.06

    * Corpus: player-seasons with at least 450 minutes in any fetched league — headline leagues 2020/21 → 2026/27, the other leagues 2024/25 → 2026/27; the target's own seasons are excluded. A path that ends early means the player left the covered leagues, not that the career ended.

    Player index

    Player index

    Every Norwegian-eligible player with a complete 2025/26 season in a covered league — 236 players — with the numbers behind the atlases. Names with a card link to it.

    236
    Table of 236 players
    PlayerPosAge ClubLeagueMin G+A / 90 adj.Style clusterNT
    Kristoffer Ajer DF 27 Brentford ENG-Premier League 1810 0.05 High-assist defenders with high playing time NT
    Ayoub Aleesami DF 28 KFUM Oslo NOR-Eliteserien 1836 0.04 Young low-minute defenders
    Haitam Aleesami DF 33 Bodø/Glimt NOR-Eliteserien 1395 0.06 Everyday starting defenders
    Ruben Alte MF 24 Kristiansund NOR-Eliteserien 1289 0.12 Young low-minute midfielders
    Leander Alvheim FW 20 Kristiansund NOR-Eliteserien 722 0.18 High-assist forwards
    Isak Amundsen DF 25 Molde NOR-Eliteserien 847 0.02 High-card-rate defenders
    Ethan Amundsen-Day DF 19 HamKam NOR-Eliteserien 898 0.04 High-card-rate defenders
    Kent-Are Antonsen MF 29 Tromsø NOR-Eliteserien 607 0.08 Everyday starting midfielders, low scoring output
    Fredrik Ardraa MF 18 Strømsgodset NOR-Eliteserien 2133 0.08 Older rotation midfielders
    Kristian Arnstad FW 21 AGF DEN-Superliga 2611 0.13 Young low-minute forwards
    Kristoffer Askildsen MF 24 Viking NOR-Eliteserien 1552 0.13 High-card-rate midfielders
    Sondre Auklend MF 21 Bodø/Glimt NOR-Eliteserien 1159 0.14 High-card-rate midfielders
    Fredrik Aursnes MF 29 Benfica POR-Primeira Liga 2291 0.14 Older rotation midfielders NT
    Edvin Austbø MF 19 Viking NOR-Eliteserien 1826 0.17 Young low-minute midfielders
    Johan Bakke MF 20 Strømsgodset NOR-Eliteserien 1140 0.09 High-scoring attacking midfielders
    Patrick Berg MF 27 Bodø/Glimt NOR-Eliteserien 2292 0.21 High-card-rate midfielders NT
    Sander Berge MF 27 Fulham ENG-Premier League 2905 0.08 Older rotation midfielders NT
    Jo Inge Berget MF 34 Sarpsborg 08 NOR-Eliteserien 1096 0.14 Young low-minute midfielders
    Fredrik Tobias Berglie DF 28 KFUM Oslo NOR-Eliteserien 1979 0.01 Young low-minute defenders
    Henrik Bjørdal MF 27 Vålerenga NOR-Eliteserien 1822 0.16 High-card-rate midfielders
    Fredrik André Bjørkan DF 26 Bodø/Glimt NOR-Eliteserien 2457 0.06 Everyday starting defenders NT
    Morten Bjørlo MF 29 Konyaspor TUR-Süper Lig 1335 0.09 Everyday starting midfielders, low scoring output
    Sondre Bjørshol DF 30 Viking NOR-Eliteserien 780 0.08 Everyday starting defenders
    Odin Bjørtuft DF 26 Bodø/Glimt NOR-Eliteserien 2266 0.10 Everyday starting defenders
    Ole Didrik Blomberg FW 24 Bodø/Glimt NOR-Eliteserien 1196 0.25 High-minutes starting forwards
    Oscar Bobb MF 22 Fulham ENG-Premier League 571 0.14 High-scoring attacking midfielders NT
    Sanel Bojadzic FW 26 Bryne NOR-Eliteserien 1419 0.19 High-minutes starting forwards
    Anders Bondhus DF 19 Haugesund NOR-Eliteserien 788 0.04 High-card-rate defenders
    Oliver Braude DF 21 Heerenveen NED-Eredivisie 2815 0.06 Goal-scoring defenders
    Daniel Braut FW 19 Tromsø NOR-Eliteserien 574 0.25 High-minutes starting forwards
    Emil Breivik MF 24 Molde NOR-Eliteserien 2497 0.11 Older rotation midfielders
    Marius Broholm FW 20 Rosenborg NOR-Eliteserien 947 0.18 Primary scorers
    Jonatan Braut Brunes FW 24 Raków POL-Ekstraklasa 2451 0.18 Young low-minute forwards
    Emil Ceide FW 23 Rosenborg NOR-Eliteserien 2274 0.19 Young low-minute forwards
    Mikkel Ceide DF 23 Rosenborg NOR-Eliteserien 2096 0.01 Young low-minute defenders
    Christopher Cheng DF 23 Sandefjord NOR-Eliteserien 1954 0.13 Everyday starting defenders
    Sander Christiansen MF 23 Sarpsborg 08 NOR-Eliteserien 1975 0.04 Older rotation midfielders
    Leo Cornic DF 24 Tromsø NOR-Eliteserien 2596 0.09 Everyday starting defenders
    Mats Møller Dæhli MF 29 Molde NOR-Eliteserien 1893 0.05 Older rotation midfielders
    Fredrik Dahl DF 26 Strømsgodset NOR-Eliteserien 2317 0.06 Goal-scoring defenders
    Petter Nosa Dahl MF 22 Rapid Wien AUT-Bundesliga 1224 0.10 High-card-rate midfielders
    Jesper Daland DF 25 Düsseldorf GER-2. Bundesliga 1561 0.01 Older defenders
    Aron Dønnum MF 27 Toulouse FRA-Ligue 1 2419 0.12 Older rotation midfielders
    Vetle Dragsnes DF 30 Brann NOR-Eliteserien 719 0.02 High-assist defenders with high playing time
    Jakob Dunsby MF 24 Sandefjord NOR-Eliteserien 1978 0.16 High-card-rate midfielders
    Oliver Edvardsen MF 26 Ajax NED-Eredivisie 640 0.12 High-minutes creative midfielders
    Sindre Egeli FW 19 Nordsjælland DEN-Superliga 540 0.20 High-minutes starting forwards
    Vetle Egeli DF 20 Sandefjord NOR-Eliteserien 1139 0.05 High-card-rate defenders
    Daniel Eid DF 26 Fredrikstad NOR-Eliteserien 2059 0.06 Goal-scoring defenders
    Magnus Wolff Eikrem FW 34 Molde NOR-Eliteserien 1623 0.16 Duel-heavy rotation forwards
    Mohamed Elyounoussi MF 30 FC Copenhagen DEN-Superliga 2283 0.19 High-card-rate midfielders
    Kristian Eriksen MF 29 Molde NOR-Eliteserien 902 0.10 Young low-minute midfielders
    Vegard Erlien FW 27 Tromsø NOR-Eliteserien 2086 0.18 Young low-minute forwards
    Håkon Evjen MF 24 Bodø/Glimt NOR-Eliteserien 2198 0.11 Older rotation midfielders
    Henrik Falchener DF 21 Viking NOR-Eliteserien 2576 0.07 Everyday starting defenders NT
    Sondre Brunstad Fet MF 28 Bodø/Glimt NOR-Eliteserien 969 0.12 Young low-minute midfielders
    Bård Finne FW 29 Brann NOR-Eliteserien 1563 0.19 Young low-minute forwards
    Iver Fossum MF 28 Rosenborg NOR-Eliteserien 1605 0.10 Older rotation midfielders
    Ulrik Fredriksen DF 25 Fredrikstad NOR-Eliteserien 2677 0.03 Young low-minute defenders
    Martin Gjone DF 19 Sandefjord NOR-Eliteserien 517 0.02 High-card-rate defenders
    Johannes Nunez Godoy FW 28 KFUM Oslo NOR-Eliteserien 884 0.24 High-minutes starting forwards
    Julian Gonstad FW 18 HamKam NOR-Eliteserien 1009 0.17 Primary scorers
    Sondre Granaas MF 18 Molde NOR-Eliteserien 919 0.08 High-scoring attacking midfielders
    Almar Grindhaug MF 18 Haugesund NOR-Eliteserien 811 0.07 High-scoring attacking midfielders
    Mathias Grundetjern FW 24 Vålerenga NOR-Eliteserien 868 0.20 High-minutes starting forwards
    Tobias Guddal DF 22 Tromsø NOR-Eliteserien 2585 0.02 Young low-minute defenders
    Fredrik Gulbrandsen FW 32 Molde NOR-Eliteserien 1269 0.18 Duel-heavy rotation forwards
    Tobias Gulliksen MF 22 Rapid Wien AUT-Bundesliga 1019 0.06 High-scoring attacking midfielders
    Jostein Gundersen DF 28 Bodø/Glimt NOR-Eliteserien 1093 0.03 High-assist defenders with high playing time
    David Hickson Gyedu MF 27 KFUM Oslo NOR-Eliteserien 2086 0.10 Older rotation midfielders
    Erling Haaland FW 25 Manchester City ENG-Premier League 2953 0.83 Older forwards with moderate playing time NT
    Elias Hagen MF 25 Vålerenga NOR-Eliteserien 1198 0.09 High-scoring attacking midfielders
    Teodor Haltvik MF 24 KFUM Oslo NOR-Eliteserien 1030 0.10 High-scoring attacking midfielders
    Victor Halvorsen MF 20 Sarpsborg 08 NOR-Eliteserien 1446 0.05 High-scoring attacking midfielders
    Andreas Hanche-Olsen DF 28 Mainz 05 GER-Bundesliga 851 0.08 High-assist defenders with high playing time
    Jakob Hansen MF 19 Viking NOR-Eliteserien 1545 0.08 High-scoring attacking midfielders
    Eirik Haugan DF 27 Molde NOR-Eliteserien 2366 0.01 Young low-minute defenders
    Jens Petter Hauge FW 25 Bodø/Glimt NOR-Eliteserien 2216 0.18 Young low-minute forwards NT
    Herman Haugen MF 24 Viking NOR-Eliteserien 601 0.10 High-minutes creative midfielders
    Kristoffer Haugen DF 30 Viking NOR-Eliteserien 1259 0.08 Goal-scoring defenders
    Vegar Eggen Hedenstad DF 33 Vålerenga NOR-Eliteserien 1529 0.05 Goal-scoring defenders
    Aune Heggebø FW 23 Brann NOR-Eliteserien 1161 0.18 Primary scorers
    Torbjørn Heggem DF 26 Bologna ITA-Serie A 2113 0.01 Young low-minute defenders NT
    Henrik Heggheim DF 23 Viking NOR-Eliteserien 1907 0.07 Goal-scoring defenders
    Eivind Helland DF 19 Brann NOR-Eliteserien 2502 0.02 Young low-minute defenders
    Andreas Helmersen FW 26 Bodø/Glimt NOR-Eliteserien 511 0.28 Older forwards with moderate playing time
    Eirik Hestad MF 29 Molde NOR-Eliteserien 1455 0.14 High-card-rate midfielders
    Simen Hestnes MF 28 KFUM Oslo NOR-Eliteserien 2258 0.09 Older rotation midfielders
    Jens Hjertø-Dahl MF 19 Tromsø NOR-Eliteserien 2372 0.11 Older rotation midfielders
    Jonas Hjorth MF 24 KFUM Oslo NOR-Eliteserien 2184 0.09 Older rotation midfielders
    Noah Holm FW 23 Rosenborg NOR-Eliteserien 969 0.14 High-minutes starting forwards
    Mikkel Hope DF 18 Haugesund NOR-Eliteserien 1108 0.03 High-card-rate defenders
    Nikolai Hopland DF 21 Heerenveen NED-Eredivisie 535 0.06 High-card-rate defenders
    Håkon Hoseth MF 25 KFUM Oslo NOR-Eliteserien 1414 0.10 High-card-rate midfielders
    Jens Husebø DF 25 Bryne NOR-Eliteserien 1711 0.01 Young low-minute defenders
    Sander Innvær MF 20 Haugesund NOR-Eliteserien 998 0.06 High-scoring attacking midfielders
    Sebastian Jarl DF 25 Vålerenga NOR-Eliteserien 1382 0.01 High-card-rate defenders
    Seedy Jatta FW 22 Sturm Graz AUT-Bundesliga 1807 0.09 Young low-minute forwards
    Fredrik Oldrup Jensen DF 32 NAC Breda NED-Eredivisie 751 0.05 High-assist defenders with high playing time
    Anders Jenssen DF 31 Tromsø NOR-Eliteserien 639 0.02 High-assist defenders with high playing time
    Ruben Yttergård Jenssen MF 36 Tromsø NOR-Eliteserien 2700 0.11 Older rotation midfielders
    Ulrik Yttergård Jenssen DF 28 Rosenborg NOR-Eliteserien 1521 0.07 Everyday starting defenders
    Simen Juklerød DF 31 Sint-Truiden BEL-Pro League 863 0.17 Goal-scoring defenders
    Markus Andreas Kaasa MF 27 Molde NOR-Eliteserien 573 0.10 Young low-minute midfielders
    Warren Kamanzi MF 24 Toulouse FRA-Ligue 1 870 0.13 High-scoring attacking midfielders
    Daniel Karlsbakk FW 21 Sarpsborg 08 NOR-Eliteserien 2347 0.17 Young low-minute forwards
    Sander Kilen MF 19 Kristiansund NOR-Eliteserien 2066 0.12 Young low-minute midfielders
    Joshua Kitolano MF 23 Sparta R. NED-Eredivisie 2623 0.13 Older rotation midfielders
    Mathias Kjølø MF 24 Twente NED-Eredivisie 533 0.09 High-scoring attacking midfielders
    Fredrik Pallesen Knudsen DF 28 Brann NOR-Eliteserien 1239 0.01 High-assist defenders with high playing time
    Magnus Knudsen MF 24 AGF DEN-Superliga 1223 0.04 High-scoring attacking midfielders
    Morten Konradsen MF 28 Haugesund NOR-Eliteserien 1296 0.04 Everyday starting midfielders, low scoring output
    Stian Kristiansen DF 26 Sandefjord NOR-Eliteserien 2565 0.04 Young low-minute defenders
    Axel Kryger DF 27 Bryne NOR-Eliteserien 1423 0.01 High-assist defenders with high playing time
    Simen Kvia-Egeskog MF 21 Viking NOR-Eliteserien 1188 0.15 Young low-minute midfielders
    Heine Larsen MF 22 Bryne NOR-Eliteserien 1497 0.11 Young low-minute midfielders
    Jørgen Strand Larsen FW 25 Wolves ENG-Premier League 1404 0.19 Primary scorers NT
    Lars Olden Larsen FW 26 Tromsø NOR-Eliteserien 624 0.17 Primary scorers
    Tobias Lauritsen FW 27 Sparta R. NED-Eredivisie 3039 0.21 Young low-minute forwards
    Kristian Strømland Lien FW 23 HamKam NOR-Eliteserien 2286 0.21 Young low-minute forwards
    Martin Linnes DF 33 Molde NOR-Eliteserien 1383 0.06 Goal-scoring defenders
    Sondre Liseth FW 27 Górnik Zabrze POL-Ekstraklasa 2341 0.15 Young low-minute forwards
    Filip Loftesnes-Bjune DF 19 Sandefjord NOR-Eliteserien 606 0.02 High-card-rate defenders
    Mathias Løvik MF 21 Parma ITA-Serie A 471 0.09 High-scoring attacking midfielders
    Mathias Løvik DF 21 Trabzonspor TUR-Süper Lig 650 0.07 Goal-scoring defenders
    Isak Määttä FW 23 Bodø/Glimt NOR-Eliteserien 750 0.22 High-minutes starting forwards
    Sivert Mannsverk MF 23 Sparta Prague CZE-First League 926 0.06 High-minutes creative midfielders
    Eman Marković FW 25 Katowice POL-Ekstraklasa 1124 0.24 Older forwards with moderate playing time
    Ulrik Mathisen FW 26 Brann NOR-Eliteserien 1411 0.16 High-minutes starting forwards
    Moses Mawa FW 28 HamKam NOR-Eliteserien 1677 0.11 Duel-heavy rotation forwards
    Marcus Mehnert MF 27 Strømsgodset NOR-Eliteserien 1148 0.09 Everyday starting midfielders, low scoring output
    Marcus Melchior MF 23 Sandefjord NOR-Eliteserien 830 0.15 High-card-rate midfielders
    Birger Meling DF 30 FC Copenhagen DEN-Superliga 802 0.02 Older defenders
    Elias Hoff Melkersen FW 22 Strømsgodset NOR-Eliteserien 507 0.14 Primary scorers
    Stian Molde DF 28 Fredrikstad NOR-Eliteserien 1856 0.04 Everyday starting defenders
    Sander Risan Mørk MF 24 Sandefjord NOR-Eliteserien 1416 0.10 High-scoring attacking midfielders
    Joel Mvuka MF 22 Lorient FRA-Ligue 1 560 0.08 High-scoring attacking midfielders
    Felix Myhre MF 25 Brann NOR-Eliteserien 2089 0.11 Older rotation midfielders
    Ivan Näsberg DF 28 Vålerenga NOR-Eliteserien 843 0.04 High-assist defenders with high playing time
    Snorre Strand Nilsen DF 28 HamKam NOR-Eliteserien 1460 0.07 Goal-scoring defenders
    Moussa Njie FW 29 KFUM Oslo NOR-Eliteserien 1209 0.12 Duel-heavy rotation forwards
    Lasse Nordås FW 23 Heerenveen NED-Eredivisie 857 0.26 Primary scorers
    Simen Bolkan Nordli MF 25 Rosenborg NOR-Eliteserien 860 0.13 High-card-rate midfielders
    Runar Norheim MF 20 Nordsjælland DEN-Superliga 1409 0.14 Young low-minute midfielders
    Runar Norheim DF 19 Tromsø NOR-Eliteserien 1311 0.09 Goal-scoring defenders
    Amin Nouri DF 35 KFUM Oslo NOR-Eliteserien 1092 0.07 High-assist defenders with high playing time
    Antonio Nusa FW 20 RB Leipzig GER-Bundesliga 2027 0.29 Young low-minute forwards NT
    Troy Nyhammer MF 18 Haugesund NOR-Eliteserien 1353 0.07 High-scoring attacking midfielders
    Sverre Nypan MF 18 Rosenborg NOR-Eliteserien 652 0.08 High-scoring attacking midfielders
    Magnar Ødegaard DF 31 Sarpsborg 08 NOR-Eliteserien 527 0.02 High-assist defenders with high playing time
    Martin Ødegaard MF 26 Arsenal ENG-Premier League 1369 0.37 High-card-rate midfielders NT
    Niklas Ødegård MF 20 Kristiansund NOR-Eliteserien 1981 0.09 Older rotation midfielders
    Mohamed Ofkir FW 28 Vålerenga NOR-Eliteserien 730 0.20 High-minutes starting forwards
    Obilor Okeke FW 22 KFUM Oslo NOR-Eliteserien 855 0.23 Older forwards with moderate playing time
    Aaron Olsen DF 23 Vålerenga NOR-Eliteserien 2283 0.03 Young low-minute defenders
    Marius Olsen DF 23 Kristiansund NOR-Eliteserien 2144 0.02 Young low-minute defenders
    Fredrik Oppegard MF 22 Auxerre FRA-Ligue 1 1246 0.05 High-scoring attacking midfielders
    Sondre Ørjasæter MF 21 Twente NED-Eredivisie 2130 0.18 High-card-rate midfielders
    William Osnes-Ringen MF 18 HamKam NOR-Eliteserien 780 0.07 High-scoring attacking midfielders
    Leo Østigård DF 25 Genoa ITA-Serie A 2622 0.14 Everyday starting defenders NT
    Robin Dahl Østrøm DF 22 Silkeborg DEN-Superliga 2581 0.01 Young low-minute defenders
    Casper Øyvann DF 25 Molde NOR-Eliteserien 687 0.04 High-card-rate defenders
    Fredrik Pedersen DF 22 Sandefjord NOR-Eliteserien 2110 0.04 Young low-minute defenders
    Marcus Pedersen MF 25 Torino ITA-Serie A 2134 0.12 Older rotation midfielders
    Thore Pedersen DF 28 Brann NOR-Eliteserien 1522 0.04 Goal-scoring defenders
    Adrian Pereira DF 25 Rosenborg NOR-Eliteserien 1704 0.04 Young low-minute defenders
    Edvard Pettersen MF 18 Sandefjord NOR-Eliteserien 726 0.09 High-scoring attacking midfielders
    Lasse Qvigstad DF 21 Bryne NOR-Eliteserien 1660 0.04 Everyday starting defenders
    Simen Rafn DF 32 Fredrikstad NOR-Eliteserien 1022 0.02 High-assist defenders with high playing time
    Mikkel Rakneberg DF 22 Kristiansund NOR-Eliteserien 2057 0.02 Young low-minute defenders
    Robin Rasch MF 31 KFUM Oslo NOR-Eliteserien 583 0.06 Everyday starting midfielders, low scoring output
    Mathias Rasmussen MF 27 Union SG BEL-Pro League 714 0.16 Everyday starting midfielders, low scoring output
    Peter Reinhardsen DF 25 Sarpsborg 08 NOR-Eliteserien 1023 0.07 Everyday starting defenders
    Erlend Dahl Reitan DF 27 Rosenborg NOR-Eliteserien 1057 0.03 High-assist defenders with high playing time
    Jesper Reitan-Sunde FW 19 Rosenborg NOR-Eliteserien 782 0.11 Primary scorers
    Magnus Riisnæs MF 20 Vålerenga NOR-Eliteserien 646 0.14 High-minutes creative midfielders
    Birk Risa DF 26 Molde NOR-Eliteserien 1218 0.02 High-card-rate defenders
    Alwande Roaldsøy MF 20 HamKam NOR-Eliteserien 1301 0.13 Young low-minute midfielders
    Jakob Romsaas MF 21 Charleroi BEL-Pro League 1753 0.13 High-scoring attacking midfielders
    Martin Ove Roseth DF 26 Viking NOR-Eliteserien 1441 0.07 Everyday starting defenders
    Julian Ryerson MF 27 Dortmund GER-Bundesliga 2266 0.39 High-card-rate midfielders NT
    Ole Christian Sæter FW 28 Rosenborg NOR-Eliteserien 501 0.14 High-assist forwards
    Ulrik Saltnes MF 32 Bodø/Glimt NOR-Eliteserien 1603 0.20 Young low-minute midfielders
    Sverre Sandal MF 21 KFUM Oslo NOR-Eliteserien 2175 0.08 High-minutes creative midfielders
    Mads Sande FW 26 Brann NOR-Eliteserien 803 0.15 Primary scorers
    Eirik Saunes DF 26 Bryne NOR-Eliteserien 2496 0.05 Young low-minute defenders
    Andreas Schjelderup MF 21 Benfica POR-Primeira Liga 1723 0.26 High-card-rate midfielders NT
    Daniel Schneider DF 22 KFUM Oslo NOR-Eliteserien 1042 0.02 High-card-rate defenders
    Sebastian Sebulonsen DF 25 Köln GER-Bundesliga 2498 0.02 Young low-minute defenders
    Ole Selnæs MF 30 Rosenborg NOR-Eliteserien 1829 0.06 Everyday starting midfielders, low scoring output
    Gard Simenstad MF 25 HamKam NOR-Eliteserien 1451 0.09 Young low-minute midfielders
    Håkon Sjåtil DF 22 Vålerenga NOR-Eliteserien 1248 0.01 High-card-rate defenders
    Fredrik Sjølstad DF 30 HamKam NOR-Eliteserien 1827 0.01 High-assist defenders with high playing time
    Fredrik Sjøvold DF 21 Bodø/Glimt NOR-Eliteserien 2594 0.10 Goal-scoring defenders
    Syver Skeide MF 20 Kristiansund NOR-Eliteserien 522 0.06 High-scoring attacking midfielders
    Vetle Skjærvik DF 24 Tromsø NOR-Eliteserien 2456 0.04 Young low-minute defenders
    Henrik Skogvold FW 20 Fredrikstad NOR-Eliteserien 1983 0.13 Young low-minute forwards
    Lars Erik Sødal MF 22 Bryne NOR-Eliteserien 2051 0.03 Older rotation midfielders
    Markus Solbakken MF 25 AGF DEN-Superliga 567 0.10 High-scoring attacking midfielders
    Ola Solbakken FW 26 Nordsjælland DEN-Superliga 1162 0.16 Primary scorers
    Vegard Solheim DF 20 Haugesund NOR-Eliteserien 593 0.02 High-card-rate defenders
    Joachim Soltvedt DF 29 Brann NOR-Eliteserien 1752 0.15 Goal-scoring defenders
    Tore André Sørås MF 26 HamKam NOR-Eliteserien 1907 0.05 Older rotation midfielders
    Sondre Sørli MF 29 Sarpsborg 08 NOR-Eliteserien 1161 0.14 Young low-minute midfielders
    Sondre Sørløkk MF 27 Fredrikstad NOR-Eliteserien 1742 0.15 Young low-minute midfielders
    Alexander Sørloth FW 29 Atlético Madrid ESP-La Liga 1980 0.44 Older forwards with moderate playing time NT
    Bent Sørmo DF 28 Strømsgodset NOR-Eliteserien 979 0.02 High-assist defenders with high playing time
    Oskar Spiten-Nysæter MF 17 Molde NOR-Eliteserien 857 0.13 Young low-minute midfielders
    Halldor Stenevik DF 24 Molde NOR-Eliteserien 1196 0.03 Older defenders
    Herman Stengel MF 29 Strømsgodset NOR-Eliteserien 2573 0.06 Older rotation midfielders
    Petter Strand MF 30 Vålerenga NOR-Eliteserien 1487 0.12 Young low-minute midfielders
    Sander Svendsen MF 27 Viking NOR-Eliteserien 1323 0.11 High-card-rate midfielders
    Jesper Tåje DF 27 Strømsgodset NOR-Eliteserien 2186 0.05 Everyday starting defenders
    Harald Tangen MF 24 Sarpsborg 08 NOR-Eliteserien 1295 0.09 High-scoring attacking midfielders
    Jonas Therkelsen MF 22 Holstein Kiel GER-2. Bundesliga 2177 0.10 Older rotation midfielders
    Mats Thornes MF 22 Bryne NOR-Eliteserien 576 0.08 High-scoring attacking midfielders
    Morten Thorsby MF 29 Genoa ITA-Serie A 731 0.22 Everyday starting midfielders, low scoring output NT
    Kristian Thorstvedt MF 26 Sassuolo ITA-Serie A 2460 0.22 Older rotation midfielders NT
    Filip Thorvaldsen MF 18 Vålerenga NOR-Eliteserien 1305 0.15 Young low-minute midfielders
    Mathias Tønnessen DF 21 KFUM Oslo NOR-Eliteserien 2422 0.02 Young low-minute defenders
    Zlatko Tripić MF 32 Viking NOR-Eliteserien 1930 0.24 High-card-rate midfielders
    David Tufekcic MF 21 Kristiansund NOR-Eliteserien 2154 0.08 Older rotation midfielders
    Dan Peter Ulvestad DF 35 Kristiansund NOR-Eliteserien 2683 0.02 Young low-minute defenders
    Fredrik Ulvestad MF 33 Pogoń Szczecin POL-Ekstraklasa 2717 0.08 Older rotation midfielders
    Bjørn Inge Utvik DF 28 Sarpsborg 08 NOR-Eliteserien 1080 0.02 High-assist defenders with high playing time
    Isak Vådebu DF 21 Tromsø NOR-Eliteserien 808 0.04 High-card-rate defenders
    Gustav Valsvik DF 31 Strømsgodset NOR-Eliteserien 2610 0.04 Young low-minute defenders
    Hugo Vetlesen MF 25 Club Brugge BEL-Pro League 1289 0.30 High-card-rate midfielders
    Lars-Christopher Vilsvik DF 36 Strømsgodset NOR-Eliteserien 2063 0.07 High-assist defenders with high playing time
    Håkon Volden DF 17 Rosenborg NOR-Eliteserien 726 0.02 High-card-rate defenders
    Sivert Westerlund DF 25 Strømsgodset NOR-Eliteserien 629 0.02 High-card-rate defenders
    Eirik Wichne DF 27 Sarpsborg 08 NOR-Eliteserien 2023 0.05 Goal-scoring defenders
    Gustav Wikheim MF 31 Strømsgodset NOR-Eliteserien 990 0.12 Everyday starting midfielders, low scoring output
    Aslak Witry DF 28 Rosenborg NOR-Eliteserien 582 0.07 Everyday starting defenders
    David Møller Wolfe MF 23 Wolves ENG-Premier League 1049 0.19 High-scoring attacking midfielders NT
    Kristoffer Zachariassen MF 30 Ferencváros HUN-NB I 783 0.06 Everyday starting midfielders, low scoring output

    * 2025/26 season, at least 450 minutes; the club is the one with the most minutes that season. NT = national-team call-up 2025–26.

    Download the tables

    Download the tables

    The tables behind this report, exactly as the pipeline produced them, committed to the public repository. An analyst can take these and work from them directly, not only from the pictures above.

    • Every player found in the pool, one row per player, before any season's numbers are attached. pool.parquet
    • Players per million people, one row per country — the number behind the very first slide. per_capita.parquet
    • The five-number season vector for every covered player, one file per position group (forwards, midfielders, defenders). features_FW.parquet · features_MF.parquet · features_DF.parquet
    • Production by country, position group and age band, the numbers behind the international comparison. cohorts.parquet
    • Youth minutes at home, the age and route of the move abroad, how exports fare, and where they land — every number behind the pathway exhibits, in one file. pathways.json
    • Season-by-season counts of home-nation players in Europe's five biggest leagues, back to the start of the fetched history. big5_history.parquet
    • Every season row this pipeline has fetched, for every league and nationality it covers — the raw table everything else in this report is built from. fbref_players.parquet
    • How each of the five season numbers contributes to the style and quality maps. pca_loadings.parquet
    • How much the top players change when a league's strength is nudged up or down. sensitivity.parquet
    • The three new numbers behind the why-the-train-left funnel: club breadth of youth minutes, the league's own age structure, and the age at a player's first move abroad. pipeline_facts.json
    • FBref’s playing-time and miscellaneous tables for every fetched league-season: starts, minutes per start, substitute appearances, on-off, crosses, interceptions, tackles won, fouls. fbref_roles.parquet
    • Every player in the pool as one row — the table behind the searchable list — with production, rank in his position group, playing-time split and role counts. pool_table.json
    • The history seasons of the home, peer and stepping-stone leagues back to the start of the fetched history, the rows behind the player atlas’s careers. fbref_history.parquet
    • Where the pool moved between the two seasons: each player’s rung in each, the move between them, and minutes by rung. season_changes.json

    How it is built, validated and where it stops

    Data sources

    From raw tables to a feature vector

    One 2025/26 player-season, traced from its raw FBref row to the five-number vector the rest of this chapter builds on: Tobias Lauritsen, the Norwegian player with the most 2025/26 minutes among those who cleared the inclusion floor.

    Tobias Lauritsen: raw row and feature row
    Raw FBref row, 2025/26
    ColumnValue
    leagueNED-Eredivisie
    season2025-2026
    teamSparta R.
    playerTobias Lauritsen
    nationNOR
    posFW
    born1997
    age27
    mp34
    min3039
    gls12
    ast5
    pk3
    crdy4
    crdr0
    Feature row after the pipeline
    FeatureRaw ShrunkQuality-adjusted Z-score
    npg_p900.2670.268 0.137−0.65
    ast_p900.1480.144 0.074−0.09
    min_share0.9930.993 0.9932.30
    age27.00027.000 27.0000.32
    cards_p900.1180.119 0.119−0.60

    Six wrangling checks turn the raw rows above into the pool used everywhere else in this report, recomputed on every run:

    The full log, with the recorded pipeline incidents, is in the data-quality log below.

    Every position group shares the same five-number vector (spec §5): a raw column becomes a per-90 rate, shrunk toward its league-season median (Efron and Morris, 1975), then multiplied by the league quality projection.

    Five candidates considered for the 2025/26 vector and what the data said about each, corpus-wide (every fetched nationality, the same population the rest of this chapter describes):

    CandidateStatistic ValueDecision
    gls_p90Correlation with npg_p900.973Replaced by npg_p90
    mpCorrelation with minutes played0.844Replaced by minutes share
    crdr_p90Share of player-seasons with zero red cards0.854Folded into cards
    ageSpread of median goals + assists per 90 across age bands (max − min band)0.028Kept
    bornShare of player-seasons missing a birth year0.003Kept — required for the player key

    Despite the high correlation, goals and non-penalty goals diverge for the players who take penalties: Jan Kalabiška (CZE-First League, 100 %); Ali Kabacalman (SUI-Super League, 100 %); Derrick Köhn (GER-Bundesliga, 100 %). Appearances (mp) correlate strongly with minutes but not perfectly — a substitute cameo counts the same as 90 minutes started, which is why minutes share, not appearances, measures playing time here. Median goals + assists per 90 by age band runs from 0.147 (30+) – 0.175 (23-25).

    The picture behind league adjustment: the same raw rate reads very differently depending on the league it was earned in.

    Small multiples: the five raw per-90 features' distributions by league, boxplots per league, the Eliteserien highlighted, 7 leagues shown.

    What shrinkage does to a low-minute player: at the inclusion floor the league median still carries most of the weight; Andreas Helmersen's rate moves the most of any Norwegian-eligible player this season.

    Scatter of raw vs shrunk non-penalty goals per 90 against minutes for Norwegian-eligible players, with the shrinkage weight on the league median as a function of minutes overlaid.

    League multipliers (quality projection)

    npG/90_quality = npG/90_shrunk × m_league

    LeagueMultiplier
    ENG-Premier League1.000
    ITA-Serie A0.856
    ESP-La Liga0.807
    GER-Bundesliga0.788
    FRA-Ligue 10.666
    POR-Primeira Liga0.630
    BEL-Pro League0.573
    NED-Eredivisie0.510
    TUR-Süper Lig0.484
    GER-2. Bundesliga0.473
    POL-Ekstraklasa0.438
    CZE-First League0.434
    NOR-Eliteserien0.374
    DEN-Superliga0.371
    SUI-Super League0.293
    AUT-Bundesliga0.268
    HUN-NB I0.265
    CRO-HNL0.249
    SVK-Super Liga0.217

    Method: uefa_coefficient. ClubElo unavailable at run time (HTTP error); fell back to Wikipedia's UEFA men's association coefficient, current 5-year ranking (2022–23–2026–27 seasons). tier-1 multiplier = coef[country] / max(coef); tier-2 multiplier = 0.6 * tier-1 multiplier of the same country (stated assumption, not derived from data). Strongest tier-1 country = 1.00. The sensitivity analysis below shows the ranking's robustness to ±20 % on any one multiplier.

    League strength: two estimates

    A season in one league is not automatically worth the same as a season in another: goals and assists come easier in some competitions than others. This section works out an exchange rate between leagues by watching the same players before and after they change league, the way a manager judges a new signing by how his output changes at the new club.

    Full method, figures and diagnostics

    How much is a Eliteserien season worth in Premier League terms? The UEFA multiplier above answers that from countries' continental results; this model answers the same question from the players who actually changed leagues.

    Movers — 2125 players observed in at least two leagues across 8108 qualifying player-seasons — anchor the model, since only a player's own before/after change of league separates their level from the league's scoring environment. A hierarchical Poisson model of non-penalty goals plus assists per 90 (Gelman et al., 2013) fits a league effect and a player effect together, sampled with NUTS (Hoffman and Gelman, 2014) in PyMC (Abril-Pla et al., 2023) and diagnosed with ArviZ (Kumar et al., 2019). Partial pooling keeps the league effects regularised while leaving player effects close to unpooled, the same within-subject logic behind plus-minus and RAPM ratings elsewhere in team sports (Kharrat, McHale and Peña, 2020; Hvattum, 2019).

    Dot and 90 % HDI of the model's league-strength multiplier m_L per league, sorted, with each league's UEFA multiplier as a hollow marker.

    In these terms, a Eliteserien season converts to 0.57 of a Premier League one (90 % HDI 0.51–0.64).

    Leaguem_L (median) 90 % HDITransitions UEFA
    ENG-Premier League1.000 1.000–1.000 588 1.000
    ITA-Serie A0.935 0.891–0.983 569 0.856
    ESP-La Liga0.907 0.864–0.956 435 0.807
    FRA-Ligue 10.810 0.772–0.848 672 0.666
    GER-Bundesliga0.777 0.739–0.812 588 0.788
    HUN-NB I0.742 0.622–0.864 35 0.265
    POR-Primeira Liga0.721 0.680–0.764 349 0.630
    BEL-Pro League0.671 0.635–0.710 474 0.573
    CZE-First League0.664 0.587–0.751 65 0.434
    TUR-Süper Lig0.660 0.625–0.696 472 0.484
    POL-Ekstraklasa0.659 0.592–0.721 131 0.438
    AUT-Bundesliga0.655 0.579–0.730 80 0.268
    DEN-Superliga0.636 0.575–0.700 117 0.371
    GER-2. Bundesliga0.600 0.554–0.642 229 0.473
    CRO-HNL0.599 0.525–0.665 64 0.249
    NED-Eredivisie0.597 0.565–0.631 386 0.510
    SUI-Super League0.596 0.539–0.652 128 0.293
    NOR-Eliteserien0.574 0.507–0.642 64 0.374

    Refit on seasons before 2025/26, the model predicts each mover's first 2025/26 row after a league change — 862 such moves — against two baselines: the same rate as before, and that rate scaled by the ratio of UEFA multipliers. This is the same population CIES Football Observatory's expatriate-player reports track (Poli, Ravenel and Besson, 2024).

    MethodLog predictive density MAE (rate)
    Same rate as before −2.628 0.137
    Rate × UEFA ratio −2.733 0.151
    Model −2.177 0.129

    Against 18 leagues in common, the model's medians and the UEFA multipliers correlate at Spearman's rho = 0.72.

    • HUN-NB I: model rank 6 vs UEFA rank 17 (m_L 0.74 vs multiplier 0.27).
    • NED-Eredivisie: model rank 16 vs UEFA rank 8 (m_L 0.60 vs multiplier 0.51).
    • NOR-Eliteserien: model rank 18 vs UEFA rank 13 (m_L 0.57 vs multiplier 0.37).

    R-hat ≤ 1.007, minimum bulk ESS 619, 0 divergent transitions across 8108 player-seasons from 2125 movers; fit in 212 s.

    Posterior predictive check

    Observed vs. replicated non-penalty goals plus assists per player-season (Vehtari, Gelman and Gabry, 2017): 15 % vs 14 % share of zeros, mean 4.45 vs 4.44, 90th percentile 11.00 vs 10.86.

    Bar chart comparing the observed and posterior-predictive-replicated distribution of non-penalty goals plus assists per player-season, grouped 0/1/2/3/4/5+.

    What the movers do not show: they are not a random sample — players tend to move up when they are good and down when they are older — so the within-player contrast describes the league difference for the kind of player who moves, and the age term absorbs only the part of that selection that is age. The interval is the model's uncertainty, not the selection's.

    The report's rankings keep the UEFA-coefficient multiplier throughout; the model above is shown alongside it as a check on that multiplier's own assumption — that continental results track player-level strength — not as a replacement for it.

    Three models, one task, five seasons

    Here we ask a simple question of five different methods: given a player's numbers this season, how well can each one guess his numbers next season. Each method is tested only on seasons it has not already seen, the way a scout's judgement only really counts for what he predicts before a season starts, not after it has finished.

    Full method, figures and diagnostics

    What does a player's season tell us about the next one, given where he plays, how old he is and — for exports — at what age he moved? In model terms: predict a player's league-adjusted production next season (non-penalty goals plus assists per 90, quality-adjusted) from this season's numbers, for every player-season pair with at least 450 minutes in both seasons, every nationality in the corpus.

    Evaluated by rolling-origin cross-validation (Hyndman and Athanasopoulos, 2021): for each of 5 target seasons in turn, every model trains only on pairs whose target season came earlier and is tested on that season's pairs — the same discipline a forecaster uses when the future is genuinely unknown at fit time, and what makes the per-season table below a real "performance over time" read rather than one blended number. Two baselines — persistence (next season = this season) and shrinkage to the league mean — sit alongside three fitted models sharing the same eight features: a hierarchical Bayesian regression (target ~ Normal(μ, σ), partial pooling on league and player, NUTS, 2 chains × 400 draws per origin (Gelman et al., 2013)), a gradient-boosted regressor and a small multilayer perceptron (scikit-learn (Pedregosa et al., 2011), hidden layers 64/32 — a small MLP, not an embedding model: PyTorch is not part of this pipeline). The Bayesian model's training rows are subsampled to at most 2500 per origin to keep five origins' worth of fits inside the runtime budget; the other four models train on the full split. The target is adjusted with the multiplier of the league the player is in next season; the features only know this season's league, so a move is a change the models cannot see coming — a limitation shared by all five.

    Line chart of RMSE per target season, one line per model, persistence dashed.
    Model 2021/222022/232023/242024/252025/26 Pooled
    Persistence 0.0768 (0.054) 0.0782 (0.056) 0.0789 (0.056) 0.0787 (0.057) 0.0684 (0.048) 0.0751 (0.053)
    Shrinkage to league mean 0.1343 (0.104) 0.1293 (0.100) 0.1311 (0.098) 0.1271 (0.098) 0.1099 (0.089) 0.1225 (0.095)
    Hierarchical Bayesian — 0.0710 (0.052) 0.0759 (0.054) 0.0722 (0.053) 0.0681 (0.051) 0.0713 (0.052)
    Gradient boosting — 0.0723 (0.051) 0.0744 (0.051) 0.0698 (0.051) 0.0677 (0.051) 0.0706 (0.051)
    Small MLP — 0.0794 (0.059) 0.0792 (0.057) 0.0735 (0.054) 0.0714 (0.054) 0.0753 (0.056)

    Each cell: RMSE (MAE), in league-adjusted npG+A per 90. The first origin's training set is empty by construction — the corpus's earliest feature season leaves no earlier target season to train on — so only the two baselines are reported there.

    The Bayesian model's 90 % predictive interval covered the observed value 92 % of the time, pooled across the 4 origins it was fit for (2022/23: 91 %, 2023/24: 90 %, 2024/25: 91 %, 2025/26: 92 %).

    By pooled RMSE, Gradient boosting wins (0.071 vs 0.075 for persistence, 6 % lower).

    The clearest season-to-season move in the winner's own RMSE is between 2023/24 and 2024/25 (0.005) — the kind of drift this rolling-origin table exists to surface.

    Dating the break and one forecast

    This section finds the two seasons when the count of home-nation players in Europe's five biggest leagues changed level for good — the rise and the fall — and puts a probability on each being the true turning point rather than an ordinary dip. It also makes one forecast for next season, as a demonstration of the method, not a prediction about any player.

    Full method, figures and diagnostics

    The series is modelled as a local level in state space (Durbin and Koopman, 2012): the log of the season count follows a Gaussian random walk (σ ~ HalfNormal(0.2)), plus two ordered step changes δ₁, δ₂ in the level at unknown seasons τ₁ < τ₂ — on the series back to 1990/91 one step is misspecified, since the count rises through the 1990s and falls after the 2000s plateau, and a single step lands on whichever change buys more likelihood. NUTS only samples continuous parameters, so the τ pair is not sampled directly — every ordered pair of candidate seasons at least 3 seasons from either end and from each other is marginalised out of the model with a single log-sum-exp potential, and each break's own posterior is recovered afterwards from the continuous draws, the standard move for a marginalised discrete parameter (Gelman et al., 2013); the changepoint idea itself is due to (Adams and MacKay, 2007), applied here to a batch, two-break setting. The report's "break" is the step that lowers the level; the other is the rise. A rolling-origin backtest (Hyndman and Athanasopoulos, 2021) refits the same local level without the step at each of 15 origins, forecasting one season ahead and scoring against the naive "same as last season" baseline — the honest forecaster's read, since no real origin knows in advance which side of a break it sits on. The one forecast below, from the same change-point-free model fitted on the full series, is a demonstration of the method on a count of players, not a statement about any player.

    Line chart of the Norway series with the change-point model's fitted level in a pale band, a vertical rule marking the most probable break season with its posterior probability, and the one-season forecast with its 90 % interval at the right edge, past a dashed divider.
    How to read it: the acid line is the observed count, the pale band the model’s fitted level; the dashed rules are the most probable rise and fall seasons with their posterior probabilities; past the divider on the right, the one-season forecast with its 90 % interval.

    For Norway, the model dates the break to 2004/05 (27 % posterior probability), a ×0.66 (0.29–1.34, 90 % HDI) change in the level; the random walk's own innovation scale is σ = 0.196.

    The recovery after it is dated to 2021/22 (23 % posterior), a ×1.32 (0.34–2.23) change in the level. The most probable seasons for each:

    • rise: 2021/22: 23 % · 2019/20: 12 % · 2020/21: 8 %
    • fall: 2004/05: 27 % · 2003/04: 13 % · 2001/02: 9 %

    The same model, fit separately for the two contrast countries:

    • Denmark: 2021/22 (23 % posterior), ×1.41 (0.88–1.97). Rise: 2017/18 (9 %), ×1.04.
    • Czechia: 2014/15 (56 % posterior), ×0.60 (0.39–1.03). Rise: 2000/01 (55 %), ×1.65.

    One-step-ahead rolling-origin backtest of the plain local level (no change point) against the naive "same as last season", 15 origins from 2010/11 to 2024/25:

    OriginForecast season ActualModel median (90 % interval) Naive
    2010/112011/128 9 (3–18)10
    2011/122012/139 8 (3–17)8
    2012/132013/1412 9 (3–18)9
    2013/142014/155 10 (4–21)12
    2014/152015/169 7 (2–15)5
    2015/162016/1711 8 (3–16)9
    2016/172017/186 9 (4–18)11
    2017/182018/197 8 (3–15)6
    2018/192019/2012 7 (3–15)7
    2019/202020/2110 9 (4–19)12
    2020/212021/2218 10 (4–19)10
    2021/222022/2317 14 (6–25)18
    2022/232023/2416 15 (7–29)17
    2023/242024/2519 16 (7–29)16
    2024/252025/2625 17 (9–30)19

    Pooled across 15 origins: MAE 3.20 for the model against 3.40 for the naive baseline, 100 % of the 90 % intervals covered the observed value.

    The one forecast, for next season (2026/27), from the same change-point-free model fitted on the full series:

    CountrySeason Median90 % interval
    Norway2026/2721 11–37
    Denmark2026/2735 22–51
    Czechia2026/2711 5–19

    A demonstration of the method on a count of players, not a statement about any player.

    Change-point fit: R-hat ≤ 1.009, minimum bulk ESS 210, 0 divergent transitions across 31 seasons.

    Cross-country youth-minutes panel

    This section asks whether the pattern from the funnel above — countries that give young players more minutes at home tend to have a deeper pool in the strongest leagues — holds up across every country in the comparison set, not only the two shown earlier. Two seasons and a handful of countries is a small sample, so the range around the estimate is wide.

    Full method, figures and diagnostics

    For each of the 8 peer countries, the 2024/25 → 2025/26 average U21 share of domestic-league minutes (x) against the 2024/25 → 2025/26 average top-9 players per million (y) — one row per country. A Bayesian simple regression, y ~ Normal(α + β·x, σ), weakly informative priors (4 chains × 1000 draws (Abril-Pla et al., 2023)), answers the between-country question slide 3 asks: does a country with a higher average U21 share also have a deeper pool, on average (Gelman et al., 2013). Cross-checked against a plain pooled least-squares slope on the full two-season panel (numpy polyfit, ignoring country structure entirely, 1000 percentile-bootstrap resamples — statsmodels is not a dependency here), which should agree in sign. Two seasons per country (previous and metrics; the current season is partial and left out); a peer whose top flight FBref does not track is absent from the panel, which is why n can be below the peer count.

    Scatter of the panel with the between-country fitted line and its 90 % band.

    β = +10.11 per 10 percentage points of U21 share (90 % HDI −1.69–23.26), R² = 0.27; OLS on the same 8 country means lands close by, at +10.65 (−3.85–20.33), and the pooled-panel OLS slope agrees in sign at +7.76 (1.12–13.99). n = 8; the interval is wide because the panel is small.

    R-hat ≤ 1.001, minimum bulk ESS 2126, 0 divergent transitions.

    A separate check: the same regression, but with a country random intercept fit on the full two-season panel instead of country means. Within-country changes across the two seasons carry no signal — β_within = −0.16 per 10 percentage points (90 % HDI −2.53–2.17), n = 16 country-seasons (R-hat ≤ 1.004, minimum bulk ESS 851, 0 divergent transitions; posterior-median country-intercept scale σ_country = 5.19). With only two seasons per country the intercept absorbs almost all of the between-country pattern the fit above isolates, leaving this estimate driven by season-to-season noise alone; reported here as a robustness check, not a second headline.

    Descriptive only: a country's average U21 share and its average per-capita count are shown together because they are the numbers on hand, not because one is claimed to produce the other.

    Panel rows (n = 16)
    CountrySeason U21 sharePer million
    CRO2024/2512.8 %15.54
    DEN2024/2513.2 %13.42
    NOR2024/2510.5 %9.01
    SUI2024/259.2 %5.36
    AUT2024/256.4 %5.02
    CZE2024/2511.1 %2.20
    HUN2024/2512.2 %1.36
    POL2024/2512.5 %1.26
    DEN2025/2615.3 %12.58
    CRO2025/2614.0 %12.18
    NOR2025/2611.5 %9.37
    SUI2025/267.7 %6.03
    AUT2025/269.2 %4.80
    CZE2025/266.3 %2.39
    HUN2025/2614.1 %1.57
    POL2025/269.3 %1.23

    What the gap is made of

    The gap between the home nation and a comparison country, in players per million, can be split into pieces that line up with three things this report already measures: how much young players play at home, how strong the domestic league is, and how old players are when they move abroad. This does not say which of the three causes the gap, only how much of it moves together with each one — the way a manager might note that a poor season lines up with an injury crisis without claiming the injuries alone explain it.

    Full method, figures and diagnostics

    Ridge regression (α = 1.00, standardised channels, converted back to original units) of top-9 players per million on three channels — U21 share of domestic minutes, domestic league strength (M2's m_L where the country's top flight is in that model's fitted set, else the UEFA-coefficient multiplier (every country in this run used the model estimate)) and median export age (recent entrants, or the all-time median for a country with none recently) — over the 8 peer countries with data on all three channels, metrics season. For one contrast country at a time, the fitted model's own prediction splits the gap exactly into one term per channel — the linear-model form of the Blinder-Oaxaca wage decomposition (Oaxaca, 1973; Blinder, 1973), applied here to a per-capita player count. Three linear channels means this split is Shapley-equivalent: the Shapley value of an additive term in a linear model equals its own coefficient's contribution, so no permutation machinery is implemented. The residual is whatever the three channels do not carry.

    Horizontal stacked bar per contrast country: three channel contributions plus the residual; whiskers show each channel's bootstrap interval.
    How to read it: the whole bar is the gap in players per million between the comparison country and Norway. Each segment is how much of that gap goes with one measured channel — youth minutes, league strength, export age — under the decomposition; the hatched remainder is what the three channels do not carry. A segment can be negative when the channel works the other way.
    ContrastChannel ContributionShare of gap 90 % interval
    Denmark
    Gap (players per million): +3.21    		 U21 minutes +3.15 98 % +1.24 – +4.49
    League strength −3.54 −110 % −4.91 – −1.27
    Export age 0.00 0 % 0.00 – 0.00
    Residual +3.60 — —
    Czechia
    Gap (players per million): −6.98    		 U21 minutes −4.25 61 % −5.94 – −1.43
    League strength −5.14 74 % −7.16 – −1.89
    Export age 0.00 0 % 0.00 – 0.00
    Residual +2.41 — —

    Bootstrap 90 % intervals on each channel's contribution, 1000 resamples of the panel's rows (the model refit on each resample; the home/contrast countries' own values held fixed); the residual itself is not bootstrapped — it is the two countries' own observed counts minus the fitted gap.

    A decomposition of a correlation this panel happens to show, not a causal accounting; with n = 8 and three correlated national-level channels, the shares are indicative, not precise. A channel's share of the gap is only shown when the gap itself is at least 3 players per million — below that, a small denominator can send a share past 100 % in either direction; the contribution itself, in players per million, is always reported.

    Age at export

    Players who move abroad younger tend to end up producing more once they are there, but that is not necessarily because moving young helps a career: clubs are also more willing to take a chance on a player they already rate highly at a younger age. This section fits a curve to that pattern while trying to hold the strength of the player's original league fixed, but it cannot fully tell the two explanations apart.

    Full method, figures and diagnostics

    For every peer-nationality player (home nation included) whose first top-9-league season lies inside the fetched window and isn't censored (the same censoring rule as "Where do Norwegian players go when they leave?", reused here) — 115 players, ages 17–31 — age at that season is modelled against league-adjusted production over the player's first one or two top-9 seasons. f(age) is a natural cubic spline with knots at 19, 21, 23 and 25 (a plain quadratic below n = 150; the quadratic branch was used here), alongside origin-league strength (the transfer-graph model, § League strength), position and a partially pooled country effect (Gelman et al., 2013), sampled with NUTS (Hoffman and Gelman, 2014), 4 chains × 1000 draws; every design column and the outcome were standardised before fitting, the youth-minutes panel's own lesson about raw-scale priors on differently-scaled covariates.

    Line chart: the fitted age-at-export curve with its 90 % band, home-nation exports as acid points against every other peer export in grey, and a rug of every export's age along the axis.
    How to read it: the horizontal axis is a player’s age in his first top-9 season; the vertical axis is his league-adjusted goals plus assists per 90 over the first two seasons there. The line is the model’s expected value at each age, the band its 90 % interval; a flat line means arriving later costs nothing measurable. Acid dots are Norwegian exports — hover for the name.
    AgeExpected G+A/90 (median)90 % HDI
    190.140.12 – 0.16
    210.140.12 – 0.16
    230.140.12 – 0.16
    250.140.12 – 0.16
    270.140.12 – 0.16

    β, per one-unit increase in origin-league strength (m_L): −0.03 (−0.11–0.05).

    Norwegian exports' own country effect: −0.01 (−0.04–0.00); Norwegian exports arrive at a median age of 21.

    The same model, fit again without origin-league strength: the country-effect scale (σ_n) is 0.135 with the league term in the model and 0.139 without it — what moves between the two is what the league term is absorbing.

    Leave-one-nation-out: excluding Norway's own 18 exports (n = 97 remaining) and refitting, the 21-vs-24 difference is +0.01 (−0.01–0.02), against 0.00 in the full fit.

    Posterior predictive check

    Observed vs. replicated y (mean league-adjusted G+A/90 over the first two top-9 seasons): mean 0.14 vs 0.14, sd 0.10 vs 0.10, 10th percentile 0.02 vs 0.01, 90th percentile 0.27 vs 0.28.

    R-hat ≤ 1.005, minimum bulk ESS 978, 0 divergent transitions across 115 players; fit in 6.9 s.

    What this model does not separate: the corpus is not a random sample of players who could have left later. Players who leave earlier tend to be the ones judged ready earliest — a selection effect the age curve mixes with any genuine development effect of arriving young, and this report does not try to tell the two apart.

    Bayesian shrinkage

    Per-90 rates of players with few minutes are shrunk towards the median of their league and season (players with at least 900 minutes) using the empirical Bayes formula (Efron and Morris, 1975), with K = 10 phantom matches expressed as 900 minutes:

    shrunk_rate = (events + K × league_median) / (minutes / 90 + K),   K = 10

    At 450 minutes (the inclusion floor) the league median carries 67 % of the weight; at 900 minutes the player's own rate and the median weigh the same. Minutes share and age are not shrunk.

    Goalkeepers are counted the same way as outfield players throughout this report, with one difference: because a goalkeeper's save numbers swing around a lot from game to game, the model needs a bigger sample of shots faced before it trusts a keeper's own numbers over the league average.

    Goalkeeper rates (chapter II's counter-example) use the same formula. GA/90 and saves/90 are shrunk toward their league-season median with the same K = 900 minutes, and GA/90 is then quality-adjusted by the league multiplier — a goal conceded in a stronger league counts less. Save percentage is shrunk the same way but against shots on target faced, not minutes: a single season's shot count sits far below K = 900, so save_pct_shrunk compresses hard toward the league median for almost every goalkeeper — a large gap in the raw, unshrunk save percentage is the more informative read there.

    PCA loadings

    One five-feature vector per position group (npg_p90, ast_p90, min_share, age, cards_p90), standardised, reduced to two components per projection. Style uses the shrunk rates; quality multiplies the rates by the league multiplier first.

    Loadings table
    GroupProjectionPC% variance npG/90A/90min shareagecards/90
    FW style PC1 25.2 % 0.533 0.215 0.651 0.430 −0.246
    FW style PC2 23.6 % −0.206 −0.271 −0.120 0.823 0.439
    FW quality PC1 28.9 % 0.636 0.380 0.482 0.217 −0.415
    FW quality PC2 24.9 % −0.108 −0.114 0.133 0.910 0.361
    MF style PC1 29.5 % 0.597 0.601 0.383 0.073 −0.361
    MF style PC2 23.2 % −0.245 −0.117 0.484 0.828 0.082
    MF quality PC1 30.0 % 0.590 0.608 0.382 0.133 −0.344
    MF quality PC2 24.2 % −0.244 −0.199 0.490 0.809 0.087
    DF style PC1 25.0 % 0.546 0.429 0.505 0.183 −0.480
    DF style PC2 21.1 % −0.343 −0.378 0.278 0.804 −0.128
    DF quality PC1 25.8 % 0.432 0.356 0.578 0.327 −0.496
    DF quality PC2 21.7 % −0.462 −0.375 0.104 0.797 −0.023

    Sensitivity analysis (±20 % multipliers)

    For each scenario the quality-adjusted ranking of Norwegian-eligible players within each position group was recomputed and compared with the baseline; "top-10" is the union of the 3 groups' own top tens (30 players at baseline). Of the 41 scenarios, 30 change nobody in that set; the largest churn is 5 (NOR-Eliteserien multiplier -20%, mean rank shift 2.70 in the top twenty)*.

    Scenario table
    ScenarioDescription Top-10 overlapTop-10 churnMean Δ rank (top 20)
    baseline current multipliers from config/league_quality.yaml 30 / 30 0 0.00
    ENG-Premier League_minus20 ENG-Premier League multiplier -20% 28 / 30 2 0.88
    ENG-Premier League_plus20 ENG-Premier League multiplier +20% 29 / 30 1 0.40
    ITA-Serie A_minus20 ITA-Serie A multiplier -20% 30 / 30 0 0.45
    ITA-Serie A_plus20 ITA-Serie A multiplier +20% 29 / 30 1 0.23
    ESP-La Liga_minus20 ESP-La Liga multiplier -20% 30 / 30 0 0.00
    ESP-La Liga_plus20 ESP-La Liga multiplier +20% 30 / 30 0 0.00
    GER-Bundesliga_minus20 GER-Bundesliga multiplier -20% 29 / 30 1 0.63
    GER-Bundesliga_plus20 GER-Bundesliga multiplier +20% 30 / 30 0 0.07
    FRA-Ligue 1_minus20 FRA-Ligue 1 multiplier -20% 30 / 30 0 0.00
    FRA-Ligue 1_plus20 FRA-Ligue 1 multiplier +20% 30 / 30 0 0.13
    NED-Eredivisie_minus20 NED-Eredivisie multiplier -20% 29 / 30 1 0.97
    NED-Eredivisie_plus20 NED-Eredivisie multiplier +20% 28 / 30 2 0.75
    POR-Primeira Liga_minus20 POR-Primeira Liga multiplier -20% 30 / 30 0 0.10
    POR-Primeira Liga_plus20 POR-Primeira Liga multiplier +20% 30 / 30 0 0.15
    BEL-Pro League_minus20 BEL-Pro League multiplier -20% 30 / 30 0 0.13
    BEL-Pro League_plus20 BEL-Pro League multiplier +20% 30 / 30 0 0.10
    TUR-Süper Lig_minus20 TUR-Süper Lig multiplier -20% 30 / 30 0 0.18
    TUR-Süper Lig_plus20 TUR-Süper Lig multiplier +20% 29 / 30 1 0.37
    CZE-First League_minus20 CZE-First League multiplier -20% 30 / 30 0 0.00
    CZE-First League_plus20 CZE-First League multiplier +20% 30 / 30 0 0.00
    SVK-Super Liga_minus20 SVK-Super Liga multiplier -20% 30 / 30 0 0.00
    SVK-Super Liga_plus20 SVK-Super Liga multiplier +20% 30 / 30 0 0.00
    AUT-Bundesliga_minus20 AUT-Bundesliga multiplier -20% 30 / 30 0 0.00
    AUT-Bundesliga_plus20 AUT-Bundesliga multiplier +20% 30 / 30 0 0.00
    HUN-NB I_minus20 HUN-NB I multiplier -20% 30 / 30 0 0.00
    HUN-NB I_plus20 HUN-NB I multiplier +20% 30 / 30 0 0.00
    POL-Ekstraklasa_minus20 POL-Ekstraklasa multiplier -20% 29 / 30 1 0.23
    POL-Ekstraklasa_plus20 POL-Ekstraklasa multiplier +20% 30 / 30 0 0.37
    CRO-HNL_minus20 CRO-HNL multiplier -20% 30 / 30 0 0.00
    CRO-HNL_plus20 CRO-HNL multiplier +20% 30 / 30 0 0.00
    DEN-Superliga_minus20 DEN-Superliga multiplier -20% 29 / 30 1 0.47
    DEN-Superliga_plus20 DEN-Superliga multiplier +20% 30 / 30 0 0.45
    SUI-Super League_minus20 SUI-Super League multiplier -20% 30 / 30 0 0.00
    SUI-Super League_plus20 SUI-Super League multiplier +20% 30 / 30 0 0.00
    NOR-Eliteserien_minus20 NOR-Eliteserien multiplier -20% 25 / 30 5 2.70
    NOR-Eliteserien_plus20 NOR-Eliteserien multiplier +20% 26 / 30 4 2.45
    GER-2. Bundesliga_minus20 GER-2. Bundesliga multiplier -20% 30 / 30 0 0.00
    GER-2. Bundesliga_plus20 GER-2. Bundesliga multiplier +20% 30 / 30 0 0.00
    all_minus20 every league multiplier -20% 30 / 30 0 0.00
    all_plus20 every league multiplier +20% 30 / 30 0 0.00

    * Churn = baseline top-10 members that leave the set under the scenario; mean Δ rank = mean absolute rank change over the baseline top-20 union. Scenarios: baseline, every league ±20 % on its own, and all leagues ±20 % at once.

    The table above is fixed at the config multipliers; the panel below is the same Norwegian-eligible top ten made interactive — drag any league's slider (0.5×–1.5× of its default) and the ranking recomputes in the browser.

    # Player League q Vs. default

    Sources This is the offline sensitivity table above (§ Sensitivity analysis) made interactive: q = (npG/90_shrunk + A/90_shrunk) × m_league, recomputed client-side from the shrunk rates of every Norwegian-eligible metrics-season player, no server round-trip. The rank-change column compares each row's rank under the current sliders to its rank at the config defaults.

    Data-quality log

    8 recomputed checks · 6 recorded incidents

    Every wrangling decision that changed a count, with the count. The first block is recomputed on every run; the second is the incident record (dates and counts as recorded at the time).

    CheckCountWhat it counts
    Women's entries filtered0 entriesFBref country-page entries dropped for a surname ending in -ová (see Limitations).
    Namesakes in the pool6 playersActive pool players sharing a normalised name (e.g. father and son), disambiguated by club.
    Pool players without season tables686 playersNorwegian professionals on FBref's country page who play in a league without season tables and carry no metrics.
    Split-season rows collapsed441 rowsPlayer-season-group rows merged into one after a mid-season transfer (minutes summed, rates minutes-weighted).
    Unmatched call-up names62 namesNational-team squad-table names that match no Norwegian-eligible row in the feature tables.
    Missing birth years0 rowsSeason-table rows of nation NOR with no birth year, which cannot form a player_key.
    Unjoined goalkeeper rows12 rowsKeeper-page rows with no matching GK row in the season tables on (league, season, team, player_key); dropped from every goalkeeper exhibit.
    Eliteserien rows without a nationality6 rowsSeason-table rows in the Eliteserien where FBref records no nationality — the highest rate of any league in this pipeline. Every “own nationals” share reads that column as its numerator while the denominator keeps the league’s full minutes, so those shares are floors, not point estimates.
    • 2026-09-14 A stale FBref season index made soccerdata fetch the season-less URL, which FBref serves as the season in progress: nine leagues' 2025/26 tables were 2026/27 after four rounds. Fixed by checking the page's own heading against the requested season. (9 leagues, recorded)
    • 2026-09-14 FBref intermittently served a squads-only page (no season table yet) for a season still in progress. Fixed by a single refetch, in the same page-heading guard.
    • 2026-09-13 ClubElo's API answered 502 for the whole run; league multipliers fell back to UEFA association coefficients.
    • 2026-09-13 The Wikidata portrait query matched on name, citizenship and birth date only; seven portraits belonged to namesakes in other sports until an occupation filter was added. (7 portraits, recorded)
    • 2026-09-13 Slovakia's top flight is not on FBref at all; this report's Slovak exhibits rest entirely on players abroad.
    • 2026-09-14 The first out-of-sample comparison of the league-strength model applied the UEFA-multiplier baseline in the wrong direction and started both baselines from a raw previous-season rate (a zero-goal season predicted zero); caught in review. Corrected: multiplier ratio m_prev / m_new, baselines from the shrunk rate. The model's margin over the baselines shrank from about 3 nats to 0.5 and is the figure reported.

    Limitations of this analysis

    Leagues without metrics

    The pipeline fetches 19 competitions from FBref. 686 of the 1197 Norwegian professionals found on FBref's country page play in a league without season tables and carry no metrics; they are listed by name and club only. The Norwegian second tier is not fetched, and Sweden's top flight is not in the fetched set, so the nearest neighbour's exhibits are absent rather than thin. The Eliteserien is a calendar-year league; its season labelled 2025/26 here is the 2025 season.

    Free-tier feature set

    The feature vector is five basic columns per 90 minutes: non-penalty goals, assists, minutes share, age and cards. No expected goals, no progressive passes, no tackles — the rule was one identical vector across every league in the corpus, and only the basic table is available for all of them. Defensive and creative contributions beyond assists are invisible to the map.

    National-team flag source

    The flag "called up 2025–26" is parsed from Wikipedia squad tables (2026 FIFA World Cup) and matched on normalised name plus birth year. 20 of the 236 mapped players carry it. A squad table edit or a name variant can drop a call-up; the flag is a tag, not a cap count.

    Photo coverage

    218 of the 1197 pool players have a Wikimedia Commons portrait (Wikidata P18, matched on name, citizenship and birth date, occupation filtered to association football player) used on the site. Players without a portrait on the site show initials.

    Season split

    The headline per-capita count and every metric use the complete 2025/26 season; trajectories run 2024/25 → 2025/26; the club on a card is the 2026/27 club (season in progress at build time).

    League multipliers

    ClubElo was unreachable at run time, so the multipliers are UEFA association coefficients scaled to the strongest league = 1.00, and second-tier leagues are set to 0.6 × the first tier of the same country by assumption. The club-strength proxy in chapter II is the club's goals-scored percentile within its league, not an Elo rating. The sensitivity table shows how far a ±20 % error in any one multiplier moves the Norwegian ranking.

    Export origins from recent entrants only

    The origin league of an export is known only when the season before the first top-9 season was fetched: 2020/21 onwards for the headline leagues, 2024/25 onwards for the peer domestic leagues. Origin shares and the recent export age are therefore computed over players whose first top-9 season is 2025/26 or 2026/27; earlier entrants count towards the full export age but not the origin mix, and a first appearance already in 2020/21 is censored (the censored share is shown).

    Player identity

    FBref's season tables carry no player id, so players are joined on normalised name plus birth year across leagues and seasons; two players sharing both would collapse into one. A mid-season transfer produces two club rows that are collapsed into one minutes-weighted row before ranking.

    Women's entries and the -ová heuristic

    FBref's country page mixes men's and women's competitions. Entries whose surname ends in -ová were dropped from the pool; a woman with a different surname ending would survive the filter, and a man with that ending would not. The suffix is specific to Czech feminine surnames, so for a nation whose naming convention doesn't use it (English, for one) the filter catches close to none of the contamination it targets; the "Women's entries filtered" count in the data-quality log below says how many it caught this run.

    No market values, no scouting

    Transfer fees, market values, video and scouting reports are outside the public sources used here. The map describes statistical footprints and counts; selection and development decisions require the federation's own data and expertise, which this method does not have.

    No event or tracking data

    Every feature here is a season aggregate from free FBref tables. The author's tracking work lives elsewhere: tactical-cz (broadcast-video player tracking for Czech football) and the hockey video PoC linked from hockey.bsandova.com. New columns enter in src/features.py::per90 and the feature list in config/feature_definitions.yaml.

    Validation & robustness

    Each model in this pipeline carries its own validation next to where it is described; this section collects one headline diagnostic from each as it lands. So far:

    Reproducibility

    The full pipeline is public: github.com/sandovabarbora/czefootball-player-pool-atlas. MIT licence. From a clean clone, uv sync && make restore-snapshot && make render renders this report from the committed data snapshot and make pages builds the site; make all refetches everything and runs the whole pipeline. Random seed 42 for every stochastic step (KMeans). Fetchers cache raw pages and are idempotent; the render step never touches the network.

    The processed tables also load into BigQuery unchanged: infra/bigquery/ has generated schemas, a bq load script and a key/unit contract for the model-output JSONs the tables don't cover. Run infra/bigquery/load.sh -n <dataset> <nation> to see the load commands without running them.

    How this was built

    Spec → plan → task agents → reviews → ledger · 25 rulings · 317 tests

    The report was produced with an agentic workflow: a written design spec, an implementation plan of small tasks, a fresh coding agent per task, a spec-compliance and code-quality review after each, a whole-branch review at the end. Every decision the controller made without the author is a dated ruling in a ledger — 25 across this edition and the last. The author wrote the framing, the cluster reads and the rulings; the agents wrote the code under 317 tests.

    Build-flow diagram Spec, then plan, then one task agent per task, a two-stage task review, up to five fix rounds, a whole-branch review, then deploy; the ledger on the side records every ruling — 25 so far, across 36 tasks. Spec Plan Task agent SONNET Task review spec + quality SONNET Fix rounds ≤ 5 SONNET Whole-branch review SONNET Deploy OPUS Ledger rulings: 25

    Implementers and reviewers: sonnet · controller: opus.

    Reviews are two-stage per task — spec compliance, then code quality — and every review's findings are written into the ledger, not just its verdict: 40 lines mentioning a review across the 36 tasks of this edition and the last.

    Spec: design · plan: tasks · ledger: rulings.

    Every edition's own design spec and ledger: v1 spec · v1 ledger · v1.2 spec · v1.2 ledger.

    What tracking data would add

    Everything above is built from season tables, because that is what exists for every league in the benchmark. The next layer of data — every player’s position ten times a second — exists for none of them publicly. So here is one match of it, from a different league, to show concretely what it answers that a season table cannot: not how many goals a player scored, but how he moves when he does not have the ball.

    How to read it: the pitch is drawn with both teams attacking left to right; every arrow is one off-ball run, from where it started to where it ended, coloured by the kind of run SkillCorner’s model labelled it. Bright arrows were passed to; thick ones were received. Pick a team, switch run types on and off, hover an arrow for the player and what came of it. Data: (SkillCorner, 2025), one A-League match, MIT licence; none of it enters any number in this report.

    For a federation this is the layer that turns “how many minutes” into “what kind of minutes”: whether a young winger’s runs are the runs the first team needs, whether an export’s physical output matches his new league, whether a squad presses as one. The atlas cannot see it and does not pretend to; the pipeline is built so that a tracking feed, when a federation has one, becomes another table beside the season tables rather than a different project.

    The methods below shaped this report's design. Each entry states what the method is, what this report took from it, and what was left out and why.

    One pairing here has no single citation behind it: the transfer-graph league-strength model (§ League strength) and the age-at-export curve (§ Age at export) are fit independently and joined only through origin-league strength as a covariate — a within-player league-identification model feeding a spline-in-age production curve is, to the author's knowledge, this report's own combination, not drawn whole from any one method below.

    Glossary

    Ten terms used in the sections above, each in one plain sentence with a football example.

    Per 90
    A rate scaled to a full match: a player with three goals in five matches, each played the full ninety minutes, has a rate of 0.6 goals per 90 — it lets a player who came on as a substitute be compared fairly with one who started every match.
    Minutes share
    How much of a club's available playing time a player actually got, out of every minute the club's matches could have offered that season; a player who played every minute of every match has a minutes share of 100 percent.
    Shrinkage
    A way of not trusting a small sample too much: a player with only a handful of matches has his numbers pulled part of the way toward the league's typical number, the way a manager waits for more than one good game before trusting that a young player's form is real.
    League multiplier / league strength
    A number saying how much a goal, an assist or a minute is worth in one league compared with another; a striker's goal in a weaker league counts for less once it is adjusted by that league's own multiplier — the same idea as judging a transfer by the level the player is coming from.
    Quality-adjusted
    A player's raw numbers after they have been multiplied by the league multiplier above, so a rate earned in a strong league and one earned in a weaker league can be compared on the same footing.
    Interval (90 percent)
    A range around a number that shows how sure the model is, not a single guess; a ninety percent interval means the model thinks the true value falls inside that range about nine times out of ten.
    Posterior
    What the model believes about a number after it has seen the data, expressed as a range of plausible values rather than one single figure; the probability attached to a break season in this report is a posterior probability.
    Out-of-sample
    Checking a method only on matches, players or seasons it was not shown while it was being built — the way a manager judges a scouting report by what actually happens once the player signs, not by how well the report described what had already happened.
    Cluster
    A group of players whose season numbers look similar to each other and different from other groups, found automatically from the data rather than assigned by hand; a cluster is a style label such as "high-volume scorers", not a formal position.
    Change point
    The point in a series of seasons where the level genuinely shifts to a new one and stays there, rather than just one unusually high or low season on its own; this report finds one for the count of home-nation players in Europe's biggest leagues.

    References

    What this is for

    A federation does not need another opinion about its pool. It needs the same measured questions asked every summer, answered the same way, with the uncertainty on the page — and a name for every player the staff ask about.

    This is one country, built from public data in the open. The pipeline takes a nationality code and a peer set; the same code produced the other editions linked below, and the comparison across them. With a federation’s own data — tracking, academy, medical — the tables get deeper and the questions stay the same.

    The one-page brief · Every player in the pool · Download the tables · The Czechia edition · The England edition · The Germany edition · The Denmark edition · The Spain edition · The player atlas · Six nations: what to take from it