Atlas of the player pool

How deep is a national player pool — and why

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

Per head, Czechia ranks 7th of 9 peer countries for players in Europe’s strongest leagues. The reasons below are measured, not guessed: 1.1 regular under-21 starters per club at home against 2.5 in Denmark; a first move abroad at 24; and a layer at the top that stepped down in 2014/15.

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 midfielders aged 23-25: 2 Czech players in the top-9 leagues against a peer median of 6. A recent Czech export first reached a top-9 roster at a median age of 22; one from Denmark at 22. Built from FBref, Wikipedia and Wikidata. Czechia 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.

* 26 CZE players with ≥ 450 minutes in the UEFA top-9 leagues, 2025/26, per 10.9 M inhabitants; peers counted the same way. Methodology.

What to take from it

1

Czechia is the one small nation whose Big-5 presence fell and has not come back.

It rose in 00/01 (×1.66) and fell in 14/15 (×0.58); 10 players with 450+ Big-5 minutes now against 26 at the 07/08 peak. At the 07/08 peak the nation's Big-5 players had a median age of 28; in the 14/15 fall season it was 32 and no debutant arrived — a generation retired and what followed was thinner.

Croatia fell too (03/04) and came back 11 seasons later; Norway fell too (04/05) and came back 17 seasons later. Every other small peer's later step is a rise.

2

Two problems at once, not one: which mechanism carries the gap depends on the peer.

Against Norway home-league strength carries 73 % of a 7.0-per-million gap; against Denmark youth minutes at home carries 72 % of a 10.2-per-million gap. They add up rather than compete: a league that gives its young few minutes and is weak besides loses on both counts.

3

The trend runs the wrong way: Czechia's own under-21s went from 10 % to 6 % of home-league minutes in 6 seasons.

Over the same seasons Norway 8 % → 11 % and +2.7 per million in the Big-5; Denmark 16 % → 15 % and +2.3 per million in the Big-5; Czechia -0.1 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: Czechia is out of line on the first rung — minutes for its own under-21s and the timing of the first move.

Out of line: minutes for its own under-21s at home: 6.4 % of league minutes and 1.1 regular under-21 starters per club, last among the peers (Denmark 15.3 %; Denmark 2.5 starters per club); the first move abroad at a median 24 (Norway 22); the exporters move at 22–23; how many leave at all: 32 first moves in the covered seasons (Denmark 112); how many clubs the exports leave from: 54 % of the 48 players who went from the home league to a top-9 league since 20/21 left from Sparta Prague or Slavia Prague (Norway: 22 % from its top two, 54 exports in all).

Not the problem: the home league itself: multiplier ×0.43, 2 of 9 among the peers; how its exports fare: a median 50 % of their club's minutes, 3 of 9.

What the peers show is reachable: two regular under-21 starters per club (from 1.1) — Denmark 2.5, Croatia 2.3, Hungary 2.2 already do; the first move at 22–23, not 24 — the route the peers that grew use; more of them, not better ones: the exports that do go hold their place.

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 Czech 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: Czech football next to Norway and Denmark 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 Czechia; 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

    CZE 6.4 % NOR 11.5 % DEN 15.3 %

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

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

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

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

    The regular-starter line is drawn at ten starts; at five it is 1.5 per club, at fifteen 0.5 (ten: 1.1).

    They are not being used as late substitutes: 6.0 % of the league’s starts went to them against 6.4 % of its minutes, and when one of them started he was on the pitch for 76 minutes against the league’s own 79. The difference between these countries is how many young players are picked at all, not how they are used once picked.

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

    Average age of a minute played in the league

    CZE 26.0 NOR 25.6 DEN 25.4

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

    CZE 21.1 % NOR 15.9 % DEN 19.5 %

  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

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

    CZE 19 % NOR 22 % DEN 10 %

  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

    CZE 2.39 NOR 9.37 DEN 12.58

    Around the 2014/15 season, the model finds a possible shift in the count of Czech players in Europe's strongest leagues, with 56 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 is different for each comparison — how strong the domestic league is for Norway, how much playing time young players get at home for Denmark. 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.
  • This share is a floor. FBref records no nationality for part of this league’s rows, and every “own nationals” share counts those players as foreign while keeping their minutes in the total, so the true figure lies between 6.4 % and 7.6 % — an error that moves this country’s number and almost none of its peers’ (see the data-quality log). The same floor applies to the share of starts.

How deep is the pool?

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

Is the Czech pool thin?

Czechia ranks 7th of 9 countries at 2.39 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: Midfielders aged 23-25, 2 Czech players vs a peer median of 6.

GroupCohortCZEPeer median
Midfielders 23-25 2 6
Midfielders 26-29 3 6
Midfielders 30+ 1 3.5
Defenders 26-29 3 4.5
Defenders U22 0 1.5
How we know

As an analytics question In numbers: Czech 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 6.4 % 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, Czechia 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 Czech players go when they leave?

27 of 192 play abroad; 63 % in the 9 strongest leagues, 19 % moved sideways (to a league no stronger than the Czech one).

17 top-9 median multiplier 0.666
63 %
10 peer country league median multiplier 0.365
37 %
How we know

As an analytics question In numbers: destination-league tier of every Czech-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 Czech-eligible player's 2025/26 row; sideways = destination multiplier ≤ Czech 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). Czech exports arrive at a median age of 23.

Line chart: the fitted age-at-export curve with its 90 % band, Czech 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 Czech exports — hover for the name.
How we know

At 21: 0.14 (0.13–0.16); at 24: 0.14 (0.12–0.16); the difference +0.001 (−0.011–0.013).

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?

Czech exports keep 50 % of their club's minutes (3rd of 9).

Dot plot: each country's median share of club minutes for players abroad, with a thin line spanning the other countries' values; Czechia 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. Czech 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 Czech First League season converts to 0.67 of a Premier League one by the transfer-graph model (0.59–0.75), against 0.43 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?

35 % of the 2026 FIFA World Cup squad plays in the 9 strongest leagues; Switzerland 88 %.

  • Top-9 %
  • Stepping %
  • Domestic %
  • Other %
  • Vladimír Coufal
  • Lukáš Horníček
  • Matěj Kovář
  • Ladislav Krejčí
  • Robin Hranáč
  • Tomáš Souček
  • Patrik Schick
  • Pavel Šulc
  • Adam Hložek
  • Vladimír Darida
  • Lukáš Červ
  • Lukáš Provod
  • Michal Sadílek
  • Tomáš Holeš
  • Tomáš Chorý
  • Štěpán Chaloupek
  • David Zima
  • Jindřich Staněk
  • Jaroslav Zelený
  • Denis Višinský
  • Jan Kuchta
  • Mojmír Chytil
  • David Douděra
  • David Jurásek
  • Alexandr Sojka
  • Hugo Sochůrek
How the peers are sourced
  • Top-9 %
  • Stepping %
  • Domestic %
  • Other %
CZE Czechia Squad 26
35 %
SUI Switzerland Squad 26
88 %
CRO Croatia Squad 26
73 %
AUT Austria Squad 26
69 %
NOR Norway Squad 26
65 %
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?

Czech players with ≥ 450 Big-5 minutes: 26 at the 2007/08 peak, 6 at the 2015/16 low, 10 in 2025/26. The break is dated to 2014/15. The rise before it is dated to 2000/01.

Line chart: Czech 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 Czechia, Denmark and Croatia.
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. Czech 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 56 %; a level change of ×0.60 (0.39–1.03). Rise posterior 55 %; ×1.65.

As an analytics question In numbers: Czech 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 Czech players of each peak season, goalkeepers included — a lineup of presence, not a quality ranking: 2007/08: Jaroslav Drobný, Jaroslav Plašil, Radim Kučera; 2002/03: Petr Čech, Jan Koller, David Jarolím; 2005/06: David Rozehnal, Tomáš Ujfaluši, Petr Čech. 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 Norway and Denmark do it?

On the same six numbers Norway gives U21 players 12 % of domestic minutes against 6 % and sends 65 % of its squad to the 9 strongest leagues against 35 %.

Slope chart: Norway, Denmark and Czechia 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 Czechia loses ground.
The six numbers, unscaled
MetricCZENORDEN
Players per million 2.39 9.37 12.58
U21 share of domestic minutes 6.4 % 11.5 % 15.3 %
Export age (recent) 22 22 22
Sideways moves 19 % 22 % 10 %
Exports' club-minutes share 50 % 37 % 46 %
National-team squad in the top-9 leagues 35 % 65 % —
Big-5 players now 10 25 38
How we know

As an analytics question In numbers: Norway and Denmark against Czechia 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?

4 Czech goalkeepers play ≥ 450 minutes in the top-9 leagues — rank 5 of 9 per million — and they get there earlier than outfield exports.

Strip plot of age at first top-9-league appearance, Czech 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: Czech top-9 goalkeepers, 2025/26
PlayerClub LeagueMinutes Club goals percentile
Lukáš HorníčekBragaPOR-Primeira Liga2959 83 %
Matej KovarPSVNED-Eredivisie2790 100 %
Vitezslav JarosAjaxNED-Eredivisie1710 83 %
Antonín KinskýTottenhamENG-Premier League630 42 %
Goalkeeper production: Czech keepers, 2025/26
PlayerClub LeagueMinutes GA/90Saves/90 Save %Clean-sheet share GA/90, quality-adj.
Antonín KinskýTottenhamENG-Premier League630 1.001.43 65.9 %29 % 1.21
Vitezslav JarosAjaxNED-Eredivisie1710 1.213.26 68.3 %26 % 2.62
Matej KovarPSVNED-Eredivisie2790 1.322.74 67.8 %23 % 2.71
Lukáš HorníčekBragaPOR-Primeira Liga2959 1.002.19 66.6 %36 % 1.70
Martin JedličkaBaník OstravaCZE-First League1440 1.503.12 67.5 %31 % 3.19
Michal ReichlBohemians 1905CZE-First League1990 1.402.89 67.5 %22 % 3.09
Hugo Jan BačkovskýDukla PragueCZE-First League1260 1.293.36 67.8 %21 % 2.88
Stanislav DostálFastav ZlínCZE-First League2700 1.473.03 67.4 %30 % 3.23
Jan HanušJablonecCZE-First League2326 1.202.24 67.2 %42 % 2.77
Aleš MandousMladá BoleslavCZE-First League720 3.122.75 66.4 %0 % 4.74
Jiří FloderMladá BoleslavCZE-First League2250 1.202.92 67.8 %36 % 2.77
Aleš MandousPardubiceCZE-First League480 1.502.81 67.4 %0 % 3.01
Jan KoutnySigma OlomoucCZE-First League2766 1.202.77 67.7 %29 % 2.77
Jakub MarkovičSlavia PragueCZE-First League1317 0.551.71 67.8 %53 % 1.87
Jindřich StaněkSlavia PragueCZE-First League1710 1.052.11 67.4 %32 % 2.55
Tomáš KoubekSlovan LiberecCZE-First League2970 1.092.73 67.9 %33 % 2.57
Jiří BorekSlováckoCZE-First League540 1.673.33 67.4 %33 % 3.17
Milan HečaSlováckoCZE-First League2250 1.403.04 67.6 %24 % 3.10
Matouš TrmalTepliceCZE-First League3060 1.242.94 67.9 %32 % 2.83
Martin JedličkaViktoria PlzeňCZE-First League1274 1.412.90 67.5 %27 % 3.06
Viktor BaierBlau-Weiß LinzAUT-Bundesliga1530 1.713.06 65.9 %18 % 5.82
Adam StejskalWSG TirolAUT-Bundesliga2790 1.652.13 65.0 %23 % 5.84

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

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

0.37 per million; first top-9 season at a median age of 22.5, against 23 for outfield exports.

As an analytics question In numbers: Czech 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, 21 % 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 6.98 players per million between Norway and Czechia, U21 minutes go with +4.22, league strength with +5.12, 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 Czechia, 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 Czechia. 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

−2.37 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?

17 cards chosen by six rules.

How the 17 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

Patrik SchickLeverkusenFW0.49G+A / 90 adj.↓ declining · −0.20 G+A / 90 adj.Career →
Leverkusen

Patrik Schick

FW · 30 · Leverkusen (2026/27) · NT 2024–26

2025/26 · Leverkusen · GER-Bundesliga

G+A / 90 adj.
0.49
Non-penalty goals / assists per 90
0.54 / 0.14
Minutes
1988 (67 %)
Style map Primary scorers Quality map High-volume scorers 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 (Schick, Patrak, Chorý).

Trajectory 2024/25 → 2025/26

declining −0.20 G+A / 90 adj. 1684 min → 1988 min

Historical analogs at age 30
  1. Serhou Guirassy GUI GER-Bundesliga 2025/26 · 2337 min · 0.48 · d = 0.47
  2. Ihlas Bebou TOG GER-Bundesliga 2023/24 · 1636 min · 0.46 · d = 0.50
  3. Pere Milla ESP ESP-La Liga 2021/22 · 1644 min · 0.44 · d = 0.62

2024–25 Nations League · 2026 FIFA World Cup · UEFA Euro 2024

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

Václav ČernýBeşiktaşMF0.22G+A / 90 adj.—Career →
Beşiktaş

Václav Černý

MF · 29 · Beşiktaş (2026/27) · NT 2024–26

2025/26 · Beşiktaş · TUR-Süper Lig

G+A / 90 adj.
0.22
Non-penalty goals / assists per 90
0.23 / 0.36
Minutes
1991 (74 %)
Style map High-minutes creative midfielders Quality map Productive midfielders in top-five leagues

Tactical read

Starting playmakers — assist rate three times the midfield median on 62 % of minutes, scoring nearly double. The creative hub of the middle third (Provod, Vlkanova, Ladra).

Historical analogs at age 29
  1. Efkan Bekiroğlu TUR-Süper Lig 2023/24 · 2126 min · 0.22 · d = 0.18
  2. Trézéguet EGY TUR-Süper Lig 2022/23 · 2127 min · 0.21 · d = 0.23
  3. João Novais POR TUR-Süper Lig 2021/22 · 1835 min · 0.21 · d = 0.23

2026 World Cup qualification · UEFA Euro 2024

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

Vladimír CoufalHoffenheimDF0.18G+A / 90 adj.↑ improving · +0.15 G+A / 90 adj.Career →
Hoffenheim

Vladimír Coufal

DF · 34 · Hoffenheim (2026/27) · NT 2024–26

2025/26 · Hoffenheim · GER-Bundesliga

G+A / 90 adj.
0.18
Non-penalty goals / assists per 90
0.03 / 0.24
Minutes
3012 (98 %)
Style map High-assist defenders with high playing time Quality map High-assist defenders in top-five leagues

Tactical read

Attacking full-backs — assist rate seven times the DF median on starter minutes (65 %). The wide defender whose job ends in the final third (Coufal, Icha, Hadaš).

Trajectory 2024/25 → 2025/26

improving +0.15 G+A / 90 adj. 1067 min → 3012 min

Historical analogs at age 34
  1. Óscar de Marcos ESP ESP-La Liga 2022/23 · 2819 min · 0.14 · d = 0.41
  2. Leandro Cabrera URU ESP-La Liga 2024/25 · 2869 min · 0.12 · d = 0.53
  3. Jeffrey Gouweleeuw NED GER-Bundesliga 2024/25 · 2944 min · 0.10 · d = 0.63

2024–25 Nations League · 2026 FIFA World Cup · UEFA Euro 2024

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

Pavel ŠulcLyonFW0.46G+A / 90 adj.—Career →
Lyon

Pavel Šulc

FW · 26 · Lyon (2026/27) · NT 2024–26

2025/26 · Lyon · FRA-Ligue 1

G+A / 90 adj.
0.46
Non-penalty goals / assists per 90
0.63 / 0.17
Minutes
1566 (54 %)
Style map Primary scorers Quality map High-volume scorers 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 (Schick, Patrak, Chorý).

Historical analogs at age 26
  1. Amine Gouiri ALG FRA-Ligue 1 2025/26 · 1326 min · 0.43 · d = 0.41
  2. Randal Kolo Muani FRA FRA-Ligue 1 2023/24 · 1268 min · 0.43 · d = 0.50
  3. Bamba Dieng SEN FRA-Ligue 1 2025/26 · 1212 min · 0.43 · d = 0.56

2024–25 Nations League · 2026 FIFA World Cup · UEFA Euro 2024

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

Tomáš SoučekWest Ham UnitedMF0.21G+A / 90 adj.↓ declining · −0.11 G+A / 90 adj.Career →
West Ham United

Tomáš Souček

MF · 31 · West Ham United (latest known) · NT 2024–26

2025/26 · West Ham · ENG-Premier League

G+A / 90 adj.
0.21
Non-penalty goals / assists per 90
0.20 / 0.00
Minutes
2200 (64 %)
Style map Older rotation midfielders Quality map Productive midfielders in top-five leagues

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 (Souček, Trávník, Daníček).

Trajectory 2024/25 → 2025/26

declining −0.11 G+A / 90 adj. 2567 min → 2200 min

Historical analogs at age 31
  1. John McGinn SCO ENG-Premier League 2024/25 · 2223 min · 0.21 · d = 0.03
  2. Rodrigo ESP ENG-Premier League 2021/22 · 2265 min · 0.22 · d = 0.14
  3. Mateusz Klich POL ENG-Premier League 2020/21 · 2393 min · 0.23 · d = 0.33

2024–25 Nations League · 2026 FIFA World Cup · UEFA Euro 2024

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

Ladislav KrejčíWolverhampton WanderersDF0.10G+A / 90 adj.→ stable · +0.05 G+A / 90 adj.Career →
Wolverhampton Wanderers

Ladislav Krejčí

DF · 27 · Wolverhampton Wanderers (latest known) · NT 2024–26

2025/26 · Wolves · ENG-Premier League

G+A / 90 adj.
0.10
Non-penalty goals / assists per 90
0.08 / 0.04
Minutes
2356 (75 %)
Style map Everyday starting defenders Quality map Goal-scoring 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 (Hůlka, Halinský, Cedidla).

Trajectory 2024/25 → 2025/26

stable +0.05 G+A / 90 adj. 2440 min → 2356 min

Historical analogs at age 27
  1. Pau Torres ESP ENG-Premier League 2023/24 · 2464 min · 0.08 · d = 0.23
  2. Pervis Estupiñán ECU ENG-Premier League 2024/25 · 2402 min · 0.07 · d = 0.26
  3. Oleksandr Zinchenko UKR ENG-Premier League 2022/23 · 2118 min · 0.09 · d = 0.32

2024–25 Nations League · 2026 FIFA World Cup · UEFA Euro 2024

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

Tomáš ChorýSlavia PragueFW0.30G+A / 90 adj.↑ improving · +0.07 G+A / 90 adj.Career →
Slavia Prague

Tomáš Chorý

FW · 31 · Slavia Prague (2026/27) · NT 2024–26

2025/26 · Slavia Prague · CZE-First League

G+A / 90 adj.
0.30
Non-penalty goals / assists per 90
0.54 / 0.24
Minutes
1838 (70 %)
Style map Primary scorers Quality map High-minutes starting forwards

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 (Schick, Patrak, Chorý).

Trajectory 2024/25 → 2025/26

improving +0.07 G+A / 90 adj. 2123 min → 1838 min

Historical analogs at age 31
  1. Serdar Dursun TUR TUR-Süper Lig 2021/22 · 1869 min · 0.29 · d = 0.25
  2. Ivi ESP POL-Ekstraklasa 2024/25 · 1759 min · 0.27 · d = 0.27
  3. Mats Seuntjens NED NED-Eredivisie 2022/23 · 1564 min · 0.31 · d = 0.54

2024–25 Nations League · 2026 FIFA World Cup · UEFA Euro 2024

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

Vladimír DaridaHradec KrálovéMF0.16G+A / 90 adj.—Career →
Hradec Králové

Vladimír Darida

MF · 36 · Hradec Králové (2026/27) · NT 2024–26

2025/26 · Hradec Králové · CZE-First League

G+A / 90 adj.
0.16
Non-penalty goals / assists per 90
0.29 / 0.13
Minutes
2764 (90 %)
Style map High-scoring attacking midfielders Quality map Everyday starting midfielders, low scoring output

Tactical read

Goal-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 (Darida, Ševčík, Višinský).

Historical analogs at age 36
  1. Alexandru Maxim ROU TUR-Süper Lig 2025/26 · 2680 min · 0.12 · d = 0.42
  2. Jesús Imaz ESP POL-Ekstraklasa 2025/26 · 2784 min · 0.23 · d = 0.60
  3. Lucas Biglia ARG TUR-Süper Lig 2021/22 · 2795 min · 0.08 · d = 0.67

2024–25 Nations League · 2026 FIFA World Cup

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

Tomáš HolešSlavia PragueDF0.06G+A / 90 adj.→ stable · 0.00 G+A / 90 adj.Career →
Slavia Prague

Tomáš Holeš

DF · 33 · Slavia Prague (2026/27) · NT 2024–26

2025/26 · Slavia Prague · CZE-First League

G+A / 90 adj.
0.06
Non-penalty goals / assists per 90
0.09 / 0.09
Minutes
2003 (77 %)
Style map Older defenders Quality map Older defenders

Tactical read

Experienced defenders on managed minutes — median age 31, 44 % of minutes, output at the floor. Leadership and cover rather than a starting role (Novák, Brabec, Fleišman).

Trajectory 2024/25 → 2025/26

stable 0.00 G+A / 90 adj. 2454 min → 2003 min

Historical analogs at age 33
  1. Piotr Mroziński POL POL-Ekstraklasa 2024/25 · 1979 min · 0.05 · d = 0.07
  2. Jakub Jugas CZE POL-Ekstraklasa 2024/25 · 2030 min · 0.05 · d = 0.08
  3. Zeki Yavru TUR TUR-Süper Lig 2023/24 · 2017 min · 0.05 · d = 0.25

2024–25 Nations League · 2026 FIFA World Cup · UEFA Euro 2024

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

Youngest national-team call-up

Christophe KabongoViktoria PlzeňFW0.25G+A / 90 adj.—Career →
Viktoria Plzeň

Christophe Kabongo

FW · 23 · Viktoria Plzeň (2026/27) · NT 2024–26

2025/26 · Mladá Boleslav · CZE-First League

G+A / 90 adj.
0.25
Non-penalty goals / assists per 90
0.45 / 0.22
Minutes
803 (27 %)
Style map Duel-heavy rotation forwards Quality map High-card-rate forwards

Tactical read

Rotation 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 (Mašek, Vojta, Kozak).

Historical analogs at age 23
  1. Hierman Barkoŭski BLR POL-Ekstraklasa 2024/25 · 962 min · 0.21 · d = 0.36
  2. Christian Rasmussen DEN GER-2. Bundesliga 2025/26 · 1054 min · 0.24 · d = 0.40
  3. Tadeáš Vachoušek CZE CZE-First League 2026/27 · 502 min · 0.24 · d = 0.40

2026 World Cup qualification

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

Hugo SochůrekSparta PragueMF0.10G+A / 90 adj.—Career →
Sparta Prague

Hugo Sochůrek

MF · 18 · Sparta Prague (2026/27) · NT 2024–26

2025/26 · Sparta Prague · CZE-First League

G+A / 90 adj.
0.10
Non-penalty goals / assists per 90
0.00 / 0.35
Minutes
510 (17 %)
Style map Young low-minute midfielders Quality map Young low-minute midfielders

Tactical read

Development midfielders — the home pool's largest midfield group: median age 22, under a third of minutes, output at median. The pipeline's waiting room (Daněk, Křišťan, Mikulenka).

Historical analogs at age 18
  1. Alexander Røssing-Lelesiit NOR NOR-Eliteserien 2024/25 · 547 min · 0.10 · d = 0.30
  2. Emirhan İlkhan TUR TUR-Süper Lig 2021/22 · 635 min · 0.09 · d = 0.32
  3. Luis Engelns GER GER-2. Bundesliga 2024/25 · 542 min · 0.06 · d = 0.45

2024–25 Nations League · 2026 FIFA World Cup

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

Jan PaluskaViktoria PlzeňDF0.01G+A / 90 adj.→ stable · −0.01 G+A / 90 adj.Career →
Viktoria Plzeň

Jan Paluska

DF · 21 · Viktoria Plzeň (latest known) · NT 2024–26

2025/26 · Viktoria Plzeň · CZE-First League

G+A / 90 adj.
0.01
Non-penalty goals / assists per 90
0.00 / 0.00
Minutes
1045 (36 %)
Style map Young low-minute defenders Quality map Young low-minute 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 (Konečný, Kolar, Prebsl).

Trajectory 2024/25 → 2025/26

stable −0.01 G+A / 90 adj. 919 min → 1045 min

Historical analogs at age 21
  1. Jan Trédl CZE-First League 2024/25 · 951 min · 0.04 · d = 0.30
  2. Aleksander Kjelsen NOR NOR-Eliteserien 2026/27 · 1097 min · 0.01 · d = 0.30
  3. Adam Dohnalek CZE-First League 2024/25 · 906 min · 0.04 · d = 0.34

UEFA European Under-21 Championship 2025

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

Most top-9 minutes

Roman KvětBohemians 1905MF0.10G+A / 90 adj.→ stable · −0.04 G+A / 90 adj.Career →
Bohemians 1905

Roman Květ

MF · 29 · Bohemians 1905 (2026/27)

2025/26 · Dender · BEL-Pro League

G+A / 90 adj.
0.10
Non-penalty goals / assists per 90
0.18 / 0.00
Minutes
2545 (81 %)
Style map Everyday starting midfielders, low scoring output Quality map Everyday starting midfielders, low scoring output

Tactical read

The engine room — nearly 80 % of minutes with median output: holding and box-to-box midfielders whose value is continuity and structure, not numbers (Hellebrand, Horák, Čermák).

Trajectory 2024/25 → 2025/26

stable −0.04 G+A / 90 adj. 2733 min → 2545 min

Historical analogs at age 29
  1. Nicolas Rommens BEL BEL-Pro League 2022/23 · 2398 min · 0.13 · d = 0.29
  2. Siebe Schrijvers BEL BEL-Pro League 2024/25 · 2719 min · 0.13 · d = 0.31
  3. Thom Haye IDN NED-Eredivisie 2023/24 · 2610 min · 0.10 · d = 0.32

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

Robin HranáčHoffenheimDF0.04G+A / 90 adj.—Career →
Hoffenheim

Robin Hranáč

DF · 26 · Hoffenheim (2026/27) · NT 2024–26

2025/26 · Hoffenheim · GER-Bundesliga

G+A / 90 adj.
0.04
Non-penalty goals / assists per 90
0.04 / 0.00
Minutes
2251 (74 %)
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 (Hůlka, Halinský, Cedidla).

Historical analogs at age 26
  1. Jonjoe Kenny ENG GER-Bundesliga 2022/23 · 2244 min · 0.04 · d = 0.03
  2. Marco Friedl AUT GER-Bundesliga 2023/24 · 2197 min · 0.03 · d = 0.10
  3. Ferland Mendy FRA ESP-La Liga 2020/21 · 2208 min · 0.03 · d = 0.14

2024–25 Nations League · 2026 FIFA World Cup · UEFA Euro 2024

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

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

Lukáš MašekSlovan LiberecFW0.16G+A / 90 adj.—Career →
Slovan Liberec

Lukáš Mašek

FW · 22 · Slovan Liberec (2026/27)

2025/26 · Slovan Liberec · CZE-First League

G+A / 90 adj.
0.16
Non-penalty goals / assists per 90
0.30 / 0.00
Minutes
1806 (57 %)
Style map Duel-heavy rotation forwards Quality map High-card-rate forwards

Tactical read

Rotation 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 (Mašek, Vojta, Kozak).

Historical analogs at age 22
  1. Isak Jensen DEN DEN-Superliga 2024/25 · 1953 min · 0.16 · d = 0.36
  2. Mustapha Isah NGA NOR-Eliteserien 2025/26 · 1653 min · 0.17 · d = 0.37
  3. Mbaye Jacques Ndiaye SEN POL-Ekstraklasa 2024/25 · 1574 min · 0.13 · d = 0.39

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

Vojtech StranskySlovan LiberecMF0.07G+A / 90 adj.→ stable · 0.00 G+A / 90 adj.Career →
Slovan Liberec

Vojtech Stransky

MF · 23 · Slovan Liberec (2026/27) · NT 2024–26

2025/26 · Slovan Liberec · CZE-First League

G+A / 90 adj.
0.07
Non-penalty goals / assists per 90
0.07 / 0.10
Minutes
2634 (84 %)
Style map Everyday starting midfielders, low scoring output Quality map Everyday starting midfielders, low scoring output

Tactical read

The engine room — nearly 80 % of minutes with median output: holding and box-to-box midfielders whose value is continuity and structure, not numbers (Hellebrand, Horák, Čermák).

Trajectory 2024/25 → 2025/26

stable 0.00 G+A / 90 adj. 2017 min → 2634 min

Historical analogs at age 23
  1. Tomas Rigo SVK CZE-First League 2024/25 · 2528 min · 0.10 · d = 0.25
  2. Eric Martel GER GER-2. Bundesliga 2024/25 · 2740 min · 0.06 · d = 0.28
  3. Jonas Therkelsen NOR GER-2. Bundesliga 2025/26 · 2722 min · 0.10 · d = 0.30

UEFA European Under-21 Championship 2025

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

Denis HalinskýPardubiceDF0.01G+A / 90 adj.→ stable · −0.03 G+A / 90 adj.Career →
Pardubice

Denis Halinský

DF · 23 · Pardubice (2026/27) · NT 2024–26

2025/26 · Teplice · CZE-First League

G+A / 90 adj.
0.01
Non-penalty goals / assists per 90
0.00 / 0.03
Minutes
2790 (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 (Hůlka, Halinský, Cedidla).

Trajectory 2024/25 → 2025/26

stable −0.03 G+A / 90 adj. 1752 min → 2790 min

Historical analogs at age 23
  1. Oskar Wójcik POL POL-Ekstraklasa 2025/26 · 2749 min · 0.01 · d = 0.08
  2. Furkan Bayır TUR TUR-Süper Lig 2022/23 · 2789 min · 0.02 · d = 0.25
  3. Marcel Beifus GER GER-2. Bundesliga 2024/25 · 2668 min · 0.01 · d = 0.25

UEFA European Under-21 Championship 2025

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

Braga

Lukáš Horníček

GK · Braga (POR-Primeira Liga) · NT 2024–26 · Career, season by season →

2025/26 · 2959 min

GA/90
1.00
Saves/90
2.19
Save %
66.6 %

Club tier

Braga (POR-Primeira Liga) · 83 % of the league's goals scored

2024–25 Nations League · 2026 FIFA World Cup · UEFA European Under-21 Championship 2025

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

Tottenham

Antonín Kinský

GK · Tottenham (ENG-Premier League) · NT 2024–26 · Career, season by season →

2025/26 · 630 min

GA/90
1.00
Saves/90
1.43
Save %
65.9 %

Club tier

Tottenham (ENG-Premier League) · 42 % of the league's goals scored

2026 World Cup qualification

Selected as: youngest home goalkeeper with minimum top-9 minutes.

* 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 31 Czech-eligible players by cluster; bright rings mark the 13 with a national-team call-up 2024–26.
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 Czech-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 102 Czech-eligible players by cluster; bright rings mark the 28 with a national-team call-up 2024–26.
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 Czech-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 59 Czech-eligible players by cluster; bright rings mark the 19 with a national-team call-up 2024–26.
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 Czech-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 217, searchable

The cards above pick by rule. This is everyone with a 2025/26 season in the data: 31 forwards, 102 midfielders, 59 defenders and 25 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.

217 of 217
    How we know

    As an analytics question In numbers: 17 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?

    6 climbed a rung, 11 came down. The stepping-stone leagues hold 0 of the pool, from 4; the top nine hold 16, from 17.

    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.

    Czech First League

    163 → 163

    266 223 → 236 747 minutes

    stepping-stone league

    4 → 0

    7 553 → 0 minutes

    top-9 league

    17 → 16

    25 734 → 26 921 minutes

    other covered league

    14 → 10

    20 690 → 14 972 minutes

    Players of the pool on each rung, 2024/25 → 2025/26, with the minutes they played there; 198 qualifying players last season, 189 this season.

    Climbed a rung 6

    • Pavel Šulc CZE-First League → FRA-Ligue 1
    • Martin Vitík CZE-First League → ITA-Serie A
    • Lukáš Sadílek CZE-First League → POL-Ekstraklasa
    • Adam Karabec GER-2. Bundesliga → FRA-Ligue 1
    • Ondřej Zmrzlý CZE-First League → POL-Ekstraklasa
    • Matej Sin CZE-First League → NED-Eredivisie

    Came down a rung 11

    • Roman Macek SUI-Super League → CZE-First League
    • Michal Sadílek NED-Eredivisie → CZE-First League
    • Pavel Kadeřábek GER-Bundesliga → CZE-First League
    • Václav Jurečka TUR-Süper Lig → CZE-First League
    • David Jurásek GER-Bundesliga → CZE-First League
    • Ondřej Karafiát GER-2. Bundesliga → CZE-First League
    • and 5 more

    New to the pool 40

    • Vladimír Darida CZE-First League
    • Martin Chlumecký HUN-NB I
    • František Čech CZE-First League
    • Filip Novák CZE-First League
    • Robin Hranáč GER-Bundesliga
    • Jakub Brabec POR-Primeira Liga
    • and 34 more

    No longer in a covered league 49

    • Lukáš Kalvach CZE-First League
    • Petr Schwarz POL-Ekstraklasa
    • Marek Suchý CZE-First League
    • Jakub Pokorný CZE-First League
    • Michal Hubínek CZE-First League
    • Jakub Klíma CZE-First League
    • and 43 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 Czech-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 CZENORDEN
    Share of league minutes that went to players aged 21 or under6.4 %11.5 %15.3 %
    Average age of a minute played in the league26.025.625.4
    Age the first time a player has real playing time in a foreign league, over the players abroad today2422.523
    Share of moves abroad to a league no stronger than the player's own19 %22 %10 %
    Players in Europe's strongest leagues, for every million people2.399.3712.58

    If you take one thing from this: the single measured link that carries the most of the gap is different for each comparison — how strong the domestic league is for Norway, how much playing time young players get at home for Denmark.

    How strong is the home league
    A season in the home league is worth about 0.67 of a season in the Premier League (0.59–0.75).
    When the count of players in the strongest leagues turned
    Around the 2014/15 season, with 56 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.
    • This share is a floor. FBref records no nationality for part of this league’s rows, and every “own nationals” share counts those players as foreign while keeping their minutes in the total, so the true figure lies between 6.4 % and 7.6 % — an error that moves this country’s number and almost none of its peers’ (see the data-quality log). The same floor applies to the share of starts.

    Explore the data

    Benchmark vs peer countries

    Structural benchmark vs peer countries

    Football intuition recognises the Czech 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+
    Czechia — 1 2 2
    Denmark 1 7 2 —
    Croatia — 3 — 2
    Austria 1 2 — 1
    Switzerland — 1 3 —

    Cohort gaps — midfielders

    Cohort U2223-2526-2930+
    Czechia 1 2 3 1
    Denmark 6 6 13 6
    Croatia — 4 8 6
    Austria 2 6 4 7
    Switzerland 4 6 10 3

    Cohort gaps — defenders

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

    * 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 Czech shortfalls against the peer median count: midfielders 23-25 (2 vs 6), midfielders 26-29 (3 vs 6), midfielders 30+ (1 vs 3.5).

    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 Czech row is outlined.
    Median non-penalty goals + assists per 90 by country, position group and age cohort, 2025/26; the outlined row is Czechia.

    Observations

    Per capita: rank 7 of 9 · The largest cohort gap: midfielders 23-25 · Trajectories 2024/25 → 2025/26: mostly stable

    Per capita: rank 7 of 9

    26 Czech players on 2025/26 rosters of the nine strongest leagues give 2.39 per million inhabitants, rank 7 of 9. Denmark leads with 12.58, 5.3 times the Czech density; Slovakia sits one place above with 3.14 from 17 players and a population 2.0 times smaller. Below Czechia: Hungary, Poland.

    The largest cohort gap: midfielders 23-25

    Counting 2025/26 top-9 players by position group and age cohort and comparing the Czech count with the median of the other eight peers, the three largest shortfalls are midfielders 23-25 — 2 Czech against a peer median of 6; midfielders 26-29 — 3 Czech against a peer median of 6; midfielders 30+ — 1 Czech against a peer median of 3.5. The cohort tables above show the medians behind the counts.

    Trajectories 2024/25 → 2025/26: mostly stable

    92 Czech-eligible players had at least 900 minutes in both 2024/25 and 2025/26: forwards 13 (5 up, 6 stable, 2 down); midfielders 43 (3 up, 31 stable, 9 down); defenders 36 (1 up, 34 stable, 1 down). A move counts as up or down when league-adjusted goals + assists per 90 changed by more than 0.05; 71 of 92 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 Czech 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 Czech members of the corpus cluster; names are the Czech 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 31 Czech-eligible players by cluster; bright rings mark the 13 with a national-team call-up 2024–26.
    Atlas of forwards 2025/26 in both projections: 31 Czech-eligible players in colour against a corpus of 924. Bright rings mark the national-team pool (call-up 2024–26, 13 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 102 Czech-eligible players by cluster; bright rings mark the 28 with a national-team call-up 2024–26.
    Atlas of midfielders 2025/26 in both projections: 102 Czech-eligible players in colour against a corpus of 2648. Bright rings mark the national-team pool (call-up 2024–26, 28 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 59 Czech-eligible players by cluster; bright rings mark the 19 with a national-team call-up 2024–26.
    Atlas of defenders 2025/26 in both projections: 59 Czech-eligible players in colour against a corpus of 1947. Bright rings mark the national-team pool (call-up 2024–26, 19 players).

    Forwards

    High-assist forwards 3 Czech of 112 · NT pool 1 · median born 1997 Vasil KušejVáclav Jurečka
    Corpus medians: 0.30 non-penalty goals and 0.21 assists per 90, 33 % of the club's minutes, age 25, 0.15 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 (Kušej, Jurečka, Pulkrab).
    Duel-heavy rotation forwards 7 Czech of 144 · NT pool 3 · median born 2003 Lukáš MašekMatyas VojtaMatyas KozakChristophe Kabongo
    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 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 (Mašek, Vojta, Kozak).
    High-minutes starting forwards 0 Czech of 148 · NT pool 0 · median born —
    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 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.
    Older forwards with moderate playing time 9 Czech of 142 · NT pool 2 · median born 1990 Jan ChramostaTomáš PoznarVáclav PilařDavid Puškáč
    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 readExperienced forwards on managed minutes — median age 31, about 40 % of minutes, output at median. The impact or target forward used in rotation (Chramosta, Poznar, Pilař).
    Young low-minute forwards 4 Czech of 255 · NT pool 0 · median born 2000 Tomás ZlatohlávekPavel JuroskaOndřej Zmrzlý
    Corpus medians: 0.27 non-penalty goals and 0.08 assists per 90, 29 % of the club's minutes, age 23, 0.13 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 (Zlatohlávek, Juroska, Zmrzlý).
    Primary scorers 8 Czech of 123 · NT pool 7 · median born 1997 Patrik SchickVojtech PatrakTomáš ChorýJan Kuchta
    Corpus medians: 0.50 non-penalty goals and 0.10 assists per 90, 51 % of the club's minutes, age 25, 0.16 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 (Schick, Patrak, Chorý).

    Midfielders

    High-card-rate midfielders 5 Czech of 387 · NT pool 2 · median born 2000 Matěj RynešDaniel LanghamerVojtěch SmržJan Matoušek
    Corpus medians: 0.08 non-penalty goals and 0.08 assists per 90, 38 % of the club's minutes, age 24, 0.33 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 (Ryneš, Langhamer, Smrž).
    Older rotation midfielders 26 Czech of 425 · NT pool 2 · median born 1993 Tomáš SoučekMichal TrávníkVlastimil DaníčekFilip Zorvan
    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 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 (Souček, Trávník, Daníček).
    Young low-minute midfielders 30 Czech of 624 · NT pool 5 · median born 2003 Kryštof DaněkJakub KřišťanMatěj MikulenkaAlexandr Sojka
    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 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 (Daněk, Křišťan, Mikulenka).
    Everyday starting midfielders, low scoring output 21 Czech of 600 · NT pool 8 · median born 1999 Patrik HellebrandDaniel HorákMarcel ČermákLukáš Červ
    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 readThe engine room — nearly 80 % of minutes with median output: holding and box-to-box midfielders whose value is continuity and structure, not numbers (Hellebrand, Horák, Čermák).
    High-scoring attacking midfielders 10 Czech of 320 · NT pool 4 · median born 2000 Vladimír DaridaMichal ŠevčíkDenis VišinskýDaniel Mareček
    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 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 (Darida, Ševčík, Višinský).
    High-minutes creative midfielders 10 Czech of 290 · NT pool 7 · median born 1997 Lukáš ProvodAdam VlkanovaTomáš LadraVáclav Černý
    Corpus medians: 0.14 non-penalty goals and 0.22 assists per 90, 59 % of the club's minutes, age 24, 0.17 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 (Provod, Vlkanova, Ladra).

    Defenders

    Everyday starting defenders 12 Czech of 431 · NT pool 5 · median born 1999 Lukáš HůlkaDenis HalinskýMartin CedidlaMartin Chlumecký
    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 readThe defensive core — 85 % of minutes, output at the DF floor. Availability and continuity are the signal; production is not (Hůlka, Halinský, Cedidla).
    High-card-rate defenders 8 Czech of 284 · NT pool 2 · median born 2000 Karel PojeznýMatěj ChalušFilip ČihákEric Hunál
    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 readDuel-heavy defenders — card rate 2.5 times the DF median with rotation minutes (46 %). The physical stopper profile (Pojezný, Chaluš, Čihák).
    Young low-minute defenders 12 Czech of 424 · NT pool 6 · median born 2003 Mikuláš KonečnýJakub KolarFilip PrebslAdam Sevinsky
    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 readDevelopment defenders — median age 22, about a third of minutes, output at the floor. The tier the 23–25 cohort draws from (Konečný, Kolar, Prebsl).
    Goal-scoring defenders 4 Czech of 218 · NT pool 1 · median born 1999 Štěpán ChaloupekJan BořilDavid Lischka
    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 readSet-piece threats — defenders scoring at five times the DF median on 60 % of minutes. Aerial presence in both boxes (Chaloupek, Bořil, Lischka).
    Older defenders 19 Czech of 363 · NT pool 2 · median born 1993 Filip NovákJakub BrabecJiří FleišmanTomáš Holeš
    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 readExperienced defenders on managed minutes — median age 31, 44 % of minutes, output at the floor. Leadership and cover rather than a starting role (Novák, Brabec, Fleišman).
    High-assist defenders with high playing time 4 Czech of 226 · NT pool 3 · median born 1997 Vladimír CoufalMarek IchaMatěj Hadaš
    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 readAttacking full-backs — assist rate seven times the DF median on starter minutes (65 %). The wide defender whose job ends in the final third (Coufal, Icha, Hadaš).
    Trajectories

    Trajectories 2024/25 → 2025/26 (Czech-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 — 13 players: 5 up, 6 stable, 2 down

    Moving up · G+A / 90 adj.

    PlayerLeagueMin 2024/25 / 2025/26Change
    Jan Kuchta CZE-First League 1863 / 1593 +0.146
    Ondřej Mihálik CZE-First League 1520 / 1184 +0.112
    Matyas Kozak CZE-First League 929 / 1124 +0.097
    Tomáš Chorý CZE-First League 2123 / 1838 +0.074
    Daniel Vašulín CZE-First League 1397 / 1061 +0.064

    Moving down · G+A / 90 adj.

    PlayerLeagueMin 2024/25 / 2025/26Change
    Patrik Schick GER-Bundesliga 1684 / 1988 −0.198
    David Puškáč CZE-First League 1418 / 904 −0.054

    Midfielders — 43 players: 3 up, 31 stable, 9 down

    Moving up · G+A / 90 adj.

    PlayerLeagueMin 2024/25 / 2025/26Change
    Patrik Vydra CZE-First League 2148 / 1402 +0.101
    Lukáš Sadílek POL-Ekstraklasa 2067 / 1077 +0.091
    Jáchym Šíp CZE-First League 1318 / 1131 +0.067

    Moving down · G+A / 90 adj.

    PlayerLeagueMin 2024/25 / 2025/26Change
    Tomáš Souček ENG-Premier League 2567 / 2200 −0.109
    Matěj Ryneš CZE-First League 1423 / 1807 −0.072
    Matěj Polidar CZE-First League 1628 / 961 −0.071
    Milan Havel CZE-First League 1599 / 1104 −0.070
    Filip Zorvan CZE-First League 2746 / 1622 −0.068

    Defenders — 36 players: 1 up, 34 stable, 1 down

    Moving up · G+A / 90 adj.

    PlayerLeagueMin 2024/25 / 2025/26Change
    Vladimír Coufal GER-Bundesliga 1067 / 3012 +0.152

    Moving down · G+A / 90 adj.

    PlayerLeagueMin 2024/25 / 2025/26Change
    Martin Vitík ITA-Serie A 2170 / 1325 −0.052
    Why does the train leave?

    Why does the train leave?

    Four exhibits comparing Czechia 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. Czech exports: 15 players, median export age 23 (recent entrants 7, median 22), 71 % of the recent ones straight from the Czech First League*.

    CountrynRecent Export age (all)Export age (recent) DomesticStepping stoneOther top-9Not covered Censored
    CZE Czechia 15 7 23 22 71 % 0 % 0 % 29 % 20 %
    SVK Slovakia 11 4 24 23 0 % 0 % 0 % 100 % 27 %
    AUT Austria 37 13 23 22 54 % 8 % 0 % 38 % 35 %
    HUN Hungary 18 11 22 22 91 % 0 % 0 % 9 % 11 %
    POL Poland 30 13 23 24 38 % 23 % 0 % 38 % 20 %
    CRO Croatia 39 10 23 24.5 60 % 10 % 0 % 30 % 31 %
    DEN Denmark 65 25 22 22 60 % 0 % 0 % 40 % 14 %
    SUI Switzerland 42 13 23 21 85 % 0 % 0 % 15 % 33 %
    NOR Norway 37 17 21 22 53 % 0 % 0 % 47 % 19 %

    * 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 %
    DEN Denmark n = 75
    56 %
    NOR Norway n = 52
    56 %
    POL Poland n = 45
    55 %
    CRO Croatia n = 47
    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. Czech count and median against the median of the peer countries' values.

    Profile table by tier and position group
    TierGroupCZE nCZE medianPeer median nPeer median
    domestic league FW 27 0.21 17 0.13
    domestic league MF 89 0.08 61 0.07
    domestic league DF 49 0.03 45 0.03
    stepping-stone league FW 0 — 2 0.28
    stepping-stone league MF 0 — 2 0.09
    stepping-stone league DF 0 — 2 0.04
    top-9 league FW 5 0.27 5 0.29
    top-9 league MF 7 0.12 18.5 0.14
    top-9 league DF 7 0.03 9.5 0.04
    other covered league FW 1 0.12 6 0.13
    other covered league MF 6 0.08 6.5 0.08
    other covered league DF 3 0.01 5.5 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 Czech exports go

    Of the 192 mapped Czech players, 27 play outside the Czech First League.

    • top-9 Vladimír Coufal · Roman Květ · Ladislav Krejčí
    • peer country league Patrik Hellebrand · Martin Chlumecký · Patrizio Stronati

    * 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, Croatia, Austria, Norway. 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+
    CZE Czechia 1826.5 0.434 1 6 10 9
    SUI Switzerland 2132 0.788 1 5 10 10
    CRO Croatia 1857 0.807 1 6 11 8
    AUT Austria 1711.5 0.788 1 6 10 9
    NOR Norway 2164.5 0.832 2 6 14 4

    * 9 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

    Patrik Schick

    FW · age 30 · GER-Bundesliga 2025/26 · 1988 min · 0.49 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Serhou Guirassy GUI GER-Bundesliga 2025/26 · 2337 min · 0.48 npG+A/90 · d = 0.47
      No later season in the corpus.
    2. 2 Ihlas Bebou TOG GER-Bundesliga 2023/24 · 1636 min · 0.46 npG+A/90 · d = 0.50
      No later season in the corpus.
    3. 3 Pere Milla ESP ESP-La Liga 2021/22 · 1644 min · 0.44 npG+A/90 · d = 0.62
      Followed by: 2022/23  ESP-La Liga · 1843 min · 0.21 2024/25  ESP-La Liga · 456 min · 0.27 2025/26  ESP-La Liga · 1973 min · 0.25
    4. 4 Michael Gregoritsch AUT GER-Bundesliga 2023/24 · 1682 min · 0.43 npG+A/90 · d = 0.64
      Followed by: 2024/25  GER-Bundesliga · 639 min · 0.36 2025/26  GER-Bundesliga · 913 min · 0.46
    5. 5 Romelu Lukaku BEL ITA-Serie A 2022/23 · 1660 min · 0.53 npG+A/90 · d = 0.64
      Followed by: 2023/24  ITA-Serie A · 2641 min · 0.43 2024/25  ITA-Serie A · 2843 min · 0.51

    Target

    Christophe Kabongo

    FW · age 23 · CZE-First League 2025/26 · 803 min · 0.25 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Hierman Barkoŭski BLR POL-Ekstraklasa 2024/25 · 962 min · 0.21 npG+A/90 · d = 0.36
      Followed by: 2025/26  POL-Ekstraklasa · 1937 min · 0.10
    2. 2 Christian Rasmussen DEN GER-2. Bundesliga 2025/26 · 1054 min · 0.24 npG+A/90 · d = 0.40
      No later season in the corpus.
    3. 3 Tadeáš Vachoušek CZE CZE-First League 2026/27 · 502 min · 0.24 npG+A/90 · d = 0.40
      No later season in the corpus.
    4. 4 Szymon Włodarczyk POL NED-Eredivisie 2025/26 · 757 min · 0.22 npG+A/90 · d = 0.43
      No later season in the corpus.
    5. 5 Lukáš Mašek CZE CZE-First League 2026/27 · 468 min · 0.24 npG+A/90 · d = 0.44
      No later season in the corpus.

    Target

    Pavel Šulc

    FW · age 26 · FRA-Ligue 1 2025/26 · 1566 min · 0.46 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Amine Gouiri ALG FRA-Ligue 1 2025/26 · 1326 min · 0.43 npG+A/90 · d = 0.41
      No later season in the corpus.
    2. 2 Randal Kolo Muani FRA FRA-Ligue 1 2023/24 · 1268 min · 0.43 npG+A/90 · d = 0.50
      Followed by: 2024/25  ITA-Serie A · 1160 min · 0.45 2025/26  ENG-Premier League · 1664 min · 0.20
    3. 3 Bamba Dieng SEN FRA-Ligue 1 2025/26 · 1212 min · 0.43 npG+A/90 · d = 0.56
      No later season in the corpus.
    4. 4 Breel Embolo SUI FRA-Ligue 1 2022/23 · 1859 min · 0.40 npG+A/90 · d = 0.65
      Followed by: 2024/25  FRA-Ligue 1 · 1841 min · 0.33 2025/26  FRA-Ligue 1 · 1872 min · 0.35
    5. 5 Jonathan Burkardt GER GER-Bundesliga 2025/26 · 1336 min · 0.47 npG+A/90 · d = 0.67
      No later season in the corpus.

    Target

    Tomáš Chorý

    FW · age 31 · CZE-First League 2025/26 · 1838 min · 0.30 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Serdar Dursun TUR TUR-Süper Lig 2021/22 · 1869 min · 0.29 npG+A/90 · d = 0.25
      Followed by: 2023/24  TUR-Süper Lig · 812 min · 0.16 2025/26  TUR-Süper Lig · 1531 min · 0.19
    2. 2 Ivi ESP POL-Ekstraklasa 2024/25 · 1759 min · 0.27 npG+A/90 · d = 0.27
      Followed by: 2025/26  POL-Ekstraklasa · 861 min · 0.12
    3. 3 Mats Seuntjens NED NED-Eredivisie 2022/23 · 1564 min · 0.31 npG+A/90 · d = 0.54
      Followed by: 2023/24  NED-Eredivisie · 956 min · 0.19 2023/24  NED-Eredivisie · 813 min · 0.12 2024/25  NED-Eredivisie · 664 min · 0.16
    4. 4 Duckens Nazon HAI TUR-Süper Lig 2024/25 · 1957 min · 0.24 npG+A/90 · d = 0.54
      No later season in the corpus.
    5. 5 Wout Weghorst NED TUR-Süper Lig 2022/23 · 2219 min · 0.30 npG+A/90 · d = 0.56
      Followed by: 2023/24  GER-Bundesliga · 1977 min · 0.33 2024/25  NED-Eredivisie · 1085 min · 0.34 2025/26  NED-Eredivisie · 1784 min · 0.27 2026/27  NED-Eredivisie · 516 min · 0.27

    Target

    Lukáš Mašek

    FW · age 22 · CZE-First League 2025/26 · 1806 min · 0.16 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Isak Jensen DEN DEN-Superliga 2024/25 · 1953 min · 0.16 npG+A/90 · d = 0.36
      Followed by: 2025/26  NED-Eredivisie · 2035 min · 0.14
    2. 2 Mustapha Isah NGA NOR-Eliteserien 2025/26 · 1653 min · 0.17 npG+A/90 · d = 0.37
      Followed by: 2026/27  NOR-Eliteserien · 1385 min · 0.22
    3. 3 Mbaye Jacques Ndiaye SEN POL-Ekstraklasa 2024/25 · 1574 min · 0.13 npG+A/90 · d = 0.39
      Followed by: 2025/26  POL-Ekstraklasa · 1626 min · 0.19 2026/27  POL-Ekstraklasa · 694 min · 0.10
    4. 4 Christian Gammelgaard DEN DEN-Superliga 2024/25 · 1582 min · 0.15 npG+A/90 · d = 0.43
      Followed by: 2025/26  DEN-Superliga · 2330 min · 0.16
    5. 5 Henrik Skogvold NOR NOR-Eliteserien 2025/26 · 1983 min · 0.13 npG+A/90 · d = 0.45
      Followed by: 2026/27  NOR-Eliteserien · 1495 min · 0.09

    Target

    Václav Černý

    MF · age 29 · TUR-Süper Lig 2025/26 · 1991 min · 0.22 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Efkan Bekiroğlu TUR-Süper Lig 2023/24 · 2126 min · 0.22 npG+A/90 · d = 0.18
      Followed by: 2024/25  TUR-Süper Lig · 1061 min · 0.16 2025/26  TUR-Süper Lig · 1062 min · 0.17
    2. 2 Trézéguet EGY TUR-Süper Lig 2022/23 · 2127 min · 0.21 npG+A/90 · d = 0.23
      Followed by: 2023/24  TUR-Süper Lig · 1604 min · 0.28
    3. 3 João Novais POR TUR-Süper Lig 2021/22 · 1835 min · 0.21 npG+A/90 · d = 0.23
      Followed by: 2023/24  TUR-Süper Lig · 2047 min · 0.11 2024/25  POR-Primeira Liga · 1402 min · 0.04
    4. 4 Emrah Başsan TUR TUR-Süper Lig 2020/21 · 2283 min · 0.21 npG+A/90 · d = 0.40
      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
    5. 5 Oussama Tannane MAR NED-Eredivisie 2022/23 · 2144 min · 0.26 npG+A/90 · d = 0.41
      No later season in the corpus.

    Target

    Hugo Sochůrek

    MF · age 18 · CZE-First League 2025/26 · 510 min · 0.10 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Alexander Røssing-Lelesiit NOR NOR-Eliteserien 2024/25 · 547 min · 0.10 npG+A/90 · d = 0.30
      No later season in the corpus.
    2. 2 Emirhan İlkhan TUR TUR-Süper Lig 2021/22 · 635 min · 0.09 npG+A/90 · d = 0.32
      Followed by: 2023/24  TUR-Süper Lig · 1052 min · 0.09 2025/26  ITA-Serie A · 1001 min · 0.19
    3. 3 Luis Engelns GER GER-2. Bundesliga 2024/25 · 542 min · 0.06 npG+A/90 · d = 0.45
      Followed by: 2025/26  GER-2. Bundesliga · 572 min · 0.08
    4. 4 Mustafa Hekimoğlu TUR TUR-Süper Lig 2024/25 · 712 min · 0.14 npG+A/90 · d = 0.47
      No later season in the corpus.
    5. 5 Luca Oyen BEL BEL-Pro League 2020/21 · 516 min · 0.10 npG+A/90 · d = 0.68
      Followed by: 2021/22  BEL-Pro League · 643 min · 0.22 2023/24  BEL-Pro League · 635 min · 0.13 2025/26  NED-Eredivisie · 471 min · 0.13

    Target

    Tomáš Souček

    MF · age 31 · ENG-Premier League 2025/26 · 2200 min · 0.21 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 John McGinn SCO ENG-Premier League 2024/25 · 2223 min · 0.21 npG+A/90 · d = 0.03
      Followed by: 2025/26  ENG-Premier League · 2137 min · 0.33
    2. 2 Rodrigo ESP ENG-Premier League 2021/22 · 2265 min · 0.22 npG+A/90 · d = 0.14
      Followed by: 2022/23  ENG-Premier League · 1935 min · 0.55
    3. 3 Mateusz Klich POL ENG-Premier League 2020/21 · 2393 min · 0.23 npG+A/90 · d = 0.33
      Followed by: 2021/22  ENG-Premier League · 2073 min · 0.15 2025/26  POL-Ekstraklasa · 2041 min · 0.07 2026/27  POL-Ekstraklasa · 627 min · 0.05
    4. 4 Pascal Groß GER ENG-Premier League 2021/22 · 2038 min · 0.24 npG+A/90 · d = 0.33
      Followed by: 2022/23  ENG-Premier League · 3239 min · 0.42 2023/24  ENG-Premier League · 3114 min · 0.34 2024/25  GER-Bundesliga · 2327 min · 0.25 2025/26  ENG-Premier League · 2249 min · 0.20
    5. 5 João Palhinha POR ENG-Premier League 2025/26 · 2198 min · 0.27 npG+A/90 · d = 0.50
      No later season in the corpus.

    Target

    Vladimír Darida

    MF · age 36 · CZE-First League 2025/26 · 2764 min · 0.16 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Alexandru Maxim ROU TUR-Süper Lig 2025/26 · 2680 min · 0.12 npG+A/90 · d = 0.42
      No later season in the corpus.
    2. 2 Jesús Imaz ESP POL-Ekstraklasa 2025/26 · 2784 min · 0.23 npG+A/90 · d = 0.60
      No later season in the corpus.
    3. 3 Lucas Biglia ARG TUR-Süper Lig 2021/22 · 2795 min · 0.08 npG+A/90 · d = 0.67
      Followed by: 2022/23  TUR-Süper Lig · 1638 min · 0.04
    4. 4 Max Gradel CIV TUR-Süper Lig 2022/23 · 2257 min · 0.14 npG+A/90 · d = 0.72
      Followed by: 2023/24  TUR-Süper Lig · 1705 min · 0.12
    5. 5 Dušan Tadić SRB TUR-Süper Lig 2023/24 · 3142 min · 0.22 npG+A/90 · d = 0.75
      Followed by: 2024/25  TUR-Süper Lig · 2485 min · 0.30

    Target

    Roman Květ

    MF · age 29 · BEL-Pro League 2025/26 · 2545 min · 0.10 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Nicolas Rommens BEL BEL-Pro League 2022/23 · 2398 min · 0.13 npG+A/90 · d = 0.29
      No later season in the corpus.
    2. 2 Siebe Schrijvers BEL BEL-Pro League 2024/25 · 2719 min · 0.13 npG+A/90 · d = 0.31
      Followed by: 2025/26  BEL-Pro League · 2115 min · 0.10
    3. 3 Thom Haye IDN NED-Eredivisie 2023/24 · 2610 min · 0.10 npG+A/90 · d = 0.32
      Followed by: 2024/25  NED-Eredivisie · 2040 min · 0.07
    4. 4 Leo Cordeiro BRA POR-Primeira Liga 2023/24 · 2607 min · 0.08 npG+A/90 · d = 0.36
      Followed by: 2024/25  POR-Primeira Liga · 1866 min · 0.08
    5. 5 Óscar Gil ESP BEL-Pro League 2025/26 · 2314 min · 0.08 npG+A/90 · d = 0.37
      Followed by: 2026/27  BEL-Pro League · 494 min · 0.04

    Target

    Vojtech Stransky

    MF · age 23 · CZE-First League 2025/26 · 2634 min · 0.07 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Tomas Rigo SVK CZE-First League 2024/25 · 2528 min · 0.10 npG+A/90 · d = 0.25
      No later season in the corpus.
    2. 2 Eric Martel GER GER-2. Bundesliga 2024/25 · 2740 min · 0.06 npG+A/90 · d = 0.28
      Followed by: 2025/26  GER-Bundesliga · 2559 min · 0.13
    3. 3 Jonas Therkelsen NOR GER-2. Bundesliga 2025/26 · 2722 min · 0.10 npG+A/90 · d = 0.30
      No later season in the corpus.
    4. 4 Michal Sadílek CZE NED-Eredivisie 2021/22 · 2582 min · 0.07 npG+A/90 · d = 0.38
      Followed by: 2022/23  NED-Eredivisie · 1006 min · 0.15 2023/24  NED-Eredivisie · 2698 min · 0.08 2024/25  NED-Eredivisie · 1599 min · 0.15 2025/26  CZE-First League · 2049 min · 0.09
    5. 5 Filip Jørgensen NOR NOR-Eliteserien 2024/25 · 2464 min · 0.06 npG+A/90 · d = 0.38
      No later season in the corpus.

    Target

    Vladimír Coufal

    DF · age 34 · GER-Bundesliga 2025/26 · 3012 min · 0.18 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Óscar de Marcos ESP ESP-La Liga 2022/23 · 2819 min · 0.14 npG+A/90 · d = 0.41
      Followed by: 2023/24  ESP-La Liga · 2258 min · 0.16 2024/25  ESP-La Liga · 1564 min · 0.13
    2. 2 Leandro Cabrera URU ESP-La Liga 2024/25 · 2869 min · 0.12 npG+A/90 · d = 0.53
      Followed by: 2025/26  ESP-La Liga · 3330 min · 0.06
    3. 3 Jeffrey Gouweleeuw NED GER-Bundesliga 2024/25 · 2944 min · 0.10 npG+A/90 · d = 0.63
      Followed by: 2025/26  GER-Bundesliga · 848 min · 0.08
    4. 4 Johan Mojica COL ESP-La Liga 2025/26 · 2724 min · 0.09 npG+A/90 · d = 0.81
      No later season in the corpus.
    5. 5 Florian Lejeune FRA ESP-La Liga 2024/25 · 3325 min · 0.09 npG+A/90 · d = 0.86
      Followed by: 2025/26  ESP-La Liga · 3230 min · 0.07 2026/27  ESP-La Liga · 450 min · 0.00

    Target

    Jan Paluska

    DF · age 21 · CZE-First League 2025/26 · 1045 min · 0.01 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Jan Trédl CZE-First League 2024/25 · 951 min · 0.04 npG+A/90 · d = 0.30
      Followed by: 2025/26  CZE-First League · 822 min · 0.01
    2. 2 Aleksander Kjelsen NOR NOR-Eliteserien 2026/27 · 1097 min · 0.01 npG+A/90 · d = 0.30
      No later season in the corpus.
    3. 3 Adam Dohnalek CZE-First League 2024/25 · 906 min · 0.04 npG+A/90 · d = 0.34
      No later season in the corpus.
    4. 4 Ege Bilsel TUR-Süper Lig 2024/25 · 1186 min · 0.04 npG+A/90 · d = 0.39
      No later season in the corpus.
    5. 5 Benjamin Boakye GER GER-2. Bundesliga 2025/26 · 1241 min · 0.03 npG+A/90 · d = 0.39
      No later season in the corpus.

    Target

    Ladislav Krejčí

    DF · age 27 · ENG-Premier League 2025/26 · 2356 min · 0.10 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Pau Torres ESP ENG-Premier League 2023/24 · 2464 min · 0.08 npG+A/90 · d = 0.23
      Followed by: 2024/25  ENG-Premier League · 2020 min · 0.02 2025/26  ENG-Premier League · 1676 min · 0.02
    2. 2 Pervis Estupiñán ECU ENG-Premier League 2024/25 · 2402 min · 0.07 npG+A/90 · d = 0.26
      Followed by: 2025/26  ITA-Serie A · 1034 min · 0.15
    3. 3 Oleksandr Zinchenko UKR ENG-Premier League 2022/23 · 2118 min · 0.09 npG+A/90 · d = 0.32
      Followed by: 2023/24  ENG-Premier League · 1722 min · 0.14 2024/25  ENG-Premier League · 527 min · 0.10
    4. 4 Daniel Ballard NIR ENG-Premier League 2025/26 · 2149 min · 0.08 npG+A/90 · d = 0.34
      No later season in the corpus.
    5. 5 Thilo Kehrer GER ENG-Premier League 2022/23 · 2230 min · 0.06 npG+A/90 · d = 0.37
      Followed by: 2023/24  FRA-Ligue 1 · 1323 min · 0.07 2024/25  FRA-Ligue 1 · 2383 min · 0.07 2025/26  FRA-Ligue 1 · 2353 min · 0.01

    Target

    Tomáš Holeš

    DF · age 33 · CZE-First League 2025/26 · 2003 min · 0.06 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Piotr Mroziński POL POL-Ekstraklasa 2024/25 · 1979 min · 0.05 npG+A/90 · d = 0.07
      No later season in the corpus.
    2. 2 Jakub Jugas CZE POL-Ekstraklasa 2024/25 · 2030 min · 0.05 npG+A/90 · d = 0.08
      Followed by: 2025/26  CZE-First League · 654 min · 0.01
    3. 3 Zeki Yavru TUR TUR-Süper Lig 2023/24 · 2017 min · 0.05 npG+A/90 · d = 0.25
      Followed by: 2024/25  TUR-Süper Lig · 2882 min · 0.11 2025/26  TUR-Süper Lig · 2008 min · 0.02
    4. 4 Nélson Semedo POR TUR-Süper Lig 2025/26 · 1933 min · 0.07 npG+A/90 · d = 0.27
      No later season in the corpus.
    5. 5 Lucas Lima BRA TUR-Süper Lig 2023/24 · 1894 min · 0.06 npG+A/90 · d = 0.28
      Followed by: 2024/25  TUR-Süper Lig · 1650 min · 0.03

    Target

    Robin Hranáč

    DF · age 26 · GER-Bundesliga 2025/26 · 2251 min · 0.04 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Jonjoe Kenny ENG GER-Bundesliga 2022/23 · 2244 min · 0.04 npG+A/90 · d = 0.03
      Followed by: 2024/25  GER-2. Bundesliga · 2869 min · 0.11
    2. 2 Marco Friedl AUT GER-Bundesliga 2023/24 · 2197 min · 0.03 npG+A/90 · d = 0.10
      Followed by: 2024/25  GER-Bundesliga · 2161 min · 0.02 2025/26  GER-Bundesliga · 2546 min · 0.04
    3. 3 Ferland Mendy FRA ESP-La Liga 2020/21 · 2208 min · 0.03 npG+A/90 · d = 0.14
      Followed by: 2021/22  ESP-La Liga · 1734 min · 0.09 2022/23  ESP-La Liga · 1351 min · 0.04 2023/24  ESP-La Liga · 1720 min · 0.02 2024/25  ESP-La Liga · 1005 min · 0.05
    4. 4 Víctor Chust ESP ESP-La Liga 2025/26 · 2166 min · 0.04 npG+A/90 · d = 0.15
      Followed by: 2026/27  ESP-La Liga · 450 min · 0.05
    5. 5 Arthur Theate BEL GER-Bundesliga 2025/26 · 2141 min · 0.04 npG+A/90 · d = 0.15
      No later season in the corpus.

    Target

    Denis Halinský

    DF · age 23 · CZE-First League 2025/26 · 2790 min · 0.01 npG+A/90 quality

    Nearest 5 at the same age

    5 nearest analogs and what followed
    1. 1 Oskar Wójcik POL POL-Ekstraklasa 2025/26 · 2749 min · 0.01 npG+A/90 · d = 0.08
      No later season in the corpus.
    2. 2 Furkan Bayır TUR TUR-Süper Lig 2022/23 · 2789 min · 0.02 npG+A/90 · d = 0.25
      Followed by: 2023/24  TUR-Süper Lig · 2015 min · 0.01 2024/25  TUR-Süper Lig · 637 min · 0.05
    3. 3 Marcel Beifus GER GER-2. Bundesliga 2024/25 · 2668 min · 0.01 npG+A/90 · d = 0.25
      Followed by: 2025/26  GER-2. Bundesliga · 813 min · 0.07
    4. 4 Sahmkou Camara GUI CZE-First League 2025/26 · 2597 min · 0.02 npG+A/90 · d = 0.26
      No later season in the corpus.
    5. 5 Bünyamin Balcı TUR TUR-Süper Lig 2022/23 · 2810 min · 0.04 npG+A/90 · d = 0.35
      Followed by: 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

    * 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 Czech-eligible player with a complete 2025/26 season in a covered league — 192 players — with the numbers behind the atlases. Names with a card link to it.

    192
    Table of 192 players
    PlayerPosAge ClubLeagueMin G+A / 90 adj.Style clusterNT
    Lukáš Ambros MF 21 Górnik Zabrze POL-Ekstraklasa 1860 0.12 High-minutes creative midfielders NT
    Lukáš Bartošák MF 35 Fastav Zlín CZE-First League 1401 0.10 Older rotation midfielders
    Michal Beran MF 24 Sigma Olomouc CZE-First League 2059 0.08 Everyday starting midfielders, low scoring output NT
    Jan Bořil DF 34 Slavia Prague CZE-First League 1754 0.06 Goal-scoring defenders
    Jiří Boula MF 26 Baník Ostrava CZE-First League 2538 0.07 Everyday starting midfielders, low scoring output
    Jakub Brabec DF 32 Rio Ave POR-Primeira Liga 2151 0.03 Older defenders
    Radim Breite MF 35 Sigma Olomouc CZE-First League 1012 0.06 Older rotation midfielders
    David Buchta MF 26 Baník Ostrava CZE-First League 1058 0.03 Young low-minute midfielders
    Alexandr Bužek MF 20 Karviná CZE-First League 862 0.08 Young low-minute midfielders
    František Čech DF 27 Hradec Králové CZE-First League 2321 0.03 Everyday starting defenders
    Martin Cedidla DF 23 Jablonec CZE-First League 2652 0.01 Everyday starting defenders NT
    Aleš Čermák MF 30 Bohemians 1905 CZE-First League 2534 0.09 Everyday starting midfielders, low scoring output
    Marcel Čermák MF 26 Dukla Prague CZE-First League 2752 0.09 Everyday starting midfielders, low scoring output
    Michal Černák MF 21 Dukla Prague CZE-First League 833 0.08 Young low-minute midfielders
    Václav Černý MF 27 Beşiktaş TUR-Süper Lig 1991 0.22 High-minutes creative midfielders NT
    Lukáš Červ MF 24 Viktoria Plzeň CZE-First League 2649 0.06 Everyday starting midfielders, low scoring output NT
    Štěpán Chaloupek DF 22 Slavia Prague CZE-First League 1815 0.12 Goal-scoring defenders NT
    Matěj Chaluš DF 27 Baník Ostrava CZE-First League 2163 0.00 High-card-rate defenders
    Martin Chlumecký DF 28 Kisvárda HUN-NB I 2491 0.02 Everyday starting defenders
    Tomáš Chorý FW 30 Slavia Prague CZE-First League 1838 0.30 Primary scorers NT
    Jan Chramosta FW 34 Jablonec CZE-First League 1501 0.21 Older forwards with moderate playing time NT
    Mojmír Chytil FW 26 Slavia Prague CZE-First League 1486 0.26 Primary scorers NT
    Jan Chytrý DF 21 Karviná CZE-First League 1127 0.03 Young low-minute defenders
    Filip Čihák DF 26 Hradec Králové CZE-First League 2059 0.02 High-card-rate defenders
    Vladimír Coufal DF 32 Hoffenheim GER-Bundesliga 3012 0.18 High-assist defenders with high playing time NT
    Tomáš Čvančara FW 24 Antalyaspor TUR-Süper Lig 525 0.17 Young low-minute forwards
    Kryštof Daněk MF 22 LASK AUT-Bundesliga 1369 0.07 Young low-minute midfielders NT
    Vlastimil Daníček MF 34 Slovácko CZE-First League 1760 0.05 Older rotation midfielders
    Vladimír Darida MF 34 Hradec Králové CZE-First League 2764 0.16 High-scoring attacking midfielders NT
    Denis Donat DF 23 Mladá Boleslav CZE-First League 935 0.03 Young low-minute defenders
    David Douděra MF 27 Slavia Prague CZE-First League 1421 0.11 High-minutes creative midfielders NT
    Václav Drchal MF 26 Bohemians 1905 CZE-First League 1682 0.07 Everyday starting midfielders, low scoring output
    Jiří Fleišman DF 40 Karviná CZE-First League 2065 0.00 Older defenders
    Christián Frýdek MF 26 Baník Ostrava CZE-First League 689 0.07 Young low-minute midfielders
    Michal Frydrych DF 35 Baník Ostrava CZE-First League 1667 0.04 Older defenders
    Michal Fukala DF 24 Fastav Zlín CZE-First League 2137 0.03 Everyday starting defenders
    Šimon Gabriel DF 24 Slovan Liberec CZE-First League 1080 0.05 Goal-scoring defenders
    Pavel Gaszczyk MF 20 Dukla Prague CZE-First League 594 0.05 Young low-minute midfielders
    Matěj Hadaš DF 21 Sigma Olomouc CZE-First League 1806 0.05 High-assist defenders with high playing time NT
    Denis Halinský DF 22 Teplice CZE-First League 2790 0.01 Everyday starting defenders NT
    Marek Hanousek MF 33 Dukla Prague CZE-First League 672 0.04 Older rotation midfielders
    Matěj Hanousek DF 32 Gençlerbirliği TUR-Süper Lig 1356 0.01 Older defenders
    Dominik Hasek DF 27 Dukla Prague CZE-First League 1243 0.01 Older defenders
    Milan Havel MF 30 Bohemians 1905 CZE-First League 1104 0.03 Older rotation midfielders
    Milan Havel DF 30 Viktoria Plzeň CZE-First League 462 0.01 Older defenders
    Marek Havlík MF 30 Slovácko CZE-First League 2633 0.06 Everyday starting midfielders, low scoring output
    Marek Havran MF 18 Baník Ostrava CZE-First League 578 0.07 Young low-minute midfielders
    Patrik Hellebrand MF 26 Górnik Zabrze POL-Ekstraklasa 2859 0.08 Everyday starting midfielders, low scoring output NT
    Michal Hlavatý MF 27 Slovan Liberec CZE-First League 1163 0.11 High-minutes creative midfielders
    Tomáš Holeš DF 32 Slavia Prague CZE-First League 2003 0.06 Older defenders NT
    Daniel Holzer MF 29 Baník Ostrava CZE-First League 1474 0.04 Older rotation midfielders
    Daniel Horák MF 25 Hradec Králové CZE-First League 2766 0.08 Everyday starting midfielders, low scoring output
    Roman Horák MF 20 Slovácko CZE-First League 552 0.07 Young low-minute midfielders
    Robin Hranáč DF 25 Hoffenheim GER-Bundesliga 2251 0.04 Everyday starting defenders NT
    Lukáš Hůlka DF 30 Bohemians 1905 CZE-First League 2820 0.05 Everyday starting defenders
    Eric Hunál DF 20 Dukla Prague CZE-First League 1879 0.01 High-card-rate defenders
    Matěj Hybš DF 32 Mladá Boleslav CZE-First League 1350 0.04 Older defenders
    Marek Icha DF 23 Slovan Liberec CZE-First League 2434 0.07 High-assist defenders with high playing time
    Dominik Janošek MF 27 Sigma Olomouc CZE-First League 784 0.09 Older rotation midfielders
    Tomáš Jedlička MF 22 Dukla Prague CZE-First League 746 0.04 Young low-minute midfielders
    Tomáš Jelínek MF 19 Pardubice CZE-First League 619 0.10 Young low-minute midfielders
    Václav Jemelka DF 30 Viktoria Plzeň CZE-First League 1768 0.03 Older defenders NT
    Jakub Jugas DF 33 Fastav Zlín CZE-First League 654 0.01 Older defenders
    Robert Jukl MF 26 Teplice CZE-First League 1210 0.05 Older rotation midfielders
    David Jurásek MF 24 Slavia Prague CZE-First League 1266 0.14 High-minutes creative midfielders NT
    David Jurásek DF 24 Beşiktaş TUR-Süper Lig 638 0.01 Young low-minute defenders NT
    Václav Jurečka FW 31 Baník Ostrava CZE-First League 1274 0.23 High-assist forwards
    Pavel Juroska FW 24 Slovácko CZE-First League 751 0.14 Young low-minute forwards
    Christophe Kabongo FW 21 Mladá Boleslav CZE-First League 803 0.25 Duel-heavy rotation forwards NT
    Pavel Kacor MF 18 Karviná CZE-First League 873 0.10 Young low-minute midfielders
    Pavel Kadeřábek MF 33 Sparta Prague CZE-First League 1362 0.10 Older rotation midfielders
    Jan Kalabiška MF 38 Fastav Zlín CZE-First League 814 0.04 Older rotation midfielders
    Adam Karabec MF 22 Lyon FRA-Ligue 1 922 0.09 Young low-minute midfielders NT
    Ondřej Karafiát DF 30 Mladá Boleslav CZE-First League 1237 0.06 Older defenders
    Jan Kliment FW 31 Sigma Olomouc CZE-First League 586 0.15 Older forwards with moderate playing time NT
    Michal Kohút MF 25 Baník Ostrava CZE-First League 872 0.10 High-card-rate midfielders
    Jakub Kolar DF 25 Fastav Zlín CZE-First League 1724 0.04 Young low-minute defenders
    Josef Kolářík MF 18 Mladá Boleslav CZE-First League 1183 0.16 High-scoring attacking midfielders
    Mikuláš Konečný DF 19 Pardubice CZE-First League 1812 0.02 Young low-minute defenders
    Dominik Kostka MF 29 Mladá Boleslav CZE-First League 2310 0.08 Everyday starting midfielders, low scoring output
    Matěj Koubek MF 25 Fastav Zlín CZE-First League 912 0.12 Young low-minute midfielders
    Jan Kovařík MF 37 Bohemians 1905 CZE-First League 999 0.06 Older rotation midfielders
    Matyas Kozak FW 24 Teplice CZE-First League 1124 0.21 Duel-heavy rotation forwards
    Josef Koželuh DF 23 Slovan Liberec CZE-First League 1010 0.05 High-card-rate defenders NT
    Daniel Kozma DF 29 Dukla Prague CZE-First League 1616 0.01 High-card-rate defenders
    Alex Král MF 27 Union Berlin GER-Bundesliga 459 0.12 Older rotation midfielders NT
    Jan Král DF 26 Sigma Olomouc CZE-First League 1876 0.01 Everyday starting defenders
    Ladislav Krejčí MF 33 Teplice CZE-First League 685 0.07 Older rotation midfielders
    Ladislav Krejčí DF 26 Wolves ENG-Premier League 2356 0.10 Everyday starting defenders NT
    Ondřej Kričfaluši MF 21 Baník Ostrava CZE-First League 2287 0.08 Everyday starting midfielders, low scoring output NT
    Jakub Křišťan MF 23 Karviná CZE-First League 1171 0.03 Young low-minute midfielders
    Michael Krmenčík FW 32 Slovácko CZE-First League 827 0.13 Older forwards with moderate playing time
    Ladislav Krobot FW 24 Pardubice CZE-First League 691 0.17 Duel-heavy rotation forwards
    Jakub Kučera MF 28 Hradec Králové CZE-First League 1435 0.08 Older rotation midfielders
    Jan Kuchta FW 28 Sparta Prague CZE-First League 1593 0.34 Primary scorers NT
    Vasil Kušej FW 25 Slavia Prague CZE-First League 1558 0.22 High-assist forwards NT
    Roman Květ MF 27 Dender BEL-Pro League 2545 0.10 Everyday starting midfielders, low scoring output
    Albert Labík MF 21 Karviná CZE-First League 1987 0.10 Everyday starting midfielders, low scoring output NT
    Tomáš Ladra MF 28 Viktoria Plzeň CZE-First League 2020 0.17 High-minutes creative midfielders NT
    Daniel Langhamer MF 22 Mladá Boleslav CZE-First League 1276 0.08 High-card-rate midfielders NT
    David Lischka DF 27 Bohemians 1905 CZE-First League 1200 0.06 Goal-scoring defenders
    Roman Macek MF 28 Mladá Boleslav CZE-First League 2233 0.08 Everyday starting midfielders, low scoring output
    David Machalík MF 29 Fastav Zlín CZE-First League 1146 0.15 High-minutes creative midfielders
    Daniel Mareček MF 27 Teplice CZE-First League 1642 0.12 High-scoring attacking midfielders
    Lukáš Mareček MF 35 Teplice CZE-First League 1197 0.05 Older rotation midfielders
    Jakub Martinec DF 27 Sparta Prague CZE-First League 841 0.05 Young low-minute defenders
    Lukáš Mašek FW 21 Slovan Liberec CZE-First League 1806 0.16 Duel-heavy rotation forwards
    Lukáš Masopust MF 32 Slovan Liberec CZE-First League 1606 0.06 Older rotation midfielders
    Jan Matoušek MF 27 Bohemians 1905 CZE-First League 879 0.10 High-card-rate midfielders
    Ondřej Mihálik FW 28 Hradec Králové CZE-First League 1184 0.28 Primary scorers NT
    Jan Mikula DF 33 Slovan Liberec CZE-First League 1290 0.06 Older defenders
    Matěj Mikulenka MF 21 Sigma Olomouc CZE-First League 1096 0.11 Young low-minute midfielders
    Štěpán Míšek MF 20 Pardubice CZE-First League 943 0.06 Young low-minute midfielders
    Jan Navrátil MF 35 Sigma Olomouc CZE-First League 619 0.07 Older rotation midfielders
    Filip Novák DF 35 Jablonec CZE-First League 2276 0.05 Older defenders
    Jan Paluska DF 20 Viktoria Plzeň CZE-First League 1045 0.01 Young low-minute defenders NT
    Filip Panák DF 29 Sparta Prague CZE-First League 920 0.03 Older defenders
    Vojtech Patrak FW 25 Pardubice CZE-First League 1847 0.22 Primary scorers NT
    David Pech MF 23 Mladá Boleslav CZE-First League 1035 0.12 Young low-minute midfielders
    Dominik Pech MF 18 Young Boys SUI-Super League 955 0.08 Young low-minute midfielders
    Tomáš Pekhart FW 36 Dukla Prague CZE-First League 481 0.13 Older forwards with moderate playing time
    Nicolas Penner MF 24 Mladá Boleslav CZE-First League 516 0.08 Young low-minute midfielders
    Tomáš Petrášek DF 33 Hradec Králové CZE-First League 1955 0.05 Older defenders
    Milan Petržela FW 42 Slovácko CZE-First League 679 0.14 Older forwards with moderate playing time
    Václav Pilař FW 36 Hradec Králové CZE-First League 1242 0.20 Older forwards with moderate playing time
    David Planka MF 20 Baník Ostrava CZE-First League 1970 0.11 Everyday starting midfielders, low scoring output NT
    Dominik Plechaty DF 26 Slovan Liberec CZE-First League 976 0.03 Young low-minute defenders
    Jakub Plšek MF 31 MTK Budapest HUN-NB I 1023 0.06 Older rotation midfielders
    Karel Pojezný DF 23 Baník Ostrava CZE-First League 2392 0.00 High-card-rate defenders
    Matěj Polidar MF 25 Jablonec CZE-First League 961 0.08 Young low-minute midfielders
    Tomáš Poznar FW 36 Fastav Zlín CZE-First League 1409 0.22 Older forwards with moderate playing time
    Filip Prebsl DF 22 Mladá Boleslav CZE-First League 1549 0.02 Young low-minute defenders NT
    Lukáš Provod MF 28 Slavia Prague CZE-First League 2401 0.22 High-minutes creative midfielders NT
    Matěj Pulkrab FW 28 Teplice CZE-First League 1265 0.25 High-assist forwards
    David Puškáč FW 32 Jablonec CZE-First League 904 0.17 Older forwards with moderate playing time
    Matěj Radosta MF 24 Teplice CZE-First League 2516 0.08 Everyday starting midfielders, low scoring output
    Petr Reinberk MF 36 Slovácko CZE-First League 1102 0.05 Older rotation midfielders
    Jan Reznicek MF 32 Pardubice CZE-First League 704 0.07 Older rotation midfielders
    Antonín Růsek MF 26 Sigma Olomouc CZE-First League 665 0.14 High-scoring attacking midfielders
    Matěj Ryneš MF 24 Sparta Prague CZE-First League 1807 0.04 High-card-rate midfielders NT
    Michal Sáček DF 28 Górnik Zabrze POL-Ekstraklasa 739 0.02 Older defenders
    Lukáš Sadílek MF 29 Górnik Zabrze POL-Ekstraklasa 1077 0.17 High-scoring attacking midfielders NT
    Michal Sadílek MF 26 Slavia Prague CZE-First League 2049 0.09 Everyday starting midfielders, low scoring output NT
    Patrik Schick FW 29 Leverkusen GER-Bundesliga 1988 0.49 Primary scorers NT
    Richard Sedláček MF 26 Jablonec CZE-First League 1708 0.04 Everyday starting midfielders, low scoring output
    Václav Sejk FW 23 Sigma Olomouc CZE-First League 792 0.20 Duel-heavy rotation forwards NT
    Michal Ševčík MF 22 Mladá Boleslav CZE-First League 1997 0.16 High-scoring attacking midfielders
    Adam Sevinsky DF 21 Sparta Prague CZE-First League 1449 0.02 Young low-minute defenders NT
    Samuel Šimek MF 23 Pardubice CZE-First League 1659 0.09 Everyday starting midfielders, low scoring output
    Matej Sin MF 21 AZ Alkmaar NED-Eredivisie 550 0.15 Young low-minute midfielders
    Jáchym Šíp MF 22 Sigma Olomouc CZE-First League 1131 0.15 High-scoring attacking midfielders
    Jiří Sláma DF 26 Sigma Olomouc CZE-First League 1610 0.02 Everyday starting defenders
    Tom Slončík MF 20 Hradec Králové CZE-First League 916 0.19 High-scoring attacking midfielders
    Vojtěch Smrž MF 28 Bohemians 1905 CZE-First League 1099 0.03 High-card-rate midfielders
    Hugo Sochůrek MF 17 Sparta Prague CZE-First League 510 0.10 Young low-minute midfielders NT
    Alexandr Sojka MF 22 Hradec Králové CZE-First League 1090 0.05 Young low-minute midfielders NT
    Tomáš Solil MF 25 Pardubice CZE-First League 592 0.07 Young low-minute midfielders
    Daniel Souček MF 27 Jablonec CZE-First League 1409 0.05 Older rotation midfielders
    Tomáš Souček MF 30 West Ham ENG-Premier League 2200 0.21 Older rotation midfielders NT
    Karel Spáčil DF 22 Viktoria Plzeň CZE-First League 1349 0.04 Young low-minute defenders NT
    Filip Špatenka MF 21 Slovan Liberec CZE-First League 612 0.04 Young low-minute midfielders
    Vojtech Stransky MF 22 Slovan Liberec CZE-First League 2634 0.07 Everyday starting midfielders, low scoring output NT
    Patrizio Stronati DF 30 Újpest HUN-NB I 1350 0.01 Older defenders
    Jan Suchan FW 29 Slovácko CZE-First League 709 0.19 Older forwards with moderate playing time
    Martin Suchomel MF 22 Hradec Králové CZE-First League 810 0.09 Young low-minute midfielders NT
    Pavel Šulc FW 24 Lyon FRA-Ligue 1 1566 0.46 Primary scorers NT
    Michal Surzyn DF 27 Pardubice CZE-First League 486 0.01 High-card-rate defenders
    Jaroslav Svozil DF 31 Dukla Prague CZE-First League 1807 0.01 Older defenders
    Daniel Tetour MF 31 Slovácko CZE-First League 1205 0.09 Older rotation midfielders
    David Tkac MF 23 Sigma Olomouc CZE-First League 581 0.07 Young low-minute midfielders
    Michal Trávník MF 31 Slovácko CZE-First League 2174 0.05 Older rotation midfielders
    Daniel Trubač MF 28 Teplice CZE-First League 1118 0.07 Older rotation midfielders
    Matej Valenta MF 25 Viktoria Plzeň CZE-First League 842 0.08 Young low-minute midfielders
    Daniel Vašulín FW 27 Sigma Olomouc CZE-First League 1061 0.25 Primary scorers
    Dalibor Večerka DF 22 Teplice CZE-First League 2475 0.03 Everyday starting defenders
    Filip Vecheta FW 22 Pardubice CZE-First League 713 0.19 Duel-heavy rotation forwards NT
    Denis Višinský MF 22 Viktoria Plzeň CZE-First League 1662 0.16 High-scoring attacking midfielders NT
    Martin Vitík DF 22 Bologna ITA-Serie A 1325 0.01 Young low-minute defenders NT
    Tomáš Vlček DF 24 Slavia Prague CZE-First League 802 0.05 High-card-rate defenders NT
    Adam Vlkanova MF 30 Hradec Králové CZE-First League 2172 0.15 High-minutes creative midfielders
    Matyas Vojta FW 21 Mladá Boleslav CZE-First League 1146 0.22 Duel-heavy rotation forwards
    Jan Vondra DF 29 Bohemians 1905 CZE-First League 1732 0.01 Older defenders
    Matěj Vydra MF 33 Viktoria Plzeň CZE-First League 1148 0.20 High-scoring attacking midfielders NT
    Patrik Vydra MF 22 Sparta Prague CZE-First League 1402 0.17 High-minutes creative midfielders NT
    Jan Žambůrek MF 24 Heracles Almelo NED-Eredivisie 1724 0.07 Everyday starting midfielders, low scoring output
    Jaroslav Zelený DF 32 Sparta Prague CZE-First League 1691 0.07 High-assist defenders with high playing time NT
    David Zima DF 24 Slavia Prague CZE-First League 1767 0.02 Everyday starting defenders NT
    Matěj Žitný MF 20 Dukla Prague CZE-First League 930 0.06 Young low-minute midfielders
    Tomás Zlatohlávek FW 25 Teplice CZE-First League 790 0.13 Young low-minute forwards
    Ondřej Zmrzlý FW 26 Górnik Zabrze POL-Ekstraklasa 559 0.12 Young low-minute forwards
    Ondřej Zmrzlý MF 26 Slavia Prague CZE-First League 522 0.07 Young low-minute midfielders
    Filip Zorvan MF 29 Jablonec CZE-First League 1622 0.09 Older rotation midfielders

    * 2025/26 season, at least 450 minutes; the club is the one with the most minutes that season. NT = national-team call-up 2024–26.

    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: Vladimír Coufal, the Czech player with the most 2025/26 minutes among those who cleared the inclusion floor.

    Vladimír Coufal: raw row and feature row
    Raw FBref row, 2025/26
    ColumnValue
    leagueGER-Bundesliga
    season2025-2026
    teamHoffenheim
    playerVladimír Coufal
    nationCZE
    posDF
    born1992
    age32
    mp34
    min3012
    gls1
    ast8
    pk0
    crdy4
    crdr0
    Feature row after the pipeline
    FeatureRaw ShrunkQuality-adjusted Z-score
    npg_p900.0300.034 0.0270.21
    ast_p900.2390.193 0.1524.31
    min_share0.9840.984 0.9841.67
    age32.00032.000 32.0001.45
    cards_p900.1190.135 0.135−0.90

    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: Nabil Touaizi (POR-Primeira Liga, 100 %); Yohan Croizet (HUN-NB I, 100 %); Luca Zuffi (SUI-Super League, 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 Czech First League 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; Antonín Růsek's rate moves the most of any Czech-eligible player this season.

    Scatter of raw vs shrunk non-penalty goals per 90 against minutes for Czech-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 Czech First League 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 Czech First League season converts to 0.67 of a Premier League one (90 % HDI 0.59–0.75).

    Leaguem_L (median) 90 % HDITransitions UEFA
    ENG-Premier League1.000 1.000–1.000 588 1.000
    ITA-Serie A0.935 0.890–0.981 569 0.856
    ESP-La Liga0.908 0.859–0.953 435 0.807
    FRA-Ligue 10.810 0.772–0.848 672 0.666
    GER-Bundesliga0.777 0.741–0.814 588 0.788
    HUN-NB I0.743 0.625–0.864 35 0.265
    POR-Primeira Liga0.721 0.680–0.762 350 0.630
    BEL-Pro League0.671 0.637–0.710 474 0.573
    CZE-First League0.665 0.585–0.749 64 0.434
    TUR-Süper Lig0.660 0.625–0.696 472 0.484
    POL-Ekstraklasa0.659 0.599–0.729 131 0.438
    AUT-Bundesliga0.657 0.576–0.729 80 0.268
    DEN-Superliga0.635 0.576–0.702 117 0.371
    GER-2. Bundesliga0.600 0.555–0.645 230 0.473
    NED-Eredivisie0.597 0.567–0.632 387 0.510
    CRO-HNL0.597 0.525–0.671 64 0.249
    SUI-Super League0.597 0.538–0.649 128 0.293
    NOR-Eliteserien0.574 0.507–0.639 64 0.374

    Refit on seasons before 2025/26, the model predicts each mover's first 2025/26 row after a league change — 863 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 −2.734 0.151
    Model −2.174 0.127

    Against 18 leagues in common, the model's medians and the UEFA multipliers correlate at Spearman's rho = 0.74.

    • HUN-NB I: model rank 6 vs UEFA rank 17 (m_L 0.74 vs multiplier 0.27).
    • NED-Eredivisie: model rank 15 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 ≤ 1.003, minimum bulk ESS 1224, 0 divergent transitions across 8108 player-seasons from 2125 movers; fit in 228 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 4.45, 90th percentile 11.00 vs 10.85.

    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 — 0.0715 (0.053) 0.0760 (0.054) 0.0722 (0.053) 0.0681 (0.052) 0.0714 (0.053)
    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 91 % of the time, pooled across the 4 origins it was fit for (2022/23: 90 %, 2023/24: 90 %, 2024/25: 91 %, 2025/26: 93 %).

    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 Czechia 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 Czechia, the model dates the break to 2014/15 (56 % posterior probability), a ×0.60 (0.39–1.03, 90 % HDI) change in the level; the random walk's own innovation scale is σ = 0.062.

    The rise before it is dated to 2000/01 (55 % posterior), a ×1.65 (0.87–2.46) change in the level. The most probable seasons for each:

    • rise: 2000/01: 55 % · 1999/00: 10 % · 1998/99: 6 %
    • fall: 2014/15: 56 % · 2013/14: 7 % · 2012/13: 7 %

    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.
    • Croatia: 2003/04 (31 % posterior), ×0.75 (0.52–1.12). Rise: 2014/15 (29 %), ×1.32.

    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
    2010/112011/1220 20 (11–31)19
    2011/122012/1316 20 (11–30)20
    2012/132013/1419 18 (10–29)16
    2013/142014/1510 18 (10–30)19
    2014/152015/166 15 (7–24)10
    2015/162016/1711 11 (5–19)6
    2016/172017/1812 11 (5–20)11
    2017/182018/1912 12 (5–20)12
    2018/192019/2015 12 (5–20)12
    2019/202020/2111 13 (6–23)15
    2020/212021/2211 12 (6–20)11
    2021/222022/239 12 (5–20)11
    2022/232023/2412 10 (5–19)9
    2023/242024/2512 11 (5–19)12
    2024/252025/2610 11 (5–20)12

    Pooled across 15 origins: MAE 2.47 for the model against 2.73 for the naive baseline, 93 % 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
    Czechia2026/2710 5–19
    Denmark2026/2735 22–51
    Croatia2026/2724 15–35

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

    Change-point fit: R-hat ≤ 1.005, minimum bulk ESS 289, 2 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 Czechia. 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
    Norway
    Gap (players per million): +6.98    		 U21 minutes +4.22 60 % +1.22 – +5.91
    League strength +5.12 73 % +1.90 – +6.77
    Export age 0.00 0 % 0.00 – 0.00
    Residual −2.37 — —
    Denmark
    Gap (players per million): +10.19    		 U21 minutes +7.35 72 % +2.17 – +10.27
    League strength +1.67 16 % +0.65 – +2.26
    Export age 0.00 0 % 0.00 – 0.00
    Residual +1.17 — —

    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 Czech 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 Czech exports — hover for the name.
    AgeExpected G+A/90 (median)90 % HDI
    190.140.12 – 0.16
    210.140.13 – 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).

    Czech exports' own country effect: 0.00 (−0.02–0.02); Czech exports arrive at a median age of 23.

    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 Czechia's own 8 exports (n = 107 remaining) and refitting, the 21-vs-24 difference is 0.00 (−0.01–0.01), 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 ≤ 1.002, minimum bulk ESS 1190, 0 divergent transitions across 115 players; fit in 5.5 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 Czech-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, 37 change nobody in that set; the largest churn is 1 (CZE-First League multiplier -20%, mean rank shift 0.88 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% 30 / 30 0 0.10
    ENG-Premier League_plus20 ENG-Premier League multiplier +20% 30 / 30 0 0.07
    ITA-Serie A_minus20 ITA-Serie A multiplier -20% 30 / 30 0 0.00
    ITA-Serie A_plus20 ITA-Serie A multiplier +20% 30 / 30 0 0.00
    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% 30 / 30 0 0.13
    GER-Bundesliga_plus20 GER-Bundesliga multiplier +20% 30 / 30 0 0.20
    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 0.03
    NED-Eredivisie_minus20 NED-Eredivisie multiplier -20% 30 / 30 0 0.20
    NED-Eredivisie_plus20 NED-Eredivisie multiplier +20% 29 / 30 1 0.23
    POR-Primeira Liga_minus20 POR-Primeira Liga multiplier -20% 30 / 30 0 0.00
    POR-Primeira Liga_plus20 POR-Primeira Liga multiplier +20% 30 / 30 0 0.00
    BEL-Pro League_minus20 BEL-Pro League multiplier -20% 30 / 30 0 0.00
    BEL-Pro League_plus20 BEL-Pro League multiplier +20% 30 / 30 0 0.02
    TUR-Süper Lig_minus20 TUR-Süper Lig multiplier -20% 30 / 30 0 0.13
    TUR-Süper Lig_plus20 TUR-Süper Lig multiplier +20% 30 / 30 0 0.05
    CZE-First League_minus20 CZE-First League multiplier -20% 29 / 30 1 0.88
    CZE-First League_plus20 CZE-First League multiplier +20% 29 / 30 1 0.75
    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 0.37
    POL-Ekstraklasa_plus20 POL-Ekstraklasa multiplier +20% 30 / 30 0 0.12
    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% 30 / 30 0 0.00
    DEN-Superliga_plus20 DEN-Superliga multiplier +20% 30 / 30 0 0.00
    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% 30 / 30 0 0.00
    NOR-Eliteserien_plus20 NOR-Eliteserien multiplier +20% 30 / 30 0 0.00
    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 Czech-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 Czech-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 filtered92 entriesFBref country-page entries dropped for a surname ending in -ová (see Limitations).
    Namesakes in the pool2 playersActive pool players sharing a normalised name (e.g. father and son), disambiguated by club.
    Pool players without season tables125 playersCzech 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 names18 namesNational-team squad-table names that match no Czech-eligible row in the feature tables.
    Missing birth years0 rowsSeason-table rows of nation CZE 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.
    Czech First League rows without a nationality232 rowsSeason-table rows in the Czech First League 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. 125 of the 440 Czech 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 Czech second tier and the Slovak top flight are not on FBref at all; Slovakia's exhibits in chapter II therefore rest on its players abroad.

    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 2024–26" is parsed from Wikipedia squad tables (2024–25 Nations League, 2026 FIFA World Cup, 2026 World Cup qualification, UEFA Euro 2024, UEFA European Under-21 Championship 2025) and matched on normalised name plus birth year. 60 of the 192 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

    300 of the 440 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 Czech 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.datasimply.eu. 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 England edition · The Germany edition · The Denmark edition · The Norway edition · The Spain edition · The player atlas · Six nations: what to take from it