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Squad planning

Start the successor search eighteen months early

Seven in ten Ekstraklasa regulars have contracts ending in 2028 at the latest. Here is how data finds a successor before it gets urgent.

2 October 2026. Data: 2025/26 season, 26 leagues, outfield players with 1,000+ minutes.

In 2025/26, 257 outfield players in Ekstraklasa played at least 1,000 minutes. 94 of them (37%) have contracts ending in 2027, and another 85 in 2028. Among the league's top quarter of players the share ending in 2027 is the same: 24 of 64.

9420278520284420291420305203115?Number of players
Contract expiry year of Ekstraklasa players with 1,000+ minutes in 2025/26 (257 players). “?”: no date in the data.

Why eighteen months

A reliable successor assessment needs a full season of data and live viewing. A club that starts searching in the January before a contract ends has one half-season, a narrower market and a weaker negotiating position. Starting eighteen months ahead gives a full season of candidates and two transfer windows.

Example: replacing a playmaker

A key central midfielder at an Ekstraklasa club: 36 years old, estimated level among the league's best (better than 98% of midfielders), contract until June 2027. We look for a player with the same style (10 passing and ball-winning metrics), aged 26 or under, no loanees, valued at up to €1.5m, and no more than 3 points below his level.

Across the 26-league database, 3 candidates remain. 19 players were excluded because they have no market valuation, so their cost is unknown. The closest in style is a 26-year-old midfielder from the Portuguese league: 89.8% similarity, level 90.6 against 90.8, valued at €0.9m.

Passes/90Pass accuracy %Forward passes/90Passes to final third/90Final-third accuracy %Through passes/90xA/90Shot assists/90Def. duels/90Interceptions/9050
A: current player, 36B: candidate, 26, Portuguese league
Percentiles of 10 style metrics against central midfielders in their own leagues, 2025/26. Names withheld.

The candidate is just as involved in build-up (passes into the final third better than 96% of his league's midfielders) and more accurate (76th vs 53rd percentile), but generates less xA (83rd vs 94th percentile). He wins the ball back far more often (57th vs 13th percentile for interceptions). Similar style does not mean an identical player, only a similar balance of strengths and weaknesses.

What the data cannot check

Data tells you whom to watch. Availability, salary expectations, fitness and dressing-room fit have to be verified separately. That is why our shortlists come with a quantitative rationale for every name and a list of what to check in person.

Want this kind of analysis for your club?

We will reply with the proposed method, data scope and timeline.

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