By OkayGoal
Women’s football has grown quickly enough that supporters now expect the same analytical depth the men’s game has enjoyed for a decade. Shot maps, passing networks and expected goals have all arrived in women’s competitions, though coverage is patchier and the samples smaller. Fans who understand both the tools and their limits get the most from them.
Where the numbers come from
Statistics begin with event data, in which analysts log every pass, shot, tackle and carry with a position on the pitch. Major providers now collect this for the top women’s leagues and international tournaments, and the output feeds broadcasts, club analysis departments and public websites. Coverage is thinner in lower divisions, where a match may be logged with fewer details.
Supporters who also follow markets such as football betting with bitcoin use the same public figures as everyone else, and the main practical point is to check which competitions a data source covers before trusting it. A statistic for a top league may rest on full data, while the same statistic for a second tier may rely on a partial record.
Public sources vary in quality. Some sites publish full event logs, others only totals, and a number quoted on social media may come from a model different from the one the competition uses. Fans who note the provider and the season behind a number can compare like with like and avoid mixing different definitions of a shot or a pass.
Opta uses a separate expected goals model for women’s competitions, which recognises that shot patterns and conversion rates differ from the men’s game. Fans comparing the two should not mix figures without checking the model.
A larger audience asking for more
Demand for detail follows audience growth. The UEFA announcement of the 2025 Women’s EURO attendance record reported a total of 657,291 spectators in Switzerland, ahead of the 574,875 recorded at the 2022 tournament in England. Larger crowds bring broadcasters, sponsors and data providers, and the supply of statistics increases with them. UEFA also reported that worldwide viewership across the tournament passed 500 million, with 45 million watching the final.
The pattern shows in daily coverage. Match reports now carry xG charts, post-match shot maps appear within minutes and podcasts dissect pressing structures. Fans who started watching during a tournament often move on to league football, and statistics help them build context for players they have only just met.
Growth also changes how fans use data. Supporters who once relied on highlights now track form across a season, compare players in the same position and follow young talent through age group tournaments.
Core metrics for following a match
Shots and shots on target remain a good starting point, and xG refines them by weighting chance quality. A team that attempts 18 shots from distance may have a lower xG than one that takes six shots inside the box, so counting attempts without location misleads. Fans look at xG alongside the actual score to check whether a result was fair.
Fans also track player development through age. Young players on loan or in academy teams produce small samples, so analysts compare per 90 minute rates rather than totals and treat anyone with fewer than 900 minutes as provisional. Shooting profiles, such as the share of shots taken from inside the box, show who gets into dangerous areas and who shoots from range.
Passing statistics show how a team builds. Progressive passes, those that move the ball substantially closer to the opponent’s goal, identify players who drive attacks, and passes into the final third show whether control becomes danger. A midfielder with many progressive passes and a high completion rate is probably central to her side’s play.
Defensive actions are harder to read. Tackles and interceptions depend on how often a team is attacked, so a defender with few of them may be doing her job well by positioning. Pressing figures give a better picture of how the team defends as a unit.
Goalkeepers and set pieces
Goalkeeper analysis has improved through post-shot expected goals, which measures the quality of a shot after it is struck, including where it is placed in the goal. A keeper who saves more than the model expects is performing above average, and a keeper conceding more than expected may be struggling with positioning.
Set pieces decide many matches in leagues where open play chances are scarce. Corners and free kicks produce a high share of goals for some clubs, and fans who track delivery type and target zone can see which teams have a repeatable routine. Tracking how many corners create a shot, rather than how many are taken, shows which clubs practise their routines well. Goalkeeper distribution adds a clue, since a keeper who plays long passes to a target forward creates a different style from one who builds through short passes. Goalkeeper distribution gives further clues, since a keeper who plays long passes to a target forward creates a different style from one who builds through short passes.
The limits of the data
Samples are small. Women’s Super League clubs play 22 league matches, so a few unusual results shift season totals more than they would across 38 matches, and international tournaments last only a few weeks. Conclusions drawn from one tournament should be held loosely.
Tracking data, which records every player’s position several times a second, is not yet available for every competition, so some analysis remains limited to event data. Reporting standards for minutes and lineups also vary between competitions, which makes comparison harder. Fans who combine statistics with careful viewing, watching how players move off the ball, get the fullest picture. Anyone wagering should keep stakes small and fixed, since limited data makes outcomes harder to predict.
Injury information is also less detailed. Clubs in some leagues publish limited team news, so fans rely on press conferences and local reporters, and a late lineup change can alter a match’s shape before the data catches up.