FIFA World Cup 2026 Player Performance Dataset

By shrijeetverma13 · June 15, 2026

An analysis of 54,600 player-match records reveals that expensive players don't necessarily deliver better results on the pitch. Market value shows only…

The analysis reveals how player ratings vary across different age groups, with two visualizations generated: a bar chart showing average ratings by age group and a line chart tracking rating trends across individual ages. The data shows that player performance generally peaks during the mid-to-late 20s, which aligns with typical athletic prime years, before gradually declining in older age groups.

Tunisia is the most efficient team at converting expected goals (xG) into actual goals, scoring 5.72 goals for every 1 xG — meaning they dramatically outperformed their chance quality. The top 5 most efficient teams all have ratios well above 1.0, indicating clinical finishing across the board. Two bar and scatter plot visualizations were generated to illustrate these findings.

The analysis compares average performance score and player rating across all four player positions. Three visualizations were generated: bar charts showing average performance score and player rating by position, plus a box plot revealing the distribution of performance scores across positions.

The analysis identifies which players deliver the most tournament rating relative to their market value, giving you a 'bang for your buck' ranking. Two visualizations were produced: a horizontal bar chart showing the Top 15 best-value players ranked by tournament rating per €1M of market value, and a scatter plot comparing all players' tournament ratings against their market values, with the top 15 value performers highlighted in red stars.

Both distance covered and stamina score have a positive relationship with player rating, but stamina score is the stronger predictor. The analysis produced scatter plots and bar charts showing these relationships across matches.

The metrics most strongly associated with earning Player of the Match awards are assists (r=0.327) and goals (r=0.324), making offensive output the top predictors. Beyond those, possession impact, dribbles attempted, shots, and successful passes also show meaningful associations. Two bar charts were generated: one showing the top 15 correlations and another comparing average metric values between award winners and non-winners.

The analysis reveals only a weak relationship between a player's market value and their actual tournament performance. The scatter plot and bar charts show that expensive players don't necessarily deliver proportionally better results on the pitch. Market value correlates weakly with goals (r=0.203), assists (r=0.174), and more moderately with tournament rating (r=0.304), with an overall combined performance index correlation of just r=0.270.

The analysis successfully produced a horizontal grouped bar chart comparing the top 15 nationalities by their average tournament rating, creativity score, and pressure resistance — filtered to only include nationalities with at least 20 player-match appearances. The chart displays all three metrics side by side for easy comparison, sorted from highest to lowest tournament rating.

The analysis produced data tables breaking down technical skill metrics across player positions — Goalkeeper, Defender, Midfielder, and Forward. Three data tables were generated showing average pass accuracy, dribble success rate, and key passes per 90 minutes for each position group.

The analysis of key performance metrics across tournament stages has been completed and presented in two data tables. These tables show how average player rating, goals per match, fouls committed, and yellow cards vary from the Group Stage through to the Final, giving you a clear picture of how player and team performance evolves as the tournament progresses.

The analysis examined all 54,600 player-match records and computed z-scores across three metrics — offensive contribution, defensive contribution, and possession impact. Using a strict threshold of z 2.5 in at least two of the three dimensions simultaneously, zero records qualified as outliers. This means that while individual metrics may have extreme values, no player-match record was simultaneously extreme in two or more dimensions at the 2.5 standard deviation threshold. A scatter plot of offensive vs. defensive contribution was generated, showing the full distribution of all records.