As one who always eager to learn more about football/soccer through data, I was happy to see that FIFA had posted some unique individual- and team-level World Cup statistics as part of its tournament coverage. That enthusiasm was tempered, however, when I saw that most of the tables focused on cumulative totals, making it much more difficult to compare players from different nations side-by-side when they had played a different number of matches.
Here, I look to rectify that situation by scaling selected metrics of interest to a standard 90 minutes of playtime. Also, I considered how some teams could have very disparate possession times based on type of play, leaving bias in some of these time-standardized metrics. The ideal would be to use possession and out-of-possession time for each individual, but that was not reported. However, I could use tournament-specific possession times to create back-of-the-envelope metrics scaled to the a standardized possession- and out-of-possession- duration. I’ve provided all these values are aggregated into tables for your exploration below.
I additionally wanted to specifically test out Tableau Stories as a vector for delivering these lengthy data tables with additional visual enhancements (filters, simple visualizations) to improve digestability. I wanted to utilize visuals that would showcase where individual performances lie amongst the entire field of players, but still leave it open to look at deeper trends with the different subgroups in the data. The entire package of data is made available as such, both embedded here and for viewing on the Tableau Public site.
Data / Methods
Accumulated World Cup statistics, including playing time in minutes, were transcribed from the FIFA website. Selected metrics were chosen based on their uniqueness, since FIFA offers some stats not found elsewhere. Those were scaled to “per 90” minute metrics based on their reported playing time.
I also considered that offensive metrics can only be accumulated when in possession of the ball, and vice versa for defensive statistics. In a coarse attempt to compensate for differing possession times that might occur because of game states and intentional tactics, I used team-level possession proportion to estimate how many minutes each player had in possession. Unfortunately, FIFA did not give the complete split with out of possession and in contest. So for each team, I made the crude assumption that 11 percent of their match time was with the ball in contest, based on statistics from the 2022 World Cup. Minutes out of possession would be assigned the remaining percentage. For simplicity and mental comparability to the “per 90” metrics, I scaled the possession-related metrics to “per 40” minutes in possession for offensive metrics and “per 40” out of possession for defensive metrics.
Lastly, I was curious about performances specifically among players set to play in the English Premier League following the Cup. To identify them, I linked the World Cup data with club rosters from Transfermarkt after the league’s deadline day.
Any comparisons between the World Cup metrics and a player’s current performance with their club were made using data from Fotmob, which posts a wide variety of per 90 metrics.
Visualization & Results
Brief Observations
I did want to use this compiled data to identify standout performances at the tournament, and then take a look at how those players have been performing for their club subsequently. Below is a quick synopsis of that information for a small selection of players.
Attacking
Deniz Undav led the tournament in Goal Contributions/90 (minimum 135 minutes played). However, he has yet to imprint his name on a score sheet in the Bundesliga, though he did assist twice in Stuttgart’s only Champions League match thus far.
Ismaila Sarr, who lead in xG, was since injured and hasn’t been able to capitalize on any personal momentum coming out of the tournament. However, runner up Raul Jimenez is 9th in xG in the English Championship, and 3rd place finisher Erling Haaland is currently ranked in the EPL.
Distribution
- While not directly comparable to his leading performance in successful defensive linebreaks per 90 at the cup, Wan Bissaka is a respectable 24th in the Prem.
Defensive
- Rayan Cherki appears a very willing presser, topping the list in Defensive Pressures Applied/90. Some of that might be related to his role more as a substitute in 6 of his 7 matches, with the team looking to him to come on and be aggressive against tiring opponents. But for Forced Turnovers, it takes until the 23rd on the list to get to an EPL player on the list, Mikel Merino.
Physical
- Maeda lead the World Cup in Sprints/90 for those with at least 135 minutes played. It’s probably no wonder he is also highest among the World Cup participants in that statistic through EPL Week 5.
- Leo Messi’s Distance Run/90 ranked dead last in the tournament, but given his placement of 8th highest in goal contributions, I don’t think his teammates minded terribly.
Discussion
Of course there are the predictable caveats arising from this application of the data. It can be a tiny sample size for some individuals, since a number of teams only played in 3 games total. This is clearly compounded by the use of tournament-level possession statistics, especially the estimated defensive possession ones. Additionally:
FIFA.com-reported playing time appear to be different from other providers, interestingly enough, resulting in noticeably discrepancies between some metrics reported by both sources.
FIFA-provided positions are broad, so, for example, central defenders and fullbacks might be listed the same despite having tangibly different roles not well-suited for direct comparison
Metrics are calculated from rounded values in FIFA tables, so numbers may be slightly off due to a lack of precision
Data linkage was done using names, so it is possible some EPL players were not identified if the names used for their club were vastly different from the ones used in the FIFA World Cup table
But ultimately, I think this visualization package does provide an interesting window into the last World Cup as it simplifies and quantifies the chaos of a 104 match tournament. The additional clarity on the relative performance of each player across a number of dimensions should better inform our expectations across a variety of applications.