Location analysis of players in UEFA EURO 2020 and 2022 using generalized valuation of defense by estimating probabilities
This work addresses the problem of analyzing defenses in football for researchers and analysts, but it is incremental as it builds on prior methods by incorporating event importance and investigating player diversity effects.
The study tackled the challenge of evaluating football team defense by proposing a generalized valuation method that score-scales predicted probabilities of events like ball gain and being attacked, using location data from UEFA EURO 2020 and 2022. Results showed that predictions for being attacked, scoring, and conceding did not require all players' information, while ball gain needed data on three to four offensive and defensive players, and game analyses explained the defensive excellence of finalist teams in Euro 2020.
Analyzing defenses in team sports is generally challenging because of the limited event data. Researchers have previously proposed methods to evaluate football team defense by predicting the events of ball gain and being attacked using locations of all players and the ball. However, they did not consider the importance of the events, assumed the perfect observation of all 22 players, and did not fully investigated the influence of the diversity (e.g., nationality and sex). Here, we propose a generalized valuation method of defensive teams by score-scaling the predicted probabilities of the events. Using the open-source location data of all players in broadcast video frames in football games of men's Euro 2020 and women's Euro 2022, we investigated the effect of the number of players on the prediction and validated our approach by analyzing the games. Results show that for the predictions of being attacked, scoring, and conceding, all players' information was not necessary, while that of ball gain required information on three to four offensive and defensive players. With game analyses we explained the excellence in defense of finalist teams in Euro 2020. Our approach might be applicable to location data from broadcast video frames in football games.