Prediction of defensive success in elite soccer using machine learning - Tactical analysis of defensive play using tracking data and explainable AI

Associations between running activities and odds of scoring goals during professional soccer matches

We aimed to assess the associations between physical activities of reference teams or the differences with their opponents, and the goals scored in professional soccer. Total distance (TD), distance >20 km/h (VHS) and goals scored during matches of the Serie-A by two Best and two Worst Teams and by their opponents were collected. Odds ratio (OR) of scoring goals when high or low levels of running, as well as higher or lower levels compared to their opponents, were estimated. Performing higher TD than the opponents increased the OR of scoring goals for Best and Worst Teams in 5-min (19.8 ± 5.2, 16.1 ± 7.3) and 15-min (11.5 ± 3.3, 11.5 ± 4.9) intervals. Performing more VHS than the opponents increased the OR of scoring for Best and Worst teams in 5-minute (3.7 ± 0.9, 2.5 ± 0.6) and for Best teams in 15-minute (3.3 ± 1.1) intervals. Odds of scoring were higher when relative differences in running activities between teams, instead of absolute levels, were considered.

The Success Factors of Rest Defense in Soccer - A Mixed-Methods Approach of Expert Interviews, Tracking Data, and Machine Learning

While the tactical behavior of soccer players differs between specific phases of play (offense, defense, offensive transition, defensive transition), little is known about successful behavior of players during defensive transition (switching behavior from offense to defense). Therefore, this study aims to analyze the group tactic of rest defense (despite in ball possession, certain players safeguard quick counterattacks in case of ball loss) in defensive transition. A mixed-methods approach was used, involving both qualitative and quantitative analysis. Semi-structured expert interviews with seven professional soccer coaches were conducted to define rest defense. In the quantitative analysis, several KPIs were calculated, based on tracking and event data of 153 games of the 2020/21 German Bundesliga season, to predict the success of rest defense situations in a machine learning approach. The qualitative interviews indicated that rest defense can be defined as the positioning of the deepest defenders during ball possession to prevent an opposing counterattack after a ball loss. For instance, the rest defending players created a numerical superiority of 1.69 ± 1.00 and allowed a space control of the attacking team of 11.51 ± 9.82 [%] in the area of rest defense. The final machine learning model showed satisfactory prediction performance of the success of rest defense (Accuracy: 0.97, Precision: 0.73, f1-Score: 0.64, AUC: 0.60). Analysis of the individual KPIs revealed insights into successful behavior of players in rest defense, including controlling deep spaces and dangerous counterattackers. The study concludes regaining possession as fast as possible after a ball loss is the most important success factor in defensive transition.