Contact times in professional football before and during the SARS-CoV-2 pandemic: Tracking data from the German Bundesliga.

The global SARS-CoV-2 pandemic led to a lockdown in team sports in March 2020. Because the risk of virus transmission seems to correlate with the duration of close contacts, data on contact times are necessary to assess the risk of virus transmission in sports. In this study, an optical tracking system was used to determine contact times between players of the two highest men's professional football leagues in Germany in the 2019-20 season and in the first half of the 2020-21 season.

Toward Automatically Labeling Situations in Soccer.

We study the automatic annotation of situations in soccer games. At first sight, this translates nicely into a standard supervised learning problem. However, in a fully supervised setting, predictive accuracies are supposed to correlate positively with the amount of labeled situations: more labeled training data simply promise better performance.

How the COVID-19 Pandemic has Changed the Game of Soccer.

This study explores the influence of corona-specific training and playing conditions - especially empty stadiums - on match performance, contact behavior, and home advantage in the Bundesliga (BL) and Bundesliga 2 (BL2). We analyzed the 2017/18, 2018/19, and 2019/20 seasons and compared matches in rounds 26-34 before shutdown with "ghost" matches after restart. Results show increased running activity for high intensity distance: (+ 6.

The origins of goals in the German Bundesliga.

We propose to analyse the origin of goals in professional football (soccer) in a purely data-driven approach. Based on positional and event data of 3,457 goals from two seasons German Bundesliga and 2nd Bundesliga (2018/20,219 and 2019/2020), we devise a rich set of 37 features that can be extracted automatically and propose a hierarchical clustering approach to identify group structures. The results consist of 50 interpretable clusters revealing insights into scoring patterns.

A Goal Scoring Probability Model for Shots Based on Synchronized Positional and Event Data in Football (Soccer).

Due to the low scoring nature of football (soccer), shots are often used as a proxy to evaluate team and player performances. However, not all shots are created equally and their quality differs significantly depending on the situation. The aim of this study is to objectively quantify the quality of any given shot by introducing a so-called model.

Constructing Spaces and Times for Tactical Analysis in Football.

A possible objective in analyzing trajectories of multiple simultaneously moving objects, such as football players during a game, is to extract and understand the general patterns of coordinated movement in different classes of situations as they develop. For achieving this objective, we propose an approach that includes a combination of query techniques for flexible selection of episodes of situation development, a method for dynamic aggregation of data from selected groups of episodes, and a data structure for representing the aggregates that enables their exploration and use in further analysis. The aggregation, which is meant to abstract general movement patterns, involves construction of new time-homomorphic reference systems owing to iterative application of aggregation operators to a sequence of data selections.