Data Analytics in Professional Soccer

The digital transformation is one of the major challenges of our time (Bounfour, 2016; Castells, 1996). On a smaller scale, this also affects the world of sport, where the ongoing digitalization and developments in the field of sensor technology have led to a rapid increase in the volume of data. In particular, spatio-temporal position data, which are now available almost everywhere in soccer, harbor huge potential for performance analysis. However, these vast amounts of data have little value in themselves. Rather, the challenge is to develop new methods of data analysis, and to use the resulting structures and correlations in sports data to augment and enhance our knowledge about sport.

This paper describes models for detecting individual and team ball possession in soccer based on position data. The types of ball possession are classified as Individual Ball Possession (IBC), Individual Ball Action (IBA), Individual Ball Control (IBC), Team Ball Possession (TBP), Team Ball Control (TBC) und Team Playmaking (TPM) according to different starting points and endpoints and the type of ball control involved. The machine learning approach used is able to determine how long the ball spends in the sphere of influence of a player based on the distance between the players and the ball together with their direction of motion, speed and the acceleration of the ball. The degree of ball control exhibited during this phase is classified based on the spatio-temporal configuration of the player controlling the ball, the ball itself and opposing players using a Bayesian network.

The evaluation and application of this approach uses data from 60 matches in the German Bundesliga season 2013/14, including 69,667 IBA intervals. The identification rate was F = 88 for IBA and F =:83 for IBP, and the classification rate for IBC was k = 67. Match analysis showed the following mean values per match: TBP 56:04 ± 5:12 min, TPM 50:01 ± 7:05 min and TBC 17:49 ± 8:13 min. There were 836 ± 424 IBC intervals per match and their number was significantly reduced by -5.1 % from the 1^st to 2^nd half. The analysis of ball possession at the player level indicates shortest accumulated IBC times for the central forwards (0:49 ± 0:43 min) and the longest for goalkeepers (1:38 ± 0:58 min), central defenders (1:38 ± 1:09 min) and central midfielders (1:27 ± 1:08 min). The results could improve performance analysis in soccer, help to detect match events automatically, and allow discernment of higher value tactical structures, which is based on individual ball possession.

This study describes an approach to quantification of attacking performance in soccer. Our procedure determines a quantitative representation of the probability of a goal being scored for every point in time at which a player is in possession of the ball – we refer to this as dangerousity. The calculation is based on the spatial constellation of the player and the ball, and comprises four components: (1) Zone describes the danger of a goal being scored from the position of the player on the ball, (2) Control stands for the extent to which the player can implement his tactical intention on the basis of the ball dynamics, (3) Pressure represents the possibility that the defending team prevent the player from completing an action with the ball and (4) Density is the chance of being able to defend the ball after the action. Other metrics can be derived from dangerousity by means of which questions relating to analysis of the play can be answered. Performance quantifies the number and quality of the attacks by a team over a period of time, while Dominance describes the difference in performance between teams. The evaluation uses the correlation between probability of winning the match (derived from betting odds) and performance indicators, and indicates that among Goal difference (r =.55), difference in Shots on Goal (r =.58), difference in Passing Accuracy (r =.56), Tackling Rate (r =.24) Ball Possession (r =.71) and Dominance (r =.82), the latter makes the largest contribution to explaining the skill of teams. We use these metrics to analyze individual actions in a match, to describe passages of play, and to characterize the performance and efficiency of teams over the season. For future studies, they provide a criterion that does not depend on chance or results to investigate the influence of central events in a match, various playing systems or tactical group concepts on success.

This study investigates the spatial relationship of performance variables for soccer free kicks. In order to suggest ways in which players might optimize their performance, we collected data from free kicks (< 35m to goal line) of two German Bundesliga seasons (2013/14, 2014/15) (n = 1;624). In the analysis, we applied the ISO-map approach using color gradients to visualize the mean values of a variable on a 2D-map of the pitch. Additionally, variograms were used to describe the degree of spatial dependence of the free kick variables. Results show that DENSITY, TYPE OF PLAY, PLAYERS IN WALL, DISTANCE TO WALL and RULE VIOLATION were strongly spatially dependent. Centrality and proximity to the goal increased the variables PLAYERS IN WALL, RULE VIOLATIONS and INTERRUPTION TIME, and the ratio of goals scored increased from 5.9 % (central far) to 10.9 % (central near). In 70.9 % of the shots, players preferred a switched laterality, which did not result in a higher success rate. Furthermore, there was no statistical advantage for the defensive team when DISTANCE TO WALL was below 9.15 m or when there was a RULE VIOLATION. Crosses had a success rate (i.e. first controlled ball contact after the cross) of 20.8 %. Played with natural laterality, they were 5 % more successful than with switched laterality. Crosses from the right side outside the penalty box were 10 % more successful than from the left side. Therefore, it might be worthwhile practicing the defense of balls coming from this side.

This research explores the influence of match importance on player activity in professional soccer. Therefore, we used an observational approach and analyzed 1,211 matches of German Bundesliga and 2nd Bundesliga. The importance measurement employed is based on post season consequences of teams involved in a match. This means, if a match result could potentially influence the final rank, and this rank would lead to different consequences for a team, such as qualification for Champions League opposed to qualification for Europe League, then this match is classified as important; otherwise not. Activity was quantified by TOTAL DISTANCE COVERED, SPRINTS, FAST RUNS, DUELS, FOULS and ATTEMPTS. Running parameters were recorded using a semi-automatic optical tracking system, while technical variables were collected by professional data loggers. Based on our importance classification, low important matches occurred at the beginning of round 29. A two-way ANOVA indicates significantly increased FAST RUNS (+4 %, d = 0:3), DUELS (+16 %, d = 1:0) and FOULS (+36 %, d = 1:2) in important matches compared to low important ones. For FAST RUNS and FOULS, this effect only exists in Bundesliga. A comparison of the two leagues show that TOTAL DISTANCE COVERED (+3 %, d = 0:9), SPRINTS (+25 %, d = 1:4) and FAST RUNS (+15 %, d = 1:4) are higher compared to 2^nd Bundesliga, whilst FOULS is less in Bundesliga (-7 %, d = 0:3). No difference in player activity was found between matches at the beginning of a season (round 1-6) and at the end of a season (round 29-34). We conclude that match importance influences player activity in German professional soccer. The most reasonable explanation is a conscious or unconscious pacing strategy, motivated by preserving abilities or preventing injury. Since this tendency mainly exists in Bundesliga, this may suggest that more skilled players show a higher awareness for the need of pacing.

This study examines the influence ambient temperature has on the distances covered by players in soccer matches. For this purpose, 1,211 games from the top German professional leagues were analysed over the course of the seasons 2011/12 and 2012/13 using an optical tracking system. The data shows a) significant differences in the total distance covered (TDC, in m (10 min)^-1) between the Bundesliga (M = 1;225) and 2^nd Bundesliga (M = 1;201) and b) a significant decrease in TDC from NEUTRAL (-4 to13 ^◦C, M = 1;229) to WARM (≥ 14 ^◦C, M = 1;217) environments. The size of the temperature effect is greater in the Bundesliga (d =.30 vs. d =.16), even though these players presumably have a higher level of fitness. This suggests that better players reduce their exertion level to a greater extent, thus preserving their ability to undertake the high intensity activities when called upon. No reduction in running performance due to COLD (≤ 5 ^◦C) temperatures was observed.

More and more sport associations introduce innovative devices to support referees and umpires respectively, affecting a strong need for the evaluation of these devices. This study evaluates the use of the new vanishing spray for free kicks in the German Bundesliga. In more detail, the aim of the study is to investigate if the spray reduces violations of the required minimum distance and consequently the respective punishments, if it reduces errors concerning the distance set by the referee and if it leads to a higher success rate of free kicks. Therefore, 1,833 free kicks of the 2013/2014 and 2014/2015 season of the German Bundesliga were screened using a self-designed observational system. For the statistical analysis two parallel samples were built of 299 free kicks each. The results showed no decrease of free kicks with distance violations but a significantly lower extent of these violations (x ² = 4.58; p <.05). However, none of these violations were punished appropriately. Concerning the success of free kicks, no significant impact was found neither for shots nor for crosses. In addition, no influence on the distance set by the referee could be identified. The main objective of the vanishing spray was basically realized, but the use didn’t lead to any further positive (side) effects. Due to the lack of punishment, the authors raise concerns about the current application of the minimum distance rule.

The studies in this book have shown how analytical methods of mathematics and computer science can be used to address performance diagnostic issues in soccer. Looking to the future, it is expected that advances in artificial intelligence, big data, and data mining will both improve existing approaches and lead to completely new applications in sports analytics. The enormous amounts of data in the professional sector allow us to expand our knowledge of performance structure of soccer and put it on an empirically founded basis. This may lead to a new evolutionary stage in training and competition theory for soccer.