On the use of passing network indicators to predict football outcomes

Summary statistics for football matches, such as ball possession and percentage of completed passes, are not always satisfyingly informative about team strategies seen on the pitch. Passing networks and their structural features can be used to evaluate the style of play in terms of passing behavior, analyzing and quantifying interactions among players. The aim of the present paper is to show how information retrieved from passing networks can have a significant impact on the match outcome. At a descriptive level, we provide useful graphic visualizations to compare teams and their individual level of connection. Therefore, we directly compute and discuss network properties, such as centralization, clustering and cliques, from a football perspective. Then, we model the probability of winning the game through four competitive machine learning models including network-based indicators as explanatory variables with a set of in-field variables. The real dataset for application includes 96 matches in the Group Stage of the 2016–2017 UEFA Champions League, involving the 32 best European teams. This approach shows that some network-based variables, such as diameter and betweenness centralization, can be related to the level of offensive actions and finalizations for a team. Furthermore, we show that such variables help improve all considered models in terms of explanatory power, compared to those presenting only in-field regressors. Among the presented models, binomial logistic regression shows the best results according to a set of performance indicators