Optimizing Football Game Strategies through Passing Network Analysis

Abstract

Objectives. This study aims to explore the potential of Passing Network as an indicator of football team outcomes. The main focus is to understand how the structure and dynamics of the Passing Network can be used to predict match results and provide practical insights for coaches and analysts in optimizing game strategies.

Materials and Methods. This study employed a mixed-methods design, combining both quantitative and qualitative approaches. Quantitative data were collected from 96 group-stage matches of the 2016–2017 UEFA Champions League season, involving 32 top European teams. The analysis was conducted using network analysis, where players were represented as nodes and passes as edges. Various indicators such as degree centrality, betweenness centrality, eigenvector centrality, diameter, and clustering coefficient were calculated to evaluate the structure of the Passing Network. Qualitative data were gathered through in-depth interviews with coaches, players, and professional analysts to provide additional contextual insights. Statistical techniques such as binomial logistic regression and machine learning were employed to analyze the relationships between Passing Network indicators and match outcomes.

Results. The results of the study indicate that teams with a more structured Passing Network tend to exhibit higher levels of offensive actions and attack effectiveness. Graphical visualizations of the Passing Network provide deep insights into player interactions and overall team strategies. The binomial logistic regression (BLR) model successfully enhanced the accuracy of match outcome predictions using network-based indicators. These findings affirm that the Passing Network can serve as a reliable predictive tool for forecasting match results.

Conclusion. This study concludes that Passing Network analysis holds significant potential as a tool for understanding team dynamics and predicting match outcomes. The combination of quantitative and qualitative approaches provides comprehensive insights that are valuable for coaches and analysts in formulating strategies. Future research is recommended to integrate player tracking data, real-time game contexts, and artificial intelligence technologies to enhance accuracy and analytical depth.