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Uncovering influence of football players' behaviour on team performance in ball possession through dynamical modelling (2504.06574v1)

Published 9 Apr 2025 in physics.soc-ph and nlin.AO

Abstract: A quest for uncovering influence of behaviour on team performance involves understanding individual behaviour, interactions with others and environment, variations across groups, and effects of interventions. Although insights into each of these areas have accumulated in sports science literature on football, it remains unclear how one can enhance team performance. We analyse influence of football players' behaviour on team performance in three-versus-one ball possession game by constructing and analysing a dynamical model. We developed a model for the motion of the players and the ball, which mathematically represented our hypotheses on players' behaviour and interactions. The model's plausibility was examined by comparing simulated outcomes with our experimental result. Possible influences of interventions were analysed through sensitivity analysis, where causal effects of several aspects of behaviour such as pass speed and accuracy were found. Our research highlights the potential of dynamical modelling for uncovering influence of behaviour on team effectiveness.

Summary

Dynamic Modeling of Player Behavior in Football and Its Impact on Team Performance

This research paper focuses on the influence of football players' behavior on team performance, particularly in the context of a three-versus-one (3v1) ball possession game, using a dynamic modeling approach. The paper builds on previous research efforts in understanding team effectiveness in football by offering a novel approach through a dynamic model that represents the interactions and movements of players and the ball.

Overview of the Model and Experiment

The researchers devised a mathematical model to simulate the dynamics of the 3v1 ball possession game. The model describes the positions and movements of three offensive players (OFs) and one defensive player (DF), including a Passer and a Receiver among the OFs. The players' positions evolve according to a system of ordinary differential equations (ODEs), incorporating multiple parameters such as pass duration, accuracy, and players' responsiveness to various forces representing interpersonal and environmental interactions.

In parallel, an experiment was conducted involving high-level and low-level football teams with players performing the 3v1 game. Empirical data on team formations (measured by the OF area) and performance (evaluated by the number of successful passes) were collected. The experimental data served to validate the dynamic model by ensuring that the model's outputs could reproduce the statistical characteristics observed in the real-world context.

Key Findings and Sensitivity Analysis

The results highlighted the model's ability to replicate both the average number of passes and the spatial positioning of players observed empirically. Notably, the model showed that faster and more precise passing could significantly enhance ball possession, echoed in the parameters T (pass duration) and σ (pass angle variance).

A sensitivity analysis was performed to determine the impact of each model parameter on the number of passes, providing insights into the factors that drive effective team behaviors in the possession game. Specific parameters such as the inverse temperature β, which affects pass direction choice, revealed non-trivial influences, suggesting an optimal amount of randomization might enhance performance. Similarly, interactions between parameters such as evasion distance (#2{L}{e}) and other variables that reflect team strategy and coordination contributed to understanding the dynamics.

Implications for Team Dynamics and Future Directions

The findings underscore the potential of dynamic models in uncovering causal relationships in team sports and have significant implications for training and strategy development in football. The model serves not only to simulate existing scenarios but also as a predictive tool for intervention strategies, where adjustments in player behavior are hypothesized to lead to improved performance metrics.

Additionally, this research lays the groundwork for future efforts in empirical parameter estimation, where the model's insights into optimal behavioral parameters can inform targeted player training regimens. The model's framework can also be adapted to other sports or team-based activities where coordination and tactical efficiency are crucial.

The paper exemplifies the utility of integrating dynamic modeling with empirical sports data, offering a robust methodology for quantifying and enhancing team performance. The ability to model complex, emergent behaviors in a controlled, computational environment paves the way for more nuanced analyses in sports science and beyond, potentially extending to broader applications in organizational and team dynamics research.

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