What are the Secrets Behind Successful Counterattacks in Football?

Exploring Gender-Specific Graph Neural Network Models and Key Factors for Predicting Counterattacks in Football.

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1. Introduction

The past few years have seen a remarkable increase in engagement with women’s football, evidenced by record-breaking viewership numbers. For example, the 2019 Women’s World Cup final between the United States and the Netherlands garnered a 56% higher audience compared to the 2015 final, and the 2022 EURO attracted more than double the viewers of 2017. Financial strides have accompanied this growth, with FIFA tripling the prize money for the Women’s World Cup from $15 million in 2015 to $50 million in 2019. Additionally, U.S. Soccer established equal pay agreements for their senior national teams in 2022, further solidifying the sport’s progress. Despite these advancements, research into tactical decision-making in women’s football remains limited. Some studies have mapped gender differences in shooting tendencies and passing behavior using event data, but no research has yet analyzed tactical differences between men’s and women’s football using tracking data.

The study “A Graph Neural Network deep-dive into successful counterattacks” pioneers the use of spatiotemporal broadcast tracking data from an entire season of both women’s and men’s professional football, synchronized with on-ball event data. The research trains gender-specific binary classification Graph Neural Networks (GNNs) to predict the success of counterattacks—a key component of football strategy. Counterattacks, defined as high-speed, high-intensity direct attacks following ball recovery, constitute a significant portion of goals in both cases. In the 2022 MLS season, 7.5% of all shots originated from counterattacks, accounting for 9.7% of goals. Similarly, in the NWSL, 6.2% of shots and 9.5% of goals were attributed to counterattacks.

Graph Neural Networks are particularly suited for this analysis due to their ability to represent football data as graphs. In these representations, players are nodes and edges describe relationships, such as inter-player distances and angles, within a specific frame of action. Historically, football analytics have relied on pass networks derived from on-ball event data, which lack information on off-ball player movements. Tracking data, providing x and y coordinates for all players and the ball at high temporal resolution, enables a richer graphical representation of in-game dynamics. Unlike image-based approaches that lose interpretability, graphs allow the integration of granular details such as player speed, acceleration, or proximity to the goal. Additionally, edge features capture critical interactions like defensive positioning and passing lanes.

“Figure 1: Schematic stylized graph representation of a single frame of tracking data”

By leveraging the flexibility of GNNs, this study overcomes challenges like incomplete data and inconsistent player counts, ensuring robust model performance. Graph-based modeling also simplifies the inclusion of diverse features, enabling a deeper understanding of tactical elements influencing counterattacking success. This methodological innovation bridges gaps in existing research, offering new insights into the tactical nuances of women’s and men’s football.