Passing Heatmap Prediction Based on Transformer Model and Tracking Data

TL;DR

This paper introduces a transformer-based deep learning model that predicts pass locations and assesses off-ball movement impact in football, enhancing understanding of players' contributions beyond scoring events.

Contribution

A novel deep learning architecture that predicts pass end locations and evaluates off-ball movement effects, providing new insights into football match analysis.

Findings

Achieved over 0.7 Top-1 accuracy in pass prediction.

Enhanced understanding of pitch control and pass options.

Provided a new metric for assessing off-ball movement contributions.

Abstract

Although the data-driven analysis of football players' performance has been developed for years, most research only focuses on the on-ball event including shots and passes, while the off-ball movement remains a little-explored area in this domain. Players' contributions to the whole match are evaluated unfairly, those who have more chances to score goals earn more credit than others, while the indirect and unnoticeable impact that comes from continuous movement has been ignored. This research presents a novel deep-learning network architecture which is capable to predict the potential end location of passes and how players' movement before the pass affects the final outcome. Once analysed more than 28,000 pass events, a robust prediction can be achieved with more than 0.7 Top-1 accuracy. And based on the prediction, a better understanding of the pitch control and pass option could be reached to measure players' off-ball movement contribution to defensive performance. Moreover, this model could provide football analysts a better tool and metric to understand how players' movement over time contributes to the game strategy and final victory.