Passing Heatmap Prediction Based on Transformer Model and Tracking Data
Yisheng Pei Varuna De Silva Mike Caine
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.