# Passing Heatmap Prediction Based on Transformer Model and Tracking Data

arXiv: https://arxiv.org/abs/2309.01526 · HTML: https://mlsanity.com/paper/2309.01526/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 4 Sep 2023.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kernel](https://mlsanity.com/paper/1908.11775/) (2019)
- [Hunting Group Clues with Transformers for Social Group Activity Recognition](https://mlsanity.com/paper/2207.05254/) (2022)
- [Using Player's Body-Orientation to Model Pass Feasibility in Soccer](https://mlsanity.com/paper/2004.07209/) (2020)
- [Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction](https://mlsanity.com/paper/2206.01899/) (2022)
- [SoccerMap: A Deep Learning Architecture for Visually-Interpretable Analysis in Soccer](https://mlsanity.com/paper/2010.10202/) (2020)
- [A Bayesian Approach to In-Game Win Probability in Soccer](https://mlsanity.com/paper/1906.05029/) (2019)
- [Distinguishing Between Roles of Football Players in Play-by-play Match Event Data](https://mlsanity.com/paper/1809.05173/) (2018)

## Abstract (arXiv)

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.