Calvin Yeung

Calvin Yeung

PhD student at Nagoya University, Research focuses on developing and applying machine learning models to sports.

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Posts

Unlock the Secrets of Football: How FIFA Ratings and Team Formations Can Predict Match Results

2 minute read

Published:

If you’re a football fan, you know how exciting it is to predict the outcome of a match. But with so many variables involved, it can be challenging to make accurate predictions. That’s where a recent paper comes in, proposing a framework that uses FIFA ratings and team formations to predict match results. Not only does this framework accurately predict match outcomes, but it also provides insights into the factors that contribute to a team’s success. In this blog, we’ll explore this framework, its effectiveness, and how it can help football enthusiasts better understand the game.

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publications

Transformer-Based Neural Marked Spatio Temporal Point Process Model for Football Match Events Analysis

Published in arXiv, 2023

With recently available football match event data that record the details of football matches, analysts and researchers have a great opportunity to develop new performance metrics, gain insight, and evaluate key performance. However, most sports sequential events modeling methods and performance metrics approaches could be incomprehensive in dealing with such large-scale spatiotemporal data (in particular, temporal process), thereby necessitating a more comprehensive spatiotemporal model and a holistic performance metric. To this end, we proposed the Transformer-Based Neural Marked Spatio Temporal Point Process (NMSTPP) model for football event data based on the neural temporal point processes (NTPP) framework. In the experiments, our model outperformed the prediction performance of the baseline models. Furthermore, we proposed the holistic possession utilization score (HPUS) metric for a more comprehensive football possession analysis. For verification, we examined the relationship with football teams’ final ranking, average goal score, and average xG over a season. It was observed that the average HPUS showed significant correlations regardless of not using goal and details of shot information. Furthermore, we show HPUS examples in analyzing possessions, matches, and between matches.

Recommended citation: Yeung, C., Sit, T., & Fujii, K. (2023). Transformer-Based Neural Marked Spatio Temporal Point Process Model for Football Match Events Analysis. arXiv preprint arXiv:2302.09276. https://arxiv.org/abs/2302.09276

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teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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