Summary of Machine Learning For Soccer Match Result Prediction, by Rory Bunker et al.
Machine Learning for Soccer Match Result Prediction
by Rory Bunker, Calvin Yeung, Keisuke Fujii
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This chapter explores machine learning approaches for predicting soccer match outcomes, surveying existing datasets, model types, and evaluation methods in this domain. The authors highlight the current best-performing models, gradient-boosted tree models like CatBoost applied to pi-ratings, but note a need for more comprehensive comparisons of deep learning and Random Forest models on diverse datasets with varying features. Additionally, they suggest investigating new rating systems incorporating player-team information and spatiotemporal tracking data. The chapter concludes by emphasizing the importance of improving model interpretability for practical applications in team management. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to use machine learning to predict the outcome of soccer games. It reviews what’s currently known about this topic, including types of models and ways to measure how well they work. The authors suggest that more research is needed to compare different types of models and features used in these predictions. They also think it would be helpful to create new rating systems that use more information about players and teams, as well as data about where players are on the field. Finally, the paper notes that making these models easier to understand could help them be more useful for coaches. |
Keywords
* Artificial intelligence * Deep learning * Machine learning * Random forest * Spatiotemporal * Tracking
