# Machine Learning for Soccer Match Result Prediction

arXiv: https://arxiv.org/abs/2403.07669 · HTML: https://mlsanity.com/paper/2403.07669/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 123 references that are in the map.
- First posted on arXiv 12 Mar 2024.

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

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Actions Speak Louder Than Goals: Valuing Player Actions in Soccer](https://mlsanity.com/paper/1802.07127/) (2018)
- [Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study](https://mlsanity.com/paper/2103.09627/) (2021)
- [A Bayesian Approach to In-Game Win Probability in Soccer](https://mlsanity.com/paper/1906.05029/) (2019)
- [The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review](https://mlsanity.com/paper/1912.11762/) (2019)
- [Combining Machine Learning and Human Experts to Predict Match Outcomes in Football: A Baseline Model](https://mlsanity.com/paper/2012.04380/) (2020)
- [Player Chemistry: Striving for a Perfectly Balanced Soccer Team](https://mlsanity.com/paper/2003.01712/) (2020)
- [Modeling outcomes of soccer matches](https://mlsanity.com/paper/1807.01623/) (2018)
- [Luck is Hard to Beat: The Difficulty of Sports Prediction](https://mlsanity.com/paper/1706.02447/) (2017)
- [Using Twitter to predict football outcomes](https://mlsanity.com/paper/1411.1243/) (2014)
- [On the dependence in football match outcomes: traditional model assumptions and an alternative proposal](https://mlsanity.com/paper/2103.07272/) (2021)
- [Predicting Football Match Outcomes with eXplainable Machine Learning and the Kelly Index](https://mlsanity.com/paper/2211.15734/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Systematic Review of Machine Learning in Sports Betting: Techniques, Challenges, and Future Directions](https://mlsanity.com/paper/2410.21484/) (2024)
- [Analysis of Line Break prediction models for detecting defensive breakthrough in football](https://mlsanity.com/paper/2511.00121/) (2025)

## Abstract (arXiv)

Machine learning has become a common approach to predicting the outcomes of soccer matches, and the body of literature in this domain has grown substantially in the past decade and a half. This chapter discusses available datasets, the types of models and features, and ways of evaluating model performance in this application domain. The aim of this chapter is to give a broad overview of the current state and potential future developments in machine learning for soccer match results prediction, as a resource for those interested in conducting future studies in the area. Our main findings are that while gradient-boosted tree models such as CatBoost, applied to soccer-specific ratings such as pi-ratings, are currently the best-performing models on datasets containing only goals as the match features, there needs to be a more thorough comparison of the performance of deep learning models and Random Forest on a range of datasets with different types of features. Furthermore, new rating systems using both player- and team-level information and incorporating additional information from, e.g., spatiotemporal tracking and event data, could be investigated further. Finally, the interpretability of match result prediction models needs to be enhanced for them to be more useful for team management.