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Soccer match outcome prediction with random forest and gradient boosting models
- 1 Jinan Foreign Language School
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Abstract
In order to accurately predict the results of soccer matches, this study introduces Machine Learning (ML) techniques in joint Random Forest (RF) and Gradient Boosting (GB) models. In order to forecast the results of the next World Cup, a model has been trained using past information from prior tournaments. The proposed model is evaluated using multiple performance criteria including precision and accuracy. The RF approach outperforms the GB approach in terms of both accuracy and precision, as concluded after the experiment. The most important features for predicting the outcome of football games are identified using feature importance scores. Football enthusiasts and analysts can use the proposed model to predict the outcome of football games with high accuracy. The implications of these findings for football teams are practical as they provide valuable insights for improving team performance and increasing their chances of winning the World Cup. By identifying the most important features for predicting the outcome of football games, teams can focus their efforts on improving these areas, increasing their chances of success. Football teams and football analysts can benefit from accurate predictions, which are enabled by machine learning techniques such as GB and RFs. Overall, this study presents a promising approach to predicting the outcome of football games, with practical implications for the field of sports analytics.
Keywords
Prediction, Soccer Matches, Machine Learning, Random Forest, Gradient Boosting
[1]. 2022 financial highlights FIFA Publications FIFA Publications. https://publications.fifa.com/en/annual-report-2022/finances/2019-2022-cycle-in-review/2022-financial-highlights/
[2]. Shobana G Suguna M 2021 Sports prediction based on RF algorithm In Springer Proceedings in Materials pp 993–1000
[3]. R Hucaljuk J Rakipović A 2011 Predicting football scores using machine learning techniques//2011 Proceedings of the 34th International Convention MIPRO IEEE pp 1623-1627
[4]. Boi Ross W J Orr M 2022 Predicting climate impacts to the Olympic Games and FIFA Men’s World Cups from 2022 to 2032 Sport in Society 25(4): pp 867-888
[5]. Groll A 2018 Prediction of the FIFA World Cup 2018 - A RF approach with an emphasis on estimated team ability parameters arXiv.org 1806.03208
[6]. Pinasthika S J Fudholi D R 2022 World Cup 2022 Knockout Stage Prediction Using Poisson Distribution Model IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 17(2)
[7]. Yoon J 2020 Forecasting of real GDP growth using machine learning models: GB and RF approach Computational Economics 57(1): pp 247–265
[8]. Glen S 2019 Decision Tree vs RF vs GB Machines: Explained Simply Data Science Central
[9]. International football results 2023 Kaggle. https://www.kaggle.com/datasets/martj42/international-football-results-from-1872-to-2017
[10]. FIFA World Ranking 2023 Kaggle. https://www.kaggle.com/datasets/cashncarry/fifaworldranking
Cite this article
Meng,X. (2024). Soccer match outcome prediction with random forest and gradient boosting models. Applied and Computational Engineering,40,99-107.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation
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