Soccer match outcome prediction with random forest and gradient boosting models

Research Article
Open access

Soccer match outcome prediction with random forest and gradient boosting models

Xiangkun Meng 1*
  • 1 Jinan Foreign Language School    
  • *corresponding author kwilsont85009@student.napavalley.edu
Published on 21 February 2024 | https://doi.org/10.54254/2755-2721/40/20230634
ACE Vol.40
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-305-0
ISBN (Online): 978-1-83558-306-7

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

Meng,X. (2024). Soccer match outcome prediction with random forest and gradient boosting models. Applied and Computational Engineering,40,99-107.
Export citation

References

[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.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation

ISBN：978-1-83558-305-0(Print) / 978-1-83558-306-7(Online)
Editor：Mustafa İSTANBULLU
Conference website: https://2023.confmla.org/
Conference date: 18 October 2023
Series: Applied and Computational Engineering
Volume number: Vol.40
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[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

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.