Esports betting technology: machine learning for match prediction and odds estimation
| dc.contributor.advisor | Watson, Neil | |
| dc.contributor.author | du Plessis, Henri Izak David | |
| dc.date.accessioned | 2025-02-07T11:23:17Z | |
| dc.date.available | 2025-02-07T11:23:17Z | |
| dc.date.issued | 2024 | |
| dc.date.updated | 2025-02-07T11:19:21Z | |
| dc.description.abstract | Esports betting is a rapidly growing segment of the online sports betting market. A key feature of this industry is the pricing of betting odds. This study investigated the online sports betting industry, odds compilation, and how machine learning can be used for sports prediction. The techniques used in the literature were then applied to one of the world's foremost esports: Counter-Strike. A substantial dataset of professional match data (n=11271) was collected and used to construct 142 relevant features for match prediction. Several supervised learning models, including random forests, feed-forward neural networks, and XGBoost, were trained to estimate win probabilities for both teams in each match. Betting odds were then calculated using these probabilities and compared to real-world betting odds. A notable aspect of the research is the implementation of Microsoft's TrueSkill rating system. It served as both a benchmark and an input feature. Among the models tested, XGBoost showed the best overall performance. The highest match prediction accuracy attained was 62.7%. It was found that incorporating a large number of statistics did not significantly improve predictive accuracy when compared to models using fewer, more important features. It was also found that LAN matches and best-of-3 map formats are more predictable than their counterparts. Despite the inherent difficulty in Counter-Strike match prediction, the models could generate efficient odds which exhibited strong correlation with real-world odds (up to 85%). A betting strategy informed by the generated odds was back-tested over a six-month period and shown to be profitable. This research therefore demonstrates how machine learning models can be used for esports match prediction, with practical applications in the online betting industry. | |
| dc.identifier.apacitation | du Plessis, H. I. D. (2024). <i>Esports betting technology: machine learning for match prediction and odds estimation</i>. (). University of Cape Town ,Faculty of Commerce ,School of Economics. Retrieved from http://hdl.handle.net/11427/40887 | en_ZA |
| dc.identifier.chicagocitation | du Plessis, Henri Izak David. <i>"Esports betting technology: machine learning for match prediction and odds estimation."</i> ., University of Cape Town ,Faculty of Commerce ,School of Economics, 2024. http://hdl.handle.net/11427/40887 | en_ZA |
| dc.identifier.citation | du Plessis, H.I.D. 2024. Esports betting technology: machine learning for match prediction and odds estimation. . University of Cape Town ,Faculty of Commerce ,School of Economics. http://hdl.handle.net/11427/40887 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - du Plessis, Henri Izak David AB - Esports betting is a rapidly growing segment of the online sports betting market. A key feature of this industry is the pricing of betting odds. This study investigated the online sports betting industry, odds compilation, and how machine learning can be used for sports prediction. The techniques used in the literature were then applied to one of the world's foremost esports: Counter-Strike. A substantial dataset of professional match data (n=11271) was collected and used to construct 142 relevant features for match prediction. Several supervised learning models, including random forests, feed-forward neural networks, and XGBoost, were trained to estimate win probabilities for both teams in each match. Betting odds were then calculated using these probabilities and compared to real-world betting odds. A notable aspect of the research is the implementation of Microsoft's TrueSkill rating system. It served as both a benchmark and an input feature. Among the models tested, XGBoost showed the best overall performance. The highest match prediction accuracy attained was 62.7%. It was found that incorporating a large number of statistics did not significantly improve predictive accuracy when compared to models using fewer, more important features. It was also found that LAN matches and best-of-3 map formats are more predictable than their counterparts. Despite the inherent difficulty in Counter-Strike match prediction, the models could generate efficient odds which exhibited strong correlation with real-world odds (up to 85%). A betting strategy informed by the generated odds was back-tested over a six-month period and shown to be profitable. This research therefore demonstrates how machine learning models can be used for esports match prediction, with practical applications in the online betting industry. DA - 2024 DB - OpenUCT DP - University of Cape Town KW - Machine Learning LK - https://open.uct.ac.za PB - University of Cape Town PY - 2024 T1 - Esports betting technology: machine learning for match prediction and odds estimation TI - Esports betting technology: machine learning for match prediction and odds estimation UR - http://hdl.handle.net/11427/40887 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/40887 | |
| dc.identifier.vancouvercitation | du Plessis HID. Esports betting technology: machine learning for match prediction and odds estimation. []. University of Cape Town ,Faculty of Commerce ,School of Economics, 2024 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/40887 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | School of Economics | |
| dc.publisher.faculty | Faculty of Commerce | |
| dc.publisher.institution | University of Cape Town | |
| dc.subject | Machine Learning | |
| dc.title | Esports betting technology: machine learning for match prediction and odds estimation | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MPhil |