Full Text Available
Note: Clicking the button above will open the full text document at the original institutional repository in a new window.
Esports betting technology: machine learning for match prediction and odds estimation
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 us...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
School of Economics
2025
|
| Subjects: | |
| Tags: |
No Tags, Be the first to tag this record!
|
| _version_ | 1867613183958581248 |
|---|---|
| access_status_str | Open Access |
| author | du Plessis, Henri Izak David |
| author2 | Watson, Neil |
| author_browse | Watson, Neil du Plessis, Henri Izak David |
| author_facet | Watson, Neil du Plessis, Henri Izak David |
| author_sort | du Plessis, Henri Izak David |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/40887 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:06.010Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | School of Economics |
| publisherStr | School of Economics |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/40887 Esports betting technology: machine learning for match prediction and odds estimation du Plessis, Henri Izak David Watson, Neil Machine Learning 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. 2025-02-07T11:23:17Z 2025-02-07T11:23:17Z 2024 2025-02-07T11:19:21Z Thesis / Dissertation Masters MPhil http://hdl.handle.net/11427/40887 eng application/pdf School of Economics Faculty of Commerce University of Cape Town |
| spellingShingle | Machine Learning du Plessis, Henri Izak David Esports betting technology: machine learning for match prediction and odds estimation |
| thesis_degree_str | Master's |
| title | Esports betting technology: machine learning for match prediction and odds estimation |
| title_full | Esports betting technology: machine learning for match prediction and odds estimation |
| title_fullStr | Esports betting technology: machine learning for match prediction and odds estimation |
| title_full_unstemmed | Esports betting technology: machine learning for match prediction and odds estimation |
| title_short | Esports betting technology: machine learning for match prediction and odds estimation |
| title_sort | esports betting technology machine learning for match prediction and odds estimation |
| topic | Machine Learning |
| url | http://hdl.handle.net/11427/40887 |
| work_keys_str_mv | AT duplessishenriizakdavid esportsbettingtechnologymachinelearningformatchpredictionandoddsestimation |