Sport analytics for cricket game results using machine learning: An experimental study
Indian Premier League (IPL) is one of the more popular cricket world tournaments, and its financial is increasing each season, its viewership has increased markedly and the betting market for IPL is growing significantly every year. With cricket being a very dynamic game, bettors and bookies are inc...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Emerald Publishing
2022-06-01
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| Series: | Applied Computing and Informatics |
| Subjects: | |
| Online Access: | https://www.emerald.com/insight/content/doi/10.1016/j.aci.2019.11.006/full/pdf |
| _version_ | 1797796869970067456 |
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| author | Kumash Kapadia Hussein Abdel-Jaber Fadi Thabtah Wael Hadi |
| author_facet | Kumash Kapadia Hussein Abdel-Jaber Fadi Thabtah Wael Hadi |
| author_sort | Kumash Kapadia |
| collection | DOAJ |
| description | Indian Premier League (IPL) is one of the more popular cricket world tournaments, and its financial is increasing each season, its viewership has increased markedly and the betting market for IPL is growing significantly every year. With cricket being a very dynamic game, bettors and bookies are incentivised to bet on the match results because it is a game that changes ball-by-ball. This paper investigates machine learning technology to deal with the problem of predicting cricket match results based on historical match data of the IPL. Influential features of the dataset have been identified using filter-based methods including Correlation-based Feature Selection, Information Gain (IG), ReliefF and Wrapper. More importantly, machine learning techniques including Naïve Bayes, Random Forest, K-Nearest Neighbour (KNN) and Model Trees (classification via regression) have been adopted to generate predictive models from distinctive feature sets derived by the filter-based methods. Two featured subsets were formulated, one based on home team advantage and other based on Toss decision. Selected machine learning techniques were applied on both feature sets to determine a predictive model. Experimental tests show that tree-based models particularly Random Forest performed better in terms of accuracy, precision and recall metrics when compared to probabilistic and statistical models. However, on the Toss featured subset, none of the considered machine learning algorithms performed well in producing accurate predictive models. |
| first_indexed | 2024-03-13T03:39:35Z |
| format | Article |
| id | doaj.art-8b26965d41da414e8dbcba877178f0c7 |
| institution | Directory Open Access Journal |
| issn | 2634-1964 2210-8327 |
| language | English |
| last_indexed | 2024-03-13T03:39:35Z |
| publishDate | 2022-06-01 |
| publisher | Emerald Publishing |
| record_format | Article |
| series | Applied Computing and Informatics |
| spelling | doaj.art-8b26965d41da414e8dbcba877178f0c72023-06-23T09:37:57ZengEmerald PublishingApplied Computing and Informatics2634-19642210-83272022-06-01183/425626610.1016/j.aci.2019.11.006Sport analytics for cricket game results using machine learning: An experimental studyKumash Kapadia0Hussein Abdel-Jaber1Fadi Thabtah2Wael Hadi3Department of Digital Technologies, Manukau Institute of Technology, Auckland, New ZealandDepartment of Information Technology and Computing, Arab Open University, Riyadh, Saudi ArabiaDepartment of Digital Technologies, Manukau Institute of Technology, Auckland, New ZealandDepartment of Computer Information Systems, Petra University, Amman, JordanIndian Premier League (IPL) is one of the more popular cricket world tournaments, and its financial is increasing each season, its viewership has increased markedly and the betting market for IPL is growing significantly every year. With cricket being a very dynamic game, bettors and bookies are incentivised to bet on the match results because it is a game that changes ball-by-ball. This paper investigates machine learning technology to deal with the problem of predicting cricket match results based on historical match data of the IPL. Influential features of the dataset have been identified using filter-based methods including Correlation-based Feature Selection, Information Gain (IG), ReliefF and Wrapper. More importantly, machine learning techniques including Naïve Bayes, Random Forest, K-Nearest Neighbour (KNN) and Model Trees (classification via regression) have been adopted to generate predictive models from distinctive feature sets derived by the filter-based methods. Two featured subsets were formulated, one based on home team advantage and other based on Toss decision. Selected machine learning techniques were applied on both feature sets to determine a predictive model. Experimental tests show that tree-based models particularly Random Forest performed better in terms of accuracy, precision and recall metrics when compared to probabilistic and statistical models. However, on the Toss featured subset, none of the considered machine learning algorithms performed well in producing accurate predictive models.https://www.emerald.com/insight/content/doi/10.1016/j.aci.2019.11.006/full/pdfCricketData scienceMachine learningPredictionSport analytics |
| spellingShingle | Kumash Kapadia Hussein Abdel-Jaber Fadi Thabtah Wael Hadi Sport analytics for cricket game results using machine learning: An experimental study Applied Computing and Informatics Cricket Data science Machine learning Prediction Sport analytics |
| title | Sport analytics for cricket game results using machine learning: An experimental study |
| title_full | Sport analytics for cricket game results using machine learning: An experimental study |
| title_fullStr | Sport analytics for cricket game results using machine learning: An experimental study |
| title_full_unstemmed | Sport analytics for cricket game results using machine learning: An experimental study |
| title_short | Sport analytics for cricket game results using machine learning: An experimental study |
| title_sort | sport analytics for cricket game results using machine learning an experimental study |
| topic | Cricket Data science Machine learning Prediction Sport analytics |
| url | https://www.emerald.com/insight/content/doi/10.1016/j.aci.2019.11.006/full/pdf |
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