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Modelling first innings totals in T20 cricket: applications in the Indian Premier League
In the game of cricket, teams batting first are faced with the question of how many runs are enough. This paper proposes a solution to this in the context of the Indian Premier League (IPL). The aim is to build a model that will allow teams to determine what scores they would need to score for any g...
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| Format: | Thesis |
| Language: | Eng |
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Department of Statistical Sciences
2024
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| _version_ | 1867613327442575360 |
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| access_status_str | Open Access |
| author | Gilbert, Arlton |
| author2 | Britz, Stefan |
| author_browse | Britz, Stefan Gilbert, Arlton |
| author_facet | Britz, Stefan Gilbert, Arlton |
| author_sort | Gilbert, Arlton |
| collection | Thesis |
| description | In the game of cricket, teams batting first are faced with the question of how many runs are enough. This paper proposes a solution to this in the context of the Indian Premier League (IPL). The aim is to build a model that will allow teams to determine what scores they would need to score for any given confidence of avoiding defeat in regular time, viz. before any Super Overs. The following machine learning methods are considered for this purpose: logistic regression, classification trees, bagging, random forest, boosting, support vector machines, artificial neu- ral networks, and naive Bayes. Features are chosen that represent various key aspects of the game, including player strengths, stadium information, the winner of the toss, and which teams are involved. The results show that logistic regression is the best performing model, having a prediction accuracy of 70.27% and a Brier score of 0.2 for the 2022 season of the IPL. The majority of the incorrect predictions occurred in prediction ranges where the model itself suggested the game could have gone either way. The model is, therefore, fit for purpose and can allow teams to pace their innings and reduce unnecessary risks. The model can also be trained and used on other limited-over tournaments, including one-day matches. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/39457 |
| institution | University of Cape Town (South Africa) |
| language | Eng |
| last_indexed | 2026-06-10T12:34:23.309Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Department of Statistical Sciences |
| publisherStr | Department of Statistical Sciences |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/39457 Modelling first innings totals in T20 cricket: applications in the Indian Premier League Gilbert, Arlton Britz, Stefan Statistical science In the game of cricket, teams batting first are faced with the question of how many runs are enough. This paper proposes a solution to this in the context of the Indian Premier League (IPL). The aim is to build a model that will allow teams to determine what scores they would need to score for any given confidence of avoiding defeat in regular time, viz. before any Super Overs. The following machine learning methods are considered for this purpose: logistic regression, classification trees, bagging, random forest, boosting, support vector machines, artificial neu- ral networks, and naive Bayes. Features are chosen that represent various key aspects of the game, including player strengths, stadium information, the winner of the toss, and which teams are involved. The results show that logistic regression is the best performing model, having a prediction accuracy of 70.27% and a Brier score of 0.2 for the 2022 season of the IPL. The majority of the incorrect predictions occurred in prediction ranges where the model itself suggested the game could have gone either way. The model is, therefore, fit for purpose and can allow teams to pace their innings and reduce unnecessary risks. The model can also be trained and used on other limited-over tournaments, including one-day matches. 2024-04-25T12:35:29Z 2024-04-25T12:35:29Z 2023 2024-04-23T13:34:52Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/39457 Eng application/pdf Department of Statistical Sciences Faculty of Science |
| spellingShingle | Statistical science Gilbert, Arlton Modelling first innings totals in T20 cricket: applications in the Indian Premier League |
| thesis_degree_str | Master's |
| title | Modelling first innings totals in T20 cricket: applications in the Indian Premier League |
| title_full | Modelling first innings totals in T20 cricket: applications in the Indian Premier League |
| title_fullStr | Modelling first innings totals in T20 cricket: applications in the Indian Premier League |
| title_full_unstemmed | Modelling first innings totals in T20 cricket: applications in the Indian Premier League |
| title_short | Modelling first innings totals in T20 cricket: applications in the Indian Premier League |
| title_sort | modelling first innings totals in t20 cricket applications in the indian premier league |
| topic | Statistical science |
| url | http://hdl.handle.net/11427/39457 |
| work_keys_str_mv | AT gilbertarlton modellingfirstinningstotalsint20cricketapplicationsintheindianpremierleague |