Automated Cricket Score Prediction
Cricket is a popular sport that involves a high degree of variability in terms of game conditions and player performance. The ability to accurately predict cricket scores could provide valuable insights for coaches, analysts, and fans, as well as offer opportunities for sports betting and fantasy ga...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/67/e3sconf_icmpc2023_01053.pdf |
| _version_ | 1827792541374742528 |
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| author | Sanjeeva Polepaka Ajith Varma Jampana Sathvik Valaparla Abhinav Sai Ratan Attemla Mishra Sanjay |
| author_facet | Sanjeeva Polepaka Ajith Varma Jampana Sathvik Valaparla Abhinav Sai Ratan Attemla Mishra Sanjay |
| author_sort | Sanjeeva Polepaka |
| collection | DOAJ |
| description | Cricket is a popular sport that involves a high degree of variability in terms of game conditions and player performance. The ability to accurately predict cricket scores could provide valuable insights for coaches, analysts, and fans, as well as offer opportunities for sports betting and fantasy games. This paper explores the use of machine learning techniques to predict cricket scores based on a variety of contextual and historical factors. The publicly available cricket dataset is used to build and evaluate several regression models that predict the total runs scored by a team in a limited-overs cricket match. This analysis includes feature engineering to extract and transform relevant input variables, model selection to compare and choose among different regression algorithms, and performance evaluation to assess the accuracy and robustness of the models. This paper also conducts sensitivity analysis to identify the most influential predictors and explore the potential biases and limitations of the models. The results indicate that machine learning techniques can effectively predict cricket scores and provide valuable insights into the factors that contribute to team performance. Automated cricket prediction is useful for cricket teams, coaches, and analysts who seek to improve their game strategies and player selection, as well as for sports betting and fantasy game platforms that seek to provide more accurate experiences for users. |
| first_indexed | 2024-03-11T18:04:35Z |
| format | Article |
| id | doaj.art-e0cc214a310d466da4d03c974f37ca3d |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-11T18:04:35Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-e0cc214a310d466da4d03c974f37ca3d2023-10-17T08:47:10ZengEDP SciencesE3S Web of Conferences2267-12422023-01-014300105310.1051/e3sconf/202343001053e3sconf_icmpc2023_01053Automated Cricket Score PredictionSanjeeva Polepaka0Ajith Varma Jampana1Sathvik Valaparla2Abhinav Sai Ratan Attemla3Mishra Sanjay4Department of CSE (AI & ML), GRIETDepartment of CSE (AI & ML), GRIETDepartment of CSE (AI & ML), GRIETDepartment of CSE (AI & ML), GRIETUttaranchal School of Computing Sciences, Uttaranchal UniversityCricket is a popular sport that involves a high degree of variability in terms of game conditions and player performance. The ability to accurately predict cricket scores could provide valuable insights for coaches, analysts, and fans, as well as offer opportunities for sports betting and fantasy games. This paper explores the use of machine learning techniques to predict cricket scores based on a variety of contextual and historical factors. The publicly available cricket dataset is used to build and evaluate several regression models that predict the total runs scored by a team in a limited-overs cricket match. This analysis includes feature engineering to extract and transform relevant input variables, model selection to compare and choose among different regression algorithms, and performance evaluation to assess the accuracy and robustness of the models. This paper also conducts sensitivity analysis to identify the most influential predictors and explore the potential biases and limitations of the models. The results indicate that machine learning techniques can effectively predict cricket scores and provide valuable insights into the factors that contribute to team performance. Automated cricket prediction is useful for cricket teams, coaches, and analysts who seek to improve their game strategies and player selection, as well as for sports betting and fantasy game platforms that seek to provide more accurate experiences for users.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/67/e3sconf_icmpc2023_01053.pdf |
| spellingShingle | Sanjeeva Polepaka Ajith Varma Jampana Sathvik Valaparla Abhinav Sai Ratan Attemla Mishra Sanjay Automated Cricket Score Prediction E3S Web of Conferences |
| title | Automated Cricket Score Prediction |
| title_full | Automated Cricket Score Prediction |
| title_fullStr | Automated Cricket Score Prediction |
| title_full_unstemmed | Automated Cricket Score Prediction |
| title_short | Automated Cricket Score Prediction |
| title_sort | automated cricket score prediction |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/67/e3sconf_icmpc2023_01053.pdf |
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