AutoCoach: an automated playing eleven selection framework in cricket
Cricket is one of the famous international sports where both teams select their best eleven against each other from the available pool of players usually a total size of 15 to 16. The team selection process is usually performed by the coaches using their observation and consultations etc. In simple...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2024
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| Online Access: | https://repository.londonmet.ac.uk/9733/1/2024194491.pdf |
| _version_ | 1824446491322744832 |
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| author | Sharma, Pulkit Hassan, Bilal Wasiq, Muhammad Farooq |
| author_facet | Sharma, Pulkit Hassan, Bilal Wasiq, Muhammad Farooq |
| author_sort | Sharma, Pulkit |
| collection | LMU |
| description | Cricket is one of the famous international sports where both teams select their best eleven against each other from the available pool of players usually a total size of 15 to 16. The team selection process is usually performed by the coaches using their observation and consultations etc. In simple words, by looking at the possible combinations of opposite sides, the best possible combinations are suggested. In our work, we tried to automate this process using machine learning models like Support Vector Regressor, Linear Regression Random Forest regressor etc. To train and test the model, the data was crawled from the Indian Premier League (IPL), which follows 20 over format and data for more than 10 seasons is used. More importantly, multiple related features for batters and bowlers were accumulated to develop all possible combinations for each playing team in a single match. In total, we selected 4 different teams and developed three combinations for each. Then, in a single match between two teams, the combinations were compared based upon accumulative score e.g., matching scores for different combinations are compared with ground truth. In our opinion, this is one of the unique contributions made for automated team selection in cricket specific to the format and encompassing performance indicators not only accumulated from IPL but also from the International Cricket Council (ICC). |
| first_indexed | 2025-02-19T01:16:00Z |
| format | Conference or Workshop Item |
| id | oai:repository.londonmet.ac.uk:9733 |
| institution | London Metropolitan University |
| language | English |
| last_indexed | 2025-02-19T01:16:00Z |
| publishDate | 2024 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | eprints |
| spelling | oai:repository.londonmet.ac.uk:97332024-10-21T08:58:53Z https://repository.londonmet.ac.uk/9733/ AutoCoach: an automated playing eleven selection framework in cricket Sharma, Pulkit Hassan, Bilal Wasiq, Muhammad Farooq 010 Bibliography 600 Technology 620 Engineering & allied operations Cricket is one of the famous international sports where both teams select their best eleven against each other from the available pool of players usually a total size of 15 to 16. The team selection process is usually performed by the coaches using their observation and consultations etc. In simple words, by looking at the possible combinations of opposite sides, the best possible combinations are suggested. In our work, we tried to automate this process using machine learning models like Support Vector Regressor, Linear Regression Random Forest regressor etc. To train and test the model, the data was crawled from the Indian Premier League (IPL), which follows 20 over format and data for more than 10 seasons is used. More importantly, multiple related features for batters and bowlers were accumulated to develop all possible combinations for each playing team in a single match. In total, we selected 4 different teams and developed three combinations for each. Then, in a single match between two teams, the combinations were compared based upon accumulative score e.g., matching scores for different combinations are compared with ground truth. In our opinion, this is one of the unique contributions made for automated team selection in cricket specific to the format and encompassing performance indicators not only accumulated from IPL but also from the International Cricket Council (ICC). Institute of Electrical and Electronics Engineers (IEEE) 2024-10-08 Conference or Workshop Item PeerReviewed text en cc_by_4 https://repository.londonmet.ac.uk/9733/1/2024194491.pdf Sharma, Pulkit, Hassan, Bilal and Wasiq, Muhammad Farooq (2024) AutoCoach: an automated playing eleven selection framework in cricket. In: 2024 International Conference on Emerging Techniques in Computational Intelligence (ICETCI), 22-24 August 2024, Mahindra University, Hyderabad (India). http://dx.doi.org/10.1109/ICETCI62771.2024.10704075 10.1109/ICETCI62771.2024.10704075 10.1109/ICETCI62771.2024.10704075 |
| spellingShingle | 010 Bibliography 600 Technology 620 Engineering & allied operations Sharma, Pulkit Hassan, Bilal Wasiq, Muhammad Farooq AutoCoach: an automated playing eleven selection framework in cricket |
| title | AutoCoach: an automated playing eleven selection framework in cricket |
| title_full | AutoCoach: an automated playing eleven selection framework in cricket |
| title_fullStr | AutoCoach: an automated playing eleven selection framework in cricket |
| title_full_unstemmed | AutoCoach: an automated playing eleven selection framework in cricket |
| title_short | AutoCoach: an automated playing eleven selection framework in cricket |
| title_sort | autocoach an automated playing eleven selection framework in cricket |
| topic | 010 Bibliography 600 Technology 620 Engineering & allied operations |
| url | https://repository.londonmet.ac.uk/9733/1/2024194491.pdf |
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