Learning Player-specific Strategies Using Cricket Text Commentary
| dc.contributor.author | Behera, Swarup Ranjan | |
| dc.date.accessioned | 2023-04-11T12:46:34Z | |
| dc.date.accessioned | 2023-10-20T04:37:34Z | |
| dc.date.available | 2023-04-11T12:46:34Z | |
| dc.date.available | 2023-10-20T04:37:34Z | |
| dc.date.issued | 2021 | |
| dc.description | Supervisor: Vedula, Vijaya Saradhi | en_US |
| dc.description.abstract | In recent times, as sports get more competitive than ever, players and teams are looking for ways to get an edge over their rivals. The progressive trend of analyzing vast amounts of data has also emerged in cricket, as it brings a significant advantage against other teams. A massive amount of information in the form of scorecards, audio commentary, video broadcasts, and tracking data is generated in every match. Various graphical representations and statistical summaries are obtained from these data to build player-specific strategies. The graphs and statistics summarize player’s - batting overview (wagon wheel, ground map, batting average, and strike rate), bowling overview (pitch map, bowling economy, and bowling average), and fielding overview (field position map). While interesting, the focus of these analyses has primarily been at an aggregate level. They capture the game’s play at a macroscopic level and do not attend to the minute details. For example, the batting average and the strike rate tell us about the batsman’s overall statistics, but not the finer details like how a batsman plays under di | en_US |
| dc.identifier.other | ROLL NO.156101015 | |
| dc.identifier.uri | http://172.17.1.107:4000/handle/123456789/2335 | |
| dc.language.iso | en | en_US |
| dc.relation.ispartofseries | TH-2907; | |
| dc.subject | COMPUTER SCIENCE AND ENGINEERING | en_US |
| dc.title | Learning Player-specific Strategies Using Cricket Text Commentary | en_US |
| dc.type | Thesis | en_US |
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