Prediction of Most Valuable Bowlers of Indian Premier League (IPL)

Santra, Arnab; Mitra, Aniket; Sinha, Abhirup; Das, Amit Kumar

doi:10.1007/978-981-15-5619-7_13

Cited by 6 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The studies by Davis et al 2015, Passi & Pandey 2018, Patel & Pandya 2019, and Santra et al 2021 focus on predicting players' performance. Davis et al 2015 introduce a new metric of "expected run differential" for player evolution in T20 cricket.…”

Section: Literature On Evaluating Player Performancementioning

confidence: 99%

See 1 more Smart Citation

Determining the playing 11 based on opposition squad: An IPL illustration

Gokul,

Sundararaman

2023

Journal of Sports Analytics

View full text Add to dashboard Cite

Indian Premier League (IPL) is the most popular T20 domestic sporting league globally. Player selection is crucial in winning the competitive IPL tournament. Thus, team management select 11 players for each match from a team’s squad of 15 to 25 players. Different player statistics are analysed to select the best playing 11 for each match. This study attempts an approach where the on-field player performance is used to determine the playing-11. A player’s on-field performance in a match is computed as a single metric considering a player’s attributes against every player present in the opposition squad. For this computation, past ball-by-ball data is cleaned and mined to generate data containing player-vs-player performance attributes. Next, the various performance attributes for a player-vs-player combination is converted into a player’s performance rating by computing a weighted score of the performance attributes. Finally, an optimisation model is proposed and developed to determine the best playing-11 using the computed performance ratings. The developed optimisation model suggests the playing-11 that maximises the possibility of winning against a given opponent. The proposed procedure to determine the playing-11 for an IPL match is demonstrated using past data from 2008-20. The demonstration indicates that for matches in the league stage, the suggested playing-11 by model and the actual playing-11 have a ∼7% similarity across all teams. The remaining ∼3% are different from those selected in the actual team. Nevertheless, this difference approximately yields a ∼ Indian Premier League (IPL) is the most popular T20 domestic sporting league globally. Player selection is crucial in winning the competitive IPL tournament. Thus, team management select 11 players for each match from a team’s squad of 15 to 25 players. Different player statistics are analysed to select the best playing 11 for each match. This study attempts an approach where the on-field player performance is used to determine the playing-11. A player’s on-field performance in a match is computed as a single metric considering a player’s attributes against every player present in the opposition squad. For this computation, past ball-by-ball data is cleaned and mined to generate data containing player-vs-player performance attributes. Next, the various performance attributes for a player-vs-player combination is converted into a player’s performance rating by computing a weighted score of the performance attributes. Finally, an optimisation model is proposed and developed to determine the best playing-11 using the computed performance ratings. The developed optimisation model suggests the playing-11 that maximises the possibility of winning against a given opponent. The proposed procedure to determine the playing-11 for an IPL match is demonstrated using past data from 2008-20. The demonstration indicates that for matches in the league stage, the suggested playing-11 by model and the actual playing-11 have a ∼7% similarity across all teams. The remaining ∼3% are different from those selected in the actual team. Nevertheless, this difference approximately yields a ∼13.32% increase in performance rating compared to the existing team.3.32% increase in performance rating compared to the existing team.

show abstract

Section: Literature On Evaluating Player Performancementioning

confidence: 99%

“…Based on the predicted fantasy score, the playing-11 is determined. Santra et al 2021 predict bowler ranking by evaluating the player's past profile. Data from IPL games between 2008-2018 is utilised in supervised machine learning models to predict top bowlers.…”

Section: Literature On Evaluating Player Performancementioning

confidence: 99%