Indian Premier League Player Selection Model Based on Indian Domestic League Performance

Ghosh, Anupam; Sinha, Abhirup; Mondal, Pritam; Roy, Anusree; Saha, Pritilata

doi:10.1109/ccwc51732.2021.9376011

Cited by 4 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This section discusses the optimization methods available in the literature to select IPL team/squad players. In addition to the optimization methods, researchers also used methods based on other techniques, such as data envelopment analysis [17], [18] and machine learning algorithms [19], [20]. However, this study is only focused on optimization methods for single and multi-objective optimization models.…”

Section: Related Workmentioning

confidence: 99%

A Balanced Squad for Indian Premier League Using Modified NSGA-II

et al. 2022

View full text Add to dashboard Cite

Selecting team players is a crucial and challenging task demanding a considerable amount of thinking and hard work by the selectors. The present study formulated the selection of an IPL team as a multiobjective optimization problem with the objectives of maximizing the batting and bowling performance of the squad, in which a player's performance is estimated using an efficient Batting Performance Factor and Combined Bowling Rate. Also, the proposed model tries to formulate a balanced squad by constraining the number of pure batters, pure bowlers and all-rounders. Bounds are considered on star players to enhance the performance of the squad and also from the income prospects of IPL. The problem in itself is treated as a 0/1 knapsack problem for which two combinatorial algorithms, namely, BNSGA-II and INSGA-I, are developed. These algorithms were compared with existing modified NSGA-II for IPL team selection and three other popular multi-objective optimization algorithms, NSGA-II, NSDE and MOPSO-CD on the basis of standard performance metrics: hypervolume and inverted generational distance. Both algorithms performed well, with BNSGA-II performing better than all the other algorithms considered in this study. The IPL 2020 players' data validated the applicability of the proposed model and algorithms. The trade-off squads have appropriate proportions of players of each expertise. Further analysis of the trade-off squads demonstrated that many theoretically selected players performed well in IPL 2020 matches.

show abstract

Section: Related Workmentioning

confidence: 99%