Optimization of team selection in fantasy cricket: a hybrid approach using recursive feature elimination and genetic algorithm

Apurva Jha (), Arpan Kumar Kar () and Agam Gupta ()
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Apurva Jha: Indian Institute of Technology Delhi
Arpan Kumar Kar: Indian Institute of Technology Delhi
Agam Gupta: Indian Institute of Technology Delhi

Annals of Operations Research, 2023, vol. 325, issue 1, No 14, 289-317

Abstract: Abstract Fantasy Sports allows individuals to assemble a virtual team to participate in free or paid tournaments and earn rewards. Selecting a good team forms a crucial decision in fantasy cricket. Existing team selection methods cater only to professional cricket and are not suited well to accommodate the differences between fantasy cricket and the on-field game. This paper proposes a two-step methodology for player assessment and team selection in fantasy cricket. Player assessment is carried out using recursive feature elimination in random forest, in which context relevant player metrics are considered and the selection of players is based on modified genetic algorithm. We illustrate the efficacy of the proposed method on Dream11, a popular fantasy sports application. The results show that the proposed method outshines the traditional team selection process in fantasy sports, which is based on hit and trial. Furthermore, we provide a typology to analyse the proposed algorithm along the dimensions of reward distribution and entry fee.

Keywords: Random forest; Genetic algorithm; Team selection; Machine learning; Fantasy sports (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10479-022-04726-z

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