Best strategy to win a match

Praveen Ranjan Srivastava, Prajwal Eachempati, Ajay Kumar (), Ashish Kumar Jha and Lalitha Dhamotharan
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Praveen Ranjan Srivastava: IIM-Rohtak - Indian Institute of Management Rohtak
Prajwal Eachempati: IIM-Rohtak - Indian Institute of Management Rohtak
Ajay Kumar: EM - EMLyon Business School
Ashish Kumar Jha: Trinity College Dublin
Lalitha Dhamotharan: University of Exeter

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Abstract: One of the significant challenges in the sports industry is identifying the factors influencing match results and their respective weightage. For appropriate recommendations to the team management and the team players, there is a need to predict the match and quantify the important factors for which prediction models need to be developed. The second thing required is identifying talented and emerging players and performing an associative analysis of the important factors to the match-winning outcome. This paper formulates a hybrid machine learning-clustering-associative rules model. This paper also implements the framework for cricket matches, one of the most popular sports globally watched by billions around the world. We predict the match outcome for One day Internationals (ODIs) and Twenty 20 s (T20s) (two formats of Cricket representing fifty over and twenty over versions respectively) adopting state-of-the-art machine learning algorithms, Random Forest, Gradient Boosting, and Deep neural networks. The variable importance is computed using machine-learning techniques and further statistically validated through the regression model. The emerging talented players are identified by clustering. Association rules are generated for determining the best possible winning outcome. The results show that environmental conditions are equally crucial for determining a match result, as are internal quantitative factors. The model is thus helpful for both team management and for players to improve their winning strategy and also for discovering emerging players to form an unbeatable team.

Keywords: Neural network; Apriori algorithm; clustering; Match result prediction; Ensemble gradient boost predictive model; Random forest; Sports analytics; Machine learning (search for similar items in EconPapers)
Date: 2023-06-01
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Published in Annals of Operations Research, 2023, 325 (1), pp.319 - 361. ⟨10.1007/s10479-022-04541-6⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05618497

DOI: 10.1007/s10479-022-04541-6

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