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Journal of Sports Analytics 7 (2021) 197–221 197 DOI 10.3233/JSA-200556 IOS Press Modeling T20I cricket bowling effectiveness: A quantile regression approach with a Bayesian extension Sulalitha M.B. Bowalaa, Ananda B.W. Manageb,∗ and Stephen M. Scarianoc aDepartment of Mathematics, Faculty of Science, University of Peradeniya, Peradeniya, Sri Lanka bMathematics and Statistics, Sam Houston State University, Huntsville, Texas, USA cStatData Consulting, LLC, Huntsville, Texas, USA Abstract. Bowling effectiveness is a key factor in winning cricket matches. The team captain should decide when to use the right bowler at the right moment so that the team can optimize the outcome of the game. In this study, we investigate the effectiveness of different types of bowlers at different stages of the game, based on the conceded percentage of runs from the innings total, for each over. Bowlers are generally categorized into three types: fast bowlers, medium-fast bowlers, and spinners. In this article, the authors divided the twenty over spell of a T20I match into four stages; namely, Stage 1: overs 1-6 (PowerPlay), Stage 2: overs 7-10, Stage 3: overs 11-15, and Stage 4: overs 16-20. To understand the broad spectrum of the behavior of game variables, a Quantile Regression methodology is used for statistical analysis. Following that, a Bayesian approach to Quantile Regression is undertaken, and it confirms the initial results. Keywords: Batsman, Bayesian, bowling, cricket, sports, T20I, quantile regression 1. Introduction and motivation fans due to its shorter match time (Manage and Scar- iano (2013)). Cricket is one of the most popular games in the The first men’s T20I took place on February 17th, world, especially in the commonwealth countries.
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