Open AccessModeling T20I cricket bowling effectiveness: A quantile regression approach with a Bayesian extension
Author(s) -
Sulalitha M.B. Bowala,
Ananda B. W. Manage,
Stephen M. Scariano
Publication year - 2021
Publication title -
journal of sports analytics
Language(s) - English
Resource type - Journals
eISSN - 2215-0218
pISSN - 2215-020X
DOI - 10.3233/jsa-200556
Subject(s) - cricket , quantile regression , statistics , bayesian probability , regression , computer science , quantile , econometrics , artificial intelligence , mathematics , ecology , biology
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.