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A machine learning approach to analyze ODI cricket predictors

As one-day international (ODI) games rise in popularity, it is important to understand the possible predictors that affect the game outcome. The home-field advantage, coin-toss result, bat-first or second, and day vs day-night game format are such popular variables being considered in the cricket literature. This article focuses on a comprehensive study of quantifying the significance of those important predictors via graphical `classification and regression tree` (CART) and the popular logistic regression approaches. This study reveals the importance of the home-field advantage for major cricket playing nations in one-day international games but questions the uniformity of such factors under different playing conditions. Importantly, the home-field advantage is investigated further based on the opponent`s geographical location. Conclusively, the CART approach provides interesting and novel interpretations for popular predictors in ODI games.
© Copyright 2018 Journal of Sports Analytics. IOS Press. All rights reserved.

Bibliographic Details
Subjects:
Notations:technical and natural sciences sport games
Tagging:Regressionsanalyse Regression
Published in:Journal of Sports Analytics
Language:English
Published: 2018
Online Access:https://doi.org/10.3233/JSA-17175
Volume:4
Issue:1
Pages:73-84
Document types:article
Level:advanced