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A machine learning approach to analyze ODI cricket predictors

(Maschinelles Lernen zur Analyse von ODI-Cricket-Prädiktoren)

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. Alle Rechte vorbehalten.

Bibliographische Detailangaben
Schlagworte:
Notationen:Naturwissenschaften und Technik Spielsportarten
Tagging:Regressionsanalyse Regression
Veröffentlicht in:Journal of Sports Analytics
Sprache:Englisch
Veröffentlicht: 2018
Online-Zugang:https://doi.org/10.3233/JSA-17175
Jahrgang:4
Heft:1
Seiten:73-84
Dokumentenarten:Artikel
Level:hoch