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