One Day International (ODI) Cricket Match Prediction in Logistic Analysis

Abstract

Cricket is now a game of enchantment, refreshing and physical strength of 22 players. As the popularity of one-day international (ODI) games increases, it is essential to understand the game results' potential predictors. The advantage at home ground, coin-toss result, decision on first batting or fielding first, and day-to-day games are such popular cricket literature variables. The Indian subcontinent is like a game of thrill, war, friendship, and finally, and the best fighting teams are India and Pakistan. This study emphases a comprehensive analysis of the importance of these important predictors by various statistical calculations. For all ODI matches played by India & Pakistan from 1978 to 2019, information was manually collected from the website, www.espncricinfo.com. For purposes of model-building, logistic regression is applied retrospectively to data already obtained from previously played matches. Univariate, bivariate, binary and skewed logistic regression on the multivariate context are considered. In bivariate analysis, only the day/day-night format match is statistically significant at a 5% level of significance in favor of winning the Indian team. In binary logistic regression, the odds of winning team India was 70.6% times more for home ground and 2.28 times more for data compared to day-night times match. For skewed logistic, the odds of conquering for both teams increase, but the model performs comparatively worse than binary logistic regression.