Statistical Analysis of Cricket Leagues Using Principal Component Analysis

Abstract

Any sport has statistics, and cricket is the one, where statistics are extremely important because players are ranked using these data. Individual runs, wickets, and highest scores, among other things, are included in these statistics. Players are chosen for tournaments all over the world based on statistics. By analysing cricket statistics and figures, this study employs Principal Component Analysis. Using the approach called Principal Component Analysis, this study examines the precise co-variation among several measurements linked to the batting and bowling talents of players in the Pakistan Super League PSL T-20 (2016-2019) and the Indian Premier League IPL T-20 (2016-2019). PCA is applied in this study to rank the PSL batsmen and bowlers based on their contributions to their clubs during these competitive seasons. The results of this research revealed the top ten ranked batters and bowlers who excelled during the series. Principal Component Analysis is widely used in applied multivariate data analysis. In the current investigation, PCA was utilized to rank the top ten best-performing batsmen and bowlers of the PSL and IPL. Principal Component Analysis is a dimension reduction technique that is used to reduce dataset dimensions into smaller variables. Here, principal component analysis is successfully used to rank the cricket batsmen and bowlers.