Harnessing the Predictive Power of Lower-division Statistics of Cricketers to Predict Their Rates of Success at the International Level
Abstract: Statistics has always been an integral part of the sporting world. Selectors pick players based on numerous factors such as averages, strike-rates, runs scored or goals scored. Teams have exclusive ‘talent hunters’, who spend weeks, if not months, trying to uncover talent from different parts of the world. With the rise of this new niche field called Sports Analytics, teams can now perform player evaluations on tons of data that is available. This paper aims to examine the factors that truly indicate the capacity of cricket players to perform at the top-most level – international cricket. Though this research has been carried out on cricket data, it is hoped that similar methods can be used to hunt for true talent in other sports! Keywords: Cricket Analytics, Random Forest, Principal Component Analysis, Dimensionality Reduction.