A robust simulation to compare meaningful batting averages in cricket

Abstract

In cricket, the traditional batting average is the most common measure of a cricket player’s batting performance. However, the batting average can easily be inflated by a high number of not-out innings. Therefore, in this research eight alternative methods are used and compared to the traditional batting average to estimate the true batting average. It is also known that there is a range of different batters within a cricket team, namely first order, middle order, tail-enders and a special class of players who can both bat and bowl known as all-rounders. There are also different formats of international cricket, namely Test, One-Day International (ODI), and Twenty20 International (T20I) cricket, where Test cricket has unlimited overs compared to the limited overs of ODI and T20I cricket. A method for estimating the batting average should be able to account for this variability. The chosen method should also work for a player’s career as well as a short series or tournament. By using the traditional bootstrap and the smoothed bootstrap in this study, the variability of each estimation method is compared for a player’s career and a series or tournament, respectively. An R Shiny application introduces alternative cricket batting performance measures, enabling accessible analysis beyond the conventional average for a comprehensive understanding of player capabilities.