Predicting Outcome of Live Cricket Match Using Duckworth- Lewis Par Score

Predicting Outcome of Live Cricket Match Using Duckworth- Lewis Par Score

Abstract: Cricket is the second most watched sport in the world after soccer, and enjoys a multi-million dollar industry. There is remarkable interest in simulating cricket and more importantly in predicting the outcome of cricket match which is played in three formats namely test match, one day international and T20 match. The complex rules prevailing in the game, along with the various natural parameters affecting the outcome of a cricket match present significant challenges for accurate prediction. Several diverse parameters, including but not limited to cricketing skills and performances, match venues and even weather conditions can significantly affect the outcome of a game. There are number of research paper on pre-match prediction of cricket match. Many papers on building a prediction model that takes in historical match data as well as the instantaneous state of a match, and predict match results. We know in the cricket match with shorter version match result keep on changing every ball. So, it is important to predict the outcome of the match on every ball. In this paper, I have developed a model that predicts match result on every ball played. Using Duckworth- Lewis formula match outcome will be predicted for live match. For every ball bowled a probability is calculated and probability figure is plotted. For betting industry this model and the probability figure will be very useful for bettor in deciding which team to on and how much to bet.

Keywords: Simulating, Duckworth-Lewis Prediction Model, Probability Figure, Betting

I. INTRODUCTION

Cricket was one of the first sports to use statistics as a tool for illustration and comparison. Although compared to other sports, there has not been much statistical modeling work done for cricket. For baseball, Ganeshapillai and Guttag (2013) developed a prediction model that decides when to change the starting pitcher as the game progresses. It is very much similar to our work-flow, where they used the combination of previous data and in game data to predict a pitchers performance. Tulabandhula and Rudin (2014) were designed a real time prediction and decision system for professional car racing. Model makes the decision of when is the best time for tire change and how many of them. Wood (1945) used the geometric distribution to model the total score, while Kimber and Hansford (1993) proposed a nonparametric approach based on runs scored for assessing batting performance.
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