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.
Highlights
Cricket was one of the first sports to use statistics as a tool for illustration and comparison
Compared to other sports, there has not been much statistical modeling work done for cricket
Results indicate that lead has a small effect on the match outcome early on but it dominates later; pre-match team strengths, ground effect and home field advantage are important predictors of a win early on; wicket resources remaining are important throughout a match
Summary
Cricket was one of the first sports to use statistics as a tool for illustration and comparison. Introduced a technique for revising the target for games that are shortened due to weather interruptions This method was well received by the cricket-playing community, and it has been using for more than 10 years. Kaluarachchi and et al (2010) takes into account various factors affecting the game including home team advantage, day/night effect and toss, etc., and uses the Bayesian classifier to predict the outcome of the match. Parag Shah: Predicting Outcome of Live Cricket Match Using Duckworth- Lewis Par Score sequence of multinomial logistic regression models. These probabilities can facilitate a team captain or management to consider an aggressive or defensive batting strategy for the coming session. Jhawar and Vikram Pudi (2016) has predicting the outcome of ODI cricket matches with a team composition based approach
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