Abstract
Indian Premier League (IPL) is one of the more popular cricket world tournaments, and its financial is increasing each season, its viewership has increased markedly and the betting market for IPL is growing significantly every year. With cricket being a very dynamic game, bettors and bookies are incentivised to bet on the match results because it is a game that changes ball-by-ball. This paper investigates machine learning technology to deal with the problem of predicting cricket match results based on historical match data of the IPL. Influential features of the dataset have been identified using filter-based methods including Correlation-based Feature Selection, Information Gain (IG), ReliefF and Wrapper. More importantly, machine learning techniques including Naïve Bayes, Random Forest, K-Nearest Neighbour (KNN) and Model Trees (classification via regression) have been adopted to generate predictive models from distinctive feature sets derived by the filter-based methods. Two featured subsets were formulated, one based on home team advantage and other based on Toss decision. Selected machine learning techniques were applied on both feature sets to determine a predictive model. Experimental tests show that tree-based models particularly Random Forest performed better in terms of accuracy, precision and recall metrics when compared to probabilistic and statistical models. However, on the Toss featured subset, none of the considered machine learning algorithms performed well in producing accurate predictive models.
Highlights
Cricket is a well-known sport and with its increasing popularity and viewership, change of formats and innovations in tournament played became necessary
To cater for potential future growth, global market research was commissioned by the International Cricket Council (ICC) which revealed that cricket has more than one billion fans worldwide, with the potential for significant growth
Conclusions and future work Applying machine learning for analysing cricket sports by considering historical game data, players performance, natural parameters, pre-game conditions and other features is beneficial for multiple stakeholders
Summary
Cricket is a well-known sport and with its increasing popularity and viewership, change of formats and innovations in tournament played became necessary. To cater for potential future growth, global market research was commissioned by the International Cricket Council (ICC) which revealed that cricket has more than one billion fans worldwide, with the potential for significant growth. Among all formats of cricket, the popularity of Twenty Internationals (T20) was the highest with 92%, with 87% of the fans stating that they would like T20 to be included in the Olympic Games [14]. In 2017, Star India bought the fiveyear global media rights of IPL for $2.55 billion and the Board of Control for Cricket in India (BCCI) disclosed that IPL contributes $600 million a year to its revenue [3]
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