Sport analytics for cricket game results using machine learning: An experimental study

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.

A Dockerized Big Data Architecture for Sports Analytics

“…Constantinou et al [23] developed probabilistic models based on possession rates and other historical statistics of various teams to predict the outcome of matches. Kapadia et al [24] used machine learning techniques to solve the same problem but for the cricket world in the Indian Premier League (IPL). Jayalath [25] considered the popular logistic regression model to study the significance of one-day international (ODI) cricket predictors.…”

Hybrid Basketball Game Outcome Prediction Model by Integrating Data Mining Methods for the National Basketball Association

“…The five well-known data mining methods, including multivariate adaptive regression splines (MARS), k-nearest neighbors (KNN), extreme learning machine (ELM), eXtreme gradient boosting (XGBoost), and stochastic gradient boosting (SGB), are used in this study for building an NBA game score prediction model, as they have been widely used in various applications such as public health [ 17 , 18 ], finance [ 19 , 20 ] and civil engineering [ 21 , 22 ]. Moreover, the five methods are also successfully applied in the sports outcomes prediction research [ 6 , 23 , 24 , 25 , 26 ].…”

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“…Moreover, continuously growing sports platforms related to games incentivize bookies and bettors to bet on match results as a game changes ball by ball. Hence, attempts have been made to predict match results based on historical match data [13].…”