An all-rounder can take an imperative role in any version of the game of cricket, whether it is a test match or any other limited-over format of the game. The study classifies the performance of all-rounders who participated in IPL based on their strike rate and economy rate. Based on the factors mentioned, the all-rounders can be divided into four non-overlapping classes, viz., Performer, Batting All-rounder, Bowling All-rounder, and Under-performer. Several predictor variables that are supposed to influence the performance of all-rounders are considered. Step-wise multinomial logistic regression (SMLR) is used to identify the significant predictors. Samples of six incumbent all-rounders who had not participated in the first three seasons of IPL are considered. The significant predictors were then used to predict the expected class of an incumbent all-rounder using naive Bayesian classification model. The relevant data were collected from the websites, www.cricinfo.org and www.cricketnirvana.com. The key points of this study are as follows: The training sample is populated with 35 all-rounders who had performed in the first three seasons of IPL. Two variables, viz., strike rate (number of runs scored per 100 balls faced) and economy rate (average number of runs scored per over against the bowler) are used to classify the all-rounders as follows: Performer: An all-rounder with strike rate above median and economy rate below median. Batting All-rounder: An all-rounder with strike rate above median and economy rate above median. Bowling All-rounder: An all-rounder with strike rate below median and economy rate below median. Under-performer: An all-rounder with strike rate below median and economy rate above median. The step-wise multinomial logistic regression (SMLR) was used to identify the significant variables that are actually responsible for classification of the all-rounders. The strike rate in ODI, strike rate in Twenty-20, economy rate in ODI, economy rate in Twenty-20 and bowling type (Spin or Fast) of the all-rounders are found to be significant in determining the class of an all-rounder. The naive Bayesian classification model is used for forecasting the expected class of allrounders based on the significant predictors for six incumbent all-rounders who had played only in fourth season of IPL. The prediction done before IPL IV was then compared with the actual situation at the end of the tournament. It is found that four predictions were performed correctly out of the six. This model would be useful for the participating teams' management while deciding the bid of an all-rounder in the upcoming season of IPL as per their requirement.
An Improved Naive Bayesian Classification Model Based on Attribute Weighting
