A Bayesian Classification Model for Predicting the Performance of All-Rounders in the Indian Premier League

SSRN Electronic Journal 06/2010; DOI: 10.2139/ssrn.1622060


The game of cricket got a new dimension, when the Indian Premier League (IPL), a competition of twenty over-a-side featuring eight teams named after various Indian cities/states started in 2008. The teams were franchisee driven and the players were selected via competitive bidding from a pool of available players. All-rounders i.e. players with the ability to both bat and bowl play a noteworthy part in cricket, whatever is the version of the game. The study measures the performance of all-rounders in Indian Premier Leagues (IPL) based on their strike rate and economy rate. The all-rounders are divided into four non-overlapping class viz. performer, batting all-rounder, bowling all-rounder and under performer. Stepwise multinomial logistic regression is used to determine the significant predictors responsible for such categorization. A Naïve Bayesian classification model is developed that can use the significant predictors to forecast the class in which an incumbent all-rounder is expected to lie. The classifier is build based on the performance of all-rounders who participated in IPL-I and II, and the validity of the classifier is subsequently tested over the incumbent all-rounders of IPL-III. The classifier though moderately successful in predicting the appropriate class of the incumbent all-rounders in IPL III, is expected to perform better in future with increase in the size of training sample. This classification would be useful for the participating teams’ management while deciding about which all-rounder to be bided for and to what amount in the next addition of the league.

33 Reads
  • [Show abstract] [Hide abstract]
    ABSTRACT: Using a dynamic programming formulation, an analysis is presented of the innings of the team which bats first (here referred to as the first innings) and the innings of the team which bats second (here referred to as the second innings). This allows a calculation, at any stage of the innings, of the optimal scoring rate, along with an estimate of the total number of runs to be scored (in the first innings) or the chance of winning (in the second innings). The analysis is used to shed some light on possible batting tactics (in terms of the best run rate at any stage of the innings), to quantify the effects of selecting extra batsmen in a side, and to suggest a method for the development of alternative measures of player performance. Results suggest that scoring rates should be more uniform than at present, and that the team batting second has an advantage. Possible extensions to the model are discussed.
    Journal of the Operational Research Society 04/1988; 39(4):331-337. DOI:10.1057/jors.1988.60 · 0.95 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: A method is described for setting revised target scores for the team batting second when a limited-overs cricket match has been forcibly shortened after it has commenced. It is designed so that neither team benefits or suffers from the shortening of the game and so is totally fair to both. It is easy to apply, requring nothing more than a single table of numbers and a pocket calculator, and is capable of dealing with any number of interruptions at any stage of either or both innings. The method is based on a simple model involving a two-factor relationship giving the number of runs which can be scored on average in the remainder of an innings as a function of the number of overs remaining and the number of wickets fallen. It is shown how the relationship enables the target score in an interrupted match to be recalculated to reflect the relative run scoring resources available to the two teams, that is overs and wickets in combination. The method was used in several international and domestic one-day competitions and tournaments in 1997.
    Journal of the Operational Research Society 03/1998; 49(3):220-227. DOI:10.1038/sj.jors.2600524 · 0.95 Impact Factor


33 Reads
Available from