Hermanus H. Lemmer

Team selection after a short cricket series

Abstract The selection of a cricket team cannot be fair unless the best available performance measures are used. The traditional batting average can be very unrealistic, especially in the case of a small number of scores with a high proportion of not out scores. In the present study the focus is on using the most suitable measures for the selection of a team after a small number of matches had been played. Provision is made for the fact that match conditions may influence the scoring rate of batsmen. These measures are used for illustration purposes to select a team from the players who played in the International Cricket Council (ICC) Champions Trophy 2009 One-Day International (ODI) Series. It is shown how an integer programming method can be used for the selection process. The approach is that a well balanced cricket team should include different kinds of specialists, namely batsmen, bowlers, all-rounders and a wicket-keeper. A selection committee may be able to rank batsmen in order of batting ability and bowlers according to bowling ability, but when it comes to all-rounders it is not so simple. The fact that an all-rounder is, by definition, a good batsman and also a good bowler, makes it difficult to rank all-rounders. Furthermore, how many of each specialist type should be selected? The purpose of this paper is to show how integer optimisation, an objective scientific method, can be used to aid in selecting a cricket team. Guidelines are also given for the selection of a team if career performance data have to be used.

A Double Weighted Tool to Measure the Fielding Performance in Cricket

Though several statistics are used to quantify the batting and bowling performance of cricketers, there is no such measure for the fielders in cricket. This article introduces a measure that can be used to gauge the fielding performance of cricketers. The various parameters that are considered for fielding are quantified to scores based on the ball-by-ball information of a match for each fielder. Subjective weights are assigned to these parameters based on the relative importance of different alternatives under various fielding circumstances. The weights are then combined with the scores of each of the on-field performances of a fielder to get his corresponding fielding score. Two different measures of fielding performance are proposed and their relative differences are discussed. To demonstrate these measures, the final of the first Twenty20 World Cup tournament played on 24 September 2007 is considered. The outcomes obtained from the two measures are accordingly compared.

A Method to Measure Choking in Cricket

Choking in cricket occurs when a team that had been strongly favoured to win loses or a team squanders a large lead late in the match. The objective of this study is to develop a measure that can be used to quantify the phenomenon of choking. By using the required run rate and the resources used (according to the Duckworth/Lewis system) at the end of each over, a measure is defined as the product of these two quantities. This product is plotted against the number of overs bowled. When choking starts, the curve bends upward with slope according to the severity of choking. A measure of choking is then based on the ratio of the slope of the right hand part of the curve to that of its initial part. It is shown that the measure serves its purpose very well. This type of choking is described as ‘typical’ choking. If the slope of the curve remains more or less constant or if the curve bends downwards and the team loses, other terms like ‘panic’ choking and ‘strangling’ are used.

Quantifying the pressure on the teams batting or bowling in the second innings of limited overs cricket matches

A team batting second in a limited overs match is under pressure to score the required number of runs for victory in the allotted number of overs without losing all its wickets. The bowling team has to prevent its opponents from reaching the target for victory. The objective of this study is to define methods that can be used to assess the pressure on the two teams’ batsmen and bowlers when playing the second innings of a Twenty20 or One-day match, and also to define methods to determine the best partnership and the turning point in such an innings. Methods are also defined to determine the batting and bowling performances of individual players in a specific match. These measures can be quite useful for selectors to assess how the players perform under pressure.

A Method to Measure Strangling, a Dramatic Form of Choking in Cricket

In limited overs cricket, the team batting first sets a target for the team batting second. The latter team may win the match, draw the match or lose it by not reaching the target. Various scenarios of losing are possible; e.g. simply not reaching the target, or some form of choking after being in a strong position. In this study we consider the situation where a team batting second in a limited overs cricket match needs to score only a few more runs with many balls still to be bowled, but is bowled out. This phenomenon is called ‘strangling’ because the bowling team succeeded in bowling their opponents, who were in a strong batting position, all out – they have strangled them. A criterion is proposed to measure the severity of strangling. The measure is based on the strength of the batting team just before this disaster struck.

A Consistency Adjusted Measure for the Success of Prediction Methods in Cricket

Two models are used to predict the outcomes of matches in a Twenty20 cricket series. The success of prediction methods is hampered by the fact that if two teams play two or more matches against each other and each team wins some of the matches, such inconsistent outcomes cannot all be predicted correctly. The challenge was to find a procedure which could compensate for inconsistent results. The consistency adjusted measure of the success of prediction is defined and shown to give a fair assessment of prediction results. For the first model the success rate of 56.8% is increased to 76.4% and for the second model from 52.7% to 70.9%. The same method can be used in any sports series where teams play against each other more than once.