Rating players in test match cricket

Akhtar, Sohail; Scarf, Philip; Rasool, Zahid

doi:10.1057/jors.2014.30

Cited by 28 publications

(16 citation statements)

References 16 publications

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“…Although a fair amount of work has been done in assessing players’ performance by introducing modifications to traditional statistical measures (Croucher, 2000; Lemmer, 2002, 2004, 2008), probabilistic modelling techniques (Akhtar et al, 2015; Davis et al, 2015) and descriptive statistics (Damodaran, 2006; Kimber, 1993; Van Staden, 2009), most of the work is focussed on individual performances of either the batsman or the bowler. Hardly any research is being done to compare players with different competencies in different departments such as batting, bowling and fielding.…”

Section: Introductionmentioning

confidence: 99%

Novel Performance Metrics to Evaluate the Duel Between a Batsman and a Bowler

Nekkanti

Bhattacharjee

2020

Management and Labour Studies

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In the current system of cricket, the most commonly used measures to evaluate players’ performance are batting and bowling averages, strike rate and economy rate. While these measures are easy to compute and understand, they fail to reflect a complete picture of the players’ performance as they do not consider the context of the match. Various factors, such as balls faced and left, wickets remaining, the skill level of the opponent, etc., are not being considered while evaluating the players’ performance. In addition, these conventional measures fail to compare a batsman’s performance against that of a bowler or vice versa. The objective of this article is to arrive at a measure that will better explain the performance of the players while incorporating some of the factors as stated above. In this article, we use strike rate for the batsman and economy rate for the bowler to arrive at a common scale for ratings. Furthermore, we use the concept of Elo ratings in order to evaluate the duel between a batsman and a bowler. Based on the developed methodology, we evaluate the duels between the players in the Indian Premier League 2018 finals.

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Section: Introductionmentioning

confidence: 99%

Novel Performance Metrics to Evaluate the Duel Between a Batsman and a Bowler

Nekkanti

Bhattacharjee

2020

Management and Labour Studies

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“…There are a number of other papers which have analysed at least one version of cricket. Some recent papers include Scarf & Shi (2005), , , Akhtar & Scarf (2012), Perera & Swartz (2012), Davis et al (2015) and Akhtar et al (2015). Each of these papers also has numerous references of interest to those in analysing cricket.…”

Section: Introductionmentioning

confidence: 99%

Using Bradley–Terry models to analyse test match cricket

Dewart

Gillard

2018

IMA Journal of Management Mathematics

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In this paper we investigate the use of Bradley-Terry models to analyse test match cricket. Specifically, we develop a new and alternative team ranking and compare our rankings with those produced by the International Cricket Council, we forecast the outcomes of a selected number of test cricket matches and show that our predictions perform well compared to bookmaker predictions. We offer ratings of individual players and use these ratings to predict the results of some recent matches. The general purpose of the paper is to illustrate the potential of Bradley-Terry models, which are effectively models of P(i is preferred to j), and thus can be applied in a number of settings where there are paired comparisons. Popular applications include analysing taste test experiments and modelling sports competitions. More creative examples of applications include statistical modelling of citation exchange among statistics journals, predicting the fighting ability of lizards and estimating driver crash risks.

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“…A players overall contribution during a given session was assessed by using the difference between the hypothetical match outcome probabilities and the actual match probabilities. The batting probability differences were observed with respect to 'not losing' and bowlers with respect to winning [5]. "These probability differences were then distributed to batsman according to their share of the runs scored in the session, and to bowlers according to their share of wickets taken in the session" [5, p.687].…”

Section: Individual Rating Systems For Cricketmentioning

confidence: 99%

“…Principal Component Analysis was applied to identify a small number of Z components that adequately explained a large proportion of the variation in the analysis datasets 5 . If such results are obtained the components would then be used to produce 'new', Z p , variables which are linear combinations of the eigenvalues (obtained from the eigenvectors) and the original performance metrics.…”

Section: Principal Component Analysismentioning

confidence: 99%

Roster-Based Optimisation for Limited Overs Cricket

Patel¹

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<p>The objective of this research was to develop a roster-based optimisation system for limited overs cricket by deriving a meaningful, overall team rating using a combination of individual ratings from a playing eleven. The research hypothesis was that an adaptive rating system accounting for individual player abilities, outperforms systems that only consider macro variables such as home advantage, opposition strength and past team performances. The assessment of performance is observed through the prediction accuracy of future match outcomes. The expectation is that in elite sport, better teams are expected to win more often. To test the hypothesis, an adaptive rating system was developed. This framework was a combination of an optimisation system and an individual rating system. The adaptive rating system was selected due to its ability to update player and team ratings based on past performances. A Binary Integer Programming model was the optimisation method of choice, while a modified product weighted measure (PWM) with an embedded exponentially weighted moving average (EWMA) functionality was the adopted individual rating system. The weights for this system were created using a combination of a Random Forest and Analytical Hierarchical Process. The model constraints were objectively obtained by identifying the player’s role and performance outcomes a limited over cricket team must obtain in order to increase their chances of winning. Utilising a random forest technique, it was found that players with strong scoring consistency, scoring efficiency, runs restricting abilities and wicket-taking efficiency are preferred for limited over cricket due to the positive impact those performance metrics have on a team’s chance of winning. To define pertinent individual player ratings, performance metrics that significantly affect match outcomes were identified. Random Forests proved to be an effective means of optimal variable selection. The important performance metrics were derived in terms of contribution to winning, and were input into the modified PWM and EWMA method to generate a player rating. The underlying framework of this system was validated by demonstrating an increase in the accuracy of predicted match outcomes compared to other established rating methods for cricket teams. Applying the Bradley-Terry method to the team ratings, generated through the adaptive system, we calculated the probability of teami beating teamj. The adaptive rating system was applied to the Caribbean Premier League 2015 and the Cricket World Cup 2015, and the systems predictive accuracy was benchmarked against the New Zealand T.A.B (Totalisator Agency Board) and the CricHQ algorithm. The results revealed that the developed rating system outperformed the T.A.B by 9% and the commercial algorithm by 6% for the Cricket World Cup (2015), respectively, and outperformed the T.A.B and CricHQ algorithm by 25% and 12%, for the Caribbean Premier League (2015), respectively. These results demonstrate that cricket team ratings based on the aggregation of individual player ratings are superior to ratings based on summaries of team performances and match outcomes; validating the research hypothesis. The insights derived from this research also inform interested parties of the key attributes to win limited over cricket matches and can be used for team selection.</p>

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Rating players in test match cricket

Cited by 28 publications

References 16 publications

Novel Performance Metrics to Evaluate the Duel Between a Batsman and a Bowler

Novel Performance Metrics to Evaluate the Duel Between a Batsman and a Bowler

Using Bradley–Terry models to analyse test match cricket

Roster-Based Optimisation for Limited Overs Cricket

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