Player evaluation in Twenty20 cricket

Davis, Jack; Perera, Harsha; Swartz, Tim B.

doi:10.3233/jsa-150002

Cited by 21 publications

(8 citation statements)

References 3 publications

(11 reference statements)

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“…Therefore, we retain symmetry in the visualization by also giving credit to batsmen for extras. Extras occur at the rate of 5.1% in Twenty20 cricket (Davis, Perera and Swartz 2015).…”

Section: Contextual Battingmentioning

confidence: 99%

Contextual batting and bowling in limited overs cricket

Thomson

2021

South African Statistical Journal

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Cricket is a sport for which many batting and bowling statistics have been proposed. However, a feature of cricket is that the level of aggressiveness adopted by batsmen is dependent on match circumstances. It is therefore relevant to consider these circumstances when evaluating batting and bowling performances. This paper considers batting performance in the second innings of limited overs cricket when a target has been set. The runs required, the number of overs completed and the wickets taken are relevant in assessing the batting performance. We produce a visualization for second innings batting which describes how a batsman performs under different circumstances. The visualization is then reduced to a single statistic "clutch batting" which can be used to compare batsmen. An analogous approach is then provided for bowlers based on the symmetry between batting and bowling, and we define the statistic "clutch bowling".

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“…Therefore, we retain symmetry in the visualization by also giving credit to batsmen for extras. Extras occur at the rate of 5.1% in Twenty20 cricket (Davis, Perera and Swartz 2015).…”

Section: Contextual Battingmentioning

confidence: 99%

Contextual batting and bowling in limited overs cricket

Thomson

2021

South African Statistical Journal

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“…Literature review reveals abundant research works in the field of measuring team or players’ performance or predicting results of a cricket match (e.g., Davis et al, 2015; Duckworth & Lewis, 1998; Koulis et al, 2014; Lewis, 2005; Scarf et al, 2011; Stefani, 2011, etc.). However, there is a serious dearth of research literature in the area of balancing different formats of cricket and on survival of test cricket.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of Research Paradigms for Managing the Cricketing Ecosystem Using Stakeholder Analysis and Text Mining

Ray

2021

Management and Labour Studies

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It took almost 400 years for test cricket to start off, and thereafter it just took 94, 34 and 13 years for the introduction of ODI, T20I and T10 formats, respectively. Length of the formats is shrinking over time at an alarming rate. If the trend continues, the format of five overs or less may not be far away and cricket pundits are fearing that the longest format, that is, test cricket, may lose its relevance. Unfortunately, the management part of the cricketing ecosystem has not drawn much attention from the academic world. Since market dynamics is playing a vital role in decision-making post commercialization of the cricket, this study applies stakeholder analysis and identifies three key stakeholders, namely administrators, players and spectators, and their aspirations. It suggests a shift from the ICC-led hierarchical model to the horizontal and more democratic model for sports governance during the post-commercialization stage. The results of the study indicate that even though ICC had taken a number of measures for advancement of the sport during 2015–2018, many of them are criticized like the world test championship or day-night test matches. The study scrapes through opinions of cricket-related professionals from open sources, applies sentiment analytics to classify them, uses text summarization to extract summary viewpoints and rates them on unanimity scale. A comparison is made between the actions taken by ICC and the pool of unanimous viewpoints using evidence-based assessment (EBA). Finally, it develops a framework of six research paradigms, taking into account the stakeholders’ aspirations and the EBA outcome. If these paradigms are acted upon, it can ensure convergence of stakeholders’ goals and balance in the cricket mix. Additionally, if ICC can make unanimous viewpoints from its stakeholders a part of its future plan, acceptability of its decisions will be more.

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“…Akhtar and Scarf (2012) suggested a method to forecast the outcome probabilities of test matches using a sequence of multinomial logistic regression models. Davis et al (2015) provided a methodology to investigate both career performances and current form of the players in Twenty20 cricket. Asif and McHale (2016) developed a dynamic logistic regression (DLR) model for forecasting the winner of ODI cricket matches at any point of the game.…”

Section: Applications Of Machine Learning In Cricketmentioning

confidence: 99%

Classification of all-rounders in limited over cricket - a machine learning approach

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Wijekularathna

2021

JSA

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In cricket, all-rounders play an important role. A good all-rounder should be able to contribute to the team by both bat and ball as needed. However, these players still have their dominant role by which we categorize them as batting all-rounders or bowling all-rounders. Current practice is to do so by mostly subjective methods. In this study, the authors have explored different machine learning techniques to classify all-rounders into bowling all-rounders or batting all-rounders based on their observed performance statistics. In particular, logistic regression, linear discriminant function, quadratic discriminant function, naïve Bayes, support vector machine, and random forest classification methods were explored. Evaluation of the performance of the classification methods was done using the metrics accuracy and area under the ROC curve. While all the six methods performed well, logistic regression, linear discriminant function, quadratic discriminant function, and support vector machine showed outstanding performance suggesting that these methods can be used to develop an automated classification rule to classify all-rounders in cricket. Given the rising popularity of cricket, and the increasing revenue generated by the sport, the use of such a prediction tool could be of tremendous benefit to decision-makers in cricket.

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Player evaluation in Twenty20 cricket

Cited by 21 publications

References 3 publications

Contextual batting and bowling in limited overs cricket

Contextual batting and bowling in limited overs cricket

Identification of Research Paradigms for Managing the Cricketing Ecosystem Using Stakeholder Analysis and Text Mining

Classification of all-rounders in limited over cricket - a machine learning approach

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