A quantitative approach to influential factors in One Day International cricket: Analysis based on Bangladesh

Khan, Jahidur Rahman; Biswas, Raaj Kishore; Kabir, Enamul

doi:10.3233/jsa-170260

Cited by 11 publications

(3 citation statements)

References 34 publications

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“…One of the factors on which team selection depends is the pitch condition and how it is expected to play out during the match. Scholars have modelled the decision to bat first after winning the toss and the probability of winning the game or have attacking instincts through the various phases of the game (both batting and bowling) (Cannonier et al, 2015; Khan et al, 2019). Researchers have speculated that combinations and strategies are often dependent on pitch conditions (Connor et al, 2021; Dawson et al, 2009; Lemmer, 2013; Sarangi & Singh, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Pitch Ratings in All Formats of Cricket

Kumar

Balasubramanian

2023

Management and Labour Studies

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The article identifies measurable attributes to find the consistency in pitch ratings which are otherwise subjective decisions made by referees for international test match cricket. To do so, the article uses statistics related to test matches, one-days and T20s played among all test playing nations between March 2017 and March 2019 (53 tests, 142 T20s and 172 one-day matches: the next two seasons ending May 2020/2021 were hit by COVID-19 and hence excluded). To measure the consistency of pitch ratings (very good, good, above average, average, below average and poor), measurable attributes like runs/day, wickets/day, runs/over, runs/wicket and overs/wicket were identified. To rank pitch ratings using these attributes, the multi-criteria decision-making technique—PROMETHEE II was used. We found that the referee pitch ratings are largely consistent and the attributes developed can be utilized to further analyse future judgements regarding pitch ratings. Further, six-pitch ratings can be clustered into two distinct groups that are significantly different from each other. The article is among the first to analyse sports pitch ratings by using team performance-based statistics. This study paves the way for similar studies and development of newer statistical flow-based attributes.

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Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Pitch Ratings in All Formats of Cricket

Kumar

Balasubramanian

2023

Management and Labour Studies

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“…They also introduced a player ranking system using the performance statistics. Khan et al (2019) used logistic and log-linear regression models to explore the association of influential factors with match results of ODI cricket games. Wickramasinghe (2020) discussed a naïve Bayes approach to predict the winner of an ODI cricket 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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Kafle

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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“…Rather than focus on a single season or league, Khan et al 23 investigate the factors contributing to Bangladesh’s performance in ODI cricket. They compare the use of a logistic regression model and a modified Poisson model, with both models providing good results but with the Poisson regression having smaller confidence intervals.…”

Section: Introductionmentioning

confidence: 99%

Calculating expected win percentage of an Indian Premier League team

Hoskins

2023

International Journal of Sports Science & Coaching

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The Indian Premier League is the most prestigious cricket league globally. There are significant finances in terms of both team ownership and player salaries. It is, therefore, essential to understanding if a team’s record is due to luck (good or bad) or if a team’s record is due to the team’s overall performance. The research presented here is motivated by how to accurately predict a team’s winning percentage in the Indian Premier League based on underlying statistics. A similar analysis has been done in other sports, mainly based on the concept of the Pythagorean expectation. This research derives a similar model for the IPL based on historical data. However, the structure of a match in the Indian Premier League is fundamentally different than the structure of games in other sports. As a result of this structural difference, this study creates additional models using both least absolute shrinkage and selection operator and stepwise regression to identify variables that are good predictors for calculating the expected winning percentage. These models compare favorably to the Pythagorean expectation model. This article presents a model combining both the determined variables and Pythagorean expectation.

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A quantitative approach to influential factors in One Day International cricket: Analysis based on Bangladesh

Cited by 11 publications

References 34 publications

Comparative Analysis of Pitch Ratings in All Formats of Cricket

Comparative Analysis of Pitch Ratings in All Formats of Cricket

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

Calculating expected win percentage of an Indian Premier League team

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