Modelling and simulation for one‐day cricket

Swartz, Tim B.; Gill, Paramjit; Muthukumarana, Saman

doi:10.1002/cjs.10017

Cited by 37 publications

(24 citation statements)

References 12 publications

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“…Their prediction model is updated when data of a match in progress streams in. Swartz et al [19] used Markov Chain Monte Carlo methods to simulate ball by ball outcome of a match with a Bayesian Latent variable model. On the basis of features of current batsman, bowler and game situation, the outcome of the next ball is estimated.…”

Section: Related Workmentioning

confidence: 99%

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Predicting The Cricket Match Outcome Using Crowd Opinions On Social Networks: A Comparative Study Of Machine Learning Methods

Mustafa

Nawaz

Lali

et al. 2017

MJCS

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Social media has become a platform of first choice where one can express his/her feelings with freedom. The sports and matches being played are also discussed on social media such as Twitter. In this article, efforts are made to investigate the feasibility of using collective knowledge obtained from microposts posted on Twitter to predict the winner of a Cricket match. For predictions, we use three different methods that depend on the total number of tweets before the game for each team, fans sentiments toward each team and fans score predictions on Twitter. By combining these three methods, we classify winning team prediction in a Cricket game before the start of game. Our results are promising enough to be used for winning team forecast. Furthermore, the effectiveness of supervised learning algorithms is evaluated where Support Vector Machine (SVM) has shown advantage over other classifiers.

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Section: Related Workmentioning

confidence: 99%

“…On the basis of features of current batsman, bowler and game situation, the outcome of the next ball is estimated. Both [12] and [19] have developed match simulators for ODI Cricket, but their models depend on games that are played over 10 years ago. In last 5 years, many rules of Cricket especially in ODI Cricket have changed.…”

Section: Related Workmentioning

confidence: 99%

Predicting The Cricket Match Outcome Using Crowd Opinions On Social Networks: A Comparative Study Of Machine Learning Methods

Mustafa

Nawaz

Lali

et al. 2017

MJCS

View full text Add to dashboard Cite

show abstract

“…Using this, they analyze betting 1 market's sensitivity to the ups and downs of the game. Swartz et al [17] use Markov Chain Monte Carlo methods to simulate ball by ball outcome of a match using a Bayesian Latent variable model. Based on the features of current batsman, bowler, and game situation (number of wickets lost and number of balls bowled), they estimate the outcome of the next ball.…”

Section: Academic Interest In Cricketmentioning

confidence: 99%

“…While both [17] and [2] have built match simulators for ODI cricket, their models rely on games played over 10 years ago. ODI cricket has since undergone a number of major rule modifications.…”

Section: Academic Interest In Cricketmentioning

confidence: 99%

Auto-play: A Data Mining Approach to ODI Cricket Simulation and Prediction

Sankaranarayanan

Sattar

Lakshmanan

2014

Proceedings of the 2014 SIAM International Conference on Data Mining

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Cricket is a popular sport played by 16 countries, is the second most watched sport in the world after soccer, and enjoys a multi-million dollar industry. There is tremendous interest in simulating cricket and more importantly in predicting the outcome of games, particularly in their one-day international format. The complex rules governing the game, along with the numerous natural parameters affecting the outcome of a cricket match present significant challenges for accurate prediction. Multiple diverse parameters, including but not limited to cricketing skills and performances, match venues and even weather conditions can significantly affect the outcome of a game. The sheer number of parameters, along with their interdependence and variance create a non-trivial challenge to create an accurate quantitative model of a game Unlike other sports such as basketball and baseball which are well researched from a sports analytics perspective, for cricket, these tasks have yet to be investigated in depth. In this paper, we build a prediction system that takes in historical match data as well as the instantaneous state of a match, and predicts future match events culminating in a victory or loss. We model the game using a subset of match parameters, using a combination of linear regression and nearestneighbor clustering algorithms. We describe our model and algorithms and finally present quantitative results, demonstrating the performance of our algorithms in predicting the number of runs scored, one of the most important determinants of match outcome.

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“…In the development of a one-day cricket simulator, Swartz, Gill and Muthukumarana (2009) consider batting behaviour in the second innings. Although match summary results are readily available from the Cricinfo website, our investigation requires ball-by-ball data, and for this, we have coded a Java script to parse the associated commentary log for each match.…”

mentioning

confidence: 99%

Duckworth–Lewis and Twenty20 cricket

Bhattacharya

Gill

Swartz

2011

Journal of the Operational Research Society

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Originally designed for one-day cricket, this paper considers the use of the DuckworthLewis method as an approach to resetting targets in interrupted Twenty20 cricket matches. The Duckworth- Lewis table is reviewed and an alternative resource table is presented. The alternative table is constructed using observed scoring rates from international Twenty20 matches. A desideratum of a resource table is monotonicity in both the rows and columns corresponding to wickets and overs respectively. Consequently, a Gibbs sampling scheme related to isotonic regression is applied to the observed scoring rates to provide a nonparametric resource table. Taking into account the more aggressive batting style of Twenty20 compared to one-day cricket, the resultant resource table is seen to possess sensible features. A discussion is provided concerning the use of the Duckworth-Lewis method applied to Twenty20.

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Modelling and simulation for one‐day cricket

Cited by 37 publications

References 12 publications

Predicting The Cricket Match Outcome Using Crowd Opinions On Social Networks: A Comparative Study Of Machine Learning Methods

Predicting The Cricket Match Outcome Using Crowd Opinions On Social Networks: A Comparative Study Of Machine Learning Methods

Auto-play: A Data Mining Approach to ODI Cricket Simulation and Prediction

Duckworth–Lewis and Twenty20 cricket

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