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Towards a time-lapse prediction system for cricket matches Veppur Sankaranarayanan, Vignesh
Abstract
Cricket is a popular sport played in over a hundred 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 phenomena 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. The goal of this work is to predict the game progression and winner of a yet-to-begin or an ongoing game. The game is modeled using a subset of match parameters, using a combination of linear regression and nearest-neighbor classification-aided attribute bagging algorithm. The prediction system takes in historical match data as well as the instantaneous state of a match, and predicts the score at key points in the future, culminating in a prediction of victory or loss. Runs scored at the end of an innings, the key factor in determining the winner, are predicted at various points in the game. Our experiments based on actual cricket game data, shows that our method predicts the winner with an accuracy of approximately 70%.
Item Metadata
| Title |
Towards a time-lapse prediction system for cricket matches
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| Creator | |
| Publisher |
University of British Columbia
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| Date Issued |
2014
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| Description |
Cricket is a popular sport played in over a hundred 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 phenomena 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. The goal of this work is to predict the game progression and winner of a yet-to-begin or an ongoing game. The game is modeled using a subset of match parameters, using a combination of linear
regression and nearest-neighbor classification-aided attribute bagging algorithm. The prediction system takes in historical match data as well as the instantaneous state of a match, and predicts the score at key points in the future, culminating
in a prediction of victory or loss. Runs scored at the end of an innings, the key factor in determining the winner, are predicted at various points in the game. Our experiments based on actual cricket game data, shows that our method predicts the winner with an accuracy of approximately 70%.
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| Genre | |
| Type | |
| Language |
eng
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| Date Available |
2015-05-31
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| Provider |
Vancouver : University of British Columbia Library
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| Rights |
Attribution-NonCommercial-NoDerivs 2.5 Canada
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| DOI |
10.14288/1.0167478
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| URI | |
| Degree | |
| Program | |
| Affiliation | |
| Degree Grantor |
University of British Columbia
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| Graduation Date |
2014-09
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| Campus | |
| Scholarly Level |
Graduate
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| Rights URI | |
| Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivs 2.5 Canada