Predicting a Starting Pitcher in Baseball by Heuristic Rules
Pages 133 - 134
Abstract
Baseball is one of the best popular sports in Japan. A large number of baseball spectators are much interested in various predictions related to the game, such as starting players and outcome of the games. In this paper, we propose a heuristic method for predicting a starting pitcher. Predicting a starting pitcher using computers is difficult as as various aspects need to be considered and the volume of information is limited. The accuracy of prediction is low even by ardent followers of baseball. Our proposed method is modeled on the human method of prediction. The preliminary evaluation results show that the proposed method attains a prediction ratio which is equal to or higher than that obtained by the human method.
References
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G. Ganeshapillai, J. Guttag, Predicting the Next Pitch, In Sport Analytics Conf., 2012.
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J. R. Bock, Pitch Sequence Complexity and Long-Term Pitcher Performance, Sports 2015, 3, pages 40--55, 2015.
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- Predicting a Starting Pitcher in Baseball by Heuristic Rules
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Published In
![cover image ACM Other conferences](/cms/asset/f8880424-2484-47dd-b4d5-7c6195ca67ce/2948992.cover.jpg)
July 2016
152 pages
ISBN:9781450340755
DOI:10.1145/2948992
- Editor:
- Evan Desai,
- General Chair:
- Bipin C. Desai,
- Program Chairs:
- Ana Alameida,
- Jorge Bernardino
Copyright © 2016 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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Association for Computing Machinery
New York, NY, United States
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Published: 20 July 2016
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- Refereed limited
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C3S2E '16
C3S2E '16: Ninth International C* Conference on Computer Science & Software Engineering
July 20 - 22, 2016
Porto, Portugal
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Overall Acceptance Rate 12 of 42 submissions, 29%
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