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Predicting a Starting Pitcher in Baseball by Heuristic Rules

Published: 20 July 2016 Publication History

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, A Data-driven Method for In-game Decision Making in MLB, In Sport Analytics Conf., 2014.
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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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X. Wei, P. Lucey, S. Morgan, P. Carr, M. Reid, S. Sridharan, Predicting Serves in Tennis using Style Priors, In ACM SIGKDD'15, pages 2207--2215, 2015.
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C. K. Leung, K. W. Joseph, Sports Data Mining: Predicting Results for the College Football Games, In KES'14, pages 710--719, 2014.
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D. Miljkovic, L. Gajic, A. Kovacevic, Z. Konjovic, The use of data mining for basketball matches outcomes prediction. In IEEE SISY'10, pages 309--312, 2010.
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Blog for baseball entertainment, http://baseball.information0.com/, accesed Jan. 20, 2016. (in Japanese)
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Love Baseball, http://www.yakyu-suki.com/ accesed Jan. 20, 2016. (in Japanese)
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Basic Investigation Report on Sports Marketing in 2013, http://macromill.com/r_data/2013025sports/20131025sports.pdf, accessed Jan. 20, 2016. (in Japanese)
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Professional Baseball Data Freak, http://baseball-data.com/, accessed Jan. 20, 2016. (in Japanese)

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Published In

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C3S2E '16: Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering
July 2016
152 pages
ISBN:9781450340755
DOI:10.1145/2948992
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

Publication History

Published: 20 July 2016

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C3S2E '16

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Overall Acceptance Rate 12 of 42 submissions, 29%

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