Fig. 1. A Batter Evaluation Model[2]
Fig. 2. Procedure for Calculating the Weights
Fig. 3. R Code for Genetic Algorithm
Fig. 4. Fitness Values by Iteration
Fig. 5. Result of Genetic Algorithm
Fig. 6. Result of Genetic Algorithm(CassPoint)
Table 1. Run Values[6]
Table 2. Win Expectation Values[13]
Table 3. Average Pitches per Game[2]
Table 4. Pitcher Weights by Grade[2]
References
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- S. L. Rubin, "Market Efficiency of Major League Baseball Player Salaries: A Look at the Moneyball Hypothesis Ten Years Later," Doctoral dissertation, University of Delaware, 2013.
- J. H. Holland, "Adaptation in natural and artificial system," University of Michigan Press, 1975.
- D. E. Goldberg, "Genetic algorithm in search, optimization & Machine Learning," Addison Wesley, 1989.
- Hyung Woo Moon, Yong Tae Woo and Yang Woo Shin, "Run expectancy and win expectancy in the Korea Baseball Organization(KBO) League," The Korean Journal of Applied Statistics, Vol. 29, No. 2, pp. 321-330, February 2016. (in Korean) https://doi.org/10.5351/KJAS.2016.29.2.321
- Com2uS Pro-Baseball Point, http://cpbpoint.mbcplus.com