DOI QR코드

DOI QR Code

Prediction Models for Racing Performance of Domestic Progeny of Thoroughbreds

  • Lee, Jeong-Ran (Department of Statistics, Seoul National University) ;
  • Lee, Jin-Woo (Horse Registry, Korea Racing Authority) ;
  • Kim, Hee-Bal (Department of Agricultural Biotechnology, Seoul National University) ;
  • Oh, Hee-Seok (Department of Statistics, Seoul National University)
  • Received : 2010.10.04
  • Accepted : 2010.12.01
  • Published : 2010.12.31

Abstract

In this study, we suggest an objective standard in selection of candidate horse mates. Korea Racing Authority provided racing records and pedigree information of 44 sires and 954 dams. The datasets were used to predict Racing Indices represented by the averages of earnings earned by offspring for each dam and sire that indicate the racing performance of its domestic progeny. Proportion of wins and second places to the number of taken races and the mean of distances for the won races of a sire were significant factors in linear model with minimum prediction errors. For dam, those factors were the average of earned money per race, number of outstanding broodmares in pedigree, and the comparable index which indicates the relative affinity with its mate. We can use the resultant model for a horse mate by choosing one of the candidates with the largest predicted value for hypothetical offspring.

Keywords

References

  1. Akaike, H. 1973. Information theory and the maximum likelihood principle. Page 267 in Second International Symposium on Information Theory. B. Petrov and F. Csaki, ed. Akademiai Kiado, Budapest.
  2. Bakhtiari, J. and Kashan, N. E. J. 2009. Estimation of genetic parameters of racing performance in Iranian Thoroughbred horses. Livestock Science 120:151-157. https://doi.org/10.1016/j.livsci.2008.05.007
  3. Breiman, L., Friedman, J., Olshen, R. A. and Stone, C. J. 1984. Classification and Regression Trees. Chapman & Hall, New York.
  4. Breiman, L. 1996. Bagging predictors. Mach. Learn. 26:123-140. https://doi.org/10.1007/BF00058655
  5. CRAN. The Comprehensive R Archive Network: http://cran.r-project.org/.
  6. Hastie, T., Tibshirani, R. and Friedman, J. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer, New York.
  7. International Federation of Horseracing Authorities. 2007. IFHA 2007 Annual Report: http://www.horseracingintfed.com/.
  8. Lee, K. J., Park, K. D., Kang, M. G., Kim, D. R. and Moon, Y. Y. 1995. Estimation of genetic parameters for racing performance of Thoroughbred horses. Kor. J. Anim. Sci. 37:11-18.
  9. Park, K. D. and Lee, K. J. 1999. Genetic evaluation of Thoroughbred racehorses in Korea. Kor. J. Anim. Sci. 41:135- 140.
  10. Ripley, B. D. 1996. Pattern Recognition and Neural Networks. Cambridge University Press, New York.
  11. The Jockey Club. 2008. Thoroughbred Racing and Breeding Worldwide: http://www.jockeyclub.com/factbook.asp?section=17.
  12. Wright, S. 1922. Coefficients of inbreeding and relationship. Am. Nat. 56:330-338. https://doi.org/10.1086/279872