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Prediction Models for Racing Performance of Domestic Progeny of Thoroughbreds

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초록

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.

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.

참고문헌 (12)

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  5. "The Comprehensive R Archive Network"[URL]
  6. Hastie, T., "The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed" (2009), Springer
  7. "IFHA 2007 Annual Report"[URL]
  8. Lee, K. J., "Estimation of genetic parameters for racing perfor- mance of Thoroughbred horses", Kor. J. Anim. Sci., 37, pp.11~18 (1995)
  9. Park, K. D., "Genetic evaluation of Thoroughbred racehorses in Korea", Kor. J. Anim. Sci., 41, pp.135~140 (1999)
  10. Ripley, B. D., "Pattern Recognition and Neural Networks" (1996), Cambridge University Press
  11. "Thoroughbred Racing and Breeding Worldwide"[URL]
  12. Wright, S., "Coefficients of inbreeding and relationship", Am. Nat., 56, pp.330~338 (1922)

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