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This paper presents an enhanced method for forecasting horserace outcomes using least-square support vector regression (LS-SVR) combined with conditional logit procedures. It introduces a novel model selection approach based on normalized discounted cumulative gain (NDCG) to improve the accuracy of predicting winning probabilities by focusing on the ranking of finishers rather than overall prediction accuracy. The findings suggest that this augmented two-stage model significantly outperforms traditional met

Author

Joseph Bloggs

Language

EN