Evaluating the predictability of distance race performance in NCAA cross country and track and field from high school race times in the United States

Abstract

Successful recruiting for collegiate track & field athletes has become a more competitive and essential component of coaching. This study aims to determine the relationship between race performances of distance runners at the United States high school and National Collegiate Athletic Association (NCAA) levels. Conditional inference classification tree models were built and analysed to predict the probability that runners would qualify for the NCAA Division I National Cross Country Meet and/or the East or West NCAA Division I Outdoor Track & Field Preliminary Round based on their high school race times in the 800 m, 1600 m, and 3200 m. Prediction accuracies of the classification trees ranged from 60.0 to 76.6 percent. The models produced the most reliable estimates for predicting qualifiers in cross country, the 1500 m, and the 800 m for females and cross country, the 5000 m, and the 800 m for males. NCAA track & field coaches can use the results from this study as a guideline for recruiting decisions. Additionally, future studies can apply the methodological foundations of this research to predicting race performances set at different metrics, such as national meets in other countries or Olympic qualifications, from previous race data.