Improvement of the passing quality of an American football training machine

Abstract

In American football, high quality training focused on catching is currently not done with passing machines due to their poor pass accuracy and precision. From a coach’s point of view, accurate and precise passing machines are needed to relieve the quarterback from too much training effort. The two aims of this study were to increase the precision of a passing machine and develop an accurate pass prediction model for it. To meet the two aims and provide evidence that a passing machine can be precise and accurate enough for high quality training, an automated passing machine was developed and two experiments were carried out. The results of the first experiment showed that the machine performs with a precision within ±1% of the throwing distance for 218 of the 225 passes. The second experiment resulted in a pass prediction model, which is based on 55 videos and a fitting approach using a neural network. The model estimates the machine configuration for a pass to a targeted point in space. In regard to precision and accuracy, the performance of the machine exceeds the performance of a skilled quarterback. This project improves the state of the art of passing machines for American football and opens possibilities for research in various fields like motion analysis for catches, hand-eye coordination and performance analysis of athletes.