Recurrent Neural Network to Forecast Sprint Performance

The present paper demonstrates that the performance of an elite track and field sprinter can be predicted by means of the dynamic, nonlinear mathematical method of recurrent neural networks (RNNs). Dataset considers three years of National Collegiate Athletics Association (NCAA) Division I competitions where the student-athlete recorded heart rate variability two days precedent to each competition. Input parameters were selected by transfer entropy via permutation tests. Subsequently, two RNN topologies, Elman and Jordan, were trained with 32 competitions, validated with 7 competitions, and tested against 6 held-out competitions. Resultant RNNs, which possess a sense of time and memory, were able to learn time-dependent sequence of acute adaptation and predict race times with an error of 0.09–0.16 s on held-out test data. Root mean sum of differences of successive R-R intervals (RMSSD), an indicator of parasympathetic tone, and direct current biopotentials, indicator of active wakefulness, were most predictive toward competitive performance for an NCAA Division I male sprinter.