Abstract
Although studies used machine learning algorithms to predict performances in
sports activities, none, to the best of our knowledge, have used and validated
two artificial intelligence techniques: artificial neural network (ANN) and
k-nearest neighbor (KNN) in the running discipline of marathon and compared the
accuracy or precision of the predicted performances. Official French rankings
for the 10-km road and marathon events in 2019 were scrutinized over a dataset
of 820 athletes (aged 21, having run 10 km and a marathon in the same
year that was run slower, etc.). For the KNN and ANN the same inputs (10-km race
time, body mass index, age and sex) were used to solve a linear regression
problem to estimate the marathon race time. No difference was found between the
actual and predicted marathon performances for either method
(p>0,05). All predicted performances were significantly
correlated with the actual ones, with very high correlation coefficients
(r>0,90; p<0,001). KNN outperformed ANN with a
mean absolute error of 2,4 vs 5,6%. The study confirms the validity of
both algorithms, with better accuracy for KNN in predicting marathon
performance. Consequently, the predictions from these artificial intelligence
methods may be used in training programs and competitions.
Key words
artificial neural networks - k-nearest neighbor - machine learning - endurance running - modeling
