Prediction on Performance of Age Group Swimming Using Machine Learning

Time for swimming at young ages may be a good indicator for swimmers' future performance. Through analyzing a large data set on swimming time, we use the machine learning algorithms to explore swimmers' performance on four different strokes in a 100m long course for both males and females. For each stroke, we divide swimmers' performance into four levels according to their time at the ages of 12-13, and predict their performance levels at the age of 18 using two well-known machine learning methods with optimal parameters. Based on the existing data, we predict the probability from each level at a young age to the top 25% at the age of 18. The predictions obtained by the machine learning approach are very close to a statistical analysis result, indicating that our approach is effective in predicting swimming performance based on swimmers' past records.