In the world of competitive sports, finding the optimal training load for individual athletes is essential to maximising their performance whilst managing the risks to injury and illness. We have datasets for state/national swimmers comprising: training data (including loads, times, distances, training loads), qualitative data (including how athletes feel, psychological measures), wellness (e.g. sleep), injury (to specific parts of the body) and illness.
There are over 90 variables in this time series dataset for multiple athletes. What are the ways we can link these datasets? How can we model the data to predict athlete resilience (maximising training load whilst avoid injury and illness)? What methods can incorporate qualitative and quantitative data, and the different types of uncertainty inherent in it?
In the world of competitive sports, finding the optimal training load for individual athletes is essential to maximising their performance whilst managing the risks to injury and illness. We have datasets for state/national swimmers comprising: training data (including loads, times, distances, training loads), qualitative data (including how athletes feel, psychological measures), wellness (e.g. sleep), injury (to specific parts of the body) and illness.
There are over 90 variables in this time series dataset for multiple athletes. What are the ways we can link these datasets? How can we model the data to predict athlete resilience (maximising training load whilst avoid injury and illness)? What methods can incorporate qualitative and quantitative data, and the different types of uncertainty inherent in it?