To Turf or Not to Turf, That is the Question [Part 2]: Applications
In the previous video I have provided “data driven” approach in deciding should your team train on the artificial grass or not. We have covered how I made sample data, how we train predictive model using CART trees (regression trees) and caret package in R language, and how to visualize the model performance.
In the video below I am deploying the model to make the decision between 5 variations of weekly plan. The goal is to minimize the morning soreness on the day of the game. We can use our model trained of observational data to help us predict athletes reactions, and hence help us in making optimal decision.
You can easily expand the model/code to make predictions for each athlete separately, rather than on the team level, which could be the next step when deciding optimal strategy for an individual rather than on the team level.
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