I just picked my topic for my econometrics course thesis. I decided to create an algorithm that forecasts NBA players' performance. My inspiration is Silver Nate's PECOTA and CARMELO algorithms, but I have no empirical experience with Data Science techniques, so I am trying to avoid any misleading transitions from theory.The algorithm is going to compare the performance of a current player with other historical players and then forecast his performance based on the of his neighbors.So, I'm going to pick all player statistics per season. Then, I'll create indexes to establish informative dimensions for kNN algorithm (eg. Offense, Defense, Physics). As next step I thought to use nave Bayesian algorithm to find the likelihood of having the same performance with his neighbors and then pick the weighted (depended on likelihood) sum of the neighbors' stats as a forecast.Empirically speaking, do you see any bias on this methodology?Thank you in advance
Econometrics Thesis methodology Suggestions?
machine learning;time series;data;naive bayes classifier