A website project built to project mlb player value through market value salary predictions
In this order:
run PlayerDatabase.py to update the postgreSQL player database with salary data, player statistics, and player IDS
run dataModels.py to update the machine learning training models for predictions
run Predictions.py to update the player databases salary predictions for each player
to run the program, run flaskbootstrapapp.py
currently the website allows user to search for a player and it will display their stats and a prediction for annual salary based on weighted war, average war and
peak war for their career. User can also explore prediction leaderboards for each salary prediction model.
SOURCES FOR DATA:
USA TODAY for Salary data
ESPN for player statistics
http://crunchtimebaseball.com/baseball_map.html for playerIDS