bksim / bball

Daily pipeline

Model (outdated - new readme to come)

1. In model.ipynb run the code block containing data_getters.download_gamelog_jsons(2014,'boxscores/2014',start_game=X, end_game=1230) where X is the first game that has not already been downloaded. The program will automatically stop once the last game that is available is downloaded.

2. Run the next code block, making sure to change latest_day = datetime.date(2014,11,16) into the correct day. All days before and including latest_day will be used to make predictions.

3. Paste the results of the model model_predictions.csv into the appropriate salary optimization sheet in excel.

Optimization

1. Export daily player information to CSV and place in first five columns of the appropriate sheet in DK Salary Optimization Sheets.

2. Copy/paste results from model_predictions.csv into the Model column of the sheet

3. Make discretionary changes if desired, then click Data -> Solver and solve using the Simplex LP algorithm.

Meta-analysis

1. Go to DraftKings lobby and save the lobby source code as draftkings scrapes/YYYYMMDD_draftkings_nba_lobby.htm.

2. Go to the main function in data_getters.py, change the variable fn to the name of the downloaded .htm file, and run, generating a list of all games and their ID's.

3. Run selenium script on logged in DK account to download the .csv info for each of these games.