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# Using Model Tuning to Beat Vegas

[1]: Betting \$110 to win \$100 is part of the edge that Vegas keeps. This keeps a player from breaking even by picking over and under randomly.

[2]: NBA stats: http://stats.nba.com, Vegas lines: http://www.nowgoal.net/nba.htm

[3]: http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html We will tune the hyperparameters of n_estimators, min_samples_leaf and min_samples_split.

[4]: 23 different team level features were chosen: points per minute, offensive rebounds per minute, defensive rebounds per minute, steals per minute, blocks per minute, assists per minute, points in paint per minute, second chance points per minute, fast break points per minute, lead changes per minute, times tied per minute, largest lead per game, point differential per game, field goal attempts per minute, field goals made per minute, free throw attempts per minute, free throws made per minute, three point field goals attempted per minute, three point field goals made per minute, first quarter points per game, second quarter points per game, third quarter points per game, and fourth quarter points per game.

[5]: The feature parameters included the number of games to look back for the slow and fast moving averages, as well as an exponential decay parameter for how much the most recent games count towards that average (with a value of 0 indicating linear decay), and the threshold for the difference between our prediction and the overunder line required to make a bet.

[6]: Even a coarse grid of width 5 would require 5^7 = 78125 evaluations, taking over 800 days to run sequentially. The coarse width would almost certainly also perform poorly compared to the Bayesian approach that SigOpt takes, for examples see this blog post.

[7]: The untuned model uses the same random forest implementation (with default hyperparameters), the same features, a fast and slow moving linear average of 1 and 10 games respectively, and a certainty threshold of 0.0 points.