I am back from San Diego and while I ran into some computer problems while I was there, thankfully the results of my trip were much better than the results of last weeks predictions.
Our discrete winner predictor is based on a Sequential Minimization Optimization (SMO) method for training the Support Vector Model (SVM). In our experiments, the SVM has proven to be one of the best binary classifiers for predicting the winner/loser of NFL games.
As I mentioned a few weeks ago, this year we have incorporated the betting line data into the classification model as a form of collective intelligence. The betting line data quickly began to dominate the output of the prediction model followed by passing efficiency and turnovers in importance to the outcome. The result of favoring the betting line is that the classifier usually follows the favorite and when there are a number of upsets like last week, then our results are below expectations.
Indeed many of the experts did not fare that well either last week. This led me to think about how " the experts" and the hypothetical average NFL fan make their choices. Are the fans influencing the betting line with with their bets or is the line influencing the bets of the fans. Some form of endogeneity rearing its head and interfering with the model.
Washington and Buffalo will be playing in Toronto this week.
|At HOU||7.2||JAX||At HOU||At HOU|
-- Greg Szalkowski