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Showing posts from October, 2011

2011-10-28: 2011 NFL Season Week 8

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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 ei…

2011-10-22: 2011 NFL Season Week 7

I have been on travel in San Diego all this week and I have had computer issues the entire time. Therefore I am posting these picks at the last minute and do not have much in the way of commentary especially since I was on an airplane during most of the games this past Sunday. I would like to thank my wife for typing in the commands I told her over the phone so that we could run the algorithms in order to get the picks done. I would not have been able to do it without her.

FavoriteSpreadUnderdogDiscretePagerankTB2.2CHITBCHIat CAR4.9WASCARWASSD0.3At NYJSDNYJAt CLE2.7SEACLESEAAt TEN5HOUTENTENAt MIA1.3DENMIADENAt DET3.6ATLATLDETAt OAK5.5KCOAKOAKPIT4.9At ARIPITPITAt DAL10.3STLDALDALGB1.5At MINGBGBAt NO4.5INDNONOBAL4At JAXBALJAX-- Greg Szalkowski

2011-10-14: 2011 NFL Season Week 6

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Our neural network predictor was 68% correct straight up this past week but overall our results were not awe inspiring. Two of the games that almost everyone got wrong were the Eagles-Bills and Seahawks-Giants games. In both games the favorite lost and one of the crucial stats was interceptions. Michael Vick of the Eagles threw four interceptions and Eli Manning threw three for the Giants. This is completely out of character for either of the quarterbacks.

So far this year our Support Vector Machine (SVM) predictor has tracked the favorites very closely. With the addition of the line data this year, the line value has driven the output of the SVM. Ignoring the Line values, passing efficiency and turnovers forced by the defense have been two of the most dominant statistics.

Predictions for week 6:

FavoriteSpreadUnderdogDiscretePagerankAt GB14.5STLGBGBAt PIT9.5JAXPITPITPHI-0.7At WASPHIWASAt DET3.4SFDETSF At ATL-0.3CARATLATLAt CIN3.2INDCINCINAt NYG4.7BUFNYG BUFAt BAL5.6HOUBALBALAt OAK6.4C…

2011-10-06: Week 5 2011 NFL Season

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Week 4 performance was rather pleasing. Straight up and against the spread were 80% and 75% correct. Buffalo lost with that last minute field goal and as to why Philadelphia fell apart in the second half and lost a 20 point lead has been the subject of numerous commentator's discussions. Hopefully the predictions continue to perform at this level but pessimism indicates that they will regress to the mean. 

Week 5 of the NFL season means the commencement of bye weeks. This week's teams on bye are the Baltimore Ravens, Cleveland Browns, Dallas Cowboys, Miami Dolphins, St. Louis Rams and Washington Redskins.

For comparison purposes we have included one of the better performing algorithms from the past two years. The PageRank algorithm that we modified to indicate strong teams averaged 68% for straight up predictions over the past two years. A more detailed explanation is provided in one of our previous posts.

The predictions for Week 5:

FavoriteLineUnderdogDiscretePageRankAt IND…

2011-10-02: 2011 NFL Season Under Way

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The 2011 NFL season is underway and we are ready to put some of our improved algorithms to the test. Last year we primarily used box score data for our predictions. This resulted in adequate performance but nothing spectacular.

This year we are increasing the collective intelligence quotient in our algorithm by incorporating betting line data and line movement. The purpose of the betting line is to make the sportsbooks money by splitting the betting population in half. The line will move as a result of betting pressure presented by the betting population. e.g. The favorite team is favored by 5 points. Many bettors may feel that the favorite team is not that good and place bets on the underdog. With an unbalanced wager profile the sportsbook has the potential to lose money so they will move the bet line until the incoming bets are equal on each side. This movement is a form of collective intelligence of the betting population.

Another change this year is that in addition to choosing th…