This experiment started around the beginning of October, when some friends challenged me to use my program to predict football against a couple of other guys. In addition to predicting games, we would be "betting" against the line. We could use any betting strategy we desired to allocate $40 per week. The default strategy was to bet the biggest differences between the prediction and the line, allocating bets of $10, $8, $6... etc. My own betting strategy was a bit more complex. I allocated money according to the formula:
$$ = 80*ABS(Prediction-Line)/(100+5*ABS(Line)))
.The idea being to scale the bet to the relative magnitude of the difference between the prediction and the line. A difference of 3 points is much more significant when the line is 3 than when the line is 27.
I predicted games from Oct 16 through the end of the bowl season. My program doesn't account for neutral site games, so the bowl games were treated as home games for the higher-ranked team. (This works well in practice on the basketball side for the NCAA tournament.) I predicted a total of 224 games. The results:
|Correct game winner||73%|
|Correct pick against the line||56%|
Overall, better results than I expected. 56% against the line is sufficient to be a winning bettor (if it can be maintained).