OVERALL | Correct | Games | Win% |
| STRAIGHT-UP | 522 | 706 | 73.9% |
| SU UNDERDOG WIN | 39 | 74 | 52.7% |
| ***** | 209 | 355 | 58.9% |
| NOVEMBER | Correct | Games | Win% |
| STRAIGHT-UP | 136 | 173 | 78.6% |
| SU UNDERDOG WIN | 23 | 31 | 74.2% |
| ***** | 63 | 96 | 65.6% |
| DECEMBER | Correct | Games | Win% |
| STRAIGHT-UP | 132 | 169 | 78.1% |
| SU UNDERDOG WIN | 5 | 11 | 45.5% |
| ***** | 57 | 83 | 68.7% |
| JANUARY | Correct | Games | Win% |
| STRAIGHT-UP | 135 | 198 | 68.2% |
| SU UNDERDOG WIN | 6 | 16 | 37.5% |
| ***** | 46 | 95 | 48.4% |
| FEBRUARY | Correct | Games | Win% |
| STRAIGHT-UP | 109 | 152 | 71.7% |
| SU UNDERDOG WIN | 4 | 13 | 30.8% |
| ***** | 38 | 71 | 53.5% |
The season got off to a great start with +65% accuracy against the spread, ***** picks, for each of the first 2 months. Accuracy declined in January, dropping to 48.4%, but began to recover in February, reaching a profitable 53.5% by month's end. Overall, NBA Black Box is still an impressive 58.9% over 355 games.
If you've got an explanation for the irrational monthly performance, I'd love to hear it.
3 comments:
Don't know if you use season to date averages but if you do perhaps you should look into using current games (maybe L5, L10, L20)instead?
Tons of trades, injuries and coaching changes make STD data a bit unreliable to use. That said, I am not a fan of using recent perf. only. Based on your results, it appears you are on to something, but I think modeling the impact of missing players is the next step -- though a very time-consuming one.
Right... the main reason I don't use recent stats, as opposed to entire season stats, is that writing the code and setting up custom stats I need is a logistical nightmare. It so easy using them straight from the internet.
I had hoped that performance would keep up as the season continued, but since that's not the case, it looks like I'll have to suck it up and do it myself.
Thanks for the input and I'll be sure to keep you all posted.
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