Notes from the Sports Nerds

NFL Early Season Over-Reaction: Bettor Beware

• 0 comments - NFL - posted by tom on 09.23.09

In one of his recent posts on ESPN Insider, Chad Millman reported that most of his wiseguy friends love to bet NFL Week 2 games, primarily because most square bettors tend to over-react to Week 1 results.

We don’t have the data to prove that assertion as fact, but it seems like a reasonable hypothesis. We’ve certainly noticed that a lot of sports fans tend to read far too much into small sample sizes of information.

Example: The fact that a team is 3-0 under the lights in October is probably due as much (or more) to randomness than to having a team full of vampire cyborgs that draw superhuman energy from the dark of night.

This problem is only amplified by the sports media, who are always looking for a number (no matter how meaningless it may be) to back up their opinions. Similarly, many sports betting related sites and publications focus on identifying “trends” and implying that they mean something, when 99% of the time they absolutely don’t.

(That’s a huge pet peeve of ours, to be explored more in a future post.)

But given Chad’s column, I looked forward to seeing how our NFL picks from our two primary math models did this week. It was a crazy week in the NFL, with lots of close games, and games expected to be close.

In fact, closing lines for 10 of the 16 NFL games in Week 2 showed favorites with an expected advantage of 3.5 points or less — a much tighter range than Week 1.

As it turns out, our Similar Games model was on the wrong end of several close games and went a poor 6-10 picking straight up winners, 3 games behind the worst ESPN expert at 9-7. ATS Performance was better at 8-8 overall, including going 4-2 in picks with predicted cover odds of 55% or better.

However, our Power Ratings model — which, compared to Chad’s assessment of the betting public, tends to UNDER-react to recent games — went 10-6 picking winners, better than 6 of the 8 ESPN experts. It also went an impressive 11-5 ATS.

(Note: Neither model adjusts for recent injuries, so both favored Philadelphia over New Orleans despite McNabb being out. In the future, we plan to introduce “injury warnings” for the math model predictions.)

Again, these sample sizes are all too small to prove anything, but it was interesting to see our “tortoise” model significantly outperform the hare, so to speak, in NFL Week 2.

Soon we plan to launch model performance pages up that show a five year pick history with results, which will give users a much deeper look into how our math models have performed in various situations with more meaningful sample sizes.