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.

Week 1 NFL Picks (SU) & Patriots-Bills Learnings

• 5 comments - NFL - posted by tom on 09.15.09

Early season predictions are always somewhat of a crapshoot for mathematically driven models, but it’s always fun to review how our NFL picks and college football picks do at the start of a new year.

Since we’ve recently launched our over under picks and money line value picks, we’re also keeping close tabs on how those models perform.

The most fun about this whole early season thing is that the average sports bar jock cannot believe that our math models, which incorporate literally zero knowledge about off-season player and coaching changes, could possibly perform well in the opening weeks. During some years, they’re right.

But the fact is, it’s difficult if not impossible to assess how things have changed in the off-season before you have at least several weeks of game results to substantiate your theories. Until then, you’re guessing. If you know football really well and also guess relatively well, you’ll probably outperform our models in the opening weeks of a season. But your assumptions and guesses also could be wrong — and you could underperform an 100% objective approach as a result.

A case in point was last nights Patriots-Bills game.

I spent some time on the phone yesterday with a friend who could not believe our Similar Games Model highly favored the Bills to cover a +13 spread. (The spread was +11.5 earlier in the day, and the model still strongly favored Buffalo.) That model also saw Buffalo +680 as one of the top three money line value plays of the week.

I went through the whole litany of early season warnings about our data-driven algorithms with my buddy. The model had no idea Tom Brady was back, or that the Bills had just fired their offensive coordinator. For Week 1, it essentially assumes that everything is the same as it was at the end of the season last year, until this season’s results begin to prove otherwise.

He was absolutely convinced of a Patriots blowout and discredited the model’s approach.

We all know what happened. At the last minute, the Pats barely escaped with a one-point win. Last year, New England beat Buffalo by 10 in Foxboro. At this point, this result could mean almost anything, including:

1) The Bills are significantly better than they were last year, despite a new OC.
2) The Patriots are actually worse than they were last year, despite Brady being back.
3) More random factors played a major role in the outcome of this game being so different than expectations.

Only time will shed light on the most likely explanation.

Overall for Week 1, our Power Ratings model was 13-3 picking straight up NFL winners and our Similar Games model was 12-4. In comparison, five of the eight ESPN experts had 4 losses, one had 3 losses, and two finished with 2 losses.

Not bad for not knowing anything about the off-season…