Turnover Index - Final 2012 Results

Here are the final results for the Turnover Index for the 2012 season.  In my original post on the topic, I excluded week 17 games from the analysis, so there are no more betting opportunities this season.

Week 16 Results

Things ended on a low note, with the week 16 picks going 1-3 against the spread.

Turnover Index through Week 16 (Against the Spread): 18-16-1

That's 52.9% against the spread.  Assuming the standard sportsbook bet (lay $110 for a chance at a $100 profit), you would have realized a whopping 1% return on investment.  That's below the long term performance of 59% against the spread observed from seasons 1998-2011, but at least it's positive.

So what, if anything, went wrong?

  • Random Variation - Assuming each bet truly had a 59% chance of success, the probability of less than 19 successes out of 35 trials is 23%, or about 1 in 4.  Looking back over past seasons' results against the spread, 2000 was at 41.1%, 2002 was at 38.9%, and 2004 was at 52.9%, identical to this year's results.  So it's not as if 2012 was an unprecedented statistical anomaly.
  • Total Turnovers vs. Turnovers Per Game - The betting criterion I came up with was to bet on any team with less than 10 defensive turnovers than its opponent.  It may have been best to convert that criterion into a "per game" difference.  Bets in the latter half of the season didn't perform well.  The more games a team plays the wider the spread in aggregate turnovers, which may have led me to cast too wide a net.
  • The Betting Market has Caught On - The biggest problem with a betting approach like this is that it only works to the extent that the betting market remains inefficient.  This is also what plagues stock market trading strategies based on technical analysis.  And while I don't think the market has changed its evaluation of turnover performance, it is a possibility.

Next Season

I plan on continuing this feature next NFL season with the only potential tweak being redefining the betting criterion in terms of turnovers per game, rather than accumulated season-to-date turnovers.   When faced with lackluster performance in a prediction model, there is a strong temptation to dive deeper into the numbers, searching for increasingly complicated patterns in the data that have led to higher returns (Should I ignore fumbles? Should I weight recent games more heavily? Is the pattern different for home teams vs. away teams?  Are 4th quarter turnovers less "random" than turnovers early in the game?).

And while it is possible that by digging deeper, you find a betting strategy more likely to result in consistent profits, you are just as likely (and probably more likely) to be searching for green jelly beans.

I am open to suggestions for other potential betting market efficiencies to exploit and track.  In the comments to these posts, Nate has laid out a fairly promising approach involving home field advantage that may be worth a separate investigation. Another commenter suggested looking at special teams touchdowns.

Steven Levitt (of Freakonomics fame) published a paper in 2004 which demonstrated that home underdogs tend to cover the spread more often than not.  In this 2009 post on the Freakonomics blog, Levitt acknowledged that this market inefficiency had disappeared in recent years (at the time 2007 and 2008).  In a separate post, I plan on taking a look at the 2009-2012 results to see if the pattern is gone for good, or whether 2007 and 2008 were just bad years for the home underdog strategy.

Are there other sports where the betting market and/or the general public tends to over or under value certain aspects of the game?  In basketball, 3 point field goal percentage, perhaps?
Powered by Blogger.