Next Year's Wins

With a valid and predictive model of team wins (r-squared =0.80), we can estimate how many wins a team "should" have earned, known as expected wins. Based on a regression model using (offensive and defensive) passing, running, and turnover efficiency, plus penalties, we can compare expected wins with actual wins and make some observations.

The first thing I noticed was that expected win totals were much steadier from year to year than actual wins. This supports the notion that the efficiency model is actually a better evaluation of how good a team truly is than a team's actual wins. A team may be "X wins good," but might get lucky or unlucky by a game or two.

Further, if we want to predict the record of a team before the season starts, we'd be much better off by starting from their previous year's expected wins rather than their actual wins. By simply using prior expected wins to predict the following year's wins, the standard error is only about 1.7. That is, the estimate is usually wrong by less than a game or two. By using prior actual wins the standard error is about 3.0--not a very accurate predictor.

Take the recent records of the Jaguars for example. Their actual wins appear to be fluctuating around a relatively steady expected win trend. This gives the appearance that they are an inconsistent team, but in reality they are performing very steadily judging by their passing and running efficiencies, turnover rates, and penalties.

In another example, we see that the Falcons' season in 2004 was something of a fluke. They came back down to Earth after posting a win total over 3 wins higher than what their stats told us to expect. Sure enough, they returned to predictability in 2005. The likely reasons for such a deviation are an easy schedule and some lucky bounces of the ball (and perhaps the upreparedness of league defenses for Vick's running ability).

The Ravens illustrate another observation. Their mediocre performance through 2005 suddenly improved in 2006. This is due to the increase in offensive pass efficiency from 5.1 to 6.4 yards per attempt. The offensive line was the same, and they had the same receivers and tight ends. They only real difference was the quarterback--Steve McNair replaced Kyle Boller. The biggest changes in team fortunes are due to quarterback changes. We can also note that although Baltimore won 13 games, they had about 10 expected wins. This tells us not to hold our breath for another 13 win season in Baltimore, but still count on a solidly above-average team.

Both Baltimore's and Atlanta's graphs illustrate the importance of scheduling. Teams tend to overperform their stats when they have a low number of wins the previous year. Because of the NFL's scheduling system, and possibly somewhat due to draft placement, teams benefit following an "off" year.

Some teams are amazingly consistent and predictable. Notice how flat the Colts' trend line is, and how small the deviation between expected and actual wins.

The Jets are the counter-example. They've been on a roller coaster ride the past 5 years, in terms of actual wins. But their performance as a team has been very steady. It appears to be slightly above average. To Jets fans, it might appear that they had a miraculous turnaround from 4 wins in 2005 to 10 wins and a playoff berth in 2006. But to me, they underperformed in '05, benefitted from an easier schedule, and got a little lucky in '06 to scrape together 10 wins. This is a good measure of "are they for real?" The Jets are, unfortunately, not for real.

The biggest surprise "storybook" season in 2006 belonged to the Saints. So were they for real, or just lucky? Remember that true changes in team fortunes start with changes behind center, and Drew Brees made all the difference. The Saints' chart shows they are for real. They'll be a playoff contender again in 2007.

We can apply the reverse analysis to the "for real" logic. There are some teams that underperformed their stats, and can anticipate improving their win total next year, even without any fundamental improvement in team performance. Look for TEN, STL, MIA, PHI, PIT, MIN, ARI, and JAX to probably improve on their win total from last year.

  • Spread The Love
  • Digg This Post
  • Tweet This Post
  • Stumble This Post
  • Submit This Post To Delicious
  • Submit This Post To Reddit
  • Submit This Post To Mixx

8 Responses to “Next Year's Wins”

  1. Anonymous says:

    This is great stuff. Found you through FO. Will try to keep checking in periodically.

  2. Anonymous says:

    Very interesting. Keep up the good work

  3. Anonymous says:

    Great stuff. Not many people out there can fathom the idea that there are meausurable and predictable trends in sports.

  4. Brian Burke says:

    Thanks, however the only trend or pattern I'm proposing is that last year's performance is the best starting point for estimating this year's performance.

    I'm not suggesting teams are on multi-year upswings or downswings.

  5. Anonymous says:

    You seem to think that "turnover efficiency" is some sustainable attribute of a team. Most turnovers are luck, and thus should not be a large factor in assessing a team's true strength for predictive purposes.

  6. Brian Burke says:

    Turnovers most definitely are a repeatable enduring skill of a team. For example, offensive fumbles correlate year-to-year at 0.31, which is not terribly strong but still considerable and statistically significant. Further, we would expect "within-year" correlations to be even higher. Therefore, their predictive power is

    I partially agree with you. There is an enormous amount of luck involved when turnovers occur, but that's true of every play in any sport. But part of what the statistical models do is devine what component of the variable is sustainable (the fitted value) and what component is luck (the residual value).

    One technique I used to reduce the noise of luck in turnover stats is to use forced fumble stats for team defenses instead of fumbles or fumbles recovered (see my posts on fumbles from April). The forced fumble stat is most definitely a repeatable skill, but who recovers fumbles usually is random. Forced fumbles are a better predictor of future fumble recoveries than past fumble recoveries themselves.

    Additionally, many defensive play calls are designed as interception plays. They are crafted to sucker a QB to throw the ball where a defender is lurking to pick off the pass. Teams with great safeties can allow their cornerbacks to gamble and "short the pass routes" for interceptions.

    And I think it goes without saying that some QBs, and some offensive schemes, are better than others at preventing interceptions. If it's not a skill, then someone please explain why Peyton Manning has been able to throw 10 or fewer interceptions each of the past 4 years.

  7. Unknown says:

    I found your site and have been digging through the archives. I love the analysis here and have a few thoughts:

    1 - Does the penalties variable really account for a good deal of the error in the model? You use significant variables such as offensive and defensive rushing and passing yards that it seems unusual to include penalties into the regression.

    2 - I agree with the inclusion of the turnover variable in the model. Teams with fumbles recovered as opposed to forced fumbles may tend to follow through on a play and never give up - both good signs of a well coached and successful team. I suppose that I have to read your post on fumbles from April...

    It would be interesting to see a model where 2006s actual wins are included in a regression as the dependent variable with 2005s offensive and defensive efficiency stats.

    But keep up the great work! I already love the site.

  8. Brian Burke says:

    Hi Shephan-

    1-Penalties do account for a small but measurable part of winning and losing. One standard deviation increase in penalty yds will cost a team 0.4 wins on average in a season.

    2-Fumbles are a difficult subject, as are turnovers in general. They are relatively rare events and are hard to predict. But they are even harder to ignore. I'm still trying to find the right balance.

    See my post here for what I've learned so far:

    Part 2 of the article discusses penalties and turnovers.

    Regarding a regression of 2005 efficiency stats onto 2006 wins, see the post titled "Leading Indicators." If I recall, penalties was one of the things that carried over from year-to-year. Turnovers, however, had a really interesting effect. The more above average a team was one year, the more below average it would be the next. Still not sure how to interpret that.

Leave a Reply

Note: Only a member of this blog may post a comment.