Skinning the NFL cat

by Bruce D

We've all heard the saying "there's more than one way to skin a cat", and that's what I've attempted to do. Skin the same "cat" as Brian Burke attempts to, but in a totally different way.

In this case the "cat" is the value, power, efficiency, likely hood to win, whatever you want to call it, of each NFL team. As I see it, Mr. Burke uses sophisticated statistical analysis and analyzes pertinent information in pertinent situations, such as tracking the success and efficiency of individual plays outside of the "garbage time" and odd-ball situations during NFL games. He strives to "eliminate the noise" and get to the truth about a team's true ability to score points, prevent points, and win games.

I'm totally with Brian on that goal, but I don't have Brian's statistical skill set (I spend too much time on wikipedia just looking up some of the words he uses as it is), and I really don't trust anything or anyone(except Brian at this point) that I can't prove to myself. So I have to do it the old fashioned, "empirical" way (I learned that big word on this web-site, had to look it up on wikipedia, did I use it in the right way?). In my analysis I came up with a simple formula that virtually anyone would agree with.

In any NFL game, if Team A scores more points than team B scores, team A wins.

So team A can win by any positive combination of scoring points and preventing team B from scoring points.

So its points. More points you win, less points you lose. I'll state it again, "its points". So that's what I use, because nothing else allows a team to win. I use points for, points allowed, and then pull out a portion of "lucky" points for each lucky play, and give a portion of "unlucky" points back to a team in random unlucky plays. The same sort of goal that Brian has, yet I look at the actual results data rather than what should be expected. The following is an example of what I've come up with. Here's an excerpt from my web-site that shows my version of team efficiency, power, ability to win, whatever you may call it, that Brian and I are trying to determine.



The power rankings and ratings listed to the left is a new feature to be posted each week. Power is derived by calculating each team's offensive and defensive points production and prevention for each game, comparing it to the league average each week, and then adjusting for strength of opponent. For each game calculated, there is a MAX and a MIN power that a team cannot go beyond, so a blow out win is great, but it can't become unrealistically excessive, likewise, getting shut out can't give a team a 0 power rating for offense or defense.

Next, all lucky type plays, good or bad, such as interceptions, fumbles lost, blocked punts, kick off return touchdowns, missed field goals etc. are valued, and each team's power is adjusted by a portion of this luck value (about half seems to be appropriate to distinguish between aggressive or sloppy team play vs dumb luck). If a team has been overly lucky, their power will be adjusted lower due to the fact that they can't be expected to continue to be so lucky. For the same reasons, an unlucky team's power will be increased.

A power rating of 1 is considered league average, so a team with a rating of 1.2 can be thought to be 20% better than the average team, and a team with a rating of .85 can be thought to be 15% worse than average.

A few things to keep in mind about the ratings. This is average power year to date, so a "hot" team won't jump above a consistently good team, all power is based on the ability to score and prevent points, so wins have no affect on the ratings, and player and coaching changes are not factored in.

You may also want to consider that a ranking system of 1 to 32 can be misleading. Rank and rating are 2 different things. You can see that the power rating of some teams are equal and/or very similar to the group of teams they're listed close to. A team ranked at #5 could have a rating that is virtually identical to a team ranked #10, yet a 5 rank difference infers a large difference. In other words, I'd pay more attention to the ratings than the rankings.

One last thing. In the 2010 NFL season, home advantage looks to be about 4% as far as these ratings go. In a match-up/prediction situation, I'd add about .04 to the home team's power rating.

So I put my calculated power ratings up against my NFL hero Mr. Burke's, and I was amazed. The expected results are pretty damn close to actual results.

What do I deduct from this? To me, its proof that Brian was right all along, its proof that Brian is a very smart "out of the box" NFL analyst, and that I never would have come up with my formulas if it weren't for this web-site.

In my humble opinion, this is "the" web-site for NFL statistical analysis.