Predicting The NFC East: What The Bill James Indicators Tell Us

At this time of year there's one question floating around somewhere in the minds of NFL fans: Just how good will my team be this year? At this point in the offseason, when training camps haven't even officially started, all 32 NFL fan bases agree on one thing: Their team will be a playoff contender in 2012. And that's okay. We're fans. It's our job to be optimistic.

Yet at the end of the season, only 12 teams will have locked up their post-season berth, leaving 20 other teams to wonder what went wrong. And predicting who those twelve teams will be is no easy task.

Bill James, godfather of advanced baseball stats, used to look at six indicators in his Baseball Abstracts that would help predict whether a team would improve or decline in the next season. After the break, we'll look at five of those indicators to see what they can tell us about the NFC East heading into the 2012 season.

Just a small word of caution up front. James developed the indicators for baseball, not for football. I can't vouch for their validity in baseball, much less in the NFL, but I thought they'd provide an interesting basis for discussion, as they provide at least some form of a basis for preseason predictions. Also, because the indicators look at stats from a macro perspective, they can help highlight trends that we might miss as we get lost in the minutiae of roster battles, position rankings and draft pick evaluations.

So without further ado, here are the indicators, and how each NFC East team stacks up:

1. Pythagorean Wins. The Pythagorean formula predicts a teams’ W/L record based on points scored and points allowed. When a team wins more games than it is projected to win based on the number of points scored and points allowed, that team will tend to decline in the following season. Conversely, when a team wins significantly fewer games than projected, that team will tend to improve in the following season.

Back in February, we used Pythagorean Wins to look at who the luckiest teams were in 2011, and the table below summarizes the win projections and variances for the NFC East teams.

Team 2011 W/L Points
scored
Points
allowed
Projected
Wins
Variance
Giants 9-7 394 400 7.9 -1.1
Eagles 8-8 396 328 9.8 +1.8
Cowboys 8-8 369 347 8.6 +0.6
Redskins 5-11 288 367 5.8 +0.8

Of the four NFC East teams, the Eagles have by far the biggest variance between their actual wins and their projected wins. Overall, this indicator suggests that the Eagles, Cowboys and Redskins all are likely to improve their records in 2012, while the Giants are likely to regress.

2. "It’s not how you start, it’s how you finish". Teams who play better in the second half of the season tend to improve the following season. It follows that teams who play worse in the second half tend to decline the following season. Because the NFL-season is only 16 games versus the 162 in a baseball season, this indicator is closely related to the first indicator.

So we'll use the Pythagorean Formula once more to calculate the projected wins for the NFC East teams based on the last eight regular season games from 2011 of each team.

Team 2011 W/L* Points
scored*
Points
allowed*
Projected
Wins*
Variance*
Giants 3-5 196 216 3.5 +0.5
Eagles 5-3 193 146 5.3 +0.3
Cowboys 4-4 190 172 4.5 +0.5
Redskins 2-6 161 209 2.8 +0.8
* Last eight regular season games

By this reckoning, all four teams played slightly better in the last eight games than their record indicates, so all four should expect to improve a little in 2012. Of course, this is where we run into the plague of NFL stats: small sample size. Applying the Pythagorean Formula to a 16-game schedule is already tricky, and it doesn't get any better using it against an eight-game schedule, but there's nothing we can change about it except to exercise some in caution in not overinterpreting these results.

3. "Plexiglass Principle". In simple terms, teams that show a dramatic improvement (or decline) from one season to the next have a tendency to relapse (or bounce back) in the following season.

Versus 2010, the 2011 Cowboys improved by two games, the Giants and Redskins declined by a game each and the Eagles declined by two - none of which I'd consider significant, so I'll not take this indicator into consideration as I look at the 2012 season projections at the end of this post.

One team that could however be affected by this indicator are the 49ers, who jumped from a six-win season in 2010 to a 13-win season in 2011. Football Outsiders specifically point out the 49ers as an example for this indicator in this year's Almanac, and even quantify the Plexiglass Principle:

San Francisco's total DVOA was 29.8 percentage points better in 2011 than it was in 2010. If you look at the 41 teams between 1992 and 2010 that showed similar improvement from one year to the next (i.e., between 25 and 35 percentage points), you find that they declined an average of 12.1 DVOA percentage points and 2.3 wins the following season.

4. Regression to the mean. This statistical phenomenon describes the fact that if a variable is extreme on its first measurement, it will tend to be closer to the average on a second measurement. In sports, this means that teams with a winning record tend to decline, teams with a losing record tend to improve.

One of the simplest ways to predict the next season - and one with which you'll likely achieve a higher accuracy than most of the expert predictions - is to start with eight wins for each team, then, for teams with a winning record in the previous year, add 0.5 games for each win above eight wins. For teams with a losing record, deduct 0.5 games for each win below eight wins. For the East, this is what that calculation would look like:

Team 2011 W/L Base wins 0.5 per game
above or below .500
Projected
Wins
Variance
Giants 9-7 8 +0.5 8.5 -0.5
Eagles 8-8 8
0 8.0 0
Cowboys 8-8 8 0 8.0 0
Redskins 5-11 8 -1.5 6.5 +1.5

The Redskins, because they are the statistical outlier in this group, benefit the most from the Regression To The Mean indicator. And bad news once more for the Giants.

5. Team Age. Young teams improve; old teams don't. This early in the season, we have only the vaguest idea of what the rosters for the NFL teams will look like, and looking at team age doesn't make a lot of sense before the final rosters are set. But here we are in July and want to make a prediction, so I went and looked at the ages of the 22 starters for each team as penciled in by ourlads.com.

By that method, the Eagles have the youngest team, with a combined age of 26.8 (590 combined years for their 22 projected starters). The Cowboys and Redskins are tied at 27.1 (both with 597 combined years) while the Giants have the oldest group of starters at 27.5 (604 combined years). A lot is likely to change with these roster projections between now and opening day, but for now, I'm awarding the Eagles 0.25 games based on their youth and deducting 0.25 games from the Giants for their age.

Summing up the Indicators

Bill James has a sixth indicator on his list (AAA performance), but since that doesn’t apply to the NFL, I’m simply skipping it. The other five indicators though are added up in the table below, sorted by the cumulative 2012 win projection based on the five indicators above:

Team 2011 W/L Pythagorean
Wins
Last 8
games
Plexiglass Regression
to the mean
Team Age Projected
Wins 2012
Eagles 8-8 +1.8 +0.3 - - 0 +0.25 10.35
Cowboys 8-8 +0.6
+0.5 - - 0 0 9.10
Giants 9-7 -1.1 +0.5 - - -0.5 -0.25 8.15
Redskins 5-11 +0.8 +0.8 - - +1.5 0 8.10

What this exercise shows is that there are some larger-scale statistical trends that can have an influence on how a season progresses. These larger-scale trends favor the Eagles and Redskins the most, while coming down hard on the Giants. Based on the five indicators alone, the Eagles look like the favorite for the division, with the Cowboys a solid second, and the Giants and Redskins in a surprisingly tight battle for third place.

But the NFL is not baseball. And picking five largely random stats to make a projection is very arbitrary. There are so many different factors determining who wins and who loses each Sunday that an accurate projection is nearly impossible, and that's without even factoring in the biggest driver of NFL game results: randomness.

In a season that only sees 16 games played, it's relatively hard to project anything, as the degree of randomness inherent in the game is probably one of the biggest determinants of the game results. And there simply is no way to predict randomness.

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