2012 Basic NFL Predictions

The NFL season begins tonight, and there are predictions flying everywhere. I thought I’d take a basic analytical approach and produce a baseline projection. If your predictions can’t beat this (dumb) system, you aren’t very good at predictions!

What got me started down this trail was Bill Barnwell’s article on Grantland breaking down some good basic predictive NFL statistics. He discussed Pythagorean Wins, record in close games, the Plexiglass principle, and team turnover margin, and how those stats help in predictions.

So, I thought I’d throw together a quick projection system that includes those principles to churn out baseline NFL predictions.

Evaluating NFL Pythagorean Wins

First, I looked at whether there is any persistence to winning more games than expected from point differential.  This is also tied into the close game fact–if you win a bunch of close games, you will win more games than your point differential would indicate likely.

I took all NFL team season since 1990 (thanks, Pro-Football-Reference!) and calculated each team’s Pythagorean Winning percentage for each season.  I then compared the difference between actual win% and expected win% in year Y to year Y+1.  The correlation was only 0.014, indicating that if you out perform your Pythagorean Win% one year, you aren’t more likely to do so the next year.  Thus the “regression to the mean” effect of looking at Pythagorean Win % and close game win%.

So, in my projection, I will project point differential and calculate wins based off of that (and SoS).

Effect of Turnover Margin

Teams that have excellent turnover margins are somewhat more likely to have good turnover margins again; the correlation of turnover margin from 1 year to the next is 0.13.  However, there is a strong “regression to the mean” component.

I ran a regression to predict turnover margin for Year Y from the previous 6 years.  The weights came out as follows:

Year	Weight
NFL Average (=0.0)	5.76
Y-1	1.00
Y-2	0.47
Y-3	0.22
Y-4	0.10
Y-5	0.05
Y-6	0.02

So most of the weight is on NFL average–there is a very strong regression to the mean.

The Projections

To create my actual projections, I used a 3-step process:

1. Adjust team point differential to remove turnover margin effect
2. Predict point differential this year (using last 6 years plus regression to the mean)
3. Convert point differential to wins based on strength of schedule and the Pythagorean equation

I ran a regression on the last 22 years of data (since 1990) to concurrently adjust for turnover margin and find the weights for predicting point differential.

The regression found that for every turnover-per-game-margin, the team’s point differential should be discounted by 2.4 points/game.  So if a team (San Fransisco last year) had a point differential of +9.44, with at turnover margin per game of +1.75, their adjusted point differential for predictive purposes would be 9.4 – (2.4*1.75) = 5.24.

Okay, now I have adjusted point differentials for every historical season.  On to the projections!

I ran the regression to predict point differential just like I did for turnover margins above.  The resulting weights:

Year	Weight
NFL Average (=0.0)	0.83
Y-1	1.00
Y-2	0.38
Y-3	0.15
Y-4	0.06
Y-5	0.02
Y-6	0.01

Notice: not nearly as strong of a regression to the NFL mean, nor a very strong reversion to the team’s historical past.  Last year is a better predictor of this year than NFL average or the team’s total history before last year.

Okay, on to part 3: converting to wins.

I took the NFL schedule for next year, and based on the predicted team strengths I just calculated, I generated a strength of schedule for each team.

So, taking the team’s strength + SoS, and predicted team pace (I won’t bore you with those calcs) I generated Points For and Points Against and thus the team’s Pythagorean Win%, and Wins and Losses.

Note: I made 2 manual tweaks to account for 2 changes for this season: I added 5 points to the Broncos historical seasons and removed 5 points from the Colts for the seasons Peyton Manning played there, and I removed 5 points from the Saints for the coaching/suspension issues.

Here are the results:

AFC Predictions

Team	W	L	Pythag	Pts	PtsO	PtDif	MoV	SoS	SRS	Seed
AFC East
New England Patriots	10	6	0.648	436	338	98	6.1	-0.7	5.5	1
New York Jets	9	7	0.537	366	344	21	1.3	0.1	1.4	6
Miami Dolphins	8	8	0.523	349	337	12	0.8	-0.4	0.3
Buffalo Bills	7	9	0.435	344	385	-41	-2.6	-0.3	-2.9
AFC North
Pittsburgh Steelers	10	6	0.632	364	291	73	4.6	0.2	4.7	2
Baltimore Ravens	10	6	0.594	367	313	54	3.4	0.6	4.0	5
Cincinnati Bengals	8	8	0.484	342	353	-10	-0.6	0.5	-0.2
Cleveland Browns	6	10	0.383	290	355	-66	-4.1	0.6	-3.5
AFC South
Houston Texans	9	7	0.557	378	344	34	2.1	-0.1	2.0	4
Tennessee Titans	8	8	0.503	348	347	1	0.1	0.3	0.4
Jacksonville Jaguars	7	9	0.411	316	369	-53	-3.3	0.1	-3.2
Indianapolis Colts	6	10	0.394	329	395	-67	-4.2	0.0	-4.2
AFC West
San Diego Chargers	9	7	0.587	403	349	54	3.4	-0.3	3.1	3
Denver Broncos	8	8	0.511	367	361	6	0.4	0.4	0.7
Oakland Raiders	7	9	0.448	350	384	-33	-2.1	0.0	-2.0
Kansas City Chiefs	6	10	0.399	307	365	-59	-3.7	0.0	-3.7

NFC Predictions

Team	W	L	Pythag	Pts	PtsO	PtDif	MoV	SoS	SRS	Seed
NFC East
Philadelphia Eagles	9	7	0.589	397	341	55	3.5	0.0	3.4	3
Dallas Cowboys	8	8	0.504	366	365	1	0.1	0.3	0.4
New York Giants	8	8	0.482	370	383	-13	-0.8	0.9	0.1
Washington Redskins	7	9	0.443	325	359	-34	-2.1	0.1	-2.0
NFC North
Green Bay Packers	10	6	0.649	432	334	98	6.1	-0.8	5.4	1
Chicago Bears	8	8	0.528	358	342	16	1.0	-0.5	0.5	6
Detroit Lions	8	8	0.509	386	381	5	0.3	0.0	0.3
Minnesota Vikings	7	9	0.452	350	380	-31	-1.9	-0.5	-2.4
NFC South
New Orleans Saints	10	6	0.598	421	356	64	4.0	-0.2	3.8	2
Atlanta Falcons	9	7	0.563	381	343	38	2.4	-0.5	1.9	5
Carolina Panthers	7	9	0.435	343	384	-41	-2.6	0.0	-2.5
Tampa Bay Buccaneers	6	10	0.382	323	397	-74	-4.6	-0.2	-4.8
NFC West
San Francisco 49ers	9	7	0.564	355	319	36	2.2	-0.5	1.8	4
Seattle Seahawks	8	8	0.472	339	356	-17	-1.1	-0.4	-1.5
Arizona Cardinals	8	8	0.470	347	366	-19	-1.2	0.0	-1.2
St. Louis Rams	5	11	0.314	279	390	-111	-6.9	0.0	-6.9

Obviously, by pundit standards, these projections seem conservative. They represent, however, a maximum likelihood estimate for each team–we should give each team about a 50% chance of doing better than or worse than their prediction here.

The standard deviation on the projection is 5.4 points, so there is a 68% chance that each team will be within 5.4 points of the predicted margin per game.

Put another way–there is about a 50% chance that a team over performs its expected point differential by 10 or more points.  If that team is yours–you could be the San Fransisco 49ers of this  season (+10.4 above last year) or the Lions or Saints (about +8.5 above expectation for each last year).