1. NFL Team Ranking Methods and Their Abilities to Predict Games
Eric Eager
July 13th, 2017

2. Data Journalism

3. Pro Football Focus
Pro Football Focus is a company that was originated by Neil Hornsby in the UK.
Neil was unsatisfied with the way in which traditional statistics were used to measure player performance.
For example, whether or not a defensive player makes a tackle right in front of the line of scrimmage (good for his team) or 15 yards downfield (bad for his team), traditional football statistics count this action as a “tackle” just the same.
Pro Football Focus developed a system to grade every player on each play of each game, measuring the good and bad things each player did on the field on each play, controlling for the performance of his teammates and the context of each action.

4. Pro Football Focus
PFF now has each NFL player from 2006 – 2016 (2005 in progress) graded, as well as 2014 – 2016 for all FBS players.
Twenty-nine of the 32 NFL teams have contracts with PFF to help with self and opponent scouting, as well a roster construction.
Sunday Night Football, often the highest-rated show on television during a given week, displays PFF rankings as a part of their broadcast.

5. Pro Football Focus

7. Why Develop a Rating System to Rank Teams?
Determine true team strength relative to outcome-driven realities
Predict future performance and help develop long-term strategies
Adjust metrics for opponent strength
Player valuation
Predict games (ATS/OU/MoneyLine) and seasons
It’s fun!

8. The ELO Method

9. The ELO Method

10. The ELO Method
The ELO rating system is an iterative system wherein ratings are updated via the results of a matchup between two teams.
In this system subsequent strengths are updated not only on the results of previous matchups, but the strengths of the teams played in such matchups.
The average ELO rating is a 1500, and ELO ratings are carried over from year to year after a procedure for regression to the mean.

11. The ELO Method
𝐸𝐿𝑂 𝑖,𝑛+1 = 𝐸𝐿𝑂 𝑖,𝑛 +𝑓( 𝐸𝐿𝑂 𝑖,𝑛 , 𝐸𝐿𝑂 𝑗,𝑛 , 𝑠𝑐𝑜𝑟𝑒 𝑖,𝑛 , 𝑠𝑐𝑜𝑟𝑒 𝑗,𝑛
Here, the function f uses
the actual score of the game between team i and team j
the expected score of the game between team i and team j
how much ratings should be updated on the basis of one game

12. The ELO Ratings for 2016 1 NE 1774.6 2 ATL 1672.6 3 KC 1667.3 4 PIT#
TEAM
RATING 1 NE
1774.6 2
ATL
1672.6 3 KC
1667.3 4
PIT
1643.7 5 GB
1624.2 6
SEA
1603.4 7
DAL
1599.0 8
DEN
1580.4 #
TEAM
RATING 32 SF
1278.0 31
CLE
1299.6 30
JAX
1320.5 29
CHI
1323.3 28 LA
1345.5 27 SD
1405.4 26
NYJ
1429.1 25
TEN
1437.1

13. The PFF ELO Method
Due to the discrete and fluky nature of the game of football, using simple results like points for and against may not be the best approach for ranking teams.
Luckily, at PFF we grade players on every play during every game, and could more-properly assign win team strength based on our grades instead of final score.

14. The PFF ELO Method Week 7 of 2016 Season: DET 20 WAS 17
Using PFF grades, Week 7 of 2016 Season:
WAS 23 DET 21

15. Comparison of Rating Systems

16. The PFF ELO Ratings for 2016 1 NE 1767.6 2 ATL 1698.6 3 PIT 1644.3 4#
TEAM
RATING 1 NE
1767.6 2
ATL
1698.6 3
PIT
1644.3 4 KC
1621.4 5
DAL
1619.1 6 GB
1597.4 7
SEA
1594.1 8
ARI
1590.9 #
TEAM
RATING 32 SF
1293.6 31
CLE
1297.9 30
JAX
1319.7 29 LA
1360.0 28
CHI
1379.9 27
NYJ
1419.8 26 SD
1435.0 25
NYG
1450.3

17. The Keener Method

18. The Keener Method A matrix-based approach
Strength evaluated by calculating a pair-wise share of all games played based on points scores/points against
For example, when the 2014 Kansas City Chiefs beat the New England Patriots 41-14, the Chiefs were awarded share of that game, with the Patriots the other
These shares populate the Keener matrix K.

19. The Keener Method Week 1 of 2016
BAL (3) beat BUF (4) 13-7, giving BAL share relative to BUF’s

20. The Keener Method
The 32-by-32 Keener matrix is populated using all games during a specified window, and scaled by the number of games in the window (per team).
The teams are then rated using a 32- dimensional vector r that solves the eigenvalue-eigenvector equation
Kr = cr.

21. The Perron-Frobenius Theorem
The Perron-Frobenius theorem says that, with a sufficient level of connectivity between teams, there is a unique real number c and a corresponding vector r that solve
Kr = cr,
where c is the largest such solution and its corresponding r makes sense as a ranking vector.

22. The Keener and PFF Keener Ratings for 2016#
TEAM
RATING 1 NE
100.0 2
DAL
98.2 3
DEN
95.6 4
ATL
94.8 5 KC
94.7 6
PHI
92.2 7
OAK
89.1 8
SEA
87.2 #
TEAM
RATING 1 NE
100.0 2
DAL
99.1 3
ATL
98.8 4
DEN
92.1 5
PHI
91.8 6
WAS
91.3 7
OAK
91.2 8 KC
90.5

23. The Keener and PFF Keener Ratings for 2016#
TEAM
RATING 32 SF
44.2 31 LA
48.2 30
NYJ
52.4 29
CHI
52.5 28
CLE
54.4 27
JAX
59.8 26
MIA
64.9 25
HOU
71.7 #
TEAM
RATING 32 SF
47.0 31
NYJ
55.1 30 LA
55.7 29
CLE
56.0 28
JAX
60.5 27
CHI
62.4 26
MIA
66.7 25
HOU
70.6

24. Comparison of Rating Systems

25. The Massey Method

26. The Massey Method A matrix-based approach
Strength evaluated by a vector f with point differential for each team
Connectivity determined by a Massey matrix M with a -n in the i,jth element if team i has played team j n times. Along the diagonal of the matrix is a number denoting how many games each team has played.
The ranking vector r is the (usually approximate) solution to the matrix-vector equation
Mr = f.

27. The Massey Method

28. The Massey and PFF Massey Ratings for 2016#
TEAM
OFF
DEF 1
ATL
20.7
-10.9 2 NO
15.2
-13.7 3 NE
14.8
-4.0 4 GB
14.7
-11.6 5
DAL
12.7
-6.6 6
PIT
11.9
-7.1 7
OAK
11.8
-9.5 8 SD
11.5
-11.4 #
TEAM
OFF
DEF 1
DEN
-2.4
3.6 2
HOU
-4.1
2.9 3
NYG
-2.9
2.4 4
SEA
-0.4
2.2 5
BAL
-1.7
2.1 6
ARI
0.3
1.9 7 NE
4.8
1.6 8 SD
-0.3
1.5

29. CURRENT PLAYER VALUATION
WINS ABOVE AVERAGE (2016) #
PLAYER
POS.
WAA 1
Tom Brady QB
2.5 2
Matt Ryan
1.9 3
Aaron Rodgers 4
Andrew Luck
1.7 5
Julio Jones WR
1.3 6
Mike Evans
1.2 7
Derek Carr
1.1 8
Russell Wilson
1.0 9
Chris Harris Jr. CB 10
Dak Prescott
0.9 11
Aaron Donald DI

30. How Do These Ratings Predict Wins One Year to the Next?#
RATING
COR
NODE IMP 1
PFFELO
0.442
597.8 2
ELO
0.434
614.4 3
Massey Off.
0.386
513.3 4
Keener
0.368
446.6 5
PFFKeener
0.360
423.3 6
PFF Massey Off.
0.303
385.2 7
PFF Massey Def.
0.264
380.8 8
Massey Def.
0.225
387.2

31. How Do These Ratings Win Percentage?#
RATING
AUC
2016 Win% 1
ELO
0.694
0.638 2
PFFELO
0.692
0.626 3
Massey
0.687
0.634 4
Keener
0.672
0.615 5
PFFMassey
0.669
0.657 6
PFFKeener
0.667

32. How Do These Ratings Win Percentage?
Correctly predicted 67.9% of 2016 games straight up

33. How Do These Ratings the Vegas Spread?#
RATING
R SQUARED 1
ELO
0.747 2
PFFELO
0.743 3
Massey
0.681 4
PFFKeener
0.574 5
Keener
0.569 6
PFFMassey
0.489

34. How Do These Ratings Predict Vegas Spread?

35. How Do These Ratings the Vegas OU?#
RATING
R SQUARED 1
Massey
0.359 2
PFFMassey
0.275 3
ELO
0.062 4
PFFELO
0.053 5
Keener
0.038 6
PFFKeener
0.025

36. How Do These Ratings Predict Vegas OU?

37. Conclusions
We examined three ways of rating NFL teams, both using simply points for and against and using PFF grades.
In some instances the PFF versions of the ratings provided more predictive and explanatory value than their traditional counterparts, and in every case they provided a correction term that increased the efficacy of a larger model.
Using PCA, 8/16 principal components are required to realize 95% of the variance in different ranking combinations between opposing teams from , 12/16 required for 99%.
None of the rankings discussed today involved issues of player availability, and future models will include concern for this issue in additional features.
Final models will include deep learning techniques, e.g. neural networks.

38. Acknowledgements Cris Collinsworth Neil Hornsby Rick Drummond
Nathan Jahnke
Steve Palazzolo
George Chahrouri
Paul Bessire
Chase Howell