The NFL regular season is over, and it’s the NFL wild card weekend. There are no more simulations of the season at this point, and I plan to work on that tool so it better implements tiebreakers and gives more accurate playoff probabilities at the end of the season. The final regular season ratings are in, and they’re dominated by the NFC west. This is the last weekend that I’ll post schedule strength just because I’m not sure there’s a lot of value updating it during the playoffs.
The most lopsided of the games this weekend is the Rams-Panthers game, but I suspect that the predictions again underestimate the chances of the Panthers winning. They already played once this season, and the Panthers won that game, also in Charlotte. The Rams should again be the favorite, but I don’t think the Panthers’ chances are as low as the rating system suggests. I’ll probably switch to using the logistic distribution in future seasons, and that would certainly give the Panthers a better chance of winning this game. The Rams are correctly favored, but it would not be a huge surprise to me if the Panthers won this weekend. I’ve been talking a lot about the Panthers the past few weekends, but if there’s another surprise this season, it’s that the Patriots are not only in the playoffs, but they’re division champs and favored by a significant margin to advance. And although I generally agree with the teams that the computer ratings favor to win this weekend’s games, I suspect the probabilities are a bit more even than what you see here.
After 18 weeks, let’s get to the final regular season ratings.
Predictive Ratings
These are forward looking ratings, meaning that they’re intended to evaluate how good a team is and predict its future success, but they don’t evaluate the quality of a team’s achievements earlier in the season. These ratings are based purely on points. They don’t factor in wins and losses, only the margin of victory. The ratings don’t explicitly calculate the strength of schedule, though I calculate this afterwards. However, because of how the ratings are calculated, the quality of opponents does influence the ratings.
The offense and defense columns refer to each team’s point scoring tendencies instead of the efficiency ratings that some other rating systems use. The overall rating is approximately the sum of a team’s offense and defense ratings. To predict the score of a game for the home team, take the home team’s offense rating, add half of the home advantage, subtract the visiting team’s defense rating, and add the mean score. Predicting the score is similar for the visiting team. Take the visiting team’s offense rating, subtract half of the home advantage, subtract the home team’s defense rating, and add the mean score. Predicting the margin of victory for a game is done by taking the home team’s rating, adding the home advantage, and subtracting the away team’s rating. For neutral site games, the home advantage is set to zero.
While I call these forward looking ratings, they’re a good measure of a team’s quality as well. This can be viewed as the final assessment of the quality of the NFL teams that have no more games to play this season.
Predictive Ratings
Home advantage: 1.95 points
Mean score: 22.42 points
Rank Move Rating Change Team Offense Defense
1 12.95 +0.81 Seattle Seahawks 6.32 6.65
2 12.12 +0.15 Los Angeles Rams 8.08 4.05
3 +1 8.34 +1.07 Jacksonville Jaguars 4.98 3.34
4 -1 7.89 +0.43 Houston Texans 0.98 6.93
5 5.59 +0.01 San Francisco 49ers 3.62 1.95
6 5.47 +0.61 New England Patriots 3.63 1.85
7 +1 4.44 +0.27 Buffalo Bills 4.77 -0.32
8 -1 4.39 -0.05 Indianapolis Colts 5.50 -1.09
9 4.19 +0.14 Detroit Lions 5.22 -1.03
10 +1 3.53 +0.52 Denver Broncos 0.15 3.38
11 -1 2.93 -0.93 Philadelphia Eagles -1.85 4.78
12 1.84 -0.62 Kansas City Chiefs -1.85 3.69
13 +1 1.47 -0.18 Baltimore Ravens 1.85 -0.37
14 -1 1.21 -0.52 Green Bay Packers -0.40 1.62
15 +1 0.91 -0.13 Chicago Bears 2.46 -1.54
16 +2 0.85 +0.57 Minnesota Vikings -3.17 4.01
17 -2 0.80 -0.57 Los Angeles Chargers -0.94 1.69
18 -1 0.48 -0.02 Pittsburgh Steelers 0.10 0.39
19 -1.38 -0.09 Tampa Bay Buccaneers 0.09 -1.45
20 -2.27 -0.11 Atlanta Falcons -1.86 -0.42
Rank Move Rating Change Team Offense Defense
21 +1 -3.56 +0.07 Carolina Panthers -4.90 1.35
22 +1 -3.57 +0.75 New York Giants -0.79 -2.80
23 -2 -3.93 -0.90 Dallas Cowboys 4.65 -8.57
24 +1 -4.69 +0.14 Arizona Cardinals -1.34 -3.37
25 -1 -4.77 -0.37 Cincinnati Bengals 1.56 -6.34
26 -4.83 +0.20 New Orleans Saints -5.16 0.28
27 +1 -5.88 +0.65 Washington Commanders -2.09 -3.78
28 -1 -6.56 -1.12 Miami Dolphins -3.90 -2.66
29 +1 -7.49 +0.02 Cleveland Browns -7.91 0.42
30 -1 -7.78 -0.75 Tennessee Titans -4.01 -3.78
31 +1 -10.70 +0.62 Las Vegas Raiders -8.12 -2.60
32 -1 -12.02 -0.72 New York Jets -5.70 -6.32 Overachievers and Underachievers
A few weeks ago, I calculated each team’s expected winning percentage based on their rating and schedule, then I compared it to their actual winning percentage. It’s a lot like the strength of record ratings I use in college football, but I use each team’s actual rating as the benchmark instead of a hypothetical team with a rating 1.5 standard deviations above the FBS mean. Because it’s so similar to strength of record, the column name is TeamSOR.
This statistic shows which teams have overachieved or underachieved the most based on their rating and schedule. Because of how this is calculated, a team’s rating really needs to be well above or below zero for it to be significant. I suggest a threshold of around +.100 to consider a team as overachieving and -.100 to consider a team an underachiever.
Strength of Record for Each Team's Rating
Rank TeamSOR Team Win% Predictive
1 .185 Denver Broncos .824 3.53 (10)
2 .112 San Francisco 49ers .706 5.59 (5)
3 .102 Los Angeles Chargers .647 0.80 (17)
4 .096 Chicago Bears .647 0.91 (15)
5 .074 Carolina Panthers .471 -3.56 (21)
6 .074 New England Patriots .824 5.47 (6)
7 .073 Jacksonville Jaguars .765 8.34 (3)
8 .065 Pittsburgh Steelers .588 0.48 (18)
9 .059 Houston Texans .706 7.89 (4)
10 .058 Philadelphia Eagles .647 2.93 (11)
11 .044 Seattle Seahawks .824 12.95 (1)
12 .040 Miami Dolphins .412 -6.56 (28)
13 .032 Buffalo Bills .706 4.44 (7)
14 .027 Atlanta Falcons .471 -2.27 (20)
15 .023 Tampa Bay Buccaneers .471 -1.38 (19)
16 .012 Minnesota Vikings .529 0.85 (16)
17 .005 Green Bay Packers .559 1.21 (14)
18 .003 Dallas Cowboys .441 -3.93 (23)
19 -.028 Cincinnati Bengals .353 -4.77 (25)
20 -.035 New Orleans Saints .353 -4.83 (26)
Rank TeamSOR Team Win% Predictive
21 -.039 Los Angeles Rams .706 12.12 (2)
22 -.041 New York Jets .176 -12.02 (32)
23 -.041 Cleveland Browns .294 -7.49 (29)
24 -.044 Las Vegas Raiders .176 -10.70 (31)
25 -.048 Washington Commanders .294 -5.88 (27)
26 -.055 Tennessee Titans .176 -7.78 (30)
27 -.074 Baltimore Ravens .471 1.47 (13)
28 -.087 Detroit Lions .529 4.19 (9)
29 -.096 Indianapolis Colts .471 4.39 (8)
30 -.126 Arizona Cardinals .176 -4.69 (24)
31 -.171 New York Giants .235 -3.57 (22)
32 -.198 Kansas City Chiefs .353 1.84 (12) Schedule Strength
The first column is the expected losing percentage (1 minus winning percentage) for a hypothetical average NFL team in each team’s games played to date. Larger numbers mean a tougher schedule. The second column is the same thing, just for future games instead of past games.
The third column is the average opponent rating, with an adjustment for the site of games, for previously played games. The fourth column is the average opponent rating for the future games that each team will play. These two columns are the same schedule strength metrics from my previous NFL articles.
In college football, the two approaches to schedule strength would differ more just because the approach used in the first two columns limits the influence of truly lopsided blowout games. In the NFL, there just aren’t that many blowouts, and the teams are more evenly balanced. Therefore, there’s just not too much of a difference in the two approaches to measuring schedule strength.
Schedule Strength for an Average Team
Home advantage: 1.95 points
Mean score: 22.42 points
Rank Team SOS Future OppRtg Future
1 Seattle Seahawks .535 (7) --- 1.43 (7) ---
2 Los Angeles Rams .560 (3) .452 (12) 2.30 (3) -1.62 (12)
3 Jacksonville Jaguars .514 (10) .573 (8) 0.48 (10) 2.49 (8)
4 Houston Texans .553 (4) .571 (9) 2.00 (4) 2.43 (9)
5 San Francisco 49ers .546 (5) .641 (5) 1.96 (5) 4.87 (5)
6 New England Patriots .381 (32) .466 (11) -4.39 (32) -1.15 (11)
7 Buffalo Bills .440 (31) .776 (1) -2.23 (31) 10.29 (1)
8 Indianapolis Colts .542 (6) --- 1.65 (6) ---
9 Detroit Lions .497 (15) --- -0.02 (13) ---
10 Denver Broncos .451 (30) --- -1.84 (30) ---
11 Philadelphia Eagles .490 (20) .607 (6) -0.41 (21) 3.65 (6)
12 Kansas City Chiefs .498 (14) --- -0.13 (16) ---
13 Baltimore Ravens .495 (18) --- -0.30 (18) ---
14 Green Bay Packers .481 (24) .584 (7) -0.68 (24) 2.86 (7)
15 Chicago Bears .474 (26) .478 (10) -0.96 (26) -0.73 (10)
16 Minnesota Vikings .506 (12) --- 0.31 (12) ---
17 Los Angeles Chargers .476 (25) .708 (3) -0.91 (25) 7.42 (3)
18 Pittsburgh Steelers .489 (21) .670 (4) -0.41 (20) 5.95 (4)
19 Tampa Bay Buccaneers .517 (9) --- 0.76 (8) ---
20 Atlanta Falcons .497 (16) --- -0.06 (15) ---
Rank Team SOS Future OppRtg Future
21 Carolina Panthers .511 (11) .774 (2) 0.46 (11) 10.17 (2)
22 New York Giants .496 (17) --- -0.17 (17) ---
23 Dallas Cowboys .453 (29) --- -1.69 (28) ---
24 Arizona Cardinals .585 (2) --- 3.33 (1) ---
25 Cincinnati Bengals .492 (19) --- -0.43 (22) ---
26 New Orleans Saints .487 (22) --- -0.33 (19) ---
27 Washington Commanders .501 (13) --- -0.03 (14) ---
28 Miami Dolphins .455 (28) --- -1.72 (29) ---
29 Cleveland Browns .466 (27) --- -1.29 (27) ---
30 Tennessee Titans .587 (1) --- 3.17 (2) ---
31 Las Vegas Raiders .520 (8) --- 0.69 (9) ---
32 New York Jets .484 (23) --- -0.57 (23) --- Wild Card Weekend Predictions
The thresholds for close games, blowouts, and high and low scoring games are different in the NFL than in college football. That’s because NFL teams are balanced enough in talent to usually avoid truly lopsided scores and there’s just less scoring overall.
Games are ranked based on the projected quality. This factors in the overall strength of the two teams and the potential for a competitive game. Game quality ratings are not directly comparable between college football and the NFL for many of the same reasons I just mentioned. This is just for predicting which NFL games are most and least compelling each weekend.
Beside each team, there are two numbers in parentheses. One is the predicted margin of victory (positive) or defeat (negative), the other is the probability of winning. In the event that a margin is larger than what’s indicated by the predicted score, that’s because there’s nothing in the math that prevents a forecast of negative points with a sufficiently lopsided matchup. This isn’t even close to possible with the current NFL ratings, even with the weakest offense against the strongest defense, but it does occur once in awhile in college football. A negative score is impossible, of course, so the score would be set to zero in those instances. However, there’s no upper limit on how many points a team can be projected to score. But with more parity between NFL teams, even the highest scoring predictions aren’t going to be nearly as crazy as what is possible with the college football ratings.
Games on Saturday, January 10, 2026
#1: Green Bay Packers (-1.65, 45.14%) at Chicago Bears (1.65, 54.86%)
Estimated score: 22.58 - 24.23, Total: 46.81
Quality: 68.35%, Team quality: 56.70%, Competitiveness: 99.32%
Blowout probability (margin >= 17.0 pts): 21.57%
Close game probability (margin <= 3.0 pts): 17.46%
High scoring probability (total >= 56.0 pts): 26.06%
Low scoring probability (total <= 33.0 pts): 16.76%
#2: Los Angeles Rams (13.73, 84.40%) at Carolina Panthers (-13.73, 15.60%)
Estimated score: 28.18 - 14.44, Total: 42.62
Quality: 58.68%, Team quality: 56.83%, Competitiveness: 62.56%
Blowout probability (margin >= 17.0 pts): 41.82%
Close game probability (margin <= 3.0 pts): 10.43%
High scoring probability (total >= 56.0 pts): 17.51%
Low scoring probability (total <= 33.0 pts): 25.10%
Games on Sunday, January 11, 2026
#1: Buffalo Bills (-5.85, 33.26%) at Jacksonville Jaguars (5.85, 66.74%)
Estimated score: 22.87 - 28.69, Total: 51.56
Quality: 85.19%, Team quality: 82.06%, Competitiveness: 91.81%
Blowout probability (margin >= 17.0 pts): 25.35%
Close game probability (margin <= 3.0 pts): 15.99%
High scoring probability (total >= 56.0 pts): 37.84%
Low scoring probability (total <= 33.0 pts): 9.75%
#2: San Francisco 49ers (0.72, 52.13%) at Philadelphia Eagles (-0.72, 47.87%)
Estimated score: 20.28 - 19.59, Total: 39.87
Quality: 83.20%, Team quality: 75.95%, Competitiveness: 99.87%
Blowout probability (margin >= 17.0 pts): 21.30%
Close game probability (margin <= 3.0 pts): 17.57%
High scoring probability (total >= 56.0 pts): 13.01%
Low scoring probability (total <= 33.0 pts): 31.58%
#3: Los Angeles Chargers (-6.62, 31.21%) at New England Patriots (6.62, 68.79%)
Estimated score: 18.66 - 25.33, Total: 43.98
Quality: 72.12%, Team quality: 64.70%, Competitiveness: 89.62%
Blowout probability (margin >= 17.0 pts): 26.48%
Close game probability (margin <= 3.0 pts): 15.56%
High scoring probability (total >= 56.0 pts): 20.08%
Low scoring probability (total <= 33.0 pts): 22.16%
Games on Monday, January 12, 2026
#1: Houston Texans (5.46, 65.71%) at Pittsburgh Steelers (-5.46, 34.29%)
Estimated score: 22.03 - 16.57, Total: 38.60
Quality: 78.47%, Team quality: 72.16%, Competitiveness: 92.80%
Blowout probability (margin >= 17.0 pts): 24.84%
Close game probability (margin <= 3.0 pts): 16.18%
High scoring probability (total >= 56.0 pts): 11.23%
Low scoring probability (total <= 33.0 pts): 34.78%Yes, I’m a bit late getting this posted. That’s due to working on other projects during the week, then realizing right before posting the article that I needed to make an adjustment for the postseason. To date, my rating system has been set up to make predictions for the regular season, where ties are possible, and there’s usually just under a 0.5% chance of any game ending in a tie. In the postseason, there are no ties, and that required making a last minute change to my code that took longer than I planned. It happens. I’m actually less concerned with the actual predictions or being a pundit and doing analysis, though both of those can be interesting at times. The biggest reason I do these is because I’m interested in the science of how to make accurate ratings and predictions.
But sometimes those predictions turn out more accurate than I ever expected. For many weeks, my rating system put Indiana atop the college football ratings, and they showed yet again they belong with a dominant win over Oregon. I’ll have a couple more articles about college football before winding that down for the winter, but Indiana will absolutely be the favorite over Ole Miss.
Thanks for reading!
This article uses data from Pro Football Reference to calculate the ratings.