Sep 14

NFL 2016 TABRankings

Like with just about every football publication, we like to do weekly rankings to track the progress of each of the 32 NFL teams. This allows us to create a points system for our weekly NFL Pick’em competition. This allows us to see the “power” of each match-up in the NFL season. In order to do that, we need a rankings system that includes key statistics in various areas of the game. That’s where the TABRankings come into play. Here, we can see which teams are truly the best and which teams are truly the worst.

For those who are new to the TABRankings, this is a system created long ago (and continue to tweak on almost a yearly basis) to look at the quality of play by a team. This is expected to go beyond each team’s overall record, although the standings matter a whole lot to the rankings formula. Other key factors, including outcome-based stats and efficiency stats, play a role in the formula as well. These rankings will then be used to help categorize (and even predict) games for the weekly Pick’em competition. Here’s our formula:

The TABRankings formula:
(Win Pct x 2)+(Pyth Pct)+(Div Pct x 0.5)+(Qual Pct x 0.5)+(3rd/4th Down Diff x 0.1n)+(TO Margin x 4)-(FK Diff x 0.8)+(Sack Diff x 0.8)+(Time Ahead Avg x 1.25)+(Time Ahead Big Avg x 1.5)+(Real Quarterback Rating Diff x 1.25)+(Relative Pts Avg x 1.8)

NOTE: The quality win percentage and relative points average will both be excluded until 2016 TABR adjustments are excluded.

This is only one change to the formula for the 2016 season. Instead of using a static 1.75 multiple for Third/Fourth Down Differential, the formula will account for sample size. That’s why the 0.1n multiple will be used, with “n” representing the number of games play. By season’s end, this stat will have a little less value relative to previous years, but we think it’s an all-around more accurate representation.

The TABRankings formula is based on three statistical categories (record stats, basic stats, advanced stats) with four totals in each category. Let’s remind everyone what’s going on by giving a breakdown of the stats that make up the rankings formula:

  • Overall Win Percentage: This should be quite simple to understand. This looks at percentage of wins out of games played.
  • Pythagorean Win Percentage: In a concept originally introduced by Pro Football Reference, the Pythagorean winning percentage focuses on an expected W-L record based on points scored and points against. While PFR uses 2.37 for the exponent, I have adjusted the total to 2.4 to compensate for the recent upturn in scoring in the NFL.
  • Divisional Win Percentage: See the overall win percentage, only for the subset of divisional games.
  • Quality Win Percentage: In this instance, the subset involves the team’s W-L percentage against teams with a .500 record or better. Cold, Hard Football Facts introduced their Quality Standings that only considered teams with a winning record. However, there are .500 teams that are strong teams and even successful playoff teams (see: 2011 Broncos, 2004 Vikings, etc.).
  • Third/Fourth Down Differential: This measures percentage difference from offensive third- and fourth-down success and defensive third- and fourth-down success. (Example: Team X goes 39 of 95 on third down and 1 of 5 on fourth down, resulting in a 40-of-100 conversion rate. Their opponents collectively convert 28 of 90 third down attempts and 2 of 10 fourth down attempts, resulting in a 30-of-100 conversion rate. This means the calculation becomes 40 percent minus 30 percent.)
  • Turnover Margin: This is very simple as well. Just subtract giveaways from takeaways for the margin.
  • Fumbles Kept Differential: To compensate a bit for luck, I’ve added a few points to take away from the teams that recovered more fumbles than their opponents. Simply subtract opponents’ fumbles kept from the team’s fumbles kept. (Example: Team Y recovers 4 of their 12 fumbles. Their opponents recover 8 of 12 their fumbles. This results in a negative-4 differential.)
  • Sack Differential: This is quite self-explanatory. Simply subtract the times sacked (offense) from the total sacks (defense).
  • Time Ahead Average: This involves the total time each team held a lead, divided by the total games played. (Example: Team Z led for 90 minutes through three games, thus having a 30-minute time ahead average.)
  • Time Ahead Big Average: This involved the total time each team held a lead by more than two possessions (15 points or more), divided by the total games played. (Example: Team Z led by 15 points or more for 15 minutes through three games, thus having a five-minute time ahead average.) This reflects bonus points for “leading big” in a game.
  • Real Quarterback Rating Differential: This stat originates from Cold, Hard Football Facts. Rushing stats, sacks and fumbles by the quarterbacks are combined with the passing stats to complete the passer rating formula. Given the slightly improved correlation of victory over Passer Rating Differential and the improving athletic stature of the average quarterback, this stat replaces Passer Rating Differential from past publications of these rankings.
  • Relative Points Average per Game: This might be a bit complicated. More or less, this total is meant to reflect a team’s point differential relative to their average opponent’s differential. First, subtract the opponents’ average points allowed from the team’s average points scored. Second, subtract the opponents’ average points scored from the team’s average points allowed. Finally, add those two totals together to get the Relative Points Average per Game. This idea originated from CHFF’s Relativity Index.

Check out this post every week to see the newest results for the TABRankings. There will be a new page for every week in the NFL season. We plan to update these rankings every Wednesday, just in time for the upcoming week’s predictions.

Sep 08

NFL 2016 Pick’em

With another NFL season set to kick off tonight, we begin another year of our NFL Pick’em competition. Both the 2013 and 2014 seasons ended in rousing fashion, with TABMathletics reigning supreme over all other experts. Last year saw some of the much-discussed regression come into play, but TABMathletics still finished as one of the best experts in the land. While no three-peat was recognized, a simple bounce back towards our three-year mean could result in a third Pick’em title in four seasons. Will we live up to the hype in 2016?

The weekly predictions involve the straight-up choice, meaning that no spread will be considered. Meanwhile, there will also be a points system used to put some emphasis on correctly predicting the tough games. Using the TABRankings as a basis for illustrating the “power” of each game, the week’s schedule will be divided into three groups:

  • Games of the Week (3 points each): Any game with two teams having a combined ranking total of 24 or less. Both teams MUST be within 10 spots of each other, unless both teams are within top 12. (Theoretically, 1 v. 12 is a playoff-quality match-up.)
  • Non-GOTW Divisional Games (2 points each): Any divisional battle that isn’t in the aforementioned group.
  • The Rest (1 point each): Any match-up that is neither a Game of the Week or a divisional battle.

Each week will also include one upset pick and one lock pick. The lock picks will be made in the elimination pool format, with the winner only allowed to be used once during a present win streak. The upset picks will be used to collect ranking points, which will be determined by the difference in the TABRankings. If there’s enough time at season’s end, we’ll compare the power of the upset to other notable experts (something which we failed to analyze in each of the previous three seasons). For now, though, it’ll just be for personal keep.

Now for the competition. Experts from several different publications were selected to highlight the most well-known sources of football analysis. Two litmus tests of comparison to TABM would be Football Outsiders and the FiveThirtyEight Elo-based predictions. Of course, there also needs to be that mainstream presence. Thus, the competition also includes the nine experts from ESPN who publish their picks online and the four experts from NFL Network who work on GameDay Morning. The picks for Pete Prisco of CBS Sports and Vinnie Iyer of Sporting News will be monitored as well. Finally, we add in five experts from USA Today along with seven experts from Pro Football Focus (excluding Cris Collinsworth, who does not pick games he will broadcast for Sunday Night Football) to complete this year’s competition panel. This group of experts provides at least some perspective to compare from other top publications.

Before we move on, we’d like to send our best to two previous mainstays in Don Banks and Chris Mortensen. Banks was one of the most reliable hands to write for Sports Illustrated during the NFL’s “fantasy” era, but the publication laid him off. His last day was just last Thursday. It only gets tougher when thinking about Mortensen, who was diagnosed back in January that he had Stage IV throat cancer. Mortensen was arguably the classiest and most reliable of ESPN’s NFL reporters, and he was a great predicting hand to boot. Upon last word, Mortensen can now “focus on recovery,” which is some positive news that warms our hearts with hope. Bless them both.

Aug 18

Reimagining the Expected Win Differential for 2016

NFL Preview

In our eyes, 2015 was a step back for TABMathletics. Of course, this assessment is relative to the rousing success of the 2013 NFL Preview and 2014 NFL Preview. The 2015 NFL Preview was still an overall successful endeavor, but it still left more to be desired when compared from the first two voyages. Part of this had to do with unexpected defiance of Expected Win Differential by two teams last year. The tempered expectations of the Arizona Cardinals and Cincinnati Bengals were overcome by two great regular season campaigns. For that, 2015 marked the first step back in the three-year run of Expected Win Differential. The results from last year were as followed:

  • Arizona Cardinals (-3.34 EWD): **Improved from 11-5 to 13-3**
  • Tampa Bay Buccaneers (+3.18 EWD): Improved from 2-14 to 6-10
  • Detroit Lions (-2.37 EWD): Declined from 11-5 to 7-9
  • Tennessee Titans (+2.27 EWD): Improved from 2-14 to 3-13
  • Green Bay Packers (-2.15 EWD): Declined from 12-4 to 10-6
  • Cincinnati Bengals (-1.93 EWD): **Improved from 10-5-1 to 12-4**
  • Dallas Cowboys (-1.91 EWD): Declined from 12-4 to 4-12
  • New York Jets (+1.79 EWD): Improved from 4-12 to 10-6
  • New York Giants (+1.68 EWD): Owned 6-10 record both seasons
  • Washington Redskins (+1.65 EWD): Improved from 4-12 to 7-9
  • San Francisco 49ers (-1.61 EWD): Declined from 8-8 to 5-11
  • Denver Broncos (-1.60 EWD): Owned 12-4 record both seasons
  • New Orleans Saints (+1.59 EWD): Owned 7-9 record both seasons
  • Oakland Raiders (+1.47 EWD): Improved from 3-13 to 7-9

As you can see, EWD experiences its first two failures in its three-year history. While nine teams regressed as expected, two teams defied expectations. Both the Cardinals and Bengals improved despite their notably poor EWD totals. Arizona’s improvement can in large part be explained by the healthy return of quarterback Carson Palmer, who built off of his strong showings from late 2013 and early 2014 (re: he owned a 94.09 passer rating over his previous 16 starts before last season). One should be able to see logic in the quarterback stability leading to unrecognized regression. However, Cincinnati’s improvement is less accounted by conventional wisdom. The team improved virtually all around, and it did so without any major factor explaining away this dynamic. It’s simply best left unexplained.

To add some more frustrations, the correlation between each team’s 2014 true wins and 2015 true wins was 0.3783, while the correlation between each team’s 2014 EWD-adjusted wins and 2015 true wins was 0.3148. This marks the first time EWD-adjusted wins had a weaker correlation than true wins. With those failures in mind, EWD is still proving to be a find. So far, through 36 teams over a three-year span, 24 regressed and 10 maintained their record. Normality seems to state that teams at worst (or best) maintain their record when facing prospect of record-based regression. However, the odds normally favor for that regression to happen.

With all that said, we’re still looking to improve this formula to paint a better picture of turnover regression. Therefore, a new formula for Turnover Win Impact regression (TWIr) is under consideration. The new formula compensates for three things not considered in the previous TWIr formula: (1) the takeaways v. giveaways dynamic, which regresses at different rates, (2) usage of per-drive turnover rates, which accounts for pace’s impact on outlier turnover margins, and (3) the use of linear regression, which more accurately predicts future turnover rates than a “regression correlation coefficient.” The formula was developed after assessing offensive and defensive turnover rates from 1998-2014, giving us a 17-year span of data (special teams excluded) to consider for future regression projections.

This new formula is much more complicated to calculate than before, but it should make much more sense. Basically, we looked over every team from every season (from 1998 to 2014) and found correlation data based on their offensive and defensive drive-based turnover rates form one year to the next. For example, if team X turned the ball on 15 percent of drives in 2013 and 20 percent of drives in 2014, the team was assigned values of x=15 and y=5. This allowed us to create linear regression lines for the takeaway rate and giveaway rate of non-special teams play. As for special teams, we will give 100 percent regression for the turnover margin, as we simply don’t have enough information to create an accurate linear regression line for the unit.

Using this for 2014, we adjusted the linear regression lines to account the league’s mean turnover rates on offense and defense. Last year’s average drive-based turnover rate was 12.1 percent, thus we adjust the line so y=0 when x=12.1. As a result, the offensive linear regression line for 2014 is YO = 9.524 – 0.7871x and the defensive linear regression line for 2014 is YD = 10.820 – 0.8942x.

In order to see how these changes affect TWIr results, check out Table 1. The table shows the TWIr differential for each team in 2014.

Table 1: Adjustments to Turnover Win Impact Regression, 2014 season

Team TWIr-1 TWIr-2 Differential Team TWIr-1 TWIr-2 Differential
BAL -0.17 -0.16 +0.01 wins CHI +0.43 +0.34 -0.09 wins
CIN 0 -0.10 -0.10 wins DET -0.60 -0.55 +0.05 wins
CLE -0.52 -0.47 +0.05 wins GB -1.20 -1.16 -0.04 wins
PIT 0 +0.01 +0.01 wins MIN +0.09 +0.15 +0.06 wins
BUF -0.60 -0.63 -0.03 wins DAL -0.52 -0.58 -0.06 wins
MIA -0.17 -0.13 +0.04 wins NYG +0.17 +0.24 +0.07 wins
NE -1.03 -1.03 +0.00 wins PHL +0.69 +0.74 +0.05 wins
NYJ +0.95 +0.99 +0.04 wins WSH +1.03 +0.91 -0.12 wins
HOU -1.03 -1.09 -0.06 wins ATL -0.43 -0.61 -0.18 wins
IND +0.43 +0.33 -0.10 wins CAR -0.26 -0.17 +0.09 wins
JAX +0.52 +0.53 +0.01 wins NO +1.12 +1.12 +0.00 wins
TEN +0.86 +0.95 +0.09 wins TB +0.69 +0.71 +0.02 wins
DEN -0.43 -0.46 -0.03 wins ARZ -0.69 -0.63 +0.06 wins
KC +0.26 +0.38 +0.12 wins SEA -0.86 -0.89 -0.03 wins
OAK +1.29 +1.37 +0.08 wins SF -0.60 -0.68 -0.08 wins
SD +0.43 +0.51 +0.08 wins STL +0.17 +0.10 -0.07 wins

TWIr-1: -[0.8175 * (ToM * 4) / 38]; TWIr-2: [(YD / 100 * DDr) – (YO / 100 * ODr) – ToMS] * 4 / 38

As the table shows, there is only a small difference from the “regression correlation coefficient” that was previously used. Albeit from a lack of strong statistical foundation, the coefficient used was fairly accurate of what the numbers from turnover rates spit out for nearly the past two decades. These new adjustments will be small on a functional level, but it’s the correct adjustment on a practical level.

Ultimately, if you want to understand the big difference between this formula and the past formula, you must look at the “slope” of the lines. Even if you go by rates for this current formula, one turnover over or under the mean is equivalent to a 0.8942-turnover difference in regression on defense and a 0.7871-turnover difference in regression on offense (along with 1-turnover difference in regression on special teams). It used to be a 0.8175-turnover difference in regression under the old formula.

The Atlanta Falcons (-0.18 differential) experienced the biggest change in 2014, given their plus-4 turnover margin on special teams. Remember we decided to regress the entirety of special teams turnovers, which proved to be accurate in this case, as the 2015 Falcons earned no takeaways and suffered no giveaways on special teams. Still, this most notable difference did not change their regression status heading into the 2015 season. However, one team did earn a change in status, as the Kansas City Chiefs (+0.12 differential to give them +1.52 EWD) would’ve been the 15th team in 2014 to face regression. They improved as expected, going from 9-7 to 11-5.

In the end, this formula change does nothing to explain the Bengals and Cardinals breaking regression. However, since we had 14 (now 15) teams on the hook as opposed to the 11 teams each of the two previous years, maybe some broken regression was bound to happen. Chalk up 2015 as an atypical year for EWD-based regression, and let’s move forward.


To calculate the Expected Win Differential and determine which teams may be in line for win-loss record regression in 2016, the Pythagorean Win Differential (PWD) of each team (Table 2) must first be calculated. Remember that the Pythagorean win formula used here is slightly different that what was originally constructed by Bill James and crew, as a result of the league’s recent uptick in scoring. The PWD results should give you an initial idea which teams are most at risk for regression, but turnovers could possibly explain away the scoring dynamic. Therefore, take these results with some amount of caution until the product is finished.

Table 2: Pythagorean Win Differential, 2015 season

Team PythW Record Differential Team PythW Record Differential
BAL 6.11 5-11 +1.11 wins CHI 6.39 6-10 +0.39 wins
CIN 11.62 12-4 -0.38 wins DET 6.94 7-9 -0.06 wins
CLE 4.12 3-13 +1.12 wins GB 9.24 10-6 -0.76 wins
PIT 10.61 10-6 +0.61 wins MIN 9.79 11-5 -1.21 wins
BUF 8.52 8-8 +0.52 wins DAL 5.18 4-12 +1.18 wins
MIA 5.87 6-10 -0.13 wins NYG 7.51 6-10 +1.51 wins
NE 11.49 12-4 -0.51 wins PHL 6.75 7-9 -0.25 wins
NYJ 9.97 10-6 -0.03 wins WSH 8.23 9-7 -0.77 wins
HOU 8.76 9-7 -0.24 wins ATL 7.83 8-8 -0.17 wins
IND 6.09 8-8 -1.91 wins CAR 12.19 15-1 -2.81 wins
JAX 6.34 5-11 +1.34 wins NO 6.54 7-9 -0.46 wins
TEN 4.85 3-13 +1.85 wins TB 6.13 6-10 +0.13 wins
DEN 9.72 12-4 -2.28 wins ARZ 11.92 13-3 -1.08 wins
KC 11.13 11-5 +0.13 wins SEA 11.75 10-6 +1.75 wins
OAK 6.99 7-9 -0.01 wins SF 3.80 5-11 -1.20 wins
SD 5.95 4-12 +1.95 wins STL 6.44 7-9 -0.56 wins

PythW: Pythagorean Wins, or (Points Scored ^ 2.4) / ((Points Scored ^ 2.4) + (Points Allowed ^ 2.4))

It comes with some degree of expectation that the two combatants of Super Bowl 50 are the first two teams on the PWD regression chopping block. However, both teams are at least two games below expectation in Pythagorean record. It could lead to some significant regression for both teams, which could mean some major changes to the power structure of the NFL. Meanwhile, the Colts return to their previous place upon the precipice of regression. This may very well mark their third time surpassing negative regression in four years. On the flip side, there is a mix of teams who could be line for notable improvement. Whether it’s bottom feeders like the Chargers and Titans, or a playoff team like Seahawks, several teams are potentially well on their way to positively surpassing the regression threshold.

Moving on to the next step, the Turnover Win Impact regression (TWIr) will be shown in Table 3. This will use the aforementioned new formula for each team in 2015. Adjusting for the 11.7 per-drive turnover rate in the NFL last year, the offensive and defensive linear regression lines are adjusted so y=0 when x=11.7. As a result, the offensive linear regression line for 2015 is YO = 9.209 – 0.7871x and the defensive linear regression line for 2015 is YD = 10.462 – 0.8942x. Special teams will continue to have 100 percent regression. Let’s also note that the 38 points-per game-differential equivalency holds for 2015, given the scoring-based PWD results.

Table 3: Turnover Win Impact Regression, 2015 season

Team ORate DRate STToM TWIr Team ORate DRate STToM TWIr
BAL 14.0% 7.0% -1 +1.29 wins CHI 11.2% 8.0% +2 +0.33 wins
CIN 9.3% 15.0% +1 -1.03 wins DET 12.3% 9.1% 0 +0.54 wins
CLE 15.2% 11.7% -3 +0.83 wins GB 8.9% 11.5% +1 -0.52 wins
PIT 13.7% 14.3% +1 -0.24 wins MIN 9.3% 11.9% 0 -0.38 wins
BUF 8.6% 11.0% +1 -0.46 wins DAL 18.0% 5.6% -1 +2.02 wins
MIA 9.6% 8.4% -1 +0.37 wins NYG 11.3% 14.2% +1 -0.61 wins
NE 5.7% 10.5% -2 -0.53 wins PHL 15.3% 11.8% +2 +0.38 wins
NYJ 11.6% 14.4% +1 -0.61 wins WSH 11.1% 14.9% -2 -0.42 wins
HOU 9.8% 11.1% +3 -0.52 wins ATL 17.2% 13.4% 0 +0.52 wins
IND 14.4% 12.4% -1 +0.41 wins CAR 9.6% 19.4% +1 -1.87 wins
JAX 14.0% 8.8% 0 +0.90 wins NO 10.4% 11.7% 0 -0.20 wins
TEN 16.2% 8.1% +2 +1.13 wins TB 14.8% 12.8% -2 +0.48 wins
DEN 15.1% 11.9% +2 +0.31 wins ARZ 11.8% 16.7% 0 -0.86 wins
KC 7.1% 15.3% -1 -1.21 wins SEA 8.6% 13.2% -1 -0.59 wins
OAK 10.3% 11.5% -1 -0.09 wins SF 8.2% 6.6% -2 +0.56 wins
SD 12.4% 10.0% 0 +0.39 wins STL 10.2% 12.1% 0 -0.17 wins

ORate: Turnover percentage per offensive drive; DRate: Turnover percentage per defensive drive; STToM: Special teams turnover margin;
TWIr: [(YD / 100 * DDr) – (YO / 100 * ODr) – ToMS] * 4 / 38

Oh, the poor Dallas Cowboys. Without their quarterback Tony Romo for 12 of the team’s 16 games, the team was already in enough of a hole. However, the defense went from first to worst in per-drive turnover rate. That contributed by very heavily to the team’s 4-12 record. Thankfully, turnover regression alone is projected to add two wins of life onto Big D’s 2016 expectancy. What a nice remedy. On the flip side, the Panthers rode their dynamic turnover differential to the league’s best record and a Super Bowl 50 appearance. Simple turnover regression accounts for nearly two wins. No other teams come close to the extremes of these two Thanksgiving 2015 combatants.

The final step in today’s study involves putting it all together. The Expected Win Differential (EWD) for each team in 2015 (Table 3) uses the sum of the Pythagorean Win Differential and the Turnover Win Impact regression (PWD + TWIr). With a new formula for TWIr, we’ve decided to also change how we determine the regression threshold. Given that TWIr totals aren’t significantly different under the new formula, we use the previous three seasons of data to get our threshold. Data from 2012 to 2014 yields an EWD standard deviation of 1.551 wins, which will become our regression threshold. Note that the EWD to Win Differential correlation of 0.5758 suggests a moderate positive relation, meaning that teams with a greater absolute EWD (re: further from zero) generally are more likely to have a greater absolute win differential the following season. In other words, we have good reason to believe in using a regression threshold to classify the teams expected to regress. Teams in line for improvement are denoted in blue, while teams in line for decline are denoted in red.

Table 4: Expected Win Differential, 2015 season

BAL +1.11 +1.29 +2.40 wins CHI +0.39 +0.33 +0.72 wins
CIN -0.38 -1.03 -1.41 wins DET -0.06 +0.54 +0.48 wins
CLE +1.12 +0.83 +1.95 wins GB -0.76 -0.52 -1.28 wins
PIT +0.61 -0.24 +0.37 wins MIN -1.21 -0.38 -1.59 wins
BUF +0.52 -0.06 +0.06 wins DAL +1.18 +2.02 +3.20 wins
MIA -0.13 +0.37 +0.24 wins NYG +1.51 -0.61 +0.90 wins
NE -0.51 -0.53 -1.04 wins PHL -0.25 +0.38 +0.13 wins
NYJ -0.03 -0.61 -0.64 wins WSH -0.77 -0.42 -1.19 wins
HOU -0.24 -0.52 -0.76 wins ATL -0.17 +0.52 +0.35 wins
IND -1.91 +0.41 -1.50 wins CAR -2.81 -1.87 -4.68 wins
JAX +1.34 +0.90 +2.24 wins NO -0.46 -0.20 -0.66 wins
TEN +1.85 +1.13 +2.98 wins TB +0.13 +0.48 +0.61 wins
DEN -2.28 +0.31 -1.97 wins ARZ -1.08 -0.86 -1.94 wins
KC +0.13 -1.21 -1.08 wins SEA +1.75 -0.59 +1.16 wins
OAK -0.01 -0.09 -0.10 wins SF -1.20 +0.56 -0.64 wins
SD +1.95 +0.39 +2.34 wins STL -0.56 -0.17 -0.73 wins

EWD: PWD – TWIr; Note: The threshold for regression is ±1.551 wins.

These are much of the expected or even obvious teams to face regression. On the plus-side, the Browns and Titans come off of sharing the league’s worst record with a 3-13 record each in 2015. Then there’s the Chargers, Cowboys and Ravens. Each team dropped at least five games in the standings last year after losing a combined 24 one-possession games. All three teams have more than capable quarterbacks, with Tony Romo (Cowboys) and Joe Flacco (Ravens) returning from season-ending injuries. EWD regression seems like a mere formality. On the minus-side, the Panthers and Broncos come off of Super Bowl appearances. Carolina’s 15-1 record obviously plays a big role as well, given that no team has ever won 15+ games in consecutive seasons. Note that they are more than three(!) standard deviations away from zero. Then there’s the Cardinals and their 13-3 season that ended in an NFC Championship Game appearance. Sure, they broke regression last year, but perhaps there’s no place to move up in 2016.

Truly, the only remotely unexpected teams facing regression are the Jaguars and Vikings. Jacksonville got here thanks in much part to Blake Bortles’ 29 touchdown passes when trailing, which was the highlight of his unusually trailing-waited success in 2015. Minnesota got here mostly because of its grounded offense in 2015, as Teddy Bridgewater still hasn’t taken full control of his offense. The regression at play makes for an interesting wrinkle as Bortles and Bridgewater both enter their third season, respectively. Given that Minnesota has a six-game head start from last season, perhaps these tales converge to share a similar script in win-loss record in 2016.

Hopefully, the formula change and a seemingly calm results table lead to a return to glory for Expected Win Differential.

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