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.

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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

Team PWD TWIr EWD Team PWD TWIr EWD
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.

Apr 03

Final Four 2016: Clash of Historic Greats

Villanova WildcatsNorth Carolina Tar HeelsFinal Four 2016

As the 2016 NCAA Division I Men’s Basketball Tournament comes to a close, we will see two of the three best teams in college basketball battle for a national championship. Perhaps some didn’t see it that way during a regular season of unprecedented parity. Every Division I team lost at least four games. All but one team in a major conference (Villanova) lost at least three conference games. And yet in the end, we will see two dominant forces clash during the season’s final game.

On one side, we have a North Carolina Tar Heels team that looks as strong this tournament as they did when lasting winning a national championship in 2009. Meanwhile, the Villanova Wildcats has the four blowout wins and one win over the top overall seed to their credit. In their own way, each team has established a historic level of dominance. Obviously, only one can win tomorrow night.

Before we get to watch that game, let’s analyze the statistical precedence behind North Carolina’s and Villanova’s dominance.

NORTH CAROLINA: DOUBLE-DIGIT WIN IN EACH ROUND
We predicted the Tar Heels to win it all when the bracket came out in mid-March. Since then, there’s been little threat of North Carolina being upset. All five UNC wins came with a scoring margin in the teens. It makes for some remarkable consistency: +16 v. Florida Gulf Coast, +19 v. Providence, +15 v. Indiana, +14 Notre Dame and +17 v. Syracuse. This is a true sign of dominance.

In fact, if the Tar Heels win tomorrow night by double digits, they would become the first national champions since the 2009 North Carolina Tar Heels to win all six games by double digits. Check out how it compares to past champions.

Table 1: Game-by-Game Point Differential (National Champions since 1985)

Year Team R1 R2 S16 E8 F4 NCG Tally
2016 UNC/Nova N/A N/A N/A N/A N/A ?? N/A
2015 Duke +29 +19 +6 +14 +20 +5 4 gms
2014 Connecticut +8* +12 +5 +6 +10 +6 2 gms
2013 Louisville +31 +26 +8 +22 +4 +6 3 gms
2012 Kentucky +15 +16 +12 +12 +8 +8 4 gms
2011 Connecticut +29 +11 +7 +2 +1 +12 3 gms
2010 Duke +29 +15 +13 +7 +21 +2 4 gms
2009 North Carolina +42 +14 +21 +12 +14 +17 6 gms
2008 Kansas +24 +19 +15 +2 +18 +7* 4 gms
2007 Florida +43 +7 +8 +8 +10 +9 2 gms
2006 Florida +26 +22 +4 +13 +15 +16 5 gms
2005 North Carolina +28 +27 +1 +6 +16 +5 3 gms
2004 Connecticut +17 +17 +20 +16 +1 +9 4 gms
2003 Syracuse +11 +12 +1 +16 +11 +3 4 gms
2002 Maryland +15 +30 +10 +8 +9 +12 4 gms
2001 Duke +38 +13 +13 +10 +11 +10 6 gms
2000 Michigan State +27 +12 +17 +11 +12 +13 6 gms
1999 Connecticut +25 +22 +10 +5 +6 +3 3 gms
1998 Kentucky +15 +27 +26 +2 +1* +9 3 gms
1997 Arizona +8 +4 +3 +4* +8 +5* 0 gms
1996 Kentucky +38 +24 +31 +19 +7 +9 4 gms
1995 UCLA +26 +1 +19 +6 +13 +11 4 gms
1994 Arkansas +15 +12 +19 +8 +9 +4 3 gms
1993 North Carolina +20 +45 +6 +7* +10 +6 3 gms
1992 Duke +26 +13 +12 +1* +3 +20 4 gms
1991 Duke +29 +15 +14 +17 +2 +7 4 gms
1990 UNLV +30 +11 +2 +30 +9 +30 4 gms
1989 Michigan +5 +9 +5 +37 +2 +1* 1 gm
1988 Kansas +13 +3 +13 +13 +7 +4 3 gms
1987 Indiana +24 +17 +6 +1 +4 +1 2 gms
1986 Louisville +20 +14 +15 +8 +11 +3 4 gms
1985 Villanova +2 +4 +3 +12 +7 +2 1 gm

R1: Round 1, R2: Round 2, S16: Sweet 16, E8: Elite 8, F4: Final 4, NCG: National Championship Game

Only three teams in the modern tournament era achieved what this Tar Heels can achieve with win by double figures tomorrow night. This speaks more to consistency in dominance than overall dominance. Two of these three champions (2000 Michigan State and 2001 Duke) didn’t win by 20+ points past the opening round. The third champion (2009 North Carolina) had no win bigger than 21 points past the opening round. That seems to fit in very well with what the 2016 Tar Heels achieved. However, it will be very tough for North Carolina to complete the job tomorrow night, given what their opponents have achieved so far in the tourney.

VILLANOVA: BUNCH OF BLOWOUTS COULD LEAD TO ALL-TIME BEST MODERN SCORING MARGIN
Most were shocked to see Villanova dismantle Oklahoma by 44 points in yesterday’s national semifinal game. However, this is far from the first blowout victory for ‘Nova this tournament. The Wildcats have actually won four of their five games by 19 points or more (North Carolina’s largest margin of victory). In fact, Villanova at some point held a 25+ point lead in each of those four games. The one exception? That was the victory over the overall number one seed, Kansas.

At first glance, the 2016 Villanova Wildcats have the makings of possibly becoming the greatest modern day tournament team, if they can pull off the victory tomorrow night. They have won their first five tournament games by a combined 121 points, which nears the record for best scoring margin in a single tournament. That also gives Villanova a 24.2-point average margin of victory thus far, which would also be a tie for the overall tournament record. We don’t think that average scoring margin will stick, given how good North Carolina is, but even a single-digit margin of victory tomorrow night still could make history for Villanova.

Here are all the teams with a +100 scoring margin or better in a single tournament (per College Basketball Reference):

  • 1996 Kentucky Wildcats: +129 scoring margin (in six games — 6-0)
  • 1999 Duke Blue Devils: +123 scoring margin (in six games — 5-1)
  • 2016 Villanova Wildcats: +121 scoring margin (in five games — 5-0)
  • 1993 Kentucky Wildcats: +121 scoring margin (in five games — 4-1)
  • 2009 North Carolina Tar Heels: +121 scoring margin (in six games — 6-0)
  • 1963 Loyola (IL) Ramblers: +115 scoring margin (in five games — 5-0)
  • 1981 Indiana Hoosiers: +113 scoring margin (in five games — 5-0)
  • 1990 UNLV Runnin’ Rebels: +112 scoring margin (in six games — 6-0)
  • 1979 Michigan State Spartans: +103 scoring margin (in five games — 5-0)
  • 2001 Duke Blue Devils: +100 scoring margin (in six games — 6-0)

Look at it like this: Villanova could lose by 21 points tomorrow night and still be the first team with a +100 scoring margin or better since that famed 2009 Tar Heels team. (North Carolina would also having a +100 scoring margin or better with a win by 19 points or more.) And yet, we don’t think all this gives Villanova complete justice. Remember, the Wildcats held a 25+ point lead four times in this tournament. This included games against 7-seed Iowa, 3-seed Miami and 2-seed Oklahoma.

We unfortunately don’t have a significant amount of data to compare this to the other teams on the above list. There will be some necessary legwork to find an apt comparison. However, we can at least compare Villanova to North Carolina. Tar Heels faced no team better than 5-seed Indiana. They avoided Kentucky, Xavier and Virginia to get there. Villanova, on the other hand, faced the tougher possible road (re: we thought Oklahoma was better than 1-seed Oregon out west).

Judging by this, Villanova has the advantage heading into tomorrow’s game. However, because we picked North Carolina to win it all, we’re not convinced who will win. All we can do is hope this match-up lives up to its potential.

Feb 11

NFL 2015 Recap: The Five Factors

With the regular season now over, we can take a look back to see how we fared in the TABMathletics 2015 NFL Preview. For this preview, a big part of our analysis focused on the “five factors” of regression for each team. Over the span of eight days, these posts exposed numerous themes for what makes a team get better or worse in each season by means of mathematical application. This helped to paint the picture for our season predictions.

Note that the statistical factors discussed below involved the REGULAR SEASON ONLY unless otherwise noted.

Editor’s Note: The remaining results for the Five Factors were added on March 4 to account for the regression factors that address published material by Football Outsiders in regards to the Adjusted Games Lost metric. Now, results for all 160 factors are available.
Update (8/18): Expected Win Differential results from the 2015 season are made to reflect the changes made for the 2016 NFL Preview.

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Baltimore Ravens (5 of 5 factors correct)
1. More sacks allowed and fewer rush yards per attempt: The 2014 Ravens allowed 19 sacks and rushed for 4.51 yards per attempt. The 2015 Ravens allowed 24 sacks and rushed for 3.86 yards per attempt.
2. Fewer sacks and worse sack differential: The 2014 Ravens totaled 49 sacks to create a plus-30 sack differential. The 2015 Ravens totaled 37 sacks to create a plus-13 sack differential.
3. Lower penalty beneficiary count for fewer yards: The 2014 Ravens benefited from 110 penalties for 1007 yards. The 2015 Ravens benefited from 103 penalties for 748 yards.
4. Closer DVOA rank from red zone subset to overall offense: The 2014 Ravens ranked 9th in Offense DVOA, but 22nd in the red zone (per Football Outsiders). The 2015 Ravens ranked 20th in Offense DVOA while ranking 20th in the red zone.
5. Fewer yards per kick return: The Ravens dropped from 28.34 yards per kick return to 25.21.

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Cincinnati Bengals (3.5 of 5 factors correct)
1. More sacks: The Bengals improved from 20 sacks to 42.
2. Fewer rush yards per attempt: The Bengals declined from 4.36 rush yards per attempt to 3.85.
3. More yards per pass attempt: The Bengals improved from 7.06 yards per pass attempt to 8.13.
4. More first half points allowed and worse first half Defense DVOA: The 2014 Bengals allowed 122 first-half points with a -13.3% DVOA (per Football Outsiders). The 2015 Bengals allowed 107 first-half points with a -12.1% DVOA.
5. Worse W-L record: The Bengals improved from 10-5-1 to 12-4.

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Cleveland Browns (4 of 5 factors correct)
1. Better third down OPR and red zone OPR: The 2014 Browns owned a 62.80 third down Offensive Passer Rating and a 67.73 red zone OPR. The 2015 Browns owned a 104.45 third down OPR and a 80.03 red zone OPR.
2. Fewer yards per pass attempt: The Browns declined from 7.33 yards per pass attempt to 6.82.
3. More points per red zone trip and a better Offense DVOA: The 2014 Browns averaged 4.36 points per red zone trip and owned a -10.2% Offense DVOA (per Football Outsiders). The 2015 Browns averaged 4.08 points per red zone trip and a owned a -13.2% Offense DVOA.
4. Either fewer total rush yards allowed or fewer rush yards allowed per attempt: The 2014 Browns allowed 2265 rush yards for a 4.53-yard average. The 2015 Browns allowed 2055 rush yards for a 4.49-yard average.
5. Worse Defensive Passer Rating: The Browns declined from a 74.08 DPR to 101.80.

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Pittsburgh Steelers (5 of 5 factors correct)
1. Fewer touchdown receptions for Antonio Brown: Brown declined from 13 touchdowns to 10.
2. Better Defensive Passer Rating: The Steelers improved from a 98.33 DPR to 90.89.
3. Fewer yards per interception return and fewer interception return touchdowns: The 2014 Steelers averaged 22 yards per return en route to four touchdowns. The 2015 Steelers averaged 16.59 yards per return en route to two touchdowns.
4. More Offensive Adjusted Games Lost: The Steelers declined from 4.8 AGL to 43.2 (per Football Outsiders).
5. More points per red zone trip: The Steelers improved from 4.82 points per trip to 5.43.

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Chicago Bears (5 of 5 factors correct)
1. Better passing first down percentage allowed and better passing touchdown percentage allowed: The 2014 Bears allowed a first down in 41.42 percent and a touchdown in 6.20 percent of passes. The 2015 Bears allowed a first down in 35.48 percent and a touchdown in 6.04 percent of passes.
2. Better defensive scoring drive percentage: The Bears improved from a 44.63 defensive scoring drive percentage to 36.57.
3. Fewer Matt Forte receptions: Forte declined from 102 receptions to 44.
4. More yards per punt return and more yards per kick return allowed: The 2014 Bears averaged 5.19 yards per punt return while allowing an average of 17.54 yards per kick return. The 2015 Bears averaged 7.81 yards per punt return while allowing an average of 25.21 yards per kick return.
5. Worse red zone touchdown percentage: The Bears declined from a 65.30 red zone touchdown percentage to 49.02.

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Detroit Lions (5 of 5 factors correct)
1. Unit decline in rush yards, rush yards per attempt and first down percentage: The 2014 Lions allowed 1109 rush yards to the account of 3.17 yards per attempt and a 16.86 first down percentage. The 2015 Lions allowed 1808 rush yards to the account of 4.22 yards per attempt and a 22.43 first down percentage.
2. More pass yards allowed per attempt: The Lions declined from 6.76 pass yards allowed per attempt to 7.70.
3. More yards per rush attempt: The Lions improved from 3.59 yards per rush attempt to 3.77.
4. Better field goal percentage: The Lions improved from a 65.79 field goal percentage to 91.67.
5. Worse W-L record: The Lions declined from 11-5 to 7-9.

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Green Bay Packers (5 of 5 factors correct)
1. Better rush first down percentage allowed: The Packers improved from a 28.79 rush first down percentage allowed to 22.86.
2. Worse home W-L record and fewer points scored: The 2014 Packers scored 318 points en route to an 8-0 home record. The 2015 Packers scored 186 points en route to a 5-3 home record.
3. Worse Offensive Passer Rating: The Packers declined from a 109.88 OPR to 92.72.
4. Better kick return average: The Packers improved from 19.14 yards per kick return to 24.47.
5. Better Expected Win Differential: The Packers improved from a negative-2.15 EWD to negative-1.28.

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Minnesota Vikings (5 of 5 factors correct)
1. More rush yards and rush yards per attempt: The 2014 Vikings totaled 1804 rush yards on 4.37 yards per attempt. The 2015 Vikings totaled 2211 yards on 4.66 yards per attempt.
2. Fewer sacks allowed: The Vikings improved from 51 sacks allowed to 45.
3. Worse offensive fumble recovery percentage and better defensive fumble recovery percentage: The 2014 Vikings recovered 81.81 percent of team fumbles and 30 percent of opponent fumbles. The 2015 Vikings recovered 61.90 percent of team fumbles and 50 percent of opponent fumbles.
4. Better field goal percentage: The Vikings improved from a 74.29 field goal percentage to 87.18.
5. Better W-L record in one-possession games: The Vikings improved from 4-5 to 4-2 in one-possession games.

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Buffalo Bills (4 of 5 factors correct)
1. More Adjusted Net Yards Allowed per Dropback: The Bills declined from 4.50 Adjusted Net Yards Allowed per Dropback to 6.11.
2. Closer DVOA rank between red zone and overall defense: The 2014 Bills ranked second in Defense DVOA, but 16th in the red zone (per Football Outsiders). The 2015 Bills ranked 24th in Defense DVOA while ranking 24th in the red zone.
3. More outside rushes of 10+ yards: The Bills improved from 21 outside rushes of 10+ yards to 54.
4. More points per drive: The Bills improved from 1.58 points per drive to 1.95.
5. More yards allowed per kick return: The Bills improved from 19.97 yards allowed per kick return to 17.2.

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Miami Dolphins (5 of 5 factors correct)
1. Worse Offensive Passer Rating: The Dolphins declined from a 92.46 OPR to 88.90.
2. Fewer points scored: The Dolphins declined from 388 points scored to 310.
3. Better defensive third down percentage and better defensive red zone DVOA: The 2014 Dolphins allowed a 43.95 third down conversion percentage and owned an 11.4% Defense DVOA in the red zone (per Football Outsiders). The 2015 Dolphins allowed a 43.44 third down conversion percentage and owned an 8.2% Defense DVOA in the red zone.
4. More defensive penalties committed: The Dolphins declined from 25 defensive penalties committed to 58.
5. Better Special Teams DVOA: The Dolphins improved from a negative-6.1% Special Teams DVOA to negative-2.7% (per Football Outsiders).

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New England Patriots (5 of 5 factors correct)
1. Worse Net Yards per Point Scored: The Patriots declined from plus-5.10 Net Yards per Point Scored to plus-1.50.
2. More yards from the leading rusher: Jonas Gray led the 2014 Patriots with 412 rush yards. LeGarrette Blount led the 2015 Patriots with 703 rush yards.
3. Worse field goal percentage: The Patriots declined from a 94.59 field goal percentage to 91.67.
4. Fewer defensive penalties committed: The Patriots improved from 60 defensive penalties committed to 28.
5. More fourth-quarter points allowed: The Patriots declined from 62 fourth-quarter points allowed to 104.

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New York Jets (5 of 5 factors correct)
1. Better Defensive Passer Rating: The Jets improved from a 101.49 DPR to 79.05.
2. Better third down Defensive Passer Rating and fewer points allowed per red zone trip: The 2014 Jets owned a 114.90 third down DPR and allowed 5.31 points per red zone trip. The 2015 Jets owned a 61.36 third down DPR and allowed 3.75 points per red zone trip.
3. Fewer yards allowed per pass attempt: The Jets improved from 7.54 yards allowed per pass attempt to 6.72.
4. More Adjusted Games Lost: The Jets declined from 41.5 Adjusted Games Lost to 61.6 (per Football Outsiders).
5. Better W-L record: The Jets improved from 4-12 to 10-6.

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Dallas Cowboys (5 of 5 factors correct)
1. Worse Offensive Passer Rating: The Cowboys declined from a 110.87 OPR to 76.55.
2. Worse road W-L record: The Cowboys declined from an 8-0 road record to 3-5..
3. Fewer takeaways: The Cowboys declined from 31 takeaways to 11.
4. Better red zone touchdown percentage allowed and better first quarter Defensive Passer Rating: The 2014 Cowboys allowed a 61.22 red zone touchdown percentage and owned a 100.02 first quarter DPR. The 2015 Cowboys allowed a 54.54 red zone touchdown percentage and owned a 91.99 first quarter DPR.
5. Worse W-L record: The Cowboys declined from 12-4 to 4-12.

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New York Giants (3 of 5 factors correct)
1. Fewer Odell Beckham receptions, receiving yards and touchdowns per game: Beckham averaged 7.58 receptions for 108.75 yards and one touchdown per game in 2014. He averaged 6.4 receptions for 96.67 yards and 0.87 touchdowns per game in 2015.
2. Fewer yards allowed per pass attempt and fewer yards allowed per rush attempt: The 2014 Giants allowed 7.98 yards per pass attempt and 4.94 yards per rush attempt. The 2015 Giants allowed 7.71 yards per pass attempt and 4.37 yards per rush attempt.
3. Fewer Adjusted Games Lost: The Giants declined from 137.1 Adjusted Games Lost to 138.7 (per Football Outsiders).
4. Worse first quarter Offensive Passer Rating: The Giants declined from a 114.74 first quarter OPR to 85.20.
5. Better W-L record: The Giants owned a 6-10 record in both 2014 and 2015.

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Philadelphia Eagles (5 of 5 factors correct)
1. Worse Net Yards per Point Scored: The Eagles declined from plus-1.63 Net Yards per Point Scored to minus-0.52.
2. Fewer DeMarco Murray rush yards: Murray declined from 1845 rush yards to 702.
3. Fewer yards allowed per pass attempt: The Eagles improved from 7.77 yards allowed per pass attempt to 7.13.
4. Fewer sacks: The Eagles declined from 49 sacks to 37.
5. Fewer giveaways: The Eagles improved from 36 giveaways to 31.

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Washington Redskins (5 of 5 factors correct)
1. Better Net Yards per Point Scored: Washington improved from minus-5.86 Net Yards per Point Scored to plus-1.48.
2. Fewer yards per pass attempt: Washington declined from 8.16 yards per pass attempt to 7.74.
3. More points scored: Washington improved from 301 points scored to 388.
4. Better Defensive Passer Rating: Washington improved from a 108.31 DPR to 96.11.
5. Better W-L record: Washington improved from 4-12 to 9-7.

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Houston Texans (4 of 5 factors correct)
1. Worse Net Yards per Point Scored: The Texans declined from plus-3.33 Net Yards per Point Scored to minus-0.56.
2. Fewer Watt sacks and touchdowns: Watt totaled 20.5 sacks and five touchdowns in 2014. He totaled 17.5 sacks and no touchdowns in 2015.
3. Worse Defensive Rusher Rating: The Texans declined from a 77.04 DRR to 90.60 (per Ken Crippen’s formula).
4. More yards per rush attempt from running backs (excluding Arian Foster): The 2014 Texans supporting cast rushed for 3.20 yards per attempt. The 2015 Texans supporting cast rushed for 4.00 yards per attempt.
5. Worse W-L record: The Texans owned a 9-7 record in both 2014 and 2015.

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Indianapolis Colts (4 of 5 factors correct)
1. Worse Offense Passer Rating: The Colts declined from a 96.78 OPR to 77.53.
2. More rush yards: The Colts declined from 1612 rush yards to 1438.
3. Better red zone touchdown percentage allowed and worse third down conversion percentage allowed: The 2014 Colts owned a 64.71 red zone touchdown percentage allowed and a 33.33 third down conversion percentage allowed. The 2015 Colts owned a 61.82 red zone touchdown percentage allowed and a 39.21 third down conversion percentage allowed.
4. Fewer Adjusted Games Lost: The Colts improved from 104.7 AGL to 65.1 (per Football Outsiders).
5. Fewer fumbles and better drop percentage for Colts and their opponents: In 2014, the Colts fumbled 31 times and dropped 40 passes, while their opponents fumbled 29 times and dropped 36 passes. In 2015, the Colts fumbled 19 times and dropped 19 passes, while their opponents fumbled 20 times and dropped 25 passes.

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Jacksonville Jaguars (5 of 5 factors correct)
1. More yards per play: The Jaguars improved from 4.69 yards per play to 5.51.
2. Fewer sacks allowed: The Jaguars improved from 71 sacks allowed to 51.
3. Fewer sacks: The Jaguars declined from 45 sacks to 36.
4. Worse net penalties: The Jaguars declined from a plus-42 penalty differential to plus-9.
5. Fewer Julius Thomas touchdown receptions: Thomas declined from 12 touchdown receptions to five.

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Tennessee Titans (5 of 5 factors correct)
1. More points per drive: The Titans improved from 1.25 points per drive to 1.50.
2. Fewer points allowed per drive: The Titans improved from 2.17 points allowed per drive to 2.06.
3. More takeaways: The Titans improved from 16 takeaways to 19.
4. More yards allowed per pass attempt: The Titans declined from 7.33 yards allowed per pass attempt to 7.85.
5. Better W-L record: The Titans improved from 2-14 to 3-13.

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Atlanta Falcons (3 of 5 factors correct)
1. Fewer yards allowed per play and fewer points allowed per drive: The 2014 Falcons allowed 6.14 yards per play and 2.25 points per drive. The 2015 Falcons allowed 5.58 yards per play and 1.94 points per drive.
2. Better sack rate: The Falcons declined from a 3.74 sack percentage to 3.28.
3. Fewer Julio Jones receiving yards: Jones improved from 1593 received yards to 1871.
4. Fewer opponent return touchdowns: The Falcons improved from five return touchdowns allowed to two.
5. Worse intra-divisional record and better inter-divisional record: The 2014 Falcons went 5-1 against the NFC South and 1-9 against all other teams. The 2015 Falcons went 1-5 against the NFC South and 7-3 against all other teams.

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Carolina Panthers (5 of 5 factors correct)
1. Better Defensive Rusher Rating: The Panthers improved from a 100.07 DRR to 84.88.
2. Fewer points allowed per red zone trip: The Panthers improved from 5.15 points allowed per red zone trip to 4.29.
3. More defensive Adjusted Games Lost: The Panthers declined from 11.7 Adjusted Games Lost on defense to 22.7 (per Football Outsiders).
4. More Cam Newton rushing touchdowns and worse Cam Newton fourth quarter passer rating: Newton scored five rushing touchdowns and finished with a 110.33 fourth quarter passer rating in 2014. He scored 10 rushing touchdowns and finished with a 105.23 fourth quarter passer rating.
5. Better kick return average and punt return average allowed: The 2014 Panthers allowed 32.44 yards per kick return and 15.54 yards per punt return. The 2015 Panthers allowed 26.62 yards per kick return and 7.78 yards per punt return.

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New Orleans Saints (4 of 5 factors correct)
1. Better Defensive Hog Index ranking: The Saints improved from 32nd to 31st in the Defensive Hog Index (per Cold, Hard Football Facts).
2. More yards allowed per punt return: The Saints declined from 4.13 yards allowed per punt return to 8.27.
3. More points scored: The Saints improved from 401 points scored to 408.
4. Better fumble recovery differential and worse opponent field goal percentage: The 2014 Saints owned a minus-8 fumble differential, while their opponents owned a 96.88 field goal percentage. The 2015 Saints owned a plus-5 fumble differential, while their opponents owned a 81.48 field goal percentage.
5. Better W-L record: The Saints owned a 7-9 record in both 2014 and 2015.

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Tampa Bay Buccaneers (3 of 5 factors correct)
1. Better red zone Offensive Passer Rating: The Buccaneers improved from a 62.66 red zone OPR to 83.80.
2. Better completion percentage allowed: The Buccaneers declined from a 68.74 completion percentage allowed to 69.87.
3. More yards allowed per punt return: The Buccaneers declined from 5.78 yards allowed per punt return to 5.19.
4. Better opponent field goal percentage: Tampa Bay’s opponents improved from a 74.36 field goal percentage to 91.18.
5. Better W-L record: The Buccaneers improved from 2-14 to 6-10.

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Denver Broncos (3 of 5 factors correct)
1. More Relative Yards Allowed per Play: The Broncos improved from 0.70 RYAPP+ to 1.09.
2. Fewer points allowed: The Broncos improved from 354 points allowed to 296.
3. Fewer Demaryius Thomas receiving yards: Thomas declined from 111 receptions to 105.
4. Closer differential between home Offense DVOA and road Offense DVOA: The 2014 Broncos owned a 49.5% DVOA differential on offense between home and the road (per Football Outsiders). The 2015 Broncos owned a 2.4% DVOA differential on offense between home and the road.
5. Better Expected Win Differential: The Broncos declined from a negative-1.60 EWD to negative-1.97.

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Kansas City Chiefs (5 of 5 factors correct)
1. Fewer Justin Houston sacks: Houston declined from 22.0 sacks to 7.5.
2. More takeaways: The Chiefs improved from 14 takeaways to 29.
3. More receptions, yards and touchdowns from wide receivers: The 2014 Chiefs receivers caught 129 passes for 1588 yards (and no touchdowns). The 2015 Chiefs receivers caught 172 passes for 2003 yards and 12 touchdowns.
4. More return touchdowns allowed: The Chiefs declined from no return touchdowns allowed to two.
5. Worse Net Points per Red Zone Trip: The Chiefs declined from plus-1.09 Net Points per Red Zone Trip to 0.03.

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Oakland Raiders (5 of 5 factors correct)
1. Fewer games against quality opponents: The Raiders changed from facing 13 quality opponents to nine.
2. More Relative Yards per Play: The Raiders improved from 0.90 RYPP- to 0.18.
3. Fewer points per red zone opportunity: The Raiders declined from 5.55 points per red zone opportunity to 5.10.
4. Better Offensive Rusher Rating: The Raiders improved a 67.79 ORR to 79.82.
5. Better W-L record: The Raiders from 3-13 to 7-9.

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San Diego Chargers (5 of 5 factors correct)
1. Fewer Adjusted Games Lost: The Chargers improved from 119.1 AGL to 88.5 (per Football Outsiders).
2. More yards per rush attempt: The Chargers improved from 3.43 rush yards per attempt to 3.46.
3. Greater passing first down percentage allowed: The Chargers declined from a 32.06 passing first down percentage allowed to 33.33.
4. Better Negative Pass Play Percentage: The Chargers improved from a 6.00 NPP% to 7.93.
5. Worse one-possession W-L record: The Chargers declined from a 5-2 one-possession record to 3-9.

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Arizona Cardinals (4 of 5 factors correct)
1. Fewer yards per point allowed: The Cardinals declined from 19.70 Yards per Point Allowed to 16.44.
2. More yards per rush attempt: The Cardinals improved from 3.29 yards per rush attempt to 4.24.
3. More giveaways: The Cardinals declined from 17 giveaways to 24.
4. More yards per kick return: The Cardinals improved from 18.95 yards per kick return to 24.06.
5. Worse W-L record: The Cardinals improved from 11-5 to 13-3.

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San Francisco 49ers (5 of 5 factors correct)
1. Worse interception differential: The 49ers declined from a plus-13 interception differential to minus-3.
2. Better fumble differential: The 49ers improved from a minus-6 fumble differential to minus-2.
3. More fourth-quarter points and better fourth-quarter Offensive Passer Rating: The 2014 49ers scored 33 fourth-quarter points and owned a 63.51 fourth-quarter OPR. The 2015 49ers scored 69 fourth-quarter points and owned a 83.79 fourth-quarter OPR.
4. Better third down conversion percentage allowed and better red zone touchdown percentage allowed: The 2014 49ers allowed a 43.35 third down conversion percentage and a 60 red zone touchdown percentage allowed. The 2015 49ers allowed a 39.05 third down conversion percentage and a 56.90 red zone touchdown percentage allowed.
5. Worse W-L record: The 49ers declined from 8-8 to 5-11.

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Seattle Seahawks (5 of 5 factors correct)
1. Fewer rush yards, touchdowns and yards per attempt: The 2014 Seahawks rushed for 2762 yards and 20 touchdowns while averaging 5.26 yards per attempt. The 2015 Seahawks rushed for 2268 yards and 10 touchdowns while averaging 4.54 yards per attempt.
2. More Relative Yards Allowed per Play: The Seahawks declined from 0.81 RYAPP+ to 0.55.
3. Fewer net penalties: The Seahawks improved from minus-60 net penalties to minus-23.
4. Better recovery percentage, for and against: The 2014 Seahawks recovered 69.57 percent of fumbles, while their opponents recovered 63.33 percent of fumbles. The 2015 Seahawks recovered 55.56 percent of fumbles, while their opponents recovered 59.09 of fumbles.
5. Fewer points allowed per red zone trip: The Seahawks improved from 5.14 points allowed per red zone trip to 4.37.

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St. Louis Rams (5 of 5 factors correct)
1. Better turnover margin: The Rams improved from a minus-2 turnover margin to plus-4.
2. Fewer return touchdowns allowed: The Rams improved from 10 return touchdowns allowed to four.
3. Better turnover margin and scoring margin during one-possession fourth-quarter play: The 2014 Rams owned a minus-8 turnover margin and a minus-42 scoring margin during one-possession fourth-quarter play. The 2015 Rams owned a minus-4 turnover margin and a plus-2 scoring margin during one-possession fourth-quarter play.
4. Fewer yards per punt return: The Rams declined from 13.03 yards per punt return to 7.69.
5. More first-quarter points allowed: The Rams declined from 42 first-quarter points allowed to 77.

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Overall score: 144.5 of 160 factors correct (90.31 percent success rate)
This ends up being a worst success rate in the three-year history of the Five Factors. However, there was a perfect 5-for-5 finish for 21 teams, which is the same as last year. This seems to indicate a little strong “hit-or-miss” atmosphere to our 2015 Five Factors. We were close to finishing with failing grades for the Giants, Falcons, Buccaneers and Broncos. There was no prevailing theme in our misses either; we just need to be more prudent all around in order to improve the success rate in 2016.

Over three years, we correctly projected 91.58 percent of factors. Furthermore, we projected all five factors correctly for a team 61 times in 96 tries (63.54 percent). Makes no bones about it; our “disappointment” is still Grade-A material. Where else can one find a set of season predictions that are more than 90 percent accurate? It’s certainly not to be found anywhere in the Mainstream Sports Media.

Moving ahead to 2016, we’re going use this off-season to find continuously relevant factors that will help to project how teams will fare in the NFL next season. With another year of experience, hopefully the 2016 Five Factors will provide the best success rate yet.

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