As #1 teams go, Kansas City has had an exceptionally weak set of opponents and as a #1, Kansas City looks to be had. The schedule doesn’t give them very many hard opponents. Denver looms in week 11 and 13, and Indianapolis in week 16, but otherwise the schedule favors this team — until the playoffs.

Week 7 NFL Stats:

2013_stats_week_7

To explain the columns above, Median is a median point spread, and can be used to get a feel for how good a team is without overly weighting a blowout win or blowout loss. HS is Brian Burke’s Homemade Sagarin, as implemented in Maggie Xiong’s PDL::Stats. Pred is the predicted Pythagorean expectation. The exponent for this measure is fitted to the data set itself. SOS, SRS, and MOV are the simple ranking components, analyzed via this Perl implementation. MOV is margin of victory, or point spread divided by games played. SOS is strength of schedule. SRS is the simple ranking.

OSRS and DSRS stats look like this:

2013_stats_week_7_srs

The OSRS and SDSR stats are calculated as described here. The top 5 teams in OSRS turn out to be Denver, Chicago, Indianapolis, Dallas, and Green Bay. The top 5 teams in DSRS are Carolina, Kansas City, Seattle, San Francisco, and New Orleans. Carolina’s stats in general are notable, as they have the second best Pythagorean in the league.

Enough data has been published previously on Denver and Indianapolis to do a direct comparison, but what do they look like historically?

Denver’s data set looks like this:

Denver Broncos 2005-2013
Year Team W L T SRS OSRS DSRS MOV SOS
2005 DEN 13 3 0 10.79 6.30 4.49 8.56 2.23
2006 DEN 9 7 0 1.32 -0.72 2.04 0.88 0.44
2007 DEN 7 9 0 -3.95 -1.57 -2.38 -5.56 1.61
2008 DEN 8 8 0 -5.79 1.15 -6.94 -4.88 -0.91
2009 DEN 8 8 0 0.32 -1.09 1.41 0.12 0.20
2010 DEN 4 12 0 -8.91 -0.54 -8.37 -7.94 -0.97
2011 DEN 8 8 0 -5.30 -2.87 -2.43 -5.06 -0.23
2012 DEN 13 3 0 10.10 7.08 3.02 12.00 -1.90
2013 DEN 6 0 0 13.95 21.22 -7.26 17.83 -3.88

 

And Indianapolis’s data set looks like this:

Indianapolis Colts 2005-2013
Year Team W L T SRS OSRS DSRS MOV SOS
2005 IND 14 2 0 10.80 6.82 3.98 12.00 -1.20
2006 IND 12 4 0 5.88 7.31 -1.43 4.19 1.69
2007 IND 13 3 0 12.01 6.44 5.57 11.75 0.26
2008 IND 12 4 0 6.49 1.58 4.91 4.94 1.55
2009 IND 14 2 0 5.93 3.65 2.28 6.81 -0.88
2010 IND 10 6 0 2.88 5.15 -2.27 2.94 -0.06
2011 IND 2 14 0 -11.28 -6.99 -4.29 -11.69 0.40
2012 IND 11 5 0 -4.71 -2.39 -2.32 -1.88 -2.84
2013 IND 4 2 0 8.89 1.72 7.18 8.33 0.56

 

Using SRS, you would say that Denver has a slight advantage. Let’s look at three different predictive techniques and what they say about point spread and odds of winning the game. (1) These three, for the Denver-Indianapolis game, yield very different results.

Odds of Denver Winning and Predicted Point Spread
Pythagorean Expectation Simple Ranking System Median Analysis
Pct Points Pct Points Pct Points
0.48 -0.6 0.57 2.1 0.77 9

 

The two techniques I trust more, Pythagoreans and SRS, yield different results for the winner but both say the game will be decided by less than three points. With games this close, small factors – a single turnover, a great punt return – can decide the results. I add the median prediction largely as a comparison. I don’t trust it as much as the other two methods in terms of predicting results.

All three predictions include a home field advantage effect.

Notes:

1. For a simple relationship between point spreads and winning percentages, look here. A different approach is given in the book “Mathletics“, worth reading if you’re into betting football.

I haven’t followed this team closely, but some people I associate with are huge Pittsburgh Steelers fans. So for them, we’ll drop this set of SRS quick hits.

Pittsburgh Steelers 2004-2013
Year Team W L T SRS OSRS DSRS MOV SOS
2004 PIT 15 1 0 9.00 1.77 7.23 7.56 1.44
2005 PIT 11 5 0 7.81 3.32 4.49 8.19 -0.37
2006 PIT 8 8 0 3.42 1.40 2.01 2.38 1.04
2007 PIT 10 6 0 5.21 2.46 2.75 7.75 -2.54
2008 PIT 12 4 0 9.80 -0.29 10.09 7.75 2.05
2009 PIT 9 7 0 1.69 0.70 0.99 2.75 -1.06
2010 PIT 12 4 0 10.22 1.40 8.82 8.94 1.28
2011 PIT 12 4 0 5.29 -2.71 7.99 6.12 -0.84
2012 PIT 8 8 0 -0.65 -2.16 1.51 1.38 -2.03
2013 PIT 1 4 0 -8.30 -8.05 -0.25 -5.60 -2.70

 

The bigger loss of productivity on the Steelers, so far, has been on the offensive side. They won their last game, so perhaps the doldrums will begin to abate, and the team will begin to score more consistently. If they had as rock ribbed a defense as in 2008, then they would likely be in the playoff hunt despite the poor offensive showing (see the 2005 Chicago Bears as an example), but the defense is just ordinary at this point.

For this set of data, I wanted to look at the Chicago Bear’s stats from the Lovie Smith era to the present.

Chicago Bears 2004-2013
Year Team W L T SRS OSRS DSRS MOV SOS
2004 CHI 5 11 0 -8.24 -7.77 -0.46 -6.25 -1.99
2005 CHI 11 5 0 1.39 -6.60 7.99 3.62 -2.23
2006 CHI 13 3 0 7.90 4.54 3.36 10.75 -2.85
2007 CHI 7 9 0 1.22 0.58 0.65 -0.88 2.10
2008 CHI 9 7 0 2.10 2.00 0.10 1.56 0.54
2009 CHI 7 9 0 -3.89 -1.03 -2.86 -3.00 -0.89
2010 CHI 11 5 0 4.11 -1.16 5.27 3.00 1.11
2011 CHI 8 8 0 1.65 0.79 0.87 0.75 0.90
2012 CHI 10 6 0 6.94 0.78 6.17 6.12 0.82
2013 CHI 4 2 0 -0.87 4.76 -5.63 1.83 -2.70

 

We had touched a bit on Chicago’s stats in our article about the Carolina Panthers, but I was still curious about performance of this team into the present. How much better is Marc Trestman‘s offense? It is substantially better, but the fall off in defensive productivity is potentially undermining to the new found offensive prowess.

I’m curious about the Panthers, because of their striking DSRS ranking. Is this something that built up without much notice, or did it appear out of nowhere?

Carolina Panthers 2010-2013
Year Team W L T SRS OSRS DSRS MOV SOS
2010 CAR 2 14 0 -13.19 -9.79 -3.40 -13.25 0.06
2011 CAR 6 10 0 -1.30 3.34 -4.63 -1.44 0.14
2012 CAR 7 9 0 0.81 0.83 -0.03 -0.38 1.19
2013 CAR 2 3 0 5.54 -2.51 8.06 8.20 -2.66

 

For now I’d say it bears watching. The 2010 to 2011 season saw a huge improvement in the Panther’s defense, going from bad to ordinary. An 8 point jump in a single season would be exceptional, but not impossible.

Looking at the Panther’s drafts, there is a heavy defensive emphasis in the first three picks in 2012 and 2013, with LB Luke Keuchly the number 1 pick in 2012 and DT Star Lotulelei the number 1 pick in 2013.

The Panther’s head coach, Ron Rivera, was the defensive coordinator for the Chicago Bears in 2004-2006, and first started receiving head coaching attention at the end of the 2005 season, when he was credited with helping to make Chicago one of the best defenses in the league. From 2004 to 2005, there was an approximately 8.5 point improvement in Chicago’s DSRS.

Chicago Bears 2004-2006
Year Team W L T SRS OSRS DSRS MOV SOS
2004 CHI 5 11 0 -8.24 -7.77 -0.46 -6.25 -1.99
2005 CHI 11 5 0 1.39 -6.60 7.99 3.62 -2.23
2006 CHI 13 3 0 7.90 4.54 3.36 10.75 -2.85

 

Week 6 stats:

2013_stats_week_6

To explain the columns above, Median is a median point spread, and can be used to get a feel for how good a team is without overly weighting a blowout win or blowout loss. HS is Brian Burke’s Homemade Sagarin, as implemented in Maggie Xiong’s PDL::Stats. Pred is the predicted Pythagorean expectation. The exponent for this measure is fitted to the data set itself. SOS, SRS, and MOV are the simple ranking components, analyzed via this Perl implementation. MOV is margin of victory, or point spread divided by games played. SOS is strength of schedule. SRS is the simple ranking.

Week 6 OSRS and DSRS stats:

2013_stats_week_6_srs

It’s interesting that while the #1 DSRS is Kansas City (expected), the #2 is Carolina (not so expected). The #1 OSRS is Denver, and the #2 for now appears to be Dallas. Dallas’s offense SRS can’t be assigned entirely to the offense. Monte Kiffin’s defense emphasizes turnovers and the Dallas special teams are scoring as well.

It is difficult to get a feel on Dallas fan sentiment, which tends often to sound a bit like Linus of Peanuts, convinced he’s missed the Great Pumpkin. Are they happy with their team? Are they dissatisfied? Are they one of those folks convinced that only Jerry Jones can save the situation by firing himself? Let’s see what the numbers say about the first six games of the 2013 Dallas campaign.

Dallas Cowboys 2003-2013
Year Team W L T SRS OSRS DSRS MOV SOS
2003 DAL 10 6 0 -0.46 -5.04 4.58 1.81 -2.27
2004 DAL 6 10 0 -7.77 -3.94 -3.83 -7.00 -0.77
2005 DAL 9 7 0 3.16 1.79 1.37 1.06 2.09
2006 DAL 9 7 0 3.66 5.90 -2.24 4.69 -1.03
2007 DAL 13 3 0 9.47 7.62 1.86 8.12 1.35
2008 DAL 9 7 0 0.57 0.65 -0.08 -0.19 0.75
2009 DAL 11 5 0 7.15 1.31 5.84 6.94 0.21
2010 DAL 6 10 0 -2.15 3.06 -5.21 -2.62 0.47
2011 DAL 8 8 0 1.63 1.13 0.49 1.38 0.25
2012 DAL 8 8 0 0.28 2.62 -2.34 -1.50 1.78
2013 DAL 3 3 0 4.93 7.45 -2.52 5.17 -0.24

 

The closest comparable, so far, appears to be the 2006 Dallas campaign. The defense is playing at about the same level, the offense is a little overrated because of turnovers on defense and excellent special teams play. That bodes well, giving the team a shot at a playoff berth. For now the Philadelphia Eagles appear to be the major obstacle in the path to a NFC East playoff berth. The conclusion to be drawn is that the play in the two Eagles games appears to be key so far. That and the health of Dallas players, which has been pretty awful for Dallas linemen the past couple years.

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