Code and Football

If the NFL were to stop playing altogether, and judge teams on their current records, and assign division winners, the playoff rankings in the AFC and NFC would look something like the table below. The teams’ associated playoff stats, according to my playoff model, are also presented.

The first thing that pops out in this chart is the remarkable strength of schedule advantage Indianapolis has so far. Given their remaining schedule (games with the Texans, Titans, Rams and Jaguars), that advantage is likely to evaporate over the next 8 games, but it leads to the interesting assertion that Indianapolis would be favored by some 14 points over Kansas City should Indianapolis play KC in a second round playoff game, right now.

Kansas City, by contrast, is an exceptionally weak #1 team in terms of playoff experience and strength of schedule. The Chiefs do start playing some tougher teams in the second half, but just a few.

In the NFC, all the possible playoff teams have had about the same strength of schedule except for the Saints, whose schedule has been a lot easier than most. The one factor an analyst will have to decide for themselves is whether the Saints are a team with previous playoff experience, despite the formal definition of the term, in my formula, as experience in last year’s playoffs. I’ve done that in the past, for the 2011 New York Giants.

Conversation around Atlanta is that the Falcons are effectively out of the playoff hunt, as they would need to go 8-1 to be back in it. Personally, I don’t see how they can become a better team than Carolina at this point, much less New Orleans.

Week 8 NFL Stats:

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:

I’m very tempted to start figuring out who would win playoffs if results were frozen and teams were to go into rounds of games at this time. That’s something for a future post, though.

Detroit for me is a puzzling team, I suspect in part it is because casual fans watching football in Georgia expected Matthew Stafford to mature into a leading NFL quarterback. That he’s become a good quarterback is given, but I guess the hope he would be a modern day Bobby Layne hasn’t left those of us living in the Southeast.

Detroit hosts Dallas this weekend and not at all surprisingly, for two teams fairly closely matched, the three predictive systems yield three different results. Pythagoreans have Dallas and Detroit as essentially even. Simple rankings would suggest that Dallas has a slight edge. Medians suggest the opposite, that Detroit will win by about 5 points.

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:

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:

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:

And Indianapolis’s data set looks like this:

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.

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.

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.

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?

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.

Week 6 stats:

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:

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.

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.