October 2012
Monthly Archive
October 30, 2012
Week 8, NFL scoring 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.
NFC East teams with aspirations of playoff contention, Dallas and Philadelphia, appear to be having them derailed by a single common cause. Neither has an offensive line healthy enough or good enough to let their quarterbacks shine. Further, being 1.5 games behind their wild card competition in the NFC North leaves them with precious few chances. Perhaps they’ll improve, perhaps Philadelphia will find a miracle LT and Dallas will scrape together a center and a right guard, but don’t hold your breath waiting. Washington is dynamic, but needs a few pieces here and there. Early season defensive line injuries did that team little good.
For now, what interests me are things like: who in the AFC can compete with Houston? Baltimore looks as if it’s winning on tradition more than dominance. New England looks great, except when it doesn’t. Denver is a powerful work in progress. In the NFC, it’s entirely possible that the NFC North will field 3 playoff teams. Chicago, Green Bay and Minnesota look good. Atlanta continues to roll up wins, the last perhaps its most impressive so far. The Giants continue to show strength. The San Francisco 49ers may be the best team in football right now, leading in HS, #2 in Simple Ranking, and no more than 0.3% off the top of the Pythagoreans.
Totally off the subject: this is the political season, and some of my favorite bloggers are tweeting some politics these days. One of the most interesting of the lot is @skepticalsports. I don’t share the political sentiments of Benjamin Morris, but polite and political – which he manages to do – is a rare combination, and it actually takes some work to be offended by his tweets.
October 24, 2012
Week 7, NFL scoring 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.
One of the things dogging Atlanta sports talk radio is “just how good are the Atlanta Falcons”? Statistically, they’re in the top 5-10 but not the very top in the various scoring stats. A lot of their success is based on turnover differential, not a good predictor of success over the long term. The Houston Texans, by contrast, shook off their one game blues are are back in the top 2 or 3 once again.
Chicago for now is at the head of the scoring stats, and those of us familiar with Jay Cutler’s ability to have really bad games will be watching to see if he can keep it up. Coming hard are both the Giants and the Packers. Denver is the best looking of the 3-3 teams.
October 17, 2012
Week 6, NFL scoring stats:
Atlanta leads in no statistical category except their won-loss record.
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.
Houston lost to Green Bay, and so for now, they’re no longer the statistical darling of the NFL. Chicago is now the top dog. The Bears are a team that can play great offensive games or horrible ones, and it’s anyone’s guess how long their offensive explosion will last. Minnesota appears to be competitive, and Green Bay and Detroit are coming out of their funks, so I expect a tough divisional battle.
That said, the surprise of the NFC is the tough division race in the NFC West. 3 of the 4 teams have real chances this year, and maybe even Saint Louis will be in the race in a year or two. The conference overall seems to be improved, with tough defenses becoming the norm this season.
The New York Giants won a game that impressed the critics, and if both Dallas and Philadelphia remain snake bitten teams that shoot themselves in their own feet, that could manifest in a great set of statistics over the year. More likely though, the Giants will play everyone tough, perhaps even play a great 8 game stretch, and then have 2-3 mystifying losses to teams they are better than. The lack of a running game makes it hard for the Giants to close out games. What they do will be on the backs of a calm collected QB, their pass rush, and large, gifted wide receivers.
October 10, 2012
Week 5, NFL scoring stats:
Stats are still extremely fluid.
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.
Houston is still the darling of all the scoring stats. New Orleans has now won a game. The record of the Saints is now more reflective of the ball they are playing. Atlanta fans may be wondering what happened to a 5-0 team, and the answer is nothing. They’re playing more or less as they were, but Chicago and San Francisco had big wins. With stats easily changed by one or two blowout wins, you have to expect some of the unexpected.
The toughest schedules so far? Green Bay, Jacksonville, Dallas, Saint Louis, and Indianapolis.
October 3, 2012
Ed Bouchette has a good article, with Steelers defenders talking about Michael Vick. Neil Payne has two interesting pieces (here and here) on how winning early games is correlated with the final record for the season.
Brian Burke has made an interesting attempt to break down EP (expected points) data to the level of individual teams. I’ve contributed to the discussion there. There is a lot to the notion that slope of the EP curve reflects the ease with which a team can score, and the more shallow the slope, the easier it is for a team to score.
Note that the defensive contribution to a EP curve will depend on how expected points are actually scored. In a Keith Goldner type Markov chain model (a “raw” EP model), a defense cannot affect its own EP curve. It can only affect an opponent’s curve. In a Romer/Burke type EP formulation, the defensive effect on a team’s EP curve and the opponent’s EP curve is complex. Scoring by the defense has an “equal and opposite” effect on team and opponent EP, the slope being affected by frequency of the scoring as a function of yard line. Various kinds of stops could also affect the slope as well. Since scoring opportunities increase for an offense the closer to the goal line the offense gets, an equal stop probability per yard line would end up yielding nonequal scoring chances, and thus slope changes.
October 2, 2012
Week 4 of my NFL stats:
The Houston Texans dominate most scoring stats by week 4.
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.
Houston, by far, is the best team by these statistical measures. The second best team is a pick ’em between Atlanta, Arizona, Chicago, and San Francisco. The Tennessee Titans might look awful in some statistical measures, but they appear to have played the toughest opposition so far. Philadelphia is the tail ender of the 3-1 teams, and Dallas and/or the Jets are tail ending the 2-2 teams. Don’t read too much into early blowouts at this stage, for any team. Those things tend to even out over time.
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