Betting Market Power Rankings – Week 15

by Michael Beuoy

Here are the week 15 betting market power rankings. As noted last week (link), I have modified the methodology to now incorporate the point spreads for the upcoming week, if they’re available (week 16, in this case).

Methodology
The goal I had was to generate a set of point-based rankings that would best predict how the betting market would set the point spread for the coming week’s matchups. The better I was able to match the point spread, the better the rankings were a reflection of the market’s estimate of team by team strength. Through trial and error experimentation, I found that using point spreads for the most recent five weeks (with higher weighting given to more recent weeks), combined with an adjustment that accounted for actual game outcomes, generated the best predictive accuracy. More detail here

Will Tim Tebow be Denver's Dick Jauron? Or: The Meaning of Clutch Victories

by Jim Glass

Is the Tim Tebow story real? I enjoy Tebow, for years have wanted to see somebody try the option in the NFL, am entertained by watching the challenges it throws up for opposing teams, and am rooting for Tebow football to work. But is the Tim Tebow story as heard everywhere from his fans and admirers (and worshipers) real?

Tebow is a type of QB unique in the modern game. But the Tebow story is very old and familiar: the tale of "clutch winning". It's the story of *not* having the stats that go with winning, but winning anyhow. Thus, credit for winning goes to character, leadership, inspiring teammates, making others better, and - most of all - coming through with big plays in the last minutes to win close games in the clutch.

Betting Market Power Rankings – Week 14

by Michael Beuoy

Here are the week 14 betting market power rankings. Here is a link to the first set of rankings I generated last week.

First off, thanks to Brian for making the Community site available as well as calling out last week’s post on the main blog. There was a lot of good feedback in the comments section. Most of it I’m still chewing over, but I have decided to incorporate a suggestion from Jim A, who has been creating a very similar set of rankings over at Nutshellsports.com. More on that later.

Methodology

The original post has more detail, but here’s an overview:

The goal I had was to generate a set of point-based rankings that would best predict how the betting market would set the point spread for the coming week’s matchups. The better I was able to match the point spread, the better the rankings were a reflection of the market’s estimate of team by team strength. Through trial and error experimentation, I found that using point spreads for the most recent five weeks (with higher weighting given to more recent weeks), combined with an adjustment that accounted for actual game outcomes, generated the best predictive accuracy. More detail here.

Betting Market Power Rankings

by Michael Beuoy

The purpose of this post is to use the point spreads from recent weeks of the season to derive an implied power ranking. Basically, the point is to try to figure out what the betting market thinks are the best and worst teams in the NFL. From a broader perspective, I hope to provide insight into how the betting market “thinks” in general. One result that emerged from this analysis was a measure of how much the betting market reacts to the result of a particular game.

The challenge in deriving a power ranking from the point spreads is that the point spread only tells you the relative strength of the two teams. For example, Green Bay is favored by 7.0 points on the road against the NY Giants this week. We know that home teams are favored on average by 2.5 points, so after removing the home team bias, the betting market appears to think that Green Bay is 9.5 points better than the Giants. New England is favored by 21(!) points at home against Indianapolis So the betting market thinks that New England is 18.5 points better than Indianapolis.

Adjusting Strength of Schedule

by Michael Beuoy

One of the more interesting components (to me) of Brian Burke's efficiency model is the strength of schedule adjustment. I found it interesting the way you could bootstrap yourself into a self-consistent opponent adjustment. Here is the description (link):

"To adjust for opponent strength, I could adjust each team efficiency stat according to the average opponents’ corresponding stat. In other words, I could adjust the Cardinals’ passing efficiency according to their opponents’ average defensive efficiency. I’d have to do that for all the stats in the model, which would be insanely complex. But I have a simpler method that produces the same results.

Peyton Manning - Colts Defensive MVP??

by Steven Buzzard

A growing sentiment that has started to crop up with a lot of the talking heads in the league is that the Colts would be pretty terrible if Peyton Manning was playing because the defense has been so atrocious. These same people love to state the obvious and point out that Peyton Manning doesn’t make tackles. However, a lot of stats analysts have known over the years that the Colts offense has actually helped the Colts defense in 3 key ways.

1) They stay on the field a long time and limit the total number of drives per game
2) By staying on the field they give the defense great field position despite terrible special teams
3) By getting leads the defense can force more turnovers

MRE - Measure of Random Events through week 5

by Bruce D

MRE = (good luck points)-(bad luck points), so positive numbers are the luckiest teams.

For a more in-depth explanation of what "luck" points are, go to a previous post here.

If random events can't be repeated, then past points due to MRE can NOT be considered as being due to skill. Likewise, past performance due to MRE can't be included in analyzing future performance. In a nutshell, "lucky" teams can't be expected to be so "lucky", and "unlucky" teams are better than we may think.

Points for(+) the lucky team, are the same amount of points against(-) the unlucky team.

MRE is valued as follows:

punts blocked=3
interceptions=2.5
fumbles lost=2.5
field goal miss/block=2.5
punt returns for a TD=4.5
ko returns for a TD=4.5