Towards a Better Pythagorean: Should Football Outsiders "hold the update"?

by Jim Glass

The good people at Football Outsiders say they are changing the formula for Pythagorean Expectation that they use to gauge team strength, and intend to update all the Pythagorean data on their website via their new formula during the offseason.

The "traditional" Pythagorean formula they've used until now produces a win expectation with about a 91% correlation to actual team wins. They say the new, improved formula increases the correlation to .9134 from .9120.

That's something - but a much simpler adjustment to applying the standard Pythagorean formula that I've been using increases the correlation to 95%. Also, while FO's new methodology uses a log function applied to year-to-date data that will be opaque to the average fan, the method I've been using gives a clear game-by-game result that anybody can easily grasp.

So I submit, for their consideration and yours, the formula adjustment below as being more accurate, simpler to apply, and easier for fans to refer to, understand, and play with.

Betting Market Power Rankings – Week 16

by Michael Beuoy

Here are the week 16 betting market power rankings. I have provided two versions this time because I think the default methodology is breaking down this late in the season. The default methodology incorporates the following week’s spreads into the rankings, if they’re available. Because week 17 often includes meaningless games for teams trying to stay healthy for the playoffs, the spreads don’t necessarily reflect the true strength differential.

For example, the week 17 line for the Green Bay- Detroit game has Green Bay as a 3 point underdog. At home. It’s not very likely that the market thinks that Detroit is truly 5.5 points better than Green Bay, but it does appear to expect Green Bay to have things wrapped up by week 17 and not be playing at full strength.

So, I have provided two versions of the rankings. The first is the standard approach, which includes week 17 lines (probably not reliable). The second excludes week 17 from the rankings, and appears to make more sense.

Dispatches From the Anti-League: #2

by James Sinclair

In September I wrote an overview of a fantasy football league some friends and I started this year where the goal is to compile the worst team possible. Here's the long-awaited follow-up—a bit later than intended, but at least I got it in before the end of the season. Barely.

First, a few observations:

1. The draft was considerably less important than in conventional fantasy football, because in the Anti-League it's actually possible for a player to be too "good" (and with that, I'll stop using quotation marks to indicate ironic reversals of good and bad, because it would definitely get out of hand). Case in point: my first round pick, Jacksonville's Luke McCown. He put up a decent score in week 1, and then an outstanding score against the Jets in week two (the fifth-highest single-game point total of the year, as you'll see below). And even as I was watching that game I was thinking "oh crap, he's going to be benched", which is exactly what happened. Fortunately I was able to pick up Blaine Gabbert off waivers, and even more fortunately the Jaguars have stuck with Gabbert all season (presumably because their backup is Luke McCown).

The Favorite/Long Shot Bias – Analysis of Superbowl Futures

by Michael Beuoy

In the week14 post, I outlined an approach to test the validity of the rankings by looking at Superbowl futures. At the time, I thought that Chris Cox’s NFL Forecast tool allowed for customization at the team strength level. Unfortunately, it doesn’t, but Chris was nice enough to run a one-off of his model, using the GWP’s from the Week 15 Betting Market Rankings.

Here are the results of the analysis. I took Superbowl futures from footballocks.com (thanks Ed) as of December 14. I then removed the “vig” from the odds under the assumption that each bet had the same (negative) expected value. In this case, the vig turned out to be a whopping 28%.

Revisiting the Big Wins Index: the kind of wins today that do predict wins tomorrow

by Jim Glass

All wins count equally in the standings ñ but they don't count equally as a measure of team strength or indicator of probable future won-loss record.

Brian Burke has written that random chance determines more than 40% of NFL game outcomes. Thirty years ago Bill James showed that baseball teams that win many (or few) close game later consistently see their W-L record regress to their record in decisive games - indicating that close game outcomes are heavily luck, and teams with good W-L records based on luck see their luck run out. At the same time, teams with disappointing W-L records due to having a lot of close losses offsetting decisive wins are primed for a turn to the better. This has since also been well documented for football and across other sports.

Of course, all this goes 100% *against* the great popular belief in the importance of "clutch play" - but that's reality. Last year, I looked at the results of 15 years of NFL playoff games for all teams that had won 11 or more games during the regular season. Sorting them by record in terms of "big wins", by 10 points or more, and "clutch, close wins", by less, produced these numbers...

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.