Betting Market Power Rankings – Conference Finals Edition

by Michael Beuoy

Editor's Note: Michael submitted this earlier this week and I was late in posting it. EA

Here are the final Betting Market Power Rankings of the season, updated with the results of the prior week and the lines for this week. As promised last week, I will also revisit my predictions for the lines and over/unders for the conference final games.

Refer to last week’s post for more detail on the weights used.
Here is a glossary of terms:

LSTWK - The betting market rank as of the prior week
GPF - Stands for Generic Points Favored. It’s what you would expect a team to be favored by against a league average opponent at a neutral site.
oGPF – Offensive Generic Points Favored. The component of a team’s total GPF attributable to its ability to score points.
dGPF – Defensive Generic Points Favored. The component of a team’s total GPF attributable to its ability to prevent the other team from scoring points
O RANK – The team’s oGPF ranking.
D RANK – The team’s dGPF ranking.
GWP - Stands for Generic Win Probability. I converted the GPF into a generic win probability using the following formula: GWP = 1/(1+exp(-GPF/7)).

Best (And Worst) Post-Season Coaching Records Since 1950, via a Binomial View

by Jim Glass

Vince Lombardi's post-season coaching W-L record of 9-1, .900, is the best ever. Or maybe not, some say Joe Gibb's record of 17-7 is standard to beat - "only" .708, but over a run of 2.4 times as many games it is much harder to keep a big winning record. How can we compare them?

One way is to compute the binomial probability of a coach attaining a given won-loss record by random chance. For instance, in Herm Edwards' first year coaching my Jets, he went 10-6. I was happy. That .625 winning record was better than Tom Landry's .607 and our former coach Bill Parcells' .570 - better than a Hall of Famer's and sure future Hall of Famer's! It looked like we had a great coach. Except that Parcells earned his .570 over 303 games and Landry earned his .607 over 418, while Herm had earned his .625 over only 16.

The binomial calculation can give the probability of winning a given number of games out of any number played, and so providea a common standard to apply to the W-L performances of different coaches with different W-L percentages over differing numbers of games. It told me that, assuming game outcomes were random with a 50% chance of winning/losing, Herm's record of .625 (or better) had only a 22% probability of occurring by random chance, which was pretty encouraging - but Bill's .570 record had only a 1% chance, and Tom's .607 had only a 0.001% chance. So perhaps it was premature to declare Herm a better coach than Tuna and Tom (as indeed it turned out to be).

Betting Market Power Rankings – Divisional Round Edition

by Michael Beuoy

In last week’s post, I showed how one can use the betting over/under in conjunction with the point spread to decompose team strength into an offensive and defensive Generic Points Favored (GPF = oGPF + dGPF). The post was essentially a redo of the Week 16 rankings, and unfortunately, I did not have enough time to apply the new method to the Wildcard Round of the playoffs. This week, I do have time, so here is a peek into the mind of the Betting Market for the Divisional Round of the Playoffs. In addition, I’ve laid out a table of the predicted lines and over/unders for each possible matchup in the Conference Finals and Superbowl. I’ll return to the predictions in the following weeks to see how the model did (testable predictions! science!).

For those of you that are interested, I’ve decided to start a blog for the purposes of publishing these rankings for various sports. I’ll start off with the NBA (see the first set of rankings here). After that, I’ll take a crack at NCAA Basketball, and then hopefully move on to Major League Baseball (which presents some interesting opportunities for decomposing team strength into offense, defense, and pitching, and creating a separate set of starting pitcher rankings). The blog will probably be pretty rough in the early going (i.e. ugly and confusing), but I hope to learn quickly.

NFL Coach Quality: A Bayesian Approach To Approximating the Value of Coaches - UPDATED

by David Durschlag

You are currently viewing version 1 of this article. To view version two, please click here.

Summary

Evaluating NFL coaches is a difficult task, popular among fans and vitally important to franchises. This is a brief attempt at the task, using purely quantitative data.

Data

The numbers of regular season games each team won each year are treated as data points. No information beyond number of regular season wins was used.

While the "metagame" of the NFL continues to evolve, the data used herein is from 1993 onward, when the last Collective Bargaining Agreement was signed. While victories now come in different environments, they are all under (roughly) the same rules. Data from before this period could be skewed based on the different rules for control of players, so it was excluded.

Also excluded was the performance of any team in a year in which it had multiple head coaches. This was done to ensure that credit for a season was easy to assign.

Betting Market Power Rankings – Offense and Defense Rankings

by Michael Beuoy

The purpose of this post is to show how the Betting Market Power Rankings can be decomposed into Offensive and Defensive strength by looking at the over/under betting lines in conjunction with the point spreads.

One of the best features of the Advanced NFL Stats efficiency model is that it not only tells you who the best teams are, it also tells you why those teams are the best (passing efficiency, rushing success rate, penalty rate, etc.). Unfortunately, the betting markets don’t tell us why they favor one team over the other; all we get is the final point spread. However, by looking at the betting over/under for each game, and combining it with the point spread, we can at least get a sense as to whether it is offense or defense (or both) that is driving the market’s evaluation of each team.

Respect Randomness, K.I.S.S., Beat Vegas.

by Jim Glass

The 2011 end-of-season result is in for BigWin% - the team rating system that treats games decided by one score, 8 points or less, as ties in computing each team's W-L%, then adds a strength-of-schedule adjustment, that is all. (Rationale and background explained previously.)

Here are game prediction and efficiency results for the final eight weeks of the season. ("Efficiency" = correct game predictions divided by expected correct predictions. E.g., if among a week's 16 predictions the average predicted winner is a .750 favorite then 12 correct predictions would be expected. If the actual number is 10 then efficiency is .833, if 11 then efficiency is .917, etc.)