And with the start of the season comes my call for volunteers! I should be able to post the weekly graphs, but there’s definitely room for more contributors.

If you have any interest in writing about any of the following subjects, let me know:

• Weekly rankings (Our dear friend Kenneth is unavailable this season) and commentary
• Weekly picks
• Pick performance
• Other statistical studies based off of the beatpaths system

What I can offer in terms of support is various output from the data to make your article-writing easier, and accounts on the wordpress system so you have free reign to publish. I can’t offer pay, but I don’t get paid either, this has always been a labor of geeky, irreverent love!

If you’re interested in helping out, please contact me through my personal site at http://curtsiffert.com/feedback . Thanks!

It was a great year, despite the Broncos falling to last place. Thanks again to all of the commenters for making this site fun, Moose for continuing to keep track of the other graphs, sports, and counting variants, Nick for helping to give the site a much-needed facelift, and of course Kenneth for doing such a phenomenal job with all the rankings and commentary this year.

I’m sure we’ll be back next season (if there is one), but in the meantime I’ll toy with some other ideas of posting content. If any of you would like to assist in posting commentary about other sports, let me know, as I’d be happy to generate graphs and rankings and whatever else folks might be interested in.

So without further ado, the final 2010 Beatpath Graph! Spoiler: it’s identical to the graph in the conference round, although I believe that an “invisible” arrow somewhere might have been removed. Hopefully Kenneth will be able to clarify on that.

Have a great off-season!

Nick Bell stepped forward to volunteer his time and effort to get us set up with a new theme, and he’s done a great job suggesting and implementing a theme that is organized, clean, and has a lot of room for future tweaks if we decide to add more functionality to it.

Go ahead and leave any comments about the new design below, we’ll be able to make some adjustments if anyone notices anything wonky. Beyond that, thanks to Nick! And also for everyone’s patience since I think I first noted I’d be putting a new design in place something like two years ago.

If you’re interested in representing a slice of the content – whether it’s a posting once a week or something more irregular, drop me a note and make a proposal. I can set up a mailing list of us to do some coordination.

For those who don’t know, the backend system is written in perl – I may be interested in allowing others to collaborate on the programming if there’s enough interest.

If you’re interested, contact me through this webform.

New to the Panel this year is Curt Siffert, proprietor of Beatpaths, a Web site that does something with math and charts that I’ve spent a good seven minutes trying to figure out over the last three years, without success. But it looks cool.

Siffert has warned his readers that the Beatpaths picks, which are based on what has happened so far this season, will be sort of ridiculous in the early going.

A few years back, I was reading one of those websites that purports to power-rank sports teams based off of every single in-game stat in the book. And, being a Broncos homer, I was peeved that the Broncos were ranked so low.

My gut-based thought – completely lacking in any evidence and data – was that wins aren’t always linked to stats. Sometimes it’s heart! And guts! Or the intelligence of a team’s scheme – sometimes they just play the matchup game better. But really, I was just thinking about how it all comes down to wins and losses, and damn the rest of the data.

The advanced ranking schemes pay attention to a lot of data – all the data except for wins and losses. So my perverse idea was to do the inverse. Focus only on wins and losses.

Turns out there are other schemes that also only pay attention to wins and losses – they use big words and probability and math and statistical theory.

I decided to use graphs. Here’s how the idea came about.

I started graphing the NFL season based off of one simple rule – if a team beats another, you draw a line from the winner to the loser. The End.

Soon, you start seeing long sequences of wins and losses – The Broncos beat the Steelers, who beat the Patriots, who beat the Giants — oh wait, they didn’t beat the Giants. Anyway, those sequences of wins become what I call a beatpath.

Now, early in the season, you see sportswriters try in vain to structure their own power rankings by always having teams ranked ahead of other teams they’ve beaten.

But soon, you notice that contradictions start appearing. Every year, there’s some silly article over in college football land about how Captain Doohickey Tech is better than Ohio State, since they beat Team A, who beat Team B, who beat Team C…. who beat Team X, who upset Ohio State back in week 2. When you know just as well that Ohio State has demolished someone who has in turn demolished the Raiders, I mean, Captain Doohickey Tech.

That contradiction is called a beatloop.

And so we, that is the Royal We, not to be confused with Eddie Royal, worked out a system to resolve these beatloops and come up with automatic power rankings. And surprisingly, it’s ended up weirdly accurate. Well, sometimes just weird. But a hell of a lot of fun.

Every week I’ll post graphs that are a snapshot of the NFL season so far. And the automatic power rankings that come from the graph. And we’ll be continuing to discuss ways to tweak the algorithm – commenters often come up with variants to the algorithm that might even be more accurate. And we’ll have arguments about whether the beatpath algorithm is actually predictive (when it’s doing well), or merely descriptive of the season so far (when there are lots of upsets). And there will probably be some subtle Broncos homerism littered throughout, until someone calls me out on it.

Welcome to the site (redesign coming soon!), and enjoy your stay!

A beatpath graph is a graphical representation of all of the games/matches in a season. It pays attention to one stat and one stat only: wins and losses. It doesn’t do any math or assign any equations.

We then generate power rankings. We started with one rule – if a team has beaten another team, it should be ranked ahead of that team. As wins and losses increase, there are less and less possible ways to rank the teams.

It also becomes impossible after only a few rounds, because you might have A beating B beating C beating A. Or more simply, two teams might play each other twice and split the series.

It’s simple: we call those beatloops. We take them out, and we graph what’s left. Since most leagues are highly interrleated, we assume that there’s enough other information in the graph to make up for losing those ambiguous relationships.

So what’s left? A picture of how generally good each team is, by examining wins, losses, and who beat who. It gives more accurate information than records and standings, because they ignore who beats who. I believe it gives more accurate information about playoff seedings, because, at least in the case of the NFL, win/loss records are affected by schedules, and schedules are designed by relying on the strength of teams from the previous season, rather than the current season. This means that teams can be unfairly penalized by having a schedule that is unexpectedly strong, and vice versa. We saw that in 2006 – Dallas should have been in the playoffs, but their schedule was murderous – they didn’t make it.

So what’s the point? If you’ve been using other stats to try and get an understanding of how good a team is, it’s worth checking out the beatpath graph as a supplement. The graph pays attention to wins, losses, who beats who, and nothing else. Same rules for every team. If you’re surprised by what you see, then that means you’re getting valuable perspective.

Plus, it’s just kind of fun to watch how the graphs change from round to round as teams wins and lose.