Friday, March 1, 2013

The 2013 Sloan Sports Analytics Conference

Hello, Sloan Sports Analytics attendees!

This is the third year I'll be attending Sloan, but unfortunately my co-author can't make it again this year. For those of you who have never visited the blog before: welcome. (For those of you who have, this post will look eerily like last year's post.) We're a pair of Silicon Valley software engineers who moonlight as college football stats analysts, and occasionally we're not completely wrong. I'm Justin, the founder of the blog and curator of the Tempo-Free Gridiron (TFG) ranking and prediction system. A year into the blog, I managed to rope Eddie, a co-worker and Arkansas Razorback fanatic, into creating his own competing system; the result was the Regression-Based Analysis (RBA) algorithm.

To get a feel for what we do here, we invite you to take a look at some of the posts describing tempo-free statistics in general (courtesy of Dean Oliver via Ken Pomeroy), the motivation behind this particular blog, and a peek under the hood of the TFG system (and a recent update). We also have info about the RBA model (along with an update and yet another update). Recently we've also started exploring how to create viable in-game win probabilities.

Given that it's the college football offseason, there's not too much happening on an ongoing basis right now, and it'll be kind of quiet until August. However we invite you to look at some of the "best of" we've produced, as other posts of interest, including
During the regular season you can expect to find weekly posts showcasing
This is all on top of predictions for each and every game between two FBS teams.

All-in-all it's grown into a pretty complicated system backed by a lot of code we've written over the last few years. If you have any questions or feedback for us, don't hesitate to email us at our tempo-free-gridiron.com addresses (justin@ or eddie@), leave a comment here, or hit us up on Twitter.

Enjoy the conference, and we hope to see you there.

Thursday, February 28, 2013

Exploring In-Game Win Probabilities [Updated]

This past year we've been working on providing live in-game win probabilities for every FBS game. By this, we mean that given the current situation on the field -- the teams playing, the score, the time remaining, possession, down, distance, and field position -- what are the odds that each time will win?

However the in-game win probabilities we posted this past season were a function only of team strength, offensive and defensive efficiency so far in the game, the magnitude of the lead, and the amount of time left. The obvious glaring deficiency here is that without possession, down, distance, and field position, we're blind to a late-game situation in which a team is driving down the field for a go-ahead score, or when a team with a lead has iced it by preventing the opposing team from ever getting the ball back. Can we quantify that, though? And to what extent are we flying blind by not having this data? Which matters most: possession, down, distance, or field position?

Let's examine our current model, see how it works, test how it's done, and then figure out how to improve it.

Monday, January 7, 2013

Sunday, January 6, 2013

Saturday, January 5, 2013

Friday, January 4, 2013

Thursday, January 3, 2013