| Wisconsin | 17 | Alabama | 35 | Final |
| BYU | 33 | Nebraska | 28 | Final |
| Stanford | 6 | Northwestern | 16 | Final |
| Penn State | 10 | Temple | 27 | Final |
Saturday, September 5, 2015
Week 1: Saturday In-Game Win Probabilities, Big Ten
Last updated: Sat Sep 5 23:40:11 2015
Week 1: Saturday In-Game Win Probabilities, Sun Belt
Last updated: Sun Sep 6 02:14:10 2015
| Arkansas St. | 6 | USC | 48 | 4:55 4th |
| Texas State | 16 | Florida St. | 59 | Final |
| New Mexico St. | 13 | Florida | 61 | Final |
| LA-Monroe | 14 | Georgia | 51 | Final |
| LA-Lafayette | 33 | Kentucky | 40 | Final |
| Troy | 21 | North Carolina St. | 49 | Final |
| Georgia Southern | at | West Virginia | Later |
Week 1: Saturday Predictions
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| Close game |
Certain victory |
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Follow us on Twitter at @TFGridiron and @TFGLiveOdds.
We're back!
This is just a quick post to let you know that even though the games have already started for this year and you haven't seen anything here (yet) we're still going on with this season. There are two main reasons we're running a bit behind this year:
This blog depends heavily on Twitter and Blogger APIs to upload and manage posts. Over the last year or so both Twitter and Google have added extra security requirements to those APIs; e.g., requiring secure connections and blocking ones that used older, less secure methods. Ultimately that's good, but it just means more code to rewrite.
So because of (2) that means extra work needed to happen, and because of (1) there wasn't time to do it. We're still working on addressing these, but starting today you should see some of the regular content you've come to expect, and over the next few days we should be coming back to our full, regular schedule, both here and on Twitter.
There might even be some new features. Stay tuned, and thanks for your patience.
- My own busy schedule; and
- Edward Snowden.
This blog depends heavily on Twitter and Blogger APIs to upload and manage posts. Over the last year or so both Twitter and Google have added extra security requirements to those APIs; e.g., requiring secure connections and blocking ones that used older, less secure methods. Ultimately that's good, but it just means more code to rewrite.
So because of (2) that means extra work needed to happen, and because of (1) there wasn't time to do it. We're still working on addressing these, but starting today you should see some of the regular content you've come to expect, and over the next few days we should be coming back to our full, regular schedule, both here and on Twitter.
There might even be some new features. Stay tuned, and thanks for your patience.
Friday, February 27, 2015
Sloan Sports Analytics Conference, v2015
Hello from the 2015 Sloan Sports Analytics Conference.
Another year has passed and there's been some major strides in college football analytics. Behind the scenes there's been good work at Football Study Hall, and I really wish I had more time to do the kind of analysis and data collection they're doing over there.
Looking forward, I think the biggest advances in football understanding are going to come from computer vision and automated understanding of where players are and what they're doing. Current efforts at data collection are manual, spotty, and time-consuming. Computer vision is the up-and-coming technology which has the most potential to provide detailed and reliable information about where the players are, where they were moving, where the ball was, and what a play did or didn't succeed.
The counter-argument is that it's difficult to spot the football, or it's difficult to know where a player was supposed to go on a certain play, but I believe issues such as those can be overcome. Compared to the money necessary to hire a person, adding more cameras connected to vision software is a more efficient, economic, and scalable solution. Hardware will get faster. Software will get better.
The question after that, obviously, is how to use that data? Right now I believe there are two broad types of questions we're using stats to answer: what happened, and how did it happen? The "what" part can be something like how many (opponent-and-pace-adjusted) points did a team score. The "how" part is whether they got those points on long drives or big plays or because of field position or some combination of the two.
There's a spectrum, and as someone who tends to approach problems from the big picture and drill down, I'm initially more interested in the "what" part since that has immediate predictive value. I realize that other people are more interested in digging into the data and explaining what happened at the line or in a certain type of pass route, but my interest is more in whether or not they should be calling a run play or that type of route in the first place.
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
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 tfgridiron.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.
Follow us on Twitter at @TFGridiron and @TFGLiveOdds.
Another year has passed and there's been some major strides in college football analytics. Behind the scenes there's been good work at Football Study Hall, and I really wish I had more time to do the kind of analysis and data collection they're doing over there.
Looking forward, I think the biggest advances in football understanding are going to come from computer vision and automated understanding of where players are and what they're doing. Current efforts at data collection are manual, spotty, and time-consuming. Computer vision is the up-and-coming technology which has the most potential to provide detailed and reliable information about where the players are, where they were moving, where the ball was, and what a play did or didn't succeed.
The counter-argument is that it's difficult to spot the football, or it's difficult to know where a player was supposed to go on a certain play, but I believe issues such as those can be overcome. Compared to the money necessary to hire a person, adding more cameras connected to vision software is a more efficient, economic, and scalable solution. Hardware will get faster. Software will get better.
The question after that, obviously, is how to use that data? Right now I believe there are two broad types of questions we're using stats to answer: what happened, and how did it happen? The "what" part can be something like how many (opponent-and-pace-adjusted) points did a team score. The "how" part is whether they got those points on long drives or big plays or because of field position or some combination of the two.
There's a spectrum, and as someone who tends to approach problems from the big picture and drill down, I'm initially more interested in the "what" part since that has immediate predictive value. I realize that other people are more interested in digging into the data and explaining what happened at the line or in a certain type of pass route, but my interest is more in whether or not they should be calling a run play or that type of route in the first place.
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
- the current rankings for TFG and RBA; and
- our trivia posts, particularly:
- a countdown of the best BCS champions from 2003 - 2010;
- an examination of the fact that not all undefeated non-BCS teams are created equal;
- a definition of an examination of the state of parity in college football; and
- some musings on what's up with the ACC.
During the regular season you can expect to find weekly posts showcasing
- top 25 and full rankings for TFG and RBA;
- weekly projections of final conference standings;
- analysis and predictions for all remaining undefeated FBS teams; and
- live in-game win probabilities, posted to the blog and our Twitter feed.
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 tfgridiron.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.
Follow us on Twitter at @TFGridiron and @TFGLiveOdds.
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