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
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 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.

Monday, January 19, 2015

Week 23: Top 25 — TFG

Mouse over column headers for definitions, or see this page
Rank +/- Team WinPct SoS Adjusted
Off. Def. Pace
1 -- Alabama  ( 11 - 2 ) 0.933 0.722 3 26.6 7 9.9 2 163.0 103
2 -- TCU  ( 11 - 1 ) 0.914 0.532 62 26.8 6 11.0 7 179.9 18
3 +1 Ohio St.  ( 14 - 1 ) 0.906 0.658 12 29.8 1 12.7 16 171.6 51
4 +1 Michigan St.  ( 10 - 2 ) 0.888 0.575 51 24.9 9 11.4 9 164.4 99
5 -2 Oregon  ( 12 - 2 ) 0.887 0.640 20 27.3 3 12.5 15 177.5 25
6 -- Georgia  ( 9 - 3 ) 0.884 0.664 9 27.2 4 12.7 17 165.2 93
7 -- Baylor  ( 10 - 2 ) 0.844 0.566 56 26.8 5 14.2 30 192.7 1
8 -- Arkansas  ( 6 - 6 ) 0.839 0.714 4 20.3 25 10.9 6 155.8 124
9 -- Clemson  ( 9 - 3 ) 0.832 0.587 48 17.4 58 9.5 1 172.3 46
10 -- Mississippi St.  ( 9 - 3 ) 0.829 0.662 10 20.8 23 11.5 10 178.7 21
11 -- Georgia Tech  ( 10 - 3 ) 0.827 0.674 7 27.6 2 15.3 43 159.2 114
12 -- Auburn  ( 7 - 5 ) 0.817 0.771 1 26.2 8 14.9 40 171.4 52
13 -- Florida St.  ( 12 - 1 ) 0.806 0.651 15 23.5 11 13.8 25 167.9 73
14 +1 Wisconsin  ( 10 - 3 ) 0.804 0.588 46 22.0 16 12.9 19 157.4 119
15 +1 Mississippi  ( 8 - 4 ) 0.802 0.752 2 17.0 65 10.0 3 168.5 69
16 -2 Stanford  ( 7 - 5 ) 0.802 0.580 50 17.5 55 10.4 4 156.7 122
17 -- LSU  ( 7 - 5 ) 0.798 0.688 5 17.8 50 10.6 5 157.0 120
18 -- Kansas St.  ( 8 - 4 ) 0.786 0.612 34 23.4 12 14.3 32 158.4 116
19 -- Marshall  ( 12 - 1 ) 0.780 0.356 111 22.9 13 14.2 29 178.0 24
20 -- Missouri  ( 10 - 3 ) 0.773 0.657 13 18.5 40 11.6 11 169.8 62
21 -- Louisville  ( 8 - 4 ) 0.754 0.589 45 19.5 32 12.8 18 164.9 95
22 -- USC  ( 9 - 4 ) 0.739 0.570 53 19.9 27 13.4 21 175.2 35
23 +1 Boise St.  ( 12 - 2 ) 0.738 0.444 74 24.1 10 16.4 55 175.6 30
24 +1 Oklahoma  ( 8 - 5 ) 0.737 0.587 47 22.0 15 14.9 39 171.0 56
25 -2 UCLA  ( 10 - 3 ) 0.736 0.657 14 21.6 20 14.7 36 178.7 22
Rankings through games of 2015-01-18

New entries: none.

Dropped out: none.

Follow us on Twitter at @TFGridiron and @TFGLiveOdds.

Week 23: Full Rankings — TFG

Biggest jumps: Toledo (0.044); Ohio St. (0.010); Central Michigan (0.006); Ball St. (0.004); Cincinnati (0.004)

Biggest drops: Arkansas St. (-0.027); Oregon (-0.013); Arizona (-0.007); Utah (-0.006); Oregon St. (-0.006)

Full rankings after the jump.

Week 23: Top 25 — RBA

Mouse over column headers for definitions, or see this page
Rank +/- Team WinPct SoS Adjusted
Off. Def. Pace
1 -- Alabama  ( 11 - 2 ) 0.901 0.552 12 25.4 6 8.7 2 156.6 128
2 -- Mississippi  ( 8 - 4 ) 0.884 0.539 34 21.3 24 7.6 1 165.0 94
3 -- LSU  ( 7 - 5 ) 0.870 0.550 15 22.1 19 9.4 4 160.4 125
4 -- Michigan St.  ( 10 - 2 ) 0.855 0.539 35 25.8 4 10.8 10 166.5 78
5 -- TCU  ( 11 - 1 ) 0.849 0.482 78 24.4 11 9.7 7 169.3 42
6 -- Georgia  ( 9 - 3 ) 0.844 0.547 22 29.0 1 11.7 15 161.0 122
7 +2 Ohio St.  ( 14 - 1 ) 0.837 0.526 56 27.1 2 11.9 17 161.4 118
8 -1 Florida St.  ( 12 - 1 ) 0.835 0.541 30 25.2 8 11.2 12 165.8 84
9 -1 Mississippi St.  ( 9 - 3 ) 0.834 0.553 11 21.5 21 10.9 11 164.0 106
10 -- Stanford  ( 7 - 5 ) 0.824 0.546 24 21.2 26 9.4 5 166.1 82
11 -- Auburn  ( 7 - 5 ) 0.824 0.556 7 24.9 9 12.0 18 160.6 123
12 -- Oregon  ( 12 - 2 ) 0.820 0.532 49 25.7 5 12.3 20 177.7 2
13 -- Kansas St.  ( 8 - 4 ) 0.809 0.538 36 25.4 7 13.1 29 165.0 95
14 -- Baylor  ( 10 - 2 ) 0.807 0.556 6 25.9 3 13.0 27 177.3 4
15 -- Wisconsin  ( 10 - 3 ) 0.795 0.534 46 22.6 15 11.5 14 161.3 119
16 -- Arkansas  ( 6 - 6 ) 0.783 0.574 1 24.8 10 12.8 25 164.6 101
17 -- Missouri  ( 10 - 3 ) 0.780 0.525 59 19.0 35 9.9 8 174.3 9
18 -- Oklahoma  ( 8 - 5 ) 0.777 0.549 18 22.9 14 12.6 23 171.9 22
19 -- Clemson  ( 9 - 3 ) 0.766 0.536 43 17.7 45 9.3 3 169.1 45
20 -- Florida  ( 6 - 5 ) 0.759 0.562 3 18.2 41 10.0 9 161.3 120
21 -- Texas  ( 6 - 7 ) 0.749 0.534 47 16.7 58 9.5 6 169.0 48
22 -- Nebraska  ( 8 - 4 ) 0.724 0.535 45 22.2 17 13.3 31 168.1 60
23 -- Texas A&M  ( 7 - 5 ) 0.717 0.549 19 21.5 22 13.6 33 174.1 11
24 -- Louisville  ( 8 - 4 ) 0.717 0.485 75 18.6 37 12.0 19 164.7 98
25 -- USC  ( 9 - 4 ) 0.716 0.538 39 21.2 25 13.6 34 167.6 66
Rankings through games of 2015-01-18

New entries: none.

Dropped out: none.

Follow us on Twitter at @TFGridiron and @TFGLiveOdds.

Week 23: Full Rankings — RBA

Biggest jumps: Toledo (0.007); Alabama (0.007); Ohio St. (0.004)

Biggest drops: Arkansas St. (-0.005); TCU (-0.002)

Full rankings after the jump.

Sunday, January 4, 2015

Week 21: Sunday In-Game Win Probabilities, Sun Belt

Last updated: Mon Jan 5 00:41:06 2015

Toledo63Arkansas St.44Final

Week 21: Sunday In-Game Win Probabilities, Mid-American

Last updated: Mon Jan 5 00:41:05 2015

Toledo63Arkansas St.44Final

Saturday, January 3, 2015

Week 19: Saturday In-Game Win Probabilities, SEC

Last updated: Sat Jan 3 15:43:08 2015

East Carolina20Florida28Final

Week 19: Saturday In-Game Win Probabilities, American Athletic

Last updated: Sat Jan 3 15:43:06 2015

East Carolina20Florida28Final

Week 19: Saturday Predictions

 28Florida30
 54East Carolina27
 18Florida34
 46East Carolina21


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Follow us on Twitter at @TFGridiron and @TFGLiveOdds.