Week 2: Sunday In-Game Win Probabilities

Last updated: Mon Sep 2 07:58:04 2013

Colorado	41	Colorado St.	27	Final
Ohio	7	Louisville	49	Final

Week 1: Saturday In-Game Win Probabilities

Last updated: Sun Sep 1 05:28:04 2013

Virginia Tech	10	Alabama	35	Final
LA-Lafayette	14	Arkansas	34	Final
Washington St.	24	Auburn	31	Final
Northwestern	44	California	30	Final
Nevada	20	UCLA	58	Final
Purdue	7	Cincinnati	42	Final
Georgia	35	Clemson	38	Final
Toledo	6	Florida	24	Final
Northern Ill.	30	Iowa	27	Final
Western Kentucky	35	Kentucky	26	Final
TCU	27	LSU	37	Final
Miami-OH	14	Marshall	52	Final
FIU	10	Maryland	43	Final
Central Michigan	9	Michigan	59	Final
Oklahoma St.	21	Mississippi St.	3	Final
Wyoming	34	Nebraska	37	Final
UTSA	21	New Mexico	13	Final
LA Tech	14	North Carolina St.	40	Final
Idaho	6	North Texas	40	Final
Temple	6	Notre Dame	28	Final
Buffalo	20	Ohio St.	40	Final
LA-Monroe	0	Oklahoma	34	Final
Syracuse	17	Penn State	23	Final
Texas State	22	Southern Miss.	15	Final
Rice	31	Texas A&M	52	Final
New Mexico St.	7	Texas	56	Final
UAB	31	Troy	34	Final
BYU	16	Virginia	19	Final
Boise St.	6	Washington	38	Final
Massachusetts	0	Wisconsin	45	Final

Week 1: Saturday Predictions

1 Alabama 34  45 Virginia Tech 18

1 Alabama 41  54 Virginia Tech 10

55 Arkansas 31  62 LA-Lafayette 29

37 Arkansas 38  67 LA-Lafayette 24

92 Auburn 27  99 Washington St. 24

73 Auburn 35  99 Washington St. 20

84 California 25  29 Northwestern 33

65 California 27  35 Northwestern 31

27 Cincinnati 32  80 Purdue 24

25 Cincinnati 41  72 Purdue 17

22 Clemson 30   8 Georgia 34

18 Clemson 28   8 Georgia 27

5 Florida 32  65 Toledo 22

9 Florida 34  61 Toledo 17

68 Iowa 23  28 Northern Ill. 28

69 Iowa 24  49 Northern Ill. 27

113 Kentucky 24  82 Western Kentucky 28

93 Kentucky 28  76 Western Kentucky 24

7 LSU 28  34 TCU 22

3 LSU 34  30 TCU 14

83 Marshall 36 110 Miami-OH 32

85 Marshall 38 110 Miami-OH 27

91 Maryland 25  90 FIU 27

90 Maryland 28  96 FIU 20

16 Michigan 36 100 Central Michigan 22

12 Michigan 55 102 Central Michigan 13

46 Mississippi St. 27  12 Oklahoma St. 33

31 Mississippi St. 31  19 Oklahoma St. 32

23 Nebraska 38  93 Wyoming 26

26 Nebraska 38  91 Wyoming 14

107 New Mexico 29 103 UTSA 31

112 New Mexico 31 113 UTSA 27

60 North Carolina St. 34  53 LA Tech 36

63 North Carolina St. 34  43 LA Tech 31

95 North Texas 32 121 Idaho 24

107 North Texas 31 121 Idaho 17

3 Notre Dame 32  85 Temple 19

2 Notre Dame 42  82 Temple 3

19 Ohio St. 34 111 Buffalo 21

17 Ohio St. 42 109 Buffalo 14

14 Oklahoma 36  73 LA-Monroe 26

14 Oklahoma 45  68 LA-Monroe 17

24 Penn State 27  40 Syracuse 26

21 Penn State 31  51 Syracuse 20

105 Southern Miss. 29  97 Texas State 30

106 Southern Miss. 32 100 Texas State 31

26 Texas 41 123 New Mexico St. 21

29 Texas 52 122 New Mexico St. 7

2 Texas A&M 40  69 Rice 25

6 Texas A&M 49  83 Rice 17

77 Troy 35 108 UAB 29

79 Troy 38 111 UAB 24

49 UCLA 35  64 Nevada 32

40 UCLA 38  64 Nevada 27

88 Virginia 19  15 BYU 28

87 Virginia 20  22 BYU 31

58 Washington 23  17 Boise St. 28

53 Washington 24  15 Boise St. 28

11 Wisconsin 39 124 Massachusetts 16

11 Wisconsin 59 125 Massachusetts 7

Key
Close game							Certain victory

Follow us on Twitter at @TFGridiron and @TFGLiveOdds.

Week 1: Friday Predictions

61 Miami-FL 34 106 FL-Atlantic 26

46 Miami-FL 45 115 FL-Atlantic 20

25 Michigan St. 30  89 Western Michigan 22

28 Michigan St. 38  94 Western Michigan 13

52 SMU 32  56 Texas Tech 31

69 SMU 34  56 Texas Tech 31

Key
Close game							Certain victory

Follow us on Twitter at @TFGridiron and @TFGLiveOdds.

Week 1: Friday In-Game Win Probabilities

Last updated: Sat Aug 31 04:58:03 2013

FL-Atlantic	6	Miami-FL	34	Final
Western Michigan	13	Michigan St.	26	Final
Texas Tech	at	SMU		Later

Week 1: Thursday Predictions

66 Bowling Green 22  38 Tulsa 26

75 Bowling Green 24  50 Tulsa 28

44 Fresno St. 24  42 Rutgers 25

39 Fresno St. 25  36 Rutgers 24

115 Hawaii 21  21 USC 36

116 Hawaii 13  13 USC 48

87 Minnesota 29 114 UNLV 24

84 Minnesota 34 104 UNLV 17

10 South Carolina 32  47 North Carolina 26

5 South Carolina 42  45 North Carolina 17

32 UCF 37 120 Akron 21

42 UCF 45 118 Akron 17

67 Utah 23  18 Utah St. 30

52 Utah 24  27 Utah St. 27

36 Vanderbilt 27  33 Mississippi 28

23 Vanderbilt 31  38 Mississippi 24

Key
Close game							Certain victory

Follow us on Twitter at @TFGridiron and @TFGLiveOdds.

Week 1: Thursday In-Game Win Probabilities

Last updated: Fri Aug 30 04:58:03 2013

Tulsa	7	Bowling Green	34	Final
USC	30	Hawaii	13	Final
UNLV	23	Minnesota	51	Final
North Carolina	10	South Carolina	27	Final
Utah St.	26	Utah	30	Final
Mississippi	39	Vanderbilt	35	Final
Rutgers	at	Fresno St.		Later
Akron	at	UCF		Later

Welcome to the 2013-2014 Season

Welcome to the 2013-2014 college football season!

The new season is only three days away, and Tempo-Free Gridiron is back for yet another year. Last year we rolled out our first real trial of in-game win probabilities. During each and every game of the 2013-2014 season we created graphs showing the current odds of each team winning. This year we're going to refine those models to provide better accuracy and precision.

We'll still be tweeting the probabilities for each game at the end of each quarter, as well as upset watches (a heavy pre-game favorite is likely to lose), upset warnings (a heavy pre-game favorite is on the verge of losing) and upset emergency notices (they're pretty much toast). So subscribe to our twitter feed (@TFGLiveOdds) for all that and more.

Along with the new content, there's the usual content, including

• Undefeated Countdown. Every other Thursday starting in October we'll examine the remaining undefeated teams, their possible road to a perfect season, and the main obstacles in their way.
• Conference Projections. Starting in October we'll have a weekly breakdown of the twelve conferences, including both the current standings and the projected final standings.
• Games of the Week. Along with weekly predictions we'll focus on specific Saturday games that are interesting in some way or another.

This is all on top of our weekly predictions, discussions, rankings, and complete bowl coverage that we've provided the previous years. Keep an eye on the blog or follow us on Twitter.

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

• the current rankings for TFG and RBA;
• our "Ask an Expert" feature, where readers get to ask the questions; 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;
• in-depth analysis and predictions for two games of the week, highlighting our "Games You Gotta See" (GUGS) game quality rating metric;
• a recap of the past week, focusing specifically on the games of the week;
• bowl previews for each and every bowl game (including the lesser-known ones); and
• live in-game win probabilities, posted to the blog and our Twitter feed.

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.

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.