NFL lineman John Urschel retires to focus on math

When I interviewed Baltimore Ravens lineman John Urschel, I was surprised by one of his answers.

I asked him why he plays football when he could protect his brain in pursuit of his math career. He’s currently working on his Ph.D. in math at MIT.

A few highlights from his response.

I got my pension last year, which is huge. Trust me, I celebrated on the inside.

This is the league allowed me to buy my mother a house.

I expected an answer about how he loved the physical contact. Instead, he alluded to the financial rewards of the job.

Urschel retired from football before the start of the 2017 season. He’ll work on his math career now.

In my interview, I try to show how Urschel isn’t your ordinary MIT graduate student. He could have submitted one of his undergraduate papers as a Ph.D. thesis. He continues to both publish and work on interesting new topics.

To listen to my interview with John Usrchel, click on the right pointing triangle.

You can listen to all episodes of The Football Analytics Show on iTunes by clicking here.

Podcast: The Top 3 Stories From The 2017 Preseason College Football Rankings

On this episode of the Football Analytics Show, I discuss the 3 stories that jumped out at me when I first looked at my 2017 preseason college football rankings. Among the topics of discussion:

  • The factor that can really screw up my team rankings based on points.
  • The 3 teams that might have been one hit wonders in 2016.
  • The blue blood program primed for a rebound in 2017. Texas? Notre Dame? Nebraska? So many teams to pick from.
  • The Pac-12 team in the top 10 that could be overrated despite their strong past performance.

To listen on iTunes, click here.

To listen to this episode here, click on the right pointing triangle:

How Jim Harbaugh is like Mandy Moore

In the HBO series Entourage, Vincent Chase is the big movie star. He takes the leading role in the biggest Hollywood movies, then does what he wants with the ladies around town.

Then he meets Mandy Moore. They date twice. It ends twice, with Vinny on the break up diet of orange juice and crackers both times. Even the the most desirable people meet their match, and for Vinny it was Mandy Moore.

Just like Vinny, The Power Rank’s preseason rankings are typically stellar. Over the past 3 season, the model has predicted the game winner in 70.8% of games (1452-598 with no prediction in 235 games), a rate that doesn’t include cupcake games with FBS teams against FCS opponents.

Note that the preseason model makes these predictions without using any data from the regular season.

While I’m usually confident in the predictions of this model, Jim Harbaugh broke it this season. Let me explain.

The model feature that doesn’t apply to Michigan

My preseason college football rankings come from a regression model that considers the last 4 years of team performance, turnovers and returning starters. The team performance comes from my ranking algorithm that takes margin of victory and adjusts for strength of schedule.

Four years might seem like a long window to use, but college football teams tend to persist in their performances from season to season. Alabama has the tradition, financial resources and the coach to stay near the top of college football every season. Rice has none of these advantages to dig them out of the bottom of FBS.

Because of this 4 year period, the preseason model gives poor predictions when teams get better or worse in a rapid manner. To see this, check out the visual of Jim Harbaugh’s tenure at Stanford, which shows their rating, or an expected margin of victory against an average FBS team.

Any model that attempts to predict Harbaugh’s 3rd year at Stanford from the previous four years would underestimate the strength of that team.

For Michigan in 2017, the preseason model has the same problem. Harbaugh has been coach for two years, so the model still considers the last two seasons of the Brady Hoke era.

To make things worse, Michigan returns only 5 starters, the lowest in all of FBS. This contributes to my preseason rank for Michigan of 30th.

From following this team closely, a rank of 30th is too low. I’ll make an adjustment to this model before calculating a win total for Michigan in 2017.

Reasons for optimism

Beyond the clear problems of using a large window of team performance, a look at the roster gives other reasons for optimism.

Neither Rashan Gary nor Maurice Hurst, defensive linemen, count as returning starters. However, the two combined for 16.5 tackles for loss last season, and both players have the potential to be first team All-American.

Michigan loses all of their starters in the secondary. While this would be a concern for most teams, most Michigan fans believe there’s enough talent to perform well in 2017. The same holds for the receivers on offense that will get the ball from QB Wilton Speight.

The big question for Michigan in 2017 is the offensive line. This unit struggled last season, making the NFL starters that Harbaugh and offensive line coach Tim Drevno turned out at Stanford seem like a distant memory.

If Michigan performs anywhere near where my preseason model predicts, the offensive line will take the blame.

To check out the full 2017 preseason rankings, click here.

Finally!! Preseason college football rankings for 2017

My preseason college football rankings consider recent team performance, turnovers and returning starters.

While simple, the model has predicted the game winner in 70.8% of games the past 3 seasons (1452-598 with no prediction in 235 games). This set of games excludes FCS cupcake games.

Here is my quick reaction to the these rankings:

— Ohio State seems a bit low at #4, although QB J.T. Barrett and the offensive line struggled last season.

— Stanford at #7 seems a too high. The Cardinal return a ton of talent on both sides of the ball but have a big question mark at QB with Keller Chryst.

— Washington, Penn State, USC. These teams had spectacular seasons in 2016. However, my model doesn’t like them since they haven’t been elite over the 4 year window the model considers.

— LSU at #9 seems about right for this program. However, they have a new coach that went 10-25 at his last SEC head coaching job.

— Auburn had a great defense last season, and transfer QB Jarrett Stidham looked like a star in limited time at Baylor. But I was still surprised how high my numbers put Auburn.

— Michigan. Can we talk about them later? Let’s just say their preseason rank seems low, way low. They will require an adjustment before I release their win total.

— Notre Dame had an awful 2016, but the preseason model thinks that might have been an outlier for Brian Kelly’s program.

— Tom Herman takes over at Texas, but the model’s rank of 37th shows just how bad the program got over the past 3 years under Charlie Strong.

— My alma mater Rice can return 16 starters yet still rank 116th in FBS and 5th worst in an awful Conference USA. It’s been a rough two seasons for the Owls.

The following list gives the rank, team and rating, which is an expected margin of victory against an average FBS team, for the 2017 preseason college football rankings.

1. Alabama, 22.67
2. Clemson, 18.43
3. Florida State, 18.21
4. Ohio State, 16.90
5. Oklahoma, 16.12
6. Auburn, 14.32
7. Stanford, 13.57
8. Wisconsin, 13.56
9. LSU, 13.01
10. Washington, 12.39
11. Penn State, 12.02
12. Miami (FL), 11.58
13. Louisville, 11.56
14. Georgia Tech, 9.94
15. USC, 9.70
16. Virginia Tech, 9.67
17. Georgia, 9.67
18. Florida, 9.63
19. Texas A&M, 9.45
20. Tennessee, 9.23
21. Washington State, 9.04
22. TCU, 8.64
23. Notre Dame, 8.60
24. North Carolina State, 8.28
25. North Carolina, 8.26
26. Mississippi, 7.90
27. Houston, 7.50
28. Iowa, 7.22
29. Baylor, 7.17
30. Michigan, 7.06
31. Oklahoma State, 6.96
32. Northwestern, 6.93
33. Kansas State, 6.84
34. Oregon, 6.37
35. Vanderbilt, 6.35
36. Pittsburgh, 6.21
37. Texas, 6.08
38. Mississippi State, 6.02
39. UCLA, 5.52
40. Minnesota, 5.24
41. Arkansas, 5.17
42. Kentucky, 5.04
43. Western Michigan, 4.51
44. Texas Tech, 4.20
45. Duke, 3.89
46. Utah, 3.86
47. South Florida, 3.65
48. Brigham Young, 3.48
49. South Carolina, 3.46
50. Colorado, 3.28
51. Syracuse, 2.60
52. Nebraska, 2.44
53. Indiana, 2.41
54. Toledo, 2.38
55. Troy, 2.24
56. Colorado State, 2.04
57. Memphis, 1.92
58. Missouri, 1.87
59. Wake Forest, 1.73
60. Arizona State, 1.50
61. Western Kentucky, 1.46
62. Navy, 1.44
63. West Virginia, 1.05
64. Temple, 0.88
65. Boston College, 0.42
66. Oregon State, 0.16
67. Appalachian State, 0.07
68. Boise State, -0.12
69. Arizona, -0.27
70. Tulsa, -0.48
71. California, -0.56
72. Michigan State, -0.61
73. San Diego State, -0.67
74. UCF, -1.17
75. Army, -1.23
76. Virginia, -1.56
77. Maryland, -2.01
78. Wyoming, -2.62
79. Ohio, -2.99
80. Iowa State, -3.12
81. Louisiana Tech, -3.73
82. Central Michigan, -4.13
83. SMU, -4.57
84. Northern Illinois, -4.86
85. Coastal Carolina, -5.22
86. Georgia Southern, -5.27
87. UTSA, -5.49
88. Arkansas State, -5.55
89. East Carolina, -6.03
90. Air Force, -6.09
91. Middle Tennessee State, -6.36
92. Georgia State, -6.41
93. Miami (OH), -6.51
94. Southern Miss, -6.67
95. Hawaii, -6.87
96. Akron, -6.95
97. Marshall, -6.99
98. Tulane, -7.00
99. Louisiana Lafayette, -7.11
100. Old Dominion, -7.25
101. Cincinnati, -7.63
102. South Alabama, -7.83
103. Bowling Green, -8.00
104. Purdue, -8.05
105. Rutgers, -8.05
106. San Jose State, -8.17
107. Utah State, -8.33
108. Eastern Michigan, -8.64
109. Louisiana Monroe, -8.70
110. New Mexico, -8.75
111. Florida Atlantic, -9.07
112. Ball State, -9.15
113. Connecticut, -9.19
114. Illinois, -9.27
115. Fresno State, -9.32
116. Rice, -10.65
117. Massachusetts, -10.92
118. Nevada, -11.01
119. Kent State, -11.15
120. Kansas, -11.17
121. Idaho, -12.04
122. UNLV, -12.09
123. Buffalo, -12.77
124. North Texas, -13.00
125. New Mexico State, -14.01
126. Florida International, -15.19
127. Texas State, -15.66
128. UTEP, -17.57
129. Charlotte, -19.20
130. UAB, -19.85

The most underrated team in sports

Chile gets no respect.

They won the South American championship over Brazil and Argentina in 2015. They won a special North and South American tournament in the US last year.

My numbers put them at 3rd in the world before the Confederations Cup, or the World Cup Light that FIFA holds the year before the actual World Cup. This tournament features the champions of each continent.

However, no one gave Chile a chance. The markets odds were +320, or about 24% win probability, and Chile got even less respect from commentators.

In the semi-final, Chile outplayed Portugal but the game remained scoreless after 120 minutes. Chile advanced by saving 3 straight penalty kicks against Portugal.

Now Chile faces Germany in the final on Sunday at 2pm Eastern. My numbers give the following probabilities for 90 minute regulation:

  • 41% for Germany to win
  • 31.5% for Chile to win
  • 27.5% to tie

Chile is not the favorite. However, don’t be surprised to see them win the tourney, especially since many of Germany’s stars won’t play in this game.

And there’s reason to trust my world soccer numbers. A published research paper studied the predictions from my algorithm and found them quite worthy.

I wrote about the lessons learned from this study. It’s probably most interesting for the results on ensembles, which I use in my football predictions.

To check out the article, click here.