Big 12 Expansion – Let History, Geography, and Mathematics Be Your Guide

Since the Big 12 is talking about expanding, I’ve taken a look at the teams who historically, geographically, and mathematically would be good fits in the conference.

TL;DR – Add Houston and SMU. If you want 14 teams, add Memphis and Tulsa too.

Here’s who they shouldn’t add: any team closer to the Atlantic Ocean than to the Mississippi River (Gulf of Mexico excluded), any team that’s closer to the Pacific Ocean than the Mississippi River, or anyone from Ohio.

HISTORY

The Big 12 is basically a combination of the old Missouri Valley Intercollegiate Athletic Association, which later split to eventually become the Big 8 and Missouri Valley Conference, and the Southwest Conference.

Missouri Valley (1907-1927)
Kansas
Missouri
Nebraska
Iowa St.
Kansas St.
Oklahoma
Oklahoma St.

The predecessor to the Big 8 began with Kansas, Mizzou, and Nebraska in 1907. Iowa St. joined the following year and Kansas St. in 1913. The Oklahoma schools joined later in the 1920s.

When the MVIAA broke apart, Oklahoma St. stayed in what became the MVC. Joining them later were two schools that still currently have D-IA football:

Missouri Valley Conference (1935-1956)
Oklahoma St.
Tulsa
Houston

Southwest (1915-1996)
Arkansas
Baylor
Rice
Texas
Texas A&M
SMU
TCU

Texas Tech was a member of the Border Conference and West Virginia should be in a conference with Marshall.

So our first tier of candidates are the teams with historical ties the current Big 12 teams.

  1. Missouri – recently left for the SEC, isn’t coming back
  2. Nebraska – recently left for the Big Ten, isn’t coming back
  3. Arkansas – Has been a member of the SEC for almost a quarter century, isn’t coming back.
  4. Rice – current member of C-USA, potential candidate
  5. Texas A&M – recently left for the SEC, isn’t coming back
  6. SMU – current member of American conference, potential candidate
  7. Colorado was an addition to the original seven schools to create the Big 8 – recently left for the Pac-12, isn’t coming back.
  8. Tulsa – current member of American conference, potential candidate
  9. Houston – current member of American conference, potential candidate

GEOGRAPHY

The second tier of candidates will be those who geographically “make sense.” Excluding West Virginia and Texas Tech as geographical outliers, the biggest traveler is Iowa St., who travels a little over 600 miles on average to play other Big 12 schools. Using that number as a guide, the following schools reside in cities approximately 600 miles away or less than at least three current Big 12 members (excluding TTU and WVU). Excluded from the list are those that have already been taken out of consideration above and members of other Power 5 conferences. They are ranked by the number of Big 12 members that are 600 miles away or less.

Within 600 Miles of Eight Schools
Tulsa

Seven Schools
North Texas and SMU (all but Iowa St.)
Arkansas St. (all but Texas)

Six Schools
Memphis (all but Texas and Iowa St.)

Five Schools
Louisiana Tech, Louisiana-Monroe, Louisiana-Lafayette, Houston, Rice, UTSA, and Texas St. (all but North schools)

Four Schools
Air Force (OU, OK St., K-State, and KU)

Three Schools
Northern Illinois (North schools)
Tulane (UT, BU, and TCU)

MATHEMATICS

Next, I’d assume the Big 12 would want to add as high-quality of a team as possible. Here are the records of the remaining candidates (in no particular order) over the last five years along with their S&P+ five-year average and the three-year trend of their S&P+ numbers.

School 5-Yr. Record S&P+ 5-Yr. Rk. 3-Yr. Trend
Rice 34-30 (.531) #99 (-7.2) down (m = -9.1)
SMU 23-39 (.371) #95 (-5.9) down (-3.4)
Tulsa 30-34 (.469) #76 (-2.0) stable (-1.85)
North Texas 23-38 (.377) #105 (-9.8) down (-15.5)
Houston 47-20 (.701) #50 (2.8) stable (0.3)
Texas St. 20-28 (.417) #114 (-11.6) stable (0.25)
UTSA 22-26 (.458) #109 (-10.2) down (-8.75)
Arkansas St. 44-21 (.677) #68 (0.2) stable (0.6)
Memphis 28-34 (.452) #88 (-5.0) up (4.4)
Louisiana Tech 39-25 (.609) #65 (0.4) up (14.5)
Louisiana-Monroe 24-38 (.387) #110 (-10.3) down (-4.45)
Louisiana-Lafayette 40-24 (.625) #94 (-5.8) down (-3)
Northern Illinois 54-16 (.771) #56 (1.5) stable (0.1)
Tulane 17-45 (.274) #118 (-12.6) down (-9.0)
Air Force 33-32 (.508) #92 (-5.2) up (10.45)

Finally, much like the Big 10 added Rutgers and Maryland for basically no other reason than the markets in which they play, I’d include size of the metro area and attendance as a factor in the decision.

School Metro Pop. Avg. 2015 Attendance
Rice Houston, TX (6.3 million) 19,300
SMU Dallas, TX (7.1 million) 21,000
Tulsa Tulsa, OK (1 million) 19,600
North Texas Dallas, TX (7.1 million) 13,600
Houston Houston, TX (6.3 million) 34,000
Texas St. Austin, TX (1.7 million) 18,200
UTSA San Antonio, TX (2.1 million) 23,000
Arkansas St. Jonesboro-Paragould, AR (0.17 million) 23,000
Memphis Memphis, TN (1.3 million) 39,300
Louisiana Tech Ruston, LA (0.06 million) 21,000
Lousisiana-Monroe Monroe, LA (0.17 million) 13,200
Lousisiana-Lafayatte Lafayette, LA (0.48 million) 21,600
Northern Illinois DeKalb, IL (0.11 million) 13,900
Tulane New Orleans, LA (1.2 million) 22,900
Air Force Colorado Springs, CO (0.69 million) 26,000

Big 12 average attendance: 56,800

After all that, I ranked and gave all of the above information a percent value. I weighted the historical information at 10%, geography at 15% and team quality/market* at 75%. Here are the results.

Team History Geography Math Weighted Total
Houston 50% 40% 84% 74%
SMU 100% 80% 43% 54%
Memphis 0% 60% 51% 47%
Tulsa 50% 100% 35% 46%
Louisiana Tech 0% 40% 50% 43%
Rice 100% 40% 36% 43%
Arkansas St. 0% 80% 41% 43%
Air Force 0% 20% 45% 37%
North Texas 0% 80% 24% 30%
UL-Lafayette 0% 40% 31% 29%
Northern Illinois 0% 0% 37% 28%
Texas St. 0% 40% 29% 27%
UTSA 0% 40% 28% 27%
ULM 0% 40% 14% 17%
Tulane 0% 0% 18% 13%

*Since the Big 12 already has a share of the Dallas-Fort Worth area, I halved UNT’s and SMU’s population numbers. Also, since Houston finished first, I halved Rice’s population number since they would be a second addition to the Houston area.

There you have it. If the Big 12 expands by two teams, those spots should go to Houston and SMU. Both are fits historically and geographically. The addition of the Houston area would have to be a good thing in the eyes of the Big 12. Houston is an up-an-coming program who has done well nationally despite being in a Group of Five conference. SMU would bring in the eastern half of DFW and, hopefully, being part of the Big 12 would improve recruiting and a cross-town rivalry could develop with TCU.

If the Big 12 were to expand to 14 teams (a proposition I am in support of since they play nine conference games), they should also add Memphis and Tulsa. Both fit well geographically and while Memphis doesn’t have any direct historical ties to the Big 12, they were members of the MVC with Tulsa for a short time. Memphis and Tulsa are both relatively large markets and would give the Big 12 a corner of the South. Memphis is another team on the rise and would likely be an upper-half team in the expanded Big 12.

Here’s what my 12- and 14-team conference divisions would look like.

12-Teams 14-Teams
North
Iowa St. Iowa St.
K-State K-State
Kansas Kansas
West Virginia West Virginia
Oklahoma St. Oklahoma St.
Oklahoma Oklahoma
Tulsa
South
TCU TCU
SMU SMU
Baylor Baylor
Texas Tech Texas Tech
Texas Texas
Houston Houston
Memphis

What would have been a likely conference championship game in 2015 with these divisions? TCU vs. Oklahoma. I think I’d have been OK with that.

Arizona State Football Schedule Ranked Using Advanced Stats

Using Brian Fremeau‘s, Bill Connelly‘s, and ESPN‘s advanced stats rating systems, I have ranked ASU’s 2015 games from worst to best in terms of the projected quality of the opponent and the closeness of the matchup. In other words, the games are ranked by projecting which ones are going to be the most exciting to watch. ASU’s rating is added to the opponent’s rating to get an idea of the overall quality of the two teams. Then, the ratings are subtracted from each other to show how well the two teams match up.

This is not a ranking of ASU’s opponents. This is a ranking of game quality using opponent strength with how well ASU matches up with each team. A good team that ASU matches up well with will get a high ranking. A good team that outmatches ASU significantly will get a lower ranking, and vice versa.

The advanced stats ratings and nation-wide rankings are given for each team. Also, rating sums and differences (ASU’s rating plus/minus the opponent’s) are ranked 1-12 based on where they fall in ASU’s schedule.

Here are ASU’s projected advanced stat ratings.

FEI (Fremeau) FEI Nat’l Rk S&P+ (Connelly) S&P+ Nat’l Rk FPI (ESPN) FPI Nat’l Rk
.170 #14 12.7 #23 13.4 #22

12. Cal Poly

Duh.

11. New Mexico

FEI FEI Nat’l Rk S&P+ S&P+ Nat’l Rk FPI FPI Nat’l Rk
-0.143 #110 -10.1 #98 -10.8 #110

Game Data

Sum – Overall Quality of the Teams

System Rating Sum Rank in Sched.
FEI 0.027 #11
S&P+ 2.6 #11
FPI 2.6 #11

Difference – Closeness of the Matchup

System Rating Difference Rank in Sched.
FEI 0.313 #11
S&P+ 22.8 #11
FPI 24.2 #11

10. Colorado

FEI FEI Nat’l Rk S&P+ S&P+ Nat’l Rk FPI FPI Nat’l Rk
-0.085 #94 -2.5 #69 0.5 #66

Game Data

Sum – Overall Quality of the Teams

System Rating Sum Rank in Sched.
FEI 0.085 #10
S&P+ 10.2 #10
FPI 13.9 #10

Difference – Closeness of the Matchup

System Rating Difference Rank in Sched.
FEI 0.255 #10
S&P+ 15.2 #10
FPI 12.9 #10

9. Washington State – 2-Star Game

FEI FEI Nat’l Rk S&P+ S&P+ Nat’l Rk FPI FPI Nat’l Rk
-0.047 #79 -1.2 #64 2.5 #55

Game Data

Sum – Overall Quality of the Teams

System Rating Sum Rank in Sched.
FEI 0.123 #9
S&P+ 11.5 #9
FPI 15.9 #9

Difference – Closeness of the Matchup

System Rating Difference Rank in Sched.
FEI 0.217 #9
S&P+ 13.9 #9
FPI 10.9 #9

8. Washington – 2-Star Game

FEI FEI Nat’l Rk S&P+ S&P+ Nat’l Rk FPI FPI Nat’l Rk
0.025 #53 0.9 #56 3.7 #49

Game Data

Sum – Overall Quality of the Teams

System Rating Sum Rank in Sched.
FEI 0.195 #7
S&P+ 13.6 #8
FPI 17.1 #8

Difference – Closeness of the Matchup

System Rating Difference Rank in Sched.
FEI 0.145 #7
S&P+ 11.8 #8
FPI 9.7 #8

7. Cal – 2-Star Game

FEI FEI Nat’l Rk S&P+ S&P+ Nat’l Rk FPI FPI Nat’l Rk
-0.02 #71 3.6 #50 11.3 #32

Game Data

Sum – Overall Quality of the Teams

System Rating Sum Rank in Sched.
FEI 0.150 #8
S&P+ 16.3 #7
FPI 24.7 #6

Difference – Closeness of the Matchup

System Rating Difference Rank in Sched.
FEI 0.190 #8
S&P+ 9.1 #7
FPI 2.1 #2

6. Utah – 3-Star Game

FEI FEI Nat’l Rk S&P+ S&P+ Nat’l Rk FPI FPI Nat’l Rk
0.114 #27 6.5 #41 9.1 #41

Game Data

Sum – Overall Quality of the Teams

System Rating Sum Rank in Sched.
FEI 0.284 #5
S&P+ 19.2 #6
FPI 22.5 #7

Difference – Closeness of the Matchup

System Rating Difference Rank in Sched.
FEI 0.056 #4
S&P+ 6.2 #4
FPI 4.3 #4

5. Tucson – 3-Star Game

FEI FEI Nat’l Rk S&P+ S&P+ Nat’l Rk FPI FPI Nat’l Rk
0.095 #31 8.3 #36 11.8 #29

Game Data

Sum – Overall Quality of the Teams

System Rating Sum Rank in Sched.
FEI 0.265 #6
S&P+ 21.0 #5
FPI 25.2 #5

Difference – Closeness of the Matchup

System Rating Difference Rank in Sched.
FEI 0.075 #5
S&P+ 4.4 #3
FPI 1.6 #1

4. Oregon – 4-Star Game

FEI FEI Nat’l Rk S&P+ S&P+ Nat’l Rk FPI FPI Nat’l Rk
#3 20.9 #4 20.8 #8 #3

Game Data

Sum – Overall Quality of the Teams

System Rating Sum Rank in Sched.
FEI .416 #1
S&P+ 33.6 #1
FPI 34.2 #2

Difference – Closeness of the Matchup

System Rating Difference Rank in Sched.
FEI 0.076 #6
S&P+ 8.2 #6
FPI 7.4 #6

3. Texas A&M – 4-Star Game

FEI FEI Nat’l Rk S&P+ S&P+ Nat’l Rk FPI FPI Nat’l Rk
0.119 #26 14.1 #21 22.5 #6

Game Data

Sum – Overall Quality of the Teams

System Rating Sum Rank in Sched.
FEI .289 #4
S&P+ 26.8 #4
FPI 35.9 #1

Difference – Closeness of the Matchup

System Rating Difference Rank in Sched.
FEI 0.051 #3
S&P+ 1.4 #1
FPI 9.1 #7

2. UCLA – 4-Star Game

FEI FEI Nat’l Rk S&P+ S&P+ Nat’l Rk FPI FPI Nat’l Rk
0.203 #6 19.6 #6 20 #12

Game Data

Sum – Overall Quality of the Teams

System Rating Sum Rank in Sched.
FEI 0.373 #2
S&P+ 32.3 #2
FPI 33.4 #4

Difference – Closeness of the Matchup

System Rating Difference Rank in Sched.
FEI 0.033 #2
S&P+ 6.9 #5
FPI 6.6 #4

1. USC – 5-Star Game

FEI FEI Nat’l Rk S&P+ S&P+ Nat’l Rk FPI FPI Nat’l Rk
0.166 #15 16.5 #12 20.5 #9

Game Data

Sum – Overall Quality of the Teams

System Rating Sum Rank in Sched.
FEI 0.336 #3
S&P+ 29.2 #3
FPI 33.9 #3

Difference – Closeness of the Matchup

System Rating Difference Rank in Sched.
FEI 0.004 #1
S&P+ 3.8 #2
FPI 7.1 #5

A Conference Championship Game Wouldn’t Have Helped the Big 12

Soon after the playoff committee announced the four teams to play in the inaugural College Football Playoff, the Big 12 said that they felt misled about and penalized for not having a conference championship game.

I contend that it didn’t matter.

Let’s say the Big 12 actually had 12 teams and played a conference championship game. What would have happened?

Depending on who the other two teams were, Baylor would have won the Big 12 South (via tiebreaker over TCU) and K-State would have won the North.

Historically, Colorado, Nebraska and Mizzou are the missing teams from the North, while Texas A&M is missing from the South. This year, the South would have likely played out the same way as A&M wasn’t a huge contender. Missouri definitely could have taken the North title from K-State, however.

So in all likelihood, your championship game would have been Baylor vs. K-State/Mizzou. The first of those options actually happened and it still didn’t help the Big 12 enough to get in. I don’t see how Baylor beating Missouri in the same fashion would have made much of a difference either.

The lack of a championship game (at least this year) is just a crutch/excuse that the Big 12 commissioner is holding on to and not the real reason the Big 12 got left out.*

*The real reason is that TCU didn’t beat Baylor. That would have solved ALL of their problems.

The Big Ten’s bad. And the Pac-12 might be right behind them.

I’ve heard a lot about the Big Ten’s poor performance last weekend. And while I agree, I haven’t heard much about the Pac-12’s rough weekend.

  1. Arizona got lucky to beat UTSA.
  2. Washington St loses by two scores to Nevada.
  3. Colorado squeaks by after giving up 38 points to UMASS!
  4. Washington barely holds on to beat a DI-AA team in Eastern Washington AT HOME!
  5. Stanford and USC did not look good against each other.
  6. UCLA gives up 35 points to Memphis and only wins by one score.
  7. Oregon St survives a scare vs Hawaii.

I’m not saying the Big Ten doesn’t deserve the heat they’re taking, but a few plays here and there, and the Pac-12 is in the same boat.

Thank You Notes

With Thanksgiving only a few days away, I wanted to show my gratitude. So I decided to write a few thank you notes.

Thank you, Utah Utes, for playing really well at home this year and somehow beating Stanford.

Thank you, Stanford Cardinal eight-and-nine-offensive-linemen formations, for playing old-school football and beating Oregon…again.

Thank you, Andre Heidari, for kicking the game winning field goal against the Cardinal, giving them their second conference loss.

Thank you, Josh Huff, for bagging on the Granddaddy of Them All and setting your team up for what is to follow.

Thank you, un-named players from the team in Baja Arizona, for obliterating and humbling an Oregon team that may have thought too highly of themselves (and for giving them their second conference loss).

Thank you, Carl Bradford, for your pick six that ended up being the difference maker in the Sun Devils Pac-12-South-Division-title-clinching victory over the UCLA Bruins.

I wish NIU would just lose already.

UPDATE: The strengths of schedule below are based on Sagarin’s numbers. According to the NCAA, ASU has the #9 toughest schedule, UCF is at #83, and NIU is at #117 (the NCAA stats do not include DI-AA schedules like Sagarin’s do).

NIU just won’t lose. I’m starting to get annoyed. I’m rooting for Bowling Green to beat Buffalo next week. The Falcons have a lot better chance of beating NIU than the Bulls do.

I’m not an NIU hater, I just don’t think they are better than some of the teams they are ranked ahead of. I find it difficult to swallow that the voters think the NIU Huskies (with the 137th strongest schedule in the nation) are better than Arizona State (8-2 against the sixth strongest schedule) and UCF (8-1 against the #73 schedule). In case you were wondering, yes, there are only 125 teams in Division I-A (FBS). So, yes, there are 12 Division I-AA (FCS) teams that have a more difficult schedule than NIU. I understand that the quality of their conference opponents is out of their control. But here are their non-conference games this year: Iowa (an average Big Ten team who has been average for several years now), Idaho (arguable the worst team in Division I-A football), Eastern Illinois (a decent Division I-AA team that the Huskies only managed to beat by four points), and Purdue (hands-down the worst Big Ten team in existence). I don’t know when those four games were scheduled, but Iowa was last “good” in 2009, Purdue in 2003, and Idaho…well they’re Idaho.

Dear NIU, If you have a weak conference and you want people to respect you as a team (and not just a team with a really good quarterback), you have to schedule some better non-conference games. Love, Stephen

Scoring Offenses and Defenses Ranked Better 2.0 – Pac-12 Edition

As I have thought more about how to better rank offenses and defenses other than just total yards or average points per game, I began to look at percentage of opponents average points scored and given up. I have two issues with my original method of calculating the expected points percentages: 1. the points per game number being used included defensive and special teams scores and 2. it did not take into account the number of plays used to score those points.

Regarding the second problem, if both the blue team and the red team average 45 points per game and their opponents give up an average of 30 points, they both will have an expected offensive scoring percentage of 150%. However, if the blue team runs an average of 85 plays per game and the red team delivers the same results while only running an average of 75 plays per game, we can now see that the red team has a more productive (i.e. better) offense.

Therefore, I have made two major changes to the calculations. First, only offensive points will be figured into the calculations (no defensive or special teams scores). That should have already been done; I just failed to realize it until now. Secondly, the figure will now be an expected percentage of points per play.

By figuring in the number of plays run in addition to the points scored, we’ll get a better idea of actual offensive production regardless of the style of offense. By nature, the fast-paced offenses being played today are going to run more plays and have more opportunities to score points (e.g. Arizona St. ranks 9th in the nation running average of 84 plays per game) while the more power-oriented, ground-and-pound offenses will not run as many plays, but can still be just as effective (e.g. Stanford ranks 115th out of 125 in number of plays run per game with 65).

Considering plays per game also adjusts for blowout wins. Many times, in very lopsided games, the winning team will score most of its points in the first half, then run a significantly fewer number of plays in the second half as they run the ball more often attempting to keep from running up the score.

Without further ado:

Percentage of Expected Points Scored Per Play (SCORING OFFENSE)

TEAM % OF EXP PTS/PLAY OLD METHOD CHANGE IN RK
1 Arizona St 150% 162%
2 Oregon 148% 149%
3 Stanford 123% 108% +6
4 UCLA 113% 115% -1
5 Utah 111% 110% +2
6 Oregon St 110% 112% -2
7 USC 108% 95% +4
8 Washington 104% 112% -3
9 Arizona 102% 110% -1
10 Washington St 92% 112% -4
11 Cal 81% 97% -1
12 Colorado 79% 76%

Percentage of Expected Points Given Up Per Play (SCORING DEFENSE)

TEAM % OF EXP PTS/PLAY OLD METHOD CHANGE IN RK
1 Stanford 60% 58%
2 USC 66% 61%
3 Oregon 71% 76%
4 Washington 74% 76%
5 Utah 78% 78%
6 UCLA 79% 81%
7 Arizona 94% 94%
8 Washington St 100% 103% +2
9 Arizona St 102% 99%
10 Oregon St 107% 99% -2
11 Colorado 121% 121%
12 Cal 122% 122%

I’m assuming that the reason there wasn’t as much change in the defensive numbers is that teams are facing different styles of offense and varying play counts which, by this point in the season, begin to balance each other out.

Combined Difference

TEAM % OF EXP PTS/PLAY OLD METHOD CHANGE IN RK
1 Oregon 77% 73%
2 Stanford 63% 50% +1
3 Arizona St 47% 63% -1
4 USC 42% 34% +2
5 UCLA 35% 35%
6 Utah 33% 32% +1
7 Washington 30% 37% -3
8 Arizona 8% 16%
9 Oregon St 3% 12%
10 Washington St -8% -5% -2
11 Cal -41% -25%
12 Colorado -42% -45%