How to instantly evaluate a football game

During the 2014 season, Oregon looked like a clear favorite over Ohio State to win the College Football Playoff title game.

After an early loss to Arizona, Oregon dominated during the last part of the season. Only UCLA came within two touchdowns of beating Oregon. This stretch of games included a rematch against Arizona and the playoff semi-final against Florida State.

Ohio State barely made the college football playoff after an early loss to Virginia Tech, a team that went 3-5 in the ACC. They lost two quarterbacks during the 2014 season, and third stringer Cardale Jones only seemed to excel because he had talented receivers to catch his jump balls.

In addition, the markets opened with Oregon as a 7 point favorite, which implies a 70% win probability. Slam dunk, Ducks.

However, Ohio State dominated Oregon in a 42-20 game to claim the first College Football Playoff. The Buckeyes earned this massive margin of victory despite committing 3 more turnovers than their opponent.

Was there any way to predict this Ohio State victory? There was, but only if you dug past team rankings and looked into how Ohio State matched up with Oregon.

For me, data visualization played a key role in uncovering the key match up. Let me show you.

Oregon’s match up problem

In 2014, Ohio State had an elite ground game. To quantify this, let’s look an efficiency statistic: yards per carry. In college football, sacks count as rushes in the official statistics. Since sacks are pass plays, I exclude these plays in calculating yards per carry.

To adjust yards per carry for strength of schedule, I use a ranking algorithm I developed based on my research in statistical physics. While Ohio State had the 7th best raw yards per carry, these schedule adjustments move them up to first.

In contrast, Oregon had an average rush defense. They allowed 5.0 yards per carry, more than the 4.8 college football average. After schedule adjustments, Oregon ranked 62nd out of 128 teams in rush defense.

Data visualization to evaluate match ups

To look at how Oregon’s rush defense matched up against Ohio State’s rush offense, we can use data visualization based on data prior to the title game. This visual explains how it works.


For defenses, the better units appear further to the right. This makes it easy to compare with the opposing offense when both units appear on the same line.

In the visual below, the blue dots represent Ohio State’s pass and rush offense while the smaller green dots show Oregon’s defense. Better defenses appear further to the right to facilitate comparisons, as you’re looking at how a unit compares to average.

Ohio State's offense vs Oregon's defense

The gap between Ohio State’s rush offense and Oregon’s rush defense shows the clear advantage for the Buckeyes.

During the championship game, Ohio State didn’t have remarkable team rushing numbers, as they gained 5.2 yards per carry. However, running back Ezekiel Elliott dominated the Oregon defense by rushing for 246 yards on 6.8 yards per carry and 4 touchdowns.

My analysis of this rushing match up appeared on Deadspin prior to the game, and this comment appeared below the article.

It is the start of the fourth, and it is creepy how on point your predictions are.

commenter on Deadspin

It doesn’t always work out this way. Football has too much much randomness to be right all the time. But analytics provides a firm baseline for your judgments about football.

Predictions based on match ups in football

Members of The Power Rank have access to my ensemble predictions, which aggregate together many predictions to make a more accurate prediction. Before the Ohio State versus Oregon game, this ensemble predicted a 3.2 point win for Oregon, which corresponded to a 59.5% win probability.

However, you should never blindly trust numbers, especially in a game with mismatches. One of the predictors in the ensemble accounted for passing and rushing separately for each team. It considered Ohio State’s significant edge in running the ball and that Ohio State ran the ball on 59.3% of plays.

This match up model predicted a 50-50 game between Ohio State and Oregon.

A cheat sheet for every team saves time

Members of The Power Rank also have access to interactive team pages that show these match up visuals. To view a match up, click on the appropriate opponent in the schedule in the upper right corner. To check Ohio State’s team page after the title game against Oregon, click here.

I use these interactive visuals to prepare for every interview, whether its the Paul Finebaum show or my weekly appearance on WTKA in Ann Arbor. The visuals save a ton of time, as I can scan through the visual for both passing and rushing to find a potential mismatch.

Do you make this mistake with college football statistics?

boy_hitting_foreheadYou’re smarter than the average college football fan.

You crave a true understanding of the game. Team rankings do not suffice. Even breaking a team into an offense and defense isn’t enough. You require a further division into passing and rushing.

Numbers can help you in this journey, but only if you’re careful. The passing and rushing statistics on major media sites are deeply flawed. You should never look at them.

Let me explain.

How to correctly evaluate passing and rushing

Sack count as rushing plays in college football.

It makes no sense. Plays that end in a sack started as a pass play. Those negative yards should count against passing yardage.

The inclusion of sacks as rushes probably originates from teams that run the option offense. The quarterback often rushes the ball by design. This makes it difficult to distinguish between a negative rushing play by the quarterback and a sack.

No matter the reason for college football’s quirks, sacks should count as negative pass plays to evaluate rushing and passing. To my knowledge, no college football statistics site makes this adjustment for sacks (although analytics guys like Bill Connelly of SB Nation do account for this in his preseason previews).

Armed with adjustment, we still must take two additional steps to get the most accurate evaluation of passing and rushing.

Accounting for pace

College football provides a diversity of styles. Baylor plays at an up tempo pace, cramming as many plays into the game as possible. In contrast, Alabama and Stanford milk every second from the play clock before snapping the football.

Due to these differing styles, yards per game is a terrible metric to judge passing and rushing on offense. Up tempo teams like Baylor generate more yards in a game by running more plays.

This pace also effects the defense. Since Baylor runs so many plays on offense, their defense tends to face more plays and allow more yards.

To account for these contrasting styles, you need a statistic that adjusts for the pace of play. While the football analytics community has many efficiency metrics, I like the simple yet effective yards per play.

The Power Rank has raw yards per play statistics for passing and rushing, which includes numbers for both offense and defense. Please use this public resource and avoid the misleading statistics on major media sites.

Adjusting for strength of schedule

With yards per play that count sacks as pass plays, you’re 95% of the way to understanding college football teams. However, to make the last leap, you must consider strength of schedule.

College football features a wide range of team strength. Programs like Alabama and Auburn will always tower over their neighbors in the Sun Belt due to tradition and financial resources.

A team should get more credit for 6 yards per carry against an SEC power like Alabama than a Sun Belt team. The Power Rank takes yards per play in college football and adjusts for strength of schedule, the same calculation I performed for adjusted pass efficiency in the NFL.

While anyone can view the raw yards per play numbers in college football, I save the adjusted yards per play statistics for members of The Power Rank.

Let’s look at how these three steps revealed a more accurate picture for Oklahoma’s defense in 2015.

Oklahoma’s defense in 2015

Oklahoma had a magical year in 2015. Despite a startling early to Texas, the Sooners stormed back to win the Big 12 and secured a spot in the College Football Playoff.

However, pass defense might have seemed like weakness for Oklahoma. The Sooners ranked 33rd in the nation for passing yards allowed per game.

However, these misleading statistics count sacks as rush plays, and Oklahoma had a fierce pass rush led by Charles Tapper and Eric Stryker. The Sooners defense sacked the opposing quarterback on 8.3% of pass attempts, higher than the 6.1% college football average.

When you count sacks as pass attempts, Oklahoma moves from 33rd to 25th in the nation in pass defense by yards per game.

However, yards per game doesn’t account for the higher tempo of play in the Big 12. For example, Oklahoma State attempted 45 passes against the Oklahoma defense.

To account for pace, we use yards per pass attempt. Oklahoma ranked 13th in yards per play adjusted for strength of schedule. While the typical college football statistics had Oklahoma’s pass defense outside the top 25, proper adjustments reveal an excellent pass defense.

How computer rankings make you smarter about football

Predicting Super Bowl in 2016 was the ultimate test between eyes and numbers.

By the eye test, Carolina looked like the clear favorite over Denver. The Panthers had a stellar 17-1 record, and they destroyed two of the NFL’s best teams, Seattle and Arizona, to make the Super Bowl.

The eye test also favored Carolina at the quarterback position. Cam Newton had an Most Valuable Player caliber season, a touchdown machine at the pinnacle of his game.

However, the eye test for Super Bowl 50 didn’t hold up. In the first quarter, Von Miller stripped Cam Newton of the ball. Denver recovered for a touchdown that gave them a 10-0.

Carolina’s offense never left the gate to take off for flight. Despite an anemic offense, Denver won 24-10 with the help of a few critical turnovers.

In contrast to the eye test, numbers suggested Denver wasn’t as overmatched as they seemed against Carolina in Super Bowl 50. This insight was based on computer rankings and their adjustments for strength of schedule.

Let me explain.

Margin of victory

It should be obvious that a team ranking system should consider margin of victory in games.

Do you care that Amazon has lower prices than your neighborhood book store? No. It’s the 40% discount on all titles that compels you to buy online.

The same lesson applies to computer rankings.

The Power Rank’s team rankings start with margin of victory in games. However, this raw metric didn’t tell the entire story about Carolina. The Panthers had an average margin of victory of almost 13 points, by far the best in the NFL.

Let’s take the next step.

Adjusting for strength of schedule

In a nutshell, computer ranking systems take a statistic like margin of victory and adjust for strength of schedule. That’s it.

This adjustment is more critical in college football than the NFL. In college, teams divide themselves into conferences of vastly differing strength. SEC teams play a much more difficult schedule than their neighbors in the Sun Belt.

In the NFL, the salary cap levels the playing field, which makes adjustments for strength of schedule less important than in college football. However, you shouldn’t ignore these adjustments, especially for Carolina during the 2015 season.

All of my team rankings take margin of victory in games and adjust for strength of schedule. Here are the NFL rankings prior to the Super Bowl with Carolina’s opponents in italics.

1. Carolina, 10.2
2. Seattle, 8.3
3. Cincinnati, 7.0
4. Arizona, 6.9
5. Kansas City, 6.8
6. Pittsburgh, 6.1
7. New England, 6.0
8. Denver, 5.2
9. Green Bay, 4.6
10. Minnesota, 3.7
11. New York Jets, 1.0
12. Buffalo, -0.4
13. St. Louis, -0.5
14. Oakland, -1.0
15. Detroit, -1.0
16. Houston, -1.0
17. Baltimore, -1.7
18. Philadelphia, -1.9
19. Chicago, -2.0
20. New York Giants, -2.2
21. Washington, -2.3
22. Atlanta, -3.1
23. New Orleans, -3.1
24. San Diego, -3.6
25. Indianapolis, -3.7
26. Dallas, -5.6
27. Tampa Bay, -5.8
28. Jacksonville, -5.9
29. San Francisco, -6.2
30. Miami, -6.4
31. Cleveland, -6.8
32. Tennessee, -8.9

Carolina played three teams in the top half of my team rankings the entire season. Their 6 division games against Atlanta (22nd), New Orleans (23rd) and Tampa Bay (27th) didn’t present much competition. In addition, they faced the weak teams from the NFC East and AFC South in other games.

Despite this strength of schedule, Carolina still ranked first in these points based NFL rankings because of their large unadjusted margin of victory in games. To find a potential weakness for Carolina against Denver, we need to dig further.

Rankings pass offense and defense

The Power Rank algorithm can do more than rank teams on adjusted margin of victory. It can also rank offenses and defenses based on efficiency metrics.

To get a better insight into the match up between Carolina and Denver, let’s look rankings for pass offense and defense. To do this, we take yards per pass attempt and adjust for strength of schedule.

This list gives the pass defense rankings before Super Bowl 50, again with Carolina’s opponents in italics.

1. Denver, 5.1
2. Carolina, 5.5
3. Seattle, 5.5
4. Kansas City, 5.6
5. Cincinnati, 5.6
6. Houston, 5.8
7. Green Bay, 5.9
8. New England, 6.0
9. Oakland, 6.0
10. St. Louis, 6.1
11. New York Jets, 6.1
12. Minnesota, 6.1
13. Pittsburgh, 6.2
14. Philadelphia, 6.2
15. Arizona, 6.3
16. Baltimore, 6.3
17. Buffalo, 6.6
18. Chicago, 6.6
19. Tampa Bay, 6.6
20. Indianapolis, 6.7
21. Detroit, 6.7
22. Dallas, 6.7
23. Washington, 6.8
24. Atlanta, 6.8
25. Tennessee, 6.9
26. Jacksonville, 6.9
27. San Francisco, 7.0
28. San Diego, 7.0
29. Miami, 7.1
30. New York Giants, 7.2
31. Cleveland, 7.3
32. New Orleans, 7.9

Cam Newton only faced three solid pass defenses all season. Three!

I should note that Arizona’s pass defense would have made a fourth good pass defense before Carolina racked up 11.2 yards per attempt against them in the NFC championship game.

Carolina threw for almost 7 yards per attempt, 5th best in the NFL. However, strength of schedule adjustments drop Carolina to 11th in the pass offense rankings.

Also, Denver had the top ranked pass defense heading into the Super Bowl. The number next to each team gives a rating, or expected yards per pass attempt allowed against an average pass offense. Denver had a rating significantly better than second ranked Carolina.

Numbers over the eye test

It wasn’t easy trusting the numbers before Super Bowl 50. Everyone liked Carolina, as the markets closed with the Panthers as a 5 point favorite over the Broncos.

My member predictions, which use a number of metrics including the rankings discussed in this article, gave Carolina a 1 point edge. While this seemed a bit low, the match up of Denver’s pass defense against Cam Newton gave the Broncos hope.

In the game, Newton threw for 4.1 yards per pass attempt, well below his season average. This played a big role in Carolina’s loss to Denver, as numbers and analytics stood strong against the eye test in this game.

The Ultimate Guide to College Football Conference Win Probabilities

Will your college football team win a conference title in 2016? Which teams will win the Power 5 conferences, giving them the inside track towards a College Football Playoff berth?

Unlike the typical college football publication, I won’t pick a single team to win each conference. Every team has some chance to win, even Kansas, and modern sports analytics can assign each team a conference win probability.

These numbers come from my preseason college football model, which considers team performance over the past four years, turnovers and returning starters to rank all 128 FBS teams. This model predicted the winner in 73.3% of games last season, a win rate that only includes games with two FBS teams.

This preseason model gives a win probability for each game this season, and these numbers drive my win total predictions at The Power Rank.

These win probabilities also provide the parameters for simulating each conference 10,000 times with random numbers. Each simulation determines the division winners through tiebreakers, and then flips another coin to determine the outcome of the championship game. These simulations give the win probabilities below, which I compare with the market odds from Bovada.

Let’s look at the numbers and story lines for each Power 5 conference.



Is Tennessee the new beast in the SEC East?

Despite 4 losses last season, Tennessee played close games with Alabama and Oklahoma and never lost by more than a touchdown. The Vols finished last season 8th in my team rankings, which take margin of victory in games and adjusts for strength of schedule.

This season, Tennessee should fully see the fruits of strong classes Butch Jones recruited in 2014 and 2015. My preseason model ranks the Vols 7th and gives them a 44.8% chance to win the East despite a difficult cross division game with Alabama.

Tennessee’s strong chances to win the East reflect poorly on the other programs in the division.

  • Georgia hired a coach (Kirby Smart) who has never been a head coach.
  • Florida looked on the rise last season under Jim McElwain before getting demoralized by Florida State, Alabama and Michigan to end the season.
  • Steve Spurrier left South Carolina mid-season, and the Gamecocks replaced him with Will Muschamp, who never got the job done in four years at Florida.
  • Missouri won the SEC East in 2013 and 2014 but appear in rebuilding mode after the retirement of Gary Pinkel.

My model doesn’t like the prospects of these usual contenders for the SEC East crown, which enhances the odds for Tennessee. The story changes if Tennessee played in the SEC West.

My numbers rank Ole Miss right behind Tennessee at 8th in the nation. However, with Alabama (#1) and LSU (#3) ahead of them, Ole Miss has a 8.7% chance to win the division.

The lack of power balance also favors Tennessee in the overall conference odds. My preseason model would make LSU a 2.5 point favorite over Tennessee in the SEC title game. However, because they play in a weaker division, Tennessee has a better probability of winning the SEC than LSU.

SEC East

Tennessee (#7) has a 44.8% chance to win.
Georgia (#16) has a 28.4% chance to win.
Florida (#28) has a 12.2% chance to win.
South Carolina (#34) has a 8.0% chance to win.
Vanderbilt (#44) has a 4.0% chance to win.
Missouri (#52) has a 2.2% chance to win.
Kentucky (#83) has a 0.3% chance to win.

SEC West

Alabama (#1) has a 45.1% chance to win.
LSU (#3) has a 27.7% chance to win.
Mississippi (#8) has a 8.7% chance to win.
Arkansas (#12) has a 8.3% chance to win.
Texas A&M (#10) has a 5.6% chance to win.
Mississippi State (#23) has a 3.5% chance to win.
Auburn (#32) has a 1.1% chance to win.

Big Ten


A year ago, Ohio State was the toast of college football. Urban Meyer’s team had won the first playoff, and he was killing it as usual on the recruiting trail.

Then in the most inexplicable game of 2015, Ohio State lost to Michigan State, a team without star QB Connor Cook. The loss cost the Buckeyes a spot in the playoff, and they lost 10 players to the first 3 rounds of the NFL draft.

In contrast, Michigan had many unanswered questions a year ago. They went 5-7 the previous season, and their hopes for 2015 rested on their faith in new coach Jim Harbaugh.

Michigan had a few rough spots in 2015, including a demoralizing 42-13 home loss to Ohio State. However, they finished 10-3 after a convincing win over Florida in a bowl game, and Harbaugh landed a top 5 recruiting class in February.

How do Ohio State and Michigan compare in 2016?

My numbers imply a dead heat between Ohio State and Michigan in 2016. Michigan (#9) is ranked ahead of Ohio State (#11) but would be a 2.5 point underdog when they travel to Columbus in November.

However, Michigan does have a better chance to win the Big Ten East (37.8% vs 34.9% for Ohio State) due to an kinder cross division schedule. Both teams play Wisconsin, but Michigan gets Iowa (#36) and Illinois (#88) while Ohio State tangles with Nebraska (#25) and Northwestern (#58).

In the Big Ten West, it might seem strange to that Nebraska has the highest conference win probability. However, the Huskers went 1-5 in games decided by a touchdown or less, which included a loss to BYU on a Hail Mary. A turnover margin of -12 didn’t help either.

Luckily for Nebraska, my research shows that their record in close games and turnover margin most likely improve in 2016. QB Tommy Armstrong returns, and Nebraska could be really good if they can fix their porous pass defense.

However, the real reason Nebraska has the highest odds to win the Big Ten West is Wisconsin’s schedule. After two years of playing Rutgers and Maryland in cross division games, the Badgers get Ohio State, Michigan and Michigan State. Nebraska also plays Ohio State but gets Indiana and Maryland. Advantage Nebraska.

And I guess we should discuss defending Big Ten West champion Iowa. The Hawkeyes had a magical season last year, but quickly regressed in the Rose Bowl against Stanford. My numbers give them a 19.3% chance to win the division, much less than the even odds in the markets.

Big Ten East

Michigan (#9) has a 37.8% chance to win.
Ohio State (#11) has a 34.9% chance to win.
Michigan State (#29) has a 19.0% chance to win.
Penn State (#43) has a 5.7% chance to win.
Indiana (#67) has a 1.4% chance to win.
Maryland (#78) has a 0.7% chance to win.
Rutgers (#84) has a 0.5% chance to win.

Big Ten West

Nebraska (#25) has a 34.2% chance to win.
Wisconsin (#22) has a 31.1% chance to win.
Iowa (#36) has a 19.3% chance to win.
Minnesota (#60) has a 7.7% chance to win.
Northwestern (#58) has a 4.8% chance to win.
Purdue (#76) has a 2.5% chance to win.
Illinois (#88) has a 0.5% chance to win.



My numbers understand some of the hype surrounding the Washington Huskies. They return 15 starters from last season, including 7 players on a defense that ranked 9th by my adjusted yards per play.

The subjective adjustments also seem to favor Washington, as QB Jake Browning returns as a sophomore after a promising freshman season. Coach Chris Petersen enters his third year and might find the same success he had at Boise State.

But it’s pure insanity to make Washington the Pac-12 favorite, as the markets have them in early August.

While Washington showed promise last season, they finished 7-6 and 31st in my team rankings that take margin of victory and adjust for schedule. Not exactly playoff material.

Last season, Washington had one signature win, a 17-12 win at USC. However, it almost doesn’t count, as USC coach Steve Sarkesian got fired the next week and possibly wasn’t sober while preparing for the Huskies.

Washington has yet to prove itself on the field, which makes it difficult to think they’ll win the Pac-12 North over proven teams.

  • Stanford has to replace their QB and fill holes on both lines, but they still have this guy named Christian McCaffrey.
  • Even though they’re replacing the quarterback, Oregon has most of its weapons back on offense. And the defense can’t get worse, can it?

My numbers like Stanford (35.8%) and Oregon (21.4%) over Washington (10.1%) to win the Pac-12 North.

In the Pac-12 South, the odds makers might be overlooking Utah. It’s not just that Kyle Whittingham has the Utes back on the rise with stellar play on defense. It’s the schedule.

When the Pac-12 split into two divisions, UCLA and USC wanted to keep their rivalries with Stanford and California. However, this has implied a more difficult schedule for all four of these teams.

USC gets the worst of it this season, as they not only play their two northern California rivals but also Oregon. The Trojans always have talent. However, Clay Helton went an uninspiring 5-4 as interim head coach last season, so there’s reason to doubt his ability to get this talent to play at a championship level.

UCLA catches a break in getting Oregon State as their third cross division game. In addition, I think my numbers underestimate Jim Mora’s team this year. My preseason rankings are based on their team rank of 41st last season, but this seems inconsistent with an offense and defense that ranked 27th and 14th last season by my adjusted yards per play.

My numbers make Utah the favorite in the Pac-12 South because they play Oregon State, California and Oregon in cross division games. If the offense can get better with new personnel, Utah could become a legitimate Pac-12 contender.

Pac-12 North

Stanford (#6) has a 55.1% chance to win.
Oregon (#18) has a 21.4% chance to win.
Washington State (#30) has a 11.8% chance to win.
Washington (#26) has a 10.1% chance to win.
California (#56) has a 1.5% chance to win.
Oregon State (#87) has a 0.1% chance to win.

Pac-12 South

Utah (#24) has a 35.8% chance to win.
USC (#19) has a 29.9% chance to win.
UCLA (#37) has a 15.9% chance to win.
Arizona (#38) has a 12.3% chance to win.
Arizona State (#53) has a 4.9% chance to win.
Colorado (#77) has a 1.3% chance to win.



It seems like Clemson should win the ACC over Florida State in 2016.

Clemson beat Florida State on their way to the national title game against Alabama. Despite the 5 point loss against Bama, you could argue Clemson should have won. They dominated the line of scrimmage but couldn’t overcome blown coverages in the secondary.

However, my preseason ranking like Florida State, as the Seminoles rank 2nd over Clemson at 5th. The returning starters variable plays a critical role in this rank.

Florida State has 17 returning starters, which includes star running back Dalvin Cook. In contrast, Clemson returns only 12 starters. The Tigers also had heavy attrition in the secondary, including 3 players that got drafted by the NFL.

In addition, while Clemson beat Florida State by 10 last year, it was a closer game than the final score indicated. Florida State had more yards per play than Clemson. The Seminoles couldn’t overcome a 2 for 12 rate in converting third downs.

However, Clemson might have the trump card. They bring back Deshaun Watson, the best quarterback in the nation. Florida State is still deciding between returning starter Sean McGuire at QB or a few younger players.

In the Coastal division, the markets have the same odds for Miami as for defending champion North Carolina. This shows major respect for new Hurricane coach Mark Richt, who won 145 games in 15 season at Georgia.

My numbers, which do not consider the coaching change, give Miami the fourth largest win probability for the Coastal division.

ACC Atlantic

Florida State (#2) has a 53.3% chance to win.
Clemson (#4) has a 31.3% chance to win.
Louisville (#14) has a 13.9% chance to win.
Boston College (#55) has a 0.6% chance to win.
North Carolina State (#59) has a 0.4% chance to win.
Syracuse (#64) has a 0.4% chance to win.
Wake Forest (#73) has a 0.2% chance to win.

ACC Coastal

North Carolina (#20) has a 37.3% chance to win.
Virginia Tech (#31) has a 25.0% chance to win.
Pittsburgh (#33) has a 16.4% chance to win.
Miami (FL) (#40) has a 10.2% chance to win.
Georgia Tech (#47) has a 6.7% chance to win.
Duke (#75) has a 2.3% chance to win.
Virginia (#72) has a 2.1% chance to win.

Big 12


Can any team topple Oklahoma from the top of the Big 12?

Bob Stoops has had consistent success as coach at Oklahoma. Only once in his 17 years have the Sooners finished the season outside the top 25 in my team rankings (2005).

Stoops has had his bad years. In 2014, Oklahoma went 1-3 in games decided by a touchdown or less and had an 8-5 record. However, despite an embarrassing bowl loss to Clemson, they still finished 15th in my team rankings.

My preseason model that looks over the past four years appreciates this type of consistency, and it ranks Oklahoma 5th. They could slip like they did in 2014. But with QB Baker Mayfield back, don’t count on it.

The Big 12 win probabilities do not consider the departure of Baylor coach Art Briles. There should be some type of adjustment downward for the Bears. They have one starter returning on both the offense and defensive lines, which suggests they might regress even with Briles as coach.

The team most likely push Oklahoma is TCU. Gary Patterson had the 17th ranked defense by my adjusted yards per play despite a rash of season ending injuries. With the return of a few of these players, who do not count towards the number of returning starters, TCU’s defense should be elite.

On offense, TCU lost almost all of their skill players on offense. However, they will reload with Texas A&M transfer Kenny Hill at quarterback.

How many conferences will my numbers get right?

The Power Rank’s preseason model predicted the game winner in 73.3% of games in 2015. Despite appearances, it is possible to make accurate game by game college football predictions right now.

However, that doesn’t imply we know which teams will win their conferences with any certainty. Teams play a small sample size of 8 or 9 games to determine a champion, which provides an opportunity for teams to steal a conference title.

It only takes one game. In 2015, Michigan State traveled to Ohio State without QB Connor Cook. The markets made the Spartans more than a two touchdown underdog. Michigan State won the game anyway, and this one loss eliminated Ohio State from Big Ten title contention.

For the Power 5 conferences, no team has better than a 50% chance to win their conference by my numbers. Oklahoma has the highest odds at 47.6%, a number that could be higher because of the coaching changes at Baylor.

This means that if three of my predicted champions actually win, I would have benefitted from some good fortune in making my predictions. This leaves plenty of room for surprise teams to make a run at the College Football Playoff, just like Michigan State in 2015.

Will Texas A&M be sneaky good in 2016?

Yes, but it might not show up in the win column. Let me explain.

Texas A&M plays in the SEC West, the most difficult division in college football. With strength of schedule adjustments, the Aggies look better in my team performance ratings than their record over the past few seasons (8-5 in 2014, 2015).

The Power Rank’s preseason model with its schedule adjustments ranks Texas A&M 10th for 2016. The Aggies are a strong program that would be the favorite on a neutral site against all but 9 teams. However, this rank doesn’t translate into a high expected win total.

Texas A&M is the fourth of seven SEC West teams in the rankings. They travel to play the three teams below them (Arkansas in Arlington, Mississippi State and Auburn) and also play Tennessee out of division. This difficult schedule implies a predicted win total of 7.6 games.

The Aggies struggled throwing the ball last season. However, the arrival of graduate transfer QB Trevor Knight, the Sugar Bowl MVP during his time at Oklahoma, should help the passing game immediately.

On the other hand, Texas A&M has had an atrocious rush defense the past three season. They don’t match up well against strong rushing teams like Alabama and LSU.

This article was adopted from The 2016 College Football Win Totals Report, which has my predictions for all 128 bowl subdivision teams. For more information on how to get your copy, click here.