College football playoff probabilities after week 3

screen-shot-2016-09-13-at-12-14-45-pmOver on Bleacher Report, I posted my numbers for top teams to make the college football playoff based on my rankings and simulations.

The numbers only gave Houston a 3% chance, which required some explanation.

Alabama and Florida State had the top odds to make the playoff, but the third and fourth highest might surprise you.

To check out my college football playoff odds, click here.

Finally!! NFL preseason rankings based on wisdom of crowds

nfl2016_preseasonYou want to know the strength of your NFL team. You’ll take any analytics that can sort through the preseason noise of the NFL.

In college football, team strength tends to persist from year to year. This makes it possible to use previous seasons to predict the current season.

However, looking at past years does not work in the NFL since team performance regresses to the mean. The salary caps levels the playing field for all 32 teams. Injuries and luck can derail teams with the highest expectations.

However, we can use a different trick from college sports to rank NFL teams in the preseason. Let me explain.

Wisdom of many sports writers

Preseason polls in college sports are remarkable predictors of success.

In the preseason AP poll, the higher ranked team has won 60.2% of bowl games that season since 2005 (174-115 with no prediction in 91 games). The preseason Coaches poll also performs well at a 60.9% rate (182-120 with no prediction in 73 games).

The combined wisdom of sports writers or coaches lead to remarkable rankings. However, the accuracy of these polls decrease once the season starts. The writers or coaches tend to react too strongly to wins and losses. By the end of the season, the higher ranked team in the AP polls wins 56.6% of bowl games.

However, the AP poll is a remarkable tool before the season starts. Let’s created the same type poll for the NFL.

Ensemble NFL preseason rankings

Every major sports media site publishes preseason power rankings. I aggregate these rankings from over 20 sites.

To make predictions, each teams also needs a rating, or an expected margin of victory against an average NFL team. I use historical results from the last 10 years of my NFL team rankings, which take margin of victory and adjust for strength of schedule.

I’ve done this calculation for the last 3 years, and the model has predicted the winner in 64.5% of games (515-284 which doesn’t count two tie games). The opening Vegas line gets 66% of games correct on average.

Preseason rankings for 2016

In these wisdom of crowds rankings for 2016, it seems like most gave the Patriots a rank with Tom Brady, who won’t play the first four games of the season.

1. Carolina, 7.6
2. Seattle, 7.2
3. Arizona, 6.7
4. New England, 6.2
5. Green Bay, 5.9
6. Pittsburgh, 5.4
7. Denver, 4.3
8. Cincinnati, 4.2
9. Kansas City, 3.3
10. Minnesota, 1.6
11. Oakland, 1.2
12. Houston, 1.0
13. New York Jets, 0.9
14. Washington, 0.7
15. Baltimore, -0.3
16. Indianapolis, -0.4
17. New York Giants, -0.6
18. Dallas, -0.6
19. Jacksonville, -1.4
20. Buffalo, -1.4
21. Tampa Bay, -1.9
22. Atlanta, -2.3
23. Detroit, -2.4
24. Miami, -3.1
25. New Orleans, -3.4
26. Los Angeles, -4.1
27. Chicago, -4.2
28. San Diego, -4.6
29. Philadelphia, -4.7
30. Tennessee, -5.5
31. San Francisco, -7.9
32. Cleveland, -8.1

The predictions for week 1 are posted on the predictions page.

College football rankings for week 2 of 2016


After one week of college football, people typically overreact to the results.

LSU lost by 2 points to a probably decent Wisconsin team on the road. The Tigers dropped 16 spots to 21st in the AP poll.

That’s nuts. LSU drops in my rankings, but one spot to 4th. Let’s not count them out just yet (although QB Brandon Harris did look terrible).

Here are the top 30 teams. The number is a rating, or expected margin of victory against an average FBS team.

1. Alabama, 20.8
2. Florida State, 18.0
3. Clemson, 15.8
4. LSU, 15.7
5. Stanford, 14.0
6. Oklahoma, 14.0
7. Tennessee, 13.3
8. Ohio State, 12.7
9. Mississippi, 12.6
10. Michigan, 12.2
11. Georgia, 11.0
12. Texas A&M, 11.0
13. Notre Dame, 11.0
14. Baylor, 10.4
15. Louisville, 10.3
16. Arkansas, 10.3
17. Oklahoma State, 10.1
18. Oregon, 9.4
19. TCU, 9.2
20. Wisconsin, 9.2
21. North Carolina, 8.3
22. Utah, 8.3
23. Nebraska, 8.2
24. Florida, 7.9
25. Washington, 7.7
26. Michigan State, 7.5
27. Houston, 7.3
28. USC, 7.2
29. Brigham Young, 7.2
30. Mississippi State, 7.2

For the full rankings, click here.

3 overrated college football teams for 2016

Screen Shot 2016-09-06 at 4.23.39 PMLast week, I wrote about a few overrated teams by preseason analytics over at Football Study Hall of SB Nation.

How did these teams do week 1?

Houston pulled off the upset of Oklahoma, so I’ve gotten some flack for including them in the ranks of overrated. However, we have much football left to play this season.

Michigan State struggled against Furman, as they beat their FCS opponent by 15 points at home.

Iowa did what they were supposed to do and beat Miami of Ohio by 24.

We’ll see how these predictions pan out by the end of the season.

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.