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Changes to formula for 2018

I am changing the computer rankings formula on to reflect margin of victory starting with 2018.  This is big change to the core beliefs of the model which have always been based on simplicity.  To this point the model only considered wins and losses with no regard to margin of victory, away/home/neutral site for game, offensive or defensive stats, or month when game was played. A model that is this simple, this mathematical, and has excellent correlation to the final AP rankings year after year should not be tinkered with lightly.

By making this change to include strength of schedule I am hoping to accomplish 2 things:

First, this change should make early season rankings more in line with human polls starting from about weeks 3-4. Currently since margin of victory does not matter the formula can not really distinguish between a 3-0 Baylor team and 3-0 Alabama team.  It is only later in the season when there is more connectedness between Baylor’s and Alabama’s opponents or opponents opponents that the model can see Alabama’s wins to be superior to Baylor’s.   Now, with margin of victory the model will be able to reward a 60-0 Alabama win vs an average Vanderbilt team earlier in the season.

The 2nd goal deals with Auburn and the final 2017 committee prediction.  After 3 very successful years of nailing the playoff committee rankings before they came out, last year was a bust for the playoffPredictor methodology when it came to Ohio State / Alabama and the final rankings. The model put Ohio State at #4 in the final rankings, when the playoff committee had them at #5.  So what happened?   A lot of it has to do with Auburn.  Even after Auburn lost to Georgia in the SEC championship game, the computer did not punish Auburn much.  Going into the game the computer had them at #11 and after the game the computer had them at #12. So they only dropped one spot in the eyes of the computer.  But the humans dropped them from #2 pre-game to #7 post game. Because the formula uses this week’s computer rankings plus last week’s average bias, Auburn’s bias was so high (9 spots between computer at #11 and committee at #2) that when the computer only dropped them from #11 to #12, it expected the committee would similarly drop them from #2 to about #3 — what happened is that the computer was right before the committee saw it.

Let’s take a closer look — here are the week 13 computer and human rankings for 2017. Week 13 is post Auburn-Alabama game (where Auburn beat Alabama) but pre SEC championship game.  Note under the old formula (which does not take in margin of victory) Auburn is #11 in the computer.  and #2 in the humans.

Now here are the week 14 computer and human rankings. Week 14 is post SEC championship game, where Georgia solidly beat Auburn by 21 points. Again, under the old formula Auburn has moved from #11 only to #12 in the computer, and moved from #2 to #7 in the humans.

Clearly Auburn did not deserve to move from #11 in the computer to say #20 just because they lost to Georgia. Yes, they had 3 losses, but the losses were to Clemson (the #1 team in the final estimation of the committee), Georgia (played for the national championship) and LSU (average team), balanced with wins against Georgia and Alabama, who both played for the national championship.  Clearly that is a team resume that should have been right where the computer said (around 10) and not around 20.  So there is no fault in the computer here — it is the fault of the committee for not seeing what the computer saw earlier.


Now let’s look at how 2017 would have played out if margin of victory was part of the computer formula all along. At week 13 Auburn is #4 in the computer. Of course they will still be #2 in the humans — so their bias will be a lot lower – only a 2 spot bias.

At week 14 with the new formula, Auburn moves to #11 in the computer.  That coupled with the more normal team bias would have put them squarely out of the final top 4 in the models calculus, accomplishing the stated goals.


The other goal that adding strength of schedule will accomplish is get a more accurate computer ranking earlier in the season.  Back to 2017, here is the old model computer rankings for week 4

and here is what it would have been with the new margin of victory components included:

and finally here is what the AP poll was at that time:


Note the details like Wisconsin is #7 in the new method, outside of the top 15 in the old.   Alabama is at #3 instead of #5. Mathematically looking at the top 10 in all 3 lists,  the average delta of old to AP is 5.0 and the average delta of new to AP is 4.1, indicating about a 25% improvement in computer to human by week 4.   The correlation of the top 15 improves from .65 to .67.

Now, the method how I am incorporating strength of schedule is: 1 win is given for games where the final margin of victory is 16 or less points, 2 wins given for 17-32 points, and 3 wins given for a margin of victory 33 points or more.  I don’t like this, but it is a crude way to start this process and get the desired effect.  I feel there is a differentiation between a team down 16 and down 17.  At 16 points down, even late in the 4th quarter, that’s just a two score game.  Anything is possible in one play, so even if the offense has the ball and a 16 point lead, a pick six followed by a two point conversion makes a compelling game, and that is always one play away.  However, at 17 points (3 scores) down, I feel the other team will tend to give up a little bit more — you have really beaten a team when you are wining by 17 points with just 5 minutes left to play and you control the ball.   The ideal formula will take all these into consideration — If I have a 1st down, I am up by 9 points, the other team has no timeouts, and there is 3 minutes on the clock — that should all come into play.  I may use ESPNs in-game probabilities as the margin of victory component (when ESPN says team A has a 99.9% chance of winning, call the game then, and if that happens at 45:00 minutes of game time vs 59:40 minutes of game time — that is how the team earns margin of victory — but I may wait till next year to implement that.  I’m all for suggestions! Drop me a line — or at reddit under /r/cfbanalysis








College Football thoughts on the eve of the 1st rankings has as the computer top 4:

Screen Shot 2017-10-29 at 8.29.41 AM

Which really makes sense, if you think about it.  Georgia has a super-quality win over ND. Bama’s best win is over #38 Texas A&M.   Wisconsin looks solid and Penn State’s one loss (1 point on the road) is a much better loss than tOSUs (15 points at home) or OUs (7 points at home).

I am expecting the committee to come out with:

  • Bama #1
  • Georgia #2
  • Wisconsin #3
  • Ohio State or ND  #4

Right now there are 2 other unbeatens – UCF and Miami.   Miami and Notre Dame play each other on Nov 11.  What I am really interested to see is how the committee treats UCF.  My computer has them at #5 – they have some very good wins — in fact, their best win is much better than Bama or Wisconsin’s best win.   However, I suspect the committee will put them at about #20 in the initial poll. The way this season is shaking out UCF could be the only unbeaten in college football.  I will really like to see if a rematch with Memphis and them winning the AAC would be enough to get a mid-major in.





NCAA bracket simulator

Need to fill out a bracket? Use my NCAA bracket simulator! Building on last year’s attempt to come up with a perfect bracket and win Warren Buffet’s $1B (by the way, he didn’t offer the prize again this year), I am launching    The underlying idea is to give each team a number of virtual “Ping-Pong balls” based on your expectation of how well they will play. Give Kentucky 100 balls and the #16 seed 1 ball – that means 100 out of each 101 times Kentucky will win that contest and 1 time in 101 Hampton will pull the upset.

Say on the other side of the bracket you give Cincinnati and Purdue 50 balls each- that means the Cincinnati / Purdue game is a 50/50 proposition, and the winner will beat Kentucky 50 out of 150 (50+100), or 1 out of every 3 times. Think that’s too much? Then give Kentucky a higher rating or drop Cincinnati down.


The computer will use the ratings you give and randomly simulate an entire tournament for you.   Fancy graphics? Well, no. Not yet. Give it a try for your bracket: Nailed It!

As if ever there was a doubt…

Couldn’t have got it more right, and got it right the instant the games went final.

In fact, the site got every single top 4 for the entire season (with the exception of the initial poll, of course — that is needed to build the bias file between the computer ranking and the committee ranking). correctly predicted a lot of things the committe did this year, including:

  • TCU over Alabama in the October poll
  • Ohio State over TCU and Baylor in the final poll

Score 1 for big data and analytics!

Looking forward to keeping the site going next year.  I’ll probably also do some other stuff now that the math is coded — such as comparing all teams in the BCS era. Stay tuned!