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Official Statistical Analysis Thread

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sparcboxbuck said:
Essentially the upper model (multivariate multiple regression) was used to predict the lower model’s beta weights. The scores resulting from the upper model were then used as a natural conjugate prior on the lower model (multinomial probit) betas. Only chose probit (as opposed to logistic) because of the well behaved math of the distribution of the betas relative to the prior.
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Imma try this line out on the wife tonight. Who needs liquor’n oysters?
 
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muffler dragon said:
Nah, man. You're what I'd call one of those "good" accountants.
You can read into that anything that you want. :wink:
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All season long (and previous seasons) it's seemed like Ohio State opponents lay an egg the week after getting beatdown from OSU. Sure enough after taking the time to put the numbers together results have proven to be true. OSU opponents are 8-3 against ATS with an astonishing total of -112 ATS. Results for the game before also show this to a lesser extent (6-4, -33 ATS).

OSU will always be their opponents Super Bowl. Something to pay attention to next year to see if the trend continues, if so there may be some money to be made.
 
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Why I Don't Bet on Sports: Reasons 1-130

I took a look at the quarter-by-quarter performance for all FBS teams for:
  • Yards per Carry - Most reliable simple stat for rushing
  • Pass Efficiency - Most reliable simple stat for passing (see correlation to winning in the early posts in this thread)
What I found is not that surprising, but it is striking nevertheless. College football is a very volatile sport.

The AVERAGE difference in YPC between a team's best quarter and their worst is 1.59 yards. That is the difference between Ohio State's rushing attack (6th in the country) and South Alabama's (87th). And that's just the average.

For the 20 teams with the largest difference between their worst quarter and their best, 7 of them had their worst quarter in the fourth quarter, 7 of them had their worst quarter in the 2nd quarter, including Alabama and Wisconsin. Wisconsin's rushing went from 6.59 ypc in the first quarter to 4.11 ypc in the second, and that's only the 20th biggest difference between best quarter and worst quarter among FBS teams.

If any of you have any ideas why Bama and Wiscy's rushing offense falls off the table from the first quarter to the second, I would like to hear them. I am also curious why Maryland goes from 3.03 yards per carry in the 1st quarter to 6.13 in the 3rd. Maybe it's whatever they have in common with Toledo, because the Rockets go from 3.6 in the 1st to 7.58 in the 3rd. smh

Another thing that you might have fun trying to figure out is why teams on average did a little worse in the 2nd than in the first, a little better in the 3rd and then a lot worse in the fourth. Some of that is easier to explain than other parts of it...

No team fared better than 15th in the country in their worst rushing quarter (again, by ypc). The team that did that, Kentucky (15th in ypc in the 1st quarter) managed to have the 2nd best ypc in the country overall in spite of having no credible passing attack.

It falls off pretty quickly after that. In fact, Ohio State's rank of 33rd in their worst rushing quarter (the 3rd) ranks as the 6th best "worst quarter ranking". Here's how Ohio State's rankings looked, by quarter:
  • 1st qtr: 16th (3rd B1G)
  • 2nd qtr: 4th (1st B1G)
  • 3rd qtr: 33rd (3rd B1G)
  • 4th qtr: 12th (1st B1G)
Three of the 4 Playoff teams appear in the top 6 of best "worst quarter ranking". LSU is 17th in that metric, as their 2nd quarter ranks 55th in the country.

That's one thing that Rushing YPC and Pass Efficiency had in common. Pass Efficiency also shows 3 of the Playoff teams ranked very highly when you sort by best "worst quarter ranking". In fact, Pass Efficiency really separated the wheat from the chaff in this metric, as 3 of the top 4 were playoff teams and the other was Alabama (whose worst quarter (the 4th) in pass efficiency was good enough for 5th in the nation). Clemson was the other playoff team and they came in at 21st in that metric, with a worst quarter (the 3rd) that ranked 62nd in the country.

The average pass offense had a difference of 55.42 spots between the ranking of their worst quarter and the ranking of their best quarter. While this disparity is pretty big, as with the rushing game, it is a bit easier to explain. Among the teams with the biggest difference between their best quarter and their worst, 14 of 22 had their worst quarter in the 4th quarter, all of them were teams that struggled and would be throwing to try to catch up in the 4th. Not one of them had their worst quarter in the first quarter. Conversely, the teams with the smallest difference between their worst quarter and their best typically had their worst quarter in the first quarter, this being the case with 6 of the 9 (including Ohio State).

The biggest difference in quarterly performance was in Army's pass efficiency: no surprise there. In the 1st quarter they threw 14 passes all year, so most of them caught the other team by surprise. Their pass efficiency in that quarter was 238.34. In the 4th quarter they had to throw a lot of the time; the result was efficiency of 85.28, a difference of 153.06 efficiency points. The next biggest difference was for Tulane, 95.37.

More weirdness that maybe you can offer an explanation for: While Wisconsin's rushing YPC fell of the table from the first quarter to the second, their pass efficiency went from 128.2 in the first to 171.31 in the second (by far their best).

Weirder still (to me) is that, of all of the teams in the top 7 for smallest difference between their best quarterly ranking and their worst (pass efficiency), Ohio State is the only one who had their best quarter in the 4th quarter AND they are the only one who had their worst quarter in the first quarter.

Ohio State's Pass Efficiency Ranking by Quarter:
  • 1st: 13th (3rd B1G)
  • 2nd: 7th (1st B1G)
  • 3rd: 12th (2nd B1G)
  • 4th: 4th (1st B1G)

Main Takeaways
  • Ohio State is both elite and consistent
  • So were the other playoff teams
  • Everyone else was all over the place - wildly variable. Betting on sports is risky.
  • Wisconsin is weird
  • In other news: Water is wet
 
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