SECFans Podcast on Michigan/Ohio State
November 19th, 2018 at 3:05 PM ^
So their model missed and they are explaining it away.
Committed murder, let me explain that away.
Lesson is that no model is perfect. Own it. That being said, do like that they do outside of SEC games and are good natured (no curse words and not belligerent when hosting/posting etc).
November 19th, 2018 at 3:15 PM ^
I haven't listened to it, but if the explanation is plausible, it works. Explaining away murder, or domestic violence, doesn't work. Explaining why a math model didn't predict something exactly correct seems a bit easier...
November 19th, 2018 at 3:17 PM ^
True. Went a bit far there but this coming on heels of "oh hey look how accurate we were on Wisconsin vs Michigan game". Can't have it both ways then.
But hey I 100% hope their model is correct re: The Game.
November 19th, 2018 at 3:36 PM ^
It's really coming off the heels of our model nailing better teams and bigger games really well. Our model had 31-17 last year OSU, ended up 31-20. It nailed OSU/PSU last year, UM/Wisky this year.
But accounting for a back up QB coming in and throwing a pick six to break a game open that's 14-0 going into the 4th that ends up 42-7 is a lot to ask for a robot. ;)
November 19th, 2018 at 3:46 PM ^
The pick 6 made it 28-0. That game was never close.
November 19th, 2018 at 3:52 PM ^
My point is that a game that's 14-0 with 1 minute left in the third, ends up 42-7 and people were killing us for a model that didn't predict a blowout. Which is a little unreasonable.
We weren't saying that PSU was a threat in the game (we stressed this multiple times in explaining the model discrepancy), but if PSU goes into a shell instead of a high variability mode trying to come back with a backup QB that game may end up something like 21-7.
It was really just an interesting opportunity to talk about how data isn't perfect, but can still provide insight.
November 19th, 2018 at 3:56 PM ^
Better way of putting it: halfway through the 3rd quarter, with the game at 14-0, would you have guessed Michigan would score 42? That was our point, that if the model result seems in any way reasonable halfway through the 3rd quarter, it's done its job. The massive blowout that actually happened was a result of Penn State's high risk-high reward offense blowing up in their face. We talked quite a bit in our preview that week that it was a possibility, but it wasn't something we could really predict or model for.
It's also worth noting that our model predicted 5.36 yards per play for Michigan and they ended up with 5.84, which is pretty dang good. Predicted 4.33 for PSU and they were right at 5.0 when McSorely was in.
The PSU discussion (about variability) was a small point in a video about tOSU-UM. If you guys want to actually discuss The Game, let us know ;)
November 19th, 2018 at 4:56 PM ^
I don't know if you guy know who James Franklin is, but I think you need to account for moronic coaching decisions in your model.....
November 20th, 2018 at 2:11 AM ^
It might be tough to do with some of the statistics you’re using in your model (which seems to be based on average points allowed versus points scored), and I’m sure you’d need much more sophisticated, play by play data, but to the point about in-game coaching decisions by coaches like Frames Janklin, it should be possible to get more insight into offenses like Penn State’s that are “highly variable.” I don’t think “randomness” and “chance” are as much the issue as the effects associated with incomprehensible decisions.
It seems to me that Field Goal Franklin costs his team, on average, about 7 points per game. The last offensive play call against OSU is a perfect example.
That's not to say that other coaches don’t make bad calls. But I would bet that in some - maybe many - cases, the negative impacts of those decisions over time are quantifiable.
By the way, I completely agree that, at best, models like these are useful insights into how a game might go.
November 20th, 2018 at 1:03 PM ^
I have really enjoyed secfans this year. Thanks to you guys for doing it, and thanks to the MGoBloggers who posted links throughout the year.
November 19th, 2018 at 4:41 PM ^
Yes but you constantly go the other way and slip up things like it's was 14-0 in 4th quarter .yea it wasn't. It was over by 3rd quarter.
Edit: sorry didn't see your response below. That's reasonable.
November 19th, 2018 at 11:33 PM ^
They scored 14 in the first half and 14 in the third and 4th quarts. So it was 28-0 going into the fourth.
November 19th, 2018 at 3:58 PM ^
Appreciate you guys. Keep up the good work! +1 to get you to hundo.
For common opponent comparison, also look at the yards gained and allowed; e.g. vs. MSU Michigan was about 400 to 100, OSU about 350 to 275; vs. PSU Michigan 400 to 200, OSU outgained 500 to 400.
November 19th, 2018 at 4:01 PM ^
OSU scores a lot of points, but they aren't efficient scorers relative to yardage. Neither team has a good red zone TD%, but it seems like UM is able to do more with less in terms of yardage.
I suspect UM will give up more yards, drives, and 3rd down conversions than UM fans are comfortable with - but Michigan's offense will find things much easier going than it's been even against average opponents.
This is statistically the worst defense in the Meyer era by a significant, significant margin. Their run defense is worse than Ole Miss' adjusted for opponent averages. And Ole Miss has one of the worst defenses I've ever seen.
But OSU can score, and that can keep them in any game.
November 19th, 2018 at 5:24 PM ^
A lot to ask of a robot you say? I think someone begs to differ...
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November 19th, 2018 at 5:38 PM ^
Ha if he had a robot number machine that could have predicted that, he could fund that colony on Mars he wants so bad.
November 19th, 2018 at 3:26 PM ^
I mean, I'm pretty sure they explained during the week of PSU that they thought their model was off for that game.
You're right that no model is perfect, but I'm sure their model is probably better than looking at raw stats. Maybe it's not, I don't know.
Good listen.
November 21st, 2018 at 3:03 PM ^
Yeah, they use the model as the starting point for discussion, not the ending point. It's a valid way of doing things, even if you don't trust the model.
November 19th, 2018 at 4:09 PM ^
It's strange they keep mentioning OSU offense scoring 26 against MSU. They scored 17 (with 7 in garbage time). Do they not account for if it's actually the offense putting up the points?
November 19th, 2018 at 4:15 PM ^
Our point was more that Haskins had 5.8 YPA vs Michigan St and with all the extra possessions that were gifted them with Lewerke being out, they still couldn't take advantage. We weren't impressed with OSU's performance vs Michigan St, in spite of us being very high on the MSU defense.
November 19th, 2018 at 4:18 PM ^
Ah I see thanks. Good listen thanks for doing these.
November 19th, 2018 at 4:18 PM ^
Ah I see thanks. Good listen thanks for doing these.
November 19th, 2018 at 4:26 PM ^
I just listened to this part again and where you say the MSU game gave them more points at 4.31 ypp with 26 points vs purdue 5.6 ypp and only 20 points bumped up the floor for how many points they can get per ypp. But that doesn't make any sense if you are factoring defensive scores into offensive ypp as a measure of offensive production. Is that how it's factored into the model?
November 19th, 2018 at 10:28 PM ^
I agree. MSU gifted them a TD with a "Sparty No!" fumble in the end zone and they had previously decided to concede a safety, so 9 points.
November 19th, 2018 at 4:26 PM ^
I still want SECfans to come to the board abt. their model for the UM-Penn State game...
EDIT- Ah, I see they're here, already!...still love you guys, though.
November 19th, 2018 at 4:31 PM ^
In digging into the MSU comparisons... sprinkle in that all of that bonus field position that OSU enjoyed they won't with Michigan as Will Hart is a monster this year punting the ball. M has a very good shot at the 40s while OSU, they'll be excelling if they get the 24 the model suggests.
November 19th, 2018 at 4:43 PM ^
They also wont get it from stupid turnovers and inability to get the ball outside of their own goalpost shadows. MSU was just unbelievably inept in that game in a way even OSU opponents with poor offenses haven't been.
November 19th, 2018 at 10:32 PM ^
All Michigan fans are hoping that Michigan does not commit stupid turnovers.
November 19th, 2018 at 11:39 PM ^
MSU offense was in the 90s for s&p the last time a checked (a few weeks ago). They bad. They really bad.
November 21st, 2018 at 10:57 AM ^
And that's somewhat propped up by earlier performances that occurred before they had some major injuries. Lewerke is a pretty decent quarterback when he's healthy but he's been lame for a while. LJ Scott is a pretty decent back but he's out for the season. When they were scoring touchdowns (something they haven't done in three weeks) they had Lewerke chucking it up to Felton Davis, who is also out for the season.
They had a bad offense before they had key injuries. With those injuries, they're probably worse than S&P suggests.
November 19th, 2018 at 4:38 PM ^
I personally feel both scores will be lower - especially if it rains - but we'll see. Michigan still wins of course.
November 19th, 2018 at 5:09 PM ^
Not mentioned is that OSU had to work Haskins into the rush offense to beat both Sparty and Maryland. Haskins doesn't run, the Bugeyes lose to the Turtles. Don Brown sends his thanks.
November 19th, 2018 at 6:32 PM ^
Two things:
What team has pressured Haskins the most so far (causing hurry up throws, throw always, or being sacked)
Is The Don strongly preparing for their back up, Martell (pen state had a back up we needed to account for as well)
November 19th, 2018 at 7:21 PM ^
Speaking as a guy who has built plenty of models in other realms (both professional and academic), I would like to say that, unless the thing you are modelling is very simple (which precludes football), a model isn't going to be good on every prediction.
To measure the quality of a model, you look at how its predictions do over a larger set.
It sounds like the SECfans guys have said their model performs pretty well on average. If so, that is a significant accomplishment and maybe good enough to make them some money in sports betting.
Alas, sports betting is illegal in the state where I currently reside.
November 19th, 2018 at 10:23 PM ^
Last few years it was a really good model, it started off something like 15-0-1 ATS last season. This year it's been haywire as has all of college football. The fact that there's just a few teams at the top and the rest is a middling pile of mediocrity makes projections tough.
There's really not much difference between the 7th best team and the 20th best team. And that's a recipe for disaster in modeling.
But with good teams, or good vs average teams, or big time match ups, it tends to do well because you're relying less on variability for a win, and more on proven repeatable results.
November 19th, 2018 at 11:10 PM ^
Nice. From one model builder to another, well done. :)
November 19th, 2018 at 10:57 PM ^
I hope the model is accurate for this week. :) Go Blue!
November 19th, 2018 at 11:09 PM ^
I hope it is correct on who wins, but way underestimates by how much. :)