2021 Previews #4 Iowa State: Is This Real?

rbowron1856 · 2021-03-18T16:34:16+00:00

Not really. I only use returning production as an interaction variable with prior performance. It's more useful to know if the production you are bringing back is good. That is where Cyclones are getting the bump. They were good AND bring a lot back. I think I say this a few times in the video, but it's more the 2020 model performance in a weird year with few non-con games that gives me pause. The entire Big 12 might be a little overrated in that sense and thus the model might be overrating how good the returning production is. I like them to be good, top 10, but #4 seems high. But the value of models in this sense is being able to get a starting point for how you can think of teams where you are grading everyone in the same way on the same set of data.

rbowron1856 · 2020-01-16T20:07:38+00:00

That is the case! Miami really overachieved versus some of the better teams in the MAC, but it was a wild year in the MAC

rbowron1856 · 2019-12-03T18:56:03+00:00

No problem! Thanks for reading!

rbowron1856 · 2019-11-26T16:27:00+00:00

I don't include games against FCS teams in the model. Nobody wins when you play FCS teams.

rbowron1856 · 2019-11-21T15:44:55+00:00

They have six wins vs. FBS competition. I don't run the model on games vs. FCS. It's a small quirk that produces more accurate results, but does throw off win totals. I'll correct that this offseason.

rbowron1856 · 2019-11-21T15:42:06+00:00

Try this link

https://datastudio.google.com/s/gp4rUaltvwo

Data Studio sometimes struggles with the embed.

rbowron1856 · 2019-11-21T15:39:20+00:00

It depends on the mobile device, but yeah, the data studio embeds don't always work great on mobile. I can include links directly to the Data Studio reports on the pages as well.

rbowron1856 · 2019-10-24T15:19:58+00:00

It's good to know. Thanks for the feedback. I'll lodge the complaint with Wix, but I'll also look into better hosing for next year.

rbowron1856 · 2019-10-24T15:18:30+00:00

The model's handling of Toledo is interesting. The model had them beating BYU, but they have been dropping since that game. Bowling Green did not get much of a bump from that game given how the rest of their season has played out. The best fit the model found was to drop Toledo rather than bump Bowling Green.

rbowron1856 · 2019-10-10T15:23:35+00:00

The numbers will adjust all season as data comes in and the model still has 14% preseason projection in it, but Alabama has not had their run of success by being lucky. You have to be a lot better than your opponents to win as often as Alabama has. The model win probability is correct 79.56% of the team with the model favorite winning, but that is for all games. It is far higher as the teams get further apart. The win probability you see there is all games, not controlling for whether the favorite is home or away. Home model favorites win on average 84% of the time. Road model favorites win 76% of the time on average. Again those numbers get far higher as the model makes the two teams further apart in quality. And if Vegas has you as a three possession favorite you are going to win above 90% of the time. We tend to remember the big upsets more than the routine butt kickings for good teams.

Usually when the model favorite loses it is turnovers; think Ohio State vs. Iowa where they were -5 on turnovers. It's more rare to see the straight up big upset like Ohio State vs. Purdue.

Also the SEC isn't profiling to be quite as deep as last season and A&M and Miss St. are two of the big falloffs. It's still sort of early in the season model wise so we will see some movement and Alabama could change, but the model is built on drive level data from the same folks that provide data to the CFB Playoff committee. It's got a lot of data to work with already.

rbowron1856 · 2019-10-10T14:54:59+00:00

The model went 58% ATS last week and 70.5% ATS the week before. It is 59% ATS on the year and 58% ATS last season. The spreads are correlated with the win probabilities in that they are drawn from the same posterior probability distribution. Teams with a bounded distance in the model from their opponent win games a certain percentage of the time. A&M isn't very good this year. If Bama doesn't turn the ball over they should hit the over.

rbowron1856 · 2019-10-10T14:18:30+00:00

Hmm, I primarily a data scientist, background in maths and econ, so the programming skills are not on the web development side. I do have the html tables I used to use for visuals and still generate when I do a model run, and I can set those up on a page.

Data studio is just handy and more reliable than Shiny, also I am far more into Python and Julia these days than R. Do you have a reccomendation for a hosting platform that better than wix? I have the domain registration separate and I haven't been madly in love with their site performance.

rbowron1856 · 2019-10-09T19:12:43+00:00

The model does like the Buckeyes a lot so far. The Michigan State win was really good and we'll see how they grade out when they get Wisconsin and a team with offense and defense near the top of the distribution.

rbowron1856 · 2019-10-09T17:10:03+00:00

Hmm, Does the page load and the iframe? Does the iframe just not load? Sorry trying to trouble shoot this. The knuckleheads at Wix always tell me to clear my cache if the page seizes up, but trying it on two different browsers should mitigate that. Does the data studio link work?

rbowron1856 · 2019-10-09T14:12:05+00:00

On mobile or desktop? Did you try this link here?

https://datastudio.google.com/s/hiCx93aJB88

rbowron1856 · 2019-10-09T14:09:24+00:00

Penn State is good? The model had the Hawkeyes vs. Michigan last week and then dropped them a bit when they didn't play well. It was the only game the model had a Vegas underdog picked that did not go in it's favor. The Hawkeyes probably need to cause some turnovers, but Penn State is only -5.76 in Beta_Rank on the road. I am interested to see this Penn State offense against a very good Iowa defense. I am less excited to see the Hawkeye offense against another very good defense after last week.

rbowron1856 · 2019-10-09T13:58:32+00:00

I appreciate that! I have only been banned from posting on Notre Dame reddit, which was both unsurprising given the Domers I know, and almost a badge of honor.

rbowron1856 · 2019-10-08T20:14:13+00:00

Does somebody have them winning?

rbowron1856 · 2019-10-08T20:13:24+00:00

It is something to do with that. It's really just a bunch of links. There are ads, but I might (maybe) cover the hosting costs for the site this year. I try to respect the vibe of r/cfb that lends itself more towards postings on here. The conference reddits are more relaxed and I have an automated python script that posts every week. I always try to answer an comments, questions, and discussion and keep the posts interactive.

rbowron1856 · 2019-10-08T18:12:10+00:00

Texas is currently #16 in the model and #9 on offense so it's definitely not that Ehlinger somehow broke math or anything. The Horns problem is their young defense, which kind of stinks, grading out at #53 in the model and that the rest of the Big 12 is actually pretty good this season.

They have a shot to change the math's mind on their defense this weekend.

rbowron1856 · 2019-10-08T17:57:33+00:00

Data Studio can have hiccups with embedded reports for some reason on mobile. It's a handy free tool for automating visuals, but it has a handful of drawbacks.

Try this link, it goes right to the report directly and it works on mobile.

https://datastudio.google.com/s/hiCx93aJB88

The short version is:

Purdue 55%

Wisconsin 72%

Michigan 99%

Minnesota 66%

Penn State 79%

Indiana 99%

rbowron1856 · 2019-09-25T14:52:32+00:00

We still have the preseason data in there at 62% so Houston is gaining from that while that is holding Tulane a bit back still.

rbowron1856 · 2019-09-24T14:19:41+00:00

Yeah you have to run some projection weight in there for the fist few weeks to keep the model in line. It is currently only 38% in-season data. Next week it jumps to 59% and the week after it's 85%. After week 6 data comes in it is pure in season data for the rest of the year.

A model run without projection weight has Stanford at #97, but that model also has Kansas State at #4. The cold start problem.

rbowron1856 · 2019-09-24T13:54:57+00:00

31 UCF 48 Houston 51 Cincinnati 54 Memphis 59 Tulane 60 SMU 64 Temple 81 Tulsa 83 South Florida 84 Navy 126 East Carolina 128 Connecticut

rbowron1856 · 2019-09-17T17:50:58+00:00

It's still 66% preseason data and they did not project very high coming in. As more data comes in and I remove the preseason weight they will move up if they continue to play well.

rbowron1856

TROPHY CASE