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New QB Analytics Stuff


CaineDeSoulis

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https://www.fanragsports.com/nfl/introducing-semtex-new-way-look-college-production/

An extremely long article about an advanced analytic for determining QB Value.

Prologue

I’ve been pretty cavalier about my skepticism with using college production for NFL evaluation purposes in the past. But I believe in being informed and personally testing everything, even methodologies I don’t buy into. So I started to look into production so I could see exactly how effective or ineffective it truly could be on a macro scale.

I started at the quarterback position. And I’ll be honest, I thought that I would find that college production was not predictive for signal callers. I could not have been more off-base. When optimized, college production can be very predictive at quarterback.

Before you jump around and yell about “box score scouting” — believe me, that’s the last thing I buy into — keep in mind that I am a tape evaluator first and foremost. Using film to identify translatable traits is vital to the NFL evaluation process and is always the first thing that should be looked at when analyzing a player. And when I say production is predictive, I don’t mean your typical passing yards and touchdowns. In fact, typical raw box score numbers have minuscule correlations to NFL success since 1999:

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MAVPY stands for Modified Approximate Value Per Year. You can read about it and the MAVEM system here.

Situationally Enhanced Metrics

So, if these box-score numbers are essentially worthless, what do I mean by “production can predictive at QB when optimized?” I am referring to Situationally Enhanced Metrics rather than raw statistics, meant to isolate player performance by accounting for external factors outside a player’s control. Put simply, these metrics are created by adding additional numerical context to position-specific rate statistics, to provide a clearer picture of how a player performs in their respective environment. Here are the enhancements applied to our QB rate stats:

Accounting for level of competition: To account for the level of competition differences among players, all of the rate stats used are given a conference-based adjuster.

Accounting for era: To account for the period in time in which a prospect played, a year-based adjustment is applied to all the quarterback rate stats used in the dataset.

Accounting for surrounding talent: To account for supporting cast, the draft capital spent on a prospect’s college teammates is added up and factored into the dataset. For current prospects, a consensus big board is used to determine projected draft position.

Accounting for pace and translatable schemes: This adjustment requires a bit more explanation. Quantifying the translatability of a college offense to the NFL is difficult to nail down.

 

 

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Jesus that's some deep stuff...

 

May post more if/when my eyes uncross...

 

Not only that Tour- who had the time to research all that stuff? If he's not making it up, it's a mighty good predictor of QB success in the NFL... To the chagrin of a certain Pat's fan- where's Waldo? I mean Jimmy? Nowhere to be found. Uma is going to be distraught AJ is around 0.69, floperoo territory.

 

Also supports my contention you need to take into account the strength of the team he played on (Watson). Not surprised at all they love my guy too- didn't need to run through a bunch of analytics for me to see Mitch has the "it" factor.

 

This chart is part of their logic- and LOL- it does support Ghoolie. Having a bunch of picks in the fourth round or lower doesn't increase your odds much. Looks like for every pick you have in the top 15, there's around a 200% better chance of hitting than in the 4th round. So Dear Sashi and DePo, please hit on your first two picks.

 

 

image-9-1.png

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While some of the writer's "adjustments" appear to be data driven there are a couple whose descriptions make them seem a little subjective.

 

Also, and I've not yet dove into the links to the regression analysis that led to the predictive model, it looks like there may be a career length factor. May simply be axiomatic since "successful" QBs' careers last longer than unsuccessful ones, but need to check it out. Then again, Dak posted "high" after only one season...

 

 

Oh... and I still am not seeing "it"... not even lower case... ;)

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so after looking at all these data......

 

I still think we should stick with Kessler another year and worry about fixing the horrible defense with the first 2 picks. Any QB is going to have a hard time when the defense gives up 30 points a game. The data reaffirm that there's not a QB in this class that is a surefire guy who can beat out Kessler to make an impact right away, and the Browns are in no position to blow a #1 pick on a gamble. I wouldn't trade any picks for Garoppolo. Osweiler has won games in the NFL too, look how good he turned out when he was removed from a team where he wasn't depended on to win games with his arm.

 

If Trubisky is somehow still available at #12 then things get interesting. Personally, I would draft the best available secondary player but I would not be devastated if the Browns picked up OJ Howard. If Trubisky is available and they go with him, I would get over it eventually but come on. Why spend a first round draft pick on someone who will most likely have to sit on the bench? Is there anyone who honestly thinks someone who couldn't beat out Marquise Williams will beat out Kessler and Osweiler to become an NFL starter his first year in the NFL?

 

Draft day can't come soon enough. Can't wait to see what year 2 of analytics looks like.

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So is there an analysis of players in the draft that are headed by this?

 

Link is at the very bottom of the article to the college analysis. You didn't think it would be easy, did you?

 

If memory serves there are three "Gold" prospects. One is Trubs at the top. Another, at #2, is Kaaya. I forget the third. I do also remember that of Kizer and Watson, one is in the Silver group (Kizer, I think) and the other is in the Copper or Bronze or Tim group.

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