I Put ESPN's Position Rankings in a Spreadsheet

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hcebb

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#21
Did the analysis real quick. Interesting results. Because of the emphasis on DL, OL, LB, and CB/S, it does change the rankings. Don't know if it helped or not. I also had to make some assumptions based on the number of players at some of the positions. Should note that only 8 points separate the top 4 teams which is impressive when the total is 348 points.

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jobob85

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#22
Did the analysis real quick. Interesting results. Because of the emphasis on DL, OL, LB, and CB/S, it does change the rankings. Don't know if it helped or not. I also had to make some assumptions based on the number of players at some of the positions. Should note that only 8 points separate the top 4 teams which is impressive when the total is 348 points.

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Texas is back baby!!!!! At least until 9/2/2017
 

ksupoke

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#23
Something seems amiss, uo has the top rated qb and the top rated ol and their backs are rated 6th, I get that they don't return much but this is a great example of lies, damn lies and statistics, assuming that their qb is 1 or 2 and their ol is 1 it is highly unlikely their backs will perform to the bottom half of the conference. Great back can make an avg ol look good, great ol can make an avg rb look great.
 
Jun 14, 2011
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#25
Did the analysis real quick. Interesting results. Because of the emphasis on DL, OL, LB, and CB/S, it does change the rankings. Don't know if it helped or not. I also had to make some assumptions based on the number of players at some of the positions. Should note that only 8 points separate the top 4 teams which is impressive when the total is 348 points.

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Ooooo-snap! We have ourselves a good old fashion "statistics off" between an OP legend @Lab Rat and the "new" kid in town @hcebb



Bring it on! As an engineer, I love me some graphs and as on OSU fan, I love me some graphs about our Pokes even more.
 

Duke Silver

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#26
Something seems amiss, uo has the top rated qb and the top rated ol and their backs are rated 6th, I get that they don't return much but this is a great example of lies, damn lies and statistics, assuming that their qb is 1 or 2 and their ol is 1 it is highly unlikely their backs will perform to the bottom half of the conference. Great back can make an avg ol look good, great ol can make an avg rb look great.
Thank you.
 

BeatOU

--- ... ..-
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#28
Even more so when one realizes that "datum" is the singular form. Thus, "data are beautiful."

:koolaid::whistle:

American English recognizes a collective singular, though, so "data is beautiful" is perfectly acceptable in this country.

For example, it is acceptable to say "OSU has the ball" even though there are 11 people on the field. Whereas in England, they would say "OSU have the ball".
 

Lab Rat

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#33
Couple of questions/comments about this that I find interesting since I'm a statistics nerd. First, the NFL salary distribution is truncated due to the salary cap, thus, the true value of a QB could be a lot more than what is shown but the salary cap skews this. Don't think you can correct for this, but it does skew ratings a bit. The second thing, and this is something I think @Lab Rat can account for, can you do this by total POSITION spending. In other words, the average OL salary is 1.76, but since there are 5 OL, the total spending is actually 8.8 making the group as a whole more valuable than the QB. Same thing goes for WR, DB/S, LB, DL. I wonder if this changes the rankings, because having a great OL where you can run all day on the opponents defense can neutralize a sub par QB. This is where the regression might be a lot more accurate.
Did the analysis real quick. Interesting results. Because of the emphasis on DL, OL, LB, and CB/S, it does change the rankings. Don't know if it helped or not. I also had to make some assumptions based on the number of players at some of the positions. Should note that only 8 points separate the top 4 teams which is impressive when the total is 348 points.

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This is quite interesting and I understand the logic for weighing each position group by the number of players within that group. Like you said though, without doing a proper regression, imperfect assumptions are inevitable. My assumptions were admittedly imperfect.

Nevertheless, I'm suspicious that your system undervalues the QB position. The system I used assumes that the QB position as a group is more important than any other position group, regardless of the number of starting players within that group. One justification for this assumption is that the QB touches the ball on every offensive play, and thus has more control of the outcome of that play than any other position group. It also seems most practical to me, based on empirical evidence.

For instance, I think it's fair to say that the value of Texas' OL and RBs over the past few seasons has been wasted on poor QB play. Last year, Texas had a 1st team All Big 12 RB (Foreman), a 1st team All Big 12 OL (Williams), and a 2nd team All Big 12 OL (Perkins). But, they ranked 6th in points per game, 7th in the Big 12 in total offensive yards, and 8th in offensive yards per play. Those mediocre numbers can be largely attributed to their poor QBs.

There really isn't a right answer here, but I would be very suspicious of any ranking system that puts Texas as #1 in the Big 12.
 

Duke Silver

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#35
Nothing kicks ass like a data fight based on some sportswriter opinions of teams he hasn't watched.
 
Nov 28, 2008
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#38
agree on regression for parameter (weights) determination--although that may be fraught with probs too but it's probably the best option available.

I don't think the weights by unit is the best way, i.e., OL is not 8.8 simply due to the sum of salaries. QB is a "unit" in the schema as is OL, CB. So the unit of observation matters (no pun intended).
 

sc5mu93

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#39
I sat down and looked at these numbers and I struggled. But then I picked up my stat book from college as well as a programming book, and I came up with a model which I think accurately breaks down the season with a standard deviation of like 4.

So to join this cripple fight, here is my chart.

Okstate 2017 chart.jpg



This thread really needs moar chartz.
 
Jul 15, 2004
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#40
My guess is that you're right on the weighting. It would take some serious number crunching to weight it, though.

My hunch is that special teams tend to be underrated. A TD on special teams counts as much as a TD made by the offense. A punt that lands inside the 20, followed by a drive that stalls at the 30 instead of the 20, followed by a missed FG, can swing a close game.

And magical seasons are made or broken in close games. See 2011.

I haven't seen a team with no QB that could control the game through special teams. Conversely, I have seen excellent teams lose through poor special teams play.

I'd like to see where the numbers fall.
A stellar qb is no good if under duress the whole game. Look at how Pitt rattled MR - didn't set his feet and threw a ton of high balls. The old saying is it begins in the trenches on both sides.

oSu