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[Pro Golf] Topic: Heritage Discussion

blairaj

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Posted: 4/10/2012 2:18:05 AM

Been working on some new statistical correlations to determine key stats for the week. For right now I just want to see how well I can copy and paste this, if it works, tomorrow I'll explain the numbers and see if I can run some other years too. This is just based on last year's tourney.

0.60

0.31	1
0.56	0.33	2
0.55	0.30	3
0.52	0.44	4
0.45	0.25	5
ppr	0.45	0.18	6
0.37	0.12	7
0.34	0.14	8
-0.02	0.00	9

quote

blairaj

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Posted: 4/10/2012 2:20:05 AM

Well garbage. It didn't work at all. Anybody have any tips how can cut and paste a small block out of spreadsheet and put in here? It's only 10 rows by 4 colums, so there's plenty of room.

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blairaj

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Posted: 4/10/2012 2:29:43 AM

Looks like I can paste them into my covers blog. I'll use that, I'll also put some explanation up there tomorrow.

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MrBogey

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Posted: 4/10/2012 3:34:43 AM

confused as garbage here sir

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Bluefin

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Posted: 4/10/2012 8:34:18 AM

good luck bud hopefully you'll still have some hair left by the time you get it..

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Chus

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Posted: 4/10/2012 2:04:36 PM

cut and paste into notepad first. then cut and paste into your reply

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blairaj

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Posted: 4/10/2012 7:06:05 PM

QUOTE Originally Posted by Bluefin:

good luck bud hopefully you'll still have some hair left by the time you get it..

Haha Blue. I'm already bald as cue ball!!!

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blairaj

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Posted: 4/10/2012 7:08:12 PM

Slope R2 Rank

par4 0.60 0.31 1

sc 0.56 0.33 2

gir 0.55 0.30 3

par5 0.52 0.44 4

pa 0.45 0.25 5

ppr 0.45 0.18 6

par3 0.37 0.12 7

da 0.34 0.14 8

dd -0.02 0.00 9

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blairaj

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Posted: 4/10/2012 7:20:16 PM

Ok, So how this works:

1) I've used ALL scores from the first 2 rounds, including players that have missed the cut. This is different from just about any other site that I've seen doing statistical correlations between key stats and score - many of them even only use the top 5 or 10 or at best all players that make the cut. We've discussed in the past that it's often more effective to find "fades" than "fors", so I think it's important to have the bottom end of the correlations in there.

2) First thing I do is normalize all scores and stats to be between 0 and 1. 0=sucks, 1=the best

3) Then, the numbers in the table above are a simple linear regression between the Y (score) and the X (different stats).

Slope= the slope of the regression line. The closer this value is to 1, the better indicator that stat is of a players score.

R2= this is R-squared. A pure statistician probably wouldn't like my simplification, but you can think of it as the confidence you have in the slope number. R2 also has a max of 1. In other words, the closer it is to 1, the better the data fits the regression line or the fewer "outliers" we have.

The first column is the different stats:

sc=scrambling

gir=greens in reg

pa=putting avg

ppr=putts per green in reg

da=driving accuracy

dd=driving distance

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blairaj

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#10

Posted: 4/10/2012 8:18:22 PM

I added a couple more stats: BA=bogey avoidance and PB=par breakers(birdies+eagles).

Slope R2 Rank

BA 0.75 0.59 1

par4 0.60 0.31 2

sc 0.56 0.33 3

gir 0.55 0.30 4

par5 0.52 0.44 5

PB 0.51 0.43 6

pa 0.45 0.25 7

ppr 0.45 0.18 8

par3 0.37 0.12 9

da 0.34 0.14 10

dd -0.02 0.00 11

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blairaj

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#11

Posted: 4/10/2012 8:35:31 PM

To help get a better feeling for the impact of these numbers, I ran this year's masters through the spreadsheet. I think you'll find that the numbers are telling you what you're gut probably felt and this should serve as a good gage going forward to tournaments when maybe we don't have such a good gut feel for it:

Slope R2 Rank

gir 0.97 0.45 1

BA 0.91 0.67 2

PB 0.79 0.60 3

pa 0.67 0.41 4

par5 0.65 0.51 5

dd 0.58 0.20 6

sc 0.53 0.24 7

par4 0.52 0.19 8

ppr 0.35 0.12 9

par3 0.31 0.08 10

da -0.04 0.00 11