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.
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.
Well shit. 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.
Well shit. 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.
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.
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.
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:
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:
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