With all the talk about PDO, and given the time of year I figured it would be appropriate to show the final results from my work on traditional stats (G, Ast, Pts, PIM) reliability. As has been discussed a lot lately PDO numbers tend to stabilize for teams around 1000. This begs the question, what stats can we use for individual players? When are they reliable? Here's a nice chart after the jump for you to ponder. (ref early SSW work for methods here, here, and here)
Basically the second column on the left is the number of games a player must play before we can say with reasonable confidence that the stat in the row (traditional counting stat) is significant (ie. probably accurate)
For Forwards;
| Stat | Games when alpha > 0.707 | 95% Confidence Interval | alpha at 120 games |
| Goals | 40 | 27-51 | 0.860 |
| Assists | 25 | 18-34 | 0.911 |
| Points | 14 | 10-19 | 0.938 |
| +/- | n/a | n/a | 0.447 |
| PIM | 36 | 29-47 | 0.907 |
For Defensmen
| Stat | Games when alpha > 0.707 | 95% Confidence Interval | alpha at 120 games |
| Goals | 77 | 60-114 | 0.777 |
| Assists | 38 | 31-47 | 0.891 |
| Points | 31 | 21-36 | 0.918 |
| +/- | n/a | n/a | 0.449 |
| PIM | 69 | 52-107 | 0.786 |
Alpha here is cronbach's alpha which is generated from a statistical formula that calculates the reliability of a test. It's basically a fanchy-schmancy correlation coefficient. If you want you could just take it as the r at the games specified. Ref early work on how these numbers can be used to calculate regression statistics.
My intention was to compute these for advanced stats as well, but it will be a lot more time consuming. I may or may not do it, time permitting. If you really want to see those numbers I could explain how, and let you crunch the numbers.
Contact me for the full spreadsheet
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