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08-23-2011, 04:08 PM
  #189
seventieslord
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Quote:
Originally Posted by plusandminus View Post
Thanks for asking. I compare only with his team's totals.

Below are what it looks like. I have only included players with GF+GA >= 20. (Expect even larger differences for the other players.)
I think the raw data I assembled and corrected should be near 100 % correct.
The rightmost column shows GF+GA per 60 minutes. I have divided all those values by league average, to make it easier to compare.

Highest:
TeamNameposESTOIEStoi%ESGFGA%diffESGFGAnormESGFGAper60min
CARBRUNO ST. JACQUES D2970.0810.1380.057351.577
CHIBURKE HENRY D2500.0650.0940.029291.552
ATLILYA KOVALCHUK F11530.3050.3790.0741301.509
NYRREM MURRAY F3980.1050.1550.050441.479
EDMMIKE COMRIE F9100.2410.3280.0861001.470
CHIERIC DAZE F7220.1880.2570.070791.464
LA ADAM DEADMARSH F2650.0710.1060.035291.464
COLPETER FORSBERG F10910.2880.4190.1311191.459
BOSJOE THORNTON F12850.3400.4330.0931401.458
ATLKIRILL SAFRONOV D4140.1100.1310.022451.454
TORALEXANDER MOGILNY F9430.2580.3480.0901021.447
TORKAREL PILAR D2450.0670.0890.022261.420
BOSGLEN MURRAY F14000.3710.4580.0871481.414
ATLDANY HEATLEY F11640.3080.3590.0511231.414
NYIROMAN HAMRLIK D12810.3550.4600.1061341.400
PITMARIO LEMIEUX F10820.2860.3870.1011131.397
MTLNIKLAS SUNDSTROM F3840.0980.1290.031401.394
PHOLANDON WILSON F3280.0890.1250.036341.387
SJ NICHOLAS DIMITRAKOS F2440.0650.0890.023251.371
BOSMIKE KNUBLE F10610.2810.3340.0531081.362

Lowest:
TeamNameposESTOIEStoi%ESGFGA%diffESGFGAnormESGFGAper60min
WASBRIAN SUTHERBY F5540.1460.096-0.05270.652
PITIAN MORAN F10530.2780.175-0.10510.648
CGYSTEVE BEGIN F4360.1180.078-0.04210.644
STLTYSON NASH F5320.1420.087-0.05250.629
TORRICHARD JACKMAN D5350.1460.085-0.06250.625
FLAIGOR ULANOV D8230.2150.146-0.07380.618
MINJASON MARSHALL F4620.1190.084-0.04210.608
TB ALEXANDER SVITOV F5110.1320.083-0.05230.602
CARJAROSLAV SVOBODA F5490.1500.095-0.05240.585
TORPAUL HEALEY F4580.1250.068-0.06200.584
BUFVACLAV VARADA F5250.1390.082-0.06220.561
CGYSTEVE MONTADOR D6270.1700.096-0.07260.555
STLSHJON PODEIN F5400.1440.073-0.07210.520
COLSERGE AUBIN F7000.1850.095-0.09270.516
PITIAN MORAN D10530.2780.134-0.14390.496

Biggest differences between real ES%, and ES% estimated by GF+GA:
TeamNameposESTOIEStoi%ESGFGA%diffESGFGAnormESGFGAper60min
PITIAN MORAN D10530.2780.134-0.14390.496
COLPETER FORSBERG F10910.2880.4190.1311191.459
NYIMATTIAS TIMANDER D11590.3210.213-0.11620.716
NYIROMAN HAMRLIK D12810.3550.4600.1061341.400
PITMARIO LEMIEUX F10820.2860.3870.1011131.397
PHOOSSI VAANANEN D10560.2870.192-0.10520.659
PITIAN MORAN F10530.2780.175-0.10510.648
STLPAVOL DEMITRA F11510.3080.4060.0981161.348
TB VINCENT LECAVALIER F11340.2930.3900.0971081.274
BOSJOE THORNTON F12850.3400.4330.0931401.458
WASSERGEI GONCHAR D15840.4170.5090.0921431.208
TORALEXANDER MOGILNY F9430.2580.3480.0901021.447
CGYSTEPHANE YELLE F10540.2850.200-0.09540.686
COLSERGE AUBIN F7000.1850.095-0.09270.516
BOSGLEN MURRAY F14000.3710.4580.0871481.414
EDMMIKE COMRIE F9100.2410.3280.0861001.470
COLMILAN HEJDUK F12190.3220.4050.0831151.262
TB MARTIN ST. LOUIS F11230.2900.3720.0821031.227
FLAOLLI JOKINEN F11680.3050.3850.0801001.146
BOSSEAN O'DONNELL D11500.3050.229-0.08740.861
VANTODD BERTUZZI F12230.3340.4130.0791171.280
DETMATHIEU DANDENAULT D12030.3140.3920.0781221.357
PHOPAUL MARA D11290.3070.3840.0771041.233
FLAIVAN NOVOSELTSEV F9600.2500.3270.077851.185
MINMARIAN GABORIK F10780.2780.3550.077891.105
ATLILYA KOVALCHUK F11530.3050.3790.0741301.509


This makes, in my opinion, GF+GA a bit unreliable when using it to determine ice time. Maybe there are steps involved in the calculations that takes care of that, so that it really doesn't matter that much. But I felt a need to point it out.
To be honest, I think this reveals how effective a model that uses GF/GA can be.

they actaually do include an adjustment based on the line or pairing the player was on, because as you can see, goals are more frequent on a per-minute basis when the higher minute players are out there. So this will partially mitigate the effect that you're seeing, an effect that is pretty small to begin with.

I mean, if you want to try to approximate icetime across the board from 1967-onwards, be my guest. But can you improve on what exists enough that it is worth the time it would take?

As for Kovalchuk, considering two other Thrashers are in your top-20, he doesn't appear to really be that much of an outlier on his team. He just barely makes the bottom list that you provided, and only because it is based on a "raw" difference and not a percentage difference (if it was based on that, he would not be anywhere near the top)

Quote:
I agree. Unfortunately, the lower numbers the more "extreme" results. And the higher numbers, the less "extreme" results. But problem is that that rule continues to be true even for high numbers. The 40-13 case will still be slightly more vulnerable than the 72-50 case (I think, but that case it might be negligable).

Anyway, I don't like dividing GF by GA, because to me it only makes sense when
GF+GA is equally big for every player.
(But maybe there is something built in in overpass' method, that takes care of that.)

I look at it like this:
A: Player being 40-20 actually helped his team improve by +20.
B: Player being 30-15 actually helped his team improve by +15.
Both have the same GF/GA (2.0). But I think what matters is GF-GA.
Let's say B was 30-12 instead, raising his GF/GA to 2.5. That may make him look like a better player when looking at GF/GA. But he would have helped his team improve by +18.
During the whole season, I think a player's goal is to help their team improve by as many goals as possible, by getting as good GF-GA as possible.
Keep in mind that generally when we talk about r-on and r-ff we are talking about very large sample sizes - seasons at the very least, and generally blocks of seasons, or careers. Like you, I don't see any value in the micro aspect, these 10-20-game segments and single game scenarios you are providing.

overpass, as usual, did a much better job than I could have done, fuddling my way through descriptions of statistics in "normal people" words and terms.

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