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Understanding analytics


dudacek

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3 hours ago, GASabresIUFAN said:

Losing back to back games 3-0 = bad team.  No analytics needed.

Sadly there are still those who are convinced this team is just about to turn the corner when their “puck luck” and shooting % improve and also that RK is a decent coach. Been reading that crap for weeks now. Wonder when people will finally realize this is 30/31st place team. 

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On 2/28/2021 at 12:31 PM, SwampD said:

Absolutely.

I love the expected goal stat, but even that has to be taken in context. It’s a league wide average-of the shots taken from here, X% go in the net. But if it’s Crosby or McDavid taking the shot from here against Hutton, it’s a much higher expected goal than if it’s Eakin shooting on Price.

Yep. They have their place, but I wouldn’t miss them if they went away.

Given what I've briefly seen about the data that xGF% is scraped & compiled from, it would be theoretically possible to incorporate this in the stat. Each shot is catologed with the type, skater who took it, and geographic coordinates, among other things. You could scan every game to find all shots Crosby has taken within a reasonable diameter of that location (5 feet?), use HIS shooting percentage historically from that region (weighted average of some sort) in your team's xGA calculation from the game when he takes a shot there, and do that for each player that takes a shot against you, keeping an updated database on that player's mesh grid of career shooting percentages (rolling 5 year average might be good). Default to league average if some threshold for shot quantity in the region isn't met. 

Just imagining how I'd go about doing that as a novice with data/statistics is dizzying, but I don't see why it wouldn't be possible to do. 

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25 minutes ago, Randall Flagg said:

Given what I've briefly seen about the data that xGF% is scraped & compiled from, it would be theoretically possible to incorporate this in the stat. Each shot is catologed with the type, skater who took it, and geographic coordinates, among other things. You could scan every game to find all shots Crosby has taken within a reasonable diameter of that location (5 feet?), use HIS shooting percentage historically from that region (weighted average of some sort) in your team's xGA calculation from the game when he takes a shot there, and do that for each player that takes a shot against you, keeping an updated database on that player's mesh grid of career shooting percentages (rolling 5 year average might be good). Default to league average if some threshold for shot quantity in the region isn't met. 

Just imagining how I'd go about doing that as a novice with data/statistics is dizzying, but I don't see why it wouldn't be possible to do. 

Absolutely, and that is my point. It’s all possible. It’s just not happening yet.

Even a good stat that I like is woefully incomplete.

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The Hockey Statistics page is for your reference.  If you want to add, please do.

The examples of different adjustments to +/- are in the thread https://www.sabrespace.com/community/topic/27067-adjusted-plus-minus-originals/?tab=comments#comment-1183230

The comments that the stats are incomplete are 100% accurate.  That's why I like integrating different stats into my analysis of players.

 

 

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weekly TRpm update...

[18:50:27] pi2000@sfo-lvswy:~$ estoi_trpm.py BUF
==============================================================
BUF even strength goal differential through 23 games = -33
BUF even strength minutes played through 23 games = 1213.77
exp+/- is a function of %min played at ES and team ES goal diff
TRpm(team relative +/-) is the diff btwn actual+/- and exp+/-
%min is % of ES minutes played
==============================================================
Team  Player                   TRpm    GP    +/-  exp+/-  %min
BUF Jake McCabe                7.89    13     2   -5.89   17.8
BUF Riley Sheahan              5.01    22    -1   -6.01   18.2
BUF Curtis Lazar               3.98    21    -2   -5.98   18.1
BUF Jacob Bryson               3.29     7     0   -3.29   10.0
BUF Rasmus Asplund             3.04     4     2   -1.04    3.1
BUF William Borgen             1.66     4     0   -1.66    5.0
BUF Jack Eichel                0.68    21    -9   -9.68   29.3
BUF Jeff Skinner               0.63    20    -6   -6.63   20.1
BUF Henri Jokiharju            0.42    17    -7   -7.42   22.5
BUF Rasmus Ristolainen         0.21    16    -8   -8.21   24.9
BUF Tobias Rieder              0.11    21    -6   -6.11   18.5
BUF Dylan Cozens               0.03    18    -6   -6.03   18.3
BUF Tage Thompson             -0.12     9    -3   -2.88    8.7
BUF Brandon Montour           -0.26    21   -11  -10.74   32.5
BUF Cody Eakin                -0.65    23    -8   -7.35   22.3
BUF Casey Mittelstadt         -0.65     8    -3   -2.35    7.1
BUF Kyle Okposo               -1.07    18    -7   -5.93   18.0
BUF Matt Irwin                -1.13    11    -6   -4.87   14.7
BUF Brandon Davidson          -1.49     4    -3   -1.51    4.6
BUF Sam Reinhart              -2.50    21   -12   -9.50   28.8
BUF Colin Miller              -2.97    22   -13  -10.03   30.4
BUF Taylor Hall               -3.07    23   -13   -9.93   30.1
BUF Victor Olofsson           -3.83    23   -13   -9.17   27.8
BUF Eric Staal                -6.22    23   -15   -8.78   26.6
BUF Rasmus Dahlin             -9.89    23   -21  -11.11   33.7
==============================================================
[18:53:54] pi2000@sfo-lvswy:~$

 

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