Skater Influence on Shot Location in the NHL 2017... · Shot Locations How much of what happens on...
Transcript of Skater Influence on Shot Location in the NHL 2017... · Shot Locations How much of what happens on...
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Skater Influence on Shot Location in the NHL
Micah Blake McCurdyhockeyviz.com
Stevens Point, WIGreat Lakes Analytics in Sports Conference
July 13, 2017
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Shot Locations
How much of what happens on the ice in a hockey game is underthe control of an individual player?
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Shot Locations
How much of what happens on the ice in a hockey game is underthe control of an individual player?
I Isolate player impact on shot location and frequency.
I Consolidate impact into convenient units.
I Measure correlation from season to season.
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Least-Squares Regression
I α a collection of observations
I X a design matrix
I β a collection of (imagined) individual isolated impacts
Xβ = α
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Least-Squares Regression
I α a collection of observations
I X a design matrix
I β a collection of (imagined) individual isolated impacts
Xβ = α
XTXβ = XTα
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Least-Squares Regression
I α a collection of observations
I X a design matrix
I β a collection of (imagined) individual isolated impacts
Xβ = α
XTXβ = XTα
β = (XTX )−1XTα
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Least-Squares Regression
I α a collection of observations
I X a design matrix
I β a collection of (imagined) individual isolated impacts
Xβ = α
XTXβ = XTα
β = (XTX )−1XTα
Elements of α and β can be taken from any inner product spaceand the usual proof goes through.
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Observations - Ottawa with Erik Karlsson
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Closed form solutions
Usually, we compute (XTX )−1XT numerically, with computers.However, in our case, we can work it out by hand:
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Closed form solutions
Usually, we compute (XTX )−1XT numerically, with computers.However, in our case, we can work it out by hand:
βp = 5(α with p) − 4(α without p)
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Closed form solutions
Usually, we compute (XTX )−1XT numerically, with computers.However, in our case, we can work it out by hand:
βp = 5(α with p) − 4(α without p)
βp(NZ ) βp(OZ ) βp(DZ ) βp(OTF )
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Closed form solutions
Usually, we compute (XTX )−1XT numerically, with computers.However, in our case, we can work it out by hand:
βp = 5(α with p) − 4(α without p)
β′p = 0.17βp(NZ ) + 0.13βp(OZ ) + 0.11βp(DZ ) + 0.59βp(OTF )
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Erik Karlsson (attacking)
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Erik Karlsson (defending)
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Erik Karlsson (isolated)
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Threat
To compare different players we weight their isolated shotcontributions according to league average shooting percentagesfrom given locations to obtain threat.
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Threat
To compare different players we weight their isolated shotcontributions according to league average shooting percentagesfrom given locations to obtain threat.
I Expected Goals per hour, purely from individual impact onshot locations.
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Threat
To compare different players we weight their isolated shotcontributions according to league average shooting percentagesfrom given locations to obtain threat.
I Expected Goals per hour, purely from individual impact onshot locations.
For Karlsson this season:
I Threat For: +2.4 goals per hour
I Threat Against: +1.3 goals per hour
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Threat
To compare different players we weight their isolated shotcontributions according to league average shooting percentagesfrom given locations to obtain threat.
I Expected Goals per hour, purely from individual impact onshot locations.
For Karlsson this season:
I Threat For: +2.4 goals per hour (Actual: 2.4)
I Threat Against: +1.3 goals per hour (Actual: 2.1)
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Correlations - Offensive Threat Created
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Correlations - Defensive Threat Allowed
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Correlations - Net Threat
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Conclusions
I Players have small but noticeable individual effects on shotlocations.
I Effects are strongest when net threat is considered.
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Future Work
I Isolate interactions with goaltending metrics.
I Take into account shooter quality.
I Consider quality-of-competition and score state.
I Isolate coaching impacts.
I Consider non-linear effects.
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Thanks!