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Spectral Analysis of NBA Data Ran Cui Department of Mathematics University of Maryland Spotlight Talk, Apr 2016 Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 1 / 12

Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

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Page 1: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

Spectral Analysis of NBA Data

Ran Cui

Department of MathematicsUniversity of Maryland

Spotlight Talk, Apr 2016

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 1 / 12

Page 2: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

Q: How to quantify player contribution to team success?

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 2 / 12

Page 3: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

Basketball is a team sport!

What is a better playerevaluation approach?Adjusted Plus-Minus (APM)

Why stop at individual playerevaluation? What about groupevaluation?Representation Theory!

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 3 / 12

Page 4: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

Basketball is a team sport!

What is a better playerevaluation approach?Adjusted Plus-Minus (APM)

Why stop at individual playerevaluation? What about groupevaluation?Representation Theory!

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 3 / 12

Page 5: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

Basketball is a team sport!

What is a better playerevaluation approach?Adjusted Plus-Minus (APM)

Why stop at individual playerevaluation? What about groupevaluation?Representation Theory!

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 3 / 12

Page 6: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

Overview

1 How It Works

2 Golden State Warriors

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 4 / 12

Page 7: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

How It Works

Idea: Borrow techniques from algebraic signal processing. We decomposea team success signal into the contributions of player groups of all sizes,

from individuals through full five player lineups.

Some Notations

Average 15 players on team roster, number them {1, 2, · · · , 15}.Pick 5 players as a lineup L = {2, 11, 7, 9, 1}.Let X be the set of all possible lineups (unordered set of 5).

Let V = {h : X → R}.

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 5 / 12

Page 8: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

How It Works

Idea: Borrow techniques from algebraic signal processing. We decomposea team success signal into the contributions of player groups of all sizes,

from individuals through full five player lineups.

Some Notations

Average 15 players on team roster, number them {1, 2, · · · , 15}.Pick 5 players as a lineup L = {2, 11, 7, 9, 1}.Let X be the set of all possible lineups (unordered set of 5).

Let V = {h : X → R}.

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 5 / 12

Page 9: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

The Representation

X = {L | L is a lineup }.V = {h : X → R}

3003 dimensional real vector space with inner product

〈h, k〉 =1

|X |∑x∈X

h(x)k(x)

The representation

S15

V

(σ · h)(x) = h(σ−1 · x)

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 6 / 12

Page 10: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

The Representation

X = {L | L is a lineup }.V = {h : X → R}3003 dimensional real vector space with inner product

〈h, k〉 =1

|X |∑x∈X

h(x)k(x)

The representation

S15

V

(σ · h)(x) = h(σ−1 · x)

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 6 / 12

Page 11: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

The Representation

X = {L | L is a lineup }.V = {h : X → R}3003 dimensional real vector space with inner product

〈h, k〉 =1

|X |∑x∈X

h(x)k(x)

The representation

S15

V

(σ · h)(x) = h(σ−1 · x)

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 6 / 12

Page 12: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

Decompose and Extract

V = V0 ⊕ V1 ⊕ V2 ⊕ V3 ⊕ V4 ⊕ V5

Suppose f = team success function.

f = f0 + f1 + f2 + f3 + f4 + f5

Interpretations:f0: Average value of ff1: Pure 1st order effect. Individual player contribution to f beyond mean.f2: Pure 2nd order effect. Pair players contribution to f ....

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 7 / 12

Page 13: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

Decompose and Extract

V = V0 ⊕ V1 ⊕ V2 ⊕ V3 ⊕ V4 ⊕ V5

Suppose f = team success function.

f = f0 + f1 + f2 + f3 + f4 + f5

Interpretations:f0: Average value of ff1: Pure 1st order effect. Individual player contribution to f beyond mean.f2: Pure 2nd order effect. Pair players contribution to f ....

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 7 / 12

Page 14: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

Decompose and Extract

How to extract certain player group contribution?

Examples

Want to see the 5th order effect of the lineup {2, 11, 9, 7, 1}Indicator function

ι{2,11,9,7,1} := δ{2,11,9,7,1}

Let ι∗{2,11,9,7,1} := (ι{2,11,9,7,1} � V5), take

〈f , ι∗{2,11,9,7,1}〉

What’s the 1st order effect of player 2?Indicator function

ι2 :=∑2∈L

δL

Let ι∗2 := (ι2 � V5), take〈f , ι∗2〉

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 8 / 12

Page 15: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

Decompose and Extract

How to extract certain player group contribution?

Examples

Want to see the 5th order effect of the lineup {2, 11, 9, 7, 1}Indicator function

ι{2,11,9,7,1} := δ{2,11,9,7,1}

Let ι∗{2,11,9,7,1} := (ι{2,11,9,7,1} � V5), take

〈f , ι∗{2,11,9,7,1}〉

What’s the 1st order effect of player 2?Indicator function

ι2 :=∑2∈L

δL

Let ι∗2 := (ι2 � V5), take〈f , ι∗2〉

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 8 / 12

Page 16: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

Overview

1 How It Works

2 Golden State Warriors

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 9 / 12

Page 17: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

ResultsUse 2013-2014 dataDefine team success function f + and team failure function f−

68.68 = 〈f +, ι∗StephenCurry 〉, 33.17 = 〈f −, ι∗KlayThompson〉

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 10 / 12

Page 18: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

ResultsUse 2013-2014 dataDefine team success function f + and team failure function f−

68.68 = 〈f +, ι∗StephenCurry 〉, 33.17 = 〈f −, ι∗KlayThompson〉

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 10 / 12

Page 19: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

ResultsUse 2013-2014 dataDefine team success function f + and team failure function f−

68.68 = 〈f +, ι∗StephenCurry 〉, 33.17 = 〈f −, ι∗KlayThompson〉

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 10 / 12

Page 20: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

ResultsUse 2013-2014 dataDefine team success function f + and team failure function f−

68.68 = 〈f +, ι∗StephenCurry 〉, 33.17 = 〈f −, ι∗KlayThompson〉

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 10 / 12

Page 21: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

Results

Top individuals: Curry, Thompson, Green, Lee, Barnes

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 11 / 12

Page 22: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

Results

Top individuals: Curry, Thompson, Green, Lee, Barnes

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 11 / 12

Page 23: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

Results

Top individuals: Curry, Thompson, Green, Lee, Barnes

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 11 / 12

Page 24: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 12 / 12

Page 25: Spectral Analysis of NBA Data - GitHub Pages · Use 2013-2014 data De ne team success function f+ and team failure function f 68:68 = hf+; StephenCurry i; 33:17 = hf ; KlayThompson

Ran Cui (University of Maryland) Spectral Analysis of NBA Data Spotlight Talk, Apr 2016 12 / 12