here - MIT Sloan Sports Analytics Conference

“Win at Home and Draw Away”:
Automa5c Forma5on Analysis Highligh5ng the Differences in Home and Away Team Behaviors
Alina Bialkowski, Patrick Lucey, Peter Carr, Yisong Yue and Iain Ma;hews
!"#$%&'(")*+$,-$.")+$('%#$'&#$(&,/)0#0$1,&$)-1,&2'.),-$(3&(,+#+$'+$4#55$'
!"#$7'5.$8)+-#6$9,2('-6$9,&(,&'.#$:0#-.).6$;&,%&'2<$=#)."#&$."#$#-.)&#
! D E $ 7 B F! $ 8 : G = E H $ 9 I J ;B = H $ K L B = 8 $ J B = B M E J E = !
8 B! E $ 9 L E B! E 8 N $
9 L E B! E 8 $ K H N $
$
OPQRSR
J8$
&'()*+,
!"#$%
%&'(")*+$*,-.')-#0$)-$.")+$;&,%&'2>$2'6$?#$&#.,3*"#0$,&$2'-)(35'.#0$)-$
,1$."#$;&,%&'2<$!")+$&#+.&)*.),-$)-*530#+$'-6$*,5,&>$+)@#$,&$+('*#$2'-)(3
."#$&#5'.),-+")($,1$'-6$,1$."#$#5#2#-.+$.,$#'*"$,."#&<$!"#$)2'%#+$'((#'&)7'5.$8)+-#6$9,2('-6<$B-6$3+#$,1$.")+$/)+3'5$)-1,&2'.),-$,."#&$."'-$'+$#C
T$8 ) + - # 6
Research, Pittsburgh
Individual Analy?cs
CVPR 2014 Submission #1090. CONFIDENTIAL REVIEW COPY. DO NOT DISTRIBUTE.
eled the shot chart. In contr
shots may happen within th
appears to lead to a blurring
two factors have coefficient
than the short-range shot fac
the fourth factor could be d
basket. In our modeling appr
spond to a near-term shootin
our approach; it may be mor
Figure 7: Top Row: Depict
location. Bottom Row: Def
See Figure 8 for the affiniti
Sports Analy5cs
Team A
naly?cs
#1090
CVPR
?
486
487
488
489
490
491
492
493
PG
494
PG
495
496
4-­‐4-­‐2
SF
es of our prediction results. Top: Examples of ball owner prediction in soccer. Black trajectories indicate the past passing
last four time steps, yellow circle shows the predicted ball owner while blue circle shows the ground truth. Bottom:
SF
C
497
498
499
C
500
501
502
4-­‐2-­‐3-­‐1
SG
SG
PF
PF
Team Analy5cs in Con5nuous Sports
Analysis is very difficult:
• Continuous and lowscoring
1) What formation they are playing?
• Time-varying
2) How they are playing? (sitting
back or pressing)
• Annotating tactics and
playing style is subjective
• Dynamic and complex
environment
What we want:
How to Compare Two Plays?
x =
x =
Play2
Play1
Sterling
Sturridge
Cou=nho
Henderson
Gerrard
Cissoko
Toure
Skrtel
Mignolet
Play 1
Suarez
Flanagan
Sterling
Sturridge
Cou=nho
Henderson
Gerrard
Cissoko
10 players have
Toure
Skrtel
3,628,800 permutations
Mignolet
Play 2
Suarez
Flanagan
x,y
ball
x,y
ball
x,y
Mignolet
x,y
Mignolet
x,y
Cissoko
x,y
Cissoko
x,y
Toure
x,y
Toure
x,y
Flanagan
x,y
Flanagan
x,y
Gerrard
-­‐
x,y
Gerrard
x,y
Coutinho
x,y
Coutinho
x,y
Henderson
x,y
Henderson
x,y
Sterling
x,y
Sterling
x,y
Sturridge
x,y
Sturridge
x,y
Suarez
x,y
Suarez
0
0
0
0
0
0
= 0
0
0
0
100
0
0
100
0
Subs5tu5ons and Role-­‐Swaps
x =
x =
Play2
Play1
Sterling
Sturridge
Cou=nho
Henderson
Gerrard
Cissoko
Toure
Skrtel
Mignolet
Play 1
Suarez
Sturridge
Suarez
Flanagan
Cou=nho
Allen
x,y
Allen
Henderson
Gerrard
Cissoko
Toure
Skrtel
Mignolet
Play 2
Sterling
Flanagan
x,y
ball
x,y
ball
x,y
Mignolet
x,y
Mignolet
x,y
Cissoko
x,y
Cissoko
x,y
Toure
x,y
Toure
x,y
Flanagan
x,y
Flanagan
x,y
Gerrard
-­‐
x,y
Gerrard
x,y
Coutinho
x,y
Coutinho
x,y
Henderson
x,y
Henderson
x,y
Sterling
x,y
Suarez
x,y
Sturridge
x,y
Sturridge
x,y
Suarez
x,y
Sterling
0
0
0
0
0
0
= 0
0
0
80
100
0
60
Use Role Representa5on
x =
x =
Play2
Play1
ST
LW
ST
LW
ACM
RW
ACM
LCM
LCM
RCM
LB
RCM
RB
LCB
GK
Play 1
RCB
RW
LB
RB
LCB
RCB
GK
Play 2
x,y
ball
x,y
ball
x,y
Mignolet
GK
x,y
Mignolet
GK
x,y
Cissoko
LB
x,y
Cissoko
LB
x,y
Toure
LCB
x,y
Toure
LCB
x,y
Flanagan
RB
x,y
Flanagan
RB
x,y
Gerrard
LCM
-­‐
x,y
Gerrard
LCM
x,y
Coutinho
RCM
x,y
Coutinho
RCM
x,y
Henderson
ACM
x,y
Henderson
ACM
x,y
Sterling
LW
x,y
Suarez
LW
x,y
Sturridge
ST
x,y
Sturridge
ST
x,y
Suarez
RW
x,y
Sterling
RW
0
0
0
0
0
0
= 0
0
0
0
100
0
0
100
0
Automa5cally Finding Forma5ons from Data
•
•
Goal: Find Forma5ons Directly From Tracking Data (en$re season from Prozone)
Solu5on: A forma=on is a set of roles of a team
Need to find the set of roles which have minimum overlap
• Applica5ons: •
•
Allows us to do global team behavior analysis -­‐> Case Study: Home Advantage
•
We can then assign a player a role at each frame -­‐ > retrieval
EM Algorithm for Finding Roles
Procedure:
1) Choose ini?al ordering of player posi?ons 2) Ini?alize set of roles: Find mean of player posi?ons
3) Assign a role to each player in each frame according to ini?al set of roles
4) Re-­‐calculate the mean of updated roles
5) Calculate the change in means
6) If change > threshold, con?nue steps 3-­‐5 un?l convergence Examples
Iden5ty vs Role
Match Summariza5on
Case Study: Home Advantage
Home Field Advantage
Sport
Soccer
% won of home
games
Sport
MLS
69.1%
Basketball
Serie A (Italy)
League
League
% won of home
games
NCAA (college)
69.1%
67.0%
NBA
62.7%
Central America
65.2%
WNBA
61.7%
La Liga (Spain)
65.0%
Cricket
International cricket
60.1%
South America
63.6%
Hockey
NHL
59.0%
EPL (UK)
63.1%
Football
NCAA (college)
63.1%
Europe
61.0%
NFL
57.6%
Asia/Africa
60.0%
MLB
54.1%
Baseball
Moskowitz and Wertheim (2011), “Scorecas=ng”
Case Study: Win at Home and Draw Away
•
•
•
Used an en?re season of Prozone data which had player tracking data for an en?re season (home and away matches)
Captured player posi?ons at 10fps and also had ball event data
Every ball event with ?me-­‐
stamps and loca?on
Home
Away
p-value
1.61 per game
1.10 per game
p < 0.0001
Shots
15.42 per game 12.18per game
p < 0.0001
Goals
1.57 per game
1.21 per game
p < 0.0001
Passing
451 per game
436 per game
p = 0.1483
Shooting Accuracy
(On vs Off-Target)
41.7%
42.4%
p = 0.5046
Points
(3=win, 1=draw, 0=loss)
Forward-­‐Third Bias
A
B
C
D
E
−5
−5
xx1010
55
44
F
G
H
I
J
Home 2214.1%
33
11
K
L
M
N
O
Away
00
11.8%
−1−1
p<0.0001
−2−2
P
Q
R
S
T
−3−3
−4−4
−5−5
Forma5ons Across the Season
Team A Home Games
Team A Away Games
Forma5ons Across the Season
Home = Red
Away = Blue
Overall Team Movement
Team A All Games -­‐ Unnormalized
Overall Team Posi5on
When the team has possession
!
When the opposi=on has possession
Summary: Home Advantage
•
•
•
•
•
•
Big discrepancy in outcomes between home and away performances
More shots, more goals, same passing and shoo?ng proficiency
Heavy possession bias in the forward third
Teams tend to play the same forma?on
At home they press higher up the field
Can our analysis be used for other team sports?
Analyzing Team Behaviors in Other Sports
Measure Team Behavior in Basketball
Using 2012-­‐2013 STATS SportsVU NBA data, we analyzed team behavior for 3-­‐point shots
• Open shots correlated with more defensive movement and defensive role-­‐swaps
•
•
Significant for all 13 teams analyzed
Po
ste
r
!!!
Take Home Points
1) Team Behaviors are difficult to measure due to problems of permuta?ons
2) Using roles to represent a forma?on nullifies this problem
3) We use forma?on analysis to inves?gate the home advantage
•
More possession forward third, press higher up the pitch, li;le difference in forma?on
4) Our approach can be used for analysis of other team sports and for tasks like retrieval