total hockey rating (thor) - mit sloan sports analytics... · pdf filemiss wrist off 0.0159...
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Total Hockey Rating (THoR): A comprehensive statistical rating of National Hockey
League forwards and defensemen based upon
© 2013 Michael Schuckers & James Curro
St. Lawrence University, Statistical Sports Consulting LLC
& Iowa State University
Introduction
• Hockey Metrics
– Traditional
• +/-, Pts
– Advanced
• CorsiRel (Desjardins), GVT (Awad), DeltaSOT (Awad),
• Expected Goals +/- (Macdonald)
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Goals
• THoR
– Value every play on the ice
– Account for Teammates (QoT)
– Account for Opponents (QoC)
– Account for Zone Starts (ZS)
– Account for Home Ice
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Data
Schuckers&Curro (c) 2013 4
• Real Time Scoring System (RTSS)
– NHL generated
– Recording (Shots, Goals, Misses, Blocked Shots, Hits, Faceoffs, End of Period, Stoppage)
– Players on the ice for both teams
– Shot Location x,y coordinates
Data
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• Real Time Scoring System (RTSS)
– Rich, not great quality
– Known issues with X,Y coordinates (esp. MSG)
– Giveaways and Takeaways Biased
– Questions about Faceoffs (~5%), Hits
– Rink to rink variability
Adjustments
CDF Adjustment for X, Y coordinates
Start with shot recorded at X-coord t at rink R,
Then
t’= FX-1( FR(t) – (FRA(t)-FA(t) )),
FX is cdf for all X coordinates
FR is the cdf’s for x coordinates for rink R
FRA is the cdf’s for all shots taken by the away
FA is the cdf’s of x for all away shots.
Kept discreteness of X’s
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Adjustments
• Takeaways/Giveaways
– Combined into turnovers (TURN)
• Remove non-action events leaving
– GOAL, SHOT, MISS, BLOCK, HIT, FACE, PENL, TURN
• Two full seasons ~510000 events
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Valuation of Events
Hockey Low Scoring
Value each event
Short term fluctuation in scoring rates
Net Probability=
P(Goal by Home) – P(Goal by Away) in k seconds
Evaluate k = 5, 6, 7, …, 60
Stability ~ k=10, use k=20 (NP20)
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NP20
No change to NP20
HIT, FACE, TURN, BLOCK, MISS
PENL = length in minutes *league average scoring rate/min on PP
SHOT & GOAL = P(SHOT(x,y)=GOAL)+NP20(SHOT)
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NP20
SHOT & GOAL = P(SHOT(x,y)=GOAL)+NP20(SHOT)
Offensive Zone 6(X) x 9(Y) grid
Zone behind net
Neutral Zone
Defensive Zone
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http://en.wikipedia.org/wiki/Ice_hockey_rink
NP20 Examples: Events by Home
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Event Shot Type (if relevant)
Location NP20
SHOT Backhand Off 0.1348 SHOT Wrist Off 0.1096 SHOT Slap Off 0.0697 TURN (to Home Team) Off 0.0362
FAC Off 0.0167 MISS Wrist Off 0.0159 HIT (by Home) Off 0.0039 FAC Neu 0.0026 HIT (by Home) Neu -0.0008 TURN (to Home Team Neu 0.0264
FAC Def 0.0005 HIT (by Home) Def -0.0060
Model
Ridge Regression
𝑁𝑃20 = 𝜇 + 1𝑖𝑗𝐻𝜃𝑗
𝑃𝑗=1 − 1𝑖𝑗
𝐴𝜃𝑗𝑃𝑗=1 + 𝛾𝑍𝑆,
where
m is the impact of home ice advantage on each play,
qj is the effect of player j,
g is the effect of a zone start on the NP20 of each event.
Goalies included for team effects
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Results
THoR =Estimated qj’s are per play
Multiply by 67 plays/game (~1/3 of EV)
Multiply by 82 games
Divide by 6 goal differential/win
Wins more than Average
If an equal number of plays
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Results
Zone Starts (ZS) Equivalent to replace average player with top 5 forward
Start all shifts in Off Zone = 0.53 goals per game
10 Additional ZS per game = 5.4 goals per season
Home Ice 0.32 goals per game
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THoR Top 15 Forwards (2010-2011 & 2011-2012)
Team Player Position Wins
Created St. Louis Blues Alexander Steen C 6.72 Detroit Red Wings Pavel Datsyuk C 6.32 Pittsburgh Penguins Tyler Kennedy C 6.05 Boston Bruins Patrice Bergeron C 5.95 Nashville Predators Patric Hornqvist R 5.88 Phoenix Coyotes Ray Whitney* L 5.62 Pittsburgh Penguins Evgeni Malkin C 5.57 Vancouver Canucks Ryan Kesler C 5.53 Chicago Blackhawks Jonathan Toews C 5.50 Vancouver Canucks Daniel Sedin L 5.47 San Jose Sharks Joe Pavelski C 5.42 Toronto Maple Leafs Mikhail Grabovski C 5.13 Carolina Hurricanes Jeff Skinner C 5.07 Los Angeles Kings Anze Kopitar C 4.93 Pittsburgh Penguins Sidney Crosby* C 4.92
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THoR Top 15 Defensemen (2010-2011 & 2011-2012)
Team Player Position Wins
Created Philadelphia Flyers Kimmo Timonen D 5.73 Los Angeles Kings Drew Doughty D 4.07 Edmonton Oilers Tom Gilbert* D 3.32 Columbus Blue Jackets Fedor Tyutin D 3.13 Calgary Flames Mark Giordano D 3.08 Philadelphia Flyers Andrej Meszaros D 2.82 Chicago Blackhawks Brent Seabrook D 2.63 New York Rangers Ryan McDonagh D 2.50 Detroit Red Wings Niklas Kronwall D 2.48 Anaheim Ducks Lubomir Visnovsky* D 2.48 Pittsburgh Penguins Paul Martin D 2.27 Winnipeg Jets Tobias Enstrom D 2.23 Ottawa Senators Erik Karlsson D 2.22 Boston Bruins Zdeno Chara D 2.18 New York Rangers Michael Sauer D 1.95
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Reliability
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2010 - 20112009 - 20102008 - 2009
100
80
60
40
20
0
Pe
rce
nt
Goals
Penalties
Missed Shots
Blocked Shots
Turnovers
Hits
Shots
Faceoffs
Event
Percentage of Event by Year
Reliability
Choose ridge parameter for small G, here:
G =
1
𝑁𝑇 𝑛𝑘 𝜃 𝑘1−𝜃 𝑘2
2𝑇𝑘=1
1
𝑁 𝑛𝑗 𝜃 𝑗−𝜃
2𝑃𝑗=1
=0.14
Ridge regression
Shrinkage of estimate to zero
Deal with multicollinearity of linemates
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Conclusions
“… Look for players with undervalued talents: faceoffs, defensive play vs tough competition, and particularly penalties drawn….”
-Gabriel Desjardins aka Hawerchuk
In blog post about how to build a team following LAKings winning Stanley Cup
http://www.arcticicehockey.com/2012/6/12/3079834/how-did-the-kings-get-here
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Conclusions
• THoR Strengths
– Standardized reliable two-way metric (w/ SE’s)
– Adjusts for QoT, QoC, ZS & Home Ice
– Wins Over Average
– Large Sample Size
– Ridge Regression
• THoR Weakness
– RTSS system is weakness
– Adjusted for some known biases from data
– Score Effects (88% plays within 2 goals)
– Don’t see systematic team advantages (goalies in model)
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Next Steps
• THoR will go live bi-weekly sometime in March
– www.statsportsconsulting.com/thor
– 2011-12+2013(to date)
• THoR for PP and PK not included in the paper
– THoR=0.8*THoR(EV) + 0.2*THoR(PP/PK)
• Add Rink effects to the model (in process)
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Actual Plays Two Years: Top 15 Players
Team Player Position Plays WOA
1 BOSTON BRUINS PATRICE BERGERON C 9621 5.21
2 VANCOUVER CANUCKS RYAN KESLER C 9946 5.01
3 TORONTO MAPLE LEAFS MIKHAIL GRABOVSKI C 10432 4.87
4 CAROLINA HURRICANES ERIC STAAL C 11768 4.86
5 LOS ANGELES KINGS ANZE KOPITAR C 10647 4.77
6 PHILADELPHIA FLYERS KIMMO TIMONEN D 7944 4.64
7 CHICAGO BLACKHAWKS JONATHAN TOEWS C 9276 4.59
8 SAN JOSE SHARKS JOE PAVELSKI C 9316 4.28
9 DETROIT RED WINGS PAVEL DATSYUK C 7442 4.15
10 COLORADO AVALANCHE PAUL STASTNY C 10115 3.91
11 PITTSBURGH PENGUINS EVGENI MALKIN C 7600 3.85
12 DETROIT RED WINGS HENRIK ZETTERBERG L 9692 3.79
13 PHOENIX COYOTES RAY WHITNEY L 7255 3.71
14 SAN JOSE SHARKS LOGAN COUTURE C 9375 3.65
15 VANCOUVER CANUCKS DANIEL SEDIN L 7311 3.64
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Acknowledgements
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Ongoing Project:
Dennis Lock
Matt Generous
Ed Harcourt
This material is based upon work supported by the National Science Foundation under Grant No. 0959713.
Thank You
@SchuckersM
@EmpiricalSports
Schuckers&Curro (c) 2013 24
Current THoR (2011-12 + 2013) Top Forwards (82 games, as if)
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1 DAVID CLARKSON NEW JERSEY DEVILS R 5.89
2 PATRIC HORNQVIST NASHVILLE PREDATORS R 5.48
3 ERIC STAAL CAROLINA HURRICANES C 5.23 4 MATT DUCHENE COLORADO AVALANCHE C 5.18 5 RAY WHITNEY DALLAS STARS L 5.11
6 TYLER KENNEDY PITTSBURGH PENGUINS C 5.11
7 PATRICK SHARP CHICAGO BLACKHAWKS R 5.09
8 ANZE KOPITAR LOS ANGELES KINGS C 5.06 9 LOGAN COUTURE SAN JOSE SHARKS C 4.94
10 JONATHAN TOEWS CHICAGO BLACKHAWKS C 4.87 11 ZACH PARISE MINNESOTA WILD L 4.87
12 TAYLOR HALL EDMONTON OILERS L 4.85 13 PATRICE BERGERON BOSTON BRUINS C 4.82
14 TEDDY PURCELL TAMPA BAY LIGHTNING R 4.49 15 JOE PAVELSKI SAN JOSE SHARKS C 4.41
Current THoR (2011-12 + 2013) Top Defensemen(82 games)
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1 ERIK KARLSSON OTTAWA SENATORS D 4.22
2 BRENT SEABROOK CHICAGO BLACKHAWKS D 3.28
3 DAN HAMHUIS VANCOUVER CANUCKS D 3.13
4 SHEA WEBER NASHVILLE PREDATORS D 2.97
5 KIMMO TIMONEN PHILADELPHIA FLYERS D 2.55
6 NIKITA NIKITIN COLUMBUS BLUE JACKETS D 2.41
7 JASON GARRISON* VANCOUVER CANUCKS D 2.29
8 KRIS LETANG PITTSBURGH PENGUINS D 2.26
9 DMITRY KULIKOV FLORIDA PANTHERS D 2.24 10 ZDENO CHARA BOSTON BRUINS D 2.24 11 MARK GIORDANO CALGARY FLAMES D 2.05
12 NICKLAS LIDSTROM DETROIT RED WINGS D 2.03
13 SLAVA VOYNOV LOS ANGELES KINGS D 1.95
14 DREW DOUGHTY LOS ANGELES KINGS D 1.93
15 FEDOR TYUTIN COLUMBUS BLUE JACKETS D 1.86