DIGR: A Defense Independent Rating of NHL Goaltenders using Spatially Smoothed Save Percentage Maps

DIGR: A Defense Independent Rating of NHL Goaltenders using Spatially Smoothed Save Percentage Maps Michael E. Schuckers* St. Lawrence UniversityStatistical Sports Consultingschuckers@stlawu.edu
*Thanks to Chris Wells, Ken Krzywicki, Dan Downs, Dennis Lock, Matt Generous

2009-10 Save Percentage
Goalie Gi Team Pts
Brodeur (NJD) 0.916 103*
Luongo (VAN) 0.913 103*
Turco (DAL) 0.913 88
Ward (CAR) 0.916 80
* Made Stanley Cup playoffs

Gi=
Problem:
Each goalie faces different distribution of shots
Goal of this paper
Find statistical methodology to allow comparison
Save Percentage

Rethinking Save Percentage
s=shot type
Pi(s)
Ri(s)
Xi(s) = Number of saves by goalie ion shots of type s
Ti(s) = Total number of shots faced by goalie ion shots of type s
Pi(s) = performance (save percentage) of goalie ion shot type s
Ri(s) = percent/rate of all shots for goalie ithat were of type s

Save Percentage
Convert to`R(s) the league average distribution of shots faced

Data
Downloaded from ESPN.com GameCast
Every NHL regular season game 09-10
Goalie
(x,y) location of ( n= )74300 shots
Opponents strength
Shot Type
Location*
Home/Away Team
*Madison Square Garden is a statistical nightmare in hockey

Shots
s=(x-coord, y-coord, shot type, strength)
All shots converted to single offensive zone
Shot types
Backhand, Deflection, Slap, Snap, Tip-in, Wrap and Wrist
Strength
Even, Power Play, Shorthanded

Spatial Smoothing
Use LOWESS* (locally weighted scatterplotsmoothing)
Nonparametric (no specific model)
One map for each strength x shot type (21)
Use all shots for given shot type (total weight 30)
*Using loess in R

Why smooth? Luongo vs. Distance

Ryan Miller/ Slap Shots/ Even Strength

Ryan Miller/Slap Shot
Even Strength
Power Play
Shorthanded

Tomas Vokoun/Slap Shot
Even Strength
Power Play
Shorthanded

NiklasBackstrom/Slap Shot
Power Play
Even Strength
Shorthanded

Save Percentage
Shot Quality Adjusted
Save Percentage
(E. g. Krzywicki (2010))
Defense Independent
Goalie Rating (DIGR)

Application
49 goalies >600 shots faced in 2009-10 Regular Season
Each shot (n=74300), each goalie
predicted goal probability using smoothed maps
Calculated DIGR

Results: Top 10
0.01 = 20 goals for a goalie facing 2000 shots

Results: Other Notables
0.01 = 20 goals for a goalie facing 2000 shots

Results
Big* Winners(DIGR - Save Pct >> 0)
Smith(TBL), Roloson (NYI), Huet (CHI),
Pavelec (ATL), Varlamov (WSH), Biron (NYI), Theodore (WSH), Leclaire (OTT),
Toskala (TOR, CGY)
Big* Losers (DIGR - Save Pct << 0)
Rask (BOS), Howard (DET), Thomas (BOS)
Big means > 0.0075 OR 15 goals on 2000 shots

Results (2000 shots using`R(s))
Rank PlayerDIGR Goals
1 Miller(BUF) 143
…
11Hedberg(ATL) 162
….
21 Anderson (COL) 173
…
31 Ellis (NSH) 177
…
41 Huet(CHI) 191
…
49 Toskala(TOR, CGY) 206
DPts=0.35*GoalDiff
6.7
3.9
1.4
4.9
5.0
19
11
4
14
15

Discussion
Average season performance
Standard Errors (Bootstrap)
Shot target (holes 1 to 5)
Injuries (e.g. Tim Thomas)
Extension G*ij=SsPi(s) Rj(s)

Turco takes Niemi’s shots
June 2010 Blackhawks win Stanley Cup
Need Cap Space
Fail to resign Niemi and sign Turco
Saving $1.45 million
GiGi*(DIGR)
Niemi(CHI) 0.915 0.922
Turco (DAL) 0.912 0.910
G*ij=SsPi(s)Rj(s) (i=Turco, j = Niemi) = 0.903

Turco takes Niemi’s shots
Turco G*ij = 0.903 vsNiemi G*jj = 0.915
What’s the cost?
Turco on pace to face about 1000 shots in 2010-11
1000 shots *(0.012) = 12 goals
12 goal *0.35 = 4.2 pts
Turco Save Pct (2010-11) = 0.897

DIGR vs. ‘09-’10 Salary

Summary
DIGR: Defense Independent Goalie Rating
Three innovations
- Spatial smoothing maps
- Goalie ratings on comparable shot distribution
- Mathematical framework

Thank You!schuckers@stlawu.edu

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DIGR: A Defense Independent Rating of NHL Goaltenders using Spatially Smoothed Save Percentage Maps

by Sloan Sports Conference on Apr 11, 2011

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