2011 5th MIT Sloan Sports Analytics Conference

1. 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

2. 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

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

4. 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

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

6. 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

7. 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

8. 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

9. Why smooth? Luongo vs. Distance

10. Ryan Miller/ Slap Shots/ Even Strength

11. Ryan Miller/Slap Shot Even Strength Power Play Shorthanded

12. Tomas Vokoun/Slap Shot Even Strength Power Play Shorthanded

13. NiklasBackstrom/Slap Shot Power Play Even Strength Shorthanded

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

15. 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

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

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

18. 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

19. 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

20. 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)

21. 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

22. 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

23. DIGR vs. ‘09-’10 Salary

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

25. Thank You!schuckers@stlawu.edu