Goal Prevention 2004 a review of goaltending and team defense including a study of the quality of a hockey ’shots allowed Copyright Alan Ryder 2004 Goal Prevention 2004 Page 2 Introduction I recently completed an assessment of “”in the NHL for the 2002-03 “”season (http://www.HockeyAnalytics.com/Research.htm). That study revealed that the quality of shots allowed varied significantly from team to team and was not well correlated with the number of shots allowed on goal. The consequence of that study was an improved ability to assess the goal prevention performance of teams and their goaltenders. This paper applies the same methods to the analysis of the 2003-04 “”season, focusing more on the results than the method. Shot Quality In summary, the approach used to assess the quality of shots allowed by a team is: 1. Collect, from NHL game event logs, the relevant data on each shot. 2. Analyze the goal probabilities for each shooting circumstance. In my analysis I separated certain “”from “”shots and studied the probability of a goal given the shot type, the ’distance and the on-ice situation (power play vs other). 3. Build a model of goal probabilities that relies on the measured circumstance. 4. Apply the model to the shot data for the defensive team in question for the season. For each shot, determine its goal probability. 5. Determine Expected Goals: EG = the sum of the goal probabilities for each shot. 6. Neutralize the variation in the number of shots on goal by calculating Normalized Expected Goals (NEG) = EG x League Average Shots / Shots 7. Shot Quality Against (SQA) = NEG / League Average Goals Against. SQA is defined such that a team SQA of 1.05 means that the team is giving up shots which are 5% more “”than those of an average team. That team would have, all other things being equal, 5% more goals against than an average team. There were 69,010 shots on goal in the NHL in 2004 (an average of 2,300 per team). This is down 1.1% from 69,786 (2,326 per team) in the prior season. There were 6,318 goals scored (an average of 211), down 3.3% from 6,530 (218 per team) in 2002-03. Shooting percentage therefore reduced from 9.4% to 9.2%. There are three plausible explanations for this: goaltending got better and/or shooting got worse and/or shot quality was lower. Note that SQA is measured against league average, but there are some Copywrite Alan Ryder, 2004 Hockey Analytics http://www.HockeyAnalytics.com Goal Prevention 2004 Page 3 other outputs of the study that can help us with the last possibility. One of these is the average distance of a normal shot. In 2004, this increased by about 0.5 feet suggesting that shot quality did diminish overall this year. Below are the SQA results for 2004, sorted from best to worst. I have shown the 2003 results and the change so that we can get a better feel for the data. For the second year in a row, the Devils sit atop the SQA list. Last ’number 2 and 3 teams, Philadelphia and Minnesota, slipped a little in the SQA Index rankings. But this was because Montreal, Buffalo 2003 vs 2004 and Edmonton tightened up defensively. Team 2003 2004 Change NJD 0.915 0.915 0.000 Buffalo and Montreal were two of the most MON 0.982 0.927 0.055 improved teams in the NHL and part of the story PHI 0.935 0.928 0.007 would seem to be shot quality. In the case of BUF 0.969 0.939 0.030 EDM 1.018 0.942 0.075 Montreal, there was also a dramatic reduction in MIN 0.947 0.946 0.000 shots allowed (-334). ’performance VAN 1.004 0.961 0.044 remained about the same notwithstanding the second TB 0.965 0.961 0.004 best improvement in SQA (0.075). SQA was part of NYI 1.037 0.965 0.072 SJ 1.022 0.970 0.052 the significant defensive improvement, but the Oilers CBJ 1.041 0.980 0.060 gave up those gains in goal and on offense. Florida COL 1.018 0.983 0.035 escaped the bottom echelon with the largest (0.078) NAS 1.000 0.985 0.015 TOR 0.956 0.986 -0.030 improvement in SQA while other big improvements OTT 0.970 0.987 -0.017 were NY Islanders (0.072) and Columbus (0.060). BOS 1.027 0.991 0.035 WAS 0.973 0.994 -0.022 With the exception of Florida, last ’bottom five DAL 0.972 0.998 -0.025 shuffled the order. Only St. Louis got better, FLA 1.078 1.000 0.078 CAL 0.953 1.013 -0.060 improving to 1.050. Phoenix had one of the largest CAR 1.024 1.022 0.002 deteriorations in shot quality (-0.076) to move into DET 0.976 1.025 -0.049 the cellar. Other big deteriorations came from CHI 0.996 1.035 -0.039 PIT 0.996 1.043 -0.047 Anaheim (-0.077), the Rangers (-0.066), Calgary ANA 0.972 1.049 -0.077 (-0.060) and Atlanta (-0.059). Calgary had a much STL 1.087 1.050 0.037 better season in spite of this change. The Flames PHO 0.980 1.056 -0.076 had a complete turnaround in goal. This may have LA 1.048 1.072 -0.024 ATL 1.045 1.103 -0.059 contributed to a change in defensive style. Except NYR 1.057 1.124 -0.066 for Atlanta, the other teams had worse seasons in 2004. Shot quality allowed is certainly a function of short handed situations. In 2003, the correlation of these two variables was 45% (r2 = 0.20) and in 2004 the correlation was 57% (r2 = 0.32). But this means that about 75% of the variation in shot quality is still coming from elsewhere. Last year I observed that the correlation of SQA to shots on goal was very weak (r2 = .15). This year it was a bit lower (r2 = .12). This is continued evidence that shots allowed and shot quality allowed are two distinct elements of defensive ability. Copywrite Alan Ryder, 2004 Hockey Analytics http://www.HockeyAnalytics.com Goal Prevention 2004 Page 4 Do teams need to sacrifice shot quality to reduce shots on goal? The answer is in the chart below: R Y N L T 1.1 A A L O L A H T N P S A IT I P H T C ER DA A L C A C Q A L L A S S F S D A 1.0 T R O LS T O B A W J O T NO B C C I J N Y S B A N T V N M F I D U M I E B H N P O M JD N 0.9 2000 2200 2400 2600 2800 SOG The answer is clearly no. New Jersey excels at both. Philadelphia excels at both. However, poorer defensive teams may see this as a choice. They tend to form a NW-SE line through the upper right quadrant of the data. Goaltending The value of SQA is that it gives us a better understanding of goal prevention. Before consideration of shot quality goal prevention looked like a simple model: GA = SOG x (1 – SV) where SOG = Shots on Goal SV = Save Percentage We knew that Shots on Goal was basically a defensive responsibility, so we have historically attributed it to the team. We knew that Save Percentage was more a goaltender statistic than a team statistic, so we have historically attributed it to goaltenders. But we are now in a position to use a better model: Copywrite Alan Ryder, 2004 Hockey Analytics http://www.HockeyAnalytics.com Goal Prevention 2004 Page 5 GA = SQA x SOG x (1 – SQNSV) where SQNSV = 1 – (1- SV) / SQA the Shot Quality Neutral Save Percentage In this model we attribute both SQA and SOG to the defense and SQNSV to goaltending. Clearly SQNSV is a better measure of the ’contribution to team success than is SV. You can think of it as the save percentage one would expect with no variation in shot quality from team to team. To the right are the SQNSV calculations for 2004 with a comparison to raw Shot Quality Neutral Save Percentage Save Percentage and to 2003 vs 2004 SQNSV for 2003. 2003 2004 When comparing the Team SQNSV Rank SQA SV Rank SQNSV Rank two years you should FLA 0.919 2 1.000 0.922 3 0.922 1 SJ 0.902 21 0.970 0.923 2 0.920 2 note that, as goal MIN 0.919 1 0.946 0.923 1 0.919 3 scoring was down in ANA 0.916 4 1.049 0.913 9 0.917 4 2004, save percentages CAL 0.892 30 1.013 0.916 7 0.917 5 are up versus 2003. BOS 0.901 24 0.991 0.918 6 0.917 6 The average save DET 0.912 7 1.025 0.912 10 0.914 7 COL 0.918 3 0.983 0.915 8 0.913 8 percentage increased MON 0.911 8 0.927 0.918 4 0.912 9 by about 0.002. NJD 0.906 14 0.915 0.918 5 0.910 10 STL 0.900 25 1.050 0.906 20 0.910 11 Florida, nosed out by ATL 0.895 29 1.103 0.899 27 0.909 12 Minnesota for top LA 0.901 23 1.072 0.902 26 0.909 13 goaltending honours in DAL 0.916 5 0.998 0.908 14 0.908 14 VAN 0.905 16 0.961 0.911 11 0.907 15 2003, claimed the top PHO 0.905 17 1.056 0.902 24 0.907 16 spot in 2004.