Adjusted Shot Quality Neutral Save Percentage

Alan Ryder has found systemic bias in the shot quality data leaving the results showing problems with the data. It is bet summarized by Ryder himself:

I have been worried that there is a systemic bias in the data. Random errors don’t concern me. They even out over large volumes of data. I seriously doubt that the RTSS scorers bias the shot data in favour of the home team. But I do think that it is a serious possibility that the scoring in certain rinks has a bias towards longer or shorter shots, the most dominant factor in a shot quality model. And I set out to investigate that possibility [Shot Quality Product Recall].

I did a rather simple way to fix the problem. I did a regression on SQ results for all games based on two factors: team shot quality and stadium shot quality or [RTSS shot quality]. This simply calculate how much off the RTSS scores are from the standard [how the team normally performs]. Preferably we want no effect from RTSS scores so all those variables should be 0. I found a rather long list of biases, most of them small, including: Calgary, St. Louis, Columbus, Chicago, Phoenix, New Jersey, New York Rangers, Philadelphia, Buffalo, Carolina, Washington. I have deliberately over chosen, so that list likely includes teams which are simply randomly different as opposed to actual bias, but it doesn't matter. Ideally, I would want to incorporate these issues into the model directly, but the shot quality model is time consuming to build and once you get the variables you have to go through the hassle of calculating the percentages for all 7000 shots.

Simple adjustment on shot quality and it's effect on goaltending:

N lastname newSQN oldSQN Shots 1 BACKSTROM 0.925 0.926 1027 2 VOKOUN 0.920 0.921 1299 3 DIPIETRO 0.920 0.922 1917 4 THIBAULT 0.920 0.921 570 5 MASON 0.919 0.921 1244 6 HUET 0.919 0.919 1280 7 GIGUERE 0.918 0.918 1490 8 LUONGO 0.918 0.919 2169 9 LEHTONEN 0.915 0.915 2075 10 ROLOSON 0.914 0.914 1979 11 BRODEUR 0.914 0.913 2182 12 KIPRUSOFF 0.914 0.912 2190 13 NABOKOV 0.914 0.915 1227 14 KOLZIG 0.913 0.919 1771 15 KHABIBULIN 0.913 0.914 1668 16 TURCO 0.912 0.912 1554 17 BURKE 0.912 0.913 687 18 FERNANDEZ 0.912 0.913 1154 19 AEBISCHER 0.911 0.910 929 20 LEGACE 0.911 0.919 1177 21 HASEK 0.911 0.912 1309 22 LUNDQVIST 0.910 0.921 1927 23 EMERY 0.910 0.911 1691 24 DUNHAM 0.910 0.910 540 25 NIITTYMAKI 0.908 0.913 1562 26 GRAHAME 0.908 0.912 702 27 MILLER 0.907 0.899 1886 28 SMITH 0.906 0.909 510 29 FLEURY 0.904 0.905 1955 30 BELFOUR 0.903 0.904 1550 31 NORRENA 0.903 0.908 1420 32 BUDAJ 0.903 0.903 1499 33 BRYZGALOV 0.902 0.903 668 34 THOMAS 0.902 0.904 1985 35 GERBER 0.900 0.901 784 36 AULD 0.899 0.899 729 37 GARON 0.899 0.901 849 38 JOSEPH 0.899 0.904 1481 39 TOIVONEN 0.899 0.897 502 40 THEODORE 0.898 0.898 870 41 WARD 0.898 0.906 1625 42 TOSKALA 0.897 0.899 915 43 LECLAIRE 0.894 0.900 629 44 JOHNSON 0.894 0.900 894 45 BIRON 0.894 0.899 509 46 SANFORD 0.893 0.901 707 47 RAYCROFT 0.891 0.892 1939 48 BIRON 0.887 0.886 533 49 TELLQVIST 0.887 0.892 780 50 HOLMQVIST 0.886 0.890 1134 51 DENIS 0.884 0.884 1068 52 CLOUTIER 0.878 0.880 608

newSQN = adjusted for RTSS bias
oldSQN = no adjustment for RTSS bias.

I'm curious what the RTSS turnover is. That is to say, I wonder if the bias last year will be the same this year.