Which goalies face the closest/farthest shots?

Discussion in 'By The Numbers' started by Hockey Outsider, Aug 14, 2005.

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  1. Hockey Outsider

    Hockey Outsider Registered User

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    Using the data from NHL.com's game summaries, I've calculated which goalies, on average, face shots from the closest and farthest distances. You might think this is an insightful way of looking more closely at defense and goaltending; you may think this data is worthless, that's fine too.

    Shots Distance Average Goalie
    1,522 47,575 31.3 DUNHAM_NYR
    1,145 38,020 33.2 BOUCHER_PHO
    913 30,401 33.3 BURKE_PHO
    1,198 40,422 33.7 CECHMANEK_LAK
    1,792 60,578 33.8 NURMINEN_ATL
    1,623 55,067 33.9 GIGUERE_ANA
    1,179 40,057 34.0 CARON_PIT
    961 32,917 34.3 HUET_LAK
    1,970 67,933 34.5 DENIS_CBJ
    1,604 55,420 34.6 OSGOOD_STL
    932 32,464 34.8 SNOW_NYI
    1,554 54,328 35.0 CLOUTIER_VAN
    1,323 46,523 35.2 ROLOSON_MIN
    988 34,771 35.2 LEIGHTON_CHI
    68,806 2,425,317 35.2 AVERAGE (ALL)
    1,958 69,114 35.3 VOKOUN_NAS
    1,445 51,114 35.4 BIRON_BUF
    1,610 57,032 35.4 NABOKOV_SJS
    1,651 58,622 35.5 WEEKES_CAR
    1,853 65,841 35.5 THEODORE_MTL
    1,019 36,245 35.6 LEGACE_DET
    1,646 58,596 35.6 TURCO_DAL
    1,414 50,408 35.6 KHABIBULIN_TBL
    1,483 53,057 35.8 BELFOUR_TOR
    1,334 47,897 35.9 LALIME_OTT
    1,703 61,211 35.9 AEBISCHER_COL
    1,586 57,059 36.0 RAYCROFT_BOS
    1,056 38,004 36.0 FERNANDEZ_MIN
    1,261 45,894 36.4 DIPIETRO_NYI
    959 34,923 36.4 CONKLIN_EDM
    1,958 71,440 36.5 KOLZIG_WAS
    1,024 37,478 36.6 SALO_EDM
    2,476 91,049 36.8 LUONGO_FLA
    966 35,678 36.9 KIPRUSOFF_CGY
    1,845 68,318 37.0 BRODEUR_NJD
    932 35,907 38.5 ESCHE_PHI

    Some thoughts/observations:

    - Most of the teams at the top of the list (ie they allow shots from up close) generally have the reputation of being poor defensively (NYR, Atlanta, Pittsburgh). Interestingly, Boucher faced the second-closest shots in the league. This makes his shutout streak even more impressive.

    - Martin Brodeur faced the second-farthest shots in the league, so this lends some support to the statement that he's protected by his defense. (Devils fans don't flame me, that's a compliment to your team's excellent defense, not an attack on Brodeur). More interestingly, all three Vezina finalists faced shots that were much more distant than average. Coincidence? Or did that have something to do with their success?

    - Let's look at the Stanley Cup champions. Grahame faced much closer shots than Khabibulin. Fluke? Bad rebound control? Did the team open up more?

    - Three of the best puck-handling goalies are Brodeur, DiPietro and Turco. They all face farther shots than average. Perhaps their excellent puckhandling skills make the other team afraid to dump the puck in, so they'll take easy shots from far away instead.

    Again, hopefully some of you find this interesting. Don't take any of this personally, I am not trying to attack anyone's favorite teams.
     
  2. btn

    btn Gone Hollywood

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    The Luongo stats support my theory that since he is prone to giving up rebounds many teams just lob as many pucks at him as possible(hence his high shot totals against and exagerated save percentage). Since he has a poor D around him, he "should" have been on the other end of the spectrum.

    BTW, interesting stats Mr. Outsider. Thanks :bow:
     
  3. Ogopogo*

    Ogopogo* Guest

    Good work, as always, Outsider.

    The question begs to be asked - How in the world did you find the shot distances? Who keeps track of that information?
     
  4. zeke

    zeke #freeVladdy

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    Unbelievable work there.

    Great post.

    The only problem is that those NHL trackers can be way off much of the time.....still, I guess on average it gives a decent assessment.

    great work.
     
  5. zeke

    zeke #freeVladdy

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    Now what's the best way we can combine "Average Shots Against" with this new "Average Shot Distance" to come up with a solid overall stat?

    I'd start with just multiplying the two and dividing by 100 or something....but maybe there's a better way?
     
  6. AnThGrt

    AnThGrt Registered User

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    Great job thx
     
  7. Hedberg

    Hedberg MLD Glue Guy

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    Well done :handclap:
     
  8. Le Golie

    Le Golie ...

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    What? You totally contradict yourself.

    If a goalie gives up a lot of rebounds, his average distance would be really low. He doesn't kick out 35 foot rebounds, and he's ranked two spots up for Broduer.
     
  9. Seph

    Seph Registered User

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    I find this particularly interesting for people who don't think stickhandling is a worthwhile trait for a goalie. Especially considering that in that season, Snow player more of the weaker teams. Also, notice that the best stickhandling goalies, guys like Turco, Brodeur, Belfour and Dipietro, are all well above the average. Could just be coincidence, and there are clearly other factors as well, but I find it interesting nonetheless.
     
  10. Arastiroth

    Arastiroth Registered User

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    Great list and a nice job on the information! I find the list quite interesting. I'm most surprised by Luongo's name being so close to the bottom.
     
  11. OlTimeHockey

    OlTimeHockey Registered User

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    A footnote: http://www.tsn.ca/nhl/teams/player_game_log.asp?player_id=2134&hubname=NYI , http://www.tsn.ca/nhl/teams/player_game_log.asp?player_id=330&hubname=NYI

    Snow played the tough teams until the middle of the season (Rick played better late in the season and Snow got hurt).

    But watching, Rick got the defensemen to play back to protect the crease, like most young goalies do. Snow didn't get the guys playing back the same way. Older goalies often lend the confidence in them and thus, recieve less coverage. So Rick recieved shots outside the box much like Brodeur did (with NJ's defensive scheme).

    So this whole stat is kinda telling in one way, but not the least bit credible on its own in another way.

    I think it's a good part of an argument when used in conjunction with other data. Rebounds....defensemen in front....division they play in.....the offense's ability to keep the play in the other zone....and so on.

    but a good job at getting this stat. Definitely good stuff when weighed adequately.
     
  12. Hockey Outsider

    Hockey Outsider Registered User

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    Thanks for the comments everyone, I'm glad some people like stuff like this too...

    Actually, I think it works the opposite way. If Luongo gave up a lot of rebounds, we'd expect to see a lot of shots from close up, not from far away. Also, Luongo's backup Steve Shields faces shots that are on average closer (35.2 vs 36.8 for Luongo), so this may indicate that Luongo has better rebound control than Shields.

    Thanks. I sent you a PM about this but, in brief, this data was collected by Ken Krzywicki who very generously made it available for everyone for free at the Hockey Analysis Group. This is actually a small part of a bigger system (from him and Alan Ryder) that evaluates overall shot quality. It's very complex but I thought it would be interesting to look at this small part and see what insights we can get from it.

    Glad you liked this. I agree that the data trackers can be off on occasion, but since we're taking an average, and using a sample size of around 1,500 shots per goalie, I don't think there's a huge issue here.

    Like I said to Ogopogo, this is one small part of a larger system that evaluates shot quality. The best thing to read is http://www.hockeyanalytics.com/Research_files/Shot_Quality.pdf "Shot Quality" by Alan Ryder. I've posted that link a few times here before and most people are quite critical of it, but I think it's an improvement over regular save percentage.

    Agreed. It's possible that this is a coincidence, but then again it could also be proof that stickhandling goalies are actually quite valuable.

    The Islanders example is a great one. If DiPietro routinely faces shots that are farther away (and therefore easier to stop), how many goals is he saving his team each year? This is a tough question because we'd need to figure out exactly how much easier it is to stop a shot that's about 2 feet farther out. I know how to solve this, but there's so much data to work with I think my computer would just quit on me first.
     
  13. interminded

    interminded Registered User

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    Great post man !

    This had to be some work !
    Respect ! :bow:
     
  14. me2

    me2 Calling out the crap

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    Any chance of a further breakdown?
    # of shots, by distance (more or less groupings as you see fit)
    0-5
    5-10
    10-20
    20-30
    30-40
    40-50
    50+

    or barring something that complex even a simple median as opposed to mean for the average.
     
  15. Hockey Outsider

    Hockey Outsider Registered User

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    I would have liked to include the median shot distances, except you can't do that with Excel's subtotal function.

    Here (http://www.geocities.com/thehockeyoutsider/GoalieShotDistances.xls) is an Excel file with the shot distances broken down into smaller groups. I also calculated the differences between Actual and Expected shots faced in each range.

    (Expected Shots by category = total shots faced * average frequency of shot category. IE Legace faced a total of 1,019 shots, and 16.9% of shots were from 14 feet or less category, league-wide. So Legace was expected to face ~173 shots in that category). There's a ton of stuff to analyze there. I don't even know where to start.
     
  16. octopi

    octopi Registered User

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    Wow, with that kind of time on your hands, you should be out curing all disease. Or figuring ways to beat down the time space continum. Very nice, though, but I'm not really sure anything can actually be proved by this....
     
  17. I'm so glad soembody is at least TRYING to bring in Sabremetrics type thinkign into Hockey.

    Anyhoo... wrt to the rebound control/shot distance problem... any goalie with poor rebound control is simply going to decrease his avg shot distance from because he'll add many 2-10 foot shots to the opponent.

    HO what I think you should have done was to make a histogram of the distribution of shot distribution for NHL goaltenders. If a goalie faces an abnormally high percentage of point-blank shots, we can then add in the analysis of the team's defense to see whehter if the goalie is just krappy at rebound control or whether the team's letting everybody shooting from point blank.

    I should also add the fact that point blank shots generate more rebounds, creating a "synergy" effect if you will.
     
  18. me2

    me2 Calling out the crap

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    I've mangled the stats using expected scoring percentages from the article (guess between PP and normal, so I added about 1.5% to normal)
    Distance <15 15-24 25-34 35-44 45-54 55+
    Scoring% 17 14.5 10 6.5 4.5 3

    Left column is the difficultly for each goalie, ranked easiest to hardest. Right column is their expected sv% assuming all were equally skilled (not sure about these set of numbers being quite right, I'll check again tomorrow). Poor Mike Dunham.

    5.259656652 LITTLE_PHI 94.74034335
    7.181093585 NIITTYMAKI_PHI 92.81890642
    7.860756816 VALIQUETTE_NYR 92.13924318
    8.150798409 SALO_COL 91.84920159
    8.225192239 GARON_MTL 91.77480776
    8.270197614 MARKKANEN_EDM 91.72980239
    8.328586292 SABOURIN_CGY 91.67141371
    8.478910921 IRBE_CAR 91.52108908
    8.493562232 ESCHE_PHI 91.50643777
    8.507459636 OUELLET_WAS 91.49254036
    8.611325885 STORR_CAR 91.38867411
    8.631599535 MCLENNAN_CGY 91.36840047
    8.658951563 ELLIS_DAL 91.34104844
    8.695610253 MUNRO_CHI 91.30438975
    8.763114264 NORONEN_BUF 91.23688574
    8.794399097 SAUVE_COL 91.2056009
    8.808626609 DUBIELEWICZ_NYI 91.19137339
    8.823574091 TOSKALA_SJS 91.17642591
    8.864636369 MASON_NAS 91.13536363
    8.875664214 CLEMMENSEN_NJD 91.12433579
    8.876259513 TUREK_CGY 91.12374049
    8.900377162 UNDERHILL_CHI 91.09962284
    8.913154551 KIPRUSOFF_CGY 91.08684545
    8.943038374 SCHWAB_NJD 91.05696163
    8.958544887 HEDBERG_VAN 91.04145511
    8.96353582 CHIODO_PIT 91.03646418
    8.990492198 SHIELDS_FLA 91.0095078
    9.006845494 STANA_WAS 90.99315451
    9.010656267 SALO_EDM 90.98934373
    9.032308475 AUBIN_PIT 90.96769153
    9.040000537 CONKLIN_EDM 90.95999946
    9.0587347 JOHNSON_PHO 90.9412653
    9.074322968 HACKETT_PHI 90.92567703
    9.085953431 PASSMORE_CHI 90.91404657
    9.088526209 BURKE_PHI 90.91147379
    9.11927073 DAFOE_ATL 90.88072927
    9.144108889 ANDERSON_CHI 90.85589111
    9.150731565 TUGNUTT_DAL 90.84926844
    9.157337152 TELLQVIST_TOR 90.84266285
    9.173502731 FERNANDEZ_MIN 90.82649727
    9.190073966 YEATS_WAS 90.80992603
    9.199866269 POTVIN_BOS 90.80013373
    9.216964084 BROCHU_PIT 90.78303592
    9.247866851 AULD_VAN 90.75213315
    9.272042081 DIPIETRO_NYI 90.72795792
    9.322090858 LEHTONEN_ATL 90.67790914
    9.334970784 GRAHAME_TBL 90.66502922
    9.336298104 LEGACE_DET 90.6637019
    9.345983151 FLEURY_PIT 90.65401685
    9.412071467 LEIGHTON_CHI 90.58792853
    9.433362992 BRODEUR_NJD 90.56663701
    9.434140082 DIVIS_STL 90.56585992
    9.434829134 JOSEPH_DET 90.56517087
    9.448310466 BELFOUR_TOR 90.55168953
    9.487698442 BIRON_BUF 90.51230156
    9.48816597 MARKKANEN_NYR 90.51183403
    9.505824647 CHARPENTIER_WAS 90.49417535
    9.512869048 ROLOSON_MIN 90.48713095
    9.517667209 HASEK_DET 90.48233279
    9.520296726 KHABIBULIN_TBL 90.47970327
    9.525614416 RAYCROFT_BOS 90.47438558
    9.533261803 SNOW_NYI 90.4667382
    9.54607773 BIERK_PHO 90.45392227
    9.54889591 PRUSEK_OTT 90.45110409
    9.556873617 NABOKOV_SJS 90.44312638
    9.560494584 HNILICKA_LAK 90.43950542
    9.58373442 LALIME_OTT 90.41626558
    9.607127357 KOLZIG_WAS 90.39287264
    9.618686035 EMERY_OTT 90.38131397
    9.623922014 HUET_LAK 90.37607799
    9.634439676 THIBAULT_CHI 90.36556032
    9.643515126 BRATHWAITE_CBJ 90.35648487
    9.65148301 GERBER_ANA 90.34851699
    9.694848742 TURCO_DAL 90.30515126
    9.70392765 WEEKES_CAR 90.29607235
    9.738022903 AEBISCHER_COL 90.2619771
    9.758216238 MILLER_BUF 90.24178376
    9.764520192 CLOUTIER_VAN 90.23547981
    9.764667493 KIDD_TOR 90.23533251
    9.796845424 LUONGO_FLA 90.20315458
    9.805384717 MCLENNAN_NYR 90.19461528
    9.832475003 THEODORE_MTL 90.167525
    9.837815148 VOKOUN_NAS 90.16218485
    9.841497706 LAMOTHE_DET 90.15850229
    9.857363124 BURKE_PHO 90.14263688
    9.860525329 BOUCHER_PHO 90.13947467
    9.878144511 GIGUERE_ANA 90.12185549
    9.879917512 CARON_PIT 90.12008249
    9.918282352 OSGOOD_STL 90.08171765
    9.951887758 DENIS_CBJ 90.04811224
    10 VALIQUETTE_EDM 90
    10.00281764 CECHMANEK_LAK 89.99718236
    10.03218884 LABARBERA_NYR 89.96781116
    10.06191463 JOHNSON_STL 89.93808537
    10.13452612 NURMINEN_ATL 89.86547388
    10.2291028 PELLETIER_PHO 89.7708972
    10.4863003 LECLAIRE_CBJ 89.5136997
    10.5 CHOUINARD_LAK 89.5
    10.60588197 DUNHAM_NYR 89.39411803
    11.03893317 BRYZGALOV_ANA 88.96106683
     
  19. Btw... are these sample sizes significant or no?
     
  20. me2

    me2 Calling out the crap

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    I should think so (for most of the goalies with enough games). It's interesting to see how teams change game plans from backups to starters. ie did Van protect Auld much better than it protected Cloutier, did Toskala have it much easier than Nabby? Were they played against weaker teams. I'd say there is some truth in that.

    Philly are excellent at protecting all of their goalies.

    I'll try and come up with a rating comparing actual sv% vs shot difficulty.
     
    Last edited: Aug 15, 2005
  21. Hockey Outsider

    Hockey Outsider Registered User

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    Ironchef, I definitely agree that more rebounds will lead to closer shots. But the problem is, it's hard to determine whether close shots are caused by bad rebound control or bad defense. We can compare a goalie's stats to their backups, but in the case of goalies like Brodeur and Luongo, their backups played so few games, the sample size may be too small. It's also possible that the defense opens up when certain goalies are in net, and this can also affect the numbers.

    I would have liked to make a histogram for the goalies (that was my first idea, actually) but I don't know how to do it in Excel for so much data. I could do it one-by-one for each goalie, but that would take hours. The third set of data (difference between actual and expected shots) from the file I linked to (http://www.geocities.com/thehockeyoutsider/GoalieShotDistances.xls) should give us at least a rough idea of what kinds of shots the goalies face.

    Me2, excellent work. For the scoring percentages for each category, were those estimates? Or did you calculate it? If those were just estimates, I can calculate the actual values if you're curious but your values looked accurate anyway.

    Yeah, Dunham was in an awful situation last year. At a first glance his actual save percentage was 89.6%, which is very bad. But when you look at his expected save percentage (89.3%), he actually played better than expected. Salo's in the reverse situation: he had an easier time than just about any goalie in the league but still couldn't stop shots at all.

    In terms of sample sizes... I don't think it's a matter of them being significant or not, I think it's a matter of how big the confidence interval is. From what I recall, the confidence interval for most starting goalies is about +/- 2% and for a backup with around 30 games it's about +/- 5% (at the 5% level). Once you look at goalies with fewer than around 20 games, there's so much distortion the data's pretty useless. I can check the data if anyone's interested but, yes, the data is significant for starters and most backups.

    Ken Krzywicki uses the same data, but he also includes ES/PP/SH/Rebound data and shot type (wrist, slap, etc) to make an even more complex, comprehensive model (http://www.hockeyanalytics.com/Research_files/Shot_Quality_Krzywicki.pdf). It's definitely worth checking out if you're into this sort of stuff.

    -----------------------------------

    One more thing. Here's a chart showing Save Percentage as a function of shot distance: http://www.geocities.com/thehockeyoutsider/savepercentage.JPG

    A few cautions:
    1. Excel can only handle about 65,000 rows worth of data, while there were around 72,000 shots last year. So I had to randomly cut 10% of all shots. Still, 90% is a big enough sample.
    2. Unfortunately, this is the "simplest" equation I got with an R^2 of at least 0.8. Sadly, it's way too complex to be of much practical use, in my opinion. Simpler equations don't fit the data well enough.
    3. Not all distances have the same sample size. ie there were only a few dozen shots from 5 feet out, but 2,000 shots from 30 feet out. This may or may not cause the equation to over-fit the data (we're getting into really complex stats here, and I don't know enough to say anything further).
     
    Last edited: Aug 15, 2005
  22. me2

    me2 Calling out the crap

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    Just estimates based on the graphs in the article. I took the midpoint for each distance and matched it to the graph of shooting % success for non-PP then added 1-2 percent or two to allow for PP effect. Not sure what percentage of shots are on the PP but I might knock 0.5% off shooting percentage if I overestimated the number of PP shots.

    Here are a few for some of the top goalies. Compares their Actual SV% vs shooting percentage to get a measure of relative performance. Highest number for performance means they exceeded expectations.

    Shot difficulty, Goalie, expected sv%, actual sv%, performance

    8.49 ESCHE_PHI 91.51 91.50 -0.01
    9.09 BURKE_PHI 90.91 91.00 0.09
    10.60 DUNHAM_NYR 89.394 89.6 0.21
    9.52 KHABIBULIN_TBL 90.48 91.00 0.52
    10.00 CECHMANEK_LAK 90.00 90.6 0.60
    9.86 BURKE_PHO 90.14 90.80 0.66
    9.70 WEEKES_CAR 90.30 91.20 0.90
    9.92 OSGOOD_STL 90.08 91.00 0.92
    9.43 BRODEUR_NJD 90.57 91.70 1.13
    9.76 CLOUTIER_VAN 90.24 91.40 1.16
    9.45 BELFOUR_TOR 90.55 91.80 1.25
    9.88 GIGUERE_ANA 90.12 91.40 1.28
    9.56 NABOKOV_SJS 90.44 92.10 1.66
    9.95 DENIS_CBJ 90.05 91.80 1.75
    8.82 TOSKALA_SJS 91.18 93.00 1.82
    9.53 RAYCROFT_BOS 90.47 92.60 2.13
    9.74 AEBISCHER_COL 90.26 92.40 2.14
    8.91 KIPRUSOFF_CGY 91.09 93.30 2.21
    9.51 ROLOSON_MIN 90.49 93.30 2.81
    9.80 LUONGO_FLA 90.20 93.10 2.90





    Luongo wins because he faced tougher shots than Kipper and Roloson.
     
    Last edited: Aug 16, 2005
  23. friction

    friction 5-14-6-1

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    If anything, this just proves that BY FAR, Salo was the worst goalie in the league.
     
  24. me2

    me2 Calling out the crap

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    Blasting the bull***

    8.15 SALO_COL 91.85 91.2 -0.65

    Not looking good since the Col defense protected him well (easier saves and poor sv %). Mind you that is only 5 games and not significant.

    There is plenty of games for Edmonton though (44)

    9.01 SALO_EDM 90.99 89.6 -1.39
     
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