Data: NHL Goaltender Schedule Strength, 2018-19 regular season and playoffs

  1. Doctor No

    Doctor No Registered User

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    This is a standard feature on my site; although this represents the regular season, I had to wait until the Stanley Cup concluded because the power ratings (and therefore the average strengths) change during the playoffs.

    These are for the 2018-19 NHL regular season.

    AVGOSTR - average opponent strength, measured in goals/game. So someone like Jordan Binnington faced an average opponent 0.03 goals/game below average (basically an average opponent). These are weighted by minutes played, and do incorporate home ice advantage (it is harder to play on the road than at home).

    AVGOSPC - average opponent save percentage. This is what an average NHL goaltender's save percentage would be, facing the slate of opponents that the goaltender faced. (An easier way of looking at it is that it's 1 minus the average shooting percentage of the goaltender's opponents). This does not incorporate home ice advantage.

    TOPPCT - percentage of games (shots weighted) facing the top quartile of opponents. An average goaltender plays 25% of his games against the top quartile of opponents. For 2018-19, these teams are Tampa Bay, Boston, Calgary, Columbus, Toronto, NY Islanders, St. Louis, and Winnipeg (75% - since there are 31 teams, there are 7.75 "top quartile" teams).

    BOTPCT - percentage of games (shots weighted) facing the bottom quartile of opponents. For 2018-19, these teams are Los Angeles, Ottawa, Anaheim, New Jersey, Detroit, NY Rangers, Buffalo, and Edmonton (75%).

    HOMEPCT - percentage of games (shots weighted) played at home. An average goaltender plays 50% of games at home. Backups usually play less than 50% at home.
     
  2. Doctor No

    Doctor No Registered User

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    NAMETEAM AVGOSTR AVGOSPCTOPPCTBOTPCTHOMEPCT
    Bernier JonathanDET 0.13 0.91030%14%31%
    Binnington JordanSTL (0.03) 0.91022%25%55%
    Bishop BenDAL (0.07) 0.91126%26%59%
    Blackwood MackenzieNJD 0.08 0.90732%14%46%
    Bobrovsky SergeiCBJ (0.01) 0.90923%24%51%
    Bow LandonDAL 0.79 0.90047%0%0%
    Boyle KevinANA (0.15) 0.91221%16%42%
    Allen JakeSTL 0.01 0.90919%23%41%
    Andersen FrederikTOR 0.04 0.90823%25%49%
    Anderson CraigOTT 0.06 0.90831%24%55%
    Bachman RichardVAN (0.15) 0.9220%0%0%
    Brossoit LaurentWPG (0.19) 0.9139%37%42%
    Budaj PeterLOS (0.13) 0.90234%42%58%
    Campbell JackLOS 0.05 0.90830%23%43%
    Comrie EricWPG (0.15) 0.9220%0%0%
    Condon MikeOTT 0.70 0.91279%0%0%
    Copley PheonixWSH (0.14) 0.91020%39%41%
    Crawford CoreyCHI (0.02) 0.91024%22%50%
    Daccord JoeyOTT (0.37) 0.9200%100%0%
    Darling ScottCAR 0.08 0.90737%13%64%
    Delia CollinCHI 0.10 0.90924%10%52%
    Dell AaronSJS 0.01 0.90931%24%31%
    Demko ThatcherVAN (0.13) 0.91123%32%54%
    DeSmith CaseyPIT (0.10) 0.91022%28%55%
    DiPietro MichaelVAN 0.06 0.8970%0%100%
    Domingue LouisTBL (0.13) 0.91012%38%38%
    Dubnyk DevanMIN (0.00) 0.91022%23%51%
    Elliott BrianPHI 0.02 0.90826%25%53%
    Fleury Marc-AndreVGK (0.10) 0.91022%29%54%
    Francouz PavelCOL 0.03 0.9120%0%52%
    Fulcher KadenDET (0.67) 0.9200%100%100%
    Georgiev AlexanderNYR 0.17 0.90740%15%43%
    Gibson ChristopherNYI (0.07) 0.9090%0%50%
    Gibson JohnANA (0.03) 0.91119%21%57%
    Greiss ThomasNYI 0.01 0.90920%26%54%
    Grubauer PhilippCOL (0.05) 0.91218%22%48%
    Halak JaroslavBOS (0.03) 0.90917%24%48%
    Hart CarterPHI (0.01) 0.90931%24%70%
    Hellebuyck ConnorWPG 0.04 0.90925%18%53%
    Hill AdenARI (0.00) 0.90719%33%45%
    Hogberg MarcusOTT 0.07 0.90929%0%71%
    Holtby BradenWSH 0.04 0.90930%23%54%
    Howard JimmyDET (0.02) 0.90728%29%61%
    Hutchinson MichaelFLO (0.31) 0.9050%52%31%
    Hutchinson MichaelTOR (0.15) 0.91620%20%80%
    Hutton CarterBUF 0.10 0.90630%18%51%
    Jarry TristanPIT 0.64 0.90951%0%0%
    Johnson ChadANA 0.17 0.90636%23%3%
    Johnson ChadSTL (0.12) 0.91112%12%76%
    Jones MartinSJS (0.07) 0.91120%28%57%
    Khudobin AntonDAL 0.04 0.90822%18%41%
    Kinkaid KeithNJD 0.05 0.90724%19%54%
    Korpisalo JoonasCBJ (0.03) 0.90821%37%48%
    Koskinen MikkoEDM 0.01 0.91023%25%49%
    Kuemper DarcyARI (0.05) 0.91023%25%51%
    Lagace MaximeVGK 0.41 0.9200%0%0%
    Lehner RobinNYI (0.04) 0.90825%29%47%
    Lindgren CharlieMON 0.22 0.900100%0%100%
    Lundqvist HenrikNYR (0.06) 0.91020%27%54%
    Luongo RobertoFLO 0.10 0.90733%26%44%
    Lyon AlexPHI (0.25) 0.9170%28%0%
    Markstrom JacobVAN (0.04) 0.90926%29%53%
    McElhinney CurtisCAR (0.04) 0.90927%30%54%
    McKenna MikeOTT 0.09 0.91027%16%33%
    McKenna MikePHI 0.47 0.8960%0%0%
    Miller RyanANA 0.06 0.90632%21%48%
    Miska HunterARI (0.40) 0.9100%100%0%
    Montembeault SamuelFLO (0.04) 0.91421%30%51%
    Mrazek PetrCAR 0.00 0.90727%29%45%
    Murray MattPIT 0.02 0.90830%28%49%
    Nedeljkovic AlexCAR (0.21) 0.9150%0%0%
    Neuvirth MichalPHI 0.32 0.90639%18%32%
    Niemi AnttiMON (0.08) 0.91113%36%38%
    Nilsson AndersOTT (0.05) 0.91021%28%45%
    Nilsson AndersVAN 0.25 0.90523%8%33%
    Pasquale EdwardTBL 0.24 0.91233%36%0%
    Petersen CalvinLOS 0.03 0.91025%17%31%
    Pickard CalvinARI 0.27 0.90736%5%26%
    Pickard CalvinPHI 0.03 0.90429%35%37%
    Price CareyMON 0.02 0.90732%27%52%
    Quick JonathanLOS (0.01) 0.91019%20%58%
    Raanta AnttiARI (0.07) 0.90922%35%59%
    Rask TuukkaBOS (0.03) 0.90830%33%52%
    Reimer JamesFLO (0.04) 0.90828%29%59%
    Rinne PekkaNSH (0.04) 0.90926%26%54%
    Rittich DavidCGY (0.06) 0.91017%29%46%
    Saros JuuseNSH 0.06 0.91024%15%42%
    Schneider CoryNJD (0.04) 0.90929%36%47%
    Smith MikeCGY (0.07) 0.91120%22%55%
    Sparks GarretTOR (0.13) 0.90926%44%45%
    Stalock AlexMIN 0.09 0.90938%21%48%
    Stolarz AnthonyEDM 0.05 0.90554%22%71%
    Stolarz AnthonyPHI (0.05) 0.90634%40%32%
    Subban MalcolmVGK 0.07 0.90728%22%42%
    Talbot CameronEDM (0.02) 0.90920%17%50%
    Talbot CameronPHI 0.10 0.9160%28%16%
    Ullmark LinusBUF (0.07) 0.91027%31%49%
    Varlamov SemyonCOL 0.01 0.90830%23%51%
    Vasilevski AndreiTBL (0.02) 0.90929%24%58%
    Ward CamCHI 0.01 0.91029%28%50%
     
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  3. Doctor No

    Doctor No Registered User

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    I left these alphabetical to make it easier to find your goaltenders.

    Here are the weights used to composite the AVGOSTR metric:

    OPPONENTHOMEAWAY
    Tampa Bay 0.896 1.199
    Boston 0.583 0.886
    Calgary 0.446 0.749
    Columbus 0.264 0.567
    Toronto 0.223 0.527
    NY Islanders 0.215 0.518
    St. Louis 0.182 0.485
    Winnipeg 0.172 0.476
    Washington 0.170 0.474
    Pittsburgh 0.134 0.438
    Carolina 0.107 0.410
    Nashville 0.081 0.384
    Colorado 0.080 0.383
    Vegas 0.059 0.363
    San Jose 0.056 0.359
    Dallas 0.017 0.320
    Montreal (0.017) 0.286
    Florida (0.296) 0.007
    Arizona (0.302) 0.002
    Chicago (0.391) (0.088)
    Minnesota (0.450) (0.147)
    Vancouver (0.508) (0.205)
    Philadelphia (0.579) (0.275)
    Edmonton (0.664) (0.361)
    Buffalo (0.668) (0.365)
    NY Rangers (0.672) (0.368)
    Detroit (0.703) (0.400)
    New Jersey (0.765) (0.461)
    Anaheim (0.784) (0.481)
    Ottawa (0.843) (0.539)
    Los Angeles (0.885) (0.581)

    To read the table, figure out if the goaltender is at HOME or AWAY. For instance, Tampa Bay would be favored over an average team by 0.896 if you are at HOME (Tampa Bay on the road) and favored by 1.199 if you are AWAY (Tampa Bay is at home).
     
  4. Doctor No

    Doctor No Registered User

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    Clear as mud? Let me know if you have questions.
     
  5. Doctor No

    Doctor No Registered User

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    These are the same measures for the 2019 Stanley Cup playoffs. Obvious differences: the "average team" is usually above average, and you play a lot more top quartile teams (no bottom quartile teams made the playoffs).

    NAMETEAM AVGOSTR AVGOSPCTOPPCTHOMEPCT
    Binnington JordanSTL 0.36 0.91643%50%
    Bishop BenDAL 0.30 0.92353%45%
    Bobrovsky SergeiCBJ 0.86 0.908100%47%
    Allen JakeSTL 0.58 0.917100%100%
    Andersen FrederikTOR 0.76 0.917100%42%
    Dell AaronSJS 0.06 0.9260%100%
    Fleury Marc-AndreVGK 0.22 0.9060%45%
    Greiss ThomasNYI 0.41 0.9290%0%
    Grubauer PhilippCOL 0.40 0.90143%43%
    Hellebuyck ConnorWPG 0.34 0.918100%49%
    Holtby BradenWSH 0.23 0.9290%61%
    Jones MartinSJS 0.25 0.92131%54%
    Khudobin AntonDAL 0.18 0.918100%100%
    Lehner RobinNYI 0.26 0.9200%55%
    McElhinney CurtisCAR 0.41 0.919100%88%
    Mrazek PetrCAR 0.47 0.90932%27%
    Murray MattPIT 0.37 0.920100%49%
    Rask TuukkaBOS 0.34 0.91684%55%
    Rinne PekkaNSH 0.16 0.9300%54%
    Saros JuuseNSH 0.32 0.9300%0%
    Smith MikeCGY 0.20 0.9180%59%
    Vasilevski AndreiTBL 0.41 0.915100%50%
     
  6. morehockeystats

    morehockeystats Unusual hockey stats

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    Shouldn't the top 25 and bottom 25 be measured at the time of the game, and thus be different for each point in time?
     
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  7. Doctor No

    Doctor No Registered User

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    I don't trust data credibility at that level of granularity, but have no problem if others want to do it.

    On some level, even if you want to do a "point in time" strength of opponent, you need to have a large enough sample size that doesn't include the game in question (because otherwise you'd get a feedback loop). My chosen sample size is full season. Others may reasonably choose something else.
     

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