Red WingsDetroit Red Wings
5-8-2, 12pts · 13th in Eastern Conference
Player Stats
Filter Tips
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Player Name Team Name# POS GP G A P +/- PIM PIM5 HIT SHT OSB OSM SHT% SB MP PPG PPA PPP PKG PKA PKP PKM GW GT FO% FOT GA TA EG HT P/20 PSG PSS
1Warren FoegeleRed WingsRed Wings22C/LW1576131401658213212.07%624.:.14011101370026.09%2300000.7200
2Fabian ZetterlundRed WingsRed Wings7C/LW/RW152911140275014494.00%522.:.24000000140052.78%3600000.6512
3Dmitri VoronkovRed WingsRed Wings5C/LW11459140203792010.81%118.:.4401100001044.77%27700000.8700
4Ryan LindgrenRed WingsRed Wings18D15268-740291251016.67%2624.:.4010100028000.00%000000.4300
5Nate SchmidtRed WingsRed Wings17D15437-72210261221433.33%2423.:.5420200024000.00%000000.3900
6Morgan FrostRed WingsRed Wings16C/LW15437-3201737142010.81%316.:.5800000001151.82%30300000.5502
7Christian DvorakRed WingsRed Wings2C/LW1524602013214159.52%513.:.38000011410151.00%10000000.5901
8Jake BeanRed WingsRed Wings9D150661201471060.00%1716.:.280000002000.00%000000.4900
9Jon MerrillRed WingsRed Wings10D150662601816350.00%2718.:.4200000029000.00%000000.4300
10Connor ZaryRed WingsRed Wings4C/LW/RW15325-720203917357.69%117.:.4602200000052.38%2100000.3801
11Ryan SheaRed WingsRed Wings19D1514501152052820.00%1416.:.3300000028000.00%000000.4000
12Kevin HayesRed WingsRed Wings12C/LW/RW152240206304126.67%012.:.5700000030144.44%1800000.4101
13Erik BrannstromRed WingsRed Wings6D150331120348560.00%2220.:.4600000012000.00%000000.1900
14Thomas NovakRed WingsRed Wings21C15123-38072310184.35%114.:.3601100001049.82%28300000.2701
15Marcus JohanssonRed WingsRed Wings13C/LW15112-640203611152.78%016.:.20000000101063.33%3000000.1611
16Fyodor SvechkovRed WingsRed Wings8C/LW151120003284113.57%311.:.10000000150040.48%8400000.2400
17Mark KastelicRed WingsRed Wings14C/RW1511200027143157.14%406.:.5100000000050.00%11600000.3900
18Kailer YamamotoRed WingsRed Wings11LW/RW15011000091120.00%006.:.5500000000033.33%600000.1902
19Mikulas HovorkaRed WingsRed Wings15D400000000000.00%000.:.060000000000.00%000000.0000
Team Total or Average2703565100-2689153174421393037.92%15916:353581122494348.57%129700000.45211
Filter Tips
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Goalie Name Team NameGP W L OTL PCT GAA MP PIM SO GA SA SAR A EG PS % PSA ST BG S1 S2 S3
1John GibsonRed WingsRed Wings1137189.8%3.1564800343340020.0%0114110
2Spencer KnightRed WingsRed Wings521193.6%2.0526400914000081.8%11411101
Team Total or Average1658290.9%2.8391300434740020.818111515211
Filter Tips
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# VS Team GP W L T OTW OTL SOW SOL GF GA Diff P PCT G A TP SO EG GP1 GP2 GP3 GP4 SHF SH1 SP2 SP3 SP4 SHA SHB Pim Hit PPA PPG PP% PKA PK GA PK% PK GF W OF FO T OF FO OF FO% W DF FO T DF FO DF FO% W NT FO T NT FO NT FO% PZ DF PZ OF PZ NT PC DF PC OF PC NT GF% SH% SV% PDO PDOBRK
1Red WingsIslanders1100000052321.0005101500311034193120267220700.00%10100.00%025349850.80%26553949.17%11223847.06%251719814771.4%14.7%92.3%107.0LUCKY
2Red WingsRangers1010000024-200.0002461000202388702411827300.00%30100.00%025349850.80%26553949.17%11223847.06%181128813533.3%8.7%83.3%92.0Unlucky
3Red WingsLightning1010000035-200.00035800102027129603518518300.00%000.00%025349850.80%26553949.17%11223847.06%211423815737.5%11.1%85.7%96.8FUN
4Red WingsPanthers1010000023-100.00024600110031157903612616100.00%30100.00%025349850.80%26553949.17%11223847.06%211425813640.0%6.5%91.7%98.1DULL
5Red WingsCanadiens1100000041321.0004711002020311111903016427000.00%20100.00%025349850.80%26553949.17%11223847.06%241622713680.0%12.9%96.7%109.6LUCKY
6Red WingsSabres1010000025-300.0002350010103815101303282205120.00%110.00%025349850.80%26553949.17%11223847.06%211423814720.0%5.3%84.4%89.6Unlucky
7Red WingsMaple Leafs2100010076130.75071320003220651917272611914485240.00%7271.43%125349850.80%26553949.17%11223847.06%46304714281555.6%10.8%90.2%100.9FUN
8Red WingsBlues2020000018-700.00012300001056231122055161936500.00%6266.67%025349850.80%26553949.17%11223847.06%43294915261214.3%1.8%85.5%87.2Unlucky
9Red WingsOilers1000000112-110.50012300100130861452511714300.00%10100.00%025349850.80%26553949.17%11223847.06%241624815733.3%3.3%92.0%95.3DULL
10Red WingsKings1010000002-200.0000001000002579903610224600.00%10100.00%025349850.80%26553949.17%11223847.06%23162461150.0%0.0%94.4%94.4DULL
11Red WingsDucks1000001032121.00034700011121686949131024200.00%5260.00%025349850.80%26553949.17%11223847.06%1912329135100.0%14.3%95.9%110.2LUCKY
12Red WingsSharks1000100043121.000471100021132513131327224100.00%10100.00%025349850.80%26553949.17%11223847.06%241623815857.1%12.5%90.6%103.1FUN
13Red WingsGolden Knights1010000023-100.000246001010291011803511819200.00%4175.00%025349850.80%26553949.17%11223847.06%181127813650.0%6.9%91.4%98.3DULL
_Vs Division623001001820-250.4171832500083701927254642194733112914321.43%13376.92%125349850.80%26553949.17%11223847.06%1358914147844246.9%9.4%89.7%99.1FUN
_Vs Conference834001002526-170.438254671101141002499965832244914117624312.50%17382.35%125349850.80%26553949.17%11223847.06%179118189641135548.9%10.0%89.3%99.4FUN
_Since Last GM Reset1538011113646-10120.40036651012013714344215812315517476159893174336.98%35877.14%125349850.80%26553949.17%11223847.06%33422237112121010146.5%8.1%90.3%98.5Unlucky
Total1538011113646-10120.40036651012013714344215812315517476159893174336.98%35877.14%125349850.80%26553949.17%11223847.06%33422237112121010146.5%8.1%90.3%98.5Unlucky

Puck Time
Offensive Zone 22
Neutral Zone 14
Defensive Zone 24
Puck Time
Offensive Zone Start 498
Neutral Zone Start 238
Defensive Zone Start 539
Puck Time
With Puck 29
Without Puck 31
Faceoffs
Faceoffs Won 630
Faceoffs Lost 645
Team Average Shots after League Average Shots after
1st Period 10.510.69
2nd Period 18.721.35
3rd Period 29.132.03
Overtime 30.232.64
Goals in Team Average Goals after League Average Goals after
1st Period 0.91.08
2nd Period 1.32.13
3rd Period 2.32.97
Overtime 2.53.07
Even Strenght Goal 30
PP Goal 3
PK Goal 1
Empty Net Goal 2
Home Away
Win 23
Lost 44
Overtime Lost 11
PP Attempt 43
PP Goal 3
PK Attempt 35
PK Goal Against 8
Home
Shots For 29.5
Shots Against 31.7
Goals For 2.4
Goals Against 3.1
Hits 21.1
Shots Blocked 10.6
Pim 5.9