Red Wings

GP: 82 | W: 41 | L: 31 | OTL: 10 | P: 92
GF: 236 | GA: 223 | PP%: 21.04% | PK%: 81.99%
DG: Martin Dufour | Morale : 56 | Moyenne d'Équipe : 66
La résolution de votre navigateur est trop petite pour cette page. Plusieurs informations sont cachées pour garder la page lisible.

Astuces sur les Filtres (Anglais seulement)
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
# Nom du Joueur C L R D CON CK FG DI SK ST EN DU PH FO PA SC DF PS EX LD PO MO OV TA SP
1Jacob Josefson (R)X100.006232976974778279908073816572985963730
2Justin Fontaine (R)X100.006149586865727489698779636883751580720
3Alexander Kerfoot (R)X100.006748717865829479757672706456554581700
4Ryan Hartman (R)X100.008153617471819375727481645668577579700
5Brady Tkachuk (R)X100.008447657583858474737173746353569181700
6Kyle Clifford (R)X100.007456696783768571636567766584785059690
7Oskar Lindblom (R)X100.006535737076829469596769716954515676670
8Teodors Blugers (R)X100.007744756971776468716569796951506967660
9Tomas Nosek (R)X100.006835746685808165766565735674523776660
10John Hayden (R)X100.007650726386739463616462685265546479650
11Kevin Roy (R)X100.005342797261719962506162625170545776630
12Scott Wilson (R)X100.006935896869748562706161655672564476630
13Jarred Tinordi (R)X100.007954516488626679476055795784845917690
14Will Butcher (R)X100.006135786968839177307864666156525782670
15Joe Morrow (R)X100.007341846674777270306876725466567570670
16Tyler Wotherspoon (R)X100.006229996178606774256449735478736170650
17Madison Bowey (R)X100.007247726580777364307670656354516877650
18Taylor Chorney (R)X100.005635886773706270306265765878575667650
Rayé
1Logan Brown (R)X100.006735677096515170706262625550508519610
2Joseph Gambardella (R)X100.006135736570715764726760655162504219610
3Ryan Poehling (R)X100.006235776277785062705080715150558318610
4Chase De Leo (R)X100.005035707065505050707063635650506020580
5Hudson Fasching (R)X100.006139756677569950505050605159535719570
6Zach Senyshyn (R)X100.005235686576515165505959595150508419570
7Julius Honka (R)X100.006235756368746463306664645362518225610
8Sami Niku (R)X100.005535746573656565306059615751504911590
9Urho Vaakanainen (R)X100.005035716474515164305454555250508519550
10Guillaume Brisebois (R)X100.005135705975515459305454545250506719540
MOYENNE D'ÉQUIPE100.00654074677570736854666568586358625264
Astuces sur les Filtres (Anglais seulement)
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
# Nom du Gardien CON SK DU EN SZ AG RB SC HS RT PH PS EX LD PO MO OV TA SP
1Michael Hutchinson100.00767488837594949999808891704280830
2Mackenzie Blackwood (R)100.00867988918589919089857852697476820
Rayé
1Hannu Toivonen100.00684076797396969696748587975519780
2Dustin Tokarski (R)100.00687873767190898485727782815920770
MOYENNE D'ÉQUIPE100.0075688182769293929278827879584980
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Joe Sacco69737269847366USA501800,000$


Astuces sur les Filtres (Anglais seulement)
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
# Nom du Joueur Nom de l'ÉquipePOSGP G A P +/- PIM PIM5 HIT HTT SHT OSB OSM SHT% SB MP AMG PPG PPA PPP PPS PPM PKG PKA PKP PKS PKM GW GT FO% FOT GA TA EG HT P/20 PSG PSS FW FL FT S1 S2 S3
1Justin FontaineRed Wings (DET)RW80335689-211115211178353902249.35%45169721.221627437629202222626050.47%2146720221.05217003265
2Jacob JosefsonRed Wings (DET)C772945741401172942567915311.33%54165121.441417314626200062654356.38%21804334010.90113000576
3Alexander KerfootRed Wings (DET)C812449731010555144225298791908.05%39155519.21519244024010161456347.60%15825323000.9425119426
4Ryan HartmanRed Wings (DET)RW823729666124302291272837516313.07%28140317.11131124392320000372541.82%1106820010.9424114725
5Brady TkachukRed Wings (DET)LW812427518122202361241615810614.91%35158019.51811193630201131381455.83%1203422000.65511112634
6Kyle CliffordRed Wings (DET)LW7916163218345159121126427312.70%39135217.127613142170000203445.45%772223000.4712252131
7Joe MorrowRed Wings (DET)D72523281351511014411233384.46%109165923.042810202571016254000.00%03846100.3400300011
8Oskar LindblomRed Wings (DET)LW82121426-1620133121209581365.74%27117814.381125410003942052.63%954622000.4412000211
9Will ButcherRed Wings (DET)D746202635210951418732436.90%85161621.843811172310001155110.00%02346000.3200101010
10Tomas NosekRed Wings (DET)C82141024926096112140466810.00%2086510.551232231013613152.93%4271616000.5501000230
11Kevin RoyRed Wings (DET)RW8271118-18033347724479.09%85656.8901108000001142.11%1982000.6400000012
12Tyler WotherspoonRed Wings (DET)D8011617800861516329321.59%103162420.3114561500000164000.00%0848000.2100000000
13Teodors BlugersRed Wings (DET)C8261117-1835513411312345824.88%2587810.710221260001500146.25%5062923000.3903001111
14Taylor ChorneyRed Wings (DET)D8211617-1255631509336271.08%98166120.2604441700111171000.00%02339000.2000001001
15Jarred TinordiRed Wings (DET)D4246108932599795216257.69%4988321.05437181480001142000.00%0922000.2300023000
16Scott WilsonRed Wings (DET)LW823710-46048436017425.00%85747.0100003000023047.83%2377000.3500000000
17Madison BoweyRed Wings (DET)D8218947525115785624291.79%62132516.17000054000059000.00%02538000.1400014000
18John HaydenRed Wings (DET)RW82358-55715110887827523.85%3094011.47000140000241048.08%52129000.1700210021
19Sami NikuRed Wings (DET)D11011-4553136230.00%714913.570000000002000.00%012000.1300010000
20Logan BrownRed Wings (DET)C1000020341200.00%11717.2800001000020081.82%1100000.0000000000
21Hudson FaschingRed Wings (DET)RW2000000010000.00%063.390000000000000.00%000000.0000000000
22Joseph GambardellaRed Wings (DET)C1000000000000.00%000.750000000000000.00%100000.0000000000
23Julius HonkaRed Wings (DET)D43000-7100314321490.00%3461814.3900002000019000.00%0619000.0000000000
24Ryan PoehlingRed Wings (DET)C5000040313010.00%1214.3800000000000045.45%1110000.0000000000
25Chase De LeoRed Wings (DET)LW4000-100114100.00%2225.64000000000300100.00%100000.0000000000
Stats d'équipe Total ou en Moyenne14712263705964102427022592386266281915438.49%9092385216.22751241993252675347332080332351.70%5429539481340.501458111330303334
Astuces sur les Filtres (Anglais seulement)
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
# Nom du Gardien Nom de l'ÉquipeGP W L OTL PCT GAA MP PIM SO GA SA SAR A EG PS % PSA ST BG S1 S2 S3
1Michael HutchinsonRed Wings (DET)67372360.9222.524000211682151953410.712526714954
2Mackenzie BlackwoodRed Wings (DET)184840.9172.5810012043520247220.55691567201
Stats d'équipe Total ou en Moyenne854131100.9212.5350014121126711200630.6896182811155


Astuces sur les Filtres (Anglais seulement)
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
Nom du Joueur Nom de l'ÉquipePOS Âge Date de Naissance Nouveau Joueur Poids Taille Non-Échange Disponible pour Échange Ballotage Forcé Contrat Type Salaire Actuel Salaire RestantCap Salariale Cap Salariale Restant Exclus du Cap Salarial Salaire Année 2Salaire Année 3Salaire Année 4Salaire Année 5Salaire Année 6Salaire Année 7Salaire Année 8Salaire Année 9Salaire Année 10Link
Alexander KerfootRed Wings (DET)C251994-08-11 11:02:39Yes175 Lbs5 ft10NoNoNo2Pro & Farm1,137,500$0$0$No1,137,500$
Brady TkachukRed Wings (DET)LW201999-09-16 14:34:31Yes196 Lbs6 ft3NoNoNo3Pro & Farm925,000$0$0$No925,000$925,000$
Chase De LeoRed Wings (DET)LW241995-10-25 23:11:13Yes179 Lbs5 ft9NoNoNo1Pro & Farm575,000$0$0$No
Dustin TokarskiRed Wings (DET)G301989-09-16 11:11:14Yes201 Lbs5 ft11NoNoNo2Pro & Farm700,000$0$0$No700,000$
Guillaume BriseboisRed Wings (DET)D221997-07-21 14:26:41Yes175 Lbs6 ft2NoNoNo3Pro & Farm697,500$0$0$No697,500$697,500$
Hannu ToivonenRed Wings (DET)G351984-05-18 05:11:14No208 Lbs6 ft2NoNoNo2Pro & Farm800,000$0$0$No800,000$
Hudson FaschingRed Wings (DET)RW241995-07-28 10:55:45Yes204 Lbs6 ft3NoNoNo1Pro & Farm925,000$0$0$No
Jacob JosefsonRed Wings (DET)C291991-03-02 11:11:13Yes198 Lbs6 ft1NoNoNo1Pro & Farm1,000,000$0$0$No
Jarred TinordiRed Wings (DET)D281992-02-20 05:11:13Yes216 Lbs6 ft6NoNoNo3Pro & Farm1,000,000$0$0$No1,000,000$1,000,000$
Joe MorrowRed Wings (DET)D271992-12-09 05:11:13Yes196 Lbs6 ft0NoNoNo3Pro & Farm700,000$0$0$No700,000$700,000$
John HaydenRed Wings (DET)RW251995-02-14 11:00:55Yes215 Lbs6 ft3NoNoNo1Pro & Farm925,000$0$0$No
Joseph GambardellaRed Wings (DET)C261993-12-01 08:29:15Yes196 Lbs5 ft10NoNoNo3Pro & Farm725,000$0$0$No725,000$725,000$
Julius HonkaRed Wings (DET)D241995-12-03 11:03:06Yes180 Lbs5 ft11NoNoNo1Pro & Farm863,333$0$0$No
Justin FontaineRed Wings (DET)RW321987-11-06 23:11:13Yes183 Lbs5 ft10NoNoNo2Pro & Farm1,000,000$0$0$No1,000,000$
Kevin RoyRed Wings (DET)RW261993-05-20 09:32:15Yes170 Lbs5 ft9NoNoNo2Pro & Farm875,000$0$0$No875,000$
Kyle CliffordRed Wings (DET)LW291991-01-13 11:11:13Yes211 Lbs6 ft2NoNoNo2Pro & Farm900,000$0$0$No900,000$
Logan BrownRed Wings (DET)C221998-03-05 09:29:54Yes220 Lbs6 ft6NoNoNo2Pro & Farm1,604,170$0$0$No1,604,170$
Mackenzie BlackwoodRed Wings (DET)G231996-12-09 14:31:28Yes225 Lbs6 ft4NoNoNo3Pro & Farm894,167$0$0$No894,167$894,167$
Madison BoweyRed Wings (DET)D251995-04-22 09:27:31Yes198 Lbs6 ft2NoNoNo2Pro & Farm925,000$0$0$No925,000$
Michael HutchinsonRed Wings (DET)G301990-03-02 17:11:14No200 Lbs6 ft3NoNoNo1Pro & Farm1,500,000$0$0$No
Oskar LindblomRed Wings (DET)LW231996-08-15 11:12:26Yes191 Lbs6 ft1NoNoNo2Pro & Farm1,137,500$0$0$No1,137,500$
Ryan HartmanRed Wings (DET)RW251994-09-20 17:11:13Yes181 Lbs6 ft0NoNoNo3Pro & Farm1,500,000$0$0$No1,500,000$1,500,000$
Ryan PoehlingRed Wings (DET)C211999-01-03 14:22:24Yes183 Lbs6 ft2NoNoNo3Pro & Farm925,000$0$0$No925,000$925,000$
Sami NikuRed Wings (DET)D231996-10-10 05:12:25Yes176 Lbs6 ft1NoNoNo3Pro & Farm775,000$0$0$No775,000$775,000$
Scott WilsonRed Wings (DET)LW281992-04-24 05:11:13Yes185 Lbs5 ft11NoNoNo3Pro & Farm575,000$0$0$No575,000$575,000$
Taylor ChorneyRed Wings (DET)D331987-04-27 23:11:13Yes193 Lbs6 ft0NoNoNo2Pro & Farm600,000$0$0$No600,000$
Teodors BlugersRed Wings (DET)C251994-08-15 14:16:07Yes185 Lbs6 ft0NoNoNo3Pro & Farm650,000$0$0$No650,000$650,000$
Tomas NosekRed Wings (DET)C271992-09-01 11:05:59Yes210 Lbs6 ft3NoNoNo1Pro & Farm612,500$0$0$No
Tyler WotherspoonRed Wings (DET)D271993-03-12 11:11:13Yes217 Lbs6 ft2NoNoNo1Pro & Farm700,000$0$0$No
Urho VaakanainenRed Wings (DET)D211999-01-01 14:29:18Yes185 Lbs6 ft1NoNoNo3Pro & Farm925,000$0$0$No925,000$925,000$
Will ButcherRed Wings (DET)D251995-01-06 11:14:31Yes190 Lbs5 ft10NoNoNo2Pro & Farm2,775,000$0$0$No2,775,000$
Zach SenyshynRed Wings (DET)RW231997-03-30 14:24:49Yes192 Lbs6 ft1NoNoNo3Pro & Farm863,333$0$0$No863,333$863,333$
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
3225.84195 Lbs6 ft12.16959,688$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Brady TkachukJacob JosefsonJustin Fontaine35122
2Kyle CliffordAlexander KerfootRyan Hartman35122
3Oskar LindblomTeodors BlugersJohn Hayden25122
4Scott WilsonTomas NosekKevin Roy5122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Jarred TinordiJoe Morrow35122
2Will ButcherTaylor Chorney35122
3Madison BoweyTyler Wotherspoon30122
4Jarred TinordiJoe Morrow0122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Brady TkachukJacob JosefsonJustin Fontaine60122
2Kyle CliffordAlexander KerfootRyan Hartman40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Jarred TinordiJoe Morrow60122
2Will ButcherTaylor Chorney40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Jacob JosefsonJustin Fontaine60122
2Ryan HartmanBrady Tkachuk40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Jarred TinordiJoe Morrow60122
2Will ButcherTaylor Chorney40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Jacob Josefson60122Jarred TinordiJoe Morrow60122
2Justin Fontaine40122Will ButcherTaylor Chorney40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Jacob JosefsonJustin Fontaine60122
2Ryan HartmanBrady Tkachuk40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Jarred TinordiJoe Morrow60122
2Will ButcherTaylor Chorney40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Brady TkachukJacob JosefsonJustin FontaineJarred TinordiJoe Morrow
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Brady TkachukJacob JosefsonJustin FontaineJarred TinordiJoe Morrow
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Oskar Lindblom, Teodors Blugers, Tomas NosekOskar Lindblom, Teodors BlugersTomas Nosek
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Madison Bowey, Tyler Wotherspoon, Will ButcherMadison BoweyTyler Wotherspoon, Will Butcher
Tirs de Pénalité
Jacob Josefson, Justin Fontaine, Ryan Hartman, Brady Tkachuk, Alexander Kerfoot
Gardien
#1 : Michael Hutchinson, #2 : Mackenzie Blackwood


Astuces sur les Filtres (Anglais seulement)
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
LigueDomicileVisiteur
# VS Équipe GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff 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
1Avalanche2010000126-41000000112-11010000014-310.250224006286781764917866894756127859700.00%4250.00%01098211351.96%1100212851.69%609113353.75%176199418047801442709
2Blackhawks20100010440100000102111010000023-120.5004590062867817809178668947580297584125.00%110.00%01098211351.96%1100212851.69%609113353.75%176199418047801442709
3Blues2010000147-31010000024-21000000123-110.25045900628678178391786689475672316454125.00%8275.00%01098211351.96%1100212851.69%609113353.75%176199418047801442709
4Bruins623000011919031200000109131100001910-150.4171933520062867817186917866894751826414818533721.21%39976.92%01098211351.96%1100212851.69%609113353.75%176199418047801442709
5Canadiens63300000171523120000079-232100000106460.500173249106286781719991786689475193519516739717.95%35585.71%21098211351.96%1100212851.69%609113353.75%176199418047801442709
6Canucks2020000059-41010000046-21010000013-200.000561100628678176991786689475803210488225.00%5180.00%01098211351.96%1100212851.69%609113353.75%176199418047801442709
7Capitals410010021082200010016602100000142260.750101727016286781714291786689475119503210923313.04%110100.00%01098211351.96%1100212851.69%609113353.75%176199418047801442709
8Devils440000001679220000009542200000072581.000162743006286781713291786689475148523210716637.50%11190.91%01098211351.96%1100212851.69%609113353.75%176199418047801442709
9Ducks3110001079-22110000036-31000001043140.667791600628678178691786689475983820776116.67%10280.00%01098211351.96%1100212851.69%609113353.75%176199418047801442709
10Flames21001000633110000003121000100032141.00061117006286781775917866894756931055300.00%000.00%01098211351.96%1100212851.69%609113353.75%176199418047801442709
11Flyers402000021014-42010000135-22010000179-220.250101828006286781712291786689475139465312217635.29%19573.68%01098211351.96%1100212851.69%609113353.75%176199418047801442709
12Islanders4220000010100211000006602110000044040.5001018280062867817125917866894751364118931815.56%9277.78%01098211351.96%1100212851.69%609113353.75%176199418047801442709
13Kings3120000010821010000023-12110000085320.333101525106286781710991786689475933328908337.50%9277.78%01098211351.96%1100212851.69%609113353.75%176199418047801442709
14Maple Leafs650000102918113300000016883200001013103121.00029487700628678171979178668947518474110157411229.27%40880.00%01098211351.96%1100212851.69%609113353.75%176199418047801442709
15Oilers2110000056-1110000004311010000013-220.50058130062867817739178668947566259473133.33%20100.00%01098211351.96%1100212851.69%609113353.75%176199418047801442709
16Penguins4210010013121210001009902110000043150.625132235006286781712591786689475128484211713215.38%16381.25%01098211351.96%1100212851.69%609113353.75%176199418047801442709
17Predators211000007431010000013-21100000061520.500711180062867817709178668947562228694250.00%4175.00%01098211351.96%1100212851.69%609113353.75%176199418047801442709
18Rangers41300000711-4211000004402020000037-420.2507121910628678171369178668947513539419623313.04%18194.44%11098211351.96%1100212851.69%609113353.75%176199418047801442709
19Sabres713000211717030200001610-441100020117470.5001722390062867817200917866894752278615220938615.79%51590.20%01098211351.96%1100212851.69%609113353.75%176199418047801442709
20Senateurs7320101021210421000101212031101000990100.7142132530062867817215917866894752186417921541819.51%611477.05%01098211351.96%1100212851.69%609113353.75%176199418047801442709
21Sharks211000005501010000002-21100000053220.50056110062867817549178668947558206605360.00%30100.00%01098211351.96%1100212851.69%609113353.75%176199418047801442709
22Stars21100000862110000006331010000023-120.500815230062867817779178668947567244555240.00%2150.00%01098211351.96%1100212851.69%609113353.75%176199418047801442709
Total823131031792362231341161601125117119-24115150205411910415920.56123637961531628678172696917866894752674943102422993667721.04%3616581.99%31098211351.96%1100212851.69%609113353.75%176199418047801442709
24Wild200000114401000000112-11000001032130.750459006286781777917866894756424659700.00%30100.00%01098211351.96%1100212851.69%609113353.75%176199418047801442709
_Since Last GM Reset823131031792362231341161601125117119-24115150205411910415920.56123637961531628678172696917866894752674943102422993667721.04%3616581.99%31098211351.96%1100212851.69%609113353.75%176199418047801442709
_Vs Conference562419021461691521728121001113888352812901033816912670.5981692814502162867817177991786689475180961590215773026120.20%3105382.90%31098211351.96%1100212851.69%609113353.75%176199418047801442709
_Vs Division32141101042103901316770001151483167401031524210400.62510316727010628678179979178668947510043396849331924020.83%2264181.86%21098211351.96%1100212851.69%609113353.75%176199418047801442709

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
8292W1236379615269626749431024229931
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
8231313179236223
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
4116161125117119
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
4115152054119104
Derniers 10 Matchs
WLOTWOTL SOWSOL
550000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
3667721.04%3616581.99%3
Tirs en 1e PériodeTirs en 2e PériodeTirs en 3e PériodeTirs en 4e PériodeButs en 1e PériodeButs en 2e PériodeButs en 3e PériodeButs en 4e Période
9178668947562867817
Mises en Jeu
Gagnées en Zone OffensiveTotal en Zone Offensive% Gagnées en Zone Offensive Gagnées en Zone DéfensiveTotal en Zone Défensive% Gagnées en Zone DéfensiveGagnées en Zone NeutreTotal en Zone Neutre% Gagnées en Zone Neutre
1098211351.96%1100212851.69%609113353.75%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
176199418047801442709


Derniers Match Joués
Astuces sur les Filtres (Anglais seulement)
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
JourMatch Équipe Visiteuse Score Équipe Locale Score ST OT SO RI Lien
1 - 2019-10-063Senateurs7Red Wings4LSommaire du Match
3 - 2019-10-0817Red Wings4Canadiens2WR5Sommaire du Match
5 - 2019-10-1024Red Wings3Bruins4LXXSommaire du Match
7 - 2019-10-1235Red Wings4Maple Leafs3WSommaire du Match
8 - 2019-10-1346Sabres3Red Wings2LXXSommaire du Match
11 - 2019-10-1659Red Wings2Sabres1WXXSommaire du Match
13 - 2019-10-1871Senateurs1Red Wings2WXXSommaire du Match
16 - 2019-10-2184Red Wings5Sabres2WSommaire du Match
18 - 2019-10-2393Bruins3Red Wings2LSommaire du Match
21 - 2019-10-26111Canadiens2Red Wings3WR5Sommaire du Match
23 - 2019-10-28124Red Wings4Penguins1WSommaire du Match
25 - 2019-10-30135Maple Leafs1Red Wings6WSommaire du Match
26 - 2019-10-31140Red Wings5Senateurs4WXSommaire du Match
30 - 2019-11-04161Devils2Red Wings5WSommaire du Match
32 - 2019-11-06170Red Wings3Sabres2WXXSommaire du Match
34 - 2019-11-08179Red Wings4Maple Leafs3WXXSommaire du Match
35 - 2019-11-09188Penguins5Red Wings6WSommaire du Match
38 - 2019-11-12201Red Wings2Flyers3LSommaire du Match
40 - 2019-11-14214Bruins3Red Wings6WSommaire du Match
43 - 2019-11-17229Canadiens3Red Wings2LR5Sommaire du Match
45 - 2019-11-19240Red Wings4Ducks3WXXSommaire du Match
47 - 2019-11-21252Capitals4Red Wings3LXXSommaire du Match
49 - 2019-11-23259Red Wings1Capitals2LXXSommaire du Match
51 - 2019-11-25270Red Wings1Sabres2LSommaire du Match
53 - 2019-11-27282Predators3Red Wings1LSommaire du Match
55 - 2019-11-29293Red Wings6Predators1WSommaire du Match
57 - 2019-12-01304Red Wings3Capitals0WSommaire du Match
58 - 2019-12-02310Oilers3Red Wings4WSommaire du Match
62 - 2019-12-06326Red Wings1Canucks3LSommaire du Match
64 - 2019-12-08335Flyers3Red Wings2LSommaire du Match
66 - 2019-12-10346Red Wings1Avalanche4LSommaire du Match
68 - 2019-12-12358Penguins4Red Wings3LXSommaire du Match
70 - 2019-12-14370Red Wings2Stars3LSommaire du Match
72 - 2019-12-16380Red Wings5Sharks3WSommaire du Match
73 - 2019-12-17384Bruins3Red Wings2LSommaire du Match
76 - 2019-12-20402Red Wings2Blackhawks3LSommaire du Match
77 - 2019-12-21408Ducks5Red Wings1LSommaire du Match
81 - 2019-12-25427Blues4Red Wings2LSommaire du Match
83 - 2019-12-27442Red Wings5Flyers6LXXSommaire du Match
85 - 2019-12-29452Sabres4Red Wings3LSommaire du Match
87 - 2019-12-31463Red Wings2Blues3LXXSommaire du Match
91 - 2020-01-04479Canadiens4Red Wings2LR5Sommaire du Match
94 - 2020-01-07497Flyers2Red Wings1LXXSommaire du Match
96 - 2020-01-09508Red Wings5Maple Leafs4WSommaire du Match
98 - 2020-01-11518Red Wings3Wild2WXXSommaire du Match
99 - 2020-01-12526Sabres3Red Wings1LSommaire du Match
103 - 2020-01-16546Rangers2Red Wings3WSommaire du Match
106 - 2020-01-19560Red Wings0Penguins2LSommaire du Match
108 - 2020-01-21572Maple Leafs2Red Wings4WSommaire du Match
110 - 2020-01-23583Red Wings1Oilers3LSommaire du Match
111 - 2020-01-24592Kings3Red Wings2LSommaire du Match
115 - 2020-01-28608Red Wings2Bruins4LSommaire du Match
117 - 2020-01-30617Stars3Red Wings6WSommaire du Match
120 - 2020-02-02633Red Wings4Bruins2WSommaire du Match
122 - 2020-02-04642Rangers2Red Wings1LSommaire du Match
125 - 2020-02-07660Maple Leafs5Red Wings6WSommaire du Match
127 - 2020-02-09667Red Wings2Senateurs1WSommaire du Match
129 - 2020-02-11681Red Wings6Kings1WSommaire du Match
130 - 2020-02-12690Blackhawks1Red Wings2WXXSommaire du Match
133 - 2020-02-15704Red Wings3Flames2WXSommaire du Match
135 - 2020-02-17715Flames1Red Wings3WSommaire du Match
137 - 2020-02-19729Red Wings4Canadiens1WR5Sommaire du Match
139 - 2020-02-21738Wild2Red Wings1LXXSommaire du Match
142 - 2020-02-24753Red Wings2Canadiens3LR5Sommaire du Match
144 - 2020-02-26763Ducks1Red Wings2WSommaire du Match
147 - 2020-02-29780Canucks6Red Wings4LSommaire du Match
149 - 2020-03-02791Red Wings2Senateurs4LSommaire du Match
151 - 2020-03-04804Red Wings2Kings4LSommaire du Match
153 - 2020-03-06810Avalanche2Red Wings1LXXSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
156 - 2020-03-09829Senateurs3Red Wings4WSommaire du Match
158 - 2020-03-11838Red Wings3Islanders1WSommaire du Match
161 - 2020-03-14853Red Wings3Devils1WSommaire du Match
162 - 2020-03-15858Capitals2Red Wings3WXSommaire du Match
165 - 2020-03-18876Sharks2Red Wings0LSommaire du Match
167 - 2020-03-20884Red Wings1Rangers3LSommaire du Match
170 - 2020-03-23900Senateurs1Red Wings2WSommaire du Match
172 - 2020-03-25910Red Wings2Rangers4LSommaire du Match
176 - 2020-03-29925Devils3Red Wings4WSommaire du Match
179 - 2020-04-01944Red Wings1Islanders3LSommaire du Match
182 - 2020-04-04956Islanders3Red Wings4WSommaire du Match
185 - 2020-04-07966Islanders3Red Wings2LSommaire du Match
187 - 2020-04-09975Red Wings4Devils1WSommaire du Match



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité de l'Aréna20001000
Prix des Billets4525
Assistance64,83738,201
Assistance PCT79.07%93.17%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacité de l'ArénaPopularité de l'Équipe
0 2513 - 83.77% 100,776$4,131,835$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
3,723,173$ 3,071,000$ 2,181,000$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
16,163$ 2,923,172$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 0 20,374$ 0$




LigueDomicileVisiteur
Année GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff P 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
Saison Régulière
1378253404366205228-2339131903211102118-1639121501155103110-7502053265311055697116250585089074957267793475522103105417.42%3457378.84%61000193751.63%1099210352.26%576110951.94%171398917067241334667
14823131031792362231341161601125117119-241151502054119104159223637961531628678172696917866894752674943102422993667721.04%3616581.99%31098211351.96%1100212851.69%609113353.75%176199418047801442709
Total Saison Régulière16056650741315441451-1080293504336219237-188027300311092222148142441705114641117155149335201176717561643132535118771779450967613119.38%70613880.45%92098405051.80%2199423151.97%1185224252.85%347519833510150427771376
Séries
12404000001421-720200000812-42020000069-3014264010077016551554811152466411524729.17%22768.18%07113353.38%6112548.80%447856.41%955590407739
Total Séries404000001421-720200000812-42020000069-3014264010077016551554811152466411524729.17%22768.18%07113353.38%6112548.80%447856.41%955590407739