Wolves

GP: 45 | W: 15 | L: 24 | OTL: 6 | P: 36
GF: 89 | GA: 117 | PP%: 8.44% | PK%: 82.73%
DG: Michael Lévesque | Morale : 50 | Moyenne d'Équipe : N/A
Prochain matchs #501 vs Penguins
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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 SPÂgeContratSalaire
1Andy AndreoffXX100.00794495707452815446575561256364615000292677,500$
2Brett RitchieX100.009870787781566462366258552565666350002611,750,000$
3Dylan GambrellX100.007155896272617963726159732550516550002321,137,000$
4Michael ChaputXX100.007875867375747863795564706160606650002821,000,000$
5Sam GagnerXX100.006542907672657867486366576179806651003023,150,000$
6Tim SchallerXX100.00754491787859705636525991256263665000292775,000$
7Trent Frederic (R)X100.006475376575788359746053585045455850002231,137,500$
8David Gustafsson (R)X100.00594199807145575164505769254646605000203925,000$
9Jason Robertson (R)X100.00777386747377806550596766644444695000203894,167$
10Joona Luoto (R)XX100.006742957370466452255055722545456050002211,012,500$
11Laurent DauphinXXX100.007168796468727660755660625747476350002511,000,000$
12Jakub Lauko (R)XX100.00666470606455555670485958564444585000203894,167$
13Jonas SiegenthalerX100.007154816882677956254948912558596350002331,758,333$
14Noah JuulsenX100.007467905367474748254139603745455050002321,063,333$
15Ville Heinola (R)X100.007666998066505050254739623744445350001931,137,500$
16Jordan SchmaltzX100.00757282687263675125454163394949545000261700,000$
17Travis SanheimX100.006742868070809367255953802560616750002411,263,333$
18Vince DunnX100.00695388857369927225545458256061635000231894,167$
Rayé
1Boris Katchouk (R)X100.00757184617180856050565964564444635000223894,166$
2Glenn Gawdin (R)X100.00757184647174766680676165584444655000233925,000$
3Mackenzie MacEachern (R)X100.00815681627252746331566667255555665000263750,000$
4Yannick Veilleux (R)X100.00767677617659586450566865654444655000271833,333$
5MacKenzie Entwistle (R)X100.00737080637070745850545862554444615000203894,167$
6Jake Walman (R)X100.00706583716574795525474861464848585000243925,000$
7Niko Mikkola (R)X100.00757185687173794925434061384444545000263925,000$
8Andrew Peeke (R)X100.007644937674636357254848632546465850002231,491,667$
MOYENNE D'ÉQUIPE100.0073608470726472594554556641515261500
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
1Casey DeSmith100.0057688566556151605655304848585000
2Ville Husso (R)100.0059648081596456646261304444614600
Rayé
MOYENNE D'ÉQUIPE100.005866837457635462595830464660480
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Patrick Roy73717270827769CAN5631,000,000$


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
1Travis SanheimWolves (STL)D4452227252044486817477.35%61104323.713811542020111214000.00%000000.5200000214
2Sven BaertschiBluesLW40111223-3180389872235815.28%1086421.613691516000031923044.01%45900000.5314000312
3Michael ChaputWolves (STL)C/LW45715224511544938824597.95%569215.380228770000340062.55%55000000.6411012042
4Tim SchallerWolves (STL)LW/RW4081422312020568128659.88%567316.84347271710001550142.31%5200000.6500000031
5Jason RobertsonWolves (STL)LW4591322232031629737729.28%572816.190115701012581046.03%6300000.6000000300
6Sam GagnerWolves (STL)C/RW368917240153850121916.00%571619.902681315600011560252.78%59300000.4715000400
7Jonas SiegenthalerWolves (STL)D456915-6300534455173110.91%4693820.86538381790000186400.00%000000.3200000023
8Vince DunnWolves (STL)D4531215-838048224418246.82%2798621.92314371830000195000.00%000000.3000000001
9Devin ShoreBluesC/LW4021214-18015584710354.26%376419.100441016200001140048.08%67600000.3702000101
10Brett RitchieWolves (STL)RW458513278101373157103114.04%371415.8911271780000170232.65%4900000.3601011211
11Ryan LindgrenBluesD3821012174010438275217.41%3172719.14044121030001104010.00%000000.3300000000
12Noah JuulsenWolves (STL)D4501111-17601098331911120.00%3976116.93011942000098000.00%000000.2900002010
13Glenn GawdinWolves (STL)C313710-120033362151114.29%938212.3300004000070058.49%15900000.5200000102
14David GustafssonWolves (STL)C442810-1403716312353.17%249511.260223280000110051.72%37900000.4000000000
15Jason DickinsonBluesC/LW/RW22639-6140313044133613.64%639718.07516131040001222051.52%13200000.4500000104
16Austin PoganskiBluesRW18235124021121541113.33%322112.3100006000000166.67%600000.4500000010
17Barrett HaytonBluesC/LW36325-4207192251313.64%12306.410110350002330139.80%9800000.4300000000
18Laurent DauphinWolves (STL)C/LW/RW31325-314022232392113.04%235211.381012120000130048.39%3100000.2800000010
19Andy AndreoffWolves (STL)C/LW45022-92352127215140.00%33678.18000030111380043.97%11600000.1100001000
20Niko MikkolaWolves (STL)D90223801502110.00%313214.7100003000015000.00%000000.3000000000
21Ville HeinolaWolves (STL)D28022018023114440.00%945516.28000042000049000.00%000000.0900000000
22Trent FredericWolves (STL)C121011755423350.00%0695.7500019000071035.29%1700000.2900001001
23Dylan GambrellWolves (STL)C3000-620115120.00%14113.9400011000000050.00%3000000.0000000000
24Andrew PeekeWolves (STL)D2000040111100.00%03919.7700016000012000.00%000000.0000000000
25Joona LuotoWolves (STL)LW/RW3000-400132120.00%0248.030000000000000.00%000000.0000000000
26Jordan SchmaltzWolves (STL)D7000-6195561100.00%210014.390000300007000.00%000000.0000010000
27Jakub LaukoWolves (STL)C/LW1000000000000.00%066.080000000000000.00%100000.0000000000
Stats d'équipe Total ou en Moyenne80089175264-54616508368659312776279.56%2811292916.16264571256194912313164911850.95%341100000.41313037171522
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
1Ville HussoWolves (STL)44142460.8722.5126021221098510010.75016440203
2Casey DeSmithWolves (STL)41000.9321.79134004590001.0003145000
Stats d'équipe Total ou en Moyenne48152460.8762.4827361221139100010.789194545203


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 RestantSalaire MoyenSalaire Moyen 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
Andrew PeekeWolves (STL)D221998-03-17Yes198 Lbs6 ft3NoNoNo3Pro & Farm1,491,667$649,597$1,491,667$649,597$0$0$No1,491,667$1,491,667$
Andy AndreoffWolves (STL)C/LW291991-05-17No203 Lbs6 ft1NoNoNo2Pro & Farm677,500$295,040$677,500$295,040$0$0$No677,500$Lien
Boris KatchoukWolves (STL)LW221998-06-18Yes192 Lbs6 ft1NoNoNo3Pro & Farm894,166$389,395$894,166$389,395$0$0$No894,166$894,166$
Brett RitchieWolves (STL)RW261993-07-01No217 Lbs6 ft3NoNoNo1Pro & Farm1,750,000$762,097$1,750,000$762,097$0$0$NoLien
Casey DeSmithWolves (STL)G281991-08-12No181 Lbs6 ft0NoNoNo2Pro & Farm675,000$293,952$675,000$293,952$0$0$No675,000$Lien
David GustafssonWolves (STL)C202000-04-11Yes194 Lbs6 ft1NoNoNo3Pro & Farm925,000$402,823$925,000$402,823$0$0$No925,000$925,000$
Dylan GambrellWolves (STL)C231996-08-26No195 Lbs6 ft0NoNoNo2Pro & Farm1,137,000$495,145$1,137,000$495,145$0$0$No1,137,000$Lien
Glenn GawdinWolves (STL)C231997-03-25Yes191 Lbs6 ft1NoNoNo3Pro & Farm925,000$402,823$925,000$402,823$0$0$No925,000$925,000$
Jake WalmanWolves (STL)D241996-02-19Yes170 Lbs6 ft1NoNoNo3Pro & Farm925,000$402,823$925,000$402,823$0$0$No925,000$925,000$
Jakub LaukoWolves (STL)C/LW202000-03-28Yes170 Lbs6 ft0NoNoNo3Pro & Farm894,167$389,395$894,167$389,395$0$0$No894,167$894,167$
Jason RobertsonWolves (STL)LW201999-07-21Yes195 Lbs6 ft2NoNoNo3Pro & Farm894,167$389,395$894,167$389,395$0$0$No894,167$894,167$
Jonas SiegenthalerWolves (STL)D231997-05-06No220 Lbs6 ft3NoNoNo3Pro & Farm1,758,333$765,726$883,333$384,677$0$0$No883,333$883,333$Lien
Joona LuotoWolves (STL)LW/RW221997-09-26Yes185 Lbs6 ft2NoNoNo1Pro & Farm1,012,500$440,927$1,000,000$435,484$0$0$NoLien
Jordan SchmaltzWolves (STL)D261993-10-08No190 Lbs6 ft2NoNoNo1Pro & Farm700,000$304,839$700,000$304,839$0$0$NoLien
Laurent DauphinWolves (STL)C/LW/RW251995-03-27No180 Lbs6 ft1NoNoNo1Pro & Farm1,000,000$435,484$1,000,000$435,484$0$0$NoLien
MacKenzie EntwistleWolves (STL)RW201999-07-13Yes184 Lbs6 ft3NoNoNo3Pro & Farm894,167$389,395$894,167$389,395$0$0$No894,167$894,167$
Mackenzie MacEachernWolves (STL)LW261994-03-08Yes190 Lbs6 ft2NoNoNo3Pro & Farm750,000$326,613$750,000$326,613$0$0$No750,000$750,000$
Michael ChaputWolves (STL)C/LW281992-04-08No204 Lbs6 ft2NoNoNo2Pro & Farm1,000,000$435,484$1,000,000$435,484$0$0$No1,000,000$Lien
Niko MikkolaWolves (STL)D261994-04-26Yes184 Lbs6 ft4NoNoNo3Pro & Farm925,000$402,823$925,000$402,823$0$0$No925,000$925,000$
Noah JuulsenWolves (STL)D231997-04-02No175 Lbs6 ft2NoNoNo2Pro & Farm1,063,333$463,064$1,063,333$463,064$0$0$No1,063,333$Lien
Sam GagnerWolves (STL)C/RW301989-08-09No200 Lbs5 ft11NoNoNo2Pro & Farm3,150,000$1,371,774$3,150,000$1,371,774$0$0$No3,150,000$Lien
Tim SchallerWolves (STL)LW/RW291990-11-16No210 Lbs6 ft2NoNoNo2Pro & Farm775,000$337,500$775,000$337,500$0$0$No775,000$Lien
Travis SanheimWolves (STL)D241996-03-28No181 Lbs6 ft3NoNoNo1Pro & Farm1,263,333$550,161$1,263,333$550,161$0$0$NoLien
Trent FredericWolves (STL)C221998-02-11Yes203 Lbs6 ft2NoNoNo3Pro & Farm1,137,500$495,363$1,137,500$495,363$0$0$No1,137,500$1,137,500$
Ville HeinolaWolves (STL)D192001-03-02Yes181 Lbs5 ft11NoNoNo3Pro & Farm1,137,500$495,363$1,137,500$495,363$0$0$No1,137,500$1,137,500$
Ville HussoWolves (STL)G251995-02-05Yes205 Lbs6 ft3NoNoNo3Pro & Farm700,000$304,839$700,000$304,839$0$0$No700,000$700,000$
Vince DunnWolves (STL)D231996-10-28No203 Lbs6 ft0NoNoNo1Pro & Farm894,167$389,395$894,167$389,395$0$0$NoLien
Yannick VeilleuxWolves (STL)LW271993-02-22Yes206 Lbs6 ft2NoNoNo1Pro & Farm833,333$362,903$450,000$195,968$0$0$No
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2824.11193 Lbs6 ft22.251,077,958$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Tim SchallerSam GagnerBrett Ritchie35122
2Jason RobertsonMichael ChaputLaurent Dauphin30122
3Joona LuotoAndy AndreoffDylan Gambrell25122
4Jakub LaukoDylan GambrellSam Gagner10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Travis SanheimVince Dunn35122
2Jonas SiegenthalerVille Heinola30122
3Jordan SchmaltzNoah Juulsen25122
4Travis SanheimVince Dunn10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Tim SchallerSam GagnerBrett Ritchie60122
2Jason RobertsonMichael ChaputLaurent Dauphin40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Travis SanheimVince Dunn60122
2Jonas SiegenthalerVille Heinola40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Sam GagnerTim Schaller60122
2Brett RitchieJason Robertson40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Travis SanheimVince Dunn60122
2Jonas SiegenthalerVille Heinola40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Sam Gagner60122Travis SanheimVince Dunn60122
2Tim Schaller40122Jonas SiegenthalerVille Heinola40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Sam GagnerTim Schaller60122
2Brett RitchieJason Robertson40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Travis SanheimVince Dunn60122
2Jonas SiegenthalerVille Heinola40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Tim SchallerSam GagnerBrett RitchieTravis SanheimVince Dunn
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Tim SchallerSam GagnerBrett RitchieTravis SanheimVince Dunn
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
David Gustafsson, Trent Frederic, Andy AndreoffDavid Gustafsson, Trent FredericAndy Andreoff
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Jordan Schmaltz, Noah Juulsen, Jonas SiegenthalerJordan SchmaltzNoah Juulsen, Jonas Siegenthaler
Tirs de Pénalité
Sam Gagner, Tim Schaller, Brett Ritchie, Jason Robertson, Michael Chaput
Gardien
#1 : Ville Husso, #2 : Casey DeSmith


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
1Condors11000000321110000003210000000000021.000369002926304102952532753023819216116.67%6183.33%0600119950.04%600123448.62%33764252.49%11387831033331569290
2Crunch1010000023-1000000000001010000023-100.000246002926304192952532753030614225120.00%6266.67%0600119950.04%600123448.62%33764252.49%11387831033331569290
3Flames21100000651211000006510000000000020.500610161129263043529525327530351334451119.09%16381.25%0600119950.04%600123448.62%33764252.49%11387831033331569290
4IceHogs715000101217-53110001086240400000411-740.28612213300292630411929525327530143319314039410.26%42880.95%0600119950.04%600123448.62%33764252.49%11387831033331569290
5Marlies21001000862100010004311100000043141.000816240029263045429525327530407213012325.00%8187.50%0600119950.04%600123448.62%33764252.49%11387831033331569290
6Monarchs11000000431000000000001100000043121.000471100292630419295253275301561815600.00%7185.71%0600119950.04%600123448.62%33764252.49%11387831033331569290
7Moose1000010023-11000010023-10000000000010.500246002926304182952532753016714137228.57%70100.00%0600119950.04%600123448.62%33764252.49%11387831033331569290
8Penguins2020000027-5000000000002020000027-500.000246002926304402952532753034525401317.69%9277.78%0600119950.04%600123448.62%33764252.49%11387831033331569290
9Rampage63200100121113120000056-13200010075270.5831224360129263041672952532753011739581202813.57%26484.62%1600119950.04%600123448.62%33764252.49%11387831033331569290
10Rocket1000000112-1000000000001000000112-110.50012300292630419295253275302651220400.00%60100.00%0600119950.04%600123448.62%33764252.49%11387831033331569290
11Senators10000010321100000103210000000000021.00032500292630418295253275302272018700.00%100100.00%0600119950.04%600123448.62%33764252.49%11387831033331569290
12Sharks2010001035-2100000103211010000003-320.5003470029263042629525327530441332581100.00%16381.25%0600119950.04%600123448.62%33764252.49%11387831033331569290
13Soldiers61400001917-83030000048-43110000159-430.250917260029263041062952532753012041104874137.32%43783.72%1600119950.04%600123448.62%33764252.49%11387831033331569290
14Stars724001001418-442100100118330300000310-750.3571426400029263041212952532753015035961522727.41%46882.61%0600119950.04%600123448.62%33764252.49%11387831033331569290
Total4511240133389117-2822610012305353023514001033664-28360.4008916125012292630483529525327530911237636889237208.44%2784882.73%2600119950.04%600123448.62%33764252.49%11387831033331569290
15Wolf Pack50400001816-82020000048-43020000148-410.1008142200292630464295253275309614761082015.00%30873.33%0600119950.04%600123448.62%33764252.49%11387831033331569290
_Since Last GM Reset4511240133389117-2822610012305353023514001033664-28360.4008916125012292630483529525327530911237636889237208.44%2784882.73%2600119950.04%600123448.62%33764252.49%11387831033331569290
_Vs Conference31916003215976-17165700220363511549001012341-18260.4195910916801292630458629525327530628180434606165137.88%1933283.42%2600119950.04%600123448.62%33764252.49%11387831033331569290
_Vs Division26715002114763-16134700110282801338001011935-16190.365478813501292630451329525327530530146351499135107.41%1572782.80%2600119950.04%600123448.62%33764252.49%11387831033331569290

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
4536L68916125083591123763688912
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
451124133389117
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
2261012305353
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
2351401033664
Derniers 10 Matchs
WLOTWOTL SOWSOL
370000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
237208.44%2784882.73%2
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
295253275302926304
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
600119950.04%600123448.62%33764252.49%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
11387831033331569290


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 - 2020-09-276Rampage2Wolves1LSommaire du Match
3 - 2020-09-2923Wolves1Wolf Pack2LSommaire du Match
4 - 2020-09-3031Wolf Pack4Wolves2LSommaire du Match
5 - 2020-10-0143Wolves2Wolf Pack3LXXSommaire du Match
6 - 2020-10-0248IceHogs3Wolves1LSommaire du Match
9 - 2020-10-0564Wolves2Crunch3LSommaire du Match
10 - 2020-10-0669Wolves1IceHogs3LSommaire du Match
12 - 2020-10-0881Soldiers4Wolves3LSommaire du Match
14 - 2020-10-1093Stars2Wolves1LXSommaire du Match
17 - 2020-10-13108Wolves1Rampage0WSommaire du Match
18 - 2020-10-14113Soldiers2Wolves1LSommaire du Match
20 - 2020-10-16125Wolves1Soldiers5LSommaire du Match
21 - 2020-10-17135Wolves2IceHogs3LSommaire du Match
22 - 2020-10-18143Wolves1Stars3LSommaire du Match
23 - 2020-10-19154Stars2Wolves5WSommaire du Match
24 - 2020-10-20163Wolves4Rampage2WSommaire du Match
26 - 2020-10-22176Rampage1Wolves3WSommaire du Match
28 - 2020-10-24195Marlies3Wolves4WXSommaire du Match
30 - 2020-10-26200Wolves0Sharks3LSommaire du Match
32 - 2020-10-28209Wolves1Wolf Pack3LSommaire du Match
34 - 2020-10-30220Flames0Wolves3WSommaire du Match
36 - 2020-11-01234Rampage3Wolves1LSommaire du Match
38 - 2020-11-03246Condors2Wolves3WSommaire du Match
40 - 2020-11-05256Wolves1Stars5LSommaire du Match
42 - 2020-11-07270Soldiers2Wolves0LSommaire du Match
45 - 2020-11-10284Wolves1Stars2LSommaire du Match
46 - 2020-11-11296Moose3Wolves2LXSommaire du Match
48 - 2020-11-13311Wolves2Soldiers3LXXSommaire du Match
49 - 2020-11-14319Stars1Wolves4WSommaire du Match
50 - 2020-11-15330Wolves4Monarchs3WSommaire du Match
51 - 2020-11-16338IceHogs2Wolves3WXXSommaire du Match
53 - 2020-11-18355Sharks2Wolves3WXXSommaire du Match
54 - 2020-11-19366Wolves1Rocket2LXXSommaire du Match
55 - 2020-11-20373Wolves2Rampage3LXSommaire du Match
56 - 2020-11-21385IceHogs1Wolves4WSommaire du Match
58 - 2020-11-23399Wolf Pack4Wolves2LSommaire du Match
59 - 2020-11-24413Wolves4Marlies3WSommaire du Match
60 - 2020-11-25420Senators2Wolves3WXXSommaire du Match
61 - 2020-11-26424Wolves2Soldiers1WSommaire du Match
63 - 2020-11-28442Flames5Wolves3LSommaire du Match
64 - 2020-11-29447Wolves0IceHogs3LSommaire du Match
66 - 2020-12-01463Stars3Wolves1LSommaire du Match
67 - 2020-12-02472Wolves2Penguins3LSommaire du Match
68 - 2020-12-03478Wolves1IceHogs2LSommaire du Match
70 - 2020-12-05495Wolves0Penguins4LSommaire du Match
71 - 2020-12-06501Penguins-Wolves-
73 - 2020-12-08516IceHogs-Wolves-
74 - 2020-12-09528Sound Tigers-Wolves-
75 - 2020-12-10536Wolves-IceHogs-
77 - 2020-12-12551Rampage-Wolves-
79 - 2020-12-14561Wolves-Stars-
80 - 2020-12-15572Wolves-Bruins-
81 - 2020-12-16583Wolf Pack-Wolves-
83 - 2020-12-18593Wolves-Monarchs-
85 - 2020-12-20606Bruins-Wolves-
86 - 2020-12-21617Condors-Wolves-
88 - 2020-12-23633Wolves-Griffins-
89 - 2020-12-24637Wolves-Rampage-
91 - 2020-12-26647Moose-Wolves-
92 - 2020-12-27661Monarchs-Wolves-
94 - 2020-12-29677Wolves-Admirals-
95 - 2020-12-30684Wolves-Phantoms-
96 - 2020-12-31692Soldiers-Wolves-
97 - 2021-01-01704Wolves-Flames-
98 - 2021-01-02714Phantoms-Wolves-
99 - 2021-01-03723Wolves-Moose-
101 - 2021-01-05737Monarchs-Wolves-
102 - 2021-01-06745Wolves-Sharks-
103 - 2021-01-07753Wolves-Condors-
104 - 2021-01-08764Crunch-Wolves-
105 - 2021-01-09777Wolves-Admirals-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
107 - 2021-01-11788Penguins-Wolves-
109 - 2021-01-13802Rocket-Wolves-
110 - 2021-01-14813Wolves-Monsters-
112 - 2021-01-16824Admirals-Wolves-
113 - 2021-01-17832Wolves-Sound Tigers-
114 - 2021-01-18844Wolves-Senators-
115 - 2021-01-19849Wolves-Wolf Pack-
116 - 2021-01-20854Griffins-Wolves-
117 - 2021-01-21861Wolves-Soldiers-
119 - 2021-01-23880Penguins-Wolves-
122 - 2021-01-26901Monsters-Wolves-



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité de l'Aréna20001000
Prix des Billets3515
Assistance00
Assistance PCT0.00%0.00%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacité de l'ArénaPopularité de l'Équipe
19 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
2,664,783$ 3,018,284$ 2,891,201$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 2,100,267$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 54 32,406$ 1,749,924$




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