CHICAGO Wolves
GP: 76 | W: 52 | L: 20 | OTL: 4 | P: 108
GF: 304 | GA: 155 | PP%: 20.33% | PK%: 80.73%
DG: Ralph Beauchamp | Morale : 99 | Moyenne d'Équipe : 64
Prochain matchs #988 vs SAN ANTONIO Rampage
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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
1Kevin Stenlund (R)0X100.006646896288837861725959606267636899680234750,000$
2Troy Terry0X100.005839856770767369576863617265665499670223750,000$
3Carl Grundstrom0X100.006545886475847862576060636665637299670223750,000$
4Sam Steel0X100.005639856770797366837064616763647899670221900,000$
5Garrett Pilon0X100.005843826072889059685857615963626499660221750,000$
6Cody Glass (R)0X100.005639896979746167736563586661628899660213750,000$
7Joe Veleno (R)0X100.006345856277848759715556635862648099660203750,000$
8Mason Appleton0X100.006243876079737762665958616067645699650244750,000$
9Trent Frederic0X100.006356796081757458715959616063657999650222925,000$
10Rem Pitlick (R)0X100.005449856072878958555659545865636499650234750,000$
11Brandon Gignac0X100.005542856166797759546058576165636499640221750,000$
12Lukas Jasek0X100.005443865855878959545757605865616399640231750,000$
13Maksim Sushko (R)0X100.005847855971858756525457535961636299640213750,000$
14Antoine Morand (R)0X100.005344865758858755665454525361626899620213750,000$
15Brandon Hagel (R)0X100.005643885667787354515760575463625799620223750,000$
16Linus Olund (R)0X100.005089716271475062705454525450504499550233750,000$
17Travis Barron (R)0X100.005389645577505055505353515350504499540211750,000$
18Jonne Tammela0X100.005089715768445057705252515250504499530232750,000$
19Kyle Capobianco0X100.006147846477797362306359615565626899660233750,000$
20Olli Juolevi0X100.006338896577807663306453624563628899660221900,000$
21Pierre-Olivier Joseph (R)0X100.006148855977858457305653584661638099650214750,000$
22Nicolas Beaudin (R)0X100.005642885966837458305757564561628099630203750,000$
23Chad Krys (R)0X100.005441885658797854305353525063627499600224750,000$
Rayé
MOYENNE D'ÉQUIPE100.00585084617277756056585758576261679963
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
1Oscar Dansk100.00767068827574767574767571757499680
2Callum Booth (R)100.00715654837069717069717065696299630
3Jakub Skarek (R)100.00676563836665676665676661656499610
Rayé
MOYENNE D'ÉQUIPE100.0071646283706971706971706670679964
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Ross Yates62626262757548CAN622300,000$


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
1Sam SteelCHICAGO Wolves (VGK)C76425294561207618036012024611.67%28147519.428111963187202151674267.75%178921021.27020006108
2Troy TerryCHICAGO Wolves (VGK)RW7646408646535828836311426512.67%24133317.55111122681981125609566.67%9610131.2923000828
3Kyle CapobiancoCHICAGO Wolves (VGK)D761460744763510464134449210.45%96150119.7651116631810112149500.00%012010.9900000252
4Garrett PilonCHICAGO Wolves (VGK)C7632316337360781442245417714.29%12117915.522810207500056210165.79%137400001.0711000642
5Carl GrundstromCHICAGO Wolves (VGK)RW76223658385409572308712057.14%16124616.404111546180000004160.00%10000000.9303000214
6Dean KukanVEGAS Golden KnightsD6810465624255424710631599.43%89144121.204812531660001159320.00%000000.7800001124
7Brandon GignacCHICAGO Wolves (VGK)LW762129503624062591996615910.55%14133617.597613281830004673160.42%9600000.7501000134
8Mason AppletonCHICAGO Wolves (VGK)RW76212546251807656231661869.09%1492912.2300002000002062.96%5410000.9911000060
9Pierre-Olivier JosephCHICAGO Wolves (VGK)D7453136483201023744134111.36%41110914.99000216000015000.00%000000.6500000103
10Joe VelenoCHICAGO Wolves (VGK)C741619352919566871604012510.00%106969.4100002000011267.66%67100001.0000100314
11Olli JuoleviCHICAGO Wolves (VGK)D48430342643553457327495.48%4591519.0727939127011094100.00%022000.7400000003
12Christian JarosVEGAS Golden KnightsD606273320655129438532517.06%55139123.20369351410002124200.00%000000.4700000114
13Lukas JasekCHICAGO Wolves (VGK)RW7611223329952150145401187.59%136558.6300000000012078.12%3200001.0100001113
14Cody GlassCHICAGO Wolves (VGK)C7410172712201550157421006.37%105207.030113120001300071.07%51500011.0400000310
15Kevin StenlundCHICAGO Wolves (VGK)C36121426111205969114318710.53%2570419.56134191180004990164.17%72000010.7401000122
16Chad KrysCHICAGO Wolves (VGK)D74322253618039183723248.11%3888511.960002310119100.00%000000.5600000021
17Nicolas BeaudinCHICAGO Wolves (VGK)D3611617211002216146117.14%3050113.9400000000023000.00%000000.6800000000
18Brandon HagelCHICAGO Wolves (VGK)LW367714218019237119439.86%448013.3500000000001044.83%2900000.5800000011
19Peter CehlarikVEGAS Golden KnightsLW2112-10065172135.88%13718.800113500000000.00%110001.0600000000
20Trent FredericCHICAGO Wolves (VGK)C76011120527250.00%0290.3800014000000076.74%4320000.6800000000
21Dylan GambrellVEGAS Golden KnightsC2000-400148130.00%0189.3000000000000088.89%1820000.0000000000
22Rem PitlickCHICAGO Wolves (VGK)C36000000020000.00%2320.92000000000300048.48%3300000.0000000000
Stats d'équipe Total ou en Moyenne13042845268105585053511521161285784420599.94%5671842314.1347841314451607437401097481566.72%5571125180.88412102364253
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
1Oscar DanskCHICAGO Wolves (VGK)76522040.9232.004482212149192922520.92914760663
2Callum BoothCHICAGO Wolves (VGK)10000.9093.1638002220000.0000036000
3Jakub SkarekCHICAGO Wolves (VGK)10001.0000.0031000100000.0000038000
Stats d'équipe Total ou en Moyenne78522040.9231.994552212151196122520.929147674663


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
Antoine MorandCHICAGO Wolves (VGK)C212/18/1999Yes84 Kg155 CMNoNoNo3Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$
Brandon GignacCHICAGO Wolves (VGK)LW2211/7/1997No77 Kg180 CMNoNoNo1Pro & Farm750,000$57,203$750,000$57,203$0$0$NoLien
Brandon HagelCHICAGO Wolves (VGK)LW228/27/1998Yes79 Kg180 CMNoNoNo3Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$
Callum BoothCHICAGO Wolves (VGK)G235/21/1997Yes84 Kg193 CMNoNoNo3Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$
Carl GrundstromCHICAGO Wolves (VGK)RW2212/1/1997No91 Kg183 CMNoNoNo3Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$Lien
Chad KrysCHICAGO Wolves (VGK)D224/10/1998Yes84 Kg155 CMNoNoNo4Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$750,000$
Cody GlassCHICAGO Wolves (VGK)C214/1/1999Yes87 Kg188 CMNoNoNo3Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$
Garrett PilonCHICAGO Wolves (VGK)C224/13/1998No85 Kg183 CMNoNoNo1Pro & Farm750,000$57,203$750,000$57,203$0$0$NoLien
Jakub SkarekCHICAGO Wolves (VGK)G2011/10/1999Yes89 Kg191 CMNoNoNo3Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$
Joe VelenoCHICAGO Wolves (VGK)C201/13/2000Yes90 Kg185 CMNoNoNo3Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$
Jonne TammelaCHICAGO Wolves (VGK)C238/5/1997No85 Kg178 CMNoNoNo2Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$Lien
Kevin StenlundCHICAGO Wolves (VGK)C239/20/1996Yes95 Kg193 CMNoNoNo4Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$750,000$
Kyle CapobiancoCHICAGO Wolves (VGK)D238/13/1997No89 Kg185 CMNoNoNo3Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$Lien
Linus OlundCHICAGO Wolves (VGK)C236/5/1997Yes84 Kg183 CMNoNoNo3Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$Lien
Lukas JasekCHICAGO Wolves (VGK)RW238/28/1997No75 Kg155 CMNoNoNo1Pro & Farm750,000$57,203$750,000$57,203$0$0$NoLien
Maksim SushkoCHICAGO Wolves (VGK)RW212/10/1999Yes82 Kg183 CMNoNoNo3Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$
Mason AppletonCHICAGO Wolves (VGK)RW241/15/1996No88 Kg188 CMNoNoNo4Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$750,000$Lien
Nicolas BeaudinCHICAGO Wolves (VGK)D2010/7/1999Yes76 Kg180 CMNoNoNo3Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$
Olli JuoleviCHICAGO Wolves (VGK)D225/5/1998No83 Kg188 CMNoNoNo1Pro & Farm900,000$68,644$750,000$57,203$0$0$NoLien
Oscar DanskCHICAGO Wolves (VGK)G262/28/1994No89 Kg191 CMNoNoNo3Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$Lien
Pierre-Olivier JosephCHICAGO Wolves (VGK)D217/1/1999Yes84 Kg188 CMNoNoNo4Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$750,000$
Rem PitlickCHICAGO Wolves (VGK)C234/2/1997Yes89 Kg180 CMNoNoNo4Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$750,000$
Sam SteelCHICAGO Wolves (VGK)C222/3/1998No86 Kg180 CMNoNoNo1Pro & Farm900,000$68,644$750,000$57,203$0$0$NoLien
Travis BarronCHICAGO Wolves (VGK)LW2111/17/1998Yes91 Kg185 CMNoNoNo1Pro & Farm750,000$57,203$750,000$57,203$0$0$NoLien
Trent FredericCHICAGO Wolves (VGK)C222/11/1998No92 Kg188 CMNoNoNo2Pro & Farm925,000$70,551$925,000$70,551$0$0$No925,000$Lien
Troy TerryCHICAGO Wolves (VGK)RW229/10/1997No82 Kg183 CMNoNoNo3Pro & Farm750,000$57,203$750,000$57,203$0$0$No750,000$750,000$Lien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2622.0885 Kg183 CM2.65768,269$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Kevin StenlundTroy Terry31122
2Brandon GignacSam SteelCarl Grundstrom26122
3Brandon HagelGarrett PilonMason Appleton23122
4Joe VelenoLukas Jasek20122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
131122
2Kyle CapobiancoOlli Juolevi26122
3Pierre-Olivier JosephNicolas Beaudin23122
4Chad Krys20122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Kevin StenlundTroy Terry55122
2Brandon GignacSam SteelCarl Grundstrom45122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
155122
2Kyle CapobiancoOlli Juolevi45122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Kevin Stenlund55122
2Sam SteelTroy Terry45122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
155122
2Kyle CapobiancoOlli Juolevi45122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
15512255122
2Kevin Stenlund45122Kyle CapobiancoOlli Juolevi45122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Kevin Stenlund55122
2Sam SteelTroy Terry45122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
155122
2Kyle CapobiancoOlli Juolevi45122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Kevin StenlundTroy Terry
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Kevin StenlundTroy Terry
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Cody Glass, Trent Frederic, Rem PitlickCody Glass, Trent FredericRem Pitlick
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Pierre-Olivier Joseph, Nicolas Beaudin, Chad KrysPierre-Olivier JosephNicolas Beaudin, Chad Krys
Tirs de Pénalité
, Kevin Stenlund, Sam Steel, Troy Terry, Carl Grundstrom
Gardien
#1 : Oscar Dansk, #2 : Callum Booth, #3 : Jakub Skarek
Lignes d'Attaque Perso. en Prol.
, Kevin Stenlund, Sam Steel, Troy Terry, Carl Grundstrom, Garrett Pilon, Garrett Pilon, Joe Veleno, Cody Glass, Mason Appleton, Trent Frederic
Lignes de Défense Perso. en Prol.
, , Kyle Capobianco, Olli Juolevi, Pierre-Olivier Joseph


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
1BAKERSFIELD Condors55000000263232200000012393300000014014101.00026497504129106646365100510079653765171491500.00%60100.00%11792263068.14%1353220061.50%754114765.74%217416621554474863455
2BELLEVILLE Senators3300000025718220000001551011000000102861.000254469001291066461201005100796537669224855100.00%10280.00%11792263068.14%1353220061.50%754114765.74%217416621554474863455
3BINGHAMTON Devils3030000026-42020000025-31010000001-100.00024600129106646871005100796537993212458112.50%6266.67%01792263068.14%1353220061.50%754114765.74%217416621554474863455
4BRIDGEPORT Sound Tigers22000000514110000004131100000010141.00059140112910664658100510079653756256188225.00%20100.00%01792263068.14%1353220061.50%754114765.74%217416621554474863455
5BROOKLYN Wolfpack2200000015312110000007161100000082641.000152742001291066466810051007965375361220500.00%6266.67%01792263068.14%1353220061.50%754114765.74%217416621554474863455
6CAROLINA Checkers3300000035233220000002402411000000112961.00035619602129106646242100510079653730943475120.00%60100.00%21792263068.14%1353220061.50%754114765.74%217416621554474863455
7COLORADO Eagles21000010532100000102111100000032141.000571200129106646551005100796537631712373133.33%6183.33%01792263068.14%1353220061.50%754114765.74%217416621554474863455
8GRAND RAPIDS Griffins32001000945110000002022100100074361.0009162501129106646781005100796537673612465240.00%60100.00%01792263068.14%1353220061.50%754114765.74%217416621554474863455
9HERSEY Bears2010100067-11010000035-21000100032120.50061117001291066467210051007965376919102711100.00%5180.00%01792263068.14%1353220061.50%754114765.74%217416621554474863455
10IOWA Wild541000002015532100000121112200000084480.80020365600129106646211100510079653713840557114428.57%221054.55%01792263068.14%1353220061.50%754114765.74%217416621554474863455
11LAVAL Rocket2010010036-31010000002-21000010034-110.250369001291066465010051007965376514304910220.00%15380.00%01792263068.14%1353220061.50%754114765.74%217416621554474863455
12LEHIGH VALLEY Phantoms505000001016-62020000057-23030000059-400.00010192900129106646134100510079653715653529620315.00%13469.23%01792263068.14%1353220061.50%754114765.74%217416621554474863455
13MANITOBA Moose440000001358110000005233300000083581.00013223502129106646164100510079653710628165013323.08%7185.71%01792263068.14%1353220061.50%754114765.74%217416621554474863455
14MILWAUKEE Admirals31100010761110000004312010001033040.6677101700129106646118100510079653762211252100.00%5180.00%01792263068.14%1353220061.50%754114765.74%217416621554474863455
15ONTARIO Reign52300000151503120000089-12110000076140.4001527421012910664618510051007965371743839891516.67%12375.00%01792263068.14%1353220061.50%754114765.74%217416621554474863455
16PROVIDENCE Bruins2010001023-1100000102111010000002-220.50021300129106646591005100796537491928371417.14%14192.86%01792263068.14%1353220061.50%754114765.74%217416621554474863455
17ROCKFORD IceHogs2200000015312110000008171100000072541.000152843001291066466910051007965373936333266.67%30100.00%01792263068.14%1353220061.50%754114765.74%217416621554474863455
18SAN ANTONIO Rampage11000000725000000000001100000072521.00071320001291066462410051007965373410425100.00%20100.00%01792263068.14%1353220061.50%754114765.74%217416621554474863455
19SAN DIEGO Gulls220000001911811000000130131100000061541.00019375601129106646157100510079653724222111100.00%10100.00%01792263068.14%1353220061.50%754114765.74%217416621554474863455
20SCRANTON Penguins54100000188103210000010732200000081780.80018304811129106646122100510079653715354387321523.81%18288.89%01792263068.14%1353220061.50%754114765.74%217416621554474863455
21STOCKTON Flames53100100131033300000011562010010025-370.7001325380012910664620010051007965371034444933239.38%21385.71%01792263068.14%1353220061.50%754114765.74%217416621554474863455
22SYRACUSE Crunch512002001723-621100000910-130100200813-540.40017304700129106646202100510079653719358369240615.00%16568.75%01792263068.14%1353220061.50%754114765.74%217416621554474863455
23TORONTO Marlies22000000624110000002111100000041341.0006121800129106646861005100796537391316293133.33%80100.00%01792263068.14%1353220061.50%754114765.74%217416621554474863455
24TUSCON Roadrunners11000000312000000000001100000031221.0003580012910664630100510079653721126174125.00%30100.00%01792263068.14%1353220061.50%754114765.74%217416621554474863455
25UTICA Comets22000000835110000004131100000042241.0008162400129106646451005100796537591810204375.00%5180.00%01792263068.14%1353220061.50%754114765.74%217416621554474863455
Total76472002430304155149372411000201648183392390241014074661080.71130454584921212910664630011005100796537198359753712262414920.33%2184280.73%41792263068.14%1353220061.50%754114765.74%217416621554474863455
_Since Last GM Reset76472002430304155149372411000201648183392390241014074661080.71130454584921212910664630011005100796537198359753712262414920.33%2184280.73%41792263068.14%1353220061.50%754114765.74%217416621554474863455
_Vs Conference5131160130020311489261790000011564512514701300885038670.6572033635662101291066462110100510079653713504183838411833418.58%1433277.62%41792263068.14%1353220061.50%754114765.74%217416621554474863455
_Vs Division20151200300974750109600000541935106600300432815330.82597170267031291066468371005100796537509158187348821821.95%751185.33%31792263068.14%1353220061.50%754114765.74%217416621554474863455

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
76108W1304545849300119835975371226212
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
7647202430304155
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
372411002016481
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
39239241014074
Derniers 10 Matchs
WLOTWOTL SOWSOL
620200
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
2414920.33%2184280.73%4
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
1005100796537129106646
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
1792263068.14%1353220061.50%754114765.74%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
217416621554474863455


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 - 2021-01-174IOWA Wild8CHICAGO Wolves5LSommaire du Match
3 - 2021-01-1921CHICAGO Wolves4SYRACUSE Crunch7LSommaire du Match
5 - 2021-01-2138SCRANTON Penguins2CHICAGO Wolves5WSommaire du Match
6 - 2021-01-2253ONTARIO Reign3CHICAGO Wolves2LSommaire du Match
7 - 2021-01-2368CHICAGO Wolves6SAN DIEGO Gulls1WSommaire du Match
9 - 2021-01-2579CHICAGO Wolves2MANITOBA Moose0WSommaire du Match
10 - 2021-01-2691STOCKTON Flames2CHICAGO Wolves4WSommaire du Match
12 - 2021-01-28103CHICAGO Wolves5SCRANTON Penguins1WSommaire du Match
13 - 2021-01-29110CHICAGO Wolves2MILWAUKEE Admirals1WXXSommaire du Match
14 - 2021-01-30117CHICAGO Wolves1LEHIGH VALLEY Phantoms2LSommaire du Match
15 - 2021-01-31129BINGHAMTON Devils2CHICAGO Wolves1LSommaire du Match
17 - 2021-02-02150LEHIGH VALLEY Phantoms4CHICAGO Wolves3LSommaire du Match
18 - 2021-02-03157CHICAGO Wolves4BAKERSFIELD Condors0WSommaire du Match
20 - 2021-02-05175BAKERSFIELD Condors0CHICAGO Wolves3WSommaire du Match
22 - 2021-02-07193CHICAGO Wolves5ONTARIO Reign3WSommaire du Match
23 - 2021-02-08202STOCKTON Flames1CHICAGO Wolves4WSommaire du Match
25 - 2021-02-10215CHICAGO Wolves0BINGHAMTON Devils1LSommaire du Match
26 - 2021-02-11230UTICA Comets1CHICAGO Wolves4WSommaire du Match
28 - 2021-02-13242CHICAGO Wolves0PROVIDENCE Bruins2LSommaire du Match
29 - 2021-02-14256CHICAGO Wolves3HERSEY Bears2WXSommaire du Match
30 - 2021-02-15260TORONTO Marlies1CHICAGO Wolves2WSommaire du Match
31 - 2021-02-16281BELLEVILLE Senators3CHICAGO Wolves7WSommaire du Match
33 - 2021-02-18291CHICAGO Wolves3LAVAL Rocket4LXSommaire du Match
34 - 2021-02-19308SAN DIEGO Gulls0CHICAGO Wolves13WSommaire du Match
36 - 2021-02-21318HERSEY Bears5CHICAGO Wolves3LSommaire du Match
37 - 2021-02-22334CHICAGO Wolves4BAKERSFIELD Condors0WSommaire du Match
39 - 2021-02-24346BINGHAMTON Devils3CHICAGO Wolves1LSommaire du Match
41 - 2021-02-26361CHICAGO Wolves7SAN ANTONIO Rampage2WSommaire du Match
42 - 2021-02-27371CHICAGO Wolves4UTICA Comets2WSommaire du Match
43 - 2021-02-28383BAKERSFIELD Condors3CHICAGO Wolves9WSommaire du Match
45 - 2021-03-02398CHICAGO Wolves1MANITOBA Moose0WSommaire du Match
47 - 2021-03-04412MANITOBA Moose2CHICAGO Wolves5WSommaire du Match
48 - 2021-03-05425CHICAGO Wolves2ONTARIO Reign3LSommaire du Match
49 - 2021-03-06436CHICAGO Wolves10BELLEVILLE Senators2WSommaire du Match
50 - 2021-03-07443COLORADO Eagles1CHICAGO Wolves2WXXSommaire du Match
52 - 2021-03-09464SCRANTON Penguins4CHICAGO Wolves2LSommaire du Match
53 - 2021-03-10477ONTARIO Reign2CHICAGO Wolves4WSommaire du Match
55 - 2021-03-12490CHICAGO Wolves3SCRANTON Penguins0WSommaire du Match
56 - 2021-03-13504CHICAGO Wolves6BAKERSFIELD Condors0WSommaire du Match
57 - 2021-03-14514CHICAGO Wolves5MANITOBA Moose3WSommaire du Match
58 - 2021-03-15521IOWA Wild1CHICAGO Wolves2WSommaire du Match
60 - 2021-03-17538ROCKFORD IceHogs1CHICAGO Wolves8WSommaire du Match
62 - 2021-03-19557MILWAUKEE Admirals3CHICAGO Wolves4WSommaire du Match
63 - 2021-03-20571CHICAGO Wolves1BRIDGEPORT Sound Tigers0WSommaire du Match
64 - 2021-03-21583SCRANTON Penguins1CHICAGO Wolves3WSommaire du Match
66 - 2021-03-23596CHICAGO Wolves3TUSCON Roadrunners1WSommaire du Match
67 - 2021-03-24607ONTARIO Reign4CHICAGO Wolves2LSommaire du Match
69 - 2021-03-26625GRAND RAPIDS Griffins0CHICAGO Wolves2WSommaire du Match
70 - 2021-03-27636CHICAGO Wolves4TORONTO Marlies1WSommaire du Match
72 - 2021-03-29649CHICAGO Wolves4GRAND RAPIDS Griffins3WXSommaire du Match
73 - 2021-03-30661LEHIGH VALLEY Phantoms3CHICAGO Wolves2LSommaire du Match
75 - 2021-04-01674CHICAGO Wolves2LEHIGH VALLEY Phantoms4LSommaire du Match
76 - 2021-04-02683BELLEVILLE Senators2CHICAGO Wolves8WSommaire du Match
78 - 2021-04-04697CHICAGO Wolves11CAROLINA Checkers2WSommaire du Match
79 - 2021-04-05712IOWA Wild2CHICAGO Wolves5WSommaire du Match
81 - 2021-04-07730STOCKTON Flames2CHICAGO Wolves3WSommaire du Match
82 - 2021-04-08741CHICAGO Wolves1STOCKTON Flames2LXSommaire du Match
85 - 2021-04-11758SYRACUSE Crunch7CHICAGO Wolves3LSommaire du Match
86 - 2021-04-12768CHICAGO Wolves7ROCKFORD IceHogs2WSommaire du Match
87 - 2021-04-13783LAVAL Rocket2CHICAGO Wolves0LSommaire du Match
88 - 2021-04-14793CHICAGO Wolves1STOCKTON Flames3LSommaire du Match
91 - 2021-04-17808PROVIDENCE Bruins1CHICAGO Wolves2WXXSommaire du Match
92 - 2021-04-18821CHICAGO Wolves8BROOKLYN Wolfpack2WSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
93 - 2021-04-19833CAROLINA Checkers0CHICAGO Wolves12WSommaire du Match
94 - 2021-04-20836CHICAGO Wolves3COLORADO Eagles2WSommaire du Match
96 - 2021-04-22859SYRACUSE Crunch3CHICAGO Wolves6WSommaire du Match
97 - 2021-04-23872CHICAGO Wolves3IOWA Wild1WSommaire du Match
98 - 2021-04-24884BRIDGEPORT Sound Tigers1CHICAGO Wolves4WSommaire du Match
100 - 2021-04-26895CHICAGO Wolves2LEHIGH VALLEY Phantoms3LSommaire du Match
101 - 2021-04-27911BROOKLYN Wolfpack1CHICAGO Wolves7WSommaire du Match
103 - 2021-04-29923CHICAGO Wolves1MILWAUKEE Admirals2LSommaire du Match
104 - 2021-04-30937CAROLINA Checkers0CHICAGO Wolves12WSommaire du Match
105 - 2021-05-01945CHICAGO Wolves1SYRACUSE Crunch2LXSommaire du Match
107 - 2021-05-03958CHICAGO Wolves5IOWA Wild3WSommaire du Match
108 - 2021-05-04967CHICAGO Wolves3SYRACUSE Crunch4LXSommaire du Match
109 - 2021-05-05979CHICAGO Wolves3GRAND RAPIDS Griffins1WSommaire du Match
111 - 2021-05-07988SAN ANTONIO Rampage-CHICAGO Wolves-
112 - 2021-05-08997CHICAGO Wolves-MILWAUKEE Admirals-
113 - 2021-05-091013TUSCON Roadrunners-CHICAGO Wolves-
116 - 2021-05-121038SAN ANTONIO Rampage-CHICAGO Wolves-



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des Billets3515
Assistance00
Assistance PCT0.00%0.00%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacitéPopularité de l'Équipe
3 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
2,008,873$ 1,997,500$ 1,967,500$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 2,008,873$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 9 16,928$ 152,352$




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
3480204707312210263-53401418071001181162406290021292147-5561210375585017065679238276777081143232659837110822493012.05%1583776.58%11415251956.17%1187219354.13%727124858.25%1996142718405501000510
3480333701423237277-4040162000310135144-940171701113102133-3179237426663228087675262284090186441237064862113662594015.44%2695978.07%01482265155.90%1100232147.39%653125552.03%200214531871544955482
3480204707312210263-53401418071001181162406290021292147-5561210375585017065679238276777081143232659837110822493012.05%1583776.58%11415251956.17%1187219354.13%727124858.25%1996142718405501000510
3480333701423237277-4040162000310135144-940171701113102133-3179237426663228087675262284090186441237064862113662594015.44%2695978.07%01482265155.90%1100232147.39%653125552.03%200214531871544955482
36764720024303041551493724110002016481833923902410140746610830454584921212910664630011005100796537198359753712262414920.33%2184280.73%41792263068.14%1353220061.50%754114765.74%217416621554474863455
Total Saison Régulière3961531880181891011981235-37197848701484067060169199691010410510528634-106388119821473345618429410332341300942194349431520511375308925216122125718915.04%107223478.17%675861297058.49%59271122852.79%3514615357.11%1017474268978266347762441