Crunch

GP: 51 | W: 32 | L: 16 | OTL: 3 | P: 67
GF: 149 | GA: 118 | PP%: 10.10% | PK%: 87.99%
DG: Mathieu Veillet | Morale : 50 | Moyenne d'Équipe : N/A
Prochain matchs #569 vs Senators
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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
1Bokondji Imama (R)X100.00678035608062665050494657444444515000233706,667$
2Garrett WilsonXX100.00697358607372775750495863555758595000291650,000$
3Joshua Ho-Sang (R)X100.007464967764616261506057635444446250002421,075,833$
4Mitchell Stephens (R)X100.00734391657057646086585874254747635000233919,166$
5Stefan MatteauX100.00828477648471746150576069575151645000262725,000$
6Eetu Luostarinen (R)X100.00726785776770735974585662534444615000213925,000$
7Oliver Wahlstrom (R)X100.007775818075636558505458645544446250002031,462,500$
8Nathan BeaulieuX100.008192788077706257255348812566676350002731,375,000$
9Nicolas Beaudin (R)X100.006963836663687448253941583944445250002031,135,833$
Rayé
MOYENNE D'ÉQUIPE100.0074717670736669575153546645494960500
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
1Jack Campbell100.0068626179716465687368755252675000
2Joonas Korpisalo100.0077666574807574798177955959765000
Rayé
1Malcolm Subban100.0054545478575158585954955252565000
MOYENNE D'ÉQUIPE100.006661607769636668716688545466500
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Phil Housley79808187837765USA5832,535,000$


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
1Oliver WahlstromCrunch (TBL)RW5125204508351461281706214214.71%10103420.2896155524000041216150.79%6300020.8703001741
2Mitchell StephensCrunch (TBL)C512218402560161281573310614.01%2273614.4520222880001378062.06%65100001.0900000257
3Stefan MatteauCrunch (TBL)LW451522371349511259109298413.76%1997121.582682215800062250152.46%6100000.7614001264
4Garrett WilsonCrunch (TBL)LW/RW51162036294003763132377212.12%1770513.8401113360114783050.85%5900011.0212000630
5Eetu LuostarinenCrunch (TBL)C4511233413200429881205713.58%572216.06336291610110960156.99%75800000.9402000202
6Bokondji ImamaCrunch (TBL)LW517233020761075659033607.78%470513.830334271011232038.10%4200000.8512011203
7Joshua Ho-SangCrunch (TBL)RW451118291160167387176012.64%461713.7224626162000003142.22%4500000.9402000241
8Nathan BeaulieuCrunch (TBL)D4541923-110115140665622527.14%4786319.19448391460000143000.00%000000.5300012042
9Nicolas BeaudinCrunch (TBL)D514711-1795663228142014.29%21100019.62235181650000175100.00%000000.2201001012
10Jake EvansLightningC/RW923522021518102011.11%417018.990004410000220149.00%20000000.5900000000
11Nicolas DeslauriersLightningLW/RW9314-122030261981415.79%318620.770005390001391037.50%10400000.4300000000
Stats d'équipe Total ou en Moyenne4531201742941104844068275394728568712.67%156771517.0324305423712681231796324555.57%198300030.76316026232722
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
1Joonas KorpisaloCrunch (TBL)51321530.8942.2830060811410780210.87524510522
2Jack CampbellCrunch (TBL)30100.9670.7481001300000.0000045000
Stats d'équipe Total ou en Moyenne54321630.8962.2430870811511080210.875245145522


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
Bokondji ImamaCrunch (TBL)LW231996-08-03Yes220 Lbs6 ft1NoNoNo3Pro & Farm706,667$262,151$706,667$262,151$0$0$No706,667$706,667$
Eetu LuostarinenCrunch (TBL)C211998-09-02Yes179 Lbs6 ft2NoNoNo3Pro & Farm925,000$343,145$925,000$343,145$0$0$No925,000$925,000$
Garrett WilsonCrunch (TBL)LW/RW291991-03-16No199 Lbs6 ft2NoNoNo1Pro & Farm650,000$241,129$650,000$241,129$0$0$NoLien
Jack CampbellCrunch (TBL)G281992-01-08No197 Lbs6 ft3NoNoNo3Pro & Farm675,000$250,403$675,000$250,403$0$0$No675,000$675,000$Lien
Joonas KorpisaloCrunch (TBL)G261994-04-27No182 Lbs6 ft3NoNoNo1Pro & Farm900,000$333,871$900,000$333,871$0$0$NoLien
Joshua Ho-SangCrunch (TBL)RW241996-01-22Yes173 Lbs6 ft0NoNoNo2Pro & Farm1,075,833$399,099$1,075,833$399,099$0$0$No1,075,833$Lien
Malcolm SubbanCrunch (TBL)G261993-12-21No200 Lbs6 ft2NoNoNo1Pro & Farm650,000$241,129$650,000$241,129$0$0$NoLien
Mitchell StephensCrunch (TBL)C231997-02-05Yes191 Lbs6 ft0NoNoNo3Pro & Farm919,166$340,981$919,166$340,981$0$0$No919,166$919,166$
Nathan BeaulieuCrunch (TBL)D271992-12-04No200 Lbs6 ft2NoNoNo3Pro & Farm1,375,000$510,081$1,000,000$370,968$0$0$No1,000,000$1,000,000$Lien
Nicolas BeaudinCrunch (TBL)D201999-10-07Yes174 Lbs5 ft11NoNoNo3Pro & Farm1,135,833$421,357$1,135,833$421,357$0$0$No1,135,833$1,135,833$
Oliver WahlstromCrunch (TBL)RW202000-06-12Yes205 Lbs6 ft1NoNoNo3Pro & Farm1,462,500$542,540$1,462,500$542,540$0$0$No1,462,500$1,462,500$
Stefan MatteauCrunch (TBL)LW261994-02-23No220 Lbs6 ft2NoNoNo2Pro & Farm725,000$268,952$725,000$268,952$0$0$No725,000$Lien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
1224.42195 Lbs6 ft12.33933,333$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Oliver Wahlstrom35122
2Stefan MatteauEetu LuostarinenJoshua Ho-Sang30122
3Garrett WilsonMitchell StephensBokondji Imama25122
4Bokondji ImamaStefan Matteau10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Nathan BeaulieuNicolas Beaudin35122
230122
325122
4Nathan BeaulieuNicolas Beaudin10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Oliver Wahlstrom60122
2Stefan MatteauEetu LuostarinenJoshua Ho-Sang40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Nathan BeaulieuNicolas Beaudin60122
240122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Stefan Matteau60122
2Oliver Wahlstrom40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Nathan BeaulieuNicolas Beaudin60122
240122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
160122Nathan BeaulieuNicolas Beaudin60122
2Stefan Matteau4012240122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Stefan Matteau60122
2Oliver Wahlstrom40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Nathan BeaulieuNicolas Beaudin60122
240122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Oliver WahlstromNathan BeaulieuNicolas Beaudin
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Oliver WahlstromNathan BeaulieuNicolas Beaudin
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Mitchell Stephens, Garrett Wilson, Eetu LuostarinenMitchell Stephens, Garrett WilsonEetu Luostarinen
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Nathan Beaulieu, Nicolas Beaudin, Nathan BeaulieuNicolas Beaudin,
Tirs de Pénalité
, Stefan Matteau, , Oliver Wahlstrom, Eetu Luostarinen
Gardien
#1 : Joonas Korpisalo, #2 : Jack Campbell


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
1Admirals11000000303110000003030000000000021.00035801555336635415390398392681521500.00%50100.00%1726142950.80%709158844.65%33871547.27%11808341321380587274
2Bruins835000001720-34220000088041300000912-360.37517324901555336616541539039839188431131614724.26%53786.79%0726142950.80%709158844.65%33871547.27%11808341321380587274
3Flames10001000321100010003210000000000021.00036900555336625415390398391272168112.50%10100.00%0726142950.80%709158844.65%33871547.27%11808341321380587274
4Griffins733010002518731101000119242200000149580.571254570015553366183415390398391824010015134411.76%48785.42%0726142950.80%709158844.65%33871547.27%11808341321380587274
5IceHogs1010000034-1000000000001010000034-100.00036900555336626415390398393352419600.00%11190.91%0726142950.80%709158844.65%33871547.27%11808341321380587274
6Marlies7500001120101032000001743430000101367130.92920335301555336617441539039839131388013036513.89%33390.91%0726142950.80%709158844.65%33871547.27%11808341321380587274
7Monarchs21100000853110000005141010000034-120.50081624005553366454153903983949142852700.00%13284.62%1726142950.80%709158844.65%33871547.27%11808341321380587274
8Monsters32100000660110000005412110000012-140.6676101611555336667415390398394911745520315.00%24291.67%0726142950.80%709158844.65%33871547.27%11808341321380587274
9Moose2010000147-31000000134-11010000013-210.25046100055533665041539039839361020382414.17%10280.00%0726142950.80%709158844.65%33871547.27%11808341321380587274
10Penguins11000000101000000000001100000010121.00011201555336626415390398391551826700.00%60100.00%0726142950.80%709158844.65%33871547.27%11808341321380587274
11Phantoms21100000880110000004311010000045-120.500815230055533665341539039839472140458112.50%20290.00%1726142950.80%709158844.65%33871547.27%11808341321380587274
12Rocket622001101816242100100131122010001055070.58318304800555336611841539039839132408612939615.38%41880.49%0726142950.80%709158844.65%33871547.27%11808341321380587274
13Senators5400100022111132001000147722000000844101.000223759015553366127415390398399923629631412.90%29389.66%0726142950.80%709158844.65%33871547.27%11808341321380587274
14Sharks11000000312110000003120000000000021.00033600555336630415390398391232022700.00%100100.00%0726142950.80%709158844.65%33871547.27%11808341321380587274
15Sound Tigers11000000101000000000001100000010121.000112015553366204153903983934918173133.33%90100.00%0726142950.80%709158844.65%33871547.27%11808341321380587274
Total512716031221491183126164031028659272511120002063594670.657149260409185553366121841539039839110929674510292973010.10%3334087.99%3726142950.80%709158844.65%33871547.27%11808341321380587274
16Wolf Pack2110000048-4110000004311010000005-520.5004812005553366444153903983945153335900.00%15286.67%0726142950.80%709158844.65%33871547.27%11808341321380587274
17Wolves11000000321110000003210000000000021.000369005553366304153903983919412166233.33%5180.00%0726142950.80%709158844.65%33871547.27%11808341321380587274
_Since Last GM Reset512716031221491183126164031028659272511120002063594670.657149260409185553366121841539039839110929674510292973010.10%3334087.99%3726142950.80%709158844.65%33871547.27%11808341321380587274
_Vs Conference35191002121998118181130210158421617870002041392480.68699172271155553366799415390398397182035086932052210.73%2222787.84%1726142950.80%709158844.65%33871547.27%11808341321380587274
_Vs Division33147011211027527178301101533914166400020493613360.545102177279045553366767415390398397321844416671872111.23%2042886.27%0726142950.80%709158844.65%33871547.27%11808341321380587274

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
5167W114926040912181109296745102918
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
5127163122149118
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
2616431028659
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
25111200206359
Derniers 10 Matchs
WLOTWOTL SOWSOL
820000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
2973010.10%3334087.99%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
415390398395553366
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
726142950.80%709158844.65%33871547.27%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
11808341321380587274


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 - 2020-09-2711Admirals0Crunch3WSommaire du Match
2 - 2020-09-2818Crunch4Marlies3WSommaire du Match
5 - 2020-10-0134Crunch2Bruins3LSommaire du Match
6 - 2020-10-0244Bruins2Crunch0LSommaire du Match
7 - 2020-10-0355Crunch1Sound Tigers0WSommaire du Match
9 - 2020-10-0564Wolves2Crunch3WSommaire du Match
11 - 2020-10-0777Crunch1Bruins3LSommaire du Match
13 - 2020-10-0987Marlies1Crunch3WSommaire du Match
15 - 2020-10-1199Crunch3IceHogs4LSommaire du Match
17 - 2020-10-13109Bruins3Crunch4WSommaire du Match
20 - 2020-10-16122Rocket4Crunch3LXSommaire du Match
21 - 2020-10-17133Crunch6Senators4WSommaire du Match
22 - 2020-10-18141Crunch4Marlies0WSommaire du Match
23 - 2020-10-19152Senators2Crunch5WSommaire du Match
25 - 2020-10-21164Crunch2Griffins3LSommaire du Match
26 - 2020-10-22173Griffins3Crunch4WXSommaire du Match
27 - 2020-10-23186Crunch3Bruins2WSommaire du Match
29 - 2020-10-25196Bruins0Crunch2WSommaire du Match
31 - 2020-10-27208Monarchs1Crunch5WSommaire du Match
33 - 2020-10-29219Crunch3Bruins4LSommaire du Match
35 - 2020-10-31230Crunch3Monarchs4LSommaire du Match
37 - 2020-11-02239Griffins4Crunch3LSommaire du Match
39 - 2020-11-04251Crunch5Griffins2WSommaire du Match
41 - 2020-11-06262Bruins3Crunch2LSommaire du Match
43 - 2020-11-08275Sharks1Crunch3WSommaire du Match
45 - 2020-11-10289Senators4Crunch5WXSommaire du Match
47 - 2020-11-12304Crunch3Griffins4LSommaire du Match
48 - 2020-11-13312Senators1Crunch4WSommaire du Match
49 - 2020-11-14317Crunch0Monsters2LSommaire du Match
50 - 2020-11-15328Crunch1Moose3LSommaire du Match
51 - 2020-11-16337Marlies2Crunch1LXXSommaire du Match
53 - 2020-11-18354Moose4Crunch3LXXSommaire du Match
54 - 2020-11-19363Crunch3Marlies2WXXSommaire du Match
55 - 2020-11-20376Crunch1Monsters0WSommaire du Match
56 - 2020-11-21383Monsters4Crunch5WSommaire du Match
58 - 2020-11-23400Rocket2Crunch6WSommaire du Match
59 - 2020-11-24408Crunch0Wolf Pack5LSommaire du Match
60 - 2020-11-25417Crunch2Rocket3LSommaire du Match
61 - 2020-11-26427Griffins2Crunch4WSommaire du Match
63 - 2020-11-28439Crunch3Rocket2WXXSommaire du Match
64 - 2020-11-29450Crunch2Marlies1WSommaire du Match
65 - 2020-11-30457Wolf Pack3Crunch4WSommaire du Match
67 - 2020-12-02470Marlies1Crunch3WSommaire du Match
68 - 2020-12-03480Crunch2Senators0WSommaire du Match
70 - 2020-12-05494Flames2Crunch3WXSommaire du Match
71 - 2020-12-06506Crunch4Phantoms5LSommaire du Match
72 - 2020-12-07513Crunch4Griffins0WSommaire du Match
74 - 2020-12-09523Rocket1Crunch2WSommaire du Match
75 - 2020-12-10538Phantoms3Crunch4WSommaire du Match
77 - 2020-12-12550Rocket4Crunch2LSommaire du Match
78 - 2020-12-13559Crunch1Penguins0WSommaire du Match
79 - 2020-12-14569Crunch-Senators-
80 - 2020-12-15578Penguins-Crunch-
82 - 2020-12-17587Crunch-Flames-
84 - 2020-12-19601Crunch-Admirals-
85 - 2020-12-20607Crunch-Wolf Pack-
86 - 2020-12-21616Griffins-Crunch-
88 - 2020-12-23631Crunch-Penguins-
90 - 2020-12-25642Phantoms-Crunch-
91 - 2020-12-26652Sound Tigers-Crunch-
93 - 2020-12-28664Crunch-Soldiers-
94 - 2020-12-29674Senators-Crunch-
95 - 2020-12-30686Crunch-Stars-
96 - 2020-12-31697Condors-Crunch-
97 - 2021-01-01709Crunch-Condors-
99 - 2021-01-03717Crunch-Sharks-
100 - 2021-01-04726Sharks-Crunch-
101 - 2021-01-05740Phantoms-Crunch-
103 - 2021-01-07756Phantoms-Crunch-
104 - 2021-01-08764Crunch-Wolves-
105 - 2021-01-09773Crunch-Sound Tigers-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
106 - 2021-01-10785Marlies-Crunch-
108 - 2021-01-12794Crunch-Rocket-
109 - 2021-01-13807Crunch-Moose-
110 - 2021-01-14812IceHogs-Crunch-
112 - 2021-01-16828Stars-Crunch-
113 - 2021-01-17836Crunch-Rampage-
115 - 2021-01-19851Rampage-Crunch-
117 - 2021-01-21867Crunch-Rocket-
118 - 2021-01-22871Wolf Pack-Crunch-
120 - 2021-01-24886Soldiers-Crunch-
122 - 2021-01-26896Crunch-Senators-



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
15 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
2,333,403$ 1,120,000$ 1,082,500$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 738,771$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 46 29,476$ 1,355,896$




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