Crunch

GP: 8 | W: 5 | L: 3 | OTL: 0 | P: 10
GF: 17 | GA: 14 | PP%: 10.53% | PK%: 86.27%
DG: Mathieu Veillet | Morale : 50 | Moyenne d'Équipe : N/A
Prochain matchs #99 vs IceHogs
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
1Bokondji Imama (R)X100.00678035608062665050494657444444515000
2Garrett WilsonXX100.00697358607372775750495863555758595000
3Nicolas DeslauriersXX100.00889958718255715925606269256666655000
4Jake EvansXX100.00774399627156835770586870254545675000
5Joshua Ho-Sang (R)X100.00746496776461626150605763544444625000
6Mitchell Stephens (R)X100.00734391657057646086585874254747635000
7Stefan MatteauX100.00828477648471746150576069575151645000
8Eetu Luostarinen (R)X100.00726785776770735974585662534444615000
9Oliver Wahlstrom (R)X100.00777581807563655850545864554444625000
10Nathan BeaulieuX100.00819278807770625725534881256667635000
11Nicolas Beaudin (R)X100.00696383666368744825394158394444525000
Rayé
MOYENNE D'ÉQUIPE100.0075717669736470575054566642505061500
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
1Bokondji ImamaCrunch (TBL)LW8134019516202013175.00%214518.140334261011170028.57%700000.5500010000
2Garrett WilsonCrunch (TBL)LW/RW8224460811274187.41%114117.670118230111231058.33%2400000.5700000010
3Mitchell StephensCrunch (TBL)C83143001221851816.67%714918.651012210000330063.73%20400000.5400000002
4Joshua Ho-SangCrunch (TBL)RW22132001662333.33%03216.060000500000100.00%000001.8700000100
5Eetu LuostarinenCrunch (TBL)C21232405740525.00%03517.5600005000020070.73%4100001.7100000001
6Oliver WahlstromCrunch (TBL)RW8033300719179120.00%115118.970005270001390046.67%1500000.4000000000
7Stefan MatteauCrunch (TBL)LW2022120416220.00%04221.1700005000180050.00%400000.9400000010
8Nicolas BeaudinCrunch (TBL)D81121140161174514.29%716220.30101622000028100.00%000000.2500000010
9Nathan BeaulieuCrunch (TBL)D2011100443040.00%13919.880002300006000.00%000000.5000000000
10Nicolas DeslauriersCrunch (TBL)LW/RW2000-120952220.00%04120.8400016000090029.41%3400000.0000000000
11Jake EvansCrunch (TBL)C/RW2000020027050.00%13618.2200006000050051.22%4100000.0000000000
Stats d'équipe Total ou en Moyenne52101626164957110811741918.55%2097718.792462815311241733058.11%37000000.5300010133
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)85300.9291.7547902141960000.000080201
Stats d'équipe Total ou en Moyenne85300.9291.7547902141960000.000080201


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$632,581$706,667$632,581$0$0$No706,667$706,667$
Eetu LuostarinenCrunch (TBL)C211998-09-02Yes179 Lbs6 ft2NoNoNo3Pro & Farm925,000$828,024$925,000$828,024$0$0$No925,000$925,000$
Garrett WilsonCrunch (TBL)LW/RW291991-03-16No199 Lbs6 ft2NoNoNo1Pro & Farm650,000$581,855$650,000$581,855$0$0$NoLien
Jack CampbellCrunch (TBL)G281992-01-08No197 Lbs6 ft3NoNoNo3Pro & Farm675,000$604,234$675,000$604,234$0$0$No675,000$675,000$Lien
Jake EvansCrunch (TBL)C/RW241996-06-02No185 Lbs6 ft0NoNoNo3Pro & Farm925,000$828,024$925,000$828,024$0$0$No925,000$925,000$Lien
Joonas KorpisaloCrunch (TBL)G261994-04-27No182 Lbs6 ft3NoNoNo1Pro & Farm900,000$805,645$900,000$805,645$0$0$NoLien
Joshua Ho-SangCrunch (TBL)RW241996-01-22Yes173 Lbs6 ft0NoNoNo2Pro & Farm1,075,833$963,044$1,075,833$963,044$0$0$No1,075,833$Lien
Malcolm SubbanCrunch (TBL)G261993-12-21No200 Lbs6 ft2NoNoNo1Pro & Farm650,000$581,855$650,000$581,855$0$0$NoLien
Mitchell StephensCrunch (TBL)C231997-02-05Yes191 Lbs6 ft0NoNoNo3Pro & Farm919,166$822,802$919,166$822,802$0$0$No919,166$919,166$
Nathan BeaulieuCrunch (TBL)D271992-12-04No200 Lbs6 ft2NoNoNo3Pro & Farm1,375,000$1,230,847$1,000,000$895,161$0$0$No1,000,000$1,000,000$Lien
Nicolas BeaudinCrunch (TBL)D201999-10-07Yes174 Lbs5 ft11NoNoNo3Pro & Farm1,135,833$1,016,754$1,135,833$1,016,754$0$0$No1,135,833$1,135,833$
Nicolas DeslauriersCrunch (TBL)LW/RW291991-02-22No215 Lbs6 ft1NoNoNo1Pro & Farm775,000$693,750$775,000$693,750$0$0$NoLien
Oliver WahlstromCrunch (TBL)RW202000-06-12Yes205 Lbs6 ft1NoNoNo3Pro & Farm1,462,500$1,309,173$1,462,500$1,309,173$0$0$No1,462,500$1,462,500$
Stefan MatteauCrunch (TBL)LW261994-02-23No220 Lbs6 ft2NoNoNo2Pro & Farm725,000$648,992$725,000$648,992$0$0$No725,000$Lien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
1424.71196 Lbs6 ft12.29921,429$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Nicolas DeslauriersJake EvansOliver Wahlstrom35122
2Stefan MatteauEetu LuostarinenJoshua Ho-Sang30122
3Garrett WilsonMitchell StephensBokondji Imama25122
4Bokondji ImamaNicolas DeslauriersStefan 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
1Nicolas DeslauriersJake EvansOliver 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
1Nicolas DeslauriersStefan Matteau60122
2Jake EvansOliver Wahlstrom40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Nathan BeaulieuNicolas Beaudin60122
240122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Nicolas Deslauriers60122Nathan BeaulieuNicolas Beaudin60122
2Stefan Matteau4012240122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Nicolas DeslauriersStefan Matteau60122
2Jake EvansOliver Wahlstrom40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Nathan BeaulieuNicolas Beaudin60122
240122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Nicolas DeslauriersJake EvansOliver WahlstromNathan BeaulieuNicolas Beaudin
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Nicolas DeslauriersJake EvansOliver 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
Nicolas Deslauriers, Stefan Matteau, Jake Evans, 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
1Admirals11000000303110000003030000000000021.0003580178203569736602681521500.00%50100.00%112322554.67%13127148.34%479947.47%175123211609243
2Bruins3030000038-51010000002-22020000036-300.000369007820666973660801752531300.00%26484.62%012322554.67%13127148.34%479947.47%175123211609243
3Marlies22000000743110000003121100000043141.0007132000782057697366037812331119.09%6266.67%012322554.67%13127148.34%479947.47%175123211609243
4Sound Tigers11000000101000000000001100000010121.00011201782020697366034918173133.33%90100.00%012322554.67%13127148.34%479947.47%175123211609243
Total8530000017143431000009544220000089-1100.62517314802782020869736601964610914038410.53%51786.27%112322554.67%13127148.34%479947.47%175123211609243
6Wolves11000000321110000003210000000000021.00036900782030697366019412166233.33%5180.00%012322554.67%13127148.34%479947.47%175123211609243
_Since Last GM Reset8530000017143431000009544220000089-1100.62517314802782020869736601964610914038410.53%51786.27%112322554.67%13127148.34%479947.47%175123211609243
_Vs Conference523000001012-2211000003303120000079-240.40010192900782012369736601172564862414.17%32681.25%012322554.67%13127148.34%479947.47%175123211609243
_Vs Division523000001012-2211000003303120000079-240.40010192900782012369736601172564862414.17%32681.25%012322554.67%13127148.34%479947.47%175123211609243

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
810W11731482081964610914002
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
85300001714
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
431000095
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
422000089
Derniers 10 Matchs
WLOTWOTL SOWSOL
530000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
38410.53%51786.27%1
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
69736607820
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
12322554.67%13127148.34%479947.47%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
175123211609243


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 - 2020-10-1199Crunch-IceHogs-
17 - 2020-10-13109Bruins-Crunch-
20 - 2020-10-16122Rocket-Crunch-
21 - 2020-10-17133Crunch-Senators-
22 - 2020-10-18141Crunch-Marlies-
23 - 2020-10-19152Senators-Crunch-
25 - 2020-10-21164Crunch-Griffins-
26 - 2020-10-22173Griffins-Crunch-
27 - 2020-10-23186Crunch-Bruins-
29 - 2020-10-25196Bruins-Crunch-
31 - 2020-10-27208Monarchs-Crunch-
33 - 2020-10-29219Crunch-Bruins-
35 - 2020-10-31230Crunch-Monarchs-
37 - 2020-11-02239Griffins-Crunch-
39 - 2020-11-04251Crunch-Griffins-
41 - 2020-11-06262Bruins-Crunch-
43 - 2020-11-08275Sharks-Crunch-
45 - 2020-11-10289Senators-Crunch-
47 - 2020-11-12304Crunch-Griffins-
48 - 2020-11-13312Senators-Crunch-
49 - 2020-11-14317Crunch-Monsters-
50 - 2020-11-15328Crunch-Moose-
51 - 2020-11-16337Marlies-Crunch-
53 - 2020-11-18354Moose-Crunch-
54 - 2020-11-19363Crunch-Marlies-
55 - 2020-11-20376Crunch-Monsters-
56 - 2020-11-21383Monsters-Crunch-
58 - 2020-11-23400Rocket-Crunch-
59 - 2020-11-24408Crunch-Wolf Pack-
60 - 2020-11-25417Crunch-Rocket-
61 - 2020-11-26427Griffins-Crunch-
63 - 2020-11-28439Crunch-Rocket-
64 - 2020-11-29450Crunch-Marlies-
65 - 2020-11-30457Wolf Pack-Crunch-
67 - 2020-12-02470Marlies-Crunch-
68 - 2020-12-03480Crunch-Senators-
70 - 2020-12-05494Flames-Crunch-
71 - 2020-12-06506Crunch-Phantoms-
72 - 2020-12-07513Crunch-Griffins-
74 - 2020-12-09523Rocket-Crunch-
75 - 2020-12-10538Phantoms-Crunch-
77 - 2020-12-12550Rocket-Crunch-
78 - 2020-12-13559Crunch-Penguins-
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
37 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
401,011$ 1,290,000$ 1,252,500$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 135,239$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 111 30,847$ 3,424,017$




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
20208530000017143431000009544220000089-11017314802782020869736601964610914038410.53%51786.27%112322554.67%13127148.34%479947.47%175123211609243
Total Saison Régulière8530000017143431000009544220000089-11017314802782020869736601964610914038410.53%51786.27%112322554.67%13127148.34%479947.47%175123211609243