Crunch

GP: 10 | W: 6 | L: 4 | OTL: 0 | P: 12
GF: 26 | GA: 22 | PP%: 13.64% | PK%: 84.21%
DG: Mathieu Veillet | Morale : 50 | Moyenne d'Équipe : N/A
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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 SP
1Bokondji Imama (R)X100.00678035608062665050494657444444515000
2Garrett WilsonXX100.00697358607372775750495863555758595000
3Nicolas DeslauriersXX100.00889958718255715925606269256666654700
4Jake EvansXX100.00774399627156835770586870254545674700
5Joshua Ho-Sang (R)X100.00746496776461626150605763544444625000
6Mitchell Stephens (R)X100.00734391657057646086585874254747635000
7Stefan MatteauX100.00828477648471746150576069575151644700
8Eetu Luostarinen (R)X100.00726785776770735974585662534444615000
9Oliver Wahlstrom (R)X100.00777581807563655850545864554444625000
10Nathan BeaulieuX100.00819278807770625725534881256667634700
11Nicolas Beaudin (R)X100.00696383666368744825394158394444525000
Rayé
MOYENNE D'ÉQUIPE100.0075717669736470575054566642505061490
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 Campbell100.0068626179716465687368755252674700
2Joonas Korpisalo100.0077666574807574798177955959765000
Rayé
1Malcolm Subban100.0054545478575158585954955252565000
MOYENNE D'ÉQUIPE100.006661607769636668716688545466490
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)LW1057128200351833143415.15%018518.511235390000211142.86%700001.3000000211
2Nicolas BeaudinCrunch (TBL)D104486215278134930.77%1319819.84112729000025110.00%000000.8100001210
3Garrett WilsonCrunch (TBL)LW/RW10336295201324101612.50%517817.850224290000340050.00%3000000.6700010120
4Mitchell StephensCrunch (TBL)C10044160346266180.00%218918.990008260000440066.18%27500000.4200000011
5Oliver WahlstromCrunch (TBL)RW10112225514192410234.17%416716.750005240000370040.00%2000000.2400010001
Stats d'équipe Total ou en Moyenne50131932198115991041204410010.83%2491918.392572914800001632262.65%33200000.7000021553
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)106400.9032.1558601212160000.0000100111
Stats d'équipe Total ou en Moyenne106400.9032.1558601212160000.0000100111


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$706,667$706,667$706,667$0$0$No706,667$706,667$
Eetu LuostarinenCrunch (TBL)C211998-09-02Yes179 Lbs6 ft2NoNoNo3Pro & Farm925,000$925,000$925,000$925,000$0$0$No925,000$925,000$
Garrett WilsonCrunch (TBL)LW/RW291991-03-16No199 Lbs6 ft2NoNoNo1Pro & Farm650,000$650,000$650,000$650,000$0$0$NoLien
Jack CampbellCrunch (TBL)G281992-01-08No197 Lbs6 ft3NoNoNo3Pro & Farm675,000$675,000$675,000$675,000$0$0$No675,000$675,000$Lien
Jake EvansCrunch (TBL)C/RW241996-06-02No185 Lbs6 ft0NoNoNo3Pro & Farm925,000$925,000$925,000$925,000$0$0$No925,000$925,000$Lien
Joonas KorpisaloCrunch (TBL)G261994-04-27No182 Lbs6 ft3NoNoNo1Pro & Farm900,000$900,000$900,000$900,000$0$0$NoLien
Joshua Ho-SangCrunch (TBL)RW241996-01-22Yes173 Lbs6 ft0NoNoNo2Pro & Farm1,075,833$1,075,833$1,075,833$1,075,833$0$0$No1,075,833$Lien
Malcolm SubbanCrunch (TBL)G261993-12-21No200 Lbs6 ft2NoNoNo1Pro & Farm650,000$650,000$650,000$650,000$0$0$NoLien
Mitchell StephensCrunch (TBL)C231997-02-05Yes191 Lbs6 ft0NoNoNo3Pro & Farm919,166$919,166$919,166$919,166$0$0$No919,166$919,166$
Nathan BeaulieuCrunch (TBL)D271992-12-04No200 Lbs6 ft2NoNoNo3Pro & Farm1,375,000$1,375,000$1,000,000$1,000,000$0$0$No1,000,000$1,000,000$Lien
Nicolas BeaudinCrunch (TBL)D201999-10-07Yes174 Lbs5 ft11NoNoNo3Pro & Farm1,135,833$1,135,833$1,135,833$1,135,833$0$0$No1,135,833$1,135,833$
Nicolas DeslauriersCrunch (TBL)LW/RW291991-02-22No215 Lbs6 ft1NoNoNo1Pro & Farm775,000$775,000$775,000$775,000$0$0$NoLien
Oliver WahlstromCrunch (TBL)RW202000-06-12Yes205 Lbs6 ft1NoNoNo3Pro & Farm1,462,500$1,462,500$1,462,500$1,462,500$0$0$No1,462,500$1,462,500$
Stefan MatteauCrunch (TBL)LW261994-02-23No220 Lbs6 ft2NoNoNo2Pro & Farm725,000$725,000$725,000$725,000$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
1Garrett WilsonMitchell StephensOliver Wahlstrom35122
2Bokondji Imama30122
325122
4Oliver WahlstromMitchell StephensGarrett Wilson10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Nicolas Beaudin35122
230122
325122
4Nicolas BeaudinBokondji Imama10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Garrett WilsonMitchell StephensOliver Wahlstrom60122
2Bokondji Imama40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Nicolas Beaudin60122
240122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Mitchell StephensOliver Wahlstrom60122
2Garrett WilsonBokondji Imama40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Nicolas Beaudin60122
240122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Mitchell Stephens60122Nicolas Beaudin60122
2Oliver Wahlstrom4012240122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Mitchell StephensOliver Wahlstrom60122
2Garrett WilsonBokondji Imama40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Nicolas Beaudin60122
240122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Garrett WilsonMitchell StephensOliver WahlstromNicolas Beaudin
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Garrett WilsonMitchell StephensOliver WahlstromNicolas Beaudin
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Garrett Wilson, Bokondji Imama, Mitchell StephensGarrett Wilson, Bokondji ImamaMitchell Stephens
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Nicolas Beaudin, , Nicolas Beaudin,
Tirs de Pénalité
Mitchell Stephens, Oliver Wahlstrom, Garrett Wilson, Bokondji Imama,
Gardien
#1 : Joonas Korpisalo, #2 :


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
1Bruins21100000440110000003211010000012-120.5004711001286056907410706119224112325.00%11281.82%015128752.61%15229451.70%7814055.71%2271642617111051
2Griffins211000006511010000023-11100000042220.500611170012860619074107045192640700.00%12191.67%015128752.61%15229451.70%7814055.71%2271642617111051
3Marlies22000000615110000002111100000040441.0006111701128606490741070341110401317.69%50100.00%015128752.61%15229451.70%7814055.71%2271642617111051
4Rocket2020000049-51010000034-11010000015-400.00048120012860369074107046155934500.00%15473.33%015128752.61%15229451.70%7814055.71%2271642617111051
5Senators22000000633110000004221100000021141.0006111700128605490741070341028387228.57%14285.71%015128752.61%15229451.70%7814055.71%2271642617111051
Total1064000002622453200000141225320000012102120.6002648740112860271907410702207414519344613.64%57984.21%015128752.61%15229451.70%7814055.71%2271642617111051
_Since Last GM Reset1064000002622453200000141225320000012102120.6002648740112860271907410702207414519344613.64%57984.21%015128752.61%15229451.70%7814055.71%2271642617111051
_Vs Conference853000002017343100000129342200000880100.6252037570112860210907410701755511915337616.22%45882.22%015128752.61%15229451.70%7814055.71%2271642617111051
_Vs Division1053000002622453100000141225220000012102100.5002648740112860271907410702207414519344613.64%57984.21%015128752.61%15229451.70%7814055.71%2271642617111051

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
1012L12648742712207414519301
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
106400002622
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
53200001412
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
53200001210
Derniers 10 Matchs
WLOTWOTL SOWSOL
640000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
44613.64%57984.21%0
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
9074107012860
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
15128752.61%15229451.70%7814055.71%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
2271642617111051


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 - 2020-09-038Rocket4Crunch3LSommaire du Match
3 - 2020-09-0522Crunch4Griffins2WSommaire du Match
4 - 2020-09-0627Senators2Crunch4WSommaire du Match
5 - 2020-09-0738Crunch1Rocket5LSommaire du Match
7 - 2020-09-0950Bruins2Crunch3WSommaire du Match
8 - 2020-09-1064Griffins3Crunch2LSommaire du Match
10 - 2020-09-1278Crunch2Senators1WSommaire du Match
11 - 2020-09-1389Marlies1Crunch2WSommaire du Match
12 - 2020-09-1496Crunch4Marlies0WSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
13 - 2020-09-15103Crunch1Bruins2LSommaire du Match



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
0 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
0$ 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$ 0$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 2 0$ 0$




LigueDomicileVisiteur
Année GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff P G A TP SO EG GP1 GP2 GP3 GP4 SHF SH1 SP2 SP3 SP4 SHA SHB Pim Hit PPA PPG PP% PKA PK GA PK% PK GF W OF FO T OF FO OF FO% W DF FO T DF FO DF FO% W NT FO T NT FO NT FO% PZ DF PZ OF PZ NT PC DF PC OF PC NT