Marlies

GP: 76 | W: 5 | L: 69 | OTL: 2 | P: 12
GF: 123 | GA: 440 | PP%: 8.66% | PK%: 75.58%
DG: Sebastien Cloutier | Morale : 5 | Moyenne d'Équipe : 63
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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
1Matt HendricksXX100.008459735977645058756157755688782518620
2Connor BrickleyXXX100.007842866175786859536156625873676135610
3John McCarthyX100.006236905776939056635654565582795269610
4Brett PollockXXX100.006536905581908554525651585365636226590
5Stefan MatteauX100.007142745584908554585352575369657669590
6Sean MaloneX100.005935945674837755595552545167646169580
7Tom SestitoX100.008047585395736952545151595280725369580
8David Pope (R)X100.006237895378797352565153545269656069570
9Cameron SchillingX100.005636926077827059306054594679714466620
10Ashton SautnerX100.006537895976796357305854594669655178610
11Olli Juolevi (R)X100.006035946177797160306651584761638461610
12Keaton ThompsonX100.005639825571908554305351524567645669590
Rayé
1Kerby RychelX100.006036925980796958545657595869657815590
2Tyrell GoulbourneXX100.006439815571777054535554565269656315570
MOYENNE D'ÉQUIPE100.00664085577881735650575358527268595260
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 Campbell100.00818684808079818079818073777469780
2Scott Darling100.00797371957877797877797879854260770
Rayé
1Chad Johnson100.00787472837776787776787782873520750
2Troy Grosenick100.00778078747675777675777678844320740
MOYENNE D'ÉQUIPE100.0079787683787779787779787883494276
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Dan Bylsma71707174726777USA484100,000$


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
1Connor BrickleyMarlies (Tor)C/LW/RW38781513621055307815488.97%560415.912138640000340047.76%6700000.5000110101
2Ashton SautnerMarlies (Tor)D5411011101603912144157.14%224087.5700001000038000.00%000000.5400000001
3Kerby RychelMarlies (Tor)LW71233601316546.25%1679.610000000000100.00%200000.8900000000
4Brett PollockMarlies (Tor)C/LW/RW2000-100016310.00%23216.45000240000000100.00%100000.0000000000
Stats d'équipe Total ou en Moyenne10192029258410954611427687.89%30111311.0221310700000731047.14%7000000.5200110102
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
Stats d'équipe Total ou en Moyenne0.0000.0000.000


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'ÉquipePOS Âge Date de Naissance Nouveau Joueur Poids Taille Non-Échange Disponible pour Échange Ballotage Forcé Contrat Type Salaire Actuel Cap 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
Ashton SautnerMarlies (Tor)D251994-05-27No195 Lbs6 ft1NoNoNo1Pro & Farm300,000$0$0$NoLien
Brett PollockMarlies (Tor)C/LW/RW231996-03-17No190 Lbs6 ft3NoNoNo3Pro & Farm500,000$0$0$No500,000$500,000$Lien
Cameron SchillingMarlies (Tor)D301988-10-07No182 Lbs6 ft2NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Chad JohnsonMarlies (Tor)G331986-06-10No197 Lbs6 ft3NoNoNo2Pro & Farm1,000,000$0$0$No1,000,000$Lien
Connor BrickleyMarlies (Tor)C/LW/RW271992-02-25No203 Lbs6 ft0NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
David PopeMarlies (Tor)LW241994-09-27Yes187 Lbs6 ft2NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Jack CampbellMarlies (Tor)G271992-01-09No197 Lbs6 ft2NoNoNo3Pro & Farm1,397,349$0$0$No1,397,349$1,397,349$Lien
John McCarthyMarlies (Tor)LW321986-08-09No195 Lbs6 ft1NoNoNo3Pro & Farm500,000$0$0$No500,000$500,000$Lien
Keaton ThompsonMarlies (Tor)D231995-09-14No182 Lbs6 ft0NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Kerby RychelMarlies (Tor)LW241994-10-07No213 Lbs6 ft1NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Matt HendricksMarlies (Tor)C/LW381981-06-17No211 Lbs6 ft0NoNoNo1Pro & Farm500,000$0$0$NoLien
Olli JuoleviMarlies (Tor)D211998-05-05Yes182 Lbs6 ft2NoNoNo4Pro & Farm900,000$0$0$No900,000$900,000$900,000$Lien
Scott DarlingMarlies (Tor)G301988-12-22No226 Lbs6 ft5NoNoNo4Pro & Farm1,000,000$0$0$No1,000,000$1,000,000$1,000,000$Lien
Sean MaloneMarlies (Tor)C241995-04-30No197 Lbs6 ft0NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Stefan MatteauMarlies (Tor)C251994-02-23No220 Lbs6 ft2NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Tom SestitoMarlies (Tor)LW311987-09-28No228 Lbs6 ft5NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Troy GrosenickMarlies (Tor)G291989-08-27No185 Lbs6 ft1NoNoNo4Pro & Farm1,000,000$0$0$No1,000,000$1,000,000$1,000,000$Lien
Tyrell GoulbourneMarlies (Tor)LW/RW251994-01-26No195 Lbs5 ft11NoNoNo1Pro & Farm300,000$0$0$NoLien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
1827.28199 Lbs6 ft22.50577,631$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
140122
230122
320122
410122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
140122
230122
320122
410122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
160122
240122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
160122
240122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
160122
240122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
160122
240122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
16012260122
24012240122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
160122
240122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
160122
240122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
, , ,
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
, , ,
Tirs de Pénalité
, , , ,
Gardien
#1 : , #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
1Americans60600000830-2230300000414-1030300000416-1200.0008162400474134213754451957682797744952129.52%21480.95%0415171824.16%676273224.74%310120525.73%9916362786561791289
2Bears20200000210-81010000004-41010000026-400.00023500474134252544519576878234439600.00%14564.29%1415171824.16%676273224.74%310120525.73%9916362786561791289
3Bruins20200000416-121010000018-71010000038-500.0004812004741342335445195768102251441300.00%7271.43%0415171824.16%676273224.74%310120525.73%9916362786561791289
4Checkers4210010022193210001001192211000001110150.6252243650047413422045445195768205672413012433.33%12191.67%0415171824.16%676273224.74%310120525.73%9916362786561791289
5Comets605000101533-1830200010412-8303000001121-1020.167152641004741342133544519576830075549720210.00%26676.92%1415171824.16%676273224.74%310120525.73%9916362786561791289
6Crunch60600000943-3430300000519-1430300000424-2000.0009172600474134210854451957683209253872229.09%23482.61%1415171824.16%676273224.74%310120525.73%9916362786561791289
7Devils60600000429-2530300000214-1230300000215-1300.000481200474134210854451957682457873833326.06%28775.00%0415171824.16%676273224.74%310120525.73%9916362786561791289
8Monsters808000001055-4540400000331-2840400000724-1700.0001020300047413421435445195768415109821304536.67%24483.33%0415171824.16%676273224.74%310120525.73%9916362786561791289
9Moose421001001916321000100111102110000085350.625193655004741342181544519576817543318719210.53%14378.57%0415171824.16%676273224.74%310120525.73%9916362786561791289
10Penguins20200000114-131010000018-71010000006-600.000123004741342275445195768102302039400.00%8187.50%0415171824.16%676273224.74%310120525.73%9916362786561791289
11Phantoms2020000019-81010000005-51010000014-300.000123004741342445445195768862010257114.29%4250.00%0415171824.16%676273224.74%310120525.73%9916362786561791289
12Rocket10010000001057-4750500000629-2350500000428-2400.00010192900474134216854451957684821321061323412.94%431272.09%0415171824.16%676273224.74%310120525.73%9916362786561791289
13Senators12012000001472-5860600000934-2560600000538-3300.000142640004741342213544519576857017011818736513.89%591869.49%1415171824.16%676273224.74%310120525.73%9916362786561791289
14Sound Tigers20200000112-111010000005-51010000017-600.00012300474134224544519576881281433300.00%7185.71%0415171824.16%676273224.74%310120525.73%9916362786561791289
15Thunderbirds2020000019-81010000015-41010000004-400.00012300474134244544519576894251440700.00%7271.43%0415171824.16%676273224.74%310120525.73%9916362786561791289
Total7646900210123440-317382330021060216-156382360000063224-161120.07912323435700474134216435445195768364410207131260277248.66%3037475.58%4415171824.16%676273224.74%310120525.73%9916362786561791289
17Wolf Pack20200000216-141010000028-61010000008-800.000246004741342245445195768110261215500.00%6266.67%0415171824.16%676273224.74%310120525.73%9916362786561791289
_Since Last GM Reset7646900210123440-317382330021060216-156382360000063224-161120.07912323435700474134216435445195768364410207131260277248.66%3037475.58%4415171824.16%676273224.74%310120525.73%9916362786561791289
_Vs Conference282240011056148-9214111001102669-4314113000003079-4970.12556106162004741342686544519576813603762424949499.57%1092577.06%1415171824.16%676273224.74%310120525.73%9916362786561791289
_Vs Division4016000001916320800000111102080000085300.000193655004741342181544519576817543318719210.53%14378.57%0415171824.16%676273224.74%310120525.73%9916362786561791289

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
7612L19123234357164336441020713126000
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
764690210123440
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
38233021060216
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
38236000063224
Derniers 10 Matchs
WLOTWOTL SOWSOL
0100000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
277248.66%3037475.58%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
54451957684741342
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
415171824.16%676273224.74%310120525.73%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
9916362786561791289


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
3 - 2019-09-041Marlies1Comets7LSommaire du Match
4 - 2019-09-0517Marlies0Devils6LSommaire du Match
6 - 2019-09-0727Monsters7Marlies2LSommaire du Match
11 - 2019-09-1245Comets3Marlies0LSommaire du Match
12 - 2019-09-1360Comets7Marlies1LSommaire du Match
17 - 2019-09-1874Marlies1Americans4LSommaire du Match
18 - 2019-09-1983Wolf Pack8Marlies2LSommaire du Match
21 - 2019-09-22100Rocket6Marlies1LSommaire du Match
24 - 2019-09-25107Crunch5Marlies0LSommaire du Match
31 - 2019-10-02141Marlies1Monsters3LSommaire du Match
38 - 2019-10-09183Marlies2Senators7LSommaire du Match
39 - 2019-10-10196Marlies0Senators4LSommaire du Match
42 - 2019-10-13212Marlies1Rocket5LSommaire du Match
45 - 2019-10-16225Marlies1Senators8LSommaire du Match
46 - 2019-10-17240Devils7Marlies1LSommaire du Match
47 - 2019-10-18250Devils5Marlies0LSommaire du Match
52 - 2019-10-23266Marlies2Monsters7LSommaire du Match
54 - 2019-10-25293Monsters7Marlies0LSommaire du Match
57 - 2019-10-28301Marlies2Monsters6LSommaire du Match
60 - 2019-10-31320Penguins8Marlies1LSommaire du Match
61 - 2019-11-01334Phantoms5Marlies0LSommaire du Match
64 - 2019-11-04341Bruins8Marlies1LSommaire du Match
67 - 2019-11-07363Rocket4Marlies1LSommaire du Match
68 - 2019-11-08378Senators5Marlies1LSommaire du Match
71 - 2019-11-11384Crunch6Marlies4LSommaire du Match
73 - 2019-11-13397Marlies3Moose4LSommaire du Match
74 - 2019-11-14405Marlies5Moose1WSommaire du Match
78 - 2019-11-18425Devils2Marlies1LSommaire du Match
80 - 2019-11-20441Marlies1Rocket6LSommaire du Match
81 - 2019-11-21450Marlies1Rocket8LSommaire du Match
85 - 2019-11-25464Senators8Marlies1LSommaire du Match
87 - 2019-11-27481Marlies1Americans5LSommaire du Match
90 - 2019-11-30504Checkers6Marlies5LXSommaire du Match
94 - 2019-12-04521Marlies2Devils5LSommaire du Match
95 - 2019-12-05535Marlies3Crunch7LSommaire du Match
96 - 2019-12-06548Checkers3Marlies6WSommaire du Match
99 - 2019-12-09552Comets2Marlies3WXXSommaire du Match
101 - 2019-12-11568Marlies2Americans7LSommaire du Match
102 - 2019-12-12574Americans5Marlies1LSommaire du Match
109 - 2019-12-19618Marlies5Checkers6LSommaire du Match
110 - 2019-12-20631Marlies6Checkers4WSommaire du Match
113 - 2019-12-23647Marlies2Monsters8LSommaire du Match
115 - 2019-12-25656Marlies1Senators9LSommaire du Match
116 - 2019-12-26674Marlies1Senators7LSommaire du Match
122 - 2020-01-01686Rocket5Marlies1LSommaire du Match
123 - 2020-01-02697Rocket5Marlies2LSommaire du Match
126 - 2020-01-05714Senators8Marlies1LSommaire du Match
130 - 2020-01-09735Thunderbirds5Marlies1LSommaire du Match
131 - 2020-01-10753Senators4Marlies2LSommaire du Match
134 - 2020-01-13763Marlies0Wolf Pack8LSommaire du Match
136 - 2020-01-15773Marlies3Bruins8LSommaire du Match
137 - 2020-01-16788Marlies0Thunderbirds4LSommaire du Match
139 - 2020-01-18805Crunch8Marlies1LSommaire du Match
141 - 2020-01-20808Moose5Marlies6WSommaire du Match
143 - 2020-01-22817Marlies0Crunch8LSommaire du Match
144 - 2020-01-23832Marlies1Crunch9LSommaire du Match
148 - 2020-01-27850Moose6Marlies5LXSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
151 - 2020-01-30868Monsters7Marlies0LSommaire du Match
155 - 2020-02-03893Marlies0Rocket5LSommaire du Match
157 - 2020-02-05899Marlies1Rocket4LSommaire du Match
159 - 2020-02-07927Sound Tigers5Marlies0LSommaire du Match
160 - 2020-02-08931Senators4Marlies1LSommaire du Match
164 - 2020-02-12945Marlies2Bears6LSommaire du Match
165 - 2020-02-13963Marlies1Phantoms4LSommaire du Match
166 - 2020-02-14968Marlies0Penguins6LSommaire du Match
169 - 2020-02-17977Marlies1Sound Tigers7LSommaire du Match
172 - 2020-02-20994Americans6Marlies1LSommaire du Match
173 - 2020-02-211011Americans3Marlies2LSommaire du Match
178 - 2020-02-261029Marlies5Comets6LSommaire du Match
179 - 2020-02-271044Rocket9Marlies1LSommaire du Match
184 - 2020-03-031072Bears4Marlies0LSommaire du Match
185 - 2020-03-041074Marlies5Comets8LSommaire du Match
186 - 2020-03-051093Marlies0Devils4LSommaire du Match
190 - 2020-03-091108Marlies0Senators3LSommaire du Match
193 - 2020-03-121128Senators5Marlies3LSommaire du Match
194 - 2020-03-131145Monsters10Marlies1LSommaire 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
194,842$ 103,973$ 24,500$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 94,800$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 0 1,051$ 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
147646900210123440-317382330021060216-156382360000063224-1611212323435700474134216435445195768364410207131260277248.66%3037475.58%4415171824.16%676273224.74%310120525.73%9916362786561791289
Total Saison Régulière7646900210123440-317382330021060216-156382360000063224-1611212323435700474134216435445195768364410207131260277248.66%3037475.58%4415171824.16%676273224.74%310120525.73%9916362786561791289