Marlies

GP: 25 | W: 0 | L: 24 | OTL: 1 | P: 1
GF: 35 | GA: 141 | PP%: 9.01% | PK%: 69.07%
DG: Sebastien Cloutier | Morale : 34 | Moyenne d'Équipe : 56
Prochain matchs #397 vs Moose
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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
1Pierre-Cedric LabrieX100.00665558638179745550555555555050156570
2Kyle FlanaganX100.00655566626561755750565755555050156560
3Mark MacMillanX100.00665562636561755550555556555050156560
4Quentin Shore (R)X100.00565563676866605550555555555050156560
5Andrew MillerX100.00565555555859595550555555557575153550
6Brandon BolligX100.00565555555758595550555555557573153550
7Max Reinhart (R)X100.00805569555466695550555555555050153550
8Ryan GarbuttX100.00555555555555555550555555556667152540
9Ryan PennyX100.00765569555454645550555555555050153540
10Miro Aaltonen (R)X100.00565555555555555550555555555050138530
11John McCarthyX100.00565555555556555550555555555555138530
12Tom SestitoX100.00565555555555555550555555556061136530
13Brandon BurlonX100.00575555605555595525555555555353156540
14Dylan LabbeX100.00555555605555585525555555555353156540
15David ShieldsX100.00555555605555585525555555555353156540
16Keaton Thompson (R)X100.00565555605555595525555555555353156540
17Seth HelgesonX100.00555555605555665525555555555353156540
18Matt LashoffX100.00555555605555575525555555555555155540
Rayé
1Ryan HamiltonX100.00565555555555555550555555555050132530
2Sean Malone (R)X100.00555555555555555550555555555050122520
MOYENNE D'ÉQUIPE100.0059555858585861554355555555555514954
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
1Chad Johnson100.0080837671817180727876557976148740
2Troy Grosenick100.0079787773737371717070556565156700
Rayé
MOYENNE D'ÉQUIPE100.008081777277727672747355727115272
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Randy Cunneyworth61625466744756CAN574100,000$


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 GP 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
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


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 StatusType Salaire Actuel Cap Salariale Cap Salariale Restant Exclus du Cap Salarial Salaire Année 2 Salaire Année 3 Salaire Année 4 Salaire Année 5 Salaire Année 6 Salaire Année 7 Salaire Année 8 Salaire Année 9 Salaire Année 10 Link
Andrew MillerMarlies (Tor)RW281988-09-17No181 Lbs5 ft10NoNoNo4Sans RestrictionPro & Farm500,000$0$0$No500,000$500,000$500,000$
Brandon BolligMarlies (Tor)LW291987-01-30No223 Lbs6 ft2NoNoNo3Sans RestrictionPro & Farm500,000$0$0$No500,000$500,000$
Brandon BurlonMarlies (Tor)D261990-03-04No190 Lbs6 ft0NoNoNo3Avec RestrictionPro & Farm500,000$0$0$No500,000$500,000$
Chad JohnsonMarlies (Tor)G301986-06-10No193 Lbs6 ft3NoNoNo1Sans RestrictionPro & Farm2,000,000$0$0$No
David ShieldsMarlies (Tor)D251991-01-27No204 Lbs6 ft3NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No300,000$
Dylan LabbeMarlies (Tor)D221995-01-09No194 Lbs6 ft2NoNoNo2Contrat d'EntréePro & Farm500,000$0$0$No500,000$
John McCarthyMarlies (Tor)C301986-08-09No194 Lbs6 ft1NoNoNo1Sans RestrictionPro & Farm500,000$0$0$No
Keaton ThompsonMarlies (Tor)D211995-09-13Yes182 Lbs6 ft0NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$No300,000$300,000$
Kyle FlanaganMarlies (Tor)C281988-12-30No181 Lbs5 ft9NoNoNo3Sans RestrictionPro & Farm300,000$0$0$No300,000$300,000$
Mark MacMillanMarlies (Tor)LW241992-01-23No172 Lbs6 ft0NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No300,000$
Matt LashoffMarlies (Tor)D301986-09-28No207 Lbs6 ft2NoNoNo1Sans RestrictionPro & Farm477,000$0$0$No
Max ReinhartMarlies (Tor)C241992-02-03Yes190 Lbs6 ft1NoNoNo1Avec RestrictionPro & Farm500,000$0$0$No
Miro AaltonenMarlies (Tor)C231993-06-07Yes176 Lbs5 ft11NoNoNo4Avec RestrictionPro & Farm300,000$0$0$No300,000$300,000$300,000$
Pierre-Cedric LabrieMarlies (Tor)LW301986-06-11No226 Lbs6 ft3NoNoNo2Sans RestrictionPro & Farm500,000$0$0$No500,000$
Quentin ShoreMarlies (Tor)C221994-05-25Yes183 Lbs6 ft2NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$No300,000$300,000$
Ryan GarbuttMarlies (Tor)C311985-08-11No195 Lbs6 ft0NoNoNo2Sans RestrictionPro & Farm500,000$0$0$No500,000$
Ryan HamiltonMarlies (Tor)LW311985-04-14No219 Lbs6 ft2NoNoNo2Sans RestrictionPro & Farm500,000$0$0$No500,000$
Ryan PennyMarlies (Tor)LW221994-09-09No192 Lbs6 ft0NoNoNo1Contrat d'EntréePro & Farm500,000$0$0$No
Sean MaloneMarlies (Tor)C211995-04-30Yes196 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No300,000$300,000$300,000$
Seth HelgesonMarlies (Tor)D261990-10-07No215 Lbs6 ft4NoNoNo4Avec RestrictionPro & Farm500,000$0$0$No500,000$500,000$500,000$
Tom SestitoMarlies (Tor)LW291987-09-27No228 Lbs6 ft5NoNoNo1Sans RestrictionPro & Farm500,000$0$0$No
Troy GrosenickMarlies (Tor)G271989-08-26No185 Lbs6 ft1NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2226.32197 Lbs6 ft12.27494,409$



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
1Americans1000010023-1000000000001000010023-110.5002460014813021149179164149208165120.00%40100.00%013453225.19%22083026.51%10137526.93%31520091819627095
2Bruins1010000012-11010000012-10000000000000.00012300148130171491791641316419400.00%20100.00%013453225.19%22083026.51%10137526.93%31520091819627095
3Comets30300000718-1120200000412-81010000036-300.000714210014813073149179164113934285015320.00%14285.71%013453225.19%22083026.51%10137526.93%31520091819627095
4Crunch20200000311-820200000311-80000000000000.00036900148130381491791641932312301317.69%5260.00%013453225.19%22083026.51%10137526.93%31520091819627095
5Devils30300000423-1920200000316-131010000017-600.00048120014813054149179164111932245010110.00%12375.00%013453225.19%22083026.51%10137526.93%31520091819627095
6Monsters50500000726-1920200000312-930300000414-1000.00071421001481308014917916411834648812200.00%22863.64%113453225.19%22083026.51%10137526.93%31520091819627095
7Penguins1010000015-41010000015-40000000000000.00012300148130191491791641211215148112.50%5420.00%013453225.19%22083026.51%10137526.93%31520091819627095
8Phantoms1010000005-51010000005-50000000000000.00000000148130201491791641271126600.00%10100.00%013453225.19%22083026.51%10137526.93%31520091819627095
9Rocket30300000521-1620200000514-91010000007-700.00059140014813063149179164114549225214321.43%10280.00%013453225.19%22083026.51%10137526.93%31520091819627095
10Senators40400000122-211010000004-430300000118-1700.000123001481306314917916411654344591100.00%20955.00%013453225.19%22083026.51%10137526.93%31520091819627095
Total250240010035141-10615015000002486-621009001001155-4410.02035681030014813049314917916411024297218404111109.01%973069.07%113453225.19%22083026.51%10137526.93%31520091819627095
12Wolf Pack1010000045-11010000045-10000000000000.00047110014813045149179164152211127300.00%20100.00%013453225.19%22083026.51%10137526.93%31520091819627095
_Since Last GM Reset250240010035141-10615015000002486-621009001001155-4410.02035681030014813049314917916411024297218404111109.01%973069.07%113453225.19%22083026.51%10137526.93%31520091819627095
_Vs Conference706001001442-2840400000926-1730200100516-1110.0711427410014813015714917916413331035811834720.59%28485.71%013453225.19%22083026.51%10137526.93%31520091819627095

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
251L193568103493102429721840400
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
25024010035141
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
1501500002486
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
100901001155
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
111109.01%973069.07%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
1491791641148130
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
13453225.19%22083026.51%10137526.93%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
31520091819627095


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 - 2018-09-071Marlies3Comets6LSommaire du Match
4 - 2018-09-0817Marlies1Devils7LSommaire du Match
6 - 2018-09-1027Monsters7Marlies2LSommaire du Match
11 - 2018-09-1545Comets6Marlies1LSommaire du Match
12 - 2018-09-1660Comets6Marlies3LSommaire du Match
17 - 2018-09-2174Marlies2Americans3LXSommaire du Match
18 - 2018-09-2283Wolf Pack5Marlies4LSommaire du Match
21 - 2018-09-25100Rocket10Marlies3LSommaire du Match
24 - 2018-09-28107Crunch3Marlies2LSommaire du Match
31 - 2018-10-05141Marlies2Monsters3LSommaire du Match
38 - 2018-10-12183Marlies0Senators7LSommaire du Match
39 - 2018-10-13196Marlies0Senators5LSommaire du Match
42 - 2018-10-16212Marlies0Rocket7LSommaire du Match
45 - 2018-10-19225Marlies1Senators6LSommaire du Match
46 - 2018-10-20240Devils8Marlies1LSommaire du Match
47 - 2018-10-21250Devils8Marlies2LSommaire du Match
52 - 2018-10-26266Marlies0Monsters7LSommaire du Match
54 - 2018-10-28293Monsters5Marlies1LSommaire du Match
57 - 2018-10-31301Marlies2Monsters4LSommaire du Match
60 - 2018-11-03320Penguins5Marlies1LSommaire du Match
61 - 2018-11-04334Phantoms5Marlies0LSommaire du Match
64 - 2018-11-07341Bruins2Marlies1LSommaire du Match
67 - 2018-11-10363Rocket4Marlies2LSommaire du Match
68 - 2018-11-11378Senators4Marlies0LSommaire du Match
71 - 2018-11-14384Crunch8Marlies1LSommaire du Match
73 - 2018-11-16397Marlies-Moose-
74 - 2018-11-17405Marlies-Moose-
78 - 2018-11-21425Devils-Marlies-
80 - 2018-11-23441Marlies-Rocket-
81 - 2018-11-24450Marlies-Rocket-
85 - 2018-11-28464Senators-Marlies-
87 - 2018-11-30481Marlies-Americans-
90 - 2018-12-03504Checkers-Marlies-
94 - 2018-12-07521Marlies-Devils-
95 - 2018-12-08535Marlies-Crunch-
96 - 2018-12-09548Checkers-Marlies-
99 - 2018-12-12552Comets-Marlies-
101 - 2018-12-14568Marlies-Americans-
102 - 2018-12-15574Americans-Marlies-
109 - 2018-12-22618Marlies-Checkers-
110 - 2018-12-23631Marlies-Checkers-
113 - 2018-12-26647Marlies-Monsters-
115 - 2018-12-28656Marlies-Senators-
116 - 2018-12-29674Marlies-Senators-
122 - 2019-01-04686Rocket-Marlies-
123 - 2019-01-05697Rocket-Marlies-
126 - 2019-01-08714Senators-Marlies-
130 - 2019-01-12735Thunderbirds-Marlies-
131 - 2019-01-13753Senators-Marlies-
134 - 2019-01-16763Marlies-Wolf Pack-
136 - 2019-01-18773Marlies-Bruins-
137 - 2019-01-19788Marlies-Thunderbirds-
139 - 2019-01-21805Crunch-Marlies-
141 - 2019-01-23808Moose-Marlies-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
143 - 2019-01-25817Marlies-Crunch-
144 - 2019-01-26832Marlies-Crunch-
148 - 2019-01-30850Moose-Marlies-
151 - 2019-02-02868Monsters-Marlies-
155 - 2019-02-06893Marlies-Rocket-
157 - 2019-02-08899Marlies-Rocket-
159 - 2019-02-10927Sound Tigers-Marlies-
160 - 2019-02-11931Senators-Marlies-
164 - 2019-02-15945Marlies-Bears-
165 - 2019-02-16963Marlies-Phantoms-
166 - 2019-02-17968Marlies-Penguins-
169 - 2019-02-20977Marlies-Sound Tigers-
172 - 2019-02-23994Americans-Marlies-
173 - 2019-02-241011Americans-Marlies-
178 - 2019-03-011029Marlies-Comets-
179 - 2019-03-021044Rocket-Marlies-
184 - 2019-03-071072Bears-Marlies-
185 - 2019-03-081074Marlies-Comets-
186 - 2019-03-091093Marlies-Devils-
190 - 2019-03-131108Marlies-Senators-
193 - 2019-03-161128Senators-Marlies-
194 - 2019-03-171145Monsters-Marlies-



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
23 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
80,166$ 108,770$ 93,900$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 43,049$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 122 1,076$ 131,272$




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
13250240010035141-10615015000002486-621009001001155-44135681030014813049314917916411024297218404111109.01%973069.07%113453225.19%22083026.51%10137526.93%31520091819627095
Total Saison Régulière250240010035141-10615015000002486-621009001001155-44135681030014813049314917916411024297218404111109.01%973069.07%113453225.19%22083026.51%10137526.93%31520091819627095