Marlies

GP: 5 | W: 0 | L: 5 | OTL: 0 | P: 0
GF: 10 | GA: 32 | PP%: 12.50% | PK%: 69.57%
DG: Sebastien Cloutier | Morale : 47 | Moyenne d'Équipe : N/A
Prochain matchs #74 vs Americans
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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
1Andrew MillerX100.0056555555585959555055555555757514800
2Austen BrassardX100.0067556760776766555055555655726715100
3Brandon BolligX100.0056555555575859555055555555757314800
4Ryan GarbuttX100.0055555555555555555055555555666714700
5Brett Sterling (R)X100.0059556768615975575056575955747515100
6Kyle FlanaganX100.0065556662656175575056575555505015100
7Max Reinhart (R)X100.0080556955546669555055555555505014800
8Mark MacMillanX100.0066556263656175555055555655505015100
9Ryan PennyX100.0076556955545464555055555555505014800
10Quentin Shore (R)X100.0056556367686660555055555555505015100
11Pierre-Cedric LabrieX100.0066555863817974555055555555505015100
12Ryan HamiltonX100.0056555555555555555055555555505014300
13Brandon BurlonX100.0057555560555559552555555555535315100
14Dylan LabbeX100.0055555560555558552555555555535315100
15David ShieldsX100.0055555560555558552555555555535315100
16Karl StolleryX100.0055555560555575552555555555535314800
17Keaton Thompson (R)X100.0056555560555559552555555555535315100
18Seth HelgesonX100.0055555560555566552555555555535315100
19Matt LashoffX100.0055555560555557552555555555555515000
Rayé
1Miro Aaltonen (R)X100.0056555555555555555055555555505014200
2John McCarthyX100.0056555555555655555055555555555514300
3Sean Malone (R)X100.0055555555555555555055555555505014200
4Tom SestitoX100.0056555555555555555055555555606114500
MOYENNE D'ÉQUIPE100.006055595959596355425555555557561480
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 Johnson100.008083767181718072787655797614300
2Troy Grosenick100.007978777373737171707055656515100
Rayé
MOYENNE D'ÉQUIPE100.00808177727772767274735572711470
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$500,000$
Austen BrassardMarlies (Tor)RW241993-01-13No188 Lbs6 ft2NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
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$500,000$
Brett SterlingMarlies (Tor)LW321984-04-23Yes175 Lbs5 ft7NoNoNo3Sans 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
Karl StolleryMarlies (Tor)D291987-11-21No180 Lbs5 ft11NoNoNo2Sans RestrictionPro & Farm300,000$0$0$No300,000$
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
2526.56195 Lbs6 ft12.24479,080$



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
1Comets30300000718-1120200000412-81010000036-300.0007142100145073313936013934285015320.00%14285.71%0299530.53%4917028.82%278930.34%6542178395619
2Devils1010000017-6000000000001010000017-600.0001230014501831393604913814200.00%4250.00%0299530.53%4917028.82%278930.34%6542178395619
3Monsters1010000027-51010000027-50000000000000.0002460014501531393604071021700.00%5340.00%0299530.53%4917028.82%278930.34%6542178395619
Total505000001032-2230300000619-1320200000413-900.000102030001450106313936022854468524312.50%23769.57%0299530.53%4917028.82%278930.34%6542178395619
_Since Last GM Reset505000001032-2230300000619-1320200000413-900.000102030001450106313936022854468524312.50%23769.57%0299530.53%4917028.82%278930.34%6542178395619
_Vs Conference30300000718-1120200000412-81010000036-300.0007142100145073313936013934285015320.00%14285.71%0299530.53%4917028.82%278930.34%6542178395619

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
50L510203010622854468500
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
50500001032
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
3030000619
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
2020000413
Derniers 10 Matchs
WLOTWOTL SOWSOL
050000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
24312.50%23769.57%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
31393601450
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
299530.53%4917028.82%278930.34%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
6542178395619


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 - 2018-09-2174Marlies-Americans-
18 - 2018-09-2283Wolf Pack-Marlies-
21 - 2018-09-25100Rocket-Marlies-
24 - 2018-09-28107Crunch-Marlies-
31 - 2018-10-05141Marlies-Monsters-
38 - 2018-10-12183Marlies-Senators-
39 - 2018-10-13196Marlies-Senators-
42 - 2018-10-16212Marlies-Rocket-
45 - 2018-10-19225Marlies-Senators-
46 - 2018-10-20240Devils-Marlies-
47 - 2018-10-21250Devils-Marlies-
52 - 2018-10-26266Marlies-Monsters-
54 - 2018-10-28293Monsters-Marlies-
57 - 2018-10-31301Marlies-Monsters-
60 - 2018-11-03320Penguins-Marlies-
61 - 2018-11-04334Phantoms-Marlies-
64 - 2018-11-07341Bruins-Marlies-
67 - 2018-11-10363Rocket-Marlies-
68 - 2018-11-11378Senators-Marlies-
71 - 2018-11-14384Crunch-Marlies-
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
35 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
14,651$ 119,770$ 99,900$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 7,946$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 181 1,133$ 205,073$




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
13505000001032-2230300000619-1320200000413-90102030001450106313936022854468524312.50%23769.57%0299530.53%4917028.82%278930.34%6542178395619
Total Saison Régulière505000001032-2230300000619-1320200000413-90102030001450106313936022854468524312.50%23769.57%0299530.53%4917028.82%278930.34%6542178395619