Marlies

GP: 25 | W: 0 | L: 25 | OTL: 0 | P: 0
GF: 26 | GA: 148 | PP%: 4.81% | PK%: 74.73%
DG: Sebastien Cloutier | Morale : 34 | Moyenne d'Équipe : 64
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
1John McCarthyX100.006236905776939056635654565582795256610
2Stefan MatteauX100.007142745584908554585352575369657656590
3Dryden HuntX100.006938925873807456525754535567645256580
4Tom SestitoX100.008047585395736952545151595280725356580
5Sean MaloneX100.005935945674837755595552545167646156570
6David Pope (R)X100.006237895378797352565153545269656056560
7Seth HelgesonX100.007644695491949553305551594977695640640
8Cameron SchillingX100.005636926077827059306054594679714453620
9Olli Juolevi (R)X100.006035946177797160306651584761638455610
10Keaton ThompsonX100.005639825571908554305351524567645656590
Rayé
1Matt HendricksXX100.008459735977645058756157755688782511620
2Egor YakovlevXHO5736916672807165307458565375684912630
MOYENNE D'ÉQUIPE100.00664083577982765647585358517369564760
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 Campbell100.00818684808079818079818073777456770
2Scott Darling100.00797371957877797877797879854248770
Rayé
1Chad Johnson100.00787472837776787776787782873525750
2Troy Grosenick100.00778078747675777675777678844325740
MOYENNE D'ÉQUIPE100.0079787683787779787779787883493976
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'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 Link
Cameron SchillingMarlies (Tor)D301988-10-07No182 Lbs6 ft2NoNoNo2Sans RestrictionPro & Farm500,000$0$0$NoLien
Chad JohnsonMarlies (Tor)G331986-06-10No197 Lbs6 ft3NoNoNo2Sans RestrictionPro & Farm1,000,000$0$0$NoLien
David PopeMarlies (Tor)LW241994-09-27Yes187 Lbs6 ft2NoNoNo4Avec RestrictionPro & Farm300,000$0$0$NoLien
Dryden HuntMarlies (Tor)LW231995-11-24No191 Lbs6 ft0NoNoNo2Avec RestrictionPro & Farm300,000$0$0$NoLien
Egor YakovlevMarlies (Tor)D271991-09-17No190 Lbs6 ft0NoNoNo4Avec RestrictionPro & Farm300,000$0$0$NoLien
Jack CampbellMarlies (Tor)G271992-01-09No197 Lbs6 ft2NoNoNo3Avec RestrictionPro & Farm1,397,349$0$0$NoLien
John McCarthyMarlies (Tor)LW321986-08-09No195 Lbs6 ft1NoNoNo3Sans RestrictionPro & Farm500,000$0$0$NoLien
Keaton ThompsonMarlies (Tor)D231995-09-14No182 Lbs6 ft0NoNoNo2Avec RestrictionPro & Farm300,000$0$0$NoLien
Matt HendricksMarlies (Tor)C/LW381981-06-17No211 Lbs6 ft0NoNoNo1Sans RestrictionPro & Farm500,000$0$0$NoLien
Olli JuoleviMarlies (Tor)D211998-05-05Yes182 Lbs6 ft2NoNoNo4Contrat d'EntréePro & Farm900,000$0$0$NoLien
Scott DarlingMarlies (Tor)G301988-12-22No226 Lbs6 ft5NoNoNo4Sans RestrictionPro & Farm1,000,000$0$0$NoLien
Sean MaloneMarlies (Tor)C241995-04-30No197 Lbs6 ft0NoNoNo3Avec RestrictionPro & Farm300,000$0$0$NoLien
Seth HelgesonMarlies (Tor)D281990-10-08No221 Lbs6 ft4NoNoNo3Sans RestrictionPro & Farm500,000$0$0$NoLien
Stefan MatteauMarlies (Tor)C251994-02-23No220 Lbs6 ft2NoNoNo2Avec RestrictionPro & Farm300,000$0$0$NoLien
Tom SestitoMarlies (Tor)LW311987-09-28No228 Lbs6 ft5NoNoNo2Sans RestrictionPro & Farm500,000$0$0$NoLien
Troy GrosenickMarlies (Tor)G291989-08-27No185 Lbs6 ft1NoNoNo4Sans RestrictionPro & Farm1,000,000$0$0$NoLien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
1627.81199 Lbs6 ft22.81599,834$



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
1Americans1010000014-3000000000001010000014-300.00012300109702614514616004612616300.00%3166.67%09953918.37%18687821.18%8838722.74%30318793419126192
2Bruins1010000018-71010000018-70000000000000.00012300109701114514616004612822200.00%4175.00%09953918.37%18687821.18%8838722.74%30318793419126192
3Comets30300000217-1520200000110-91010000017-600.00024600109705414514616001363120461800.00%10280.00%09953918.37%18687821.18%8838722.74%30318793419126192
4Crunch20200000411-720200000411-70000000000000.00048120010970401451461600942510338112.50%50100.00%09953918.37%18687821.18%8838722.74%30318793419126192
5Devils30300000118-1720200000112-111010000006-600.00012300109705314514616001113534411317.69%12466.67%09953918.37%18687821.18%8838722.74%30318793419126192
6Monsters50500000730-2320200000214-1230300000516-1100.0007142100109708314514616002828350772827.14%19478.95%09953918.37%18687821.18%8838722.74%30318793419126192
7Penguins1010000018-71010000018-70000000000000.000123001097015145146160056111218300.00%60100.00%09953918.37%18687821.18%8838722.74%30318793419126192
8Phantoms1010000005-51010000005-50000000000000.0000000010970241451461600364412300.00%110.00%09953918.37%18687821.18%8838722.74%30318793419126192
9Rocket30300000315-1220200000210-81010000015-400.00036900109705514514616001214022311200.00%11463.64%09953918.37%18687821.18%8838722.74%30318793419126192
10Senators40400000424-201010000015-430300000319-1600.000471100109708114514616001755632571218.33%16662.50%09953918.37%18687821.18%8838722.74%30318793419126192
Total250250000026148-12215015000001591-7610010000001157-4600.00026517700109704511451461600115832420636110454.81%912374.73%09953918.37%18687821.18%8838722.74%30318793419126192
12Wolf Pack1010000028-61010000028-60000000000000.000246001097091451461600551588200.00%40100.00%09953918.37%18687821.18%8838722.74%30318793419126192
_Since Last GM Reset250250000026148-12215015000001591-7610010000001157-4600.00026517700109704511451461600115832420636110454.81%912374.73%09953918.37%18687821.18%8838722.74%30318793419126192
_Vs Conference70700000636-3040400000320-1730300000316-1300.00061218001097013514514616003038348933300.00%24770.83%09953918.37%18687821.18%8838722.74%30318793419126192

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
250L25265177451115832420636100
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
25025000026148
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
1501500001591
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
1001000001157
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
10454.81%912374.73%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
145146160010970
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
9953918.37%18687821.18%8838722.74%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
30318793419126192


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 - 2019-11-13397Marlies-Moose-
74 - 2019-11-14405Marlies-Moose-
78 - 2019-11-18425Devils-Marlies-
80 - 2019-11-20441Marlies-Rocket-
81 - 2019-11-21450Marlies-Rocket-
85 - 2019-11-25464Senators-Marlies-
87 - 2019-11-27481Marlies-Americans-
90 - 2019-11-30504Checkers-Marlies-
94 - 2019-12-04521Marlies-Devils-
95 - 2019-12-05535Marlies-Crunch-
96 - 2019-12-06548Checkers-Marlies-
99 - 2019-12-09552Comets-Marlies-
101 - 2019-12-11568Marlies-Americans-
102 - 2019-12-12574Americans-Marlies-
109 - 2019-12-19618Marlies-Checkers-
110 - 2019-12-20631Marlies-Checkers-
113 - 2019-12-23647Marlies-Monsters-
115 - 2019-12-25656Marlies-Senators-
116 - 2019-12-26674Marlies-Senators-
122 - 2020-01-01686Rocket-Marlies-
123 - 2020-01-02697Rocket-Marlies-
126 - 2020-01-05714Senators-Marlies-
130 - 2020-01-09735Thunderbirds-Marlies-
131 - 2020-01-10753Senators-Marlies-
134 - 2020-01-13763Marlies-Wolf Pack-
136 - 2020-01-15773Marlies-Bruins-
137 - 2020-01-16788Marlies-Thunderbirds-
139 - 2020-01-18805Crunch-Marlies-
141 - 2020-01-20808Moose-Marlies-
143 - 2020-01-22817Marlies-Crunch-
144 - 2020-01-23832Marlies-Crunch-
148 - 2020-01-27850Moose-Marlies-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
151 - 2020-01-30868Monsters-Marlies-
155 - 2020-02-03893Marlies-Rocket-
157 - 2020-02-05899Marlies-Rocket-
159 - 2020-02-07927Sound Tigers-Marlies-
160 - 2020-02-08931Senators-Marlies-
164 - 2020-02-12945Marlies-Bears-
165 - 2020-02-13963Marlies-Phantoms-
166 - 2020-02-14968Marlies-Penguins-
169 - 2020-02-17977Marlies-Sound Tigers-
172 - 2020-02-20994Americans-Marlies-
173 - 2020-02-211011Americans-Marlies-
178 - 2020-02-261029Marlies-Comets-
179 - 2020-02-271044Rocket-Marlies-
184 - 2020-03-031072Bears-Marlies-
185 - 2020-03-041074Marlies-Comets-
186 - 2020-03-051093Marlies-Devils-
190 - 2020-03-091108Marlies-Senators-
193 - 2020-03-121128Senators-Marlies-
194 - 2020-03-131145Monsters-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
71,412$ 92,973$ 13,000$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 34,262$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 122 995$ 121,390$




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
14250250000026148-12215015000001591-7610010000001157-46026517700109704511451461600115832420636110454.81%912374.73%09953918.37%18687821.18%8838722.74%30318793419126192
Total Saison Régulière250250000026148-12215015000001591-7610010000001157-46026517700109704511451461600115832420636110454.81%912374.73%09953918.37%18687821.18%8838722.74%30318793419126192