Marlies

GP: 76 | W: 5 | L: 68 | OTL: 3 | P: 13
GF: 118 | GA: 417 | PP%: 9.29% | PK%: 72.80%
DG: Sebastien Cloutier | Morale : 7 | Moyenne d'Équipe : 56
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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
1Stefan MatteauX100.00645558748078706150606060555050122600
2Brett Sterling (R)X100.00595567686159755750565759557475118580
3Austen BrassardX100.00675567607767665550555556557267128570
4Kyle FlanaganX100.00655566626561755750565755555050169560
5Mark MacMillanX100.00665562636561755550555556555050169560
6Quentin Shore (R)X100.00565563676866605550555555555050169560
7Andrew MillerX100.00565555555859595550555555557575166550
8Brandon BolligX100.00565555555758595550555555557573166550
9Max Reinhart (R)X100.00805569555466695550555555555050166550
10Ryan GarbuttX100.00555555555555555550555555556667165540
11Ryan PennyX100.00765569555454645550555555555050166540
12John McCarthyX100.00565555555556555550555555555555151530
13Tom SestitoX100.00565555555555555550555555556061149530
14Oliver KylingtonX100.00555557605757635725575757555353130550
15Dylan LabbeX100.00555555605555585525555555555353169540
16David ShieldsX100.00555555605555585525555555555353169540
17Keaton Thompson (R)X100.00565555605555595525555555555353169540
18Seth HelgesonX100.00555555605555665525555555555353169540
19Matt LashoffX100.00555555605555575525555555555555168540
Rayé
1Miro Aaltonen (R)X100.00565555555555555550555555555050120520
2Sean Malone (R)X100.00555555555555555550555555555050120520
3Ryan HamiltonX100.00565555555555555550555555555050120520
MOYENNE D'ÉQUIPE100.0060555959595962564355565655575715255
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 Johnson100.0080837671817180727876557976161740
2Troy Grosenick100.0079787773737371717070556565169700
Rayé
MOYENNE D'ÉQUIPE100.008081777277727672747355727116572
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
1Tyson JostToronto Maple LeafsC/LW/RW4323285129375651061885816912.23%12108725.2959144219900052223167.02%9400010.9423100474
2Joel Eriksson EkToronto Maple LeafsC/LW/RW4015254065354776101338414.85%981920.48714213119610141472172.39%16300000.9801100343
3Julius HonkaToronto Maple LeafsD3912273925560547282285814.63%3690223.14101020681810001184200.00%000010.8600000311
4Pavel ZachaToronto Maple LeafsC/LW1000-120343210.00%02121.1800001000000046.15%1300000.0000000000
5Oliver KylingtonMarlies (Tor)D7000000400000.00%0202.960000000005000.00%000000.0000000000
Stats d'équipe Total ou en Moyenne1305080130591481017325837412131213.37%57285121.93223355141579101105607269.26%27000020.912420010128
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 StatusType Salaire Actuel Cap Salariale Cap Salariale Restant Exclus du Cap Salarial Link
Andrew MillerMarlies (Tor)RW281988-09-17No181 Lbs5 ft10NoNoNo4Sans RestrictionPro & Farm500,000$0$0$No
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$0$No
Brett SterlingMarlies (Tor)LW321984-04-23Yes175 Lbs5 ft7NoNoNo3Sans RestrictionPro & Farm500,000$0$0$No
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$0$No
Dylan LabbeMarlies (Tor)D221995-01-09No194 Lbs6 ft2NoNoNo2Contrat d'EntréePro & Farm500,000$0$0$No
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$0$No
Kyle FlanaganMarlies (Tor)C281988-12-30No181 Lbs5 ft9NoNoNo3Sans RestrictionPro & Farm300,000$0$0$No
Mark MacMillanMarlies (Tor)LW241992-01-23No172 Lbs6 ft0NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No
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$0$No
Oliver KylingtonMarlies (Tor)D191997-05-19No181 Lbs6 ft0NoNoNo3Contrat d'EntréePro & Farm500,000$0$0$No
Quentin ShoreMarlies (Tor)C221994-05-25Yes183 Lbs6 ft2NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$No
Ryan GarbuttMarlies (Tor)C311985-08-11No195 Lbs6 ft0NoNoNo2Sans RestrictionPro & Farm500,000$0$0$No
Ryan HamiltonMarlies (Tor)LW311985-04-14No219 Lbs6 ft2NoNoNo2Sans RestrictionPro & Farm500,000$0$0$No
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$0$No
Seth HelgesonMarlies (Tor)D261990-10-07No215 Lbs6 ft4NoNoNo4Avec RestrictionPro & Farm500,000$0$0$No
Stefan MatteauMarlies (Tor)LW221994-02-22No220 Lbs6 ft2NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$No
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
2425.83195 Lbs6 ft12.29478,208$



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
1Americans60500100825-1730300000615-930200100210-810.0838162400463536111758060157932236547923339.09%19384.21%0527180329.23%702254627.57%326121126.92%10506852692580827303
2Bears2020000059-41010000025-31010000034-100.0005914004635361415806015793861411251516.67%3233.33%0527180329.23%702254627.57%326121126.92%10506852692580827303
3Bruins2020000016-51010000012-11010000004-400.00012300463536137580601579362272236600.00%10190.00%0527180329.23%702254627.57%326121126.92%10506852692580827303
4Checkers420011002117421000100108221001000119270.8752142630046353611875806015793187591910210550.00%9366.67%0527180329.23%702254627.57%326121126.92%10506852692580827303
5Comets606000001136-2530300000420-1630300000716-900.000112233004635361147580601579328673629427414.81%30486.67%0527180329.23%702254627.57%326121126.92%10506852692580827303
6Crunch60600000429-2530300000313-1030300000116-1500.000481200463536112058060157932686745772913.45%14564.29%0527180329.23%702254627.57%326121126.92%10506852692580827303
7Devils60600000547-4230300000424-2030300000123-2200.0005101500463536110558060157932857160942129.52%23386.96%0527180329.23%702254627.57%326121126.92%10506852692580827303
8Monsters80800000849-4140400000429-2540400000420-1600.00081624004635361136580601579333688681303300.00%321262.50%1527180329.23%702254627.57%326121126.92%10506852692580827303
9Moose412001001116-52020000027-52100010099030.375112233004635361134580601579319438419112433.33%15193.33%0527180329.23%702254627.57%326121126.92%10506852692580827303
10Penguins2020000029-71010000015-41010000014-300.000246004635361295806015793552119269111.11%7442.86%0527180329.23%702254627.57%326121126.92%10506852692580827303
11Phantoms20200000210-81010000005-51010000025-300.000246004635361455806015793802617201218.33%6266.67%0527180329.23%702254627.57%326121126.92%10506852692580827303
12Rocket10010000001258-4650500000926-1750500000332-2900.0001221330046353612035806015793475130821583738.11%381073.68%0527180329.23%702254627.57%326121126.92%10506852692580827303
13Senators1201200000872-6460600000634-2860600000238-3600.000816240046353612265806015793460119971974912.04%381950.00%0527180329.23%702254627.57%326121126.92%10506852692580827303
14Sound Tigers20200000213-111010000017-61010000016-500.000246004635361465806015793802129281218.33%7185.71%0527180329.23%702254627.57%326121126.92%10506852692580827303
15Thunderbirds21100000910-11010000035-21100000065120.50091827004635361955806015793923012579222.22%60100.00%0527180329.23%702254627.57%326121126.92%10506852692580827303
Total7646801300118417-299381360010060210-150383320120058207-149130.0861182303480046353611763580601579332578756461275323309.29%2617172.80%1527180329.23%702254627.57%326121126.92%10506852692580827303
17Wolf Pack20200000911-21010000045-11010000056-100.00091625004635361955806015793882615489111.11%4175.00%0527180329.23%702254627.57%326121126.92%10506852692580827303
_Since Last GM Reset7646801300118417-299381360010060210-150383320120058207-149130.0861182303480046353611763580601579332578756461275323309.29%2617172.80%1527180329.23%702254627.57%326121126.92%10506852692580827303
_Vs Conference283220120061146-8514112001003274-4214210011002972-43100.17961119180004635361749580601579312633572225031161714.66%1022080.39%0527180329.23%702254627.57%326121126.92%10506852692580827303
_Vs Division4015001001116-52080000027-52070010099010.125112233004635361134580601579319438419112433.33%15193.33%0527180329.23%702254627.57%326121126.92%10506852692580827303

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
7613L2411823034817633257875646127500
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
764681300118417
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
38136010060210
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
38332120058207
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
323309.29%2617172.80%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
58060157934635361
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
527180329.23%702254627.57%326121126.92%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
10506852692580827303


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 - 2018-11-16397Marlies4Moose5LXSommaire du Match
74 - 2018-11-17405Marlies5Moose4WSommaire du Match
78 - 2018-11-21425Devils8Marlies1LSommaire du Match
80 - 2018-11-23441Marlies0Rocket9LSommaire du Match
81 - 2018-11-24450Marlies2Rocket5LSommaire du Match
85 - 2018-11-28464Senators4Marlies2LSommaire du Match
87 - 2018-11-30481Marlies0Americans5LSommaire du Match
90 - 2018-12-03504Checkers5Marlies4LXSommaire du Match
94 - 2018-12-07521Marlies0Devils9LSommaire du Match
95 - 2018-12-08535Marlies1Crunch5LSommaire du Match
96 - 2018-12-09548Checkers3Marlies6WSommaire du Match
99 - 2018-12-12552Comets8Marlies0LSommaire du Match
101 - 2018-12-14568Marlies0Americans2LSommaire du Match
102 - 2018-12-15574Americans4Marlies3LSommaire du Match
109 - 2018-12-22618Marlies5Checkers4WSommaire du Match
110 - 2018-12-23631Marlies6Checkers5WXSommaire du Match
113 - 2018-12-26647Marlies0Monsters6LSommaire du Match
115 - 2018-12-28656Marlies0Senators5LSommaire du Match
116 - 2018-12-29674Marlies1Senators7LSommaire du Match
122 - 2019-01-04686Rocket4Marlies2LSommaire du Match
123 - 2019-01-05697Rocket4Marlies1LSommaire du Match
126 - 2019-01-08714Senators9Marlies2LSommaire du Match
130 - 2019-01-12735Thunderbirds5Marlies3LSommaire du Match
131 - 2019-01-13753Senators5Marlies1LSommaire du Match
134 - 2019-01-16763Marlies5Wolf Pack6LSommaire du Match
136 - 2019-01-18773Marlies0Bruins4LSommaire du Match
137 - 2019-01-19788Marlies6Thunderbirds5WSommaire du Match
139 - 2019-01-21805Crunch2Marlies0LSommaire du Match
141 - 2019-01-23808Moose3Marlies1LSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
143 - 2019-01-25817Marlies0Crunch4LSommaire du Match
144 - 2019-01-26832Marlies0Crunch7LSommaire du Match
148 - 2019-01-30850Moose4Marlies1LSommaire du Match
151 - 2019-02-02868Monsters10Marlies0LSommaire du Match
155 - 2019-02-06893Marlies1Rocket5LSommaire du Match
157 - 2019-02-08899Marlies0Rocket6LSommaire du Match
159 - 2019-02-10927Sound Tigers7Marlies1LSommaire du Match
160 - 2019-02-11931Senators7Marlies0LSommaire du Match
164 - 2019-02-15945Marlies3Bears4LSommaire du Match
165 - 2019-02-16963Marlies2Phantoms5LSommaire du Match
166 - 2019-02-17968Marlies1Penguins4LSommaire du Match
169 - 2019-02-20977Marlies1Sound Tigers6LSommaire du Match
172 - 2019-02-23994Americans4Marlies1LSommaire du Match
173 - 2019-02-241011Americans7Marlies2LSommaire du Match
178 - 2019-03-011029Marlies3Comets4LSommaire du Match
179 - 2019-03-021044Rocket4Marlies1LSommaire du Match
184 - 2019-03-071072Bears5Marlies2LSommaire du Match
185 - 2019-03-081074Marlies1Comets6LSommaire du Match
186 - 2019-03-091093Marlies0Devils7LSommaire du Match
190 - 2019-03-131108Marlies0Senators8LSommaire du Match
193 - 2019-03-161128Senators5Marlies1LSommaire du Match
194 - 2019-03-171145Monsters7Marlies1LSommaire 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
212,293$ 114,770$ 91,900$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 112,307$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 0 1,107$ 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
137646801300118417-299381360010060210-150383320120058207-149131182303480046353611763580601579332578756461275323309.29%2617172.80%1527180329.23%702254627.57%326121126.92%10506852692580827303
Total Saison Régulière7646801300118417-299381360010060210-150383320120058207-149131182303480046353611763580601579332578756461275323309.29%2617172.80%1527180329.23%702254627.57%326121126.92%10506852692580827303