Monsters

GP: 8 | W: 5 | L: 3 | OTL: 0 | P: 10
GF: 14 | GA: 16 | PP%: 9.62% | PK%: 79.49%
DG: Yvon Poulin | Morale : 50 | Moyenne d'Équipe : N/A
Prochain matchs #91 vs Wolf Pack
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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
1Charles HudonXX100.00824499746659646433575854755959625000
2Eric Cornel (R)XX100.00797296647279865670475866555151625000
3Nicolas RoyX100.00794590687859826272647163254747695000
4Kevin Stenlund (R)XX100.00674391658066817554617161254747695000
5Nick Moutrey (R)X100.00818082658069744961464764454444565000
6Rasmus Asplund (R)XXX100.00694292686457866155575685254646655000
7Tyler Benson (R)X100.00737079717075786350665763544444645000
8Paul Bittner (R)X100.00808079688066705150475164484444575000
9Sonny MilanoXX100.00654286837364636625746459255253675000
10Antoine Morand (R)X100.00716683676670755063494759454444554500
11Connor Dewar (R)XX100.00706387656369735468535060484444575000
12Brendan GuhleX100.00694289756971755725505474255858635000
13Cale Fleury (R)X100.00904694777464735325394769254747595000
14Mirco MuellerX100.00785786687972695925494890256061635000
15Slater KoekkoekX100.00804472767270596325554780255758625000
16Mike ReillyX100.0073438478737568732566476725606063500X0
17Devon ToewsX100.00714291806881877825655065636263655000
Rayé
1Pascal Laberge (R)X100.00706582646552515670466160584444595000
2Michael McCarronXX100.00788853658861625974506266594949615000
3Gabriel CarlssonX100.00797589787569755025424165395252565000
4Ryan Collins (R)X100.00828184528155574825384264404444535000
5Markus NutivaaraX100.0073439682716876622552495925626360500X0
MOYENNE D'ÉQUIPE100.0075588671736772604453546640515161500
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
1Calvin Pickard100.0054587377535852585756305757565000
2Oscar Dansk100.0060688579586556646160304444615000
Rayé
1Jakub Skarek (R)100.0049526579474850544848304444505000
MOYENNE D'ÉQUIPE100.005459747853575359555530484856500
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Rick Tocchet84927887817667CAN5731,500,000$


Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Nom du Joueur Nom de l'ÉquipePOSGP G A P +/- PIM PIM5 HIT HTT SHT OSB OSM SHT% SB MP AMG PPG PPA PPP PPS PPM PKG PKA PKP PKS PKM GW GT FO% FOT GA TA EG HT P/20 PSG PSS FW FL FT S1 S2 S3
1Devon ToewsMonsters (CLB)D8066260866280.00%219224.11022645000027000.00%000000.6200000001
2Slater KoekkoekMonsters (CLB)D81341120161193611.11%1316620.84112937000022000.00%000000.4800000001
3Nicolas RoyMonsters (CLB)C8033012014115470.00%215919.880332390000190056.76%14800000.3811000100
4Kevin StenlundMonsters (CLB)C/RW830310027144421.43%215819.812025390000180061.64%7300000.3802000100
5Mike ReillyMonsters (CLB)D80332001438370.00%419123.95000843000028000.00%000000.3100000000
6Sonny MilanoMonsters (CLB)LW/RW82131403141241216.67%117722.221126380000271133.33%3600000.3402000010
7Drake CaggiulaBlue JacketsLW/RW420228012692622.22%08922.260003130000150038.46%3900000.4511000020
8Mirco MuellerMonsters (CLB)D811212041072414.29%717021.25101638000025000.00%000000.2400000000
9Tyler BensonMonsters (CLB)LW8112-20051742725.00%012515.6900007000010100.00%200000.3200000100
10Charles HudonMonsters (CLB)LW/RW8011-26011511090.00%014518.15011340000060022.22%900000.1411000000
11Rasmus AsplundMonsters (CLB)C/LW/RW8101-2408151021210.00%013516.98000341000001051.52%6600000.1500000010
12Paul BittnerMonsters (CLB)LW8101-18081120250.00%112115.1800000000000050.00%200000.1600000010
13Brendan GuhleMonsters (CLB)D8000-420183020.00%512215.360001700005000.00%000000.0000000000
14Cale FleuryMonsters (CLB)D8000-32751684030.00%112415.5600022000011000.00%000000.0000000000
15Eric CornelMonsters (CLB)C/RW80000409111360.00%014017.510001290000110058.62%5800000.0000000000
16Nick MoutreyMonsters (CLB)C8000-220982010.00%18410.6200005000090049.21%6300000.0000000000
17Antoine MorandMonsters (CLB)C4000000011000.00%0205.0500007000010033.33%600000.0000000000
18Connor DewarMonsters (CLB)C/LW8000000121000.00%0313.9800000000000071.43%1400000.0000000000
Stats d'équipe Total ou en Moyenne136121931-6975141154109319611.01%39235717.3458135543500002413152.33%51600000.2637000352
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
1Oscar DanskMonsters (CLB)85300.8931.9649002161500000.857780200
Stats d'équipe Total ou en Moyenne85300.8931.9649002161500000.857780200


Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
Nom du Joueur Nom de l'ÉquipePOS Âge Date de Naissance Nouveau Joueur Poids Taille Non-Échange Disponible pour Échange Ballotage Forcé Contrat Type Salaire Actuel Salaire RestantSalaire MoyenSalaire Moyen RestantCap Salariale Cap Salariale Restant Exclus du Cap Salarial Salaire Année 2Salaire Année 3Salaire Année 4Salaire Année 5Salaire Année 6Salaire Année 7Salaire Année 8Salaire Année 9Salaire Année 10Link
Antoine MorandMonsters (CLB)C211999-02-18Yes185 Lbs5 ft10NoNoNo3Pro & Farm927,500$830,262$927,500$830,262$0$0$No927,500$927,500$
Brendan GuhleMonsters (CLB)D221997-07-29No186 Lbs6 ft1NoNoNo3Pro & Farm946,083$846,897$888,833$795,649$0$0$No888,833$888,833$Lien
Cale FleuryMonsters (CLB)D211998-11-19Yes203 Lbs6 ft1NoNoNo3Pro & Farm883,333$790,726$883,333$790,726$0$0$No883,333$883,333$
Calvin PickardMonsters (CLB)G281992-04-14No207 Lbs6 ft1NoNoNo1Pro & Farm1,000,000$895,161$1,000,000$895,161$0$0$NoLien
Charles HudonMonsters (CLB)LW/RW261994-06-23No188 Lbs5 ft10NoNoNo3Pro & Farm650,000$581,855$725,000$648,992$0$0$No650,000$650,000$Lien
Connor DewarMonsters (CLB)C/LW211999-06-26Yes176 Lbs5 ft10NoNoNo3Pro & Farm925,000$828,024$925,000$828,024$0$0$No925,000$925,000$
Devon ToewsMonsters (CLB)D261994-02-20No181 Lbs6 ft1NoNoNo2Pro & Farm700,000$626,613$700,000$626,613$0$0$No700,000$Lien
Eric CornelMonsters (CLB)C/RW241996-04-11Yes194 Lbs6 ft2NoNoNo3Pro & Farm925,000$828,024$925,000$828,024$0$0$No925,000$925,000$
Gabriel CarlssonMonsters (CLB)D231997-01-02No192 Lbs6 ft5NoNoNo2Pro & Farm894,166$800,423$894,166$800,423$0$0$No894,166$Lien
Jakub SkarekMonsters (CLB)G201999-11-10Yes196 Lbs6 ft3NoNoNo3Pro & Farm927,500$830,262$927,500$830,262$0$0$No927,500$927,500$
Kevin StenlundMonsters (CLB)C/RW231996-09-20Yes210 Lbs6 ft4NoNoNo3Pro & Farm925,000$828,024$925,000$828,024$0$0$No925,000$925,000$
Markus NutivaaraMonsters (CLB)D261994-06-06No191 Lbs6 ft1NoYesNo3Pro & Farm2,700,000$2,416,935$2,700,000$2,416,935$0$0$No2,700,000$2,700,000$Lien
Michael McCarronMonsters (CLB)C/RW251995-03-06No231 Lbs6 ft6NoNoNo1Pro & Farm1,075,833$963,044$1,075,833$963,044$0$0$NoLien
Mike ReillyMonsters (CLB)D261993-07-12No195 Lbs6 ft2NoYesNo3Pro & Farm1,500,000$1,342,742$1,500,000$1,342,742$0$0$No1,500,000$1,500,000$Lien
Mirco MuellerMonsters (CLB)D251995-03-21No210 Lbs6 ft3NoNoNo1Pro & Farm1,400,000$1,253,226$1,400,000$1,253,226$0$0$NoLien
Nick MoutreyMonsters (CLB)C251995-06-23Yes218 Lbs6 ft3NoNoNo2Pro & Farm925,000$828,024$925,000$828,024$0$0$No925,000$
Nicolas RoyMonsters (CLB)C231997-02-05No208 Lbs6 ft4NoNoNo1Pro & Farm815,000$729,556$815,000$729,556$0$0$NoLien
Oscar DanskMonsters (CLB)G261994-02-28No195 Lbs6 ft3NoNoNo2Pro & Farm675,000$604,234$675,000$604,234$0$0$No675,000$Lien
Pascal LabergeMonsters (CLB)C221998-04-08Yes173 Lbs6 ft1NoNoNo3Pro & Farm863,333$772,822$863,333$772,822$0$0$No863,333$863,333$
Paul BittnerMonsters (CLB)LW231996-11-03Yes214 Lbs6 ft4NoNoNo3Pro & Farm863,333$772,822$863,333$772,822$0$0$No863,333$863,333$
Rasmus AsplundMonsters (CLB)C/LW/RW221997-12-02Yes176 Lbs5 ft11NoNoNo3Pro & Farm925,000$828,024$925,000$828,024$0$0$No925,000$925,000$
Ryan CollinsMonsters (CLB)D241996-05-06Yes212 Lbs6 ft5NoNoNo3Pro & Farm925,000$828,024$925,000$828,024$0$0$No925,000$925,000$
Slater KoekkoekMonsters (CLB)D261994-02-18No193 Lbs6 ft2NoNoNo1Pro & Farm800,000$716,129$800,000$716,129$0$0$NoLien
Sonny MilanoMonsters (CLB)LW/RW241996-05-11No195 Lbs6 ft2NoNoNo1Pro & Farm1,263,333$1,130,887$1,263,333$1,130,887$0$0$NoLien
Tyler BensonMonsters (CLB)LW221998-03-15Yes192 Lbs6 ft0NoNoNo3Pro & Farm863,333$772,822$863,333$772,822$0$0$No863,333$863,333$
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2523.76197 Lbs6 ft22.361,011,910$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Sonny MilanoNicolas RoyKevin Stenlund35122
2Charles HudonRasmus AsplundEric Cornel30122
3Tyler BensonNick MoutreyPaul Bittner25122
4Paul BittnerConnor DewarSonny Milano10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Devon ToewsMike Reilly35122
2Mirco MuellerSlater Koekkoek30122
3Brendan GuhleCale Fleury25122
4Devon ToewsMike Reilly10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Sonny MilanoNicolas RoyKevin Stenlund60122
2Charles HudonRasmus AsplundEric Cornel40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Devon ToewsMike Reilly60122
2Mirco MuellerSlater Koekkoek40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Sonny MilanoNicolas Roy60122
2Kevin StenlundCharles Hudon40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Devon ToewsMike Reilly60122
2Mirco MuellerSlater Koekkoek40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Sonny Milano60122Devon ToewsMike Reilly60122
2Nicolas Roy40122Mirco MuellerSlater Koekkoek40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Sonny MilanoNicolas Roy60122
2Kevin StenlundCharles Hudon40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Devon ToewsMike Reilly60122
2Mirco MuellerSlater Koekkoek40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Sonny MilanoNicolas RoyKevin StenlundDevon ToewsMike Reilly
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Sonny MilanoNicolas RoyKevin StenlundDevon ToewsMike Reilly
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Antoine Morand, Tyler Benson, Nick MoutreyAntoine Morand, Tyler BensonNick Moutrey
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Brendan Guhle, Cale Fleury, Mirco MuellerBrendan GuhleCale Fleury, Mirco Mueller
Tirs de Pénalité
Sonny Milano, Nicolas Roy, Kevin Stenlund, Charles Hudon, Rasmus Asplund
Gardien
#1 : Oscar Dansk, #2 : Calvin Pickard


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
1Bruins1010000003-3000000000001010000003-300.0000000052531230304792661015900.00%5260.00%09719250.52%11421253.77%5911252.68%1981331886210353
2Marlies11000000211110000002110000000000021.0002460052532030304791381418600.00%6183.33%09719250.52%11421253.77%5911252.68%1981331886210353
3Penguins11000000101000000000001100000010121.00011201525311303047912323171000.00%40100.00%09719250.52%11421253.77%5911252.68%1981331886210353
4Phantoms512000201112-1311000106602010001056-160.6001114250152536630304799922509127518.52%24579.17%09719250.52%11421253.77%5911252.68%1981331886210353
Total833000201416-2421000108714120001069-3100.625141933025253109303047915039971415259.62%39879.49%09719250.52%11421253.77%5911252.68%1981331886210353
_Since Last GM Reset833000201416-2421000108714120001069-3100.625141933025253109303047915039971415259.62%39879.49%09719250.52%11421253.77%5911252.68%1981331886210353
_Vs Conference833000201416-2421000108714120001069-3100.625141933025253109303047915039971415259.62%39879.49%09719250.52%11421253.77%5911252.68%1981331886210353
_Vs Division6220002012120311000106603110001066080.667121527025253773030479111257310837513.51%28582.14%09719250.52%11421253.77%5911252.68%1981331886210353

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
810W2141933109150399714102
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
83300201416
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
421001087
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
412001069
Derniers 10 Matchs
WLOTWOTL SOWSOL
530000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
5259.62%39879.49%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
30304795253
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
9719250.52%11421253.77%5911252.68%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
1981331886210353


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
1 - 2020-09-2710Marlies1Monsters2WSommaire du Match
2 - 2020-09-2813Monsters1Phantoms3LSommaire du Match
4 - 2020-09-3030Phantoms3Monsters1LSommaire du Match
5 - 2020-10-0137Monsters4Phantoms3WXXSommaire du Match
6 - 2020-10-0247Phantoms0Monsters1WSommaire du Match
9 - 2020-10-0563Monsters0Bruins3LSommaire du Match
10 - 2020-10-0673Monsters1Penguins0WSommaire du Match
12 - 2020-10-0882Phantoms3Monsters4WXXSommaire du Match
14 - 2020-10-1091Monsters-Wolf Pack-
16 - 2020-10-12104Phantoms-Monsters-
18 - 2020-10-14116Monsters-Sound Tigers-
20 - 2020-10-16124Penguins-Monsters-
22 - 2020-10-18137Monsters-Phantoms-
23 - 2020-10-19146Sound Tigers-Monsters-
24 - 2020-10-20159Stars-Monsters-
26 - 2020-10-22178Phantoms-Monsters-
27 - 2020-10-23182Monsters-Flames-
28 - 2020-10-24190Monsters-Phantoms-
30 - 2020-10-26202Sound Tigers-Monsters-
32 - 2020-10-28216Phantoms-Monsters-
34 - 2020-10-30222Monsters-Admirals-
37 - 2020-11-02238Monsters-Penguins-
38 - 2020-11-03248Monsters-Penguins-
40 - 2020-11-05258Senators-Monsters-
43 - 2020-11-08274Bruins-Monsters-
45 - 2020-11-10290Sound Tigers-Monsters-
46 - 2020-11-11295Monsters-Wolf Pack-
48 - 2020-11-13309Monsters-Wolf Pack-
49 - 2020-11-14317Crunch-Monsters-
50 - 2020-11-15329Monsters-Bruins-
51 - 2020-11-16336Monsters-Penguins-
52 - 2020-11-17347Wolf Pack-Monsters-
54 - 2020-11-19362Wolf Pack-Monsters-
55 - 2020-11-20376Crunch-Monsters-
56 - 2020-11-21383Monsters-Crunch-
57 - 2020-11-22391Monsters-Griffins-
58 - 2020-11-23403Sharks-Monsters-
59 - 2020-11-24409Monsters-Stars-
61 - 2020-11-26426Penguins-Monsters-
63 - 2020-11-28436Monsters-Sound Tigers-
64 - 2020-11-29449Phantoms-Monsters-
66 - 2020-12-01464Moose-Monsters-
67 - 2020-12-02473Monsters-Stars-
68 - 2020-12-03485Stars-Monsters-
71 - 2020-12-06500Monsters-Condors-
72 - 2020-12-07510Condors-Monsters-
73 - 2020-12-08519Monsters-Sound Tigers-
74 - 2020-12-09530Monarchs-Monsters-
77 - 2020-12-12549Monsters-Bruins-
78 - 2020-12-13555Flames-Monsters-
79 - 2020-12-14567Monsters-Moose-
80 - 2020-12-15576Senators-Monsters-
82 - 2020-12-17589Monsters-Senators-
84 - 2020-12-19598Marlies-Monsters-
86 - 2020-12-21613Monsters-Sound Tigers-
87 - 2020-12-22622Griffins-Monsters-
88 - 2020-12-23634Monsters-Flames-
90 - 2020-12-25644Admirals-Monsters-
92 - 2020-12-27659Monsters-Sharks-
93 - 2020-12-28665Rocket-Monsters-
95 - 2020-12-30681Monsters-Rampage-
96 - 2020-12-31690Bruins-Monsters-
97 - 2021-01-01705Wolf Pack-Monsters-
98 - 2021-01-02712Monsters-Wolf Pack-
100 - 2021-01-04728Sound Tigers-Monsters-
101 - 2021-01-05741Monsters-Marlies-
102 - 2021-01-06747Penguins-Monsters-
104 - 2021-01-08758Monsters-IceHogs-
105 - 2021-01-09772Rampage-Monsters-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
106 - 2021-01-10780Monsters-Monarchs-
108 - 2021-01-12793Penguins-Monsters-
110 - 2021-01-14813Wolves-Monsters-
111 - 2021-01-15818Monsters-Soldiers-
113 - 2021-01-17837IceHogs-Monsters-
114 - 2021-01-18840Monsters-Rocket-
116 - 2021-01-20858Soldiers-Monsters-
117 - 2021-01-21860Monsters-Phantoms-
118 - 2021-01-22874Monsters-Griffins-
119 - 2021-01-23882Wolf Pack-Monsters-
120 - 2021-01-24883Monsters-Phantoms-
121 - 2021-01-25888Monsters-Rocket-
122 - 2021-01-26901Monsters-Wolves-



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
37 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
425,675$ 2,529,773$ 2,531,548$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 268,414$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 111 32,498$ 3,607,278$




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
2020833000201416-2421000108714120001069-310141933025253109303047915039971415259.62%39879.49%09719250.52%11421253.77%5911252.68%1981331886210353
Total Saison Régulière833000201416-2421000108714120001069-310141933025253109303047915039971415259.62%39879.49%09719250.52%11421253.77%5911252.68%1981331886210353