Monsters
GP: 82 | W: 31 | L: 46 | OTL: 5 | P: 67
GF: 161 | GA: 204 | PP%: 10.61% | PK%: 85.89%
DG: Yvon Poulin | Morale : 50 | Moyenne d'Équipe : N/A
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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 SPÂgeContratSalaire
1Charles HudonXX100.00824499746659646433575854755959625000263650,000$
2Eric Cornel (R)XX100.00797296647279865670475866555151625000243925,000$
3Nicolas RoyX100.00794590687859826272647163254747694600231815,000$
4Kevin Stenlund (R)XX100.00674391658066817554617161254747694900233925,000$
5Nick Moutrey (R)X100.00818082658069744961464764454444565000252925,000$
6Pascal Laberge (R)X100.00706582646552515670466160584444595000223863,333$
7Rasmus Asplund (R)XXX100.00694292686457866155575685254646655000223925,000$
8Tyler Benson (R)X100.00737079717075786350665763544444645000223863,333$
9Paul Bittner (R)X100.00808079688066705150475164484444575000233863,333$
10Michael McCarronXX100.007888536588616259745062665949496150002511,075,833$
11Sonny MilanoXX100.006542868373646366257464592552536750002411,263,333$
12Connor Dewar (R)XX100.00706387656369735468535060484444575000213925,000$
13Brendan GuhleX100.00694289756971755725505474255858635000223946,083$
14Cale Fleury (R)X100.00904694777464735325394769254747595000213883,333$
15Gabriel CarlssonX100.00797589787569755025424165395252565000232894,166$
16Markus NutivaaraX100.0073439682716876622552495925626360500X02632,700,000$
17Slater KoekkoekX100.00804472767270596325554780255758625000261800,000$
18Ben HuttonX100.007143928176758463255348792567676445002731,725,002$
19Mike ReillyX100.0073438478737568732566476725606063500X02631,500,000$
Rayé
1Antoine Morand (R)X100.00716683676670755063494759454444554500213927,500$
2Ryan Collins (R)X100.00828184528155574825384264404444535000243925,000$
3Ville Heinola (R)X100.007666998066505050254739623744445350001931,137,500$
MOYENNE D'ÉQUIPE100.0075588671736671584453536639505061490
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 Pickard100.0054587377535852585756305757564900
2Oscar Dansk100.0060688579586556646160304444615100
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
1Nicolas RoyMonsters (CLB)C812019391655117129107347118.69%16158919.6248122932420272596456.03%134400000.4939001812
2Rasmus AsplundMonsters (CLB)C/LW/RW82172138-16180381551213411514.05%15135516.534711453260000173050.14%71800000.5612000275
3Devon ToewsBlue JacketsD6433336-456078836120474.92%60154224.102810512930220294000.00%000000.4700000213
4Slater KoekkoekMonsters (CLB)D82112334-713801719380295613.75%68168520.569716703480110294000.00%000000.4000000113
5Ben HuttonMonsters (CLB)D6742933-728038778015575.00%41143521.4331316652890002284010.00%000000.4600000111
6Kevin StenlundMonsters (CLB)C/RW81191332-32004677143339513.29%10151818.7467135135011232004050.63%79200100.42611000342
7Mike ReillyMonsters (CLB)D8272431-28801379069255810.14%65179021.8471017643880220352000.00%000000.3500000025
8Sonny MilanoMonsters (CLB)LW/RW82141731-420033115115309712.17%7174721.3157123735221373563230.13%44800000.35311000121
9Tyler BensonMonsters (CLB)LW78121628-12601053110108328411.11%7124816.0102203500031832146.00%15000000.4501101301
10Brendan GuhleMonsters (CLB)D8261824-18480347343193613.95%36124015.133251977000061000.00%000000.3900000023
11Charles HudonMonsters (CLB)LW/RW8213720-13560934370196518.57%3151518.481451935300001392037.37%9900000.2613000112
12Drake CaggiulaBlue JacketsLW/RW438715-583151085550245816.00%489520.821341217211231850035.64%30300000.3414003120
13Paul BittnerMonsters (CLB)LW826814-129515114525563810.91%9114213.9300000000041048.08%5200000.2500102131
14Eric CornelMonsters (CLB)C/RW65551003610478450223910.00%894114.490005620000342054.61%53100000.2100002011
15Cale FleuryMonsters (CLB)D82099-9175517142229140.00%39122114.90000460000123000.00%000000.1500000000
16Mirco MuellerBlue JacketsD1216758061514247.14%1525321.171011050000141000.00%000000.5500000000
17Michael McCarronMonsters (CLB)C/RW30257-74757610259268.00%551017.011347137000000068.00%2500000.2701001110
18Pascal LabergeMonsters (CLB)C46426-922013222171819.05%13407.4000000000021152.26%26600000.3511000111
19Joel Eriksson EkBlue JacketsC/LW/RW8145020721132107.69%218322.950227380000380056.99%19300000.5400000010
20Gabriel CarlssonMonsters (CLB)D70044-326045177560.00%164636.62000416000076000.00%000000.1700000001
21Ville HeinolaMonsters (CLB)D50123-43553815881112.50%3288017.6210131200000121000.00%000000.0700010000
22Nick MoutreyMonsters (CLB)C81022-1421033266670.00%104245.240000390110520049.45%27500000.0900101000
23Markus NutivaaraMonsters (CLB)D11112-1205660616.67%123221.16112549000045100.00%000000.1700000011
24Antoine MorandMonsters (CLB)C631013007881412.50%01582.521015510000150064.15%5300000.1300000000
25Connor DewarMonsters (CLB)C/LW55011-34010189340.00%02224.040112260000510053.85%7800000.0900000000
26Pavel ZachaBlue JacketsC/LW3000-10011011050.00%06822.860004160001120054.41%6800000.0000000000
Stats d'équipe Total ou en Moyenne1564156276432-132117480151914461302394103111.98%4702461015.7450851355183928691527324925949.92%539500100.3516433111263233
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)80294550.8682.4346154718714140500.65143800511
2Calvin PickardMonsters (CLB)132100.9011.9034800111110110.0000279000
Stats d'équipe Total ou en Moyenne93314650.8702.3949644719815250610.651438279511


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$7,480$927,500$7,480$0$0$No927,500$927,500$
Ben HuttonMonsters (CLB)D271993-04-20No207 Lbs6 ft2NoNoNo3Pro & Farm1,725,002$13,911$1,500,000$12,097$0$0$No1,500,000$1,500,000$Lien
Brendan GuhleMonsters (CLB)D221997-07-29No186 Lbs6 ft1NoNoNo3Pro & Farm946,083$7,630$888,833$7,168$0$0$No888,833$888,833$Lien
Cale FleuryMonsters (CLB)D211998-11-19Yes203 Lbs6 ft1NoNoNo3Pro & Farm883,333$7,124$883,333$7,124$0$0$No883,333$883,333$
Calvin PickardMonsters (CLB)G281992-04-14No207 Lbs6 ft1NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Charles HudonMonsters (CLB)LW/RW261994-06-23No188 Lbs5 ft10NoNoNo3Pro & Farm650,000$5,242$725,000$5,847$0$0$No650,000$650,000$Lien
Connor DewarMonsters (CLB)C/LW211999-06-26Yes176 Lbs5 ft10NoNoNo3Pro & Farm925,000$7,460$925,000$7,460$0$0$No925,000$925,000$
Eric CornelMonsters (CLB)C/RW241996-04-11Yes194 Lbs6 ft2NoNoNo3Pro & Farm925,000$7,460$925,000$7,460$0$0$No925,000$925,000$
Gabriel CarlssonMonsters (CLB)D231997-01-02No192 Lbs6 ft5NoNoNo2Pro & Farm894,166$7,211$894,166$7,211$0$0$No894,166$Lien
Jakub SkarekMonsters (CLB)G201999-11-10Yes196 Lbs6 ft3NoNoNo3Pro & Farm927,500$7,480$927,500$7,480$0$0$No927,500$927,500$
Kevin StenlundMonsters (CLB)C/RW231996-09-20Yes210 Lbs6 ft4NoNoNo3Pro & Farm925,000$7,460$925,000$7,460$0$0$No925,000$925,000$
Markus NutivaaraMonsters (CLB)D261994-06-06No191 Lbs6 ft1NoYesNo3Pro & Farm2,700,000$21,774$2,700,000$21,774$0$0$No2,700,000$2,700,000$Lien
Michael McCarronMonsters (CLB)C/RW251995-03-06No231 Lbs6 ft6NoNoNo1Pro & Farm1,075,833$8,676$1,075,833$8,676$0$0$NoLien
Mike ReillyMonsters (CLB)D261993-07-12No195 Lbs6 ft2NoYesNo3Pro & Farm1,500,000$12,097$1,500,000$12,097$0$0$No1,500,000$1,500,000$Lien
Nick MoutreyMonsters (CLB)C251995-06-23Yes218 Lbs6 ft3NoNoNo2Pro & Farm925,000$7,460$925,000$7,460$0$0$No925,000$
Nicolas RoyMonsters (CLB)C231997-02-05No208 Lbs6 ft4NoNoNo1Pro & Farm815,000$6,573$815,000$6,573$0$0$NoLien
Oscar DanskMonsters (CLB)G261994-02-28No195 Lbs6 ft3NoNoNo2Pro & Farm675,000$5,444$675,000$5,444$0$0$No675,000$Lien
Pascal LabergeMonsters (CLB)C221998-04-08Yes173 Lbs6 ft1NoNoNo3Pro & Farm863,333$6,962$863,333$6,962$0$0$No863,333$863,333$
Paul BittnerMonsters (CLB)LW231996-11-03Yes214 Lbs6 ft4NoNoNo3Pro & Farm863,333$6,962$863,333$6,962$0$0$No863,333$863,333$
Rasmus AsplundMonsters (CLB)C/LW/RW221997-12-02Yes176 Lbs5 ft11NoNoNo3Pro & Farm925,000$7,460$925,000$7,460$0$0$No925,000$925,000$
Ryan CollinsMonsters (CLB)D241996-05-06Yes212 Lbs6 ft5NoNoNo3Pro & Farm925,000$7,460$925,000$7,460$0$0$No925,000$925,000$
Slater KoekkoekMonsters (CLB)D261994-02-18No193 Lbs6 ft2NoNoNo1Pro & Farm800,000$6,452$800,000$6,452$0$0$NoLien
Sonny MilanoMonsters (CLB)LW/RW241996-05-11No195 Lbs6 ft2NoNoNo1Pro & Farm1,263,333$10,188$1,263,333$10,188$0$0$NoLien
Tyler BensonMonsters (CLB)LW221998-03-15Yes192 Lbs6 ft0NoNoNo3Pro & Farm863,333$6,962$863,333$6,962$0$0$No863,333$863,333$
Ville HeinolaMonsters (CLB)D192001-03-02Yes181 Lbs5 ft11NoNoNo3Pro & Farm1,137,500$9,173$1,137,500$9,173$0$0$No1,137,500$1,137,500$
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2523.56197 Lbs6 ft22.481,042,410$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Sonny MilanoNicolas RoyKevin Stenlund35122
2Charles HudonRasmus AsplundMichael McCarron30122
3Tyler BensonEric CornelPaul Bittner25122
4Paul BittnerPascal LabergeSonny Milano10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Mike ReillyBen Hutton35122
2Markus NutivaaraSlater Koekkoek30122
3Brendan GuhleCale Fleury25122
4Gabriel CarlssonMike Reilly10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Sonny MilanoNicolas RoyKevin Stenlund60122
2Charles HudonRasmus AsplundMichael McCarron40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Mike ReillyBen Hutton60122
2Markus NutivaaraSlater 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
1Mike ReillyBen Hutton60122
2Markus NutivaaraSlater Koekkoek40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Sonny Milano60122Mike ReillyBen Hutton60122
2Nicolas Roy40122Markus NutivaaraSlater Koekkoek40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Sonny MilanoNicolas Roy60122
2Kevin StenlundCharles Hudon40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Mike ReillyBen Hutton60122
2Markus NutivaaraSlater Koekkoek40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Sonny MilanoNicolas RoyKevin StenlundMike ReillyBen Hutton
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Sonny MilanoNicolas RoyKevin StenlundMike ReillyBen Hutton
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Nick Moutrey, Connor Dewar, Tyler BensonNick Moutrey, Connor DewarTyler Benson
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Brendan Guhle, Cale Fleury, Gabriel CarlssonBrendan GuhleCale Fleury, Gabriel Carlsson
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
1Admirals22000000514110000002111100000030341.000591401625241164042742143555181122281417.14%100100.00%01092211551.63%1038219547.29%591113851.93%2018137619416061015522
2Bruins51400000410-62110000045-13030000005-520.200481200625241167042742143555892868872900.00%32487.50%01092211551.63%1038219547.29%591113851.93%2018137619416061015522
3Condors2010001035-2100000102111010000014-320.500336006252411634427421435554182943800.00%12375.00%01092211551.63%1038219547.29%591113851.93%2018137619416061015522
4Crunch31200000660211000002111010000045-120.3336111701625241164942742143555671466482428.33%20385.00%11092211551.63%1038219547.29%591113851.93%2018137619416061015522
5Flames3030000039-61010000023-12020000016-500.00036900625241165242742143555391538452000.00%15380.00%01092211551.63%1038219547.29%591113851.93%2018137619416061015522
6Griffins32100000862110000004222110000044040.66781422016252411640427421435555416386420315.00%18288.89%11092211551.63%1038219547.29%591113851.93%2018137619416061015522
7IceHogs2110000024-2110000001011010000014-320.500235016252411633427421435553111223712216.67%110100.00%01092211551.63%1038219547.29%591113851.93%2018137619416061015522
8Marlies3120000078-1211000005501010000023-120.3337132000625241165542742143555481752642428.33%19478.95%01092211551.63%1038219547.29%591113851.93%2018137619416061015522
9Monarchs2020000016-51010000012-11010000004-400.00012300625241163242742143555361028411100.00%14192.86%01092211551.63%1038219547.29%591113851.93%2018137619416061015522
10Moose2000000235-21000000123-11000000112-120.50035800625241163142742143555421239361119.09%90100.00%01092211551.63%1038219547.29%591113851.93%2018137619416061015522
11Penguins824000111123-1240200011612-642200000511-670.438111728016252411612342742143555149461211494924.08%49589.80%01092211551.63%1038219547.29%591113851.93%2018137619416061015522
12Phantoms1338000203038-87330001018162605000101222-10100.38530467612625241162164274214355527160202235841214.29%821482.93%21092211551.63%1038219547.29%591113851.93%2018137619416061015522
13Rampage220000001028110000005141100000051441.00010162600625241165242742143555411082011327.27%3166.67%01092211551.63%1038219547.29%591113851.93%2018137619416061015522
14Rocket3100000256-1110000002112000000235-240.6675914006252411648427421435555213385319315.79%19384.21%01092211551.63%1038219547.29%591113851.93%2018137619416061015522
15Senators31200000752211000007341010000002-220.33371320006252411645427421435555421385616425.00%19384.21%01092211551.63%1038219547.29%591113851.93%2018137619416061015522
16Sharks21100000220110000002111010000001-120.5002460062524116294274214355529722341317.69%90100.00%01092211551.63%1038219547.29%591113851.93%2018137619416061015522
17Soldiers2110000023-1110000002111010000002-220.50024600625241163542742143555205283511218.18%14192.86%01092211551.63%1038219547.29%591113851.93%2018137619416061015522
18Sound Tigers834000102530-5421000101615141300000915-680.500254469006252411612542742143555188451021653339.09%491079.59%11092211551.63%1038219547.29%591113851.93%2018137619416061015522
19Stars422000001091211000006512110000044040.500101828006252411652427421435558225599220315.00%23291.30%11092211551.63%1038219547.29%591113851.93%2018137619416061015522
20Wolf Pack817000001423-94130000079-240400000714-720.1251425390062524116114427421435551445410511728517.86%46882.61%01092211551.63%1038219547.29%591113851.93%2018137619416061015522
21Wolves20100010330100000103211010000001-120.5003470062524116204274214355531112034500.00%9188.89%01092211551.63%1038219547.29%591113851.93%2018137619416061015522
Total82254600065161204-4341181600052998910417300001362115-53670.40916127443517625241161295427421435551526439114514834624910.61%4826885.89%61092211551.63%1038219547.29%591113851.93%2018137619416061015522
_Since Last GM Reset82254600065161204-4341181600052998910417300001362115-53670.40916127443517625241161295427421435551526439114514834624910.61%4826885.89%61092211551.63%1038219547.29%591113851.93%2018137619416061015522
_Vs Conference4911320003387128-4125913000215355-224219000123473-39310.316871482351462524116772427421435559132687288542933010.24%3014784.39%31092211551.63%1038219547.29%591113851.93%2018137619416061015522
_Vs Division376220003180114-341949000214752-518213000103362-29190.257801322121362524116578427421435557522055306661942211.34%2263783.63%31092211551.63%1038219547.29%591113851.93%2018137619416061015522

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
8267L1161274435129515264391145148317
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
8225460065161204
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
41181600529989
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
41730001362115
Derniers 10 Matchs
WLOTWOTL SOWSOL
350002
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
4624910.61%4826885.89%6
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
4274214355562524116
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
1092211551.63%1038219547.29%591113851.93%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
2018137619416061015522


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 - 2020-10-1091Monsters3Wolf Pack5LSommaire du Match
16 - 2020-10-12104Phantoms2Monsters4WSommaire du Match
18 - 2020-10-14116Monsters4Sound Tigers1WSommaire du Match
20 - 2020-10-16124Penguins3Monsters0LSommaire du Match
22 - 2020-10-18137Monsters4Phantoms6LSommaire du Match
23 - 2020-10-19146Sound Tigers4Monsters5WSommaire du Match
24 - 2020-10-20159Stars4Monsters3LSommaire du Match
26 - 2020-10-22178Phantoms0Monsters3WSommaire du Match
27 - 2020-10-23182Monsters0Flames3LSommaire du Match
28 - 2020-10-24190Monsters1Phantoms3LSommaire du Match
30 - 2020-10-26202Sound Tigers2Monsters3WXXSommaire du Match
32 - 2020-10-28216Phantoms4Monsters2LSommaire du Match
34 - 2020-10-30222Monsters3Admirals0WSommaire du Match
37 - 2020-11-02238Monsters0Penguins2LSommaire du Match
38 - 2020-11-03248Monsters1Penguins7LSommaire du Match
40 - 2020-11-05258Senators1Monsters6WSommaire du Match
43 - 2020-11-08274Bruins1Monsters2WSommaire du Match
45 - 2020-11-10290Sound Tigers4Monsters7WSommaire du Match
46 - 2020-11-11295Monsters1Wolf Pack2LSommaire du Match
48 - 2020-11-13309Monsters1Wolf Pack2LSommaire du Match
49 - 2020-11-14317Crunch0Monsters2WSommaire du Match
50 - 2020-11-15329Monsters0Bruins1LSommaire du Match
51 - 2020-11-16336Monsters3Penguins2WSommaire du Match
52 - 2020-11-17347Wolf Pack1Monsters2WSommaire du Match
54 - 2020-11-19362Wolf Pack3Monsters2LSommaire du Match
55 - 2020-11-20376Crunch1Monsters0LSommaire du Match
56 - 2020-11-21383Monsters4Crunch5LSommaire du Match
57 - 2020-11-22391Monsters1Griffins4LSommaire du Match
58 - 2020-11-23403Sharks1Monsters2WSommaire du Match
59 - 2020-11-24409Monsters1Stars2LSommaire du Match
61 - 2020-11-26426Penguins3Monsters0LSommaire du Match
63 - 2020-11-28436Monsters3Sound Tigers5LSommaire du Match
64 - 2020-11-29449Phantoms4Monsters3LSommaire du Match
66 - 2020-12-01464Moose3Monsters2LXXSommaire du Match
67 - 2020-12-02473Monsters3Stars2WSommaire du Match
68 - 2020-12-03485Stars1Monsters3WSommaire du Match
71 - 2020-12-06500Monsters1Condors4LSommaire du Match
72 - 2020-12-07510Condors1Monsters2WXXSommaire du Match
73 - 2020-12-08519Monsters1Sound Tigers4LSommaire du Match
74 - 2020-12-09530Monarchs2Monsters1LSommaire du Match
77 - 2020-12-12549Monsters0Bruins1LSommaire du Match
78 - 2020-12-13555Flames3Monsters2LSommaire du Match
79 - 2020-12-14567Monsters1Moose2LXXSommaire du Match
80 - 2020-12-15576Senators2Monsters1LSommaire du Match
82 - 2020-12-17589Monsters0Senators2LSommaire du Match
84 - 2020-12-19598Marlies4Monsters3LSommaire du Match
86 - 2020-12-21613Monsters1Sound Tigers5LSommaire du Match
87 - 2020-12-22622Griffins2Monsters4WSommaire du Match
88 - 2020-12-23634Monsters1Flames3LSommaire du Match
90 - 2020-12-25644Admirals1Monsters2WSommaire du Match
92 - 2020-12-27659Monsters0Sharks1LSommaire du Match
93 - 2020-12-28665Rocket1Monsters2WSommaire du Match
95 - 2020-12-30681Monsters5Rampage1WSommaire du Match
96 - 2020-12-31690Bruins4Monsters2LSommaire du Match
97 - 2021-01-01705Wolf Pack2Monsters1LSommaire du Match
98 - 2021-01-02712Monsters2Wolf Pack5LSommaire du Match
100 - 2021-01-04728Sound Tigers5Monsters1LSommaire du Match
101 - 2021-01-05741Monsters2Marlies3LSommaire du Match
102 - 2021-01-06747Penguins3Monsters4WXXSommaire du Match
104 - 2021-01-08758Monsters1IceHogs4LSommaire du Match
105 - 2021-01-09772Rampage1Monsters5WSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
106 - 2021-01-10780Monsters0Monarchs4LSommaire du Match
108 - 2021-01-12793Penguins3Monsters2LXXSommaire du Match
110 - 2021-01-14813Wolves2Monsters3WXXSommaire du Match
111 - 2021-01-15818Monsters0Soldiers2LSommaire du Match
113 - 2021-01-17837IceHogs0Monsters1WSommaire du Match
114 - 2021-01-18840Monsters2Rocket3LXXSommaire du Match
116 - 2021-01-20858Soldiers1Monsters2WSommaire du Match
117 - 2021-01-21860Monsters1Phantoms3LSommaire du Match
118 - 2021-01-22874Monsters3Griffins0WSommaire du Match
119 - 2021-01-23882Wolf Pack3Monsters2LSommaire du Match
120 - 2021-01-24883Monsters1Phantoms4LSommaire du Match
121 - 2021-01-25888Monsters1Rocket2LXXSommaire du Match
122 - 2021-01-26901Monsters0Wolves1LSommaire du Match



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des Billets3515
Assistance00
Assistance PCT0.00%0.00%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacitéPopularité 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
4,105,755$ 2,606,023$ 2,585,298$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 2,617,828$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 1 33,113$ 33,113$




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