Monsters

GP: 46 | W: 21 | L: 24 | OTL: 1 | P: 43
GF: 101 | GA: 115 | PP%: 10.24% | PK%: 85.24%
DG: Yvon Poulin | Morale : 50 | Moyenne d'Équipe : N/A
Prochain matchs #519 vs Sound Tigers
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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$
3Drake CaggiulaXX100.008577778268616559346274672564647148002611,350,000$
4Nicolas RoyX100.00794590687859826272647163254747695000231815,000$
5Kevin Stenlund (R)XX100.00674391658066817554617161254747695000233925,000$
6Nick Moutrey (R)X100.00818082658069744961464764454444565000252925,000$
7Pascal Laberge (R)X100.00706582646552515670466160584444595000223863,333$
8Rasmus Asplund (R)XXX100.00694292686457866155575685254646655000223925,000$
9Tyler Benson (R)X100.00737079717075786350665763544444645000223863,333$
10Paul Bittner (R)X100.00808079688066705150475164484444575000233863,333$
11Sonny MilanoXX100.006542868373646366257464592552536750002411,263,333$
12Antoine Morand (R)X100.00716683676670755063494759454444554500213927,500$
13Brendan GuhleX100.00694289756971755725505474255858635000223946,083$
14Cale Fleury (R)X100.00904694777464735325394769254747595000213883,333$
15Gabriel CarlssonX100.00797589787569755025424165395252565000232894,166$
16Slater KoekkoekX100.00804472767270596325554780255758625000261800,000$
17Ben HuttonX100.007143928176758463255348792567676445002731,725,002$
18Mike ReillyX100.0073438478737568732566476725606063500X02631,500,000$
19Devon ToewsX100.00714291806881877825655065636263654200262700,000$
Rayé
1Michael McCarronXX100.007888536588616259745062665949496150002511,075,833$
2Connor Dewar (R)XX100.00706387656369735468535060484444575000213925,000$
3Ryan Collins (R)X100.00828184528155574825384264404444535000243925,000$
4Markus NutivaaraX100.0073439682716876622552495925626360500X02632,700,000$
MOYENNE D'ÉQUIPE100.0076588672736772604454546639525262490
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
1Devon ToewsMonsters (CLB)D4132831236047563510378.57%3198724.08257301950220184000.00%000000.6300000213
2Slater KoekkoekMonsters (CLB)D4661622-17001206139173415.38%4695020.665510351890110166000.00%000000.4600000013
3Nicolas RoyMonsters (CLB)C46912211360657860154415.00%1087619.051561417310161174155.66%72400000.4823000402
4Kevin StenlundMonsters (CLB)C/RW4615621-1100184577235319.48%585018.49437271981122993049.83%59200100.4925000331
5Rasmus AsplundMonsters (CLB)C/LW/RW4691221-7140317965155613.85%674616.22134201640000111045.02%21100000.5600000053
6Sonny MilanoMonsters (CLB)LW/RW46111021-1140165362174917.74%694120.483361720121351932231.85%13500000.4515000110
7Tyler BensonMonsters (CLB)LW4271017-33010286748204314.58%366815.9200001200011012045.78%8300000.5100101300
8Brendan GuhleMonsters (CLB)D4651116-10300184223112121.74%1472515.783251559000051000.00%000000.4400000023
9Drake CaggiulaMonsters (CLB)LW/RW398715-37715975044215118.18%480920.761341216011221670036.06%26900000.3714003120
10Mike ReillyMonsters (CLB)D4631215258080443415288.82%28100121.78358322220220190000.00%000000.3000000002
11Charles HudonMonsters (CLB)LW/RW468513-440055244073820.00%379917.37033121920000362021.74%4600000.3311000110
12Ben HuttonMonsters (CLB)D3111213-1802038396272.56%2067121.66156311260002131000.00%000000.3900000000
13Eric CornelMonsters (CLB)C/RW4645932610355330142113.33%366814.540004530000262052.07%33800000.2700002011
14Paul BittnerMonsters (CLB)LW465380571547283322115.15%560313.1100000000021044.44%2700000.2700102121
15Mirco MuellerBlue JacketsD1216758061514247.14%1525321.171011050000141000.00%000000.5500000000
16Cale FleuryMonsters (CLB)D46066-11075962516280.00%1769815.1800046000073000.00%000000.1700000000
17Joel Eriksson EkBlue JacketsC/LW/RW8145020721132107.69%218322.950227380000380056.99%19300000.5400000010
18Gabriel CarlssonMonsters (CLB)D34033-114022132160.00%132647.7900026000044000.00%000000.2300000001
19Nick MoutreyMonsters (CLB)C450111361018155520.00%72685.970000220000300052.20%18200000.0700101000
20Antoine MorandMonsters (CLB)C401012006861316.67%01213.031013370000130065.22%4600000.1600000000
21Pascal LabergeMonsters (CLB)C10000-120314000.00%0414.1500000000000047.06%3400000.0000000000
22Pavel ZachaBlue JacketsC/LW3000-10011011050.00%06822.860004160001120054.41%6800000.0000000000
23Connor DewarMonsters (CLB)C/LW26000-1404135110.00%0973.7700014000090057.78%4500000.0000000000
Stats d'équipe Total ou en Moyenne83797169266-206796584083970520756213.76%2381329915.892644702802133581320174517349.55%299300100.40718309162020
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)44192310.8692.482543251058030200.77818440400
2Calvin PickardMonsters (CLB)72100.8842.03237008690110.0000243000
Stats d'équipe Total ou en Moyenne51212410.8702.442780251138720310.778184643400


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$388,952$927,500$388,952$0$0$No927,500$927,500$
Ben HuttonMonsters (CLB)D271993-04-20No207 Lbs6 ft2NoNoNo3Pro & Farm1,725,002$723,388$1,500,000$629,032$0$0$No1,500,000$1,500,000$Lien
Brendan GuhleMonsters (CLB)D221997-07-29No186 Lbs6 ft1NoNoNo3Pro & Farm946,083$396,744$888,833$372,736$0$0$No888,833$888,833$Lien
Cale FleuryMonsters (CLB)D211998-11-19Yes203 Lbs6 ft1NoNoNo3Pro & Farm883,333$370,430$883,333$370,430$0$0$No883,333$883,333$
Calvin PickardMonsters (CLB)G281992-04-14No207 Lbs6 ft1NoNoNo1Pro & Farm1,000,000$419,355$1,000,000$419,355$0$0$NoLien
Charles HudonMonsters (CLB)LW/RW261994-06-23No188 Lbs5 ft10NoNoNo3Pro & Farm650,000$272,581$725,000$304,032$0$0$No650,000$650,000$Lien
Connor DewarMonsters (CLB)C/LW211999-06-26Yes176 Lbs5 ft10NoNoNo3Pro & Farm925,000$387,903$925,000$387,903$0$0$No925,000$925,000$
Devon ToewsMonsters (CLB)D261994-02-20No181 Lbs6 ft1NoNoNo2Pro & Farm700,000$293,548$700,000$293,548$0$0$No700,000$Lien
Drake CaggiulaMonsters (CLB)LW/RW261994-06-20No185 Lbs5 ft10NoNoNo1Pro & Farm1,350,000$566,129$1,350,000$566,129$0$0$NoLien
Eric CornelMonsters (CLB)C/RW241996-04-11Yes194 Lbs6 ft2NoNoNo3Pro & Farm925,000$387,903$925,000$387,903$0$0$No925,000$925,000$
Gabriel CarlssonMonsters (CLB)D231997-01-02No192 Lbs6 ft5NoNoNo2Pro & Farm894,166$374,973$894,166$374,973$0$0$No894,166$Lien
Jakub SkarekMonsters (CLB)G201999-11-10Yes196 Lbs6 ft3NoNoNo3Pro & Farm927,500$388,952$927,500$388,952$0$0$No927,500$927,500$
Kevin StenlundMonsters (CLB)C/RW231996-09-20Yes210 Lbs6 ft4NoNoNo3Pro & Farm925,000$387,903$925,000$387,903$0$0$No925,000$925,000$
Markus NutivaaraMonsters (CLB)D261994-06-06No191 Lbs6 ft1NoYesNo3Pro & Farm2,700,000$1,132,258$2,700,000$1,132,258$0$0$No2,700,000$2,700,000$Lien
Michael McCarronMonsters (CLB)C/RW251995-03-06No231 Lbs6 ft6NoNoNo1Pro & Farm1,075,833$451,156$1,075,833$451,156$0$0$NoLien
Mike ReillyMonsters (CLB)D261993-07-12No195 Lbs6 ft2NoYesNo3Pro & Farm1,500,000$629,032$1,500,000$629,032$0$0$No1,500,000$1,500,000$Lien
Nick MoutreyMonsters (CLB)C251995-06-23Yes218 Lbs6 ft3NoNoNo2Pro & Farm925,000$387,903$925,000$387,903$0$0$No925,000$
Nicolas RoyMonsters (CLB)C231997-02-05No208 Lbs6 ft4NoNoNo1Pro & Farm815,000$341,774$815,000$341,774$0$0$NoLien
Oscar DanskMonsters (CLB)G261994-02-28No195 Lbs6 ft3NoNoNo2Pro & Farm675,000$283,065$675,000$283,065$0$0$No675,000$Lien
Pascal LabergeMonsters (CLB)C221998-04-08Yes173 Lbs6 ft1NoNoNo3Pro & Farm863,333$362,043$863,333$362,043$0$0$No863,333$863,333$
Paul BittnerMonsters (CLB)LW231996-11-03Yes214 Lbs6 ft4NoNoNo3Pro & Farm863,333$362,043$863,333$362,043$0$0$No863,333$863,333$
Rasmus AsplundMonsters (CLB)C/LW/RW221997-12-02Yes176 Lbs5 ft11NoNoNo3Pro & Farm925,000$387,903$925,000$387,903$0$0$No925,000$925,000$
Ryan CollinsMonsters (CLB)D241996-05-06Yes212 Lbs6 ft5NoNoNo3Pro & Farm925,000$387,903$925,000$387,903$0$0$No925,000$925,000$
Slater KoekkoekMonsters (CLB)D261994-02-18No193 Lbs6 ft2NoNoNo1Pro & Farm800,000$335,484$800,000$335,484$0$0$NoLien
Sonny MilanoMonsters (CLB)LW/RW241996-05-11No195 Lbs6 ft2NoNoNo1Pro & Farm1,263,333$529,785$1,263,333$529,785$0$0$NoLien
Tyler BensonMonsters (CLB)LW221998-03-15Yes192 Lbs6 ft0NoNoNo3Pro & Farm863,333$362,043$863,333$362,043$0$0$No863,333$863,333$
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2623.92196 Lbs6 ft22.381,037,413$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Drake CaggiulaKevin StenlundSonny Milano35122
2Charles HudonNicolas RoyRasmus Asplund30122
3Tyler BensonEric CornelPaul Bittner25122
4Paul BittnerPascal LabergeDrake Caggiula10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Devon ToewsMike Reilly35122
2Ben HuttonSlater Koekkoek30122
3Brendan GuhleCale Fleury25122
4Gabriel CarlssonDevon Toews10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Drake CaggiulaKevin StenlundSonny Milano60122
2Charles HudonNicolas RoyRasmus Asplund40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Devon ToewsMike Reilly60122
2Ben HuttonSlater Koekkoek40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Drake CaggiulaSonny Milano60122
2Kevin StenlundNicolas Roy40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Devon ToewsMike Reilly60122
2Ben HuttonSlater Koekkoek40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Drake Caggiula60122Devon ToewsMike Reilly60122
2Sonny Milano40122Ben HuttonSlater Koekkoek40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Drake CaggiulaSonny Milano60122
2Kevin StenlundNicolas Roy40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Devon ToewsMike Reilly60122
2Ben HuttonSlater Koekkoek40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Drake CaggiulaKevin StenlundSonny MilanoDevon ToewsMike Reilly
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Drake CaggiulaKevin StenlundSonny MilanoDevon ToewsMike Reilly
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Nick Moutrey, Antoine Morand, Tyler BensonNick Moutrey, Antoine MorandTyler Benson
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Brendan Guhle, Cale Fleury, Gabriel CarlssonBrendan GuhleCale Fleury, Gabriel Carlsson
Tirs de Pénalité
Drake Caggiula, Sonny Milano, Kevin Stenlund, Nicolas Roy, Charles Hudon
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
1Admirals11000000303000000000001100000030321.00035801393127718211220268248614131000.00%60100.00%0619117852.55%556121545.76%33665351.45%11337731079338571295
2Bruins3120000025-3110000002112020000004-420.3332460039312773621122026824501636582100.00%17382.35%0619117852.55%556121545.76%33665351.45%11337731079338571295
3Condors2010001035-2100000102111010000014-320.50033600393127734211220268244182943800.00%12375.00%0619117852.55%556121545.76%33665351.45%11337731079338571295
4Crunch31200000660211000002111010000045-120.333611170139312774921122026824671466482428.33%20385.00%1619117852.55%556121545.76%33665351.45%11337731079338571295
5Flames1010000003-3000000000001010000003-300.0000000039312771421122026824124614600.00%3166.67%0619117852.55%556121545.76%33665351.45%11337731079338571295
6Griffins1010000014-3000000000001010000014-300.0001230039312778211220268241831224500.00%5260.00%0619117852.55%556121545.76%33665351.45%11337731079338571295
7Marlies11000000211110000002110000000000021.00024600393127720211220268241381418600.00%6183.33%0619117852.55%556121545.76%33665351.45%11337731079338571295
8Moose1000000123-11000000123-10000000000010.50023500393127713211220268241992527300.00%30100.00%0619117852.55%556121545.76%33665351.45%11337731079338571295
9Penguins62400000517-122020000006-642200000511-640.3335813013931277852112202682411134971143313.03%39489.74%0619117852.55%556121545.76%33665351.45%11337731079338571295
10Phantoms1136000202831-37330001018162403000101015-5100.4552842701239312771852112202682422650176188681217.65%691085.51%2619117852.55%556121545.76%33665351.45%11337731079338571295
11Senators11000000615110000006150000000000021.0006111700393127723211220268241458187342.86%40100.00%0619117852.55%556121545.76%33665351.45%11337731079338571295
12Sharks11000000211110000002110000000000021.000246003931277152112202682416210177114.29%40100.00%0619117852.55%556121545.76%33665351.45%11337731079338571295
13Sound Tigers531000102216632000010151052110000076180.80022386000393127786211220268241052666982129.52%31583.87%1619117852.55%556121545.76%33665351.45%11337731079338571295
14Stars422000001091211000006512110000044040.50010182800393127752211220268248225599220315.00%23291.30%1619117852.55%556121545.76%33665351.45%11337731079338571295
Total46172400041101115-14241280003161501122516000104065-25430.467101169270153931277705211220268248732386838412542610.24%2714085.24%5619117852.55%556121545.76%33665351.45%11337731079338571295
15Wolf Pack51400000913-4211000004403030000059-420.2009162500393127767211220268249128656915213.33%29679.31%0619117852.55%556121545.76%33665351.45%11337731079338571295
_Since Last GM Reset46172400041101115-14241280003161501122516000104065-25430.467101169270153931277705211220268248732386838412542610.24%2714085.24%5619117852.55%556121545.76%33665351.45%11337731079338571295
_Vs Conference311019000205877-191687000103430415212000102447-23240.3875896154143931277479211220268245841594685271802011.11%1872885.03%3619117852.55%556121545.76%33665351.45%11337731079338571295
_Vs Division27615000206477-13144600010373611329000102741-14160.29664104168133931277423211220268245331384044691371712.41%1682585.12%3619117852.55%556121545.76%33665351.45%11337731079338571295

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
4643W110116927070587323868384115
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
4617240041101115
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
2412800316150
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
2251600104065
Derniers 10 Matchs
WLOTWOTL SOWSOL
450001
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
2542610.24%2714085.24%5
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
211220268243931277
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
619117852.55%556121545.76%33665351.45%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
11337731079338571295


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 - 2020-12-08519Monsters-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
17 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
2,423,430$ 2,697,273$ 2,676,548$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 1,552,448$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 52 33,849$ 1,760,148$




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