Monarchs
GP: 82 | W: 37 | L: 36 | OTL: 9 | P: 83
GF: 173 | GA: 183 | PP%: 12.75% | PK%: 88.04%
DG: Patrick 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
1Alexander NylanderXX100.006341948468628068337068617560606931002231,713,333$
2Daniel SprongXX100.00726783846773756650636268596161675000232750,000$
3Casey MittelstadtXX100.006342978374638970666364587957576750002121,491,666$
4Hunter ShinkarukX100.00736593636557585650475864555252605000252833,000$
5Nicholas Merkley (R)XX100.007368857968676962786258635544446450002321,075,833$
6Jesperi KotkaniemiX100.007958818571638170735973532557576736001923,425,000$
7Joel Lowry (R)X100.00716879626869735750575162484747585000283700,000$
8Nikolai Prokhorkin (R)X100.007343957869626265626859597547476550002631,775,000$
9Alexandre Texier (R)X100.00674290837163676442657060254747695000203925,000$
10Paul ByronXXX100.007943959058676161306666788568687149003111,166,667$
11Mikhail VorobyevX100.005941907172567855856057662547476250002331,050,000$
12Ryan Poehling (R)XX100.007944947769566757335456682546466250002131,491,667$
13Jake Leschyshyn (R)X100.00746789696775834759434660444444555000213927,500$
14Ryan LombergX100.00726881666862616850617164674444685000253950,000$
15Vitaly Kravtsov (R)X100.007971967571616355505154645144446050002031,775,000$
16Brennan MenellX100.007166826466788361256342654055555950002331,091,666$
17Jacob MiddletonX100.00839975687955685825544768255151605000242735,000$
18Matt BenningX100.008358898274617558255247702561616150002621,900,000$
19Roland McKeownX100.00747277677276835125474161394444555000241894,167$
Rayé
1Martin Fehervary (R)X100.00757283727266714725384160394444535000203894,167$
2Otto Leskinen (R)X100.006461707461697452254940563844445350002311,000,000$
3Pierre-Olivier Joseph (R)X100.006963826563697450254441583944445350002031,075,833$
4Dmitri Samorukov (R)X100.00777485627465704625374061384444525000213863,333$
MOYENNE D'ÉQUIPE100.0073618674696572584455546347505061490
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
1Curtis McElhinney100.006957588074656769767071646569800
2Kevin Poulin (R)100.0059556973626055635958304444595000
Rayé
MOYENNE D'ÉQUIPE100.006456647768636166686451545564290
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Bob Hartley72707573878159CAN6133,500,000$


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
1Daniel SprongMonarchs (LAK)LW/RW82142640-362013213616447998.54%16154818.88615214839100021493140.00%14500000.5205000353
2Casey MittelstadtMonarchs (LAK)C/LW82201939-14140311501593910712.58%10146017.8098174427101161535055.42%108800010.5313000644
3Paul ByronMonarchs (LAK)C/LW/RW68122739252099158131401079.16%15141420.80311144231901122582035.62%107800000.5516000433
4Matt BenningMonarchs (LAK)D7692231-18201356966174013.64%55166021.856814473460110267000.00%000000.3700000223
5Nicholas MerkleyMonarchs (LAK)C/RW82141630-47915931151143612212.28%9141717.298715433900000654154.41%13600100.4200120121
6Alexandre TexierMonarchs (LAK)C7652227-1242046907528566.67%20107414.14123661000033044.14%44400000.5000000312
7Brennan MenellMonarchs (LAK)D64520253420604226131819.23%45139521.802911202870000267210.00%000000.3600000101
8Jesperi KotkaniemiMonarchs (LAK)C5613122518500691031103010711.82%489616.0111217891122721057.50%72700000.5602000311
9Ryan LombergMonarchs (LAK)LW76121123-115408578125441139.60%2114315.04224241790000201146.38%6900000.4000000123
10Jacob MiddletonMonarchs (LAK)D7761420-4166301674646143613.04%40151319.654913343170000230000.00%000000.2600105112
11Ryan PoehlingMonarchs (LAK)C/LW68108187320476362224216.13%976911.31000020000183022.95%6100000.4700000413
12Roland McKeownMonarchs (LAK)D8261016-1141151473845101913.33%60155318.955611272290110179010.00%200000.2100120123
13Nikolai ProkhorkinMonarchs (LAK)C605813-7220264938153213.16%35258.761125470000271047.85%18600000.4900000101
14Brian BoyleKingsC/LW152911-114041372412288.33%332421.621787750000631065.81%38900000.6801000101
15Vitaly KravtsovMonarchs (LAK)RW5356111131533485316489.43%1071813.56000219000000148.78%4100000.3100100011
16Alexander NylanderMonarchs (LAK)LW/RW17358-10018212752011.11%536421.461349760002542032.26%3100000.4401000003
17Hunter ShinkarukMonarchs (LAK)LW42437-24018252351417.39%42315.513258420001540046.34%4100000.6100000010
18Joel LowryMonarchs (LAK)LW55246-31603636289167.14%72955.380112190110740036.51%6300000.4100000010
19Mikhail VorobyevMonarchs (LAK)C74246-260824213159.52%42643.580006741011411055.56%9000000.4500000001
20Pierre-Olivier JosephMonarchs (LAK)D32066-1622030149290.00%1756317.60022951000055000.00%000000.2100000010
21Martin FehervaryMonarchs (LAK)D15123-7220331372414.29%625216.82112312000021000.00%000000.2400000000
22Otto LeskinenMonarchs (LAK)D3103313003665260.00%1451916.76000442000075000.00%000000.1200000010
23Jake LeschyshynMonarchs (LAK)C141121001634233.33%0352.55000117000020057.14%700101.1200000000
Stats d'équipe Total ou en Moyenne1297151258409-4698365139113671361415106011.09%3581994315.385495149408336426816215729648.93%459800210.41218445322936
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
1Kevin PoulinMonarchs (LAK)195720.9001.9396523313110000.50061464300
Stats d'équipe Total ou en Moyenne195720.9001.9396523313110000.50061464300


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
Alexander NylanderMonarchs (LAK)LW/RW221998-03-01No180 Lbs6 ft1NoNoNo3Pro & Farm1,713,333$13,817$1,713,333$13,817$0$0$No1,713,333$1,713,333$Lien
Alexandre TexierMonarchs (LAK)C201999-09-13Yes192 Lbs6 ft1NoNoNo3Pro & Farm925,000$7,460$925,000$7,460$0$0$No925,000$925,000$
Brennan MenellMonarchs (LAK)D231997-05-24No183 Lbs5 ft11NoNoNo3Pro & Farm1,091,666$8,804$716,666$5,780$0$0$No716,666$716,666$Lien
Casey MittelstadtMonarchs (LAK)C/LW211998-11-22No203 Lbs6 ft1NoNoNo2Pro & Farm1,491,666$12,030$1,491,666$12,030$0$0$No1,491,666$Lien
Curtis McElhinneyMonarchs (LAK)G371983-05-22No200 Lbs6 ft3NoNoNo1Pro & Farm850,000$6,855$850,000$6,855$0$0$NoLien
Daniel SprongMonarchs (LAK)LW/RW231997-03-17No180 Lbs6 ft0NoNoNo2Pro & Farm750,000$6,048$750,000$6,048$0$0$No750,000$Lien
Dmitri SamorukovMonarchs (LAK)D211999-06-16Yes196 Lbs6 ft3NoNoNo3Pro & Farm863,333$6,962$863,333$6,962$0$0$No863,333$863,333$
Hunter ShinkarukMonarchs (LAK)LW251994-10-13No181 Lbs5 ft10NoNoNo2Pro & Farm833,000$6,718$833,000$6,718$0$0$No833,000$Lien
Jacob MiddletonMonarchs (LAK)D241996-01-01No200 Lbs6 ft3NoNoNo2Pro & Farm735,000$5,927$450,000$3,629$0$0$No735,000$Lien
Jake LeschyshynMonarchs (LAK)C211999-03-09Yes185 Lbs5 ft11NoNoNo3Pro & Farm927,500$7,480$927,500$7,480$0$0$No927,500$927,500$
Jesperi KotkaniemiMonarchs (LAK)C192000-07-06No188 Lbs6 ft2NoNoNo2Pro & Farm3,425,000$27,621$450,000$3,629$0$0$No3,425,000$Lien
Joel LowryMonarchs (LAK)LW281991-11-15Yes180 Lbs6 ft1NoNoNo3Pro & Farm700,000$5,645$700,000$5,645$0$0$No700,000$700,000$
Kevin PoulinMonarchs (LAK)G251995-01-01Yes200 Lbs6 ft0NoNoNo3Pro & Farm700,000$5,645$700,000$5,645$0$0$No700,000$700,000$
Martin FehervaryMonarchs (LAK)D201999-10-06Yes194 Lbs6 ft2NoNoNo3Pro & Farm894,167$7,211$894,167$7,211$0$0$No894,167$894,167$
Matt BenningMonarchs (LAK)D261994-05-25No204 Lbs6 ft1NoNoNo2Pro & Farm1,900,000$15,323$1,900,000$15,323$0$0$No1,900,000$Lien
Mikhail VorobyevMonarchs (LAK)C231997-01-04No194 Lbs6 ft2NoNoNo3Pro & Farm1,050,000$8,468$925,000$7,460$0$0$No925,000$925,000$Lien
Nicholas MerkleyMonarchs (LAK)C/RW231997-05-23Yes194 Lbs5 ft10NoNoNo2Pro & Farm1,075,833$8,676$1,075,833$8,676$0$0$No1,075,833$Lien
Nikolai ProkhorkinMonarchs (LAK)C261993-09-17Yes183 Lbs6 ft2NoNoNo3Pro & Farm1,775,000$14,315$1,775,000$14,315$0$0$No1,775,000$1,775,000$
Otto LeskinenMonarchs (LAK)D231997-02-06Yes168 Lbs5 ft11NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Paul ByronMonarchs (LAK)C/LW/RW311989-04-26No162 Lbs5 ft9NoNoNo1Pro & Farm1,166,667$9,409$1,166,667$9,409$0$0$NoLien
Pierre-Olivier JosephMonarchs (LAK)D201999-07-01Yes161 Lbs6 ft2NoNoNo3Pro & Farm1,075,833$8,676$1,075,833$8,676$0$0$No1,075,833$1,075,833$
Roland McKeownMonarchs (LAK)D241996-01-19No195 Lbs6 ft1NoNoNo1Pro & Farm894,167$7,211$894,167$7,211$0$0$NoLien
Ryan LombergMonarchs (LAK)LW251994-12-09No190 Lbs5 ft10NoNoNo3Pro & Farm950,000$7,661$700,000$5,645$0$0$No700,000$700,000$Lien
Ryan PoehlingMonarchs (LAK)C/LW211999-01-02Yes183 Lbs6 ft2NoNoNo3Pro & Farm1,491,667$12,030$1,491,667$12,030$0$0$No1,491,667$1,491,667$
Vitaly KravtsovMonarchs (LAK)RW201999-12-23Yes183 Lbs6 ft4NoNoNo3Pro & Farm1,775,000$14,315$925,002$7,460$0$0$No1,775,000$1,775,000$
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2523.64187 Lbs6 ft12.401,202,153$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Alexander NylanderDaniel Sprong35122
2Ryan LombergCasey MittelstadtNicholas Merkley30122
3Ryan PoehlingJesperi KotkaniemiVitaly Kravtsov25122
4Alexandre TexierAlexander Nylander10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Matt BenningJacob Middleton35122
2Roland McKeown30122
3Alexandre Texier25122
4Matt BenningJacob Middleton10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Alexander NylanderDaniel Sprong60122
2Ryan LombergCasey MittelstadtNicholas Merkley40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Matt BenningJacob Middleton60122
2Roland McKeown40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Alexander Nylander60122
2Daniel SprongCasey Mittelstadt40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Matt BenningJacob Middleton60122
2Roland McKeown40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
160122Matt BenningJacob Middleton60122
2Alexander Nylander40122Roland McKeown40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Alexander Nylander60122
2Daniel SprongCasey Mittelstadt40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Matt BenningJacob Middleton60122
2Roland McKeown40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Alexander NylanderDaniel SprongMatt BenningJacob Middleton
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Alexander NylanderDaniel SprongMatt BenningJacob Middleton
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Mikhail Vorobyev, Jake Leschyshyn, Jesperi KotkaniemiMikhail Vorobyev, Jake LeschyshynJesperi Kotkaniemi
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
, Roland McKeown, Roland McKeown,
Tirs de Pénalité
, Alexander Nylander, Daniel Sprong, Casey Mittelstadt, Jesperi Kotkaniemi
Gardien
#1 : Kevin Poulin, #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
1Admirals95300010252054310000012845220001013121120.66725416600724948515955048147737151408915446613.04%40587.50%11026219446.76%1036216047.96%501111644.89%2013140219616141002497
2Bruins21100000660110000004131010000025-320.500611170072494854255048147737427202218422.22%10190.00%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
3Condors84200002181534300000112754120000168-2100.62518325001724948517455048147737133317613860813.33%36488.89%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
4Crunch2110000058-3110000004311010000015-420.500581300724948549550481477374519164213215.38%70100.00%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
5Flames9530000125187532000001578421000011011-1110.611254772007249485175550481477371583912116060915.00%49687.76%21026219446.76%1036216047.96%501111644.89%2013140219616141002497
6Griffins211000005411010000034-11100000020220.500591401724948541550481477374513322912325.00%150100.00%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
7IceHogs4020110069-33020010037-41000100032130.3756121800724948552550481477379221638615320.00%24291.67%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
8Marlies21100000431110000002021010000023-120.500471101724948542550481477373910122516318.75%50100.00%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
9Monsters22000000615110000004041100000021141.000611170172494853655048147737321822211417.14%110100.00%11026219446.76%1036216047.96%501111644.89%2013140219616141002497
10Moose825000011317-44030000149-54220000098150.31313203301724948511255048147737160371331534948.16%60690.00%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
11Penguins22000000734110000002111100000052341.000710170072494854455048147737311122411218.33%11281.82%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
12Phantoms210000016511000000123-11100000042230.7506121800724948533550481477374617204110110.00%8187.50%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
13Rampage3120000037-41010000001-12110000036-320.3333470072494857155048147737672244601616.25%17382.35%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
14Rocket21001000422100010002111100000021141.000471100724948536550481477373511203414214.29%10190.00%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
15Senators2010000115-41000000101-11010000014-310.250112007249485415504814773723212331800.00%6266.67%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
16Sharks926000011222-104220000078-150400001514-950.278122032007249485151550481477371654812117649918.37%53884.91%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
17Soldiers321000003211010000001-12200000031240.6673580172494855155048147737331626551715.88%120100.00%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
18Sound Tigers2020000028-61010000014-31010000014-300.0002460072494856255048147737481127451815.56%10280.00%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
19Stars31100100911-22010010058-31100000043130.5009182700724948562550481477376718446014214.29%21480.95%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
20Wolf Pack2020000047-31010000023-11010000024-200.0004711007249485275504814773743142649900.00%13376.92%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
21Wolves41200010910-12020000047-32100001053240.5009142301724948557550481477375914547222313.64%25388.00%01026219446.76%1036216047.96%501111644.89%2013140219616141002497
Total82333602227173183-104116180120488844411718010238599-14830.5061733004730772494851517550481477371514419100014965026412.75%4435388.04%41026219446.76%1036216047.96%501111644.89%2013140219616141002497
_Since Last GM Reset82333602227173183-104116180120488844411718010238599-14830.5061733004730772494851517550481477371514419100014965026412.75%4435388.04%41026219446.76%1036216047.96%501111644.89%2013140219616141002497
_Vs Conference55192701224105125-2027815002025164-13281112010225461-7500.45510517928405724948599255048147737102027170910283184112.89%3133788.18%11026219446.76%1036216047.96%501111644.89%2013140219616141002497
_Vs Division431318000149392121870000250391122511000124353-10320.37293160253027249485771550481477377671955407812643613.64%2382987.82%31026219446.76%1036216047.96%501111644.89%2013140219616141002497

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
8283SOL1173300473151715144191000149607
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
8233362227173183
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
41161812048884
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
41171810238599
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
5026412.75%4435388.04%4
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
550481477377249485
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
1026219446.76%1036216047.96%501111644.89%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
2013140219616141002497


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 - 2020-09-279Sharks3Monarchs1LSommaire du Match
2 - 2020-09-2819Monarchs2Admirals5LSommaire du Match
3 - 2020-09-2922Monarchs3Flames2WSommaire du Match
4 - 2020-09-3033Monarchs2Moose3LSommaire du Match
6 - 2020-10-0246Condors3Monarchs7WSommaire du Match
8 - 2020-10-0460Monarchs2Sharks3LXXSommaire du Match
10 - 2020-10-0667Flames2Monarchs1LSommaire du Match
12 - 2020-10-0878Monarchs2Condors3LSommaire du Match
14 - 2020-10-1089Admirals3Monarchs1LSommaire du Match
16 - 2020-10-12100Flames1Monarchs3WSommaire du Match
18 - 2020-10-14118Monarchs3Condors1WSommaire du Match
20 - 2020-10-16127Moose2Monarchs1LSommaire du Match
22 - 2020-10-18138Sharks1Monarchs2WSommaire du Match
23 - 2020-10-19148Monarchs1Sharks4LSommaire du Match
25 - 2020-10-21165Condors0Monarchs1WSommaire du Match
26 - 2020-10-22179Moose1Monarchs0LSommaire du Match
27 - 2020-10-23183Monarchs1Sharks2LSommaire du Match
29 - 2020-10-25197Monarchs2Griffins0WSommaire du Match
31 - 2020-10-27208Monarchs1Crunch5LSommaire du Match
33 - 2020-10-29218Griffins4Monarchs3LSommaire du Match
35 - 2020-10-31230Crunch3Monarchs4WSommaire du Match
37 - 2020-11-02241Monarchs2Soldiers1WSommaire du Match
39 - 2020-11-04252Monarchs2Bruins5LSommaire du Match
42 - 2020-11-07263Sharks3Monarchs2LSommaire du Match
43 - 2020-11-08271Monarchs2Admirals3LSommaire du Match
44 - 2020-11-09279Monarchs2Flames3LXXSommaire du Match
45 - 2020-11-10286IceHogs2Monarchs1LSommaire du Match
47 - 2020-11-12302IceHogs2Monarchs0LSommaire du Match
48 - 2020-11-13313Monarchs0Condors2LSommaire du Match
49 - 2020-11-14324Monarchs3Admirals1WSommaire du Match
50 - 2020-11-15330Wolves4Monarchs3LSommaire du Match
52 - 2020-11-17346Stars4Monarchs2LSommaire du Match
53 - 2020-11-18358Admirals1Monarchs3WSommaire du Match
55 - 2020-11-20371Monarchs5Flames2WSommaire du Match
56 - 2020-11-21382Moose2Monarchs1LXXSommaire du Match
58 - 2020-11-23398Condors1Monarchs2WSommaire du Match
59 - 2020-11-24407Monarchs4Phantoms2WSommaire du Match
60 - 2020-11-25419IceHogs3Monarchs2LXSommaire du Match
61 - 2020-11-26430Monarchs4Stars3WSommaire du Match
63 - 2020-11-28438Monarchs2Moose0WSommaire du Match
64 - 2020-11-29448Monarchs5Penguins2WSommaire du Match
65 - 2020-11-30458Flames1Monarchs5WSommaire du Match
67 - 2020-12-02471Senators1Monarchs0LXXSommaire du Match
68 - 2020-12-03484Penguins1Monarchs2WSommaire du Match
71 - 2020-12-06497Monarchs3Admirals1WSommaire du Match
72 - 2020-12-07508Monarchs3Rampage1WSommaire du Match
73 - 2020-12-08517Soldiers1Monarchs0LSommaire du Match
74 - 2020-12-09530Monarchs2Monsters1WSommaire du Match
76 - 2020-12-11540Moose4Monarchs2LSommaire du Match
77 - 2020-12-12552Monarchs3IceHogs2WXSommaire du Match
79 - 2020-12-14560Monarchs2Wolf Pack4LSommaire du Match
80 - 2020-12-15571Wolf Pack3Monarchs2LSommaire du Match
81 - 2020-12-16582Monarchs3Admirals2WXXSommaire du Match
83 - 2020-12-18593Wolves3Monarchs1LSommaire du Match
85 - 2020-12-20605Stars4Monarchs3LXSommaire du Match
86 - 2020-12-21619Bruins1Monarchs4WSommaire du Match
87 - 2020-12-22627Monarchs1Condors2LXXSommaire du Match
89 - 2020-12-24639Sharks1Monarchs2WSommaire du Match
91 - 2020-12-26653Monarchs2Rocket1WSommaire du Match
92 - 2020-12-27661Monarchs4Wolves3WXXSommaire du Match
93 - 2020-12-28670Flames1Monarchs6WSommaire du Match
94 - 2020-12-29679Monarchs0Flames4LSommaire du Match
96 - 2020-12-31691Sound Tigers4Monarchs1LSommaire du Match
97 - 2021-01-01702Monarchs2Moose1WSommaire du Match
98 - 2021-01-02713Rampage1Monarchs0LSommaire du Match
100 - 2021-01-04730Rocket1Monarchs2WXSommaire du Match
101 - 2021-01-05737Monarchs1Wolves0WSommaire du Match
102 - 2021-01-06744Monarchs1Senators4LSommaire du Match
104 - 2021-01-08759Condors3Monarchs2LXXSommaire du Match
105 - 2021-01-09767Monarchs1Sharks2LSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
106 - 2021-01-10780Monsters0Monarchs4WSommaire du Match
107 - 2021-01-11791Monarchs0Rampage5LSommaire du Match
108 - 2021-01-12799Monarchs2Marlies3LSommaire du Match
109 - 2021-01-13808Marlies0Monarchs2WSommaire du Match
111 - 2021-01-15820Monarchs3Moose4LSommaire du Match
113 - 2021-01-17831Flames2Monarchs0LSommaire du Match
114 - 2021-01-18843Admirals2Monarchs4WSommaire du Match
116 - 2021-01-20855Monarchs1Sound Tigers4LSommaire du Match
117 - 2021-01-21866Admirals2Monarchs4WSommaire du Match
118 - 2021-01-22877Monarchs0Sharks3LSommaire du Match
119 - 2021-01-23881Monarchs1Soldiers0WSommaire du Match
123 - 2021-01-27902Phantoms3Monarchs2LXXSommaire 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
6,629,704$ 3,005,384$ 2,519,384$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 3,157,927$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 1 52,463$ 52,463$




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