Admirals

GP: 47 | W: 26 | L: 17 | OTL: 4 | P: 56
GF: 108 | GA: 110 | PP%: 11.79% | PK%: 86.81%
DG: Danny Rhéaume | Morale : 50 | Moyenne d'Équipe : N/A
Prochain matchs #525 vs Stars
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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
1Axel Jonsson-FjallbyX100.007668946668585955504858635544446050002211,000,000$
2Lukas JasekX100.007468886168717558505161635844446350002211,000,000$
3Nathan Noel (R)XX100.00646366536349514455384454424444475000233925,000$
4Nicholas CaamanoX100.007944896372557854445858642545456250002111,000,000$
5Cody Glass (R)XX100.006341948067656174536464592547476650002131,713,333$
6Stefan NoesenXX100.00844577747559696437557161255859685000273450,000$
7Taylor Raddysh (R)XX100.00838089638078836075536268594444653400223894,166$
8Ryan MacInnis (R)X100.00714399647154805847605562254444615000243874,125$
9Joel Kiviranta (R)XX100.008043888059577163255059542545456147002411,000,000$
10Jordy Bellerive (R)X100.007269786769747956704959615644446150002111,000,000$
11Ondrej KaseX100.005941918668746173337564627559606850002432,600,000$
12Sasha Chmelevski (R)XX100.007368846568656662785962635944446446002111,000,000$
13Henri JokiharjuX100.00764386826771846125524875255757635000213925,000$
14Jacob LarssonX100.00674293777369855725514875256060624700233894,166$
15Lucas CarlssonX100.007470846570737854255242624044445650002211,000,000$
16Michael Anderson (R)X100.007671897771636749254141613944445450002111,000,000$
17Brogan Rafferty (R)X100.007872926472646660256045654344445950002511,000,000$
18Leon Gawanke (R)X100.007672866372687254255241633944445550002111,000,000$
Rayé
1Deven Sideroff (R)X100.00736395546348485250465163485252555000233935,833$
2Manuel Wiederer (R)XX100.00716486616456585265534760454444555000233736,667$
3Riley Sutter (R)X100.00827499637452544850464564434444555000203894,167$
4Keaton Thompson (R)X100.00736786676762665025444060384545535000243925,000$
5Noah Dobson (R)X100.006942918071626369255348592547476050002031,431,667$
6Johnathan Kovacevic (R)X100.007878796378555848253842624044445250002211,000,000$
7Mac Hollowell (R)X100.00676279676262665025434257404444525000213799,766$
MOYENNE D'ÉQUIPE100.0074608768696368574152526241474759490
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
1Adam Werner (R)100.0057597473596151605857304444575000
2Mikhail Berdin100.0059627863606351615857304444585000
Rayé
MOYENNE D'ÉQUIPE100.005861766860625161585730444458500
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Ryan Huska66707368656082CAN463850,000$


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
1Ondrej KaseAdmirals (ANA)RW47111930-200410190196312.22%7104022.1437102119600032463037.50%53600000.5829000321
2Henri JokiharjuAdmirals (ANA)D4791827-85601035138153623.68%35108523.105813281930000198300.00%000000.5000000223
3Stefan NoesenAdmirals (ANA)LW/RW471115261480965395286811.58%686118.343692717600031383040.91%13200010.6046000501
4Taylor RaddyshAdmirals (ANA)C/RW3110132384915383754192618.52%249415.96369191210000273255.87%46000000.9301011312
5Sasha ChmelevskiAdmirals (ANA)C/RW44815235100245277255110.39%465714.9525720174000002056.54%21400000.7002000130
6Cody GlassAdmirals (ANA)C/RW4791322-20037970234812.86%690219.192351719400021431247.44%86000000.4926000112
7Brogan RaffertyAdmirals (ANA)D47218205702085402420228.33%3098520.96145171720110184100.00%000000.4100004201
8Jacob LarssonAdmirals (ANA)D4721416-728047563910185.13%42107222.82257281930000205000.00%000000.3000000011
9Nick RitchieDucksLW387815-25601444169236410.14%378820.753361614910121632128.00%5000000.3817000103
10Lucas CarlssonAdmirals (ANA)D476713444065351672337.50%2899121.09426101720110187000.00%000000.2600000111
11Leon GawankeAdmirals (ANA)D4721113571158227103820.00%2676416.26101115000181000.00%000000.3400201121
12Jordy BelleriveAdmirals (ANA)C476612320034384072615.00%33908.32000000000150055.73%32300000.6100000013
13Lukas JasekAdmirals (ANA)RW47459136103730457258.89%24218.960001130000140128.57%2800000.4300001011
14Ryan MacInnisAdmirals (ANA)C47369-516019653813307.89%456512.04011330000040044.49%46300000.3200000002
15Axel Jonsson-FjallbyAdmirals (ANA)LW47538-4281025444992510.20%758412.430001140002551150.00%3400000.2700200031
16Michael AndersonAdmirals (ANA)D4707768230693294100.00%2975216.02000129000028000.00%000000.1900014000
17Deven SideroffAdmirals (ANA)RW383254201314176817.65%23408.95011319000061040.00%3000000.2900000110
18Nicholas CaamanoAdmirals (ANA)RW47235-338048414912424.08%463213.450115450000641132.69%5200000.1600000002
19Joel KivirantaAdmirals (ANA)LW/RW13235312011161021620.00%019214.84000247000010036.36%1100000.5200000020
20Nathan NoelAdmirals (ANA)C/LW18112112010321550.00%21639.06000000000120050.00%1000000.2500000010
21Manuel WiedererAdmirals (ANA)C/RW6000-100151020.00%0345.6800000000040047.62%2100000.0000000000
22Noah DobsonAdmirals (ANA)D2000140222030.00%04422.000002600007000.00%000000.0000000000
23Riley SutterAdmirals (ANA)RW2000000000000.00%042.2400000000000050.00%200000.0000000000
Stats d'équipe Total ou en Moyenne8501031872901368210096086284425361912.20%2421377016.20295281222196812313179321846.96%322600010.429314211212225
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
1Mikhail BerdinAdmirals (ANA)33181130.8882.08193003675990210.667153215210
2Adam WernerAdmirals (ANA)168610.8592.3793740372630110.778181532000
Stats d'équipe Total ou en Moyenne49261740.8792.182868431048620320.727334747210


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
Adam WernerAdmirals (ANA)G231997-01-01Yes200 Lbs6 ft0NoNoNo1Pro & Farm1,000,000$411,290$1,000,000$411,290$0$0$NoLien
Axel Jonsson-FjallbyAdmirals (ANA)LW221998-02-10No185 Lbs6 ft0NoNoNo1Pro & Farm1,000,000$411,290$1,000,000$411,290$0$0$NoLien
Brogan RaffertyAdmirals (ANA)D251995-05-28Yes192 Lbs6 ft2NoNoNo1Pro & Farm1,000,000$411,290$1,000,000$411,290$0$0$NoLien
Cody GlassAdmirals (ANA)C/RW211999-03-31Yes178 Lbs6 ft2NoNoNo3Pro & Farm1,713,333$704,677$1,713,333$704,677$0$0$No1,713,333$1,713,333$
Deven SideroffAdmirals (ANA)RW231997-04-14Yes171 Lbs5 ft11NoNoNo3Pro & Farm935,833$384,899$935,833$384,899$0$0$No935,833$935,833$
Henri JokiharjuAdmirals (ANA)D211999-06-17No180 Lbs6 ft0NoNoNo3Pro & Farm925,000$380,444$925,000$380,444$0$0$No925,000$925,000$Lien
Jacob LarssonAdmirals (ANA)D231997-04-29No195 Lbs6 ft2NoNoNo3Pro & Farm894,166$367,762$894,166$367,762$0$0$No894,166$894,166$Lien
Joel KivirantaAdmirals (ANA)LW/RW241996-03-23Yes163 Lbs5 ft10NoNoNo1Pro & Farm1,000,000$411,290$1,000,000$411,290$0$0$NoLien
Johnathan KovacevicAdmirals (ANA)D221997-07-12Yes207 Lbs6 ft4NoNoNo1Pro & Farm1,000,000$411,290$1,000,000$411,290$0$0$NoLien
Jordy BelleriveAdmirals (ANA)C211999-05-02Yes194 Lbs5 ft11NoNoNo1Pro & Farm1,000,000$411,290$1,000,000$411,290$0$0$NoLien
Keaton ThompsonAdmirals (ANA)D241995-09-14Yes182 Lbs6 ft0NoNoNo3Pro & Farm925,000$380,444$925,000$380,444$0$0$No925,000$925,000$
Leon GawankeAdmirals (ANA)D211999-05-31Yes198 Lbs6 ft1NoNoNo1Pro & Farm1,000,000$411,290$1,000,000$411,290$0$0$NoLien
Lucas CarlssonAdmirals (ANA)D221997-07-05No190 Lbs6 ft0NoNoNo1Pro & Farm1,000,000$411,290$1,000,000$411,290$0$0$NoLien
Lukas JasekAdmirals (ANA)RW221997-08-28No183 Lbs6 ft1NoNoNo1Pro & Farm1,000,000$411,290$1,000,000$411,290$0$0$NoLien
Mac HollowellAdmirals (ANA)D211998-09-26Yes170 Lbs5 ft10NoNoNo3Pro & Farm799,766$328,936$799,766$328,936$0$0$No799,766$799,766$
Manuel WiedererAdmirals (ANA)C/RW231996-11-21Yes170 Lbs6 ft0NoNoNo3Pro & Farm736,667$302,984$736,667$302,984$0$0$No736,667$736,667$
Michael AndersonAdmirals (ANA)D211999-05-25Yes196 Lbs6 ft0NoNoNo1Pro & Farm1,000,000$411,290$1,000,000$411,290$0$0$NoLien
Mikhail BerdinAdmirals (ANA)G221998-02-28No163 Lbs6 ft2NoNoNo1Pro & Farm1,000,000$411,290$1,000,000$411,290$0$0$NoLien
Nathan NoelAdmirals (ANA)C/LW231997-06-21Yes174 Lbs5 ft11NoNoNo3Pro & Farm925,000$380,444$925,000$380,444$0$0$No925,000$925,000$
Nicholas CaamanoAdmirals (ANA)RW211998-10-07No194 Lbs6 ft2NoNoNo1Pro & Farm1,000,000$411,290$1,000,000$411,290$0$0$NoLien
Noah DobsonAdmirals (ANA)D202000-01-07Yes183 Lbs6 ft4NoNoNo3Pro & Farm1,431,667$588,831$1,431,667$588,831$0$0$No1,431,667$1,431,667$
Ondrej KaseAdmirals (ANA)RW241995-11-08No185 Lbs6 ft0NoNoNo3Pro & Farm2,600,000$1,069,355$2,600,000$1,069,355$0$0$No2,600,000$2,600,000$Lien
Riley SutterAdmirals (ANA)RW201999-10-24Yes200 Lbs6 ft1NoNoNo3Pro & Farm894,167$367,762$894,167$367,762$0$0$No894,167$894,167$
Ryan MacInnisAdmirals (ANA)C241996-02-13Yes185 Lbs6 ft3NoNoNo3Pro & Farm874,125$359,519$874,125$359,519$0$0$No874,125$874,125$
Sasha ChmelevskiAdmirals (ANA)C/RW211999-06-09Yes187 Lbs6 ft0NoNoNo1Pro & Farm1,000,000$411,290$1,000,000$411,290$0$0$NoLien
Stefan NoesenAdmirals (ANA)LW/RW271993-02-12No205 Lbs6 ft1NoNoNo3Pro & Farm450,000$185,081$450,000$185,081$0$0$No450,000$450,000$Lien
Taylor RaddyshAdmirals (ANA)C/RW221998-02-18Yes216 Lbs6 ft3NoNoNo3Pro & Farm894,166$367,762$894,166$367,762$0$0$No894,166$894,166$
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2722.33187 Lbs6 ft12.041,036,996$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Stefan NoesenCody GlassOndrej Kase35122
2Joel KivirantaTaylor RaddyshSasha Chmelevski30122
3Axel Jonsson-FjallbyRyan MacInnisNicholas Caamano25122
4Nathan NoelJordy BelleriveLukas Jasek10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Henri JokiharjuJacob Larsson35122
2Brogan RaffertyLucas Carlsson30122
3Michael AndersonLeon Gawanke25122
4Henri JokiharjuJacob Larsson10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Stefan NoesenCody GlassOndrej Kase60122
2Joel KivirantaTaylor RaddyshSasha Chmelevski40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Henri JokiharjuJacob Larsson60122
2Brogan RaffertyLucas Carlsson40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Ondrej KaseStefan Noesen60122
2Cody GlassTaylor Raddysh40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Henri JokiharjuJacob Larsson60122
2Brogan RaffertyLucas Carlsson40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Ondrej Kase60122Henri JokiharjuJacob Larsson60122
2Stefan Noesen40122Brogan RaffertyLucas Carlsson40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Ondrej KaseStefan Noesen60122
2Cody GlassTaylor Raddysh40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Henri JokiharjuJacob Larsson60122
2Brogan RaffertyLucas Carlsson40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Stefan NoesenCody GlassOndrej KaseHenri JokiharjuJacob Larsson
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Stefan NoesenCody GlassOndrej KaseHenri JokiharjuJacob Larsson
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Ryan MacInnis, Nicholas Caamano, Lukas JasekRyan MacInnis, Nicholas CaamanoLukas Jasek
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Michael Anderson, Leon Gawanke, Brogan RaffertyMichael AndersonLeon Gawanke, Brogan Rafferty
Tirs de Pénalité
Ondrej Kase, Stefan Noesen, Cody Glass, Taylor Raddysh, Sasha Chmelevski
Gardien
#1 : Mikhail Berdin, #2 : Adam Werner


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
1Bruins1010000024-21010000024-20000000000000.0002350043292911252542982843922810154125.00%5260.00%0638126650.39%545126243.19%33269847.56%11277641139358603297
2Condors852000102214843000010136742200000981120.7502241630043292911147254298284391454013717246613.04%56983.93%1638126650.39%545126243.19%33269847.56%11277641139358603297
3Crunch1010000003-3000000000001010000003-300.000000004329291126254298284393581531500.00%50100.00%0638126650.39%545126243.19%33269847.56%11277641139358603297
4Flames503000111016-62020000048-43010001168-230.30010162610432929117725429828439932081932414.17%32681.25%0638126650.39%545126243.19%33269847.56%11277641139358603297
5Griffins11000000532000000000001100000053221.00058130043292911172542982843924712264375.00%6183.33%0638126650.39%545126243.19%33269847.56%11277641139358603297
6IceHogs11000000321000000000001100000032121.00036900432929111525429828439135822200.00%40100.00%0638126650.39%545126243.19%33269847.56%11277641139358603297
7Marlies32100000871110000003122110000056-140.66781624004329291165254298284395714316319421.05%13192.31%0638126650.39%545126243.19%33269847.56%11277641139358603297
8Monarchs633000001414042200000101002110000044060.50014243800432929111112542982843996348413426311.54%33681.82%0638126650.39%545126243.19%33269847.56%11277641139358603297
9Monsters1010000003-31010000003-30000000000000.00000000432929118254298284391852228600.00%100100.00%0638126650.39%545126243.19%33269847.56%11277641139358603297
10Moose52200001911-22020000025-33200000176150.5009182710432929118625429828439872494882926.90%30293.33%0638126650.39%545126243.19%33269847.56%11277641139358603297
11Phantoms10001000211100010002110000000000021.000246004329291115254298284392386131300.00%20100.00%0638126650.39%545126243.19%33269847.56%11277641139358603297
12Rampage10000010541000000000001000001054121.0005712004329291123254298284391446244125.00%3166.67%0638126650.39%545126243.19%33269847.56%11277641139358603297
13Rocket21000010642110000002111000001043141.00068140043292911332542982843935934307228.57%16193.75%0638126650.39%545126243.19%33269847.56%11277641139358603297
14Senators2020000029-71010000025-31010000004-400.00024600432929113025429828439421033441100.00%13376.92%0638126650.39%545126243.19%33269847.56%11277641139358603297
15Sharks41001011963310000118621000100010170.87591423024329291174254298284396121387221314.29%170100.00%0638126650.39%545126243.19%33269847.56%11277641139358603297
16Sound Tigers210000015501000000134-11100000021130.75058130043292911432542982843947112047900.00%10280.00%0638126650.39%545126243.19%33269847.56%11277641139358603297
17Stars11000000321000000000001100000032121.0003690043292911192542982843921716175120.00%8187.50%0638126650.39%545126243.19%33269847.56%11277641139358603297
Total47191702054108110-224910010225456-2231070103254540560.5961081872952343292911844254298284398642426869602462911.79%2733686.81%1638126650.39%545126243.19%33269847.56%11277641139358603297
18Wolf Pack21100000321211000003210000000000020.50034701432929113025429828439317394111218.18%10190.00%0638126650.39%545126243.19%33269847.56%11277641139358603297
_Since Last GM Reset47191702054108110-224910010225456-2231070103254540560.5961081872952343292911844254298284398642426869602462911.79%2733686.81%1638126650.39%545126243.19%33269847.56%11277641139358603297
_Vs Conference29157010337561141464000223631515930101139309410.707751322071243292911535254298284395081534156021461913.01%1672286.83%1638126650.39%545126243.19%33269847.56%11277641139358603297
_Vs Division2812701023646131564000223735213630100127261330.589641131772243292911495254298284394821394345591461510.27%1682386.31%1638126650.39%545126243.19%33269847.56%11277641139358603297

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
4756W110818729584486424268696023
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
4719172054108110
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
2491010225456
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
2310710325454
Derniers 10 Matchs
WLOTWOTL SOWSOL
630001
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
2462911.79%2733686.81%1
Tirs en 1e PériodeTirs en 2e PériodeTirs en 3e PériodeTirs en 4e PériodeButs en 1e PériodeButs en 2e PériodeButs en 3e PériodeButs en 4e Période
2542982843943292911
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
638126650.39%545126243.19%33269847.56%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
11277641139358603297


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 - 2020-09-2711Admirals0Crunch3LSommaire du Match
2 - 2020-09-2819Monarchs2Admirals5WSommaire du Match
4 - 2020-09-3027Sharks4Admirals3LXXSommaire du Match
5 - 2020-10-0142Condors2Admirals3WSommaire du Match
6 - 2020-10-0252Admirals3Moose4LXXSommaire du Match
8 - 2020-10-0462Admirals2Moose1WSommaire du Match
10 - 2020-10-0668Admirals2Condors4LSommaire du Match
12 - 2020-10-0879Moose3Admirals2LSommaire du Match
14 - 2020-10-1089Admirals3Monarchs1WSommaire du Match
17 - 2020-10-13107Moose2Admirals0LSommaire du Match
18 - 2020-10-14117Admirals4Flames3WXXSommaire du Match
20 - 2020-10-16128Admirals3Stars2WSommaire du Match
21 - 2020-10-17131Flames5Admirals3LSommaire du Match
23 - 2020-10-19150Condors1Admirals4WSommaire du Match
24 - 2020-10-20161Admirals3Marlies2WSommaire du Match
25 - 2020-10-21169Sharks2Admirals3WXXSommaire du Match
26 - 2020-10-22175Admirals1Condors2LSommaire du Match
28 - 2020-10-24192Admirals5Rampage4WXXSommaire du Match
30 - 2020-10-26198Wolf Pack2Admirals1LSommaire du Match
32 - 2020-10-28210Admirals1Flames2LXXSommaire du Match
34 - 2020-10-30222Monsters3Admirals0LSommaire du Match
36 - 2020-11-01237Rocket1Admirals2WSommaire du Match
38 - 2020-11-03249Admirals2Marlies4LSommaire du Match
40 - 2020-11-05253Admirals3IceHogs2WSommaire du Match
42 - 2020-11-07265Admirals1Flames3LSommaire du Match
43 - 2020-11-08271Monarchs2Admirals3WSommaire du Match
45 - 2020-11-10285Marlies1Admirals3WSommaire du Match
46 - 2020-11-11300Flames3Admirals1LSommaire du Match
47 - 2020-11-12305Admirals2Moose1WSommaire du Match
49 - 2020-11-14324Monarchs3Admirals1LSommaire du Match
51 - 2020-11-16335Condors2Admirals3WXXSommaire du Match
52 - 2020-11-17345Admirals4Condors1WSommaire du Match
53 - 2020-11-18358Admirals1Monarchs3LSommaire du Match
54 - 2020-11-19367Condors1Admirals3WSommaire du Match
55 - 2020-11-20378Admirals2Condors1WSommaire du Match
57 - 2020-11-22390Wolf Pack0Admirals2WSommaire du Match
58 - 2020-11-23401Admirals0Senators4LSommaire du Match
59 - 2020-11-24410Senators5Admirals2LSommaire du Match
60 - 2020-11-25422Bruins4Admirals2LSommaire du Match
63 - 2020-11-28434Admirals5Griffins3WSommaire du Match
64 - 2020-11-29446Sound Tigers4Admirals3LXXSommaire du Match
65 - 2020-11-30456Admirals4Rocket3WXXSommaire du Match
67 - 2020-12-02467Admirals2Sound Tigers1WSommaire du Match
68 - 2020-12-03475Admirals1Sharks0WXSommaire du Match
69 - 2020-12-04486Phantoms1Admirals2WXSommaire du Match
71 - 2020-12-06497Monarchs3Admirals1LSommaire du Match
72 - 2020-12-07511Sharks0Admirals2WSommaire du Match
74 - 2020-12-09525Admirals-Stars-
75 - 2020-12-10534Sharks-Admirals-
77 - 2020-12-12546Admirals-Sharks-
78 - 2020-12-13554Admirals-Soldiers-
79 - 2020-12-14566Soldiers-Admirals-
81 - 2020-12-16582Monarchs-Admirals-
82 - 2020-12-17588Admirals-Moose-
84 - 2020-12-19601Crunch-Admirals-
86 - 2020-12-21611Admirals-Flames-
87 - 2020-12-22625Moose-Admirals-
88 - 2020-12-23636Admirals-Soldiers-
90 - 2020-12-25644Admirals-Monsters-
91 - 2020-12-26654Penguins-Admirals-
92 - 2020-12-27662Admirals-IceHogs-
94 - 2020-12-29677Wolves-Admirals-
95 - 2020-12-30687Flames-Admirals-
96 - 2020-12-31699Admirals-Penguins-
97 - 2021-01-01707Admirals-Phantoms-
99 - 2021-01-03719IceHogs-Admirals-
100 - 2021-01-04731Admirals-Bruins-
102 - 2021-01-06742Flames-Admirals-
103 - 2021-01-07755Griffins-Admirals-
104 - 2021-01-08763Admirals-Marlies-
105 - 2021-01-09777Wolves-Admirals-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
107 - 2021-01-11787Admirals-Wolf Pack-
108 - 2021-01-12800Rampage-Admirals-
109 - 2021-01-13805Admirals-Sharks-
111 - 2021-01-15821Rampage-Admirals-
112 - 2021-01-16824Admirals-Wolves-
114 - 2021-01-18843Admirals-Monarchs-
116 - 2021-01-20853Stars-Admirals-
117 - 2021-01-21866Admirals-Monarchs-
118 - 2021-01-22873Moose-Admirals-
121 - 2021-01-25890Marlies-Admirals-
122 - 2021-01-26897Admirals-Sharks-



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
2,154,286$ 2,799,890$ 2,799,890$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 1,653,885$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 51 29,435$ 1,501,185$




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