Admirals
GP: 82 | W: 39 | L: 37 | OTL: 6 | P: 84
GF: 182 | GA: 206 | PP%: 11.66% | PK%: 87.08%
DG: Danny Rhéaume | 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
1Axel Jonsson-FjallbyX100.007668946668585955504858635544446050002211,000,000$
2Deven Sideroff (R)X100.00736395546348485250465163485252555000233935,833$
3Lukas JasekX100.007468886168717558505161635844446350002211,000,000$
4Manuel Wiederer (R)XX100.00716486616456585265534760454444555000233736,667$
5Nathan Noel (R)XX100.00646366536349514455384454424444475000233925,000$
6Nicholas CaamanoX100.007944896372557854445858642545456250002111,000,000$
7Ryan MacInnis (R)X100.00714399647154805847605562254444615000243874,125$
8Joel Kiviranta (R)XX100.008043888059577163255059542545456147002411,000,000$
9Jordy Bellerive (R)X100.007269786769747956704959615644446150002111,000,000$
10Sasha Chmelevski (R)XX100.007368846568656662785962635944446446002111,000,000$
11Riley Sutter (R)X100.00827499637452544850464564434444555000203894,167$
12Henri JokiharjuX100.00764386826771846125524875255757635000213925,000$
13Jacob LarssonX100.00674293777369855725514875256060624700233894,166$
14Lucas CarlssonX100.007470846570737854255242624044445650002211,000,000$
15Michael Anderson (R)X100.007671897771636749254141613944445450002111,000,000$
16Brogan Rafferty (R)X100.007872926472646660256045654344445950002511,000,000$
17Leon Gawanke (R)X100.007672866372687254255241633944445550002111,000,000$
Rayé
1Keaton Thompson (R)X100.00736786676762665025444060384545535000243925,000$
2Noah Dobson (R)X100.006942918071626369255348592547476050002031,431,667$
3Johnathan Kovacevic (R)X100.007878796378555848253842624044445250002211,000,000$
4Mac Hollowell (R)X100.00676279676262665025434257404444525000213799,766$
MOYENNE D'ÉQUIPE100.0074618767696168554050496240464658500
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
1Henri JokiharjuAdmirals (ANA)D82133245-1910401659795336213.68%60188723.0181321773540000367310.00%000000.4800000253
2Sasha ChmelevskiAdmirals (ANA)C/RW79202545-234050100125379716.00%6123515.6361319353331011533259.92%48400010.7304000360
3Ondrej KaseDucksRW69132639-4209150121299310.74%11152122.0548123130000033803036.99%83800000.51212000323
4Taylor RaddyshDucksC/RW641720375831585101102346916.67%6113517.74710174228110131553357.06%109000000.6513011524
5Stefan NoesenDucksLW/RW70181735397515371125398914.40%9132618.953693327610142493039.78%18100010.5359100602
6Brogan RaffertyAdmirals (ANA)D82331341011325118785428415.56%60171620.9321012453190110335100.00%000000.4000104202
7Cody GlassDucksC/RW69131730-3401510795356813.68%6130118.873582629300032122246.85%125300000.4639000212
8Jacob LarssonAdmirals (ANA)D8252126-2058066867321426.85%78186822.784711533500221373000.00%000100.2800000112
9Lucas CarlssonAdmirals (ANA)D8210142499801015339123925.64%58172421.036410253210111334100.00%000000.2800000213
10Lukas JasekAdmirals (ANA)RW82101424-15810625575164713.33%382510.061346730000602242.86%6300000.5801001311
11Joel KivirantaAdmirals (ANA)LW/RW4861622-2300417056196110.71%375215.68279172050002441028.89%4500000.5811000050
12Leon GawankeAdmirals (ANA)D8222022-5121151475017101511.76%48131816.081234330111157000.00%000000.3300201122
13Jordy BelleriveAdmirals (ANA)C82713205460518565124210.77%46848.35000010000440057.12%55500000.5811000023
14Michael AndersonAdmirals (ANA)D8221416-41203011453257168.00%47129715.821011161000064100.00%000000.2500014000
15Ryan MacInnisAdmirals (ANA)C826915-17220311067221558.33%5100112.210115640000130044.04%74700000.3000000003
16Nick RitchieDucksLW387815-25601444169236410.14%378820.753361614910121632128.00%5000000.3817000103
17Axel Jonsson-FjallbyAdmirals (ANA)LW829514-163810587481144411.11%8105512.870116630002943155.10%4900000.2700200041
18Nicholas CaamanoAdmirals (ANA)RW824711-1262084758220694.88%7114513.970331311500001261139.13%9200000.1901000022
19Deven SideroffAdmirals (ANA)RW5132532018161871116.67%44358.53011329000091043.90%4100000.2300000110
20Nathan NoelAdmirals (ANA)C/LW5122412803113631033.33%24619.05000010000270054.55%2200000.1700000010
21Riley SutterAdmirals (ANA)RW15224-113514982725.00%115210.1400000000000050.00%1000000.5300001000
22Noah DobsonAdmirals (ANA)D42022402461533.33%18421.20101413000017100.00%000000.4700000000
23Manuel WiedererAdmirals (ANA)C/RW20101-24071212168.33%11497.48000020000161051.28%3900000.1300000001
24Keaton ThompsonAdmirals (ANA)D1000000000000.00%011.570000000000000.00%000000.0000000000
25Johnathan KovacevicAdmirals (ANA)D1000-100120000.00%077.050000000000000.00%000000.0000000000
26Mac HollowellAdmirals (ANA)D1000000000000.00%144.250000000003000.00%000000.0000000000
Stats d'équipe Total ou en Moyenne1483175315490-731197115156715081421424105212.32%4322388016.1052971494523648459233306321348.55%555900120.4114486212323637
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)64292850.8822.3037250614312130220.688326220411
2Adam WernerAdmirals (ANA)2410910.8482.58125740543560110.778182062000
Stats d'équipe Total ou en Moyenne88393760.8742.3749824619715690330.720508282411


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$8,065$1,000,000$8,065$0$0$NoLien
Axel Jonsson-FjallbyAdmirals (ANA)LW221998-02-10No185 Lbs6 ft0NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Brogan RaffertyAdmirals (ANA)D251995-05-28Yes192 Lbs6 ft2NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Deven SideroffAdmirals (ANA)RW231997-04-14Yes171 Lbs5 ft11NoNoNo3Pro & Farm935,833$7,547$935,833$7,547$0$0$No935,833$935,833$
Henri JokiharjuAdmirals (ANA)D211999-06-17No180 Lbs6 ft0NoNoNo3Pro & Farm925,000$7,460$925,000$7,460$0$0$No925,000$925,000$Lien
Jacob LarssonAdmirals (ANA)D231997-04-29No195 Lbs6 ft2NoNoNo3Pro & Farm894,166$7,211$894,166$7,211$0$0$No894,166$894,166$Lien
Joel KivirantaAdmirals (ANA)LW/RW241996-03-23Yes163 Lbs5 ft10NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Johnathan KovacevicAdmirals (ANA)D221997-07-12Yes207 Lbs6 ft4NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Jordy BelleriveAdmirals (ANA)C211999-05-02Yes194 Lbs5 ft11NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Keaton ThompsonAdmirals (ANA)D241995-09-14Yes182 Lbs6 ft0NoNoNo3Pro & Farm925,000$7,460$925,000$7,460$0$0$No925,000$925,000$
Leon GawankeAdmirals (ANA)D211999-05-31Yes198 Lbs6 ft1NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Lucas CarlssonAdmirals (ANA)D221997-07-05No190 Lbs6 ft0NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Lukas JasekAdmirals (ANA)RW221997-08-28No183 Lbs6 ft1NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Mac HollowellAdmirals (ANA)D211998-09-26Yes170 Lbs5 ft10NoNoNo3Pro & Farm799,766$6,450$799,766$6,450$0$0$No799,766$799,766$
Manuel WiedererAdmirals (ANA)C/RW231996-11-21Yes170 Lbs6 ft0NoNoNo3Pro & Farm736,667$5,941$736,667$5,941$0$0$No736,667$736,667$
Michael AndersonAdmirals (ANA)D211999-05-25Yes196 Lbs6 ft0NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Mikhail BerdinAdmirals (ANA)G221998-02-28No163 Lbs6 ft2NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Nathan NoelAdmirals (ANA)C/LW231997-06-21Yes174 Lbs5 ft11NoNoNo3Pro & Farm925,000$7,460$925,000$7,460$0$0$No925,000$925,000$
Nicholas CaamanoAdmirals (ANA)RW211998-10-07No194 Lbs6 ft2NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Noah DobsonAdmirals (ANA)D202000-01-07Yes183 Lbs6 ft4NoNoNo3Pro & Farm1,431,667$11,546$1,431,667$11,546$0$0$No1,431,667$1,431,667$
Riley SutterAdmirals (ANA)RW201999-10-24Yes200 Lbs6 ft1NoNoNo3Pro & Farm894,167$7,211$894,167$7,211$0$0$No894,167$894,167$
Ryan MacInnisAdmirals (ANA)C241996-02-13Yes185 Lbs6 ft3NoNoNo3Pro & Farm874,125$7,049$874,125$7,049$0$0$No874,125$874,125$
Sasha ChmelevskiAdmirals (ANA)C/RW211999-06-09Yes187 Lbs6 ft0NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2322.13185 Lbs6 ft11.87971,365$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Joel KivirantaSasha ChmelevskiNicholas Caamano35122
2Axel Jonsson-FjallbyRyan MacInnisLukas Jasek30122
3Nathan NoelJordy BelleriveRiley Sutter25122
4Nicholas CaamanoManuel WiedererDeven Sideroff10122
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
1Joel KivirantaSasha ChmelevskiNicholas Caamano60122
2Axel Jonsson-FjallbyRyan MacInnisLukas Jasek40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Henri JokiharjuJacob Larsson60122
2Brogan RaffertyLucas Carlsson40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Nicholas CaamanoSasha Chmelevski60122
2Joel KivirantaRyan MacInnis40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Henri JokiharjuJacob Larsson60122
2Brogan RaffertyLucas Carlsson40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Nicholas Caamano60122Henri JokiharjuJacob Larsson60122
2Sasha Chmelevski40122Brogan RaffertyLucas Carlsson40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Nicholas CaamanoSasha Chmelevski60122
2Joel KivirantaRyan MacInnis40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Henri JokiharjuJacob Larsson60122
2Brogan RaffertyLucas Carlsson40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Joel KivirantaSasha ChmelevskiNicholas CaamanoHenri JokiharjuJacob Larsson
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Joel KivirantaSasha ChmelevskiNicholas CaamanoHenri JokiharjuJacob Larsson
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Jordy Bellerive, Riley Sutter, Manuel WiedererJordy Bellerive, Riley SutterManuel Wiederer
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Michael Anderson, Leon Gawanke, Brogan RaffertyMichael AndersonLeon Gawanke, Brogan Rafferty
Tirs de Pénalité
Nicholas Caamano, Sasha Chmelevski, Joel Kiviranta, Ryan MacInnis, Lukas Jasek
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
1Bruins2020000058-31010000024-21010000034-100.000581300675452164043849947359501836259222.22%16475.00%01107216451.16%1015221445.84%577118148.86%1940131220086271042515
2Condors852000102214843000010136742200000981120.7502241630067545216147438499473591454013717246613.04%56983.93%11107216451.16%1015221445.84%577118148.86%1940131220086271042515
3Crunch2020000026-41010000023-11010000003-300.00023500675452164143849947359701631451218.33%12191.67%01107216451.16%1015221445.84%577118148.86%1940131220086271042515
4Flames824000111821-3422000001111040200011710-370.438183048106754521613743849947359130351151404224.76%46784.78%01107216451.16%1015221445.84%577118148.86%1940131220086271042515
5Griffins220000001046110000005141100000053241.00010172700675452164343849947359441024447342.86%10190.00%01107216451.16%1015221445.84%577118148.86%1940131220086271042515
6IceHogs32000010954110000003122100001064261.00091524006754521648438499473594310386020210.00%16193.75%11107216451.16%1015221445.84%577118148.86%1940131220086271042515
7Marlies531000101284210000105233210000076180.8001221330167545216100438499473599925699633515.15%31196.77%01107216451.16%1015221445.84%577118148.86%1940131220086271042515
8Monarchs935000012025-5522000011213-141300000812-470.3892035551067545216151438499473591594911218840512.50%46686.96%01107216451.16%1015221445.84%577118148.86%1940131220086271042515
9Monsters2020000015-41010000003-31010000012-100.00012300675452161843849947359401130441000.00%14192.86%01107216451.16%1015221445.84%577118148.86%1940131220086271042515
10Moose825000011021-1140400000213-114210000188050.313102030106754521611643849947359153391511534237.14%55689.09%01107216451.16%1015221445.84%577118148.86%1940131220086271042515
11Penguins20200000311-81010000018-71010000023-100.00036900675452163943849947359387282617211.76%14378.57%01107216451.16%1015221445.84%577118148.86%1940131220086271042515
12Phantoms2010100035-2100010002111010000014-320.50036900675452162743849947359471822341900.00%9188.89%01107216451.16%1015221445.84%577118148.86%1940131220086271042515
13Rampage320000101486220000009451000001054161.000142337006754521667438499473596717426612325.00%19478.95%11107216451.16%1015221445.84%577118148.86%1940131220086271042515
14Rocket21000010642110000002111000001043141.00068140067545216334384994735935934307228.57%16193.75%01107216451.16%1015221445.84%577118148.86%1940131220086271042515
15Senators2020000029-71010000025-31010000004-400.00024600675452163043849947359421033441100.00%13376.92%01107216451.16%1015221445.84%577118148.86%1940131220086271042515
16Sharks823010111419-54110001198141201000511-690.5631423370267545216126438499473591554011814734411.76%51394.12%01107216451.16%1015221445.84%577118148.86%1940131220086271042515
17Soldiers311000011011-11010000024-22100000187130.500101626006754521655438499473594922365018422.22%17382.35%01107216451.16%1015221445.84%577118148.86%1940131220086271042515
18Sound Tigers210000015501000000134-11100000021130.75058130067545216434384994735947112047900.00%10280.00%01107216451.16%1015221445.84%577118148.86%1940131220086271042515
19Stars32100000743110000003032110000044040.66771320016754521663438499473595015305519210.53%15286.67%11107216451.16%1015221445.84%577118148.86%1940131220086271042515
20Wolf Pack3120000035-2211000003211010000003-320.333347016754521640438499473595714475717211.76%14192.86%01107216451.16%1015221445.84%577118148.86%1940131220086271042515
21Wolves3120000068-2211000004401010000024-220.33361218016754521657438499473595216484422418.18%23578.26%01107216451.16%1015221445.84%577118148.86%1940131220086271042515
Total82303702076182206-24411717010339598-34113200104387108-21840.51218231549736675452161421438499473591572432120115674465211.66%5036587.08%41107216451.16%1015221445.84%577118148.86%1940131220086271042515
_Since Last GM Reset82303702076182206-24411717010339598-34113200104387108-21840.51218231549736675452161421438499473591572432120115674465211.66%5036587.08%41107216451.16%1015221445.84%577118148.86%1940131220086271042515
_Vs Conference522319010451271243261290002365587261110010226266-4610.58712722335024675452169164384994735996426975610262693613.38%3184286.79%41107216451.16%1015221445.84%577118148.86%1940131220086271042515
_Vs Division4113150102484100-162167000234751-42078010013749-12360.43984149233326754521667743849947359742203633800204209.80%2543187.80%11107216451.16%1015221445.84%577118148.86%1940131220086271042515

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
8284W2182315497142115724321201156736
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
8230372076182206
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
41171710339598
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
411320104387108
Derniers 10 Matchs
WLOTWOTL SOWSOL
550000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
4465211.66%5036587.08%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
4384994735967545216
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
1107216451.16%1015221445.84%577118148.86%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
1940131220086271042515


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 - 2020-12-09525Admirals1Stars2LSommaire du Match
75 - 2020-12-10534Sharks2Admirals1LSommaire du Match
77 - 2020-12-12546Admirals0Sharks4LSommaire du Match
78 - 2020-12-13554Admirals4Soldiers2WSommaire du Match
79 - 2020-12-14566Soldiers4Admirals2LSommaire du Match
81 - 2020-12-16582Monarchs3Admirals2LXXSommaire du Match
82 - 2020-12-17588Admirals1Moose2LSommaire du Match
84 - 2020-12-19601Crunch3Admirals2LSommaire du Match
86 - 2020-12-21611Admirals1Flames2LSommaire du Match
87 - 2020-12-22625Moose4Admirals0LSommaire du Match
88 - 2020-12-23636Admirals4Soldiers5LXXSommaire du Match
90 - 2020-12-25644Admirals1Monsters2LSommaire du Match
91 - 2020-12-26654Penguins8Admirals1LSommaire du Match
92 - 2020-12-27662Admirals3IceHogs2WXXSommaire du Match
94 - 2020-12-29677Wolves0Admirals1WSommaire du Match
95 - 2020-12-30687Flames2Admirals3WSommaire du Match
96 - 2020-12-31699Admirals2Penguins3LSommaire du Match
97 - 2021-01-01707Admirals1Phantoms4LSommaire du Match
99 - 2021-01-03719IceHogs1Admirals3WSommaire du Match
100 - 2021-01-04731Admirals3Bruins4LSommaire du Match
102 - 2021-01-06742Flames1Admirals4WSommaire du Match
103 - 2021-01-07755Griffins1Admirals5WSommaire du Match
104 - 2021-01-08763Admirals2Marlies0WSommaire du Match
105 - 2021-01-09777Wolves4Admirals3LSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
107 - 2021-01-11787Admirals0Wolf Pack3LSommaire du Match
108 - 2021-01-12800Rampage1Admirals3WSommaire du Match
109 - 2021-01-13805Admirals1Sharks5LSommaire du Match
111 - 2021-01-15821Rampage3Admirals6WSommaire du Match
112 - 2021-01-16824Admirals2Wolves4LSommaire du Match
114 - 2021-01-18843Admirals2Monarchs4LSommaire du Match
116 - 2021-01-20853Stars0Admirals3WSommaire du Match
117 - 2021-01-21866Admirals2Monarchs4LSommaire du Match
118 - 2021-01-22873Moose4Admirals0LSommaire du Match
121 - 2021-01-25890Marlies1Admirals2WXXSommaire du Match
122 - 2021-01-26897Admirals3Sharks2WSommaire 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
3,555,134$ 2,234,140$ 2,234,140$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 2,711,983$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 1 24,872$ 24,872$




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