Admirals

GP: 55 | W: 31 | L: 19 | OTL: 5 | P: 67
GF: 189 | GA: 173 | PP%: 16.67% | PK%: 79.75%
DG: Marc Simard | Morale : 50 | Moyenne d'Équipe : 52
Prochain matchs #856 vs Crunch
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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 Moyen
1Garrett PilonX100.00716390686378836176566162584444050600212742,500$
2Andy AndreoffXX100.00805582707457815348575662256364050590282650,000$
3Joshua Ho-Sang (R)X100.006959937764636262505956655444440505902321,000,000$
4Patrick RussellXX100.00795388627564685746585576254949050590262925,000$
5Tomas JurcoXX100.00644787777056515945625656256263050580262750,000$
6Tanner MacMasterX100.00727078656868735872555463554444050580231560,000$
7Trent FredericX100.00687049657571836065565262504545050580212895,000$
8Ryan SpoonerXX100.004835936762626658785758514848450505602722,400,000$
9Joona Luoto (R)XX100.00674295737046645225505572254545050560221560,000$
10D'Artagnan Joly (R)X100.00524784676960755062543747395454050520204650,000$
11Dillon HeatheringtonX100.00787775668177864825403966385354050610242700,000$
12Trevor CarrickX100.00757470667476795425455065504444050600251560,000$
13Joey Keane (R)X100.00726782616775795925535162484444050590204809,166$
14Gustav Olofsson (R)X100.00787389727352544925433962374444050560242750,000$
15Bode Wilde (R)X100.00777385607356604525353961374444050540194778,333$
16Matt HunwickX100.004843846162654446354745624765570505403422,800,000$
Rayé
1Jack Badini (R)X100.00534989647256714456394146435858050500214805,000$
2Dennis EverbergXX100.00463588627243333836383866443532050460273600,000$
3Malte Stromwall (R)XX100.00414545455439394145414145433230050410252742,500$
4Brendan Warren (R)X100.00374343436435353743373743403230050390221700,000$
5Benjamin Mirageas (R)X100.00514584626747663125282944305454050480204525,000$
6James Greenway (R)X100.00394343436837373943393943413230050410212700,000$
7Linus Hultstrom (R)X100.00414545455539394145414145433230050410262825,000$
8Lukas Bengtsson (R)X100.00414545454839394145414145433230050410252742,500$
MOYENNE D'ÉQUIPE100.0060547562685761494447465741464505053
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
1Christopher Gibson100.0058638072596353626058304545050590
2Ales Stezka (R)100.0036403871353434343434333230050390
Rayé
MOYENNE D'ÉQUIPE100.004752597247494448474632393805049
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire


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
1Garrett PilonAdmirals (Ana)C552744712420088229287752009.41%19136424.8268143713301161475257.49%190300011.0429000551
2Joshua Ho-SangAdmirals (Ana)RW552535602220050152256761799.77%26100518.293811401340001673248.53%13600001.1914000126
3Tomas JurcoAdmirals (Ana)LW/RW551930491612040107215541538.84%15100918.36224251180000533042.31%5200000.9711000062
4Dominic TurgeonAnaheimC/LW391527421730010676192501467.81%2493223.9204429991016913048.94%14100200.9015000332
5Tanner MacMasterAdmirals (Ana)C5213263914100871211264910810.32%1170913.641451488000026157.16%102000001.1000000252
6Joona LuotoAdmirals (Ana)LW/RW5216203615602976182451268.79%1380515.503361982000020133.33%4500010.8900000121
7Dillon HeatheringtonAdmirals (Ana)D527273476715211677444809.46%109129424.90257131340000115210.00%000000.5300111211
8Trevor CarrickAdmirals (Ana)D52622282460126448330617.23%76107320.6526835111000286000.00%000000.5200000005
9Gustav OlofssonAdmirals (Ana)D52421251916076385920386.78%86100119.262351399000190100.00%000000.5000000100
10Patrick RussellAdmirals (Ana)LW/RW1610919420433971164114.08%332220.1723516340000461051.52%3300001.1804000231
11Andy AndreoffAdmirals (Ana)C/LW19115164160654365285416.92%1038620.361014371011330141.88%35100000.8324000202
12D'Artagnan JolyAdmirals (Ana)RW559716-20605911115839785.70%1375713.78000014000001059.18%4900000.4200000011
13Bode WildeAdmirals (Ana)D5531114-9315135435118445.88%9986215.69000421000123000.00%000000.3200100011
14Joey KeaneAdmirals (Ana)D222111361203222327226.25%3347221.460111341000153110.00%000000.5500000001
15Ryan SpoonerAdmirals (Ana)C/LW194812200229448419.09%129015.31000000110330158.72%10900000.8311000010
16Trent FredericAdmirals (Ana)C16461032004141429239.52%523414.6400014000060053.14%27100000.8500000100
17Dennis EverbergAdmirals (Ana)LW/RW14224-1240319256178.00%421515.42000040002170134.78%2300000.3700000000
18Linus HultstromAdmirals (Ana)D3044420210100.00%44715.940000001102000.00%000001.6700000001
19Matt HunwickAdmirals (Ana)D160225007911160.00%1624315.220000300006000.00%000000.1600000000
20Benjamin MirageasAdmirals (Ana)D11022-820410000.00%816114.6500000000013000.00%000000.2500000000
21James GreenwayAdmirals (Ana)D14011520801000.00%5866.190000300009000.00%000000.2300000000
22Cal FooteAnaheimD701136015711080.00%515722.57000012000118000.00%000000.1300000000
23Jack BadiniAdmirals (Ana)C14011-10401012132110.00%019213.7800005000070049.02%20400000.1000000000
24Malte StromwallAdmirals (Ana)LW/RW3000-100426200.00%03812.960000000000000.00%300000.0000000000
25Brendan WarrenAdmirals (Ana)LW14000000312000.00%1785.6300000000010033.33%300000.0000000000
Stats d'équipe Total ou en Moyenne7621773224991123342012461290200658014368.82%5861374918.04244771263118723522930261154.32%434300220.73828211203027
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
1Christopher GibsonAdmirals (Ana)49281650.9262.83290710113718410030.76025490843
2Ales StezkaAdmirals (Ana)30000.9332.3178003450000.0000045000
Stats d'équipe Total ou en Moyenne52281650.9262.81298610114018860030.760254945843


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 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
Ales StezkaAdmirals (Ana)G221997-01-06Yes192 Lbs6 ft4NoNoNo1Pro & Farm650,000$199,193$65,000$19,919$NoLien
Andy AndreoffAdmirals (Ana)C/LW281991-05-17No203 Lbs6 ft1NoNoNo2Pro & Farm650,000$199,193$65,000$19,919$No650,000$Lien
Benjamin MirageasAdmirals (Ana)D201999-05-08Yes181 Lbs6 ft1NoNoNo4Pro & Farm525,000$160,887$52,500$16,089$No525,000$525,000$525,000$Lien
Bode WildeAdmirals (Ana)D192000-01-24Yes192 Lbs6 ft3NoNoNo4Pro & Farm778,333$238,521$77,833$23,852$No778,333$778,333$778,333$Lien
Brendan WarrenAdmirals (Ana)LW221997-05-07Yes191 Lbs6 ft1NoNoNo1Pro & Farm700,000$214,516$70,000$21,452$NoLien
Christopher GibsonAdmirals (Ana)G261992-12-27No188 Lbs6 ft1NoNoNo2Pro & Farm800,000$245,161$80,000$24,516$No800,000$Lien
D'Artagnan JolyAdmirals (Ana)RW201999-04-07Yes181 Lbs6 ft3NoNoNo4Pro & Farm650,000$199,193$65,000$19,919$No650,000$650,000$650,000$Lien
Dennis EverbergAdmirals (Ana)LW/RW271991-12-31No205 Lbs6 ft4NoNoNo3Pro & Farm600,000$183,870$60,000$18,387$No600,000$600,000$Lien
Dillon HeatheringtonAdmirals (Ana)D241995-05-08No215 Lbs6 ft4NoNoNo2Pro & Farm700,000$214,516$70,000$21,452$No700,000$Lien
Garrett PilonAdmirals (Ana)C211998-04-13No175 Lbs5 ft10NoNoNo2Pro & Farm742,500$227,540$74,250$22,754$No742,500$Lien
Gustav OlofssonAdmirals (Ana)D241994-12-01Yes194 Lbs6 ft3NoNoNo2Pro & Farm750,000$229,838$75,000$22,984$No750,000$Lien
Jack BadiniAdmirals (Ana)C211998-01-19Yes203 Lbs6 ft0NoNoNo4Pro & Farm805,000$246,693$80,500$24,669$No805,000$805,000$805,000$Lien
James GreenwayAdmirals (Ana)D211998-04-27Yes205 Lbs6 ft4NoNoNo2Pro & Farm700,000$214,516$70,000$21,452$No700,000$Lien
Joey KeaneAdmirals (Ana)D201999-07-02Yes183 Lbs6 ft0NoNoNo4Pro & Farm809,166$247,970$80,917$24,797$No809,166$809,166$809,166$Lien
Joona LuotoAdmirals (Ana)LW/RW221997-09-26Yes185 Lbs6 ft2YesNoNo1Pro & Farm560,000$171,612$56,000$17,161$NoLien
Joshua Ho-SangAdmirals (Ana)RW231996-01-22Yes173 Lbs6 ft0NoNoNo2Pro & Farm1,000,000$306,451$100,000$30,645$No1,000,000$Lien
Linus HultstromAdmirals (Ana)D261992-12-09Yes181 Lbs5 ft10NoNoNo2Pro & Farm825,000$252,822$82,500$25,282$No825,000$Lien
Lukas BengtssonAdmirals (Ana)D251994-04-14Yes168 Lbs5 ft9NoNoNo2Pro & Farm742,500$227,540$74,250$22,754$No742,500$Lien
Malte StromwallAdmirals (Ana)LW/RW251994-08-24Yes180 Lbs5 ft10NoNoNo2Pro & Farm742,500$227,540$74,250$22,754$No742,500$Lien
Matt HunwickAdmirals (Ana)D341985-05-21No194 Lbs5 ft11NoNoNo2Pro & Farm2,800,000$858,064$280,000$85,806$No2,800,000$Lien
Patrick RussellAdmirals (Ana)LW/RW261993-01-03No205 Lbs6 ft1NoNoNo2Pro & Farm925,000$283,467$92,500$28,347$No925,000$Lien
Ryan SpoonerAdmirals (Ana)C/LW271992-01-30No191 Lbs5 ft11NoNoNo2Pro & Farm2,400,000$735,483$240,000$73,548$No2,400,000$Lien
Tanner MacMasterAdmirals (Ana)C231996-01-08No185 Lbs6 ft0YesNoNo1Pro & Farm560,000$171,612$56,000$17,161$NoLien
Tomas JurcoAdmirals (Ana)LW/RW261992-12-27No188 Lbs6 ft2NoNoNo2Pro & Farm750,000$229,838$75,000$22,984$No750,000$Lien
Trent FredericAdmirals (Ana)C211998-02-11No203 Lbs6 ft2NoNoNo2Pro & Farm895,000$274,274$89,500$27,427$No895,000$Lien
Trevor CarrickAdmirals (Ana)D251994-07-04No186 Lbs6 ft2YesNoNo1Pro & Farm560,000$171,612$56,000$17,161$NoLien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2623.77190 Lbs6 ft12.23870,000$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Patrick RussellGarrett PilonJoshua Ho-Sang40122
2Andy AndreoffTomas Jurco30122
3Ryan SpoonerTrent FredericJoona Luoto20122
4Garrett PilonTanner MacMasterD'Artagnan Joly10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Dillon HeatheringtonTrevor Carrick40122
2Joey KeaneGustav Olofsson30122
3Matt HunwickBode Wilde20122
4Dillon HeatheringtonTrevor Carrick10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Patrick RussellGarrett PilonJoshua Ho-Sang60122
2Andy AndreoffTomas Jurco40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Dillon HeatheringtonTrevor Carrick60122
2Joey KeaneGustav Olofsson40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Garrett PilonPatrick Russell60122
2Andy AndreoffJoshua Ho-Sang40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Dillon HeatheringtonTrevor Carrick60122
2Joey KeaneGustav Olofsson40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Garrett Pilon60122Dillon HeatheringtonTrevor Carrick60122
2Patrick Russell40122Joey KeaneGustav Olofsson40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Garrett PilonPatrick Russell60122
2Andy AndreoffJoshua Ho-Sang40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Dillon HeatheringtonTrevor Carrick60122
2Joey KeaneGustav Olofsson40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Patrick RussellGarrett PilonJoshua Ho-SangDillon HeatheringtonTrevor Carrick
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Patrick RussellGarrett PilonJoshua Ho-SangDillon HeatheringtonTrevor Carrick
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Trent Frederic, Tanner MacMaster, Ryan SpoonerTrent Frederic, Tanner MacMasterRyan Spooner
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Matt Hunwick, Bode Wilde, Joey KeaneMatt HunwickBode Wilde, Joey Keane
Tirs de Pénalité
Garrett Pilon, Patrick Russell, Andy Andreoff, Joshua Ho-Sang,
Gardien
#1 : Christopher Gibson, #2 : Ales Stezka


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
1Baby Hawks2110000056-1110000004221010000014-320.50059140076574910766577186527285318561119.09%4175.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
2Bears21000001550110000003211000000123-130.75059140076574910786577186527289201152300.00%30100.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
3Bruins1010000012-1000000000001010000012-100.00012300765749102665771865272498625200.00%30100.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
4Cabaret Lady Mary Ann11000000716000000000001100000071621.00071320007657491072657718652722412628200.00%30100.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
5Caroline22000000743110000003211100000042241.0007132000765749106465771865272742614384250.00%60100.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
6Chiefs2020000058-3000000000002020000058-300.000581300765749108065771865272882814457114.29%7185.71%0983189751.82%1068204952.12%47692051.74%13059091326411722353
7Chill30100101812-41000000134-12010010058-320.3338152310765749109965771865272118353168600.00%13284.62%0983189751.82%1068204952.12%47692051.74%13059091326411722353
8Comets11000000321110000003210000000000021.000358007657491038657718652723682195120.00%10100.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
9Cougars2020000068-21010000034-11010000034-100.0006111700765749104965771865272842418416233.33%9366.67%0983189751.82%1068204952.12%47692051.74%13059091326411722353
10Crunch11000000312110000003120000000000021.000358007657491036657718652723111820200.00%40100.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
11Heat11000000321110000003210000000000021.000358007657491042657718652724721824100.00%4175.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
12Jayhawks402010101821-3201000101011-120101000810-240.500183048107657491016265771865272152453010413323.08%15753.33%1983189751.82%1068204952.12%47692051.74%13059091326411722353
13Las Vegas3000101110911000000134-12000101075250.83310172700765749101126577186527211131106113323.08%40100.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
14Manchots11000000642000000000001100000064221.0006915007657491026657718652723311420300.00%20100.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
15Marlies1000000112-1000000000001000000112-110.500123007657491021657718652724110219000.00%10100.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
16Minnesota220000001028110000007161100000031241.0001019290076574910139657718652727317854500.00%40100.00%1983189751.82%1068204952.12%47692051.74%13059091326411722353
17Monarchs320000101064210000106331100000043161.0001017270076574910145657718652721513525871000.00%10190.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
18Monsters2110000079-2110000005231010000027-520.5007121900765749106865771865272671310409111.11%50100.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
19Monsters1010000025-3000000000001010000025-300.000235007657491030657718652723620825000.00%4325.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
20Oceanics32100000911-2220000006331010000038-540.667918270076574910766577186527212526185610330.00%8362.50%0983189751.82%1068204952.12%47692051.74%13059091326411722353
21Oil Kings1010000046-21010000046-20000000000000.00048121076574910366577186527249742111100.00%2150.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
22Phantoms20200000311-81010000017-61010000024-200.0003580076574910596577186527281282042500.00%9277.78%0983189751.82%1068204952.12%47692051.74%13059091326411722353
23Rocket10000010321000000000001000001032121.000347007657491037657718652722892169111.11%110.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
24Senators10000010431000000000001000001043121.0004610007657491040657718652724415625200.00%30100.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
25Sharks32100000141132200000012751010000024-240.667142741007657491011865771865272132402779400.00%10370.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
26Sound Tigers2020000057-21010000034-11010000023-100.000591400765749106065771865272802512664125.00%5180.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
27Spiders11000000303000000000001100000030321.000336017657491024657718652722474204250.00%10100.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
28Stars21100000660110000003211010000034-120.5006111700765749108865771865272712820474125.00%8187.50%0983189751.82%1068204952.12%47692051.74%13059091326411722353
29Thunder220000001349110000008171100000053241.0001325380076574910916577186527254116503133.33%3166.67%0983189751.82%1068204952.12%47692051.74%13059091326411722353
Total55241902154189173162516500022987226308140213291101-10670.6091893345233176574910207065771865272213662535412881502516.67%1583279.75%2983189751.82%1068204952.12%47692051.74%13059091326411722353
30Wolf Pack22000000835110000005231100000031241.0008142200765749107865771865272592312402150.00%60100.00%0983189751.82%1068204952.12%47692051.74%13059091326411722353
_Since Last GM Reset55241902154189173162516500022987226308140213291101-10670.6091893345233176574910207065771865272213662535412881502516.67%1583279.75%2983189751.82%1068204952.12%47692051.74%13059091326411722353
_Vs Conference2914900123979071392000115235171657001124555-10360.621971732701176574910100965771865272114730719468967913.43%821384.15%0983189751.82%1068204952.12%47692051.74%13059091326411722353

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
5567SOL118933452320702136625354128831
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
5524192154189173
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
2516500229872
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
30814213291101
Derniers 10 Matchs
WLOTWOTL SOWSOL
530101
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
1502516.67%1583279.75%2
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
6577186527276574910
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
983189751.82%1068204952.12%47692051.74%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
13059091326411722353


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
2 - 2020-10-2312Jayhawks5Admirals6WXXSommaire du Match
4 - 2020-10-2528Sharks4Admirals8WSommaire du Match
7 - 2020-10-2840Admirals3Cougars4LSommaire du Match
9 - 2020-10-3050Admirals6Manchots4WSommaire du Match
10 - 2020-10-3159Admirals2Monsters7LSommaire du Match
13 - 2020-11-0378Admirals1Bruins2LSommaire du Match
15 - 2020-11-0596Crunch1Admirals3WSommaire du Match
17 - 2020-11-07112Caroline2Admirals3WSommaire du Match
19 - 2020-11-09128Heat2Admirals3WSommaire du Match
21 - 2020-11-11138Admirals2Chill4LSommaire du Match
23 - 2020-11-13152Admirals3Stars4LSommaire du Match
25 - 2020-11-15168Admirals2Monsters5LSommaire du Match
26 - 2020-11-16176Admirals3Las Vegas2WXXSommaire du Match
28 - 2020-11-18187Oceanics2Admirals4WSommaire du Match
31 - 2020-11-21202Comets2Admirals3WSommaire du Match
33 - 2020-11-23219Baby Hawks2Admirals4WSommaire du Match
35 - 2020-11-25233Minnesota1Admirals7WSommaire du Match
40 - 2020-11-30269Oil Kings6Admirals4LSommaire du Match
42 - 2020-12-02278Cougars4Admirals3LSommaire du Match
44 - 2020-12-04292Sharks3Admirals4WSommaire du Match
46 - 2020-12-06309Admirals2Chiefs4LSommaire du Match
48 - 2020-12-08316Admirals2Bears3LXXSommaire du Match
51 - 2020-12-11334Admirals7Cabaret Lady Mary Ann1WSommaire du Match
53 - 2020-12-13355Admirals5Thunder3WSommaire du Match
55 - 2020-12-15372Sound Tigers4Admirals3LSommaire du Match
57 - 2020-12-17386Admirals7Jayhawks6WXSommaire du Match
59 - 2020-12-19392Oceanics1Admirals2WSommaire du Match
62 - 2020-12-22423Monarchs1Admirals3WSommaire du Match
66 - 2020-12-26451Bears2Admirals3WSommaire du Match
68 - 2020-12-28463Admirals3Oceanics8LSommaire du Match
70 - 2020-12-30476Admirals3Minnesota1WSommaire du Match
72 - 2021-01-01497Monarchs2Admirals3WXXSommaire du Match
74 - 2021-01-03503Wolf Pack2Admirals5WSommaire du Match
77 - 2021-01-06528Admirals2Phantoms4LSommaire du Match
78 - 2021-01-07535Admirals3Spiders0WSommaire du Match
81 - 2021-01-10553Admirals2Sound Tigers3LSommaire du Match
82 - 2021-01-11565Admirals3Wolf Pack1WSommaire du Match
87 - 2021-01-16591Las Vegas4Admirals3LXXSommaire du Match
89 - 2021-01-18610Phantoms7Admirals1LSommaire du Match
91 - 2021-01-20615Admirals4Las Vegas3WXSommaire du Match
93 - 2021-01-22636Admirals1Jayhawks4LSommaire du Match
96 - 2021-01-25658Chill4Admirals3LXXSommaire du Match
98 - 2021-01-27674Monsters2Admirals5WSommaire du Match
100 - 2021-01-29687Stars2Admirals3WSommaire du Match
102 - 2021-01-31699Admirals1Baby Hawks4LSommaire du Match
104 - 2021-02-02713Admirals3Chiefs4LSommaire du Match
107 - 2021-02-05735Admirals3Chill4LXSommaire du Match
108 - 2021-02-06741Admirals4Caroline2WSommaire du Match
118 - 2021-02-16773Admirals2Sharks4LSommaire du Match
120 - 2021-02-18778Jayhawks6Admirals4LSommaire du Match
122 - 2021-02-20791Thunder1Admirals8WSommaire du Match
123 - 2021-02-21804Admirals4Monarchs3WSommaire du Match
126 - 2021-02-24819Admirals4Senators3WXXSommaire du Match
128 - 2021-02-26828Admirals3Rocket2WXXSommaire du Match
129 - 2021-02-27839Admirals1Marlies2LXXSommaire du Match
131 - 2021-03-01856Admirals-Crunch-
133 - 2021-03-03876Chiefs-Admirals-
135 - 2021-03-05890Heat-Admirals-
138 - 2021-03-08909Admirals-Comets-
139 - 2021-03-09916Admirals-Heat-
141 - 2021-03-11930Cabaret Lady Mary Ann-Admirals-
143 - 2021-03-13947Monsters-Admirals-
145 - 2021-03-15965Las Vegas-Admirals-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
147 - 2021-03-17980Oil Kings-Admirals-
150 - 2021-03-20998Manchots-Admirals-
152 - 2021-03-221015Spiders-Admirals-
154 - 2021-03-241025Admirals-Baby Hawks-
155 - 2021-03-251031Admirals-Monsters-
157 - 2021-03-271048Marlies-Admirals-
159 - 2021-03-291063Minnesota-Admirals-
161 - 2021-03-311078Senators-Admirals-
165 - 2021-04-041099Admirals-Monarchs-
166 - 2021-04-051112Rocket-Admirals-
169 - 2021-04-081136Bruins-Admirals-
171 - 2021-04-101151Comets-Admirals-
174 - 2021-04-131174Admirals-Oil Kings-
176 - 2021-04-151187Admirals-Heat-
179 - 2021-04-181215Admirals-Comets-
180 - 2021-04-191221Admirals-Oil Kings-
183 - 2021-04-221241Stars-Admirals-
185 - 2021-04-241256Monarchs-Admirals-
186 - 2021-04-251271Admirals-Sharks-



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité de l'Aréna20001000
Prix des Billets3520
Assistance48,04916,272
Assistance PCT96.10%65.09%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacité de l'ArénaPopularité de l'Équipe
16 2573 - 85.76% 80,286$2,007,155$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
1,621,077$ 2,262,000$ 2,262,000$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
12,161$ 1,621,077$ 26 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
1,284,579$ 57 12,161$ 693,177$




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