Admirals

GP: 44 | W: 28 | L: 13 | OTL: 3 | P: 59
GF: 144 | GA: 91 | PP%: 16.19% | PK%: 86.07%
DG: Stéphane Fournier | Morale : 66 | Moyenne d'Équipe : 62
Prochain matchs #662 vs Wild
La résolution de votre navigateur est trop petite pour cette page. Plusieurs informations sont cachées pour garder la page lisible.

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 SP
1Jason Pominville (C)X100.005635936770738666546870636987803224660
2Filip ChytilX100.005636916882758866786564676560628462650
3Tage ThompsonX100.007437876893707367596369626463628055650
4Kiefer SherwoodXXX100.007036906473756963586362615967655225620
5Yakov TreninXX100.006637885981949558605654575563626369610
6Jansen HarkinsXX100.005837885774939156595556575663626377600
7Austin Poganski (R)XX100.006335935877918757625953585565636276600
8Sam AnasXX100.005135935961918758655955535771665977590
9Lukas SedlakXXX100.008037855675646056735459655871664729590
10Chase BalisyX100.005337885568959654585352555173675876590
11Sammy BlaisXX100.007540915881726256525758596165635646590
12Brandon GignacXX100.005436925766797156635854575263626578580
13Samuel KurkerX100.006638855881777156525353565469656774580
14Jacob MiddletonX100.006238855985796958306054635865635664620
15Dominik MasinX100.006740795483939153305251565465637271620
16Ben ThomasX100.005936905576939054305551574565636577610
17John Gilmour (A)X100.005536916272887161305956584771665320610
18Ethan BearX100.005838865972908458306252564763625973610
19Brandon Hickey (R)X100.006637885581837754305552584565636278610
20Dysin MayoX100.006238855476939053305251544865636262600
Rayé
1Tanner Kaspick (R)X100.006137885474878153555252545061636471570
2Joseph GambardellaXX100.005935945670615854555753555671664262560
3Otto Somppi (R)X100.006336905479787253585252545361636471560
4Emil JohanssonX100.005238855459928953305251554765626333580
MOYENNE D'ÉQUIPE100.00623789587583805750575658546764606060
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
1Louis Domingue100.00797775857877797877797873774849760
2Garret Sparks100.00777472847675777675777671754764740
Rayé
1Jake Oettinger (R)100.00726563937170727170727161658344690
2Jeremy Helvig (R)100.00736361847271737271737263675720690
MOYENNE D'ÉQUIPE100.0075706887747375747375746771594472
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Pascal Vincent72686671736878CAN4741,500,000$


Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Nom du Joueur Nom de l'ÉquipePOSGP G A P +/- PIM PIM5 HIT HTT SHT OSB OSM SHT% SB MP AMG PPG PPA PPP PPS PPM PKG PKA PKP PKS PKM GW GT FO% FOT GA TA EG HT P/20 PSG PSS FW FL FT S1 S2 S3
1Filip ChytilAdmirals (Nas)C3124224625601864137419017.52%1074424.013811201211124954171.09%46000111.2413000632
2Jordan SzwarzNashville PredatorsC/RW2613274018803964104256412.50%464424.7931013159601111102263.53%53200011.2402000531
3Chris WagnerNashville PredatorsC/LW/RW3719143319400108100136439413.97%1989124.104593415502251134058.41%104100020.7412000714
4Brandon GignacAdmirals (Nas)C/LW441021312680184383185512.05%463714.490117510001362059.06%12700000.9700000221
5Jansen HarkinsAdmirals (Nas)C/LW44131730122003969112428011.61%888620.16145281570000641059.05%75700000.6801000201
6Ben ThomasAdmirals (Nas)D446202615240403838132615.79%3993221.20369191390110123110.00%000000.5600000113
7Jason PominvilleAdmirals (Nas)RW221212241540103170236417.14%341819.031348651013681055.31%17900001.1511000322
8Austin PoganskiAdmirals (Nas)LW/RW401311244240484797327013.40%573118.285510281570002682060.26%7800020.6601000214
9Sam AnasAdmirals (Nas)LW/RW4461723280165010339615.83%474416.92268201180002421160.98%4100000.6201000002
10Jacob MiddletonAdmirals (Nas)D35716238400532653284113.21%3879622.77268281370001110300.00%000000.5801000122
11Ethan BearAdmirals (Nas)D32418228435373230172713.33%2673923.1133619121000098010.00%000000.6000010011
12Samuel KurkerAdmirals (Nas)RW409112012461069339420569.57%562615.6611219630000191053.85%5200000.6401002032
13Yakov TreninAdmirals (Nas)C/LW3361218722050668517587.06%373222.203710291290003942060.80%37500000.4923000201
14Chase BalisyAdmirals (Nas)C40791621120205059143911.86%546411.62000322000022055.56%45000000.6911000210
15Dysin MayoAdmirals (Nas)D29411151412016131871722.22%1443915.16202624000036110.00%000000.6800000110
16Dominik MasinAdmirals (Nas)D445101524671574275210239.62%3388820.19426311180000122010.00%000000.3400021111
17Joseph GambardellaAdmirals (Nas)C/LW3486147100112438122221.05%23299.7000000000001016.67%1200000.8500000021
18Brandon HickeyAdmirals (Nas)D4411213214755029284203.57%3468015.47011429000057000.00%000000.3800001100
19Tage ThompsonAdmirals (Nas)RW134812418025324114439.76%729522.720227571011611155.48%14600000.8101000012
20Tanner KaspickAdmirals (Nas)C38369210015293710268.11%32967.80000100000120058.66%28300000.6100000100
21Barclay GoodrowNashville PredatorsC/LW/RW414544061211599.09%47719.36112690000130073.68%7600001.2900000000
22Sammy BlaisAdmirals (Nas)LW/RW122353155233276197.41%221117.631236430000122068.42%1900000.4700001010
23Lukas SedlakAdmirals (Nas)C/LW/RW7134300171415586.67%210014.3300025000031056.82%4400000.8000000100
24Kiefer SherwoodAdmirals (Nas)C/LW/RW6134140109116109.09%110517.510224220001170058.62%2900000.7601000010
25Otto SomppiAdmirals (Nas)C38134-1201212133107.69%01423.750000110000101058.33%3600000.5600000000
26Emil JohanssonAdmirals (Nas)D13022120314150.00%1876.740000000006000.00%000000.4600000000
27John GilmourAdmirals (Nas)D61011402574614.29%913622.83101324000031000.00%000000.1500000000
Stats d'équipe Total ou en Moyenne800181298479276500408299231503459104312.04%2851378317.234075115347188635824143433960.23%473700160.70619035382730
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
1Garret SparksAdmirals (Nas)1914400.9241.71112403324200001.00051811211
2Jake OettingerAdmirals (Nas)85200.9112.0045121151680000.000079100
3Louis DomingueAdmirals (Nas)104420.9231.7860802182330000.66791014110
4Jeremy HelvigAdmirals (Nas)64200.8962.032960210960000.000067100
Stats d'équipe Total ou en Moyenne43271220.9181.81248128759170000.786144141521


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 Cap 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
Austin PoganskiAdmirals (Nas)LW/RW231996-02-16Yes198 Lbs6 ft1NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$Lien
Ben ThomasAdmirals (Nas)D231996-05-28No180 Lbs6 ft2NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Brandon GignacAdmirals (Nas)C/LW211997-11-07No170 Lbs5 ft11NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Brandon HickeyAdmirals (Nas)D231996-04-13Yes201 Lbs6 ft2NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$500,000$Lien
Chase BalisyAdmirals (Nas)C271992-02-02No179 Lbs5 ft11NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Dominik MasinAdmirals (Nas)D231996-02-01No198 Lbs6 ft3NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Dysin MayoAdmirals (Nas)D221996-08-17No194 Lbs6 ft1NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Emil JohanssonAdmirals (Nas)D231996-05-06No189 Lbs5 ft1NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Ethan BearAdmirals (Nas)D221997-06-26No197 Lbs5 ft11NoNoNo3Pro & Farm500,000$0$0$No500,000$500,000$Lien
Filip ChytilAdmirals (Nas)C191999-09-05No208 Lbs6 ft2NoNoNo4Pro & Farm900,000$0$0$No900,000$900,000$900,000$Lien
Garret SparksAdmirals (Nas)G261993-06-28No201 Lbs6 ft3NoNoNo1Pro & Farm500,000$0$0$NoLien
Jacob MiddletonAdmirals (Nas)D231996-01-02No210 Lbs6 ft3NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Jake OettingerAdmirals (Nas)G201998-12-18Yes220 Lbs6 ft5NoNoNo4Pro & Farm900,000$0$0$No900,000$900,000$900,000$Lien
Jansen HarkinsAdmirals (Nas)C/LW221997-05-23No182 Lbs6 ft1NoNoNo3Pro & Farm500,000$0$0$No500,000$500,000$Lien
Jason PominvilleAdmirals (Nas)RW361982-11-30No180 Lbs5 ft11NoNoNo3Pro & Farm5,666,000$0$0$No5,666,000$5,666,000$Lien
Jeremy HelvigAdmirals (Nas)G221997-05-25Yes188 Lbs6 ft4NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
John GilmourAdmirals (Nas)D261993-05-17No190 Lbs6 ft0NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Joseph GambardellaAdmirals (Nas)C/LW251993-12-01No196 Lbs5 ft10NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Kiefer SherwoodAdmirals (Nas)C/LW/RW241995-03-31No194 Lbs6 ft0NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Louis DomingueAdmirals (Nas)G271992-03-06No210 Lbs6 ft3NoNoNo2Pro & Farm2,500,000$0$0$No2,500,000$Lien
Lukas SedlakAdmirals (Nas)C/LW/RW261993-02-25No205 Lbs6 ft0NoNoNo1Pro & Farm300,000$0$0$NoLien
Otto SomppiAdmirals (Nas)C211998-01-12Yes192 Lbs6 ft2NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Sam AnasAdmirals (Nas)LW/RW261993-06-01No163 Lbs5 ft8NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Sammy BlaisAdmirals (Nas)LW/RW231996-06-17No205 Lbs6 ft2NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Samuel KurkerAdmirals (Nas)RW251994-04-08No202 Lbs6 ft2NoNoNo3Pro & Farm500,000$0$0$No500,000$500,000$Lien
Tage ThompsonAdmirals (Nas)RW211997-10-30No205 Lbs6 ft6NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Tanner KaspickAdmirals (Nas)C211998-01-28Yes200 Lbs6 ft0NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Yakov TreninAdmirals (Nas)C/LW221997-01-13No201 Lbs6 ft2NoNoNo3Pro & Farm500,000$0$0$No500,000$500,000$Lien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2823.64195 Lbs6 ft12.93684,500$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Kiefer SherwoodTage Thompson34113
2Austin PoganskiYakov TreninSammy Blais31113
3Brandon GignacJansen HarkinsLukas Sedlak25122
4Sam AnasTage Thompson10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Jacob MiddletonJohn Gilmour30122
2Ben ThomasDominik Masin30122
3Brandon Hickey30122
4Jacob MiddletonJohn Gilmour10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Kiefer SherwoodTage Thompson60122
2Austin PoganskiYakov TreninSammy Blais40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Jacob MiddletonJohn Gilmour60122
2Ben ThomasDominik Masin40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Tage Thompson60131
2Kiefer SherwoodYakov Trenin40131
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Jacob MiddletonJohn Gilmour60131
2Ben ThomasDominik Masin40131
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Tage Thompson60131Jacob MiddletonJohn Gilmour60131
240131Ben ThomasDominik Masin40131
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Tage Thompson60122
2Kiefer SherwoodYakov Trenin40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Jacob MiddletonJohn Gilmour60122
2Ben ThomasDominik Masin40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Kiefer SherwoodTage ThompsonJacob MiddletonJohn Gilmour
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Kiefer SherwoodTage ThompsonJacob MiddletonJohn Gilmour
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Jansen Harkins, Jason Pominville, Sammy BlaisJansen Harkins, Jason PominvilleSammy Blais
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Brandon Hickey, , Ben ThomasBrandon Hickey, Ben Thomas
Tirs de Pénalité
Tage Thompson, , Kiefer Sherwood, Yakov Trenin, Austin Poganski
Gardien
#1 : Garret Sparks, #2 : Louis Domingue


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
1Bears211000005501010000023-11100000032120.500510150054434456641539944924391113351616.25%30100.00%0791133059.47%760128359.24%37365756.77%1197866943300525277
2Griffins6600000026818220000009184400000017710121.0002650760154434451714153994492414328758320525.00%33390.91%1791133059.47%760128359.24%37365756.77%1197866943300525277
3Gulls210000016511000000101-11100000064230.7506111700544344556415399449245616314614321.43%13192.31%0791133059.47%760128359.24%37365756.77%1197866943300525277
4IceHogs660000002910193300000013673300000016412121.0002956850154434452514153994492411633489834720.59%23291.30%0791133059.47%760128359.24%37365756.77%1197866943300525277
5Monsters1010000034-11010000034-10000000000000.00036900544344514415399449243382087114.29%90100.00%0791133059.47%760128359.24%37365756.77%1197866943300525277
6Moose44000000243212200000012210220000001211181.000244670025443445151415399449246523346418738.89%17194.12%1791133059.47%760128359.24%37365756.77%1197866943300525277
7Penguins1010000023-1000000000001010000023-100.000246005443445224153994492425111621400.00%7185.71%0791133059.47%760128359.24%37365756.77%1197866943300525277
8Rampage40300001312-92020000016-52010000126-410.12535800544344575415399449241152941701417.14%18477.78%0791133059.47%760128359.24%37365756.77%1197866943300525277
9Reign2200000012111110000005051100000071641.000122335015443445784153994492437423375120.00%80100.00%0791133059.47%760128359.24%37365756.77%1197866943300525277
10Stars51201010121202110000056-13010101076160.6001220320054434451334153994492413028538628414.29%24675.00%0791133059.47%760128359.24%37365756.77%1197866943300525277
Total44251301122144915319108000015843152515501121864838590.6701442684120754434451272415399449249912784727292103416.19%2012886.07%2791133059.47%760128359.24%37365756.77%1197866943300525277
12Wild431000004401010000003-33300000041360.750471102544344597415399449247019606415213.33%18288.89%0791133059.47%760128359.24%37365756.77%1197866943300525277
13Wolves714001101824-631200000811-3402001101013-350.3571830480054434451584153994492416268581173525.71%28871.43%0791133059.47%760128359.24%37365756.77%1197866943300525277
_Since Last GM Reset44251301122144915319108000015843152515501121864838590.6701442684120754434451272415399449249912784727292103416.19%2012886.07%2791133059.47%760128359.24%37365756.77%1197866943300525277
_Vs Conference32171001121927022137600000363331910401121563719420.65692168260045443445885415399449247362053355181462114.38%1442582.64%1791133059.47%760128359.24%37365756.77%1197866943300525277
_Vs Division11137001113630656400000131216730011123185301.3643664100015443445292415399449242558811220054611.11%49981.63%0791133059.47%760128359.24%37365756.77%1197866943300525277

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
4459L2144268412127299127847272907
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
442513112214491
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
1910800015843
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
2515511218648
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
2103416.19%2012886.07%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
415399449245443445
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
791133059.47%760128359.24%37365756.77%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
1197866943300525277


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
4 - 2019-09-0519Admirals2Stars3LSommaire du Match
7 - 2019-09-0828Admirals2Rampage3LXXSommaire du Match
11 - 2019-09-1248Bears3Admirals2LSommaire du Match
12 - 2019-09-1361Admirals1Wolves2LSommaire du Match
15 - 2019-09-1668Admirals7Reign1WSommaire du Match
17 - 2019-09-1880Admirals6Gulls4WSommaire du Match
19 - 2019-09-2098Admirals5Wolves6LXSommaire du Match
21 - 2019-09-22101Rampage4Admirals1LSommaire du Match
24 - 2019-09-25112Monsters4Admirals3LSommaire du Match
25 - 2019-09-26122Moose2Admirals8WSommaire du Match
28 - 2019-09-29136Moose0Admirals4WSommaire du Match
31 - 2019-10-02149IceHogs3Admirals5WSommaire du Match
32 - 2019-10-03159Admirals4Griffins2WSommaire du Match
36 - 2019-10-07174Wolves2Admirals4WSommaire du Match
38 - 2019-10-09188IceHogs2Admirals3WSommaire du Match
40 - 2019-10-11207Admirals4Wolves3WXXSommaire du Match
43 - 2019-10-14221Admirals1Wild0WSommaire du Match
45 - 2019-10-16232Admirals2Wild1WSommaire du Match
46 - 2019-10-17242Reign0Admirals5WSommaire du Match
50 - 2019-10-21261Wild3Admirals0LSommaire du Match
52 - 2019-10-23267Admirals5Griffins1WSommaire du Match
53 - 2019-10-24280IceHogs1Admirals5WSommaire du Match
59 - 2019-10-30314Stars1Admirals2WSommaire du Match
60 - 2019-10-31326Admirals7IceHogs3WSommaire du Match
64 - 2019-11-04345Admirals2Stars1WXSommaire du Match
66 - 2019-11-06357Admirals3Stars2WXXSommaire du Match
67 - 2019-11-07371Admirals0Rampage3LSommaire du Match
70 - 2019-11-10381Griffins0Admirals5WSommaire du Match
73 - 2019-11-13398Stars5Admirals3LSommaire du Match
74 - 2019-11-14406Admirals4Griffins1WSommaire du Match
78 - 2019-11-18431Admirals7IceHogs0WSommaire du Match
81 - 2019-11-21452Wolves6Admirals2LSommaire du Match
85 - 2019-11-25469Wolves3Admirals2LSommaire du Match
88 - 2019-11-28493Admirals1Wild0WSommaire du Match
92 - 2019-12-02516Admirals2IceHogs1WSommaire du Match
95 - 2019-12-05539Admirals2Penguins3LSommaire du Match
96 - 2019-12-06549Admirals3Bears2WSommaire du Match
99 - 2019-12-09555Griffins1Admirals4WSommaire du Match
102 - 2019-12-12584Gulls1Admirals0LXXSommaire du Match
104 - 2019-12-14592Admirals6Moose0WSommaire du Match
106 - 2019-12-16600Admirals6Moose1WSommaire du Match
109 - 2019-12-19622Admirals4Griffins3WSommaire du Match
110 - 2019-12-20635Admirals0Wolves2LSommaire du Match
112 - 2019-12-22644Rampage2Admirals0LSommaire du Match
115 - 2019-12-25662Wild-Admirals-
116 - 2019-12-26668Moose-Admirals-
122 - 2020-01-01690Moose-Admirals-
123 - 2020-01-02706IceHogs-Admirals-
127 - 2020-01-06721Stars-Admirals-
129 - 2020-01-08728Penguins-Admirals-
130 - 2020-01-09734Wolves-Admirals-
133 - 2020-01-12758Rampage-Admirals-
138 - 2020-01-17795Admirals-Moose-
139 - 2020-01-18806Admirals-Moose-
143 - 2020-01-22819Admirals-Monsters-
145 - 2020-01-24841Admirals-Monsters-
148 - 2020-01-27853Admirals-Stars-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
150 - 2020-01-29864Admirals-Rampage-
152 - 2020-01-31885Admirals-Rampage-
154 - 2020-02-02889Stars-Admirals-
157 - 2020-02-05906Wolves-Admirals-
158 - 2020-02-06917Griffins-Admirals-
159 - 2020-02-07928Admirals-Wolves-
162 - 2020-02-10934Monsters-Admirals-
165 - 2020-02-13961Griffins-Admirals-
171 - 2020-02-19989IceHogs-Admirals-
172 - 2020-02-20998Admirals-IceHogs-
176 - 2020-02-241023Admirals-IceHogs-
179 - 2020-02-271049Admirals-Wolves-
180 - 2020-02-281055Admirals-Griffins-
183 - 2020-03-021067Rampage-Admirals-
185 - 2020-03-041079Wolves-Admirals-
186 - 2020-03-051091Wild-Admirals-
189 - 2020-03-081106IceHogs-Admirals-
193 - 2020-03-121131Griffins-Admirals-
194 - 2020-03-131149Admirals-IceHogs-



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
19 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
968,158$ 191,660$ 45,700$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 86,710$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 80 8,720$ 697,600$




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
1444251301122144915319108000015843152515501121864838591442684120754434451272415399449249912784727292103416.19%2012886.07%2791133059.47%760128359.24%37365756.77%1197866943300525277
Total Saison Régulière44251301122144915319108000015843152515501121864838591442684120754434451272415399449249912784727292103416.19%2012886.07%2791133059.47%760128359.24%37365756.77%1197866943300525277