Connexion

Admirals
GP: 81 | W: 38 | L: 33 | OTL: 10 | P: 86
GF: 160 | GA: 174 | PP%: 13.24% | PK%: 84.96%
DG: Yannick Masse | Morale : 50 | Moyenne d’équipe : 57
Prochains matchs #1311 vs Las Vegas
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Centre de jeu
Heat
28-48-5, 61pts
0
FINAL
1 Admirals
38-33-10, 86pts
Team Stats
L1SéquenceSOL1
15-23-2Fiche domicile18-20-3
13-25-3Fiche domicile20-13-7
4-5-1Derniers 10 matchs3-5-2
2.05Buts par match 1.98
2.89Buts contre par match 2.15
16.23%Pourcentage en avantage numérique13.24%
83.85%Pourcentage en désavantage numérique84.96%
Admirals
38-33-10, 86pts
0
FINAL
1 Monarchs
39-36-6, 84pts
Team Stats
SOL1SéquenceW4
18-20-3Fiche domicile20-18-2
20-13-7Fiche domicile19-18-4
3-5-2Derniers 10 matchs7-2-1
1.98Buts par match 1.86
2.15Buts contre par match 2.21
13.24%Pourcentage en avantage numérique15.74%
84.96%Pourcentage en désavantage numérique86.96%
Admirals
38-33-10, 86pts
2024-04-18
Las Vegas
31-41-9, 71pts
Statistiques d’équipe
SOL1SéquenceL1
18-20-3Fiche domicile17-18-5
20-13-7Fiche visiteur14-23-4
3-5-210 derniers matchs2-7-1
1.98Buts par match 1.74
2.15Buts contre par match 1.74
13.24%Pourcentage en avantage numérique14.29%
84.96%Pourcentage en désavantage numérique90.59%
Meneurs d'équipe
Buts
Nikita Nesterenko
17
Passes
Axel Andersson
24
Points
Nikita Nesterenko
39
Plus/Moins
Joey Keane
15
Victoires
Louis Domingue
37
Pourcentage d’arrêts
David Tendeck
0.915

Statistiques d’équipe
Buts pour
160
1.98 GFG
Tirs pour
1565
19.32 Avg
Pourcentage en avantage numérique
13.2%
29 GF
Début de zone offensive
38.0%
Buts contre
174
2.15 GAA
Tirs contre
1611
19.89 Avg
Pourcentage en désavantage numérique
85.0%%
34 GA
Début de la zone défensive
40.6%
Informations de l'équipe

Directeur généralYannick Masse
DivisionEst
ConférenceEst
Capitaine
Assistant #1Justin Falk
Assistant #2Mitch Moroz


Informations de l’aréna

Capacité3,000
Assistance2,564
Billets de saison300


Informations de la formation

Équipe Pro21
Équipe Mineure21
Limite contact 42 / 50
Espoirs10


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
1Mike VecchioneXX100.00584869706270706548656164656456050640291950,000$
2Nikita Nesterenko (R)X100.0057407170757860664060616066515005062N0211925,000$
3Garrett PilonX100.00604470696365646342625760635350050610241850,000$
4Nathan Legare (R)X100.00605361616662626241555451595050050570213789,167$
5Jan Mysak (R)XX100.00594069636258585947545454595050050560201850,833$
6Mikael Pyyhtia (R)X100.00564068685658575640535454595050050560203897,500$
7Cameron Hillis (R)X100.00554467675860575550515153585150050550222838,333$
8Jack BadiniX100.00614569606563605450505054555350050550241805,000$
9Liam Hawel (R)X100.00697287617245464663483861394444050530231525,000$
10Dmitry Zavgorodniy (R)XXX100.00656187616134324458404056404444050490222780,000$
11D'Artagnan JolyX100.00444780646948594259433046365454050490231650,000$
12Joni Ikonen (R)X100.00383795626227263148243138365454050410231825,000$
13Victor MeteX100.00554377747583726740646068677464050680241950,000$
14Corey SchuenemanX100.006143747168726667406258696654500506502721,255,000$
15Connor CarrickX100.00595563716269696740655769655950050640281750,000$
16Madison BoweyX100.00624865666567656140565564615750050610271750,000$
17Axel Andersson (R)X100.00614568706361595740515363605150050600222772,500$
18Joey KeaneX100.00636864606861645125464057394444050560231809,166$
19Benjamin Mirageas (R)X100.00434580596738522725232443275454050450231525,000$
Rayé
MOYENNE D’ÉQUIPE100.0057487366655958554451495754535105057
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 SPÂgeContratSalaire moyen
1Louis Domingue100.007170716871727371727171655805065N0301900,000$
2David Tendeck (R)100.0063575760605959576160555150050550223783,333$
Rayé
MOYENNE D’ÉQUIPE100.006764646466666664676663585405060
Nom de l’entraîneur 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
1Jonny BrodzinskiAnaheimC681920391160701531454010813.10%18117017.2128102515600001033154.04%116400000.67310000450
2Nikita NesterenkoAdmirals (Ana)C791722393220411321424211311.97%1297012.281343310000195148.75%80200010.8025000354
3Mike VecchioneAdmirals (Ana)C/RW55142135214051921344310910.45%1098517.924610321470000251147.79%11300000.7114000532
4Garrett PilonAdmirals (Ana)C78122234-220073106148281118.11%14121615.60246171590000945245.93%30700000.5603000315
5Axel AnderssonAdmirals (Ana)D799243314001187482163810.98%68149118.87246341360000146200%000000.4400000234
6Nathan LegareAdmirals (Ana)RW79131629140012068119347810.92%7126015.96246151840000213051.19%8400000.4601000251
7Joey KeaneAdmirals (Ana)D797192615620194473572420.00%51134417.02202535000080100%200000.3900000534
8Alex BelzileAnaheimC/RW531115264301082146136361158.09%8110020.763361912700021472262.25%129000100.4717002233
9Nikita AlexandrovAnaheimC62141226718070103116278112.07%873411.850114150000103158.80%66500000.7137000354
10Corey SchuenemanAdmirals (Ana)D7871825-154001251048924447.87%65173622.275510511930001175300%000000.2900000111
11Connor CarrickAdmirals (Ana)D7871724-13440128907418359.46%68170521.87347521900000169200%000000.2800000122
12Madison BoweyAdmirals (Ana)D78419231440111686819525.88%46152919.613710481670000148110%000000.3000000002
13Mikael PyyhtiaAdmirals (Ana)C79810180120456274296010.81%480410.190000170000141134.75%14100000.4500000031
14Jan MysakAdmirals (Ana)C/LW7910515-41801014673306113.70%7161120.401121317600001862142.86%10500000.1900000302
15Cameron HillisAdmirals (Ana)C7421214-116049656817502.94%3109214.7701105000060150.86%11600000.2600000010
16Jack BadiniAdmirals (Ana)C7377142140694666193610.61%575910.4001103000021245.45%5500000.3700000121
17Dmitry ZavgorodniyAdmirals (Ana)C/LW/RW71178426045202910263.45%5118116.64000314800001020156.10%4100000.1400000000
18D'Artagnan JolyAdmirals (Ana)RW7735886015171571220.00%594512.27000033000001161.90%4200000.1700000101
19Victor MeteAdmirals (Ana)D11314-10100102085837.50%1126724.33314529000018010%000000.3000000000
20Joni IkonenAdmirals (Ana)C3000-100000000%03311.1900000000000066.67%30000000000000
21Liam HawelAdmirals (Ana)C15000-520994250%21328.8400000000000058.33%120000000000000
22Benjamin MirageasAdmirals (Ana)D1000000000000%199.000000000000000%00000000000000
Statistiques d’équipe totales ou en moyenne1349168272440-249410152614681625453116610.34%4182208316.37335386326196100031474361754.39%494200110.401037002364237
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
1Louis DomingueAdmirals (Ana)783731100.8962.0046312815414860410.53132780734
2David TendeckAdmirals (Ana)61000.9152.091720067100000179000
Statistiques d’équipe totales ou en moyenne843831100.8972.004804281601557041327979734


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 Recrue Poids Taille Non-échange Disponible pour échange Ballotage forcé Waiver Possible Contrat Type Salaire actuel Salaire moyen restantPlafond salarial Plafond salarial restant Exclus du plafond salarial Salaire annuel 2Salaire annuel 3Salaire annuel 4Salaire annuel 5Salaire annuel 6Salaire annuel 7Salaire annuel 8Salaire annuel 9Salaire annuel 10Lien
Axel AnderssonAdmirals (Ana)D222000-02-10Yes179 Lbs6 ft0NoNoNoNo2Pro & Farm772,500$4,023$0$0$No772,500$Lien
Benjamin MirageasAdmirals (Ana)D231999-05-08Yes181 Lbs6 ft1NoNoNoNo1Pro & Farm525,000$2,734$0$0$NoLien
Cameron HillisAdmirals (Ana)C222000-06-24Yes172 Lbs5 ft10NoNoNoNo2Pro & Farm838,333$4,366$0$0$No838,333$Lien
Connor Carrick (contrat à 1 volet)Admirals (Ana)D281994-04-13No192 Lbs5 ft11NoNoYesYes1Pro & Farm750,000$3,906$0$0$NoLien
Corey Schueneman (contrat à 1 volet)Admirals (Ana)D271995-09-02No196 Lbs6 ft0NoNoYesYes2Pro & Farm1,255,000$6,536$355,000$1,849$No1,255,000$Lien
D'Artagnan JolyAdmirals (Ana)RW231999-04-07No181 Lbs6 ft3NoNoNoNo1Pro & Farm650,000$3,385$0$0$NoLien
David TendeckAdmirals (Ana)G221999-11-25Yes172 Lbs6 ft1NoNoNoNo3Pro & Farm783,333$4,080$0$0$No783,333$783,333$Lien
Dmitry ZavgorodniyAdmirals (Ana)C/LW/RW222000-08-11Yes173 Lbs5 ft9NoNoNoNo2Pro & Farm780,000$4,062$0$0$No780,000$Lien
Garrett PilonAdmirals (Ana)C241998-04-13No190 Lbs6 ft0NoNoYesYes1Pro & Farm850,000$4,427$0$0$NoLien
Jack BadiniAdmirals (Ana)C241998-01-19No203 Lbs6 ft0NoNoYesYes1Pro & Farm805,000$4,193$0$0$NoLien
Jan MysakAdmirals (Ana)C/LW202002-06-24Yes183 Lbs6 ft0NoNoNoNo1Pro & Farm850,833$4,431$0$0$NoLien
Joey KeaneAdmirals (Ana)D231999-07-02No187 Lbs6 ft0NoNoNoNo1Pro & Farm809,166$4,214$0$0$NoLien
Joni IkonenAdmirals (Ana)C231999-04-14Yes172 Lbs5 ft11NoNoNoNo1Pro & Farm825,000$4,297$0$0$NoLien
Liam HawelAdmirals (Ana)C231999-04-18Yes183 Lbs6 ft5NoNoNoNo1Pro & Farm525,000$2,734$0$0$NoLien
Louis Domingue (contrat à 1 volet)Admirals (Ana)G301992-03-06No207 Lbs6 ft3YesNoYesYes1Pro & Farm900,000$4,688$0$0$NoLien
Madison Bowey (contrat à 1 volet)Admirals (Ana)D271995-04-22No203 Lbs6 ft2NoNoYesYes1Pro & Farm750,000$3,906$0$0$NoLien
Mikael PyyhtiaAdmirals (Ana)C202001-12-17Yes154 Lbs6 ft0NoNoNoNo3Pro & Farm897,500$4,674$0$0$No897,500$897,500$
Mike Vecchione (contrat à 1 volet)Admirals (Ana)C/RW291993-02-25No194 Lbs5 ft10NoNoYesYes1Pro & Farm950,000$4,948$50,000$260$NoLien
Nathan LegareAdmirals (Ana)RW212001-01-11Yes205 Lbs6 ft0NoNoNoNo3Pro & Farm789,167$4,110$0$0$No789,167$789,167$Lien
Nikita Nesterenko (contrat à 1 volet)Admirals (Ana)C212001-09-10Yes183 Lbs6 ft2YesNoNoNo1Pro & Farm925,000$4,818$25,000$130$NoLien
Victor MeteAdmirals (Ana)D241998-06-07No187 Lbs5 ft9NoNoYesYes1Pro & Farm950,000$4,948$0$0$NoLien
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2123.71186 Lbs6 ft01.48818,135$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Jan MysakMike VecchioneJack Badini40122
2Dmitry ZavgorodniyNikita NesterenkoNathan Legare30122
3Cameron HillisGarrett PilonD'Artagnan Joly20122
4Liam HawelMikael PyyhtiaJoni Ikonen10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Victor MeteCorey Schueneman40122
2Connor CarrickMadison Bowey30122
3Axel AnderssonJoey Keane20122
4Benjamin MirageasVictor Mete10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Jan MysakMike VecchioneGarrett Pilon60122
2Dmitry ZavgorodniyNikita NesterenkoNathan Legare40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Victor MeteCorey Schueneman60122
2Connor CarrickMadison Bowey40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Mike VecchioneJan Mysak60122
2Nikita NesterenkoDmitry Zavgorodniy40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Victor MeteCorey Schueneman60122
2Connor CarrickMadison Bowey40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Mike Vecchione60122Victor MeteCorey Schueneman60122
2Nikita Nesterenko40122Connor CarrickMadison Bowey40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Mike VecchioneJan Mysak60122
2Nikita NesterenkoDmitry Zavgorodniy40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Victor MeteCorey Schueneman60122
2Connor CarrickMadison Bowey40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Jan MysakMike VecchioneGarrett PilonVictor MeteCorey Schueneman
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Jan MysakMike VecchioneGarrett PilonVictor MeteCorey Schueneman
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Jan Mysak, Mikael Pyyhtia, Cameron HillisJan Mysak, Mikael PyyhtiaJan Mysak
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Axel Andersson, Joey Keane, Benjamin MirageasAxel AnderssonAxel Andersson, Joey Keane
Tirs de pénalité
Mike Vecchione, Nikita Nesterenko, Garrett Pilon, Nathan Legare, Jan Mysak
Gardien
#1 : Louis Domingue, #2 : David Tendeck


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
TotalDomicileVisiteur
# 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 Hawks310000025501000000112-12100000143140.66756110065444611455415025085152171054700.00%50100.00%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
2Bears22000000743110000004221100000032141.000713200065444611435415025085130614433133.33%7271.43%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
3Bruins2020000017-61010000002-21010000015-400.000123006544461126541502508513881441700.00%7357.14%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
4Cabaret Lady Mary Ann22000000523110000003211100000020241.000591401654446114354150250851281512345240.00%6183.33%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
5Caroline2110000034-11010000002-21100000032120.50036900654446114254150250851339637800.00%30100.00%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
6Chiefs3110000156-1211000003301000000123-130.500581300654446116354150250851592522441000.00%90100.00%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
7Chill3020001059-41010000025-32010001034-120.333571200654446116854150250851641612507114.29%6183.33%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
8Comets3110100078-1110000002112010100057-240.66771219006544461151541502508515512275422100.00%11190.91%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
9Cougars21100000431110000003121010000012-120.50048120065444611325415025085147122248100.00%11281.82%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
10Crunch2010010035-21010000001-11000010034-110.25035800654446114554150250851521218516116.67%8187.50%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
11Heat32100000321211000002201100000010140.66736902654446116154150250851461024501119.09%10190.00%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
12Jayhawks32100000981211000007701100000021140.6679142310654446119854150250851921812747114.29%6266.67%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
13Las Vegas3120000045-1211000004311010000002-220.33347110165444611595415025085172151457500.00%7185.71%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
14Manchots210000015501000000123-11100000032130.7505914006544461134541502508514352241300.00%7185.71%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
15Marlies201000104401010000023-11000001021120.500459006544461132541502508513281233500.00%60100.00%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
16Minnesota3120000079-21010000013-22110000066020.3337132000654446116954150250851622016679222.22%8362.50%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
17Monarchs4120000136-32020000003-32100000133030.37536900654446115654150250851671724896116.67%10280.00%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
18Monsters22000000835110000005141100000032141.0008142200654446114654150250851361210347342.86%5180.00%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
19Monsters3020000137-41010000001-12010000136-310.167358006544461159541502508514611125216212.50%40100.00%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
20Oceanics3030000029-72020000016-51010000013-200.00023500654446114854150250851652210591300.00%4175.00%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
21Oil Kings420010011064220000008442000100122070.87510172702654446116954150250851742124741300.00%11281.82%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
22Phantoms21100000440110000003121010000013-220.500461000654446113454150250851341310374125.00%50100.00%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
23Rocket22000000835110000005231100000031241.00081119006544461131541502508513816631300.00%30100.00%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
24Sags42200000610-4211000002202110000048-440.50061016016544461187541502508517322287715320.00%14285.71%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
25Seattle41300000913-4211000005412020000049-520.25091625006544461150541502508518425286810330.00%13284.62%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
26Senators201000104401010000012-11000001032120.5004590065444611465415025085137114327114.29%10100.00%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
27Sound Tigers2110000024-2110000002111010000003-320.50022400654446112554150250851391112404125.00%60100.00%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
28Spiders210000013301000000112-11100000021130.75035800654446112754150250851461318496116.67%8187.50%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
29Stars3110000157-22110000034-11000000123-130.500581300654446116254150250851691531531000.00%120100.00%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
30Thunder220000001248110000004311100000081741.00012203200654446117854150250851461712555240.00%4175.00%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
31Wolf Pack2110000045-11010000013-21100000032120.50047110065444611365415025085152121842400.00%9366.67%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
Total81333302139160174-14411820000037781-4401513021368393-10860.5311602654251765444611156554150250851161144650415702192913.24%2263484.96%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
_Since Last GM Reset81333302139160174-14411820000037781-4401513021368393-10860.5311602654251765444611156554150250851161144650415702192913.24%2263484.96%01058200052.90%1157213854.12%570112350.76%1980139119225801027517
_Vs Conference361416000337081-1118610000023039-91886000314042-2370.51470114184016544461168654150250851702193220722961515.63%991881.82%01058200052.90%1157213854.12%570112350.76%1980139119225801027517

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
8186SOL116026542515651611446504157017
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
8133332139160174
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
41182000037781
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
40151321368393
Derniers 10 matchs
WLOTWOTL SOWSOL
350002
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
2192913.24%2263484.96%0
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
5415025085165444611
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
1058200052.90%1157213854.12%570112350.76%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
1980139119225801027517


Derniers matchs 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
5 - 2023-10-1430Admirals0Las Vegas2ALSommaire du match
6 - 2023-10-1534Caroline2Admirals0BLSommaire du match
10 - 2023-10-1959Stars2Admirals3BWSommaire du match
12 - 2023-10-2166Admirals2Jayhawks1AWSommaire du match
13 - 2023-10-2281Bruins2Admirals0BLSommaire du match
15 - 2023-10-2484Admirals3Monsters2AWSommaire du match
17 - 2023-10-26100Admirals1Bruins5ALSommaire du match
19 - 2023-10-28117Admirals1Phantoms3ALSommaire du match
21 - 2023-10-30132Admirals3Manchots2AWSommaire du match
23 - 2023-11-01144Jayhawks5Admirals3BLSommaire du match
27 - 2023-11-05175Las Vegas0Admirals3BWSommaire du match
29 - 2023-11-07187Manchots3Admirals2BLXXSommaire du match
32 - 2023-11-10208Phantoms1Admirals3BWSommaire du match
34 - 2023-11-12226Sags2Admirals0BLSommaire du match
36 - 2023-11-14234Admirals3Chill2AWXXSommaire du match
37 - 2023-11-15240Admirals1Monsters3ALSommaire du match
39 - 2023-11-17253Cabaret Lady Mary Ann2Admirals3BWSommaire du match
41 - 2023-11-19271Chiefs1Admirals2BWSommaire du match
44 - 2023-11-22290Rocket2Admirals5BWSommaire du match
46 - 2023-11-24299Monarchs1Admirals0BLSommaire du match
48 - 2023-11-26320Admirals1Oil Kings0AWXSommaire du match
50 - 2023-11-28336Admirals1Comets4ALSommaire du match
52 - 2023-11-30352Bears2Admirals4BWSommaire du match
54 - 2023-12-02366Monsters1Admirals0BLSommaire du match
57 - 2023-12-05386Admirals2Monsters3ALXXSommaire du match
59 - 2023-12-07399Admirals1Baby Hawks2ALXXSommaire du match
62 - 2023-12-10425Oceanics2Admirals1BLSommaire du match
65 - 2023-12-13444Admirals0Sound Tigers3ALSommaire du match
67 - 2023-12-15456Admirals3Wolf Pack2AWSommaire du match
69 - 2023-12-17476Admirals2Spiders1AWSommaire du match
70 - 2023-12-18479Admirals1Cougars2ALSommaire du match
73 - 2023-12-21508Heat2Admirals1BLSommaire du match
75 - 2023-12-23523Seattle2Admirals4BWSommaire du match
79 - 2023-12-27540Las Vegas3Admirals1BLSommaire du match
81 - 2023-12-29553Jayhawks2Admirals4BWSommaire du match
83 - 2023-12-31569Oil Kings4Admirals7BWSommaire du match
86 - 2024-01-03588Marlies3Admirals2BLSommaire du match
88 - 2024-01-05604Oceanics4Admirals0BLSommaire du match
90 - 2024-01-07620Cougars1Admirals3BWSommaire du match
92 - 2024-01-09629Admirals0Chill2ALSommaire du match
94 - 2024-01-11639Admirals3Caroline2AWSommaire du match
96 - 2024-01-13662Admirals8Thunder1AWSommaire du match
98 - 2024-01-15674Admirals2Cabaret Lady Mary Ann0AWSommaire du match
99 - 2024-01-16683Admirals3Bears2AWSommaire du match
103 - 2024-01-20714Admirals3Sags2AWSommaire du match
104 - 2024-01-21722Wolf Pack3Admirals1BLSommaire du match
106 - 2024-01-23737Crunch1Admirals0BLSommaire du match
108 - 2024-01-25751Admirals2Stars3ALXXSommaire du match
110 - 2024-01-27770Admirals3Minnesota5ALSommaire du match
114 - 2024-01-31780Sags0Admirals2BWSommaire du match
123 - 2024-02-09803Oil Kings0Admirals1BWSommaire du match
127 - 2024-02-13825Admirals3Rocket1AWSommaire du match
129 - 2024-02-15841Admirals3Senators2AWXXSommaire du match
131 - 2024-02-17858Admirals2Marlies1AWXXSommaire du match
133 - 2024-02-19866Admirals3Crunch4ALXSommaire du match
135 - 2024-02-21886Monsters1Admirals5BWSommaire du match
138 - 2024-02-24915Admirals3Monarchs2AWSommaire du match
139 - 2024-02-25922Chill5Admirals2BLSommaire du match
143 - 2024-02-29952Admirals1Sags6ALSommaire du match
144 - 2024-03-01955Spiders2Admirals1BLXXSommaire du match
146 - 2024-03-03973Comets1Admirals2BWSommaire du match
149 - 2024-03-06992Senators2Admirals1BLSommaire du match
151 - 2024-03-081007Stars2Admirals0BLSommaire du match
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
153 - 2024-03-101026Sound Tigers1Admirals2BWSommaire du match
155 - 2024-03-121038Admirals3Baby Hawks1AWSommaire du match
157 - 2024-03-141054Admirals3Minnesota1AWSommaire du match
158 - 2024-03-151057Admirals1Oceanics3ALSommaire du match
160 - 2024-03-171079Admirals2Chiefs3ALXXSommaire du match
162 - 2024-03-191091Minnesota3Admirals1BLSommaire du match
164 - 2024-03-211105Baby Hawks2Admirals1BLXXSommaire du match
167 - 2024-03-241131Thunder3Admirals4BWSommaire du match
169 - 2024-03-261146Admirals2Seattle4ALSommaire du match
171 - 2024-03-281162Admirals2Seattle5ALSommaire du match
173 - 2024-03-301167Admirals1Oil Kings2ALXXSommaire du match
174 - 2024-03-311180Admirals4Comets3AWXSommaire du match
176 - 2024-04-021195Admirals1Heat0AWSommaire du match
179 - 2024-04-051215Seattle2Admirals1BLSommaire du match
181 - 2024-04-071235Chiefs2Admirals1BLSommaire du match
183 - 2024-04-091249Monarchs2Admirals0BLSommaire du match
186 - 2024-04-121268Heat0Admirals1BWSommaire du match
187 - 2024-04-131281Admirals0Monarchs1ALXXSommaire du match
192 - 2024-04-181311Admirals-Las Vegas-



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets3520
Assistance77,73927,382
Assistance PCT94.80%66.79%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
0 2564 - 85.46% 95,664$3,922,206$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursSalaire total moyen des joueursSalaire des entraineurs
1,049,912$ 1,165,083$ 1,165,083$ 0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
6,068$ 1,049,912$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
0$ 1 6,068$ 6,068$




Admirals Leaders statistiques des joueurs (saison régulière)

# Nom du joueur GP G A P +/- PIM HIT HTT SHT SHT% SB MP AMG PPG PPA PPP PPS PKG PKA PKP PKS GW GT FO% HT P/20 PSG PSS

Admirals Leaders des statistiques des gardiens (saison régulière)

# Nom du gardien GP W L OTL PCT GAA MP PIM SO GA SA SAR A EG PS % PSA

Admirals Statistiques de l'Équipe de Carrière

TotalDomicileVisiteur
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

Admirals Leaders statistiques des joueurs (séries éliminatoires)

# Nom du joueur GP G A P +/- PIM HIT HTT SHT SHT% SB MP AMG PPG PPA PPP PPS PKG PKA PKP PKS GW GT FO% HT P/20 PSG PSS

Admirals Leaders des statistiques des gardiens (séries éliminatoires)

# Nom du gardien GP W L OTL PCT GAA MP PIM SO GA SA SAR A EG PS % PSA