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Moose
GP: 82 | W: 50 | L: 25 | OTL: 7 | P: 107
GF: 319 | GA: 190 | PP%: 14.12% | PK%: 84.14%
DG: Mika | Morale : 50 | Moyenne d’équipe : 59

Centre de jeu
Firebirds
6-75-1, 13pts
2
FINAL
9 Moose
50-25-7, 107pts
Team Stats
OTW1SéquenceW4
4-36-1Fiche domicile28-11-2
2-39-0Fiche domicile22-14-5
1-9-0Derniers 10 matchs8-1-1
1.65Buts par match 3.89
6.99Buts contre par match 2.32
6.31%Pourcentage en avantage numérique14.12%
65.79%Pourcentage en désavantage numérique84.14%
Moose
50-25-7, 107pts
9
FINAL
1 Ice Hogs
6-74-2, 14pts
Team Stats
W4SéquenceL40
28-11-2Fiche domicile3-37-1
22-14-5Fiche domicile3-37-1
8-1-1Derniers 10 matchs0-10-0
3.89Buts par match 1.62
2.32Buts contre par match 7.40
14.12%Pourcentage en avantage numérique7.76%
84.14%Pourcentage en désavantage numérique71.64%
Meneurs d'équipe
Buts
Aleksanteri Kaskimaki
44
Passes
Aleksanteri Kaskimaki
61
Points
Aleksanteri Kaskimaki
105
Plus/Moins
Samuel Savoie
64
Chris DriedgerVictoires
Chris Driedger
29
Chris DriedgerPourcentage d’arrêts
Chris Driedger
0.865

Statistiques d’équipe
Buts pour
319
3.89 GFG
Tirs pour
2211
26.96 Avg
Pourcentage en avantage numérique
14.1%
50 GF
Début de zone offensive
42.8%
Buts contre
190
2.32 GAA
Tirs contre
1391
16.96 Avg
Pourcentage en désavantage numérique
84.1%%
75 GA
Début de la zone défensive
34.6%
Informations de l'équipe

Directeur généralMika
EntraîneurTodd Richards
DivisionCentral Division
ConférenceWestern Conference
Capitaine
Assistant #1
Assistant #2


Informations de l’aréna

Capacité3,000
Assistance2,927
Billets de saison0


Informations de la formation

Équipe Pro19
Équipe Mineure21
Limite contact 40 / 51
Espoirs29


Historique d'équipe

Saison actuelle50-25-7 (107PTS)
Historique0-0-0
Coupe Stanley0


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
1Tyler PitlickX100.007775837375737564806065666244466750640341800,000$
2Clark BishopXX100.007672866872828762785663666049506650630301775,000$
3Aleksanteri Kaskimaki (R)X100.007870956670747860755957655444446350610223870,000$
4Samu TuomaalaX100.007065816665636462506160625744446250600232863,333$
5Judd CaulfieldX100.008282826382768255505056665344446150600252837,500$
6Roby JarventieX100.008477998077363160757244684244445950590232995,000$
7Konsta Helenius (R)X100.007367876667555460755758635544446150580193975,000$
8Samuel Savoie (R)X100.007468886568798751505047614544445650570223846,666$
9Felix Unger Sorum (R)X100.007263936563717652505346604444445650560203831,667$
10Tucker RobertsonX100.007467896267565850634748614644445450540222950,000$
11Eetu LiukasX100.007877806577505050504550634844445550540233867,500$
12Uvis BalinskisX100.00765488757265956425534867255656625065N0292850,000$
13Steven SantiniX100.00827989667969755025394368415859565062N0313775,000$
14Corey SchuenemanX100.00757088667075825025434162394747555060N0302775,000$
15Dakota MermisX100.00747180697155574825403963375454525058N0323812,000$
16Michael KarowX100.007676756476535646253739613744445050560272775,000$
17Aleksi Heimosalmi (R)X100.007063866463677347253742584044445350560223805,833$
Rayé
MOYENNE D’ÉQUIPE100.00767086677165695550515064464747585059
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
1Joel Blomqvist (R)100.00595453806552696065597545456050600242925,000$
2Chris Driedger100.0044405084434250514647304444465050N0311795,000$
Rayé
MOYENNE D’ÉQUIPE100.0052475282544760565653534545535055
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Todd Richards75737168878160USA581925,000$


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
1Aleksanteri KaskimakiMoose (WPG)C82446110556495731562997521214.72%5150918.41101525552950004874366.54%136600021.39000001272
2Judd CaulfieldMoose (WPG)RW824147886210010140822307016717.83%11154818.8946102722321381785259.00%10000031.14131011056
3Samu TuomaalaMoose (WPG)RW823341745148078912255214814.67%10138616.9081018362750000117162.16%11100001.0700000447
4Clark BishopMoose (WPG)C/LW8230447457600781522247217513.39%17159319.4448122213022484163265.68%132300000.9323000248
5Samuel SavoieMoose (WPG)LW82175269645757282130379313.08%9151518.48156811511271854067.09%7900000.9100000072
6Uvis BalinskisMoose (WPG)D821251635310001248372215316.67%55172020.98246211090112307400%000000.7300000334
7Roby JarventieMoose (WPG)LW822239614372109596134258516.42%4137216.7437102122900021273260.32%6300000.8900101321
8Konsta HeleniusMoose (WPG)C822131524024041951324811815.91%9106412.982461316812382374264.90%88900000.9813000321
9Tyler PitlickMoose (WPG)RW822824522835570621643610617.07%37509.15561126176000042174.47%4700001.3900010321
10Eetu LiukasMoose (WPG)LW82123143341022012569101349111.88%12150318.334371215201142170158.69%44300000.5700121142
11Steven SantiniMoose (WPG)D8273441521120159367718529.09%56172221.01279362781452352400%100000.4800000144
12Corey SchuenemanMoose (WPG)D824374155580141317425545.41%58168920.60257472830112211000%000000.4900000132
13Mikael PyyhtiaJetsC52191332410017126145308813.10%1475714.57000022025851255.80%67200000.8400000114
14Michael KarowMoose (WPG)D8262531445801581832193118.75%46150118.32167111860111293200%000000.4100000410
15Dakota MermisMoose (WPG)D82623295298101433240183915.00%43157019.16257102600001148100%000000.3700101102
16Felix Unger SorumMoose (WPG)RW821017276280395386255011.63%483810.23000112000003061.90%4200000.6400000000
17Aleksi HeimosalmiMoose (WPG)D823111450500101282951310.34%54158019.270335630000243100%000000.1800000100
18Tucker RobertsonMoose (WPG)C3033654091417121717.65%21665.5500000000001068.89%13500000.7200000000
Statistiques d’équipe totales ou en moyenne1394318584902756106565166313062211622159214.38%4122379217.075094144351296391423543109491663.80%527100050.7649434504946
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
1Chris DriedgerMoose (WPG)48291330.8652.25263825997330010.50024339021
2Pheonix CopleyJets42211240.8662.28231324886570000.50063942101
Statistiques d’équipe totales ou en moyenne90502570.8652.27495149187139000188281122


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 Pays Recrue Poids Taille Non-échange Disponible pour échange Acquis Par Date de la Dernière Transaction Ballotage forcé Waiver Possible Contrat Date du Signature du Contrat Forcer UFA Rappel d'urgence Type Salaire actuel Salaire restantPlafond salarial Plafond salarial restant Exclus du plafond salarial Salaire année 2Salaire année 3Salaire année 4Salaire année 5Salaire année 6Salaire année 7Salaire année 8Salaire année 9Salaire année 10Plafond salarial année 2Plafond salarial année 3Plafond salarial année 4Plafond salarial année 5Plafond salarial année 6Plafond salarial année 7Plafond salarial année 8Plafond salarial année 9Plafond salarial année 10Non-échange année 2Non-échange année 3Non-échange année 4Non-échange année 5Non-échange année 6Non-échange année 7Non-échange année 8Non-échange année 9Non-échange année 10Lien
Aleksanteri KaskimakiMoose (WPG)C222004-02-06FINYes193 Lbs6 ft0NoNoAssign ManuallyNoNo32025-08-27FalseFalsePro & Farm870,000$0$0$No870,000$870,000$-------870,000$870,000$-------NoNo-------Lien
Aleksi HeimosalmiMoose (WPG)D222003-05-08FINYes170 Lbs5 ft11NoNoProspectNoNo32025-08-27FalseFalsePro & Farm805,833$0$0$No805,833$805,833$-------805,833$805,833$-------NoNo-------Lien
Chris DriedgerMoose (WPG)G311994-05-18MBNo205 Lbs6 ft4YesNoFree Agent2024-08-16NoYes12025-08-21FalseFalsePro & Farm795,000$0$0$No---------------------------Lien / Lien NHL
Clark BishopMoose (WPG)C/LW301996-03-29CANNo197 Lbs6 ft1NoNoFree AgentNoYes12024-06-16FalseFalsePro & Farm775,000$0$0$No---------------------------Lien / Lien NHL
Corey SchuenemanMoose (WPG)D301995-09-02USANo197 Lbs5 ft11YesNoFree AgentNoYes22025-08-21FalseFalsePro & Farm775,000$0$0$No775,000$--------775,000$--------Yes--------Lien
Dakota MermisMoose (WPG)D321994-01-05USANo197 Lbs6 ft0YesNoFree AgentNoYes32025-08-21FalseFalsePro & Farm812,000$0$0$No812,000$812,000$-------812,000$812,000$-------YesYes-------Lien / Lien NHL
Eetu LiukasMoose (WPG)LW232002-09-25FINNo205 Lbs6 ft3NoNoAssign ManuallyNoNo32025-08-27FalseFalsePro & Farm867,500$0$0$No867,500$867,500$-------867,500$867,500$-------NoNo-------Lien
Felix Unger SorumMoose (WPG)RW202005-09-14NORYes170 Lbs5 ft11NoNoProspectNoNo32025-08-27FalseFalsePro & Farm831,667$0$0$No831,667$831,667$-------831,667$831,667$-------NoNo-------Lien
Joel BlomqvistMoose (WPG)G242002-01-10FINYes200 Lbs6 ft3NoNoAssign ManuallyNoNo22024-06-29FalseFalsePro & Farm925,000$0$0$No925,000$--------925,000$--------No--------Lien
Judd CaulfieldMoose (WPG)RW252001-03-19USANo220 Lbs6 ft3NoNoAssign ManuallyNoYes22024-06-29FalseFalsePro & Farm837,500$0$0$No837,500$--------837,500$--------No--------Lien
Konsta HeleniusMoose (WPG)C192006-05-11FINYes189 Lbs5 ft11NoNoAssign ManuallyNoNo32025-08-27FalseFalsePro & Farm975,000$0$0$No975,000$975,000$-------975,000$975,000$-------NoNo-------Lien
Michael KarowMoose (WPG)D271998-12-18USANo209 Lbs6 ft2NoNoTrade2025-02-01NoYes22024-07-07FalseFalsePro & Farm775,000$0$0$No775,000$--------775,000$--------No--------Lien
Roby JarventieMoose (WPG)LW232002-08-08FINNo209 Lbs6 ft3NoNoFree AgentNoNo22025-08-02FalseFalsePro & Farm995,000$0$0$No995,000$--------995,000$--------No--------Lien
Samu TuomaalaMoose (WPG)RW232003-01-08FINNo180 Lbs5 ft10NoNoAssign ManuallyNoNo22024-06-29FalseFalsePro & Farm863,333$0$0$No863,333$--------863,333$--------No--------Lien
Samuel SavoieMoose (WPG)LW222004-03-25CANYes190 Lbs5 ft10NoNoProspectNoNo32025-08-27FalseFalsePro & Farm846,666$0$0$No846,666$846,666$-------846,666$846,666$-------NoNo-------Lien
Steven SantiniMoose (WPG)D311995-03-07USANo214 Lbs6 ft3YesNoFree AgentNoYes32025-08-21FalseFalsePro & Farm775,000$0$0$No775,000$775,000$-------775,000$775,000$-------YesYes-------Lien / Lien NHL
Tucker RobertsonMoose (WPG)C222003-06-22CANNo189 Lbs5 ft11NoNoAssign ManuallyNoNo22024-06-29FalseFalsePro & Farm950,000$0$0$No950,000$--------950,000$--------No--------Lien
Tyler PitlickMoose (WPG)RW341991-11-01USANo201 Lbs6 ft2NoNoN/ANoYes1FalseFalsePro & Farm800,000$0$0$No---------------------------Lien / Lien NHL
Uvis BalinskisMoose (WPG)D291996-08-01LATNo196 Lbs6 ft0YesNoFree AgentNoYes22025-08-21FalseFalsePro & Farm850,000$0$0$No850,000$--------850,000$--------Yes--------Lien
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
1925.74196 Lbs6 ft12.26848,658$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Roby JarventieClark BishopTyler Pitlick40122
2Samuel SavoieAleksanteri KaskimakiSamu Tuomaala30122
3Eetu LiukasKonsta HeleniusJudd Caulfield20122
4Roby JarventieTucker RobertsonFelix Unger Sorum10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Uvis BalinskisSteven Santini40122
2Corey SchuenemanDakota Mermis30122
3Michael KarowAleksi Heimosalmi20122
4Uvis BalinskisSteven Santini10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Roby JarventieClark BishopTyler Pitlick60122
2Samuel SavoieAleksanteri KaskimakiSamu Tuomaala40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Uvis BalinskisSteven Santini60122
2Corey SchuenemanDakota Mermis40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Clark BishopRoby Jarventie60122
2Aleksanteri KaskimakiSamuel Savoie40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Uvis BalinskisSteven Santini60122
2Corey SchuenemanDakota Mermis40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Clark Bishop60122Uvis BalinskisSteven Santini60122
2Aleksanteri Kaskimaki40122Corey SchuenemanDakota Mermis40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Clark BishopRoby Jarventie60122
2Aleksanteri KaskimakiSamuel Savoie40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Uvis BalinskisSteven Santini60122
2Corey SchuenemanDakota Mermis40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Roby JarventieClark BishopTyler PitlickUvis BalinskisSteven Santini
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Roby JarventieClark BishopTyler PitlickUvis BalinskisSteven Santini
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Judd Caulfield, Roby Jarventie, Konsta HeleniusJudd Caulfield, Roby JarventieJudd Caulfield
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Dakota Mermis, Michael Karow, Aleksi HeimosalmiDakota MermisDakota Mermis, Michael Karow
Tirs de pénalité
Tyler Pitlick, Clark Bishop, Aleksanteri Kaskimaki, Samu Tuomaala, Judd Caulfield
Gardien
#1 : Chris Driedger, #2 :
Lignes d’attaque personnalisées en prolongation
Tyler Pitlick, Clark Bishop, Aleksanteri Kaskimaki, Samu Tuomaala, Judd Caulfield, Roby Jarventie, Konsta Helenius, Samuel Savoie, Felix Unger Sorum, Eetu Liukas
Lignes de défense personnalisées en prolongation
Uvis Balinskis, Steven Santini, Corey Schueneman, Dakota Mermis, Michael Karow


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
1 Americans4220000089-12110000035-22110000054140.5008142201120979787170171677924592773712229.09%34585.29%01460223965.21%1104181160.96%799118667.37%245518271544511956531
2Admirals4310000014113220000006332110000088060.75014264000120979781137017167792479106710513215.38%26676.92%01460223965.21%1104181160.96%799118667.37%245518271544511956531
3Barracuda2110000067-1110000005411010000013-220.500611170012097978417017167792445928366233.33%12283.33%01460223965.21%1104181160.96%799118667.37%245518271544511956531
4Bears220000001248110000006151100000063341.00012233500120979787270171677924325274911218.18%11190.91%11460223965.21%1104181160.96%799118667.37%245518271544511956531
5Bruins2110000010911010000046-21100000063320.50010182800120979784770171677924341532309222.22%16287.50%01460223965.21%1104181160.96%799118667.37%245518271544511956531
6Canucks2020000059-41010000035-21010000024-200.000581300120979783270171677924561545371119.09%20385.00%01460223965.21%1104181160.96%799118667.37%245518271544511956531
7Checkers22000000707110000003031100000040441.00071421021209797839701716779242811203810110.00%90100.00%01460223965.21%1104181160.96%799118667.37%245518271544511956531
8Comets20002000642100010002111000100043141.000611170012097978677017167792421616577114.29%7271.43%01460223965.21%1104181160.96%799118667.37%245518271544511956531
9Condors220000001129110000008171100000031241.00011223300120979785670171677924317224710220.00%9188.89%01460223965.21%1104181160.96%799118667.37%245518271544511956531
10Crunch21000100660110000004311000010023-130.7506121800120979783270171677924401026418225.00%13284.62%01460223965.21%1104181160.96%799118667.37%245518271544511956531
11Eagles22000000853110000004311100000042241.00081624001209797844701716779243913223412216.67%11190.91%01460223965.21%1104181160.96%799118667.37%245518271544511956531
12Firebirds550000004343933000000264222200000017017101.000437812102120979782587017167792438103010610550.00%15193.33%01460223965.21%1104181160.96%799118667.37%245518271544511956531
13Griffins3000010236-31000000112-12000010124-230.50035800120979786070171677924411828572000.00%14192.86%01460223965.21%1104181160.96%799118667.37%245518271544511956531
14Gulls2200000016214110000007161100000091841.0001627430012097978877017167792422518374250.00%90100.00%01460223965.21%1104181160.96%799118667.37%245518271544511956531
15Ice Hogs44000000332312200000017116220000001611581.000336497021209797815270171677924401240726233.33%190100.00%11460223965.21%1104181160.96%799118667.37%245518271544511956531
16Islanders2200000013310110000007251100000061541.0001324370012097978887017167792417820423133.33%9188.89%01460223965.21%1104181160.96%799118667.37%245518271544511956531
17Marlies2110000057-2110000004311010000014-320.500591400120979784670171677924268253514214.29%8362.50%01460223965.21%1104181160.96%799118667.37%245518271544511956531
18Monsters21000100642110000004131000010023-130.75061117001209797834701716779244910293415213.33%12375.00%01460223965.21%1104181160.96%799118667.37%245518271544511956531
19Penguins3200100014592200000010281000100043161.000142438001209797890701716779244815265512216.67%12283.33%21460223965.21%1104181160.96%799118667.37%245518271544511956531
20Phantoms2010010047-31010000013-21000010034-110.2504812001209797829701716779244313342510110.00%15473.33%01460223965.21%1104181160.96%799118667.37%245518271544511956531
21Reign3030000016-51010000013-22020000003-300.00012310120979785070171677924541842711400.00%20195.00%01460223965.21%1104181160.96%799118667.37%245518271544511956531
22Roadrunners220000001367110000006421100000072541.0001325380012097978687017167792431531605360.00%11372.73%21460223965.21%1104181160.96%799118667.37%245518271544511956531
23Rocket21001000532110000002111000100032141.000591400120979784470171677924287284218211.11%13284.62%11460223965.21%1104181160.96%799118667.37%245518271544511956531
24Senators21100000550110000003211010000023-120.500591400120979785070171677924278124414214.29%60100.00%01460223965.21%1104181160.96%799118667.37%245518271544511956531
25Silver Knights40400000416-122020000039-62020000017-600.000461000120979788370171677924873061681800.00%27677.78%01460223965.21%1104181160.96%799118667.37%245518271544511956531
26Stars43100000191182110000010462200000097260.7501933520012097978115701716779249832581121218.33%27677.78%11460223965.21%1104181160.96%799118667.37%245518271544511956531
27Thunderbirds4300001024321220000001311221000010112981.000244468021209797816270171677924531036826233.33%18194.44%01460223965.21%1104181160.96%799118667.37%245518271544511956531
28Wild40300100713-62010010047-32020000036-310.1257101700120979787670171677924923262812514.00%30776.67%11460223965.21%1104181160.96%799118667.37%245518271544511956531
29Wolf Pack22000000624110000003121100000031241.00061218001209797848701716779243911273614214.29%11190.91%01460223965.21%1104181160.96%799118667.37%245518271544511956531
30Wolves2020000016-51010000013-21010000003-300.0001230012097978297017167792438205730800.00%18572.22%01460223965.21%1104181160.96%799118667.37%245518271544511956531
31Wranglers20200000413-91010000048-41010000005-500.000471100120979782870171677924561227297114.29%11372.73%01460223965.21%1104181160.96%799118667.37%245518271544511956531
Total824525045123191901294127110110117594814118140341114496481070.65231958490319120979782211701716779241391412106916633545014.12%4737584.14%91460223965.21%1104181160.96%799118667.37%245518271544511956531
_Since Last GM Reset824525045123191901294127110110117594814118140341114496481070.65231958490319120979782211701716779241391412106916633545014.12%4737584.14%91460223965.21%1104181160.96%799118667.37%245518271544511956531
_Vs Conference482917001102111101012416700100117585924131000010945242610.635211385596171209797814047017167792482423261710111702715.88%2794384.59%51460223965.21%1104181160.96%799118667.37%245518271544511956531
_Vs Division2420110011011851671211500100602337129600010582830430.896118218336041209797873070171677924432114316546791316.46%1422483.10%51460223965.21%1104181160.96%799118667.37%245518271544511956531

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
82107W4319584903221113914121069166319
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
8245254512319190
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
412711110117594
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
411814341114496
Derniers 10 matchs
WLOTWOTL SOWSOL
810100
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
3545014.12%4737584.14%9
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
7017167792412097978
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
1460223965.21%1104181160.96%799118667.37%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
245518271544511956531


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
212Firebirds1Moose10WSommaire du match
428Moose11Firebirds0WSommaire du match
643Moose3Thunderbirds2WXXSommaire du match
856Ice Hogs1Moose10WSommaire du match
1075Moose7Ice Hogs0WSommaire du match
1287Penguins1Moose7WSommaire du match
15107Stars1Moose8WSommaire du match
17118Moose4Stars3WSommaire du match
19136Moose3Wild4LSommaire du match
21151Admirals1Moose3WSommaire du match
24172Moose0Reign2LSommaire du match
26187Wild4Moose3LXSommaire du match
28200 Americans4Moose1LSommaire du match
30210Moose3 Americans0WSommaire du match
33235Silver Knights6Moose2LSommaire du match
35248Moose1Silver Knights5LSommaire du match
37263Moose6Bruins3WSommaire du match
39277Wolves3Moose1LSommaire du match
41292Moose0Silver Knights2LSommaire du match
43307Wranglers8Moose4LSommaire du match
46328Moose1Marlies4LSommaire du match
47342Islanders2Moose7WSommaire du match
50363 Americans1Moose2WSommaire du match
54388Eagles3Moose4WSommaire du match
56401Moose4Checkers0WSommaire du match
58421Condors1Moose8WSommaire du match
60438Moose2Canucks4LSommaire du match
62449Moose2Crunch3LXSommaire du match
63461Reign3Moose1LSommaire du match
67485Canucks5Moose3LSommaire du match
69500Moose1Griffins2LXXSommaire du match
71518Monsters1Moose4WSommaire du match
74536Moose3Wolf Pack1WSommaire du match
76549Ice Hogs0Moose7WSommaire du match
78564Moose2 Americans4LSommaire du match
80580Moose3Rocket2WXSommaire du match
81589Phantoms3Moose1LSommaire du match
84611Moose6Bears3WSommaire du match
86622Griffins2Moose1LXXSommaire du match
88634Moose0Reign1LSommaire du match
90650Marlies3Moose4WSommaire du match
92668Moose0Wild2LSommaire du match
94683Checkers0Moose3WSommaire du match
96698Moose4Penguins3WXSommaire du match
98714Admirals2Moose3WSommaire du match
101740Moose5Stars4WSommaire du match
102749Wild3Moose1LSommaire du match
106774Roadrunners4Moose6WSommaire du match
108786Moose0Wolves3LSommaire du match
110805Comets1Moose2WXSommaire du match
112816Moose7Roadrunners2WSommaire du match
114832Moose8Thunderbirds0WSommaire du match
116842Silver Knights3Moose1LSommaire du match
119867Moose6Firebirds0WSommaire du match
121877Gulls1Moose7WSommaire du match
123889Moose3Condors1WSommaire du match
125905Senators2Moose3WSommaire du match
127923Moose4Comets3WXSommaire du match
129934Moose1Barracuda3LSommaire du match
130944Wolf Pack1Moose3WSommaire du match
133967Bruins6Moose4LSommaire du match
135977Moose0Wranglers5LSommaire du match
1381001Moose1Griffins2LXSommaire du match
1391008Stars3Moose2LSommaire du match
1431034Crunch3Moose4WSommaire du match
1441041Moose3Admirals5LSommaire du match
1481066Thunderbirds0Moose7WSommaire du match
1511095Rocket1Moose2WSommaire du match
1531107Moose6Islanders1WSommaire du match
1561127Penguins1Moose3WSommaire du match
1581143Moose2Monsters3LXSommaire du match
1601160Barracuda4Moose5WSommaire du match
1611165Moose2Senators3LSommaire du match
1641191Thunderbirds1Moose6WSommaire du match
1651196Moose5Admirals3WSommaire du match
1691223Bears1Moose6WSommaire du match
1701230Moose9Gulls1WSommaire du match
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
1731253Moose3Phantoms4LXSommaire du match
1741260Firebirds1Moose7WSommaire du match
1751268Moose4Eagles2WSommaire du match
1781289Firebirds2Moose9WSommaire du match
1801303Moose9Ice Hogs1WSommaire du match



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets330
Assistance81,69238,318
Assistance PCT99.62%93.46%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
0 2927 - 97.57% 65,752$2,695,836$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursPlafond Salariale total des joueursSalaire des entraineurs
2,580,285$ 1,612,450$ 1,612,450$ 925,000$0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
8,763$ 1,655,317$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
0$ 0 13,790$ 0$




Moose 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

Moose 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

Moose 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

Moose 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

Moose 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