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Moose
GP: 16 | W: 12 | L: 4 | OTL: 0 | P: 24
GF: 92 | GA: 37 | PP%: 21.13% | PK%: 87.34%
DG: Mika | Morale : 50 | Moyenne d’équipe : 59

Centre de jeu
Silver Knights
12-4-0, 24pts
2
FINAL
3 Moose
12-4-0, 24pts
Team Stats
L2SéquenceW3
7-1-0Fiche domicile6-2-0
5-3-0Fiche domicile6-2-0
7-3-0Derniers 10 matchs7-3-0
5.00Buts par match 5.75
1.56Buts contre par match 2.31
18.64%Pourcentage en avantage numérique21.13%
88.31%Pourcentage en désavantage numérique87.34%
Moose
12-4-0, 24pts
5
FINAL
2 Firebirds
10-4-2, 22pts
Team Stats
W3SéquenceL2
6-2-0Fiche domicile5-2-1
6-2-0Fiche domicile5-2-1
7-3-0Derniers 10 matchs5-4-1
5.75Buts par match 5.38
2.31Buts contre par match 2.13
21.13%Pourcentage en avantage numérique16.07%
87.34%Pourcentage en désavantage numérique87.85%
Meneurs d'équipe
Konsta HeleniusButs
Konsta Helenius
15
Roby JarventiePasses
Roby Jarventie
21
Roby JarventiePoints
Roby Jarventie
32
Tucker RobertsonPlus/Moins
Tucker Robertson
23
Joel BlomqvistVictoires
Joel Blomqvist
12
Joel BlomqvistPourcentage d’arrêts
Joel Blomqvist
0.857

Statistiques d’équipe
Buts pour
92
5.75 GFG
Tirs pour
662
41.38 Avg
Pourcentage en avantage numérique
21.1%
15 GF
Début de zone offensive
46.5%
Buts contre
37
2.31 GAA
Tirs contre
259
16.19 Avg
Pourcentage en désavantage numérique
87.3%%
10 GA
Début de la zone défensive
30.7%
Informations de l'équipe

Directeur généralMika
EntraîneurJessica Campbell
DivisionCentral Division
ConférenceWestern Conference
Capitaine
Assistant #1
Assistant #2


Informations de l’aréna

Capacité3,000
Assistance0
Billets de saison0


Informations de la formation

Équipe Pro24
Équipe Mineure20
Limite contact 44 / 51
Espoirs28


Historique d'équipe

Saison actuelle12-4-0 (24PTS)
Historique0--0
Apparitions en séries éliminatoires 1
Historique en séries éliminatoires (W-L)1 - 4 (0.200%)
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
1Konsta Helenius (R)XX100.007569886569828667506763656044446750640202975,000$
2Felix Unger Sorum (R)X100.007670906570838865506760655744446650640212850,000$
3Mikael PyyhtiaX100.007265886665767965506263666053536650630241925,000$
4Aleksanteri Kaskimaki (R)XX100.007871946571798462505862665944446550620222870,000$
5Ryan SuzukiX100.00767186647179836278635865554444645062N0252850,000$
6Roby Jarventie (R)X100.007469856469767964506065646244446650620241995,000$
7Samu TuomaalaX100.007065816665636462506160625744446250600231863,333$
8Tucker RobertsonX100.007570856270747858735358635544446250600231950,000$
9Alex Suzdalev (R)X100.007972966572494661505662665944446350590223865,000$
10Eetu LiukasX100.007977856377434057505356655344445950560242867,500$
11Josh Filmon (R)X100.006962865662555749504251584844445350520223913,333$
12Mason PrimeauX100.00657247807232304455384455424444485050N0251850,000$
13Steven SantiniX100.007144947080546362254747722559605950630312850,000$
14Dakota MermisX100.006994727074615760253950772555555950620322850,000$
15Reilly WalshX100.00747083667078855325494261404444565060N0272850,000$
16Marc Del GaizoX100.00726882616873785525464962474949575060N0273875,000$
17Michael KarowX100.007975886275545553254448644644445750580271850,000$
18Corey SchuenemanX100.007973926373403756255443654147475650570311850,000$
Rayé
1Judd CaulfieldX100.008277946677818760505660675744446550630251850,000$
2Samuel SavoieX100.007168786068727756705157615444446050580222850,000$
3Aleksi HeimosalmiX100.006763775663636750254341573944445150550232850,000$
MOYENNE D’ÉQUIPE100.00747084657165685845535464504646605060
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 Blomqvist100.00534751776253645860555244445350570241925,000$
2Georgi Romanov100.0042496185494850504642304444475051N0263875,000$
Rayé
1Pheonix Copley100.00445164845149565449453054555050550341850,000$
MOYENNE D’ÉQUIPE100.0046495982545057545247374748505054
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Jessica Campbell64666770505093CAN334750,000$


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
1Roby JarventieMoose (WPG)RW161121322160301485336112.94%233420.9014515530005381066.07%5600011.9100000223
2Konsta HeleniusMoose (WPG)LW/RW161515301840242982296118.29%839224.5234711572136722064.41%17700011.5311000220
3Tucker RobertsonMoose (WPG)C161415292380292365194821.54%129718.59224452000150068.22%32100011.9500000130
4Aleksanteri KaskimakiMoose (WPG)LW/RW1610182823120132785316111.76%130118.821121552000052165.00%2000001.8600000121
5Mikael PyyhtiaMoose (WPG)RW1681927194021278917678.99%738524.1114514560334651047.83%2300001.4011000202
6Ryan SuzukiMoose (WPG)C16520251312018205221459.62%734621.660777580004370071.30%32400001.4400000012
7Samu TuomaalaMoose (WPG)RW1687159100152360144113.33%322714.1900039000061061.11%1800001.3200000020
8Marc Del GaizoMoose (WPG)D16391215200313188916.67%535822.39303962011142000%000000.6700000001
9Josh FilmonMoose (WPG)LW167512118051340102117.50%321813.64000050001111076.47%1700011.1000000101
10Alex SuzdalevMoose (WPG)RW1639124160211022121913.64%725215.78235646000001033.33%300000.9500000001
11Mason PrimeauMoose (WPG)C161101191802519194145.26%522514.12000050002200067.91%18700000.9700000001
12Dakota MermisMoose (WPG)D43363604291233.33%25814.682023300005100%000002.0400000100
13Reilly WalshMoose (WPG)D1605515140235168110%236022.560001260000145000%000000.2800000000
14Eetu LiukasMoose (WPG)RW162242215184160512.50%423814.9101194500000100%000000.3400100000
15Steven SantiniMoose (WPG)D412310010110100.00%3225.5502201101118000%000002.7000000000
16Corey SchuenemanMoose (WPG)D4011300421010%05313.250000000001000%000000.3800000000
17Felix Unger SorumMoose (WPG)RW4000000000000%000.220000000000000%00000000000000
18Michael KarowMoose (WPG)D4000120400000%2164.2500000000016000%00000000000000
Statistiques d’équipe totales ou en moyenne22891161252190161528622166020846613.79%62409017.941528431085703582639311167.80%114600041.2322100101212
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
1Joel BlomqvistMoose (WPG)1612400.8572.3196300372590100.6673160001
Statistiques d’équipe totales ou en moyenne1612400.8572.3196300372590103160001


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)LW/RW222004-02-06FINYes195 Lbs6 ft0NoNoAssign ManuallyNoNo22025-08-27FalseFalsePro & Farm870,000$870,000$0$0$No870,000$--------870,000$--------No--------Lien / Lien NHL
Aleksi HeimosalmiMoose (WPG)D232003-05-08FINNo170 Lbs5 ft11NoNoAssign ManuallyNoNo22026-10-05FalseFalsePro & Farm850,000$850,000$0$0$No850,000$--------850,000$--------No--------Lien / Lien NHL
Alex SuzdalevMoose (WPG)RW222004-03-05SWEYes190 Lbs6 ft2NoNoAssign ManuallyNoNo32026-06-14FalseFalsePro & Farm865,000$865,000$0$0$No865,000$865,000$-------865,000$865,000$-------NoNo-------Lien / Lien NHL
Corey SchuenemanMoose (WPG)D311995-09-02USANo200 Lbs6 ft0NoNoAssign ManuallyNoNo12026-10-05FalseFalsePro & Farm850,000$850,000$0$0$No---------------------------Lien / Lien NHL
Dakota MermisMoose (WPG)D321994-01-05USANo197 Lbs6 ft0NoNoAssign ManuallyNoNo22026-10-05FalseFalsePro & Farm850,000$850,000$0$0$No850,000$--------850,000$--------No--------Lien / Lien NHL
Eetu LiukasMoose (WPG)RW242002-09-25FINNo205 Lbs6 ft3NoNoAssign ManuallyNoNo22025-08-27FalseFalsePro & Farm867,500$867,500$0$0$No867,500$--------867,500$--------No--------Lien / Lien NHL
Felix Unger SorumMoose (WPG)RW212005-09-14NORYes190 Lbs6 ft0NoNoAssign ManuallyNoNo22026-10-05FalseFalsePro & Farm850,000$850,000$0$0$No850,000$--------850,000$--------No--------Lien / Lien NHL
Georgi RomanovMoose (WPG)G261999-12-15RUSNo210 Lbs6 ft4YesNoFree AgentNoNo32026-08-08FalseFalsePro & Farm875,000$875,000$0$0$No875,000$875,000$-------875,000$875,000$-------YesYes-------Lien / Lien NHL
Joel BlomqvistMoose (WPG)G242002-01-10FINNo187 Lbs6 ft2NoNoAssign ManuallyNoNo12024-06-29FalseFalsePro & Farm925,000$925,000$0$0$No---------------------------Lien / Lien NHL
Josh FilmonMoose (WPG)LW222004-03-18CANYes160 Lbs6 ft1NoNoAssign ManuallyNoNo32026-06-14FalseFalsePro & Farm913,333$913,333$0$0$No913,333$913,333$-------913,333$913,333$-------NoNo-------Lien / Lien NHL
Judd CaulfieldMoose (WPG)RW252001-03-19USANo207 Lbs6 ft3NoNoAssign ManuallyNoNo12026-10-05FalseFalsePro & Farm850,000$850,000$0$0$No---------------------------Lien / Lien NHL
Konsta HeleniusMoose (WPG)LW/RW202006-05-11FINYes190 Lbs5 ft11NoNoAssign ManuallyNoNo22025-08-27FalseFalsePro & Farm975,000$975,000$0$0$No975,000$--------975,000$--------No--------Lien / Lien NHL
Marc Del GaizoMoose (WPG)D271999-10-11USANo193 Lbs5 ft10YesNoFree AgentNoNo32026-08-08FalseFalsePro & Farm875,000$875,000$0$0$No875,000$875,000$-------875,000$875,000$-------YesYes-------Lien / Lien NHL
Mason PrimeauMoose (WPG)C252001-07-28ONTNo183 Lbs6 ft5YesNoFree AgentNoNo12026-08-08FalseFalsePro & Farm850,000$850,000$0$0$No---------------------------Lien / Lien NHL
Michael KarowMoose (WPG)D271998-12-18USANo201 Lbs6 ft2NoNoAssign Manually2025-02-01NoNo12026-10-05FalseFalsePro & Farm850,000$850,000$0$0$No---------------------------Lien / Lien NHL
Mikael PyyhtiaMoose (WPG)RW242001-12-17FINNo178 Lbs6 ft0NoNoAssign ManuallyNoNo12024-06-29FalseFalsePro & Farm925,000$925,000$0$0$No---------------------------Lien / Lien NHL
Pheonix CopleyMoose (WPG)G341992-01-18USANo205 Lbs6 ft4NoNoAssign Manually2024-07-16NoNo12026-10-05FalseFalsePro & Farm850,000$850,000$0$0$No---------------------------Lien / Lien NHL
Reilly WalshMoose (WPG)D271999-04-21USANo192 Lbs6 ft0YesNoFree AgentNoNo22026-08-08FalseFalsePro & Farm850,000$850,000$0$0$No850,000$--------850,000$--------Yes--------Lien / Lien NHL
Roby JarventieMoose (WPG)RW242002-08-08FINYes184 Lbs6 ft2NoNoFree AgentNoNo12025-08-02FalseFalsePro & Farm995,000$995,000$0$0$No---------------------------Lien / Lien NHL
Ryan SuzukiMoose (WPG)C252001-05-28CANNo190 Lbs6 ft1YesNoFree AgentNoNo22026-08-08FalseFalsePro & Farm850,000$850,000$0$0$No850,000$--------850,000$--------Yes--------Lien / Lien NHL
Samu TuomaalaMoose (WPG)RW232003-01-08FINNo180 Lbs5 ft10NoNoAssign ManuallyNoNo12024-06-29FalseFalsePro & Farm863,333$863,333$0$0$No---------------------------Lien / Lien NHL
Samuel SavoieMoose (WPG)C222004-03-25CANNo190 Lbs5 ft10NoNoAssign ManuallyNoNo22026-10-05FalseFalsePro & Farm850,000$850,000$0$0$No850,000$--------850,000$--------No--------Lien / Lien NHL
Steven SantiniMoose (WPG)D311995-03-07USANo217 Lbs6 ft3NoNoAssign ManuallyNoNo22026-10-05FalseFalsePro & Farm850,000$850,000$0$0$No850,000$--------850,000$--------No--------Lien / Lien NHL
Tucker RobertsonMoose (WPG)C232003-06-22CANNo195 Lbs5 ft11NoNoAssign ManuallyNoNo12024-06-29FalseFalsePro & Farm950,000$950,000$0$0$No---------------------------Lien / Lien NHL
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2425.17192 Lbs6 ft11.75879,132$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Konsta HeleniusRyan SuzukiMikael Pyyhtia40122
2Aleksanteri KaskimakiTucker RobertsonRoby Jarventie30122
3Josh FilmonMason PrimeauSamu Tuomaala20122
4Mikael PyyhtiaRyan SuzukiAlex Suzdalev10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Marc Del GaizoReilly Walsh40122
2Alex SuzdalevEetu Liukas30122
3Corey SchuenemanDakota Mermis20122
4Marc Del GaizoReilly Walsh10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Konsta HeleniusRyan SuzukiMikael Pyyhtia60122
2Aleksanteri KaskimakiTucker RobertsonRoby Jarventie40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Marc Del GaizoReilly Walsh60122
2Alex SuzdalevEetu Liukas40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Konsta HeleniusMikael Pyyhtia60122
2Roby JarventieRyan Suzuki40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Marc Del GaizoReilly Walsh60122
2Michael KarowSteven Santini40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Konsta Helenius60122Marc Del GaizoReilly Walsh60122
2Mikael Pyyhtia40122Michael KarowSteven Santini40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Konsta HeleniusMikael Pyyhtia60122
2Roby JarventieRyan Suzuki40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Marc Del GaizoReilly Walsh60122
2Corey SchuenemanDakota Mermis40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Konsta HeleniusRyan SuzukiMikael PyyhtiaMarc Del GaizoReilly Walsh
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Konsta HeleniusRyan SuzukiMikael PyyhtiaMarc Del GaizoReilly Walsh
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Samu Tuomaala, Josh Filmon, Mason PrimeauSamu Tuomaala, Josh FilmonMason Primeau
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Marc Del Gaizo, Reilly Walsh, Corey SchuenemanMarc Del GaizoReilly Walsh, Dakota Mermis
Tirs de pénalité
Konsta Helenius, Mikael Pyyhtia, Roby Jarventie, Ryan Suzuki, Aleksanteri Kaskimaki
Gardien
#1 : Joel Blomqvist, #2 : Georgi Romanov
Lignes d’attaque personnalisées en prolongation
Konsta Helenius, Mikael Pyyhtia, Roby Jarventie, Ryan Suzuki, Aleksanteri Kaskimaki, Tucker Robertson, Samu Tuomaala, Alex Suzdalev, Eetu Liukas, Josh Filmon
Lignes de défense personnalisées en prolongation
Marc Del Gaizo, Reilly Walsh, Michael Karow, Steven Santini, Corey Schueneman


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 Americans2110000034-1110000003121010000003-320.500369004428192102240221200316618502150.00%80100.00%038153271.62%21235260.23%18426170.50%52641126489174102
2Admirals220000001569110000007521100000081741.00015264100442819210024022120032402441000%11281.82%238153271.62%21235260.23%18426170.50%52641126489174102
3Firebirds211000007611010000024-21100000052320.500712190044281925124022120033610222924416.67%10190.00%038153271.62%21235260.23%18426170.50%52641126489174102
4Ice Hogs220000002021811000000918110000001111041.000203656004428192122240221200324712437228.57%6183.33%038153271.62%21235260.23%18426170.50%52641126489174102
5Silver Knights22000000844110000003211100000052341.000816240044281924724022120032811203317317.65%90100.00%138153271.62%21235260.23%18426170.50%52641126489174102
6Stars220000002131811000000112911000000101941.000213556004428192119240221200327616505120.00%8187.50%038153271.62%21235260.23%18426170.50%52641126489174102
7Thunderbirds2020000057-21010000034-11010000023-100.0005914004428192502402212003531049399444.44%21480.95%038153271.62%21235260.23%18426170.50%52641126489174102
8Wild210000101358100000105411100000081741.00013213400442819271240221200351171433700.00%6183.33%038153271.62%21235260.23%18426170.50%52641126489174102
Total16114000109237558520001043232086200000491435240.75092161253004428192662240221200325967175318711521.13%791087.34%338153271.62%21235260.23%18426170.50%52641126489174102
_Since Last GM Reset16114000109237558520001043232086200000491435240.75092161253004428192662240221200325967175318711521.13%791087.34%338153271.62%21235260.23%18426170.50%52641126489174102
_Vs Conference16114000109237558520001043232086200000491435240.75092161253004428192662240221200325967175318711521.13%791087.34%338153271.62%21235260.23%18426170.50%52641126489174102
_Vs Division1011400010742351552000103516195620000039732241.2007412720100442819246224022120031794011520628725.00%52982.69%238153271.62%21235260.23%18426170.50%52641126489174102

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
1624W3921612536622596717531800
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
1611400109237
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
85200104323
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
86200004914
Derniers 10 matchs
WLOTWOTL SOWSOL
730000
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
711521.13%791087.34%3
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
24022120034428192
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
38153271.62%21235260.23%18426170.50%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
52641126489174102


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
19Moose8Wild1WSommaire du match
217Moose5Silver Knights2WSommaire du match
331Wild4Moose5WXXSommaire du match
553Admirals5Moose7WSommaire du match
773Firebirds4Moose2LSommaire du match
895Moose10Stars1WSommaire du match
9105Moose0 Americans3LSommaire du match
11121Ice Hogs1Moose9WSommaire du match
12138 Americans1Moose3WSommaire du match
15168Stars2Moose11WSommaire du match
16191Thunderbirds4Moose3LSommaire du match
17194Moose11Ice Hogs1WSommaire du match
19219Moose2Thunderbirds3LSommaire du match
20228Moose8Admirals1WSommaire du match
21235Silver Knights2Moose3WSommaire du match
22246Moose5Firebirds2WSommaire du match



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets330
Assistance00
Assistance PCT0.00%0.00%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
0 0 - 0.00% 0$0$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursPlafond Salariale total des joueursSalaire des entraineurs
0$ 2,109,916$ 2,109,916$ 750,000$0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
0$ 0$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
0$ 0 0$ 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
Saison régulière
Total Saison régulière00000000000000%0%0%0%0%000000
Séries éliminatoires
202551400000923-142110000055030300000418-1429152400324074322121011631878520315.00%381073.68%06010955.05%7312060.83%568665.12%10568127396632
Total Séries éliminatoires51400000923-142110000055030300000418-1429152400324074322121011631878520315.00%381073.68%06010955.05%7312060.83%568665.12%10568127396632

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
1Tyler Pitlick5134-1622714.29%06713.53011000000066.67%01.1800
2Clark Bishop5224023121118.18%010320.72011200011061.06%00.7700
3Aleksanteri Kaskimaki5303-445111030.00%09719.54202200000063.00%00.6100
4Dakota Mermis50222811220%410020.0100020000000%00.4000
5Roby Jarventie5202-12715450.00%19519.16101100000080.00%00.4200

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
1Chris Driedger51400.7795.7017900177700000