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Connexion

Moose
GP: 24 | W: 11 | L: 11 | OTL: 2 | P: 24
GF: 103 | GA: 111 | PP%: 24.35% | PK%: 77.57%
DG: Pascal Verret | Morale : 36 | Moyenne d’équipe : 65
Prochains matchs #315 vs Icehogs
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Centre de jeu
Islander
13-10-3, 29pts
4
FINAL
6 Moose
11-11-2, 24pts
Team Stats
L1SéquenceL1
5-5-1Fiche domicile6-5-1
8-5-2Fiche domicile5-6-1
5-3-2Derniers 10 matchs5-4-1
3.65Buts par match 4.29
3.62Buts contre par match 4.63
23.71%Pourcentage en avantage numérique24.35%
75.00%Pourcentage en désavantage numérique77.57%
Bears
10-10-2, 22pts
7
FINAL
3 Moose
11-11-2, 24pts
Team Stats
W3SéquenceL1
5-6-1Fiche domicile6-5-1
5-4-1Fiche domicile5-6-1
4-4-2Derniers 10 matchs5-4-1
4.14Buts par match 4.29
4.27Buts contre par match 4.63
19.19%Pourcentage en avantage numérique24.35%
74.55%Pourcentage en désavantage numérique77.57%
Moose
11-11-2, 24pts
Jour 62
Icehogs
15-7-1, 31pts
Statistiques d’équipe
L1SéquenceW2
6-5-1Fiche domicile7-4-1
5-6-1Fiche visiteur8-3-0
5-4-110 derniers matchs6-4-0
4.29Buts par match 4.39
4.63Buts contre par match 4.39
24.35%Pourcentage en avantage numérique25.51%
77.57%Pourcentage en désavantage numérique78.64%
Moose
11-11-2, 24pts
Jour 64
Wranglers
14-9-1, 29pts
Statistiques d’équipe
L1SéquenceOTW1
6-5-1Fiche domicile7-4-1
5-6-1Fiche visiteur7-5-0
5-4-110 derniers matchs7-2-1
4.29Buts par match 4.71
4.63Buts contre par match 4.71
24.35%Pourcentage en avantage numérique23.16%
77.57%Pourcentage en désavantage numérique77.27%
Phantoms
9-11-3, 21pts
Jour 65
Moose
11-11-2, 24pts
Statistiques d’équipe
OTW1SéquenceL1
5-6-1Fiche domicile6-5-1
4-5-2Fiche visiteur5-6-1
5-5-010 derniers matchs5-4-1
3.96Buts par match 4.29
4.48Buts contre par match 4.29
22.34%Pourcentage en avantage numérique24.35%
78.65%Pourcentage en désavantage numérique77.57%
Meneurs d'équipe
Buts
Nick Paul
17
Passes
Michael McLeod
18
Points
Michael McLeod
33
Plus/Moins
Kieffer Bellows
9
Victoires
Filip Gustavsson
11
Pourcentage d’arrêts
Filip Gustavsson
0.867

Statistiques d’équipe
Buts pour
103
4.29 GFG
Tirs pour
762
31.75 Avg
Pourcentage en avantage numérique
24.3%
28 GF
Début de zone offensive
36.5%
Buts contre
111
4.63 GAA
Tirs contre
775
32.29 Avg
Pourcentage en désavantage numérique
77.6%%
24 GA
Début de la zone défensive
37.4%
Informations de l'équipe

Directeur généralPascal Verret
EntraîneurJared Bednar
DivisionThayer-Tutt
ConférenceRobert-Lebel
Capitaine
Assistant #1
Assistant #2


Informations de l’aréna

Capacité3,000
Assistance2,804
Billets de saison300


Informations de la formation

Équipe Pro33
Équipe Mineure18
Limite contact 51 / 250
Espoirs0


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
1Michael McLeod (R)X100.006237778671756786717175697855515337730212975,000$
2Chase De Leo (R)X100.007130827474767368747977586652494742700231900,000$
3Nick Paul (R)X100.006949857176747371697576627152483642700241900,000$
4Anders Bjork (R)X100.006829778366726981637364617148474042680231800,000$
5Vinni Lettieri (R)X100.006639867477797559697963666152534143680242850,000$
6Jake Leschyshyn (R)X100.007736707179678174716964585945436342660204750,000$
7Boris Katchouk (R)X100.007242786966707475706368587044475942660214750,000$
8Kieffer Bellows (R)X100.006524836176697167696675507245465442650212550,000$
9Lukas Rousek (R)X100.005832797758605069546060457443445642600202500,000$
10Riley Damiani (R)X100.005723735752626364706859455341416521580191500,000$
11Aliaksei Protas (R)X100.006936616769537359495958555340408433580182500,000$
12Juuso Parssinen (R)X100.006634665358546359575860445940407220550182500,000$
13Ludwig Bystrom (R)X100.007623768077768178537364796476513542740251950,000$
14Matthew Benning (R)X100.007122827375776470527363765559543732710251900,000$
15Juuso Valimaki (R)X100.006229738270776981418255735452455842700212900,000$
16Caleb Jones (R)X100.007544676673724765426358754648464939670223750,000$
17Josh Mahura (R)X100.005838767764676679387258694748454945660212650,000$
18Adam Ginning (R)X100.006544646676714951326547584841415821610191500,000$
Rayé
1Mitchell Stephens (R)X100.005031967368846854756963665144444539650222600,000$
2Nathan Bastian (R)X100.005344766163756570837065536344464120640221500,000$
3Max Jones (R)X100.007146696873607765517264584943434720630211500,000$
4William Bitten (R)X100.007445656970597367536267546243444620630211500,000$
5Kristian Vesalainen (R)X100.007028695860626664696367606144445742620204500,000$
6Jonatan Berggren (R)X100.005629786358616467697157374641416020580191500,000$
7Jaret Anderson-Dolan (R)X100.006944576059446660505963514442425423570202550,000$
8Alexander Khovanov (R)X100.004423726246626254666352414741415620540191500,000$
9Nelson Nogier (R)X100.006441796970706666467062755152483920680231800,000$
10Joey Keane (R)X100.006642846667706370546951666143435241650201500,000$
11Jonny Tychonick (R)X100.005533726155446274275743624241416620580191500,000$
MOYENNE D’ÉQUIPE100.00653575696767676858686259584745523364
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
1Filip Gustavsson99.00727581716684856560837259475948720212950,000$
2Daniel Vladar100.00577054716270627468606146465548640221750,000$
Rayé
1Karel Vejmelka100.00766172817762596674517249504528670232650,000$
2Joey Daccord100.00706969535577776057735746454328640231500,000$
MOYENNE D’ÉQUIPE99.7569696969657371666567665047513867
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Jared Bednar53568572375954CAN552950,000$


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
1Nick PaulMoose (Win)LW24171633-980342685204620.00%1353222.185101520981012632135.48%62178111.2401000302
2Michael McLeodMoose (Win)C18151833-3160164392345416.30%1441623.13551019701013621144.08%633195021.5900000140
3Anders BjorkMoose (Win)LW24917260120462270254512.86%1142317.64551011710003580180.00%10186001.2300000100
4Chase De LeoMoose (Win)C24101525140333861183716.39%1044118.392686710112680046.10%308155001.1301000030
5Jake LeschyshynMoose (Win)C2471219-6220323354193812.96%435414.761454280000391153.48%27399001.0701000111
6Ludwig BystromMoose (Win)D2431619-430040555229295.77%4466827.872579109011180100%03420000.5700000101
7Vinni LettieriMoose (Win)RW2411718-49522333382133.33%839416.44437480000002156.00%25611010.9100001110
8Juuso ValimakiMoose (Win)D2401616-1018018443519140%1848320.16022365000159000%0214000.6600000000
9Matthew BenningMoose (Win)D2421416-220050433613175.56%3966827.871346108011284000%01221000.4800000002
10Boris KatchoukMoose (Win)LW2457121120352745102811.11%427211.3700016000010042.86%7105000.8800000001
11Lukas RousekMoose (Win)RW247512-32014737112518.92%01817.580000000000100%2101001.3200000110
12Josh MahuraMoose (Win)D241670175102820865.00%2228812.0300001000013000%026000.4800100000
13Kieffer BellowsMoose (Win)RW2433696026171841716.67%326811.19000015000000028.57%1442000.4500000001
14Joey KeaneMoose (Win)D22055355122510350%1527612.5800000000029000%018000.3600001000
15Kristian VesalainenMoose (Win)LW21325040206202915.00%41647.8200003000040033.33%322000.6100000000
16Mitchell StephensMoose (Win)C20314100716187916.67%01668.3200000000040046.67%7522000.4800000000
17Caleb JonesMoose (Win)D24033-8415332417590%2648320.16000364000059000%0312000.1200010000
18Aliaksei ProtasMoose (Win)RW23213-6602014107520.00%72038.8600001000000033.33%912000.2900000010
19Jaret Anderson-DolanMoose (Win)C10101-34019542325.00%3727.25000000000120051.72%2900000.2800000000
20Riley DamianiMoose (Win)C2000-100000010%0168.1700000000000066.67%30000000000000
21Adam GinningMoose (Win)D2000-220111100%12512.680000000000000%00000000000000
22Juuso ParssinenMoose (Win)LW3000-300210020%2237.840000000000000%11000000000000
Statistiques d’équipe totales ou en moyenne43399164263-492382049050871824542013.79%248682915.7725436886799235146428546.35%1454168139140.77031129118
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
1Filip GustavssonMoose (Win)2311920.8674.2912730091684303210.6673231000
2Daniel VladarMoose (Win)60200.8006.32171001890340000123000
Statistiques d’équipe totales ou en moyenne29111120.8594.531445001097743372132424000


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 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 Non Activé 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 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
Adam GinningMoose (Win)D192000-01-01Yes196 Lbs6 ft3NoNoN/ANoNo1FalseFalsePro & Farm500,000$349,010$0$0$No------------------
Alexander KhovanovMoose (Win)C192000-01-01Yes192 Lbs5 ft11NoNoN/ANoNo1FalseFalsePro & Farm500,000$349,010$0$0$No------------------
Aliaksei ProtasMoose (Win)RW182001-01-01Yes225 Lbs6 ft2NoNoTrade2024-12-09NoNo22024-07-10FalseFalsePro & Farm500,000$349,010$0$0$No500,000$--------No--------Lien
Anders BjorkMoose (Win)LW231996-01-01Yes190 Lbs6 ft0NoNoFree AgentNoNo12024-08-21FalseFalsePro & Farm800,000$558,416$0$0$No------------------
Boris KatchoukMoose (Win)LW211998-01-01Yes206 Lbs6 ft2NoNoFree AgentNoNo42024-08-21FalseFalsePro & Farm750,000$523,515$0$0$No800,000$850,000$950,000$------NoNoNo------
Caleb JonesMoose (Win)D221997-01-01Yes194 Lbs6 ft1NoNoFree AgentNoNo32024-08-21FalseFalsePro & Farm750,000$523,515$0$0$No800,000$850,000$-------NoNo-------
Chase De LeoMoose (Win)C231996-01-01Yes179 Lbs5 ft9NoNoN/ANoNo1FalseFalsePro & Farm900,000$628,218$0$0$No------------------
Daniel VladarMoose (Win)G221997-01-01No185 Lbs6 ft5NoNoN/ANoNo1FalseFalsePro & Farm750,000$523,515$0$0$No------------------
Filip GustavssonMoose (Win)G211998-01-01No183 Lbs6 ft2NoNoN/ANoNo2FalseFalsePro & Farm950,000$663,119$0$0$No950,000$--------No--------
Jake LeschyshynMoose (Win)C201999-01-01Yes192 Lbs5 ft11NoNoFree AgentNoNo42024-08-21FalseFalsePro & Farm750,000$523,515$0$0$No800,000$850,000$950,000$------NoNoNo------
Jaret Anderson-DolanMoose (Win)C201999-01-01Yes200 Lbs5 ft11NoNoFree AgentNoNo22024-10-11FalseFalsePro & Farm550,000$383,911$0$0$No550,000$--------No--------
Joey DaccordMoose (Win)G231996-01-01No197 Lbs6 ft2NoNoN/ANoNo1FalseFalsePro & Farm500,000$349,010$0$0$No------------------
Joey KeaneMoose (Win)D201999-01-01Yes187 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm500,000$349,010$0$0$No------------------
Jonatan BerggrenMoose (Win)LW192000-01-01Yes195 Lbs5 ft11NoNoN/ANoNo1FalseFalsePro & Farm500,000$349,010$0$0$No------------------
Jonny TychonickMoose (Win)D192000-01-01Yes187 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm500,000$349,010$0$0$No------------------
Josh MahuraMoose (Win)D211998-01-01Yes185 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm650,000$453,713$0$0$No650,000$--------No--------
Juuso ParssinenMoose (Win)LW182001-01-01Yes212 Lbs6 ft3NoNoAssign ManuallyNoNo22024-07-10FalseFalsePro & Farm500,000$349,010$0$0$No500,000$--------No--------Lien
Juuso ValimakiMoose (Win)D211998-01-01Yes212 Lbs6 ft2NoNoFree AgentNoNo22024-08-21FalseFalsePro & Farm900,000$628,218$0$0$No950,000$--------No--------
Karel VejmelkaMoose (Win)G231996-01-01No224 Lbs6 ft4NoNoFree AgentNoNo22024-08-21FalseFalsePro & Farm650,000$453,713$0$0$No650,000$--------No--------
Kieffer BellowsMoose (Win)RW211998-01-01Yes194 Lbs6 ft1NoNoN/ANoNo2FalseFalsePro & Farm550,000$383,911$0$0$No550,000$--------No--------
Kristian VesalainenMoose (Win)LW201999-01-01Yes207 Lbs6 ft3NoNoFree Agent2024-08-03NoNo42024-08-21FalseFalsePro & Farm500,000$349,010$0$0$No550,000$600,000$700,000$------NoNoNo------
Ludwig BystromMoose (Win)D251994-01-01Yes169 Lbs6 ft0NoNoFree AgentNoNo12024-08-21FalseFalsePro & Farm950,000$663,119$0$0$No------------------
Lukas RousekMoose (Win)RW201999-01-01Yes172 Lbs5 ft11NoNoAssign ManuallyNoNo22024-07-10FalseFalsePro & Farm500,000$349,010$0$0$No500,000$--------No--------Lien
Matthew BenningMoose (Win)D251994-01-01Yes180 Lbs6 ft0NoNoFree AgentNoNo12024-08-21FalseFalsePro & Farm900,000$628,218$0$0$No------------------
Max JonesMoose (Win)LW211998-01-01Yes220 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm500,000$349,010$0$0$No------------------
Michael McLeodMoose (Win)C211998-01-01Yes187 Lbs6 ft2NoNoN/ANoNo2FalseFalsePro & Farm975,000$680,569$0$0$No975,000$--------No--------
Mitchell StephensMoose (Win)C221997-01-01Yes193 Lbs5 ft11NoNoFree AgentNoNo22024-08-21FalseFalsePro & Farm600,000$418,812$0$0$No700,000$--------No--------
Nathan BastianMoose (Win)RW221997-01-01Yes205 Lbs6 ft4NoNoFree AgentNoNo12024-08-21FalseFalsePro & Farm500,000$349,010$0$0$No------------------
Nelson NogierMoose (Win)D231996-01-01Yes191 Lbs6 ft2NoNoFree AgentNoNo12024-08-21FalseFalsePro & Farm800,000$558,416$0$0$No------------------
Nick PaulMoose (Win)LW241995-01-01Yes185 Lbs6 ft2NoNoN/ANoNo1FalseFalsePro & Farm900,000$628,218$0$0$No------------------
Riley DamianiMoose (Win)C192000-01-01Yes170 Lbs5 ft10NoNoN/ANoNo1FalseFalsePro & Farm500,000$349,010$0$0$No------------------
Vinni LettieriMoose (Win)RW241995-01-01Yes185 Lbs5 ft11NoNoFree AgentNoNo22024-08-21FalseFalsePro & Farm850,000$593,317$0$0$No850,000$--------No--------
William BittenMoose (Win)RW211998-01-01Yes179 Lbs5 ft10NoNoN/ANoNo1FalseFalsePro & Farm500,000$349,010$0$0$No------------------
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
3321.21193 Lbs6 ft11.70664,394$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Nick PaulMichael McLeodKieffer Bellows40122
2Anders BjorkChase De LeoVinni Lettieri30122
3Boris KatchoukJake LeschyshynAliaksei Protas20311
4Juuso ParssinenRiley DamianiLukas Rousek10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Ludwig BystromMatthew Benning40122
2Juuso ValimakiCaleb Jones30122
3Josh MahuraAdam Ginning20122
4Ludwig BystromMatthew Benning10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Nick PaulMichael McLeodVinni Lettieri60122
2Anders BjorkChase De LeoKieffer Bellows40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Ludwig BystromMatthew Benning60122
2Juuso ValimakiCaleb Jones40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Michael McLeodNick Paul60122
2Chase De LeoAnders Bjork40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Ludwig BystromMatthew Benning60122
2Juuso ValimakiCaleb Jones40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Michael McLeod60122Ludwig BystromMatthew Benning60122
2Chase De Leo40122Juuso ValimakiCaleb Jones40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Michael McLeodNick Paul60122
2Chase De LeoAnders Bjork40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Ludwig BystromMatthew Benning60122
2Juuso ValimakiCaleb Jones40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Nick PaulMichael McLeodVinni LettieriLudwig BystromMatthew Benning
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Nick PaulMichael McLeodVinni LettieriLudwig BystromMatthew Benning
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Anders Bjork, Boris Katchouk, Jake LeschyshynAnders Bjork, Boris KatchoukAnders Bjork
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Caleb Jones, Josh Mahura, Ludwig BystromCaleb JonesCaleb Jones, Josh Mahura
Tirs de pénalité
Michael McLeod, Nick Paul, Chase De Leo, Vinni Lettieri, Anders Bjork
Gardien
#1 : Filip Gustavsson, #2 : Daniel Vladar
Lignes d’attaque personnalisées en prolongation
Michael McLeod, Nick Paul, Chase De Leo, Vinni Lettieri, Anders Bjork, Boris Katchouk, Boris Katchouk, Jake Leschyshyn, Kieffer Bellows, Lukas Rousek, Aliaksei Protas
Lignes de défense personnalisées en prolongation
Ludwig Bystrom, Matthew Benning, Juuso Valimaki, Caleb Jones, Josh Mahura


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
1Barracuda312000001013-32110000089-11010000024-220.33310182800293341010026724724837528326216637.50%16287.50%024554045.37%26055347.02%18338647.41%509284509225427214
2Bears1010000037-41010000037-40000000000000.0003580029334102726724724834111893266.67%4325.00%024554045.37%26055347.02%18338647.41%509284509225427214
3Condors523000002427-321100000910-1312000001517-240.4002440641029334101462672472483176555011828621.43%24483.33%224554045.37%26055347.02%18338647.41%509284509225427214
4Eagles211000009901010000035-21100000064220.50091524002933410592672472483643314339111.11%7357.14%024554045.37%26055347.02%18338647.41%509284509225427214
5Griffins42100100161421100000072531100100912-350.625162541002933410115267247248312735478022313.64%21385.71%024554045.37%26055347.02%18338647.41%509284509225427214
6Gulls20100001713-61000000134-11010000049-510.250713200029334107126724724837217344110330.00%12558.33%024554045.37%26055347.02%18338647.41%509284509225427214
7Islander11000000642110000006420000000000021.0006111700293341041267247248336132206350.00%110.00%024554045.37%26055347.02%18338647.41%509284509225427214
8Little Stars21100000981211000009810000000000020.50091827002933410702672472483671617525240.00%60100.00%024554045.37%26055347.02%18338647.41%509284509225427214
9Marlies2110000079-2000000000002110000079-220.50071017002933410642672472483521714451317.69%7185.71%024554045.37%26055347.02%18338647.41%509284509225427214
10Phantoms11000000752110000007520000000000021.000713200029334103526724724834010122111100.00%6266.67%024554045.37%26055347.02%18338647.41%509284509225427214
11Rockets11000000523000000000001100000052321.00057120029334103426724724832512627200.00%30100.00%024554045.37%26055347.02%18338647.41%509284509225427214
Total24111100101103111-8126500001555411256001004857-9240.50010317527810293341076226724724837752472365081152824.35%1072477.57%224554045.37%26055347.02%18338647.41%509284509225427214
_Since Last GM Reset24111100101103111-8126500001555411256001004857-9240.50010317527810293341076226724724837752472365081152824.35%1072477.57%224554045.37%26055347.02%18338647.41%509284509225427214
_Vs Conference1888001017683-774200001343041146001004253-11180.500761262021029334105652672472483567174195394922021.74%891780.90%224554045.37%26055347.02%18338647.41%509284509225427214
_Vs Division1457001015767-106320000127252825001003042-12120.42957961531029334104322672472483450135163301761823.68%731480.82%224554045.37%26055347.02%18338647.41%509284509225427214

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
2424L110317527876277524723650810
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
2411110101103111
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
126500015554
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
125601004857
Derniers 10 matchs
WLOTWOTL SOWSOL
540100
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
1152824.35%1072477.57%2
Tirs en 1e périodeTirs en 2e périodeTirs en 3e périodeTirs en 4e périodeButs en 1e périodeButs en 2e périodeButs en 3e périodeButs en 4e période
26724724832933410
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
24554045.37%26055347.02%18338647.41%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
509284509225427214


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
413Little Stars2Moose6WSommaire du match
522Moose5Marlies3WSommaire du match
839Barracuda7Moose5LR1Sommaire du match
1049Moose2Barracuda4LSommaire du match
1259Moose4Gulls9LR1Sommaire du match
1466Griffins2Moose7WR1Sommaire du match
1885Condors3Moose5WSommaire du match
2096Moose5Condors7LR1Sommaire du match
22112Gulls4Moose3LXXSommaire du match
25128Moose2Marlies6LSommaire du match
27135Moose2Griffins8LR1Sommaire du match
29146Little Stars6Moose3LSommaire du match
31159Moose6Condors3WR1Sommaire du match
32171Moose5Rockets2WSommaire du match
34178Phantoms5Moose7WSommaire du match
37191Moose1Griffins2LXR1Sommaire du match
39202Condors7Moose4LSommaire du match
41211Moose6Eagles4WSommaire du match
46231Barracuda2Moose3WR1Sommaire du match
48241Moose6Griffins2WR1Sommaire du match
50253Moose4Condors7LSommaire du match
51260Eagles5Moose3LSommaire du match
55281Islander4Moose6WSommaire du match
60304Bears7Moose3LSommaire du match
62315Moose-Icehogs-
64324Moose-Wranglers-
65332Phantoms-Moose-
68351Moose-Canucks-
70356Moose-Marlies-
71362Canucks-Moose-
74382Barracuda-Moose-
78401Rockets-Moose-
81418Moose-Bears-
83427Moose-Wranglers-
85435Crunch-Moose-
88452Moose-Little Stars-
90460Little Stars-Moose-
92473Moose-Penguins-
94484Checkers-Moose-
99507Little Stars-Moose-
104527Moose-Firebirds-
105536Firebirds-Moose-
108549Moose-Gulls-
110561Wolfpack-Moose-
114580Moose-Americans-
115590Thunderbirds-Moose-
118601Moose-Gulls-
120614Penguins-Moose-
123630Moose-Icehogs-
125640Wolfpack-Moose-
127649Moose-Admirals-
129664Americans-Moose-
131671Moose-Comets-
133686Moose-Phantoms-
134695Gulls-Moose-
138717Wranglers-Moose-
140727Moose-Rockets-
142737Moose-Senators-
143747Punishers-Moose-
147767Admirals-Moose-
149781Moose-Americans-
151792Moose-Thunderbirds-
153798Moose-Admirals-
154805Griffins-Moose-
157821Moose-Crunch-
159829Griffins-Moose-
162848Moose-Checkers-
163857Condors-Moose-
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
167876Icehogs-Moose-
169884Moose-Islander-
173905Senators-Moose-
175915Moose-Punishers-
177927Marlies-Moose-
181951Senators-Moose-
185970Comets-Moose-
189985Moose-Barracuda-
191995Gulls-Moose-
1941012Moose-Barracuda-
1961022Icehogs-Moose-
2001038Moose-Wolfpack-



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets7040
Assistance22,29711,349
Assistance PCT92.90%94.58%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
28 2804 - 93.46% 176,291$2,115,487$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursSalaire total moyen des joueursSalaire des entraineurs
946,819$ 2,192,500$ 2,157,500$ 0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
10,854$ 659,936$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
4,936,136$ 141 15,557$ 2,193,537$




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