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Moose
GP: 80 | W: 41 | L: 34 | OTL: 5 | P: 87
GF: 360 | GA: 337 | PP%: 24.84% | PK%: 79.08%
DG: Pascal Verret | Morale : 45 | Moyenne d’équipe : 64
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Centre de jeu
Reign
42-28-10, 94pts
4
FINAL
2 Moose
41-34-5, 87pts
Team Stats
W4SéquenceW1
22-15-3Fiche domicile20-16-4
20-13-7Fiche domicile21-18-1
9-1-0Derniers 10 matchs3-6-1
4.16Buts par match 4.50
3.78Buts contre par match 4.21
22.79%Pourcentage en avantage numérique24.84%
79.02%Pourcentage en désavantage numérique79.08%
Moose
41-34-5, 87pts
6
FINAL
4 Rocket
38-30-12, 88pts
Team Stats
W1SéquenceL1
20-16-4Fiche domicile24-12-4
21-18-1Fiche domicile14-18-8
3-6-1Derniers 10 matchs5-3-2
4.50Buts par match 4.38
4.21Buts contre par match 4.55
24.84%Pourcentage en avantage numérique19.63%
79.08%Pourcentage en désavantage numérique79.14%
Meneurs d'équipe
Buts
Dylan Strome
56
Passes
Dylan Strome
78
Points
Dylan Strome
134
Plus/Moins
Dylan Strome
28
Victoires
Filip Gustavsson
37
Pourcentage d’arrêts
Karel Vejmelka
0.876

Statistiques d’équipe
Buts pour
360
4.50 GFG
Tirs pour
2608
32.60 Avg
Pourcentage en avantage numérique
24.8%
80 GF
Début de zone offensive
36.1%
Buts contre
337
4.21 GAA
Tirs contre
2622
32.78 Avg
Pourcentage en désavantage numérique
79.1%%
64 GA
Début de la zone défensive
36.5%
Informations de l'équipe

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


Informations de l’aréna

Capacité3,000
Assistance2,786
Billets de saison300


Informations de la formation

Équipe Pro34
Équipe Mineure18
Limite contact 52 / 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
1Dylan Strome (R)X100.006628817171837878878373575856476071710213975,000$
2Mathieu Joseph (R)X100.007638747676687776627177736852465366710212975,000$
3Nikolay Goldobin (R)X100.006937827774726182677472717951475165710223975,000$
4Michael McLeod (R)X100.005837768567716284676773677647445865700203975,000$
5Chase De Leo (R)X100.006930837272727370727875556449465256690222900,000$
6Nick Paul (R)X100.006649847173707068677374596945454064680232800,000$
7Vinni Lettieri (R)X100.006539867379787457687860656050504516670231500,000$
8Anders Bjork (R)X100.006429758262686582617061597144454460660221650,000$
9Julien Gauthier (R)X100.006429816175686070647175546446435155650212650,000$
10Boris Katchouk (R)X100.007042766863687272676366536743456565640201500,000$
11Kieffer Bellows (R)X100.006324815676696965666473457043445943630203550,000$
12Jake Leschyshyn (R)X100.007636656677618071636560525442427065620191500,000$
13Ludwig Bystrom (R)X100.007423777978767979507361786169503857730241975,000$
14Juuso Valimaki (R)X100.005630708062696479368151715043436525660201500,000$
15Nelson Nogier (R)X100.006641766668696463426860724845454327660221650,000$
16Caleb Jones (R)X100.007344596075704459345752703943435522630211550,000$
17Josh Mahura (R)X100.005537757458636277326956664343435468630203650,000$
18Joey Keane (R)X100.006342806063645966486546595642415822610192500,000$
Rayé
1Tomas JurcoX100.007633757183747073737772657267623341710261775,000$
2Mitchell Stephens (R)X100.004929937167826651736859634843435220620211500,000$
3Nathan Bastian (R)X100.005142746061736369816762526143454620620211500,000$
4Max Jones (R)X100.006844676671567563486962564642425419610202500,000$
5William Bitten (R)X100.007242636669557166515966516042435220600202500,000$
6Riley Damiani (R)X100.005521725547586162686757425040407420560182500,000$
7Jonatan Berggren (R)X100.005528766153566364677055364440406819560182500,000$
8Alexander Khovanov (R)X100.004121696041606052646151394640406320520182500,000$
9Matthew Benning (R)X63.917022817171746568497161755752514142690241850,000$
10Adam Ginning (R)X100.006241626475694848316345564540406620590182500,000$
11Jonathan Kovacevic (R)X100.006234645758655647366240623643454420570211400,000$
12Jonny Tychonick (R)X100.005332695949395972265541604140407520550182500,000$
MOYENNE D’ÉQUIPE98.80643475686767666857696159574645544064
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 Gustavsson98.00697278686182836155826949446564680203950,000$
2Daniel Vladar100.00546848695968597266575843446132610212700,000$
Rayé
1Karel Vejmelka100.00756071817661576573497346484920660221500,000$
2Joey Daccord100.00696767505474755754705545444920620222500,000$
MOYENNE D’ÉQUIPE99.5067676667637169646265644645563464
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Jared Bednar53568572375954CAN543950,000$


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
1Dylan StromeMoose (Win)C685678134282151121742769216020.29%37151022.2215284338228224121795255.24%23483828131.77011001077
2Mathieu JosephMoose (Win)RW804364107207751331032568617416.80%38162620.34111829372560004791042.03%1385628021.3214001336
3Michael McLeodMoose (Win)C80425395-44001071182807817215.00%26141317.67716232421701171074544.97%12035925011.3411000465
4Nikolay GoldobinMoose (Win)LW80335184-10100128952428615713.64%28150518.8279162622211291735251.77%1415124021.1234000652
5Ludwig BystromMoose (Win)D80145165042015016416356728.59%161223027.8891221292770113266100%04372000.5800000303
6Nick PaulMoose (Win)LW82233457-9140107901875812612.30%25123315.04459121360114871258.33%484423000.9200000154
7Kyle ConnorJetsLW3627255226005135136438319.85%1579021.9510919251112025904143.68%87359031.3211000624
8Chase De LeoMoose (Win)C78242549-111008274143538016.78%2897412.494375340000344351.42%494389001.0100000133
9Anders BjorkMoose (Win)LW77262147-1330087631655611315.76%1895412.400112250003851255.56%364817000.9800000231
10Tomas JurcoMoose (Win)RW59123042-73601065812954729.30%1798716.73371091450002263148.65%372126000.8511000001
11Juuso ValimakiMoose (Win)D6852631-238410811096730397.46%86138620.391894177000211501100.00%12147000.4501010120
12Julien GauthierMoose (Win)RW8091827-780624774233512.16%1686810.861011100110211141.18%341712000.6200000010
13Boris KatchoukMoose (Win)LW80141125-81606937106337013.21%186738.4200002000062060.00%20212000.7400000000
14Matthew BenningMoose (Win)D6631922-3200891157631353.95%63151923.0224681840221187000%01647000.2900000100
15Erik CernakJetsD4041721638081786626286.06%95107826.9734791330113121010%01332000.3900000021
16Jake LeschyshynMoose (Win)C8013720-11420944277235216.88%127209.00000080001701046.45%2821213000.5600000001
17Josh MahuraMoose (Win)D8021517633529735126183.92%53104413.06101438000051000%0628000.3300100000
18Nelson NogierMoose (Win)D5601616128045684517240%52102218.260111970113135000%0645000.3100000000
19Caleb JonesMoose (Win)D62310139460715428111610.71%5891514.77101350000173000%0232000.2800000100
20Vinni LettieriMoose (Win)RW30471124045282051220.00%841613.87123166000001047.37%1947000.5300000000
21Kieffer BellowsMoose (Win)RW583811-76016133711308.11%34097.0700000000000047.06%1788000.5400000010
22Mirco MuellerJetsD506644014810440%813226.41000010000014000%013000.9100000010
23Joey KeaneMoose (Win)D2514574091632333.33%2127310.940000200001000%027000.3700000000
24Riley DamianiMoose (Win)C101233006433633.33%3727.2800000000000051.72%2900000.8200000010
25Max JonesMoose (Win)LW12022-340856530%2655.450000000000000%020000.6100000000
26Mitchell StephensMoose (Win)C5011000112100%0316.3900000000000025.00%800000.6300000000
27Adam GinningMoose (Win)D3000040042000%33411.620000000001000%00100000000000
28Nathan BastianMoose (Win)RW5000000000000%020.540000000002000%00000000000000
29Jonatan BerggrenMoose (Win)LW9000000000000%0111.310000000000000%10000000000000
Statistiques d’équipe totales ou en moyenne1494362601963-462125178316762650913158413.66%8942390816.0080127207238244151116601937342150.96%49435645451110.81713211394238
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)74372740.8754.0741286028022411064500.63611719101
2Karel VejmelkaMoose (Win)102500.8763.7436900231851061000428000
3Daniel VladarMoose (Win)82210.8276.163310034196110001.0002543000
Statistiques d’équipe totales ou en moyenne92413450.8714.194829603372622128060138080101


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)D182000-01-01Yes196 Lbs6 ft3NoNoN/ANoNo2FalseFalsePro & Farm500,000$2,488$0$0$No500,000$--------No--------Lien
Alexander KhovanovMoose (Win)C182000-01-01Yes192 Lbs5 ft11NoNoN/ANoNo2FalseFalsePro & Farm500,000$2,488$0$0$No500,000$--------No--------Lien
Anders BjorkMoose (Win)LW221996-01-01Yes190 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm650,000$3,234$0$0$No------------------
Boris KatchoukMoose (Win)LW201998-01-01Yes206 Lbs6 ft2NoNoN/ANoNo1FalseFalsePro & Farm500,000$2,488$0$0$No------------------
Caleb JonesMoose (Win)D211997-01-01Yes194 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm550,000$2,736$0$0$No------------------
Chase De LeoMoose (Win)C221996-01-01Yes179 Lbs5 ft9NoNoN/ANoNo2FalseFalsePro & Farm900,000$4,478$0$0$No900,000$--------No--------
Daniel VladarMoose (Win)G211997-01-01No185 Lbs6 ft5NoNoN/ANoNo2FalseFalsePro & Farm700,000$3,483$0$0$No750,000$--------No--------
Dylan StromeMoose (Win)C211997-01-01Yes200 Lbs6 ft3NoNoN/ANoNo3FalseFalsePro & Farm975,000$4,851$0$0$No1,500,000$2,250,000$-------NoNo-------
Filip GustavssonMoose (Win)G201998-01-01No183 Lbs6 ft2NoNoN/ANoNo3FalseFalsePro & Farm950,000$4,726$0$0$No950,000$950,000$-------NoNo-------
Jake LeschyshynMoose (Win)C191999-01-01Yes192 Lbs5 ft11NoNoN/ANoNo1FalseFalsePro & Farm500,000$2,488$0$0$No------------------
Joey DaccordMoose (Win)G221996-01-01No197 Lbs6 ft2NoNoN/ANoNo2FalseFalsePro & Farm500,000$2,488$0$0$No500,000$--------No--------
Joey KeaneMoose (Win)D191999-01-01Yes187 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm500,000$2,488$0$0$No500,000$--------No--------Lien
Jonatan BerggrenMoose (Win)LW182000-01-01Yes195 Lbs5 ft11NoNoN/ANoNo2FalseFalsePro & Farm500,000$2,488$0$0$No500,000$--------No--------Lien
Jonathan KovacevicMoose (Win)D211997-01-01Yes208 Lbs6 ft4NoNoN/ANoNo1FalseFalsePro & Farm400,000$1,990$0$0$No------------------
Jonny TychonickMoose (Win)D182000-01-01Yes187 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm500,000$2,488$0$0$No500,000$--------No--------Lien
Josh MahuraMoose (Win)D201998-01-01Yes185 Lbs6 ft0NoNoN/ANoNo3FalseFalsePro & Farm650,000$3,234$0$0$No650,000$650,000$-------NoNo-------
Julien GauthierMoose (Win)RW211997-01-01Yes227 Lbs6 ft4NoNoN/ANoNo2FalseFalsePro & Farm650,000$3,234$0$0$No650,000$--------No--------
Juuso ValimakiMoose (Win)D201998-01-01Yes212 Lbs6 ft2NoNoN/ANoNo1FalseFalsePro & Farm500,000$2,488$0$0$No------------------
Karel VejmelkaMoose (Win)G221996-01-01No224 Lbs6 ft4NoNoN/ANoNo1FalseFalsePro & Farm500,000$2,488$0$0$No------------------
Kieffer BellowsMoose (Win)RW201998-01-01Yes194 Lbs6 ft1NoNoN/ANoNo3FalseFalsePro & Farm550,000$2,736$0$0$No550,000$550,000$-------NoNo-------
Ludwig BystromMoose (Win)D241994-01-01Yes169 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm975,000$4,851$0$0$No------------------
Mathieu JosephMoose (Win)RW211997-01-01Yes190 Lbs6 ft1NoNoN/ANoNo2FalseFalsePro & Farm975,000$4,851$0$0$No1,000,000$--------No--------
Matthew Benning (sur la masse salariale)Moose (Win)D241994-01-01Yes180 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm850,000$4,229$0$0$Yes------------------
Max JonesMoose (Win)LW201998-01-01Yes220 Lbs6 ft1NoNoN/ANoNo2FalseFalsePro & Farm500,000$2,488$0$0$No500,000$--------No--------
Michael McLeodMoose (Win)C201998-01-01Yes187 Lbs6 ft2NoNoN/ANoNo3FalseFalsePro & Farm975,000$4,851$0$0$No975,000$975,000$-------NoNo-------
Mitchell StephensMoose (Win)C211997-01-01Yes193 Lbs5 ft11NoNoN/ANoNo1FalseFalsePro & Farm500,000$2,488$0$0$No------------------
Nathan BastianMoose (Win)RW211997-01-01Yes205 Lbs6 ft4NoNoN/ANoNo1FalseFalsePro & Farm500,000$2,488$0$0$No------------------
Nelson NogierMoose (Win)D221996-01-01Yes191 Lbs6 ft2NoNoN/ANoNo1FalseFalsePro & Farm650,000$3,234$0$0$No------------------
Nick PaulMoose (Win)LW231995-01-01Yes185 Lbs6 ft2NoNoN/ANoNo2FalseFalsePro & Farm800,000$3,980$0$0$No900,000$--------No--------
Nikolay GoldobinMoose (Win)LW221996-01-01Yes196 Lbs5 ft11NoNoN/ANoNo3FalseFalsePro & Farm975,000$4,851$0$0$No975,000$975,000$-------NoNo-------
Riley DamianiMoose (Win)C182000-01-01Yes170 Lbs5 ft10NoNoN/ANoNo2FalseFalsePro & Farm500,000$2,488$0$0$No500,000$--------No--------Lien
Tomas JurcoMoose (Win)RW261992-01-01No203 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm775,000$3,856$0$0$No------------------
Vinni LettieriMoose (Win)RW231995-01-01Yes185 Lbs5 ft11NoNoN/ANoNo1FalseFalsePro & Farm500,000$2,488$0$0$No------------------
William BittenMoose (Win)RW201998-01-01Yes179 Lbs5 ft10NoNoN/ANoNo2FalseFalsePro & Farm500,000$2,488$0$0$No500,000$--------No--------
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
3420.82194 Lbs6 ft11.76645,588$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Nikolay GoldobinDylan StromeMathieu Joseph40122
2Nick PaulMichael McLeodVinni Lettieri30122
3Anders BjorkChase De LeoJulien Gauthier20122
4Boris KatchoukJake LeschyshynKieffer Bellows10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Ludwig BystromJuuso Valimaki40122
2Nelson NogierCaleb Jones30122
3Josh MahuraJoey Keane20122
4Ludwig BystromJuuso Valimaki10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Nikolay GoldobinDylan StromeMathieu Joseph60122
2Nick PaulMichael McLeodVinni Lettieri40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Ludwig BystromJuuso Valimaki60122
2Nelson NogierCaleb Jones40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Dylan StromeNikolay Goldobin60122
2Michael McLeodNick Paul40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Ludwig BystromJuuso Valimaki60122
2Nelson NogierCaleb Jones40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Dylan Strome60122Ludwig BystromJuuso Valimaki60122
2Michael McLeod40122Nelson NogierCaleb Jones40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Dylan StromeNikolay Goldobin60122
2Michael McLeodNick Paul40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Ludwig BystromJuuso Valimaki60122
2Nelson NogierCaleb Jones40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Nikolay GoldobinDylan StromeMathieu JosephLudwig BystromJuuso Valimaki
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Nikolay GoldobinDylan StromeMathieu JosephLudwig BystromJuuso Valimaki
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Chase De Leo, Nick Paul, Vinni LettieriChase De Leo, Nick PaulChase De Leo
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Caleb Jones, Josh Mahura, Joey KeaneCaleb JonesCaleb Jones, Josh Mahura
Tirs de pénalité
Mathieu Joseph, Nikolay Goldobin, Dylan Strome, Michael McLeod, Chase De Leo
Gardien
#1 : Filip Gustavsson, #2 : Daniel Vladar
Lignes d’attaque personnalisées en prolongation
Mathieu Joseph, Nikolay Goldobin, Dylan Strome, Michael McLeod, Chase De Leo, Nick Paul, Nick Paul, Vinni Lettieri, Anders Bjork, Julien Gauthier, Boris Katchouk
Lignes de défense personnalisées en prolongation
Ludwig Bystrom, Juuso Valimaki, Nelson Nogier, 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
1Admirals3300000017981100000063322000000116561.00017294600991291231390868843873381054224587342.86%12466.67%1937180152.03%907181949.86%715137052.19%173198016597431446722
2Americans312000001516-121100000101001010000056-120.333152540009912912313113868843873389737207411436.36%10460.00%0937180152.03%907181949.86%715137052.19%173198016597431446722
3Barracuda624000003033-33120000015150312000001518-340.33330477700991291231320686884387338225763811829931.03%19573.68%1937180152.03%907181949.86%715137052.19%173198016597431446722
4Bears301000201011-11010000014-32000002097240.6671016260099129123138886884387338104432290600.00%11190.91%0937180152.03%907181949.86%715137052.19%173198016597431446722
5Canucks220000001174110000006511100000052341.000111829009912912313778688438733855164382150.00%2150.00%0937180152.03%907181949.86%715137052.19%173198016597431446722
6Checkers20100100912-31000010045-11010000057-210.25091524009912912313628688438733862291444700.00%7271.43%0937180152.03%907181949.86%715137052.19%173198016597431446722
7Comets20200000611-51010000035-21010000036-300.00068140099129123135486884387338722624415360.00%12558.33%0937180152.03%907181949.86%715137052.19%173198016597431446722
8Condors6420000029227321000001413132100000159680.66729497800991291231320286884387338186746713929827.59%31487.10%1937180152.03%907181949.86%715137052.19%173198016597431446722
9Crunch20002000972100010003211000100065141.000918270099129123137286884387338672418367114.29%9277.78%0937180152.03%907181949.86%715137052.19%173198016597431446722
10Eagles3020100078-12020000046-21000100032120.33371118009912912313808688438733884172667700.00%130100.00%0937180152.03%907181949.86%715137052.19%173198016597431446722
11Griffins613002002633-73100020016142303000001019-940.33326426800991291231317586884387338191616214235720.00%31874.19%0937180152.03%907181949.86%715137052.19%173198016597431446722
12Icehogs31200000191901010000046-2211000001513220.3331932510099129123131008688438733811536166013646.15%8275.00%0937180152.03%907181949.86%715137052.19%173198016597431446722
13Islander20200000510-51010000046-21010000014-300.0005813009912912313608688438733863412561119.09%6183.33%0937180152.03%907181949.86%715137052.19%173198016597431446722
14Little Stars21000100972110000006331000010034-130.750916250099129123136886884387338602984712325.00%3166.67%0937180152.03%907181949.86%715137052.19%173198016597431446722
15Marlies5410000025131233000000176112110000087180.80025426700991291231317186884387338149573710918422.22%16381.25%0937180152.03%907181949.86%715137052.19%173198016597431446722
16Penguins211000006601010000023-11100000043120.50061218009912912313758688438733874112054700.00%10280.00%0937180152.03%907181949.86%715137052.19%173198016597431446722
17Phantoms31101000181621100000094520101000912-340.667183250009912912313109868843873389337325713215.38%11190.91%2937180152.03%907181949.86%715137052.19%173198016597431446722
18Punishers2110000010911010000024-21100000085320.5001017270099129123135986884387338642516444125.00%9277.78%0937180152.03%907181949.86%715137052.19%173198016597431446722
19Reign302000101215-32020000059-41000001076120.333121830009912912313104868843873389925285611545.45%13192.31%1937180152.03%907181949.86%715137052.19%173198016597431446722
20Rocket641000103428633000000181353110001016151100.83334589200991291231320686884387338196677112929724.14%30776.67%0937180152.03%907181949.86%715137052.19%173198016597431446722
21Senators3300000015872200000010551100000053261.000152641009912912313898688438733810230166614428.57%8362.50%0937180152.03%907181949.86%715137052.19%173198016597431446722
22Silver Knights31200000141311010000026-421100000127520.33314253900991291231312486884387338953326528450.00%13284.62%0937180152.03%907181949.86%715137052.19%173198016597431446722
23Thunderbirds2020000068-21010000023-11010000045-100.000612180099129123135286884387338732910456116.67%5260.00%0937180152.03%907181949.86%715137052.19%173198016597431446722
24Wolfpack320010001156210010005321100000062461.00011182900991291231393868843873389429246717423.53%12191.67%0937180152.03%907181949.86%715137052.19%173198016597431446722
25Wranglers30200100711-41000010034-12020000047-310.1677142100991291231379868843873389729106514214.29%50100.00%0937180152.03%907181949.86%715137052.19%173198016597431446722
Total80323405540360337234018160240017115714401418031401891809870.544360608968009912912313260886884387338262288664517543228024.84%3066479.08%6937180152.03%907181949.86%715137052.19%173198016597431446722
_Since Last GM Reset80323405540360337234018160240017115714401418031401891809870.544360608968009912912313260886884387338262288664517543228024.84%3066479.08%6937180152.03%907181949.86%715137052.19%173198016597431446722
_Vs Conference50242001320247223242515700300127102252591301020120121-1570.570247414661009912912313164486884387338165557142110732236127.35%1944278.35%6937180152.03%907181949.86%715137052.19%173198016597431446722
_Vs Division241110002101191163127300200635581247000105661-5260.542119196315009912912313789868843873387982782385281223125.41%1112478.38%2937180152.03%907181949.86%715137052.19%173198016597431446722

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
8087W136060896826082622886645175400
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
8032345540360337
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
4018162400171157
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
4014183140189180
Derniers 10 matchs
WLOTWOTL SOWSOL
360100
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
3228024.84%3066479.08%6
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
868843873389912912313
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
937180152.03%907181949.86%715137052.19%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
173198016597431446722


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
11Barracuda5Moose8WR1Sommaire du match
29Moose6Rocket5WXXSommaire du match
635Griffins5Moose4LXR1Sommaire du match
847Moose2Barracuda6LSommaire du match
1058Condors5Moose7WR1Sommaire du match
1369Moose7Condors2WSommaire du match
1687Rocket6Moose7WR1Sommaire du match
20109Marlies1Moose3WSommaire du match
25131Senators2Moose5WSommaire du match
26138Moose3Griffins8LR1Sommaire du match
29157Condors5Moose2LSommaire du match
31170Moose4Phantoms8LSommaire du match
34184Griffins5Moose4LXR1Sommaire du match
37199Moose6Griffins7LSommaire du match
39208Moose5Bears4WXXSommaire du match
40215Punishers4Moose2LSommaire du match
44229Moose8Punishers5WSommaire du match
46242Bears4Moose1LSommaire du match
50261Moose3Marlies5LSommaire du match
52265Wranglers4Moose3LXSommaire du match
54280Moose1Griffins4LR1Sommaire du match
56291Penguins3Moose2LSommaire du match
61312Moose5Senators3WSommaire du match
62319Admirals3Moose6WSommaire du match
66339Marlies2Moose6WSommaire du match
67352Moose5Canucks2WSommaire du match
70366Condors3Moose5WR1Sommaire du match
72380Moose5Admirals3WSommaire du match
74391Canucks5Moose6WSommaire du match
80415Moose9Icehogs6WSommaire du match
81419Barracuda4Moose3LR1Sommaire du match
84435Moose4Bears3WXXSommaire du match
85445Barracuda6Moose4LR1Sommaire du match
88465Moose5Phantoms4WXSommaire du match
91472Phantoms4Moose9WSommaire du match
93483Moose7Reign6WXXSommaire du match
95496Rocket3Moose4WR1Sommaire du match
99521Islander6Moose4LSommaire du match
101531Moose5Marlies2WSommaire du match
104546Little Stars3Moose6WSommaire du match
106559Moose3Comets6LSommaire du match
109574Crunch2Moose3WXSommaire du match
111582Moose6Wolfpack2WSommaire du match
113593Moose5Condors2WR1Sommaire du match
115601Marlies3Moose8WSommaire du match
117616Moose2Wranglers4LSommaire du match
119627Moose6Admirals3WSommaire du match
121632Wolfpack2Moose3WXSommaire du match
123651Moose5Americans6LSommaire du match
125658Americans4Moose2LSommaire du match
127672Moose3Condors5LR1Sommaire du match
128682Icehogs6Moose4LSommaire du match
131690Moose5Checkers7LSommaire du match
133705Moose6Crunch5WXSommaire du match
135714Rocket4Moose7WR1Sommaire du match
138731Moose1Islander4LSommaire du match
139739Moose9Barracuda6WR1Sommaire du match
140744Griffins4Moose8WR1Sommaire du match
143765Thunderbirds3Moose2LSommaire du match
145774Moose4Rocket6LR1Sommaire du match
146784Moose4Penguins3WSommaire du match
148796Silver Knights6Moose2LSommaire du match
150807Moose3Eagles2WXSommaire du match
154822Eagles3Moose2LSommaire du match
158842Senators3Moose5WSommaire du match
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
159849Moose6Icehogs7LSommaire du match
164869Moose3Little Stars4LXSommaire du match
165873Wolfpack1Moose2WSommaire du match
169894Comets5Moose3LSommaire du match
170902Moose4Thunderbirds5LSommaire du match
173918Moose2Wranglers3LSommaire du match
175925Checkers5Moose4LXSommaire du match
179947Reign5Moose3LSommaire du match
181962Moose4Barracuda6LR1Sommaire du match
183972Americans6Moose8WSommaire du match
185985Moose4Silver Knights6LSommaire du match
188997Eagles3Moose2LSommaire du match
1891002Moose8Silver Knights1WSommaire du match
1971027Reign4Moose2LSommaire du match
1981030Moose6Rocket4WR1Sommaire du match



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets7040
Assistance73,98537,435
Assistance PCT92.48%93.59%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
0 2786 - 92.85% 175,254$7,010,166$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursSalaire total moyen des joueursSalaire des entraineurs
3,551,451$ 2,110,000$ 2,085,000$ 0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
10,498$ 2,597,580$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
0$ 1 15,224$ 15,224$




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