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Moose
GP: 80 | W: 32 | L: 41 | OTL: 7 | P: 71
GF: 312 | GA: 335 | PP%: 19.09% | PK%: 77.16%
DG: Pascal Verret | Morale : 21 | Moyenne d’équipe : 64
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Centre de jeu
Rampage
45-28-7, 97pts
4
FINAL
2 Moose
32-41-7, 71pts
Team Stats
W5StreakL2
22-15-3Home Record14-22-4
23-13-4Away Record18-19-3
9-0-1Last 10 Games0-9-1
4.33Buts par match 3.90
3.95Buts contre par match 4.19
19.39%Pourcentage en avantage numérique19.09%
79.82%Pourcentage en désavantage numérique77.16%
Reign
41-32-7, 89pts
4
FINAL
3 Moose
32-41-7, 71pts
Team Stats
L1StreakL2
21-15-4Home Record14-22-4
20-17-3Away Record18-19-3
5-3-2Last 10 Games0-9-1
4.25Buts par match 3.90
4.18Buts contre par match 4.19
22.94%Pourcentage en avantage numérique19.09%
78.81%Pourcentage en désavantage numérique77.16%
Meneurs d'équipe
Buts
Mathieu Joseph
43
Passes
Dylan Strome
75
Points
Dylan Strome
116
Plus/Moins
Mathieu Joseph
14
Victoires
Filip Gustavsson
17
Pourcentage d’arrêts
Filip Gustavsson
0.877

Statistiques d’équipe
Buts pour
312
3.90 GFG
Tirs pour
2696
33.70 Avg
Pourcentage en avantage numérique
19.1%
63 GF
Début de zone offensive
37.9%
Buts contre
335
4.19 GAA
Tirs contre
2571
32.14 Avg
Pourcentage en désavantage numérique
77.2%%
74 GA
Début de la zone défensive
36.2%
Informations de l'équipe

Directeur généralPascal Verret
EntraîneurAdams Oates
DivisionThayer-Tutt
ConférenceRobert-Lebel
Capitaine
Assistant #1Ludwig Bystrom
Assistant #2


Informations de l’aréna

Capacité3,000
Assistance2,784
Billets de saison300


Informations de la formation

Équipe Pro36
Équipe Mineure18
Limite contact 54 / 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
1Nathan Walker (R)X100.006931727772786977717572647351515033700231850,000$
2Kyle Connor (R)X100.006134897572747873596088517751466231690213800,000$
3Nikolay Goldobin (R)X100.006537827568705880647170687947445625690211850,000$
4Mathieu Joseph (R)X100.007337707375627474596774696445435844680203950,000$
5Dylan Strome (R)X100.006128786763797473848169525349436733670204950,000$
6Michael McLeod (R)X100.005237738362635682616470637343426441660191500,000$
7Julien Gauthier (R)X100.006129775574626167586973486144425641620201500,000$
8Nick Merkley (R)X100.005735816157666370806862395942425617610204550,000$
9Mitchell Stephens (R)X100.004827907066796549706658614642425918600202500,000$
10Boris Katchouk (R)X100.006742726364626968625863476342437220600192500,000$
11Kieffer Bellows (R)X100.006024795174666661636171406741426514590191500,000$
12Jake Leschyshyn (R)X100.007436596075577866565853444740407926570182500,000$
13Mirco Mueller (R)X100.007222827174787472557463815655454948720221990,000$
14Ludwig Bystrom (R) (A)X100.007323787874727677497158775861474141710231975,000$
15Erik Cernak (R)X100.007735726979786356417264775049446210700202950,000$
16Matthew Benning (R)X100.006722796866716166457061725549494518660231750,000$
17Juuso Valimaki (R)X100.005030667654615977297947674641417326620192500,000$
18Josh Mahura (R)X100.00513774725457597526665262384141598600191500,000$
Rayé
1Lane MacDermidX100.007744787675677473667472696869662811710281700,000$
2Tomas JurcoX100.007733767282716871717673657364603625710251700,000$
3Chase De Leo (R)X100.006530817066697167697673516145445730660211750,000$
4Vinni Lettieri (R)X100.006539867278777353667656635749484919650222500,000$
5Anders Bjork (R)X100.006029738056626181586863567143434818640211600,000$
6Nathan Bastian (R)X100.004941725760726166796561495942445214600201500,000$
7Max Jones (R)X100.006742666570557360466659544441416116590191500,000$
8William Bitten (R)X100.007140616468516964495664505841425916590191500,000$
9Tim Gettinger (R)X100.005629655164595460505862316541416020540191500,000$
10Roman JosiX100.0073308671747379725370757365645233207102711,000,000$
11Nelson Nogier (R)X100.006641746466676260386657694543434716640211600,000$
12Urho Vaakanainen (R)X100.005623765957575747285936643840407717560182500,000$
13Jonathan Kovacevic (R)X100.006133635655635443356137593542444920550201350,000$
MOYENNE D’ÉQUIPE100.00643375686867666756686359584745562464
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 Gustavsson (R)100.00646774635478795648786445427346630191500,000$
2Daniel Vladar (R)100.00536644675767557065555742436820590203500,000$
Rayé
1Brandon Halverson100.00727772707572727167687348445239700211850,000$
2Karel Vejmelka (R)100.00725869787460556271487145475514640212500,000$
3Joey Daccord100.00676665485273735352685344435520600211600,000$
MOYENNE D’ÉQUIPE100.0066676565627067626163644544612863
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Adams Oates53568572375954CAN531950,000$


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)C77417511684001101492437615616.87%35157620.48112031402660000692353.82%24193630001.4703000944
2Nathan WalkerMoose (Win)LW75285785670011197285881659.82%44148719.83517223023900051486149.49%1965833001.1437000141
3Mathieu JosephMoose (Win)RW7743378014555147971966210421.94%30144018.71913223321711261224051.69%1184431011.1104100337
4Kyle ConnorMoose (Win)LW78363470-141751159227010418113.33%42169021.679101944255303151872342.49%1935824100.8369000534
5Tomas JurcoMoose (Win)RW71333164-546094612044810616.18%39120917.037714231551012572054.24%1184226021.0602000541
6Nikolay GoldobinMoose (Win)LW73293160410087712156611713.49%3699813.68751216621012911445.26%1373920101.2036000451
7Michael McLeodMoose (Win)C793120518320661052306911813.48%28111414.1126810851013925143.90%7704925010.9223000322
8Mirco MuellerMoose (Win)D7664046-738012716212545504.80%138202926.705611242230006258110.00%03773000.4500000304
9Chase De LeoMoose (Win)C73142741-13200905915354869.15%2296013.15369101230001320048.31%5613414000.8501000113
10Ludwig BystromMoose (Win)D8053439154013915313860673.62%146211926.49257222930002255000.00%04669000.3700000121
11Julien GauthierMoose (Win)RW781213251120613670145617.14%1684710.870113280001533044.44%451210000.5900000210
12Erik CernakMoose (Win)D7232124-156951171087427254.05%87151821.0910161590331157000.00%01456000.3201000111
13Roman JosiMoose (Win)D7432124-231808111811953472.52%93158921.47246182220110166000.00%03555000.3012000001
14Anders BjorkMoose (Win)LW3951318-314042178636555.81%1441210.57000070005440053.33%15225000.8722000100
15Vinni LettieriMoose (Win)RW4841317-102044215219337.69%1759612.43112142000000054.55%2266000.5700000000
16Jake LeschyshynMoose (Win)C696915-7400743437122916.22%76599.55011060000731041.29%264111000.4600000010
17Juuso ValimakiMoose (Win)D59114151338020472223154.55%4275612.83011425000034000.00%0322000.4000000010
18Nick MerkleyMoose (Win)RW386915240191146162413.04%32977.8200009000000160.00%1022001.0100000011
19Boris KatchoukMoose (Win)LW636915-10140422346224413.04%134897.78000122000070040.00%15195000.6101000010
20Lane MacDermidMoose (Win)LW185611-910022175618238.93%1129016.132026150001260148.48%3374000.7601000010
21Matthew BenningMoose (Win)D74279624045865323153.77%65105014.19011173000177000.00%0830000.1700000010
22Kieffer BellowsMoose (Win)RW38415-178016162472516.67%22827.4300000000010033.33%394000.3500000000
23Josh MahuraMoose (Win)D30134712072616596.25%1234611.5600022200006000.00%0611000.2300000000
24Mitchell StephensMoose (Win)C14112-50051383312.50%11359.7000002000030157.14%5614000.2900000000
25Nelson NogierMoose (Win)D17022-9808218310.00%1421012.3800000011115000.00%029000.1900000000
26Nathan BastianMoose (Win)RW91121401352220.00%2404.5300001000080066.67%320000.9800000001
27Sam ReinhartJetsRW2011-500846160.00%14422.2401138000060050.00%1441000.4500000000
28William BittenMoose (Win)RW16011-6407510050.00%0915.700000000001000.00%211000.2200000000
29Urho VaakanainenMoose (Win)D8000320153200.00%3769.620000000002000.00%000000.00%00000000
30Max JonesMoose (Win)LW7000-200010000.00%181.230000000000000.00%000000.00%00000000
Statistiques d’équipe totales ou en moyenne1532326531857-8666515170616582800958156711.64%9642437015.916610517129725697613522006271649.98%4994597581240.701742100393632
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)41171640.8773.932247201471197596310.625163734021
2Brandon HalversonMoose (Win)41151930.8743.952249201481179589310.591223736002
3Karel VejmelkaMoose (Win)70500.8156.52276003016281000.00%0510000
4Daniel VladarMoose (Win)10100.8714.07590043115000.00%010000
Statistiques d’équipe totales ou en moyenne90324170.8724.084834403292569128162388080023


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 Nouveau joueur Poids Taille Non-échange Disponible pour échange Ballotage forcé Contrat Type Salaire actuel Salaire restantPlafond salarial Plafond salarial restant Exclus du plafond salarial Salaire annuel 2Salaire annuel 3Salaire annuel 4Salaire annuel 5Salaire annuel 6Salaire annuel 7Salaire annuel 8Salaire annuel 9Salaire annuel 10Link
Anders BjorkMoose (Win)LW211996-01-01Yes190 Lbs6 ft0NoNoNo1Pro & Farm600,000$0$0$NoLien
Boris KatchoukMoose (Win)LW191998-01-01Yes206 Lbs6 ft2NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Brandon HalversonMoose (Win)G211996-01-01No203 Lbs6 ft4NoNoNo1Pro & Farm850,000$0$0$NoLien
Chase De LeoMoose (Win)C211996-01-01Yes179 Lbs5 ft9NoNoNo1Pro & Farm750,000$0$0$NoLien
Daniel VladarMoose (Win)G201997-01-01Yes185 Lbs6 ft5NoNoNo3Pro & Farm500,000$0$0$No700,000$750,000$Lien
Dylan StromeMoose (Win)C201997-01-01Yes200 Lbs6 ft3NoNoNo4Pro & Farm950,000$0$0$No975,000$1,500,000$2,250,000$Lien
Erik CernakMoose (Win)D201997-01-01Yes233 Lbs6 ft3NoNoNo2Pro & Farm950,000$0$0$No975,000$Lien
Filip GustavssonMoose (Win)G191998-01-01Yes183 Lbs6 ft2NoNoNo1Pro & Farm500,000$0$0$NoLien
Jake LeschyshynMoose (Win)C181999-01-01Yes192 Lbs5 ft11NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Joey DaccordMoose (Win)G211996-01-01No197 Lbs6 ft2NoNoNo1Pro & Farm600,000$0$0$NoLien
Jonathan KovacevicMoose (Win)D201997-01-01Yes208 Lbs6 ft4NoNoNo1Pro & Farm350,000$0$0$NoLien
Josh MahuraMoose (Win)D191998-01-01Yes185 Lbs6 ft0NoNoNo1Pro & Farm500,000$0$0$NoLien
Julien GauthierMoose (Win)RW201997-01-01Yes227 Lbs6 ft4NoNoNo1Pro & Farm500,000$0$0$NoLien
Juuso ValimakiMoose (Win)D191998-01-01Yes212 Lbs6 ft2NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Karel VejmelkaMoose (Win)G211996-01-01Yes224 Lbs6 ft4NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Kieffer BellowsMoose (Win)RW191998-01-01Yes194 Lbs6 ft1NoNoNo1Pro & Farm500,000$0$0$NoLien
Kyle ConnorMoose (Win)LW211996-01-01Yes182 Lbs6 ft1NoNoNo3Pro & Farm800,000$0$0$No950,000$1,750,000$Lien
Lane MacDermidMoose (Win)LW281989-01-01No215 Lbs6 ft3NoNoNo1Pro & Farm700,000$0$0$No
Ludwig BystromMoose (Win)D231994-01-01Yes169 Lbs6 ft0NoNoNo1Pro & Farm975,000$0$0$NoLien
Mathieu JosephMoose (Win)RW201997-01-01Yes190 Lbs6 ft1NoNoNo3Pro & Farm950,000$0$0$No975,000$1,000,000$Lien
Matthew BenningMoose (Win)D231994-01-01Yes180 Lbs6 ft0NoNoNo1Pro & Farm750,000$0$0$NoLien
Max JonesMoose (Win)LW191998-01-01Yes220 Lbs6 ft1NoNoNo1Pro & Farm500,000$0$0$NoLien
Michael McLeodMoose (Win)C191998-01-01Yes187 Lbs6 ft2NoNoNo1Pro & Farm500,000$0$0$NoLien
Mirco MuellerMoose (Win)D221995-01-01Yes185 Lbs6 ft4NoNoNo1Pro & Farm990,000$0$0$NoLien
Mitchell StephensMoose (Win)C201997-01-01Yes193 Lbs5 ft11NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Nathan BastianMoose (Win)RW201997-01-01Yes205 Lbs6 ft4NoNoNo1Pro & Farm500,000$0$0$NoLien
Nathan WalkerMoose (Win)LW231994-01-01Yes186 Lbs5 ft9NoNoNo1Pro & Farm850,000$0$0$NoLien
Nelson NogierMoose (Win)D211996-01-01Yes191 Lbs6 ft2NoNoNo1Pro & Farm600,000$0$0$NoLien
Nick MerkleyMoose (Win)RW201997-01-01Yes194 Lbs5 ft10NoNoNo4Pro & Farm550,000$0$0$No550,000$650,000$650,000$Lien
Nikolay GoldobinMoose (Win)LW211996-01-01Yes196 Lbs5 ft11NoNoNo1Pro & Farm850,000$0$0$NoLien
Roman JosiMoose (Win)D271990-01-01No198 Lbs6 ft2NoNoNo1Pro & Farm1,000,000$0$0$No
Tim GettingerMoose (Win)LW191998-01-01Yes220 Lbs6 ft6NoNoNo1Pro & Farm500,000$0$0$NoLien
Tomas JurcoMoose (Win)RW251992-01-01No203 Lbs6 ft1NoNoNo1Pro & Farm700,000$0$0$No
Urho VaakanainenMoose (Win)D181999-01-01Yes200 Lbs6 ft2NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Vinni LettieriMoose (Win)RW221995-01-01Yes185 Lbs5 ft11NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
William BittenMoose (Win)RW191998-01-01Yes179 Lbs5 ft10NoNoNo1Pro & Farm500,000$0$0$NoLien
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
3620.78197 Lbs6 ft11.56646,250$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Kyle ConnorDylan StromeNick Merkley40122
2Nathan WalkerMichael McLeodMathieu Joseph30122
3Nikolay GoldobinMitchell StephensJulien Gauthier20122
4Boris KatchoukJake LeschyshynKieffer Bellows10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Mirco MuellerJuuso Valimaki40122
2Josh MahuraErik Cernak30122
3Matthew BenningLudwig Bystrom20122
4Mirco MuellerLudwig Bystrom10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Kyle ConnorDylan StromeNick Merkley60122
2Nikolay GoldobinMichael McLeodMathieu Joseph40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Mirco MuellerJuuso Valimaki60122
2Ludwig BystromJosh Mahura40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Dylan StromeNathan Walker60122
2Mitchell StephensKyle Connor40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Mirco MuellerLudwig Bystrom60122
2Matthew BenningErik Cernak40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Mitchell Stephens60122Mirco MuellerLudwig Bystrom60122
2Jake Leschyshyn40122Matthew BenningErik Cernak40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Dylan StromeKyle Connor60122
2Michael McLeodMathieu Joseph40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Mirco MuellerLudwig Bystrom60122
2Juuso ValimakiErik Cernak40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Kyle ConnorDylan StromeMathieu JosephMirco MuellerLudwig Bystrom
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Kyle ConnorDylan StromeMathieu JosephMirco MuellerErik Cernak
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Mathieu Joseph, Dylan Strome, Kyle ConnorMathieu Joseph, Dylan StromeMathieu Joseph
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Erik Cernak, Matthew Benning, Josh MahuraErik CernakMatthew Benning, Josh Mahura
Tirs de pénalité
Dylan Strome, Nathan Walker, Kyle Connor, Nikolay Goldobin, Mathieu Joseph
Gardien
#1 : Daniel Vladar, #2 : Filip Gustavsson


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
1Admirals311000011213-12100000110821010000025-330.50012193100969711416117890884912499139186311218.18%9366.67%0973191850.73%882183048.20%677131251.60%172195916947491430718
2Barracuda613001102428-4302001001014-4311000101414050.4172439630096971141621789088491249202666211331619.35%311067.74%0973191850.73%882183048.20%677131251.60%172195916947491430718
3Bears2100000110641000000145-11100000061530.750101626009697114166789088491249692512356350.00%6183.33%0973191850.73%882183048.20%677131251.60%172195916947491430718
4Comets2020000069-31010000045-11010000024-200.000611170096971141679890884912496219143911218.18%7357.14%0973191850.73%882183048.20%677131251.60%172195916947491430718
5Condors622010102116531200000910-131001010126680.6672132530096971141620089088491249181875411935514.29%28389.29%3973191850.73%882183048.20%677131251.60%172195916947491430718
6Crunch2020000059-41010000035-21010000024-200.0005813109697114166889088491249682512451200.00%6266.67%0973191850.73%882183048.20%677131251.60%172195916947491430718
7Devils211000009811010000045-11100000053220.50091423009697114165789088491249572122444125.00%11281.82%1973191850.73%882183048.20%677131251.60%172195916947491430718
8Eagles201000101213-11010000046-21000001087120.5001217291096971141656890884912497422185010220.00%9188.89%0973191850.73%882183048.20%677131251.60%172195916947491430718
9Griffins623000102021-132100000121203020001089-160.5002031510096971141618289088491249194595214539512.82%26965.38%1973191850.73%882183048.20%677131251.60%172195916947491430718
10Heat30300000815-720200000712-51010000013-200.000813210096971141680890884912499227286413323.08%14285.71%0973191850.73%882183048.20%677131251.60%172195916947491430718
11Icehogs30300000913-41010000024-22020000079-200.00091524009697114169189088491249883015764125.00%60100.00%0973191850.73%882183048.20%677131251.60%172195916947491430718
12Little Stars412001001315-2311001001011-11010000034-130.37513223500969711416133890884912491344634898112.50%17288.24%0973191850.73%882183048.20%677131251.60%172195916947491430718
13Marlies321000001917211000000105521100000912-340.6671933520096971141611489088491249892426639444.44%13653.85%0973191850.73%882183048.20%677131251.60%172195916947491430718
14Monsters30300000514-91010000024-220200000310-700.00059140096971141610089088491249109443263600.00%16568.75%0973191850.73%882183048.20%677131251.60%172195916947491430718
15Penguins30200100814-61010000035-22010010059-410.1678152300969711416108890884912499737264610220.00%13192.31%0973191850.73%882183048.20%677131251.60%172195916947491430718
16Phantoms321000001515021100000911-21100000064240.6671524390096971141699890884912491113827519111.11%11463.64%0973191850.73%882183048.20%677131251.60%172195916947491430718
17Punishers220000001055110000005231100000053241.0001018280096971141671890884912495220164110330.00%8275.00%0973191850.73%882183048.20%677131251.60%172195916947491430718
18Rampage2010000147-31010000024-21000000123-110.250459009697114166689088491249522820367114.29%10190.00%0973191850.73%882183048.20%677131251.60%172195916947491430718
19Reign422000002218420200000810-222000000148640.500223860009697114161508908849124912645388615640.00%19289.47%0973191850.73%882183048.20%677131251.60%172195916947491430718
20Rocket624000002331-83120000013103312000001021-1140.3332337600096971141619289088491249195676514126934.62%32778.13%0973191850.73%882183048.20%677131251.60%172195916947491430718
21Senators3300000016791100000063322000000104661.0001629450096971141698890884912499044106315426.67%50100.00%0973191850.73%882183048.20%677131251.60%172195916947491430718
22Sound Tigers210000011091110000006421000000145-130.750101626009697114167189088491249653110511119.09%5340.00%0973191850.73%882183048.20%677131251.60%172195916947491430718
23Thunderbirds20200000711-41010000036-31010000045-100.0007142100969711416728908849124972251241800.00%6183.33%0973191850.73%882183048.20%677131251.60%172195916947491430718
24Wolfpack21100000880110000004221010000046-220.5008152300969711416638908849124970291435700.00%7185.71%0973191850.73%882183048.20%677131251.60%172195916947491430718
25Wolves43100000161332200000073421100000910-160.75016284400969711416145890884912491314018901317.69%9366.67%0973191850.73%882183048.20%677131251.60%172195916947491430718
Total80274101344312335-2340142200202157166-940131901142155169-14710.44431251883020969711416269689088491249257193865516893306319.09%3247477.16%5973191850.73%882183048.20%677131251.60%172195916947491430718
_Since Last GM Reset80274101344312335-2340142200202157166-940131901142155169-14710.44431251883020969711416269689088491249257193865516893306319.09%3247477.16%5973191850.73%882183048.20%677131251.60%172195916947491430718
_Vs Conference49172601131194208-14248140010198103-5259120103096105-9440.44919431951300969711416164089088491249156857042710472134621.60%2105175.71%4973191850.73%882183048.20%677131251.60%172195916947491430718
_Vs Division24712011308896-81247001004446-21235010304450-6230.4798813922700969711416791890884912497722792335181312519.08%1172975.21%4973191850.73%882183048.20%677131251.60%172195916947491430718

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
8071L231251883026962571938655168920
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
8027411344312335
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
4014220202157166
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
4013191142155169
Derniers 10 matchs
WLOTWOTL SOWSOL
090001
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
3306319.09%3247477.16%5
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
89088491249969711416
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
973191850.73%882183048.20%677131251.60%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
172195916947491430718


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
1 - 2022-10-272Rocket5Moose4BLR1Sommaire du match
3 - 2022-10-2911Moose3Condors2AWXXR1Sommaire du match
6 - 2022-11-0131Moose5Barracuda4AWXXSommaire du match
9 - 2022-11-0446Little Stars2Moose4BWSommaire du match
12 - 2022-11-0763Griffins3Moose4BWR1Sommaire du match
14 - 2022-11-0973Moose3Griffins2AWXXSommaire du match
16 - 2022-11-1181Moose3Rocket8ALR1Sommaire du match
19 - 2022-11-1496Barracuda4Moose2BLR1Sommaire du match
21 - 2022-11-16115Wolves2Moose3BWSommaire du match
23 - 2022-11-18126Moose5Wolves3AWSommaire du match
26 - 2022-11-21139Condors4Moose3BLR1Sommaire du match
28 - 2022-11-23151Moose6Condors2AWSommaire du match
32 - 2022-11-27171Condors4Moose3BLR1Sommaire du match
35 - 2022-11-30185Moose8Barracuda4AWSommaire du match
37 - 2022-12-02193Griffins5Moose7BWR1Sommaire du match
39 - 2022-12-04209Moose2Comets4ALSommaire du match
42 - 2022-12-07221Eagles6Moose4BLSommaire du match
45 - 2022-12-10237Monsters4Moose2BLSommaire du match
48 - 2022-12-13250Moose6Phantoms4AWSommaire du match
52 - 2022-12-17265Rocket3Moose2BLR1Sommaire du match
55 - 2022-12-20284Moose4Wolfpack6ALSommaire du match
57 - 2022-12-22294Little Stars5Moose4BLXSommaire du match
61 - 2022-12-26313Moose2Rampage3ALXXSommaire du match
62 - 2022-12-27319Condors2Moose3BWR1Sommaire du match
66 - 2022-12-31335Moose1Heat3ALSommaire du match
67 - 2023-01-01345Griffins4Moose1BLR1Sommaire du match
70 - 2023-01-04360Moose5Punishers3AWSommaire du match
72 - 2023-01-06370Moose2Admirals5ALSommaire du match
73 - 2023-01-07376Thunderbirds6Moose3BLSommaire du match
77 - 2023-01-11396Heat5Moose3BLSommaire du match
81 - 2023-01-15417Moose7Reign4AWSommaire du match
82 - 2023-01-16424Senators3Moose6BWSommaire du match
85 - 2023-01-19437Moose6Bears1AWSommaire du match
87 - 2023-01-21449Bears5Moose4BLXXSommaire du match
91 - 2023-01-25469Moose7Reign4AWSommaire du match
92 - 2023-01-26475Punishers2Moose5BWSommaire du match
95 - 2023-01-29497Wolves1Moose4BWSommaire du match
97 - 2023-01-31506Moose8Eagles7AWXXSommaire du match
101 - 2023-02-04524Little Stars4Moose2BLSommaire du match
104 - 2023-02-07540Moose6Marlies4AWSommaire du match
106 - 2023-02-09551Phantoms4Moose5BWSommaire du match
108 - 2023-02-11562Moose3Marlies8ALSommaire du match
110 - 2023-02-13574Moose4Thunderbirds5ALSommaire du match
111 - 2023-02-14581Penguins5Moose3BLSommaire du match
115 - 2023-02-18597Moose2Crunch4ALSommaire du match
117 - 2023-02-20606Sound Tigers4Moose6BWSommaire du match
119 - 2023-02-22618Moose1Rocket9ALR1Sommaire du match
122 - 2023-02-25634Rocket2Moose7BWSommaire du match
124 - 2023-02-27644Moose5Devils3AWSommaire du match
126 - 2023-03-01653Moose4Icehogs5ALSommaire du match
128 - 2023-03-03662Wolfpack2Moose4BWSommaire du match
130 - 2023-03-05680Moose4Wolves7ALSommaire du match
132 - 2023-03-07687Heat7Moose4BLSommaire du match
134 - 2023-03-09701Moose3Condors2AWXR1Sommaire du match
136 - 2023-03-11713Moose2Penguins5ALSommaire du match
137 - 2023-03-12717Devils5Moose4BLSommaire du match
141 - 2023-03-16738Crunch5Moose3BLSommaire du match
144 - 2023-03-19751Moose6Rocket4AWR1Sommaire du match
147 - 2023-03-22764Comets5Moose4BLSommaire du match
149 - 2023-03-24776Moose3Little Stars4ALSommaire du match
152 - 2023-03-27791Moose3Penguins4ALXSommaire du match
153 - 2023-03-28797Admirals3Moose6BWSommaire du match
156 - 2023-03-31812Moose4Sound Tigers5ALXXSommaire du match
158 - 2023-04-02820Icehogs4Moose2BLSommaire du match
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
161 - 2023-04-05834Moose3Icehogs4ALSommaire du match
164 - 2023-04-08849Marlies5Moose10BWSommaire du match
166 - 2023-04-10856Moose4Senators1AWSommaire du match
168 - 2023-04-12871Barracuda4Moose3BLXR1Sommaire du match
170 - 2023-04-14882Moose6Senators3AWSommaire du match
172 - 2023-04-16894Moose3Griffins4ALR1Sommaire du match
174 - 2023-04-18900Moose2Monsters3ALSommaire du match
176 - 2023-04-20908Barracuda6Moose5BLR1Sommaire du match
178 - 2023-04-22921Moose2Griffins3ALR1Sommaire du match
179 - 2023-04-23927Moose1Barracuda6ALSommaire du match
181 - 2023-04-25936Phantoms7Moose4BLSommaire du match
183 - 2023-04-27946Moose1Monsters7ALSommaire du match
186 - 2023-04-30961Reign6Moose5BLSommaire du match
192 - 2023-05-06986Admirals5Moose4BLXXSommaire du match
198 - 2023-05-121010Rampage4Moose2BLSommaire du match
203 - 2023-05-171027Reign4Moose3BLSommaire du match



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets3515
Assistance73,60137,754
Assistance PCT92.00%94.39%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
0 2784 - 92.80% 82,487$3,299,461$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursSalaire total moyen des joueursSalaire des entraineurs
3,208,984$ 2,326,500$ 1,976,500$ 0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
11,294$ 2,233,988$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
0$ 0 15,905$ 0$




Moose Leaders statistiques (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 (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