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Moose
GP: 34 | W: 15 | L: 13 | OTL: 6 | P: 36
GF: 69 | GA: 77 | PP%: 22.97% | PK%: 75.76%
DG: Quentin Robb | Morale : 43 | Moyenne d’équipe : 58
Prochains matchs #540 vs Islanders

Centre de jeu
Checkers
13-16-5, 31pts
0
2 Moose
15-13-6, 36pts
Team Stats
L2SéquenceL1
8-8-2Fiche domicile7-8-3
5-8-3Fiche domicile8-5-3
4-6-0Derniers 10 matchs6-3-1
2.38Buts par match 2.03
3.03Buts contre par match 2.26
27.03%Pourcentage en avantage numérique22.97%
68.49%Pourcentage en désavantage numérique75.76%
Bears
27-6-1, 55pts
3
1 Moose
15-13-6, 36pts
Team Stats
W7SéquenceL1
13-4-0Fiche domicile7-8-3
14-2-1Fiche domicile8-5-3
9-1-0Derniers 10 matchs6-3-1
3.82Buts par match 2.03
2.47Buts contre par match 2.26
35.71%Pourcentage en avantage numérique22.97%
73.68%Pourcentage en désavantage numérique75.76%
Moose
15-13-6, 36pts
Jour 43
Islanders
19-13-3, 41pts
Statistiques d’équipe
L1SéquenceW1
7-8-3Fiche domicile10-6-1
8-5-3Fiche visiteur9-7-2
6-3-110 derniers matchs3-6-1
2.03Buts par match 3.83
2.26Buts contre par match 3.83
22.97%Pourcentage en avantage numérique18.45%
75.76%Pourcentage en désavantage numérique73.33%
Moose
15-13-6, 36pts
Jour 44
Roadrunners
15-13-7, 37pts
Statistiques d’équipe
L1SéquenceL4
7-8-3Fiche domicile7-6-3
8-5-3Fiche visiteur8-7-4
6-3-110 derniers matchs2-4-4
2.03Buts par match 3.91
2.26Buts contre par match 3.91
22.97%Pourcentage en avantage numérique23.26%
75.76%Pourcentage en désavantage numérique76.03%
Checkers
13-16-5, 31pts
Jour 45
Moose
15-13-6, 36pts
Statistiques d’équipe
L2SéquenceL1
8-8-2Fiche domicile7-8-3
5-8-3Fiche visiteur8-5-3
4-6-010 derniers matchs6-3-1
2.38Buts par match 2.03
3.03Buts contre par match 2.03
27.03%Pourcentage en avantage numérique22.97%
68.49%Pourcentage en désavantage numérique75.76%
Meneurs d'équipe
Derek RyanButs
Derek Ryan
15
Tyson JostPasses
Tyson Jost
15
Tyson JostPoints
Tyson Jost
27
Plus/Moins
Adam Boqvist
5
James ReimerVictoires
James Reimer
15
Pourcentage d’arrêts
Marcus Hogberg
0.925

Statistiques d’équipe
Buts pour
69
2.03 GFG
Tirs pour
455
13.38 Avg
Pourcentage en avantage numérique
23.0%
17 GF
Début de zone offensive
34.1%
Buts contre
77
2.26 GAA
Tirs contre
612
18.00 Avg
Pourcentage en désavantage numérique
75.8%%
16 GA
Début de la zone défensive
39.3%
Informations de l'équipe

Directeur généralQuentin Robb
EntraîneurBob Essensa
DivisionDivision 4
ConférenceConference 2
CapitaineJohn Klingberg
Assistant #1Adam Boqvist
Assistant #2Tyson Jost


Informations de l’aréna

Capacité3,000
Assistance2,866
Billets de saison1,500


Informations de la formation

Équipe Pro27
Équipe Mineure19
Limite contact 46 / 100
Espoirs149


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
1Phillip Di GiuseppeX100.0092469473705960613170576863676865406403231,750,000$
2Jakub VranaX100.0065429181715659633662745869707268426402931,600,000$
3Chris WagnerXX95.0085908576724788595550567163717461526303431,600,000$
4Derek RyanXX99.0069428970655466608761586975757861596303912,250,000$
5Tyson Jost (A)XXX99.0077577676685670645262606025727562606202732,000,000$
6Ross JohnstonXX100.0096995671895262622557546525656660606103212,160,000$
7Alex Barre-BouletXX100.0068638167635755668065636160454563556002821,524,000$
8Joseph BlandisiX100.0065676166677174637860635859454561396003112,190,000$
9Taylor WardX100.007576746776778355504956615446455951590271972,000$
10Kurtis MacDermidX100.0082997374904448572550545825666657535803122,000,000$
11Wyatt BongiovanniX100.0074708666705250605049666364454563535802611,063,333$
12Chase WoutersX100.0068677465675556536850525850454556525502621,064,000$
13Oliver KylingtonX100.0059418887675946602555486925646558526302832,000,000$
14Adam Boqvist (A)X100.0063419169706456742563516325656661556202531,484,000$
15John Klingberg (C)X100.0065427968707139592560506825778159366203331,750,000$
16Chase PriskieX100.0075708766707377622554586350464559546202912,157,000$
17Dillon HeatheringtonX100.0081817962816874472537416237464650535903012,126,000$
18Gustav OlofssonX100.0079768564766266512545446239454553465803121,200,000$
19Nikolas BrouillardX100.0066627866625455532549435640464552525503111,041,000$
Rayé
1Devin Kaplan (R)X100.0081739980734546445038446342444453295302231,265,000$
2Kevin ConleyXXX100.0074727962724443486044476045444452205102911,026,000$
3Travis HoweX100.0071834260834445455040445842444448205003211,007,000$
4Kalan Lind (R)X100.0059644780643230445038445242444447204802131,326,000$
5Connor Kelley (R)X100.0081739980733230412528396237444450335402331,330,000$
MOYENNE D’ÉQUIPE99.71746779717255575644525362455556574559
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
1James Reimer100.006054577865577065676183737462796503732,211,000$
2Marcus Hogberg (R)100.005952529665527059655971505060616203132,180,000$
Rayé
1Thomas Milic100.004440506543425051464730444446334702231,025,000$
MOYENNE D’ÉQUIPE100.0054495380585063585956615656565858
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Bob Essensa45444345434337TUR812800,000$


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
1Tyson JostMoose (WPG)C/LW/RW341215274140505063194819.05%770720.803475400001402146.58%73207000.7600000324
2Jakub VranaMoose (WPG)LW2891221-240334753234316.98%1156420.1544810480000142042.08%1831711000.7403000131
3Derek RyanMoose (WPG)C/RW3415318095364451153329.41%667920.0020210490001212160.00%4079000.5312001213
4Chris WagnerMoose (WPG)C/RW346915-64830634338112015.79%1270720.810663480000363049.57%117915000.4202123211
5Adam BoqvistMoose (WPG)D340141452022413616110%3276122.38011247000013000%01517000.3700000022
6Chase PriskieMoose (WPG)D34212142121048592920146.90%2986225.38112365000043000%01220000.3200011101
7Phillip Di GiuseppeMoose (WPG)LW276713-130042404382413.95%554620.263258430001312041.49%9497000.4811000210
8Alex Barre-BouletMoose (WPG)C/RW345510210033432631419.23%651615.18101112000031055.84%19777000.3900000001
9Ross JohnstonMoose (WPG)LW/RW3428104571533271551313.33%242012.36156623000001039.26%16324000.4800102112
10Oliver KylingtonMoose (WPG)D34077-620274811660%1467619.8800004000044000%377000.2100000010
11Dillon HeatheringtonMoose (WPG)D34167-7295395218275.56%1879723.45011247000053000%0115000.1800001010
12Taylor WardMoose (WPG)RW34325-101603941112327.27%1155016.191011280000160041.41%128611000.1812000000
13Joseph BlandisiMoose (WPG)C24044-4753928145110%436515.220220200112180056.76%11167000.2200001100
14Adam KlapkaJetsRW11314-216101313182916.67%423721.620110211012181039.44%7165000.3400101111
15Kurtis MacDermidMoose (WPG)LW34134080302915446.67%235910.5700008000190033.33%914000.2200000000
16Nikolas BrouillardMoose (WPG)D34202-860211980525.00%1358717.27101368000036000%013000.0700000000
17Gustav OlofssonMoose (WPG)D25011-1006150000%12409.630000100002000%011000.0800000000
18Wyatt BongiovanniMoose (WPG)LW34101300157113100.00%01243.660000000002000%101000.1600000001
19Chase WoutersMoose (WPG)C34011120841020%0992.9400000000000054.55%2200000.2000000000
20Devin KaplanMoose (WPG)RW9011100330010%1616.800110300000000%000000.3300000000
21John KlingbergMoose (WPG)D23000-2759304000%727311.8700000000020000%00500000001000
22Connor KelleyMoose (WPG)D16000000000000%0120.800000000000000%00000000000000
Statistiques d’équipe totales ou en moyenne63968111179-392498560968345514227114.95%1851015115.8917284554583112842714246.62%1212127156000.353103311141417
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
1James ReimerMoose (WPG)34151260.8722.1519530570545310100.6679340040
2Marcus HogbergMoose (WPG)30100.9252.8610500567400000034000
Statistiques d’équipe totales ou en moyenne37151360.8772.19205905756123501093434040


Astuces sur les filtres (anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
Nom du joueur Nom de l’équipePOS Âge Date de naissance Pays Recrue Poids Taille Non-échange Disponible pour échange Acquis Par Date de la Dernière Transaction Ballotage forcé Waiver Possible Contrat Date du Signature du Contrat Forcer UFA Rappel d'urgence Type Salaire actuel Salaire restantPlafond salarial Plafond salarial restant Exclus du plafond salarial Salaire année 2Salaire année 3Salaire année 4Salaire année 5Salaire année 6Salaire année 7Salaire année 8Salaire année 9Salaire année 10Plafond salarial année 2Plafond salarial année 3Plafond salarial année 4Plafond salarial année 5Plafond salarial année 6Plafond salarial année 7Plafond salarial année 8Plafond salarial année 9Plafond salarial année 10Non-échange année 2Non-échange année 3Non-échange année 4Non-échange année 5Non-échange année 6Non-échange année 7Non-échange année 8Non-échange année 9Non-échange année 10Lien
Adam BoqvistMoose (WPG)D252000-08-15SWENo191 Lbs6 ft0NoNoN/ANoNo32025-10-22FalseFalsePro & Farm1,484,000$927,500$0$0$No1,484,000$1,484,000$-------1,484,000$1,484,000$-------NoNo-------Lien
Alex Barre-BouletMoose (WPG)C/RW281997-05-21QUENo178 Lbs5 ft10NoNoN/ANoNo2FalseFalsePro & Farm1,524,000$952,500$0$0$No1,524,000$--------1,524,000$--------No--------Lien / Lien NHL
Chase PriskieMoose (WPG)D291996-03-19USANo197 Lbs5 ft11NoNoN/ANoNo1FalseFalsePro & Farm2,157,000$1,348,125$0$0$No---------------------------Lien
Chase WoutersMoose (WPG)C262000-02-08SKWNo182 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm1,064,000$665,000$0$0$No1,064,000$--------1,064,000$--------No--------Lien
Chris WagnerMoose (WPG)C/RW341991-05-27USANo192 Lbs6 ft0NoNoAssign ManuallyNoNo32025-11-06FalseFalsePro & Farm1,600,000$1,000,000$0$0$No1,600,000$1,600,000$-------1,600,000$1,600,000$-------NoNo-------Lien / Lien NHL
Connor KelleyMoose (WPG)D232002-07-30USAYes195 Lbs6 ft2NoNoProspectNoNo32025-10-22FalseFalsePro & Farm1,330,000$831,250$0$0$No1,330,000$1,330,000$-------1,330,000$1,330,000$-------NoNo-------Lien
Derek RyanMoose (WPG)C/RW391986-12-29USANo185 Lbs5 ft10NoNoFree AgentNoNo12025-08-28FalseFalsePro & Farm2,250,000$1,406,250$0$0$No---------------------------Lien / Lien NHL
Devin KaplanMoose (WPG)RW222004-01-10USAYes199 Lbs6 ft2NoNoProspectNoNo32025-10-22FalseFalsePro & Farm1,265,000$790,625$0$0$No1,265,000$1,265,000$-------1,265,000$1,265,000$-------NoNo-------Lien
Dillon HeatheringtonMoose (WPG)D301995-05-09CANNo215 Lbs6 ft4NoNoN/ANoNo1FalseFalsePro & Farm2,126,000$1,328,750$0$0$No---------------------------Lien
Gustav OlofssonMoose (WPG)D311994-12-01SWENo201 Lbs6 ft3NoNoFree AgentNoNo22024-09-18FalseFalsePro & Farm1,200,000$750,000$0$0$No1,200,000$--------1,200,000$--------No--------Lien
Jakub VranaMoose (WPG)LW291996-02-28CZENo197 Lbs6 ft0NoNoAssign ManuallyNoNo32025-11-06FalseFalsePro & Farm1,600,000$1,000,000$0$0$No1,600,000$1,600,000$-------1,600,000$1,600,000$-------NoNo-------Lien / Lien NHL
James ReimerMoose (WPG)G371988-03-15CANNo200 Lbs6 ft2NoNoAssign ManuallyNoNo32025-12-08FalseFalsePro & Farm2,211,000$1,381,875$0$0$No2,211,000$2,211,000$-------2,211,000$2,211,000$-------NoNo-------Lien / Lien NHL
John KlingbergMoose (WPG)D331992-08-14SWENo185 Lbs6 ft2NoNoAssign ManuallyNoNo32025-11-06FalseFalsePro & Farm1,750,000$1,093,750$0$0$No1,750,000$1,750,000$-------1,750,000$1,750,000$-------NoNo-------Lien / Lien NHL
Joseph BlandisiMoose (WPG)C311994-07-18CANNo183 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm2,190,000$1,368,750$0$0$No---------------------------Lien / Lien NHL
Kalan LindMoose (WPG)LW212005-01-25SKYes170 Lbs6 ft0NoNoDraftNoNo32025-10-22FalseFalsePro & Farm1,326,000$828,750$0$0$No1,326,000$1,326,000$-------1,326,000$1,326,000$-------NoNo-------Lien
Kevin ConleyMoose (WPG)C/LW/RW291997-02-17USANo198 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm1,026,000$641,250$0$0$No---------------------------Lien
Kurtis MacDermidMoose (WPG)LW311994-03-25CANNo233 Lbs6 ft5NoNoFree AgentNoNo22024-09-18FalseFalsePro & Farm2,000,000$1,250,000$0$0$No2,000,000$--------2,000,000$--------No--------Lien / Lien NHL
Marcus HogbergMoose (WPG)G311994-11-25SWEYes234 Lbs6 ft5NoNoAssign ManuallyNoNo32025-12-08FalseFalsePro & Farm2,180,000$1,362,500$0$0$No2,180,000$2,180,000$-------2,180,000$2,180,000$-------NoNo-------Lien
Nikolas BrouillardMoose (WPG)D311995-02-07QUENo172 Lbs5 ft10NoNoN/ANoNo1FalseFalsePro & Farm1,041,000$650,625$0$0$No---------------------------Lien
Oliver KylingtonMoose (WPG)D281997-05-19SWENo183 Lbs6 ft0NoNoAssign ManuallyNoNo32025-11-06FalseFalsePro & Farm2,000,000$1,250,000$0$0$No2,000,000$2,000,000$-------2,000,000$2,000,000$-------NoNo-------Lien
Phillip Di GiuseppeMoose (WPG)LW321993-10-09CANNo193 Lbs6 ft0NoNoAssign ManuallyNoNo32025-11-06FalseFalsePro & Farm1,750,000$1,093,750$0$0$No1,750,000$1,750,000$-------1,750,000$1,750,000$-------NoNo-------Lien
Ross JohnstonMoose (WPG)LW/RW321994-02-18CANNo232 Lbs6 ft5NoNoN/ANoNo1FalseFalsePro & Farm2,160,000$1,350,000$0$0$No---------------------------Lien / Lien NHL
Taylor WardMoose (WPG)RW271998-03-31CANNo207 Lbs6 ft2NoNoN/ANoNo1FalseFalsePro & Farm972,000$607,500$0$0$No---------------------------Lien
Thomas MilicMoose (WPG)G222003-04-13BCNo179 Lbs6 ft0NoNoN/ANoNo32025-10-17FalseFalsePro & Farm1,025,000$640,625$0$0$No1,025,000$1,025,000$-------1,025,000$1,025,000$-------NoNo-------Lien
Travis HoweMoose (WPG)RW321994-02-10USANo229 Lbs6 ft3NoNoN/ANoNo1FalseFalsePro & Farm1,007,000$629,375$0$0$No---------------------------Lien
Tyson JostMoose (WPG)C/LW/RW271998-03-14CANNo187 Lbs5 ft11NoNoAssign ManuallyNoNo32025-11-06FalseFalsePro & Farm2,000,000$1,250,000$0$0$No2,000,000$2,000,000$-------2,000,000$2,000,000$-------NoNo-------Lien / Lien NHL
Wyatt BongiovanniMoose (WPG)LW261999-07-24USANo193 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm1,063,333$664,583$0$0$No---------------------------Lien
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2729.11197 Lbs6 ft12.111,603,753$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Jakub VranaAlex Barre-BouletChris Wagner40122
2Tyson JostRoss JohnstonDerek Ryan30122
3Derek RyanChris WagnerTyson Jost20122
4Derek RyanTaylor WardChris Wagner10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Adam BoqvistChase Priskie40122
2Oliver KylingtonDillon Heatherington30122
3Dillon HeatheringtonAdam Boqvist20122
4Nikolas BrouillardChase Priskie10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Tyson JostRoss JohnstonJakub Vrana60122
2Derek RyanTaylor WardChris Wagner40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Chase PriskieNikolas Brouillard60122
2Adam BoqvistDillon Heatherington40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Taylor WardChris Wagner60122
2Derek RyanTyson Jost40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Dillon HeatheringtonOliver Kylington60122
2Chase PriskieNikolas Brouillard40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Derek Ryan60122Dillon HeatheringtonOliver Kylington60122
2Chris Wagner40122Nikolas BrouillardChase Priskie40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Chris WagnerDerek Ryan60122
2Tyson JostTaylor Ward40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Nikolas BrouillardChase Priskie60122
2Dillon HeatheringtonOliver Kylington40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Derek RyanChris WagnerJakub VranaDillon HeatheringtonChase Priskie
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Derek RyanChris WagnerJakub VranaDillon HeatheringtonChase Priskie
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Derek Ryan, Tyson Jost, Chris WagnerDerek Ryan, Chris WagnerChris Wagner
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Dillon Heatherington, Chase Priskie, Nikolas BrouillardDillon HeatheringtonChase Priskie, Dillon Heatherington
Tirs de pénalité
Taylor Ward, Chris Wagner, Jakub Vrana, Derek Ryan, Tyson Jost
Gardien
#1 : James Reimer, #2 : Marcus Hogberg


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
1Admirals42100100121021010000045-13200010085350.62512193100202322671122153165259828487712325.00%9188.89%019040347.15%21346545.81%16231551.43%567182609378884455
2Americans2010100049-5000000000002010100049-520.50046100020232262312215316525702732335120.00%50100.00%019040347.15%21346545.81%16231551.43%567182609378884455
3Barracuda21000001211110000002021000000101-130.750235012023226161221531652515611333133.33%30100.00%019040347.15%21346545.81%16231551.43%567182609378884455
4Bears1010000013-21010000013-20000000000000.0001120020232261212215316525177812000%4250.00%019040347.15%21346545.81%16231551.43%567182609378884455
5Bruins1010000001-11010000001-10000000000000.000000002023226121221531652552219100.00%10100.00%019040347.15%21346545.81%16231551.43%567182609378884455
6Checkers22000000514220000005140000000000041.0005813012023226251221531652520311396233.33%30100.00%019040347.15%21346545.81%16231551.43%567182609378884455
7Comets1010000003-31010000003-30000000000000.0000000020232261012215316525126425300.00%20100.00%019040347.15%21346545.81%16231551.43%567182609378884455
8Condors1010000025-31010000025-30000000000000.0002460020232261812215316525481024144125.00%20100.00%019040347.15%21346545.81%16231551.43%567182609378884455
9Crunch11000000312000000000001100000031221.00035800202322618122153165252172115200.00%3166.67%019040347.15%21346545.81%16231551.43%567182609378884455
10Eagles2010001023-11010000013-21000001010120.50021301202322626122153165253112648800.00%3166.67%019040347.15%21346545.81%16231551.43%567182609378884455
11Gulls210001007521000010034-11100000041330.750711180020232263212215316525231113313133.33%4250.00%019040347.15%21346545.81%16231551.43%567182609378884455
12Heat3110010045-1210001004311010000002-230.500461000202322631122153165254168438112.50%40100.00%119040347.15%21346545.81%16231551.43%567182609378884455
13Marlies11000000422110000004220000000000021.000461000202322622122153165258442022100.00%2150.00%019040347.15%21346545.81%16231551.43%567182609378884455
14Monsters10001000431000000000001000100043121.000461000202322618122153165251551424200.00%220.00%019040347.15%21346545.81%16231551.43%567182609378884455
15Penguins11000000312110000003120000000000021.00035800202322619122153165251214242150.00%2150.00%019040347.15%21346545.81%16231551.43%567182609378884455
16Reign1010000046-21010000046-20000000000000.00046100020232262012215316525501410194375.00%5180.00%019040347.15%21346545.81%16231551.43%567182609378884455
17Rocket1010000012-1000000000001010000012-100.0001230020232261312215316525114419000%2150.00%019040347.15%21346545.81%16231551.43%567182609378884455
18Senators2020000015-41010000013-21010000002-200.0001230020232261112215316525358728300.00%110.00%019040347.15%21346545.81%16231551.43%567182609378884455
19Stars2010010037-41000010012-11010000025-310.25036900202322627122153165253078313133.33%4175.00%019040347.15%21346545.81%16231551.43%567182609378884455
20Thunderbirds1000000123-1000000000001000000123-110.50024600202322610122153165253913625200.00%3166.67%019040347.15%21346545.81%16231551.43%567182609378884455
21Wolf Pack21001000514100010001011100000041341.000510150120232262112215316525114430100.00%20100.00%019040347.15%21346545.81%16231551.43%567182609378884455
Total341113034126977-81868013003642-61655021123335-2360.5296911118004202322645512215316525612185249609741722.97%661675.76%119040347.15%21346545.81%16231551.43%567182609378884455
_Since Last GM Reset341113034126977-81868013003642-61655021123335-2360.5296911118004202322645512215316525612185249609741722.97%661675.76%119040347.15%21346545.81%16231551.43%567182609378884455
_Vs Conference2077013114450-611540020027261923011111724-7220.550446811202202322628612215316525416122145371521426.92%44979.55%119040347.15%21346545.81%16231551.43%567182609378884455
_Vs Division66501310131124420020057-222301110844191.58313223501202322680122153165256723341158112.50%12558.33%019040347.15%21346545.81%16231551.43%567182609378884455

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
3436L16911118045561218524960904
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
34111334126977
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
186813003642
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
165521123335
Derniers 10 matchs
WLOTWOTL SOWSOL
630001
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
741722.97%661675.76%1
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
122153165252023226
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
19040347.15%21346545.81%16231551.43%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
567182609378884455


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
18Checkers1Moose3WSommaire du match
224Condors5Moose2LSommaire du match
441Moose0Americans6LSommaire du match
554Moose2Stars5LSommaire du match
666Heat2Moose1LXSommaire du match
774Moose1Admirals2LXSommaire du match
990Moose0Heat2LSommaire du match
10112Penguins1Moose3WSommaire du match
11129Heat1Moose3WSommaire du match
12140Moose1Eagles0WXXSommaire du match
14162Eagles3Moose1LSommaire du match
16188Moose3Admirals1WSommaire du match
17199Admirals5Moose4LSommaire du match
18216Moose4Americans3WXSommaire du match
19229Comets3Moose0LSommaire du match
20250Moose4Gulls1WSommaire du match
22262Stars2Moose1LXSommaire du match
23274Moose4Admirals2WSommaire du match
24289Moose4Monsters3WXSommaire du match
26302Reign6Moose4LSommaire du match
27321Senators3Moose1LSommaire du match
28334Moose0Senators2LSommaire du match
29349Moose0Barracuda1LXXSommaire du match
30362Gulls4Moose3LXSommaire du match
31378Barracuda0Moose2WSommaire du match
33399Moose1Rocket2LSommaire du match
34414Marlies2Moose4WSommaire du match
35434Bruins1Moose0LSommaire du match
37449Moose4Wolf Pack1WSommaire du match
38467Wolf Pack0Moose1WXSommaire du match
39479Moose2Thunderbirds3LXXSommaire du match
40493Moose3Crunch1WSommaire du match
41511Checkers0Moose2WSommaire du match
42524Bears3Moose1LSommaire du match
43540Moose-Islanders-
44554Moose-Roadrunners-
45570Checkers-Moose-
46582Moose-Condors-
47598Monsters-Moose-
48616Rocket-Moose-
49629Moose-Reign-
50644Moose-Bruins-
52663Canucks-Moose-
53684Griffins-Moose-
54702Moose-Wild-
56715Admirals-Moose-
57723Moose-Eagles-
58746Phantoms-Moose-
60763Moose-Checkers-
61778Comets-Moose-
63794Moose-Comets-
64813Stars-Moose-
65829Moose-Condors-
67842Wolves-Moose-
68851Moose-Wolves-
69872Americans-Moose-
70888Moose-Americans-
72902Condors-Moose-
74926Moose-Penguins-
75938Islanders-Moose-
76954Moose-Heat-
78969IceHogs-Moose-
79985Moose-Heat-
811000Thunderbirds-Moose-
821019Moose-Bears-
831029Heat-Moose-
851055Crunch-Moose-
861059Moose-Marlies-
871074Moose-IceHogs-
881089Americans-Moose-
901109Moose-Canucks-
911121Moose-Stars-
921135Penguins-Moose-
931151Moose-Checkers-
941162Roadrunners-Moose-
951176Moose-Canucks-
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
971189Penguins-Moose-
981212Moose-Silver Knights-
991218Wild-Moose-
1021247Silver Knights-Moose-
1041271Eagles-Moose-
1051277Moose-Penguins-
1071303Eagles-Moose-
1081307Moose-Phantoms-
1091313Moose-Griffins-
1101323Moose-Stars-



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets2510
Assistance33,79517,784
Assistance PCT93.88%98.80%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
25 2866 - 95.52% 84,658$1,523,846$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursPlafond Salariale total des joueursSalaire des entraineurs
1,940,385$ 4,330,133$ 4,330,133$ 800,000$0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
38,662$ 1,640,379$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
2,116,453$ 70 45,805$ 3,206,350$




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