Wolf Pack
GP: 14 | W: 7 | L: 7
GF: 30 | GA: 33 | PP%: 12.94% | PK%: 87.14%
DG: Sebastien Regnier | Morale : 50 | Moyenne d'Équipe : N/A
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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
1Aleksi SaarelaX100.007670916670768063796161665847486550002331,152,500$
2Alexander TrueX100.008445946778557763657055562556566350002231,050,000$
3Kaapo Kakko (R)X100.006141928272677874346568617552526850001933,575,000$
4Rudolfs BalcersX100.00784399656162817326615859254949635000232925,000$
5Jack Hughes (R)XX100.005940948262727584696860554851516550001933,775,000$
6Isac LundestromX100.00614199806866687276725564254646645000202925,000$
7Logan BrownX100.006744946285596972627556572546466350002221,573,333$
8Sam LaffertyXXX100.00845786677154806165626276254848675000253975,000$
9Vitaly AbramovXX100.00686183696176796550626461614444655000222880,000$
10Gabriel Vilardi (R)X100.005741878076634868877575542544446954002031,627,000$
11Joachim Blichfeld (R)X100.007369836669727564505965646244446550002131,290,000$
12Rem Pitlick (R)XX100.00747082657066686075536263594444625000233925,000$
13Vladislav KamenevXXX100.00734392807252566462645661255151625000233800,000$
14Kody Clark (R)X100.00716683606659625050494759454444545000203894,167$
15Brendan LemieuxXX100.009396508379637867386559717559596750002411,039,167$
16Matt RoyX100.008445956274758762255448752556566250002531,200,000$
17Adam Boqvist (R)X100.007142948064697274255650602547476250001931,744,167$
18Ryan GravesX100.00815581818376815925595489255656685000252650,000$
19Christian DjoosX100.00594099806277677925535070255961635000251650,000$
Rayé
1Hayden VerbeekXX100.00726589546550524455384458424444505000223776,666$
2Aleksi Heponiemi (R)XX100.006656896156636750635046574444445350002131,775,000$
3Logan StanleyX100.007887566287687447253741623944445250002221,075,833$
MOYENNE D'ÉQUIPE100.0072558771716572645159566440494962500
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 SP
1Connor Ingram100.0069648076707172777373304444704600
2Igor Shesterkin (R)100.0069636071776288697769754545715000
Rayé
MOYENNE D'ÉQUIPE100.006964707474678073757153454571480
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Steve Ott70656067545089CAN393975,000$


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
1Adam BoqvistWolf Pack (NYR)D141910-3607611199.09%323616.931561078000017100.00%000000.8400000200
2Gabriel VilardiWolf Pack (NYR)C1437101000312151814.29%028720.552356650000621063.99%33600000.7000000010
3Brendan LemieuxWolf Pack (NYR)LW/RW1417813004013103910.00%227119.410553641013490022.73%2200000.5900000000
4Kaapo KakkoWolf Pack (NYR)RW146170204212651223.08%023116.522021068000002068.75%1600000.6100000000
5Sam LaffertyWolf Pack (NYR)C/LW/RW1434731201862232713.64%120414.61000070001371040.00%1000000.6800000112
6Ryan GravesWolf Pack (NYR)D14156-32002728134167.69%1228220.191231260000044000.00%000000.4200000011
7Jack HughesWolf Pack (NYR)C/LW14325-32012182192514.29%023316.68112874000070150.00%1400000.4300000110
8Isac LundestromWolf Pack (NYR)C142353001331841611.11%320714.82000080001390059.69%19100000.4800000110
9Christian DjoosWolf Pack (NYR)D14235-3205171981710.53%531022.202241976000057000.00%000000.3200000000
10Logan BrownWolf Pack (NYR)C14134-34091214287.14%125117.960222720000270056.49%23900000.3200000100
11Vitaly AbramovWolf Pack (NYR)LW/RW14224-6403121641112.50%318112.94112468000000050.00%1400000.4400000010
12Matt RoyWolf Pack (NYR)D14123-218025711099.09%1226619.04112867011058100.00%000000.2300000020
13Rem PitlickWolf Pack (NYR)C/LW142133201012113918.18%116311.6700003000001077.78%900000.3700000101
14Alexander TrueWolf Pack (NYR)C14022-6607158240.00%31168.3200001000000055.10%9800000.3400000000
15Rudolfs BalcersWolf Pack (NYR)LW14202-62029121816.67%01168.3500001000000025.00%400000.3400000001
16Vladislav KamenevWolf Pack (NYR)C/LW/RW14022-3601411183120.00%019013.580002160000220033.33%1200000.2100000000
Stats d'équipe Total ou en Moyenne224305383-2711601842512515721011.95%46355215.861122338473611254267158.45%96500000.4700000785
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
1Igor ShesterkinWolf Pack (NYR)147430.8742.2679701302380000.0000140002
Stats d'équipe Total ou en Moyenne147430.8742.2679701302380000.0000140002


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 RestantSalaire MoyenSalaire Moyen RestantCap Salariale Cap Salariale Restant Exclus du Cap Salarial Salaire Année 2Salaire Année 3Salaire Année 4Salaire Année 5Salaire Année 6Salaire Année 7Salaire Année 8Salaire Année 9Salaire Année 10Link
Adam BoqvistWolf Pack (NYR)D192000-08-14Yes179 Lbs5 ft11NoNoNo3Pro & Farm1,744,167$1,744,167$0$0$No1,744,167$1,744,167$
Aleksi HeponiemiWolf Pack (NYR)C/RW211999-01-09Yes150 Lbs5 ft10NoNoNo3Pro & Farm1,775,000$1,775,000$0$0$No1,775,000$1,775,000$
Aleksi SaarelaWolf Pack (NYR)C231997-01-07No198 Lbs5 ft11NoNoNo3Pro & Farm1,152,500$902,500$0$0$No902,500$902,500$Lien
Alexander TrueWolf Pack (NYR)C221997-07-17No201 Lbs6 ft5NoNoNo3Pro & Farm1,050,000$800,000$0$0$No800,000$800,000$Lien
Brendan LemieuxWolf Pack (NYR)LW/RW241996-03-14No210 Lbs6 ft1NoNoNo1Pro & Farm1,039,167$1,039,167$0$0$NoLien
Christian DjoosWolf Pack (NYR)D251994-08-06No169 Lbs6 ft0NoNoNo1Pro & Farm650,000$650,000$0$0$NoLien
Connor IngramWolf Pack (NYR)G231997-03-31No204 Lbs6 ft1NoNoNo2Pro & Farm925,000$925,000$0$0$No925,000$Lien
Gabriel VilardiWolf Pack (NYR)C201999-08-15Yes201 Lbs6 ft3NoNoNo3Pro & Farm1,627,000$1,627,000$0$0$No1,627,000$1,627,000$
Hayden VerbeekWolf Pack (NYR)C/LW221997-10-17No183 Lbs5 ft10NoNoNo3Pro & Farm776,666$776,666$0$0$No776,666$776,666$Lien
Igor ShesterkinWolf Pack (NYR)G241995-12-30Yes187 Lbs6 ft1NoNoNo2Pro & Farm3,775,000$3,775,000$0$0$No3,775,000$
Isac LundestromWolf Pack (NYR)C201999-11-06No185 Lbs6 ft0NoNoNo2Pro & Farm925,000$450,000$0$0$No925,000$Lien
Jack HughesWolf Pack (NYR)C/LW192001-05-13Yes171 Lbs5 ft10NoNoNo3Pro & Farm3,775,000$3,775,000$0$0$No3,775,000$3,775,000$
Joachim BlichfeldWolf Pack (NYR)RW211998-07-16Yes180 Lbs6 ft2NoNoNo3Pro & Farm1,290,000$790,000$0$0$No790,000$790,000$Lien
Kaapo KakkoWolf Pack (NYR)RW192001-02-13Yes190 Lbs6 ft2NoNoNo3Pro & Farm3,575,000$3,575,000$0$0$No3,575,000$3,575,000$
Kody ClarkWolf Pack (NYR)RW201999-10-12Yes179 Lbs6 ft1NoNoNo3Pro & Farm894,167$894,167$0$0$No894,167$894,167$
Logan BrownWolf Pack (NYR)C221998-03-04No220 Lbs6 ft6NoNoNo2Pro & Farm1,573,333$1,573,333$0$0$No1,573,333$Lien
Logan StanleyWolf Pack (NYR)D221998-05-25No228 Lbs6 ft7NoNoNo2Pro & Farm1,075,833$1,075,833$0$0$No1,075,833$Lien
Matt RoyWolf Pack (NYR)D251995-02-28No200 Lbs6 ft1NoNoNo3Pro & Farm1,200,000$700,000$0$0$No700,000$700,000$Lien
Rem PitlickWolf Pack (NYR)C/LW231997-04-01Yes196 Lbs5 ft11NoNoNo3Pro & Farm925,000$925,000$0$0$No925,000$925,000$
Rudolfs BalcersWolf Pack (NYR)LW231997-04-08No165 Lbs5 ft11NoNoNo2Pro & Farm925,000$925,000$0$0$No925,000$Lien
Ryan GravesWolf Pack (NYR)D251995-05-20No216 Lbs6 ft5NoNoNo2Pro & Farm650,000$650,000$0$0$No650,000$Lien
Sam LaffertyWolf Pack (NYR)C/LW/RW251995-03-06No194 Lbs6 ft1NoNoNo3Pro & Farm975,000$925,000$0$0$No925,000$925,000$Lien
Vitaly AbramovWolf Pack (NYR)LW/RW221998-05-08No172 Lbs5 ft9NoNoNo2Pro & Farm880,000$880,000$0$0$No880,000$Lien
Vladislav KamenevWolf Pack (NYR)C/LW/RW231996-08-12No194 Lbs6 ft2NoNoNo3Pro & Farm800,000$750,000$0$0$No750,000$750,000$Lien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2422.17191 Lbs6 ft12.501,415,743$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Brendan LemieuxGabriel VilardiKaapo Kakko35023
2Jack HughesLogan BrownVladislav Kamenev30023
3Rem PitlickIsac LundestromSam Lafferty25032
4Rudolfs BalcersAlexander TrueVitaly Abramov10032
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Ryan GravesChristian Djoos35122
2Matt RoyAdam Boqvist30122
325122
4Ryan GravesChristian Djoos10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Jack HughesLogan BrownKaapo Kakko60122
2Brendan LemieuxGabriel VilardiVitaly Abramov40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Adam BoqvistChristian Djoos60122
2Ryan GravesMatt Roy40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Gabriel VilardiBrendan Lemieux60122
2Isac LundestromSam Lafferty40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Ryan GravesChristian Djoos60122
2Matt Roy40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Sam Lafferty60122Ryan GravesChristian Djoos60122
2Isac Lundestrom40122Matt Roy40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Gabriel VilardiKaapo Kakko60122
2Brendan LemieuxJack Hughes40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Ryan GravesChristian Djoos60122
2Matt RoyAdam Boqvist40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Jack HughesGabriel VilardiKaapo KakkoRyan GravesChristian Djoos
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Brendan LemieuxSam LaffertyKaapo KakkoRyan GravesMatt Roy
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Logan Brown, Gabriel Vilardi, Vladislav KamenevLogan Brown, Kaapo KakkoLogan Brown
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Adam Boqvist, Matt Roy, Christian DjoosAdam BoqvistMatt Roy, Adam Boqvist
Tirs de Pénalité
Gabriel Vilardi, Kaapo Kakko, Brendan Lemieux, Jack Hughes, Isac Lundestrom
Gardien
#1 : Igor Shesterkin, #2 :


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
LigueDomicileVisiteur
# 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
1Bruins7430000017152422000008803210000097280.5711731480115780151698495313238569748714.58%27581.48%022037858.20%22138257.85%12320560.00%34523733511017888
2Moose734000001318-53210000065141300000713-660.42913223500157801006984953118239211037410.81%43490.70%122037858.20%22138257.85%12320560.00%34523733511017888
Total1477000003033-37430000014131734000001620-4140.5003053830115780251698495325061148207851112.94%70987.14%122037858.20%22138257.85%12320560.00%34523733511017888
_Since Last GM Reset1477000003033-37430000014131734000001620-4140.5003053830115780251698495325061148207851112.94%70987.14%122037858.20%22138257.85%12320560.00%34523733511017888
_Vs Conference7430000017152422000008803210000097280.5711731480115780151698495313238569748714.58%27581.48%022037858.20%22138257.85%12320560.00%34523733511017888

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
1414OTL13053832512506114820701
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
147700003033
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
74300001413
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
73400001620
Derniers 10 Matchs
WLOTWOTL SOWSOL
520300
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
851112.94%70987.14%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
698495315780
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
22037858.20%22138257.85%12320560.00%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
34523733511017888


Derniers Match 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 - 2021-03-151Bruins2Wolf Pack3WSommaire du Match
2 - 2021-03-163Bruins3Wolf Pack1LSommaire du Match
3 - 2021-03-175Wolf Pack1Bruins5LSommaire du Match
4 - 2021-03-187Wolf Pack4Bruins1WSommaire du Match
5 - 2021-03-199Bruins3Wolf Pack2LXSommaire du Match
6 - 2021-03-2011Wolf Pack4Bruins1WSommaire du Match
7 - 2021-03-2113Bruins0Wolf Pack2WSommaire du Match
8 - 2021-03-2215Wolf Pack3Moose1WSommaire du Match
9 - 2021-03-2316Wolf Pack0Moose6LSommaire du Match
10 - 2021-03-2417Moose1Wolf Pack2WSommaire du Match
11 - 2021-03-2518Moose3Wolf Pack2LSommaire du Match
12 - 2021-03-2619Wolf Pack2Moose3LXSommaire du Match
13 - 2021-03-2720Moose1Wolf Pack2WSommaire du Match
14 - 2021-03-2821Wolf Pack2Moose3LXSommaire du Match



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des Billets3515
Assistance00
Assistance PCT0.00%0.00%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacitéPopularité de l'Équipe
-7 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
0$ 3,397,784$ 3,190,284$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 0$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 0 0$ 0$




LigueDomicileVisiteur
Année GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff P G A TP SO EG GP1 GP2 GP3 GP4 SHF SH1 SP2 SP3 SP4 SHA SHB Pim Hit PPA PPG PP% PKA PK GA PK% PK GF W OF FO T OF FO OF FO% W DF FO T DF FO DF FO% W NT FO T NT FO NT FO% PZ DF PZ OF PZ NT PC DF PC OF PC NT
Saison Régulière
202082472301155217162554123120103211385284124110012310477271122173916080674776013170354958155847145341591913974826713.90%3864688.08%31300231056.28%1160207255.98%638113256.36%214815311846588979505
Total Saison Régulière82472301155217162554123120103211385284124110012310477271122173916080674776013170354958155847145341591913974826713.90%3864688.08%31300231056.28%1160207255.98%638113256.36%214815311846588979505
20201477000003033-37430000014131734000001620-4143053830115780251698495325061148207851112.94%70987.14%122037858.20%22138257.85%12320560.00%34523733511017888
Total Séries1477000003033-37430000014131734000001620-4143053830115780251698495325061148207851112.94%70987.14%122037858.20%22138257.85%12320560.00%34523733511017888