Connexion

67s
GP: 17 | W: 5 | L: 10 | OTL: 2 | P: 12
GF: 34 | GA: 47 | PP%: 12.50% | PK%: 87.10%
DG: Farlou Ferland | Morale : 50 | Moyenne d’équipe : N/A
Prochains matchs #203 vs Eagles
La résolution de votre navigateur est trop petite pour cette page. Plusieurs informations sont cachées pour garder la page lisible.

Centre de jeu
67s
5-10-2, 12pts
2
FINAL
1 Thunderbird
7-10-0, 14pts
Team Stats
L1SéquenceL2
2-6-1Fiche domicile4-5-0
3-4-1Fiche domicile3-5-0
3-6-1Derniers 10 matchs3-7-0
2.00Buts par match 1.41
2.76Buts contre par match 2.47
12.50%Pourcentage en avantage numérique12.79%
87.10%Pourcentage en désavantage numérique82.86%
67s
5-10-2, 12pts
2
FINAL
3 Bruins
14-3-1, 29pts
Team Stats
L1SéquenceW2
2-6-1Fiche domicile8-0-1
3-4-1Fiche domicile6-3-0
3-6-1Derniers 10 matchs8-2-0
2.00Buts par match 3.33
2.76Buts contre par match 2.00
12.50%Pourcentage en avantage numérique13.79%
87.10%Pourcentage en désavantage numérique89.13%
Eagles
11-6-0, 22pts
Jour 35
67s
5-10-2, 12pts
Statistiques d’équipe
W5SéquenceL1
6-2-0Fiche domicile2-6-1
5-4-0Fiche visiteur3-4-1
7-3-010 derniers matchs3-6-1
2.18Buts par match 2.00
1.76Buts contre par match 2.00
11.88%Pourcentage en avantage numérique12.50%
92.22%Pourcentage en désavantage numérique87.10%
67s
5-10-2, 12pts
Jour 37
Eagles
11-6-0, 22pts
Statistiques d’équipe
L1SéquenceW5
2-6-1Fiche domicile6-2-0
3-4-1Fiche visiteur5-4-0
3-6-110 derniers matchs7-3-0
2.00Buts par match 2.18
2.76Buts contre par match 2.18
12.50%Pourcentage en avantage numérique11.88%
87.10%Pourcentage en désavantage numérique92.22%
Barracuda
10-6-1, 21pts
Jour 39
67s
5-10-2, 12pts
Statistiques d’équipe
L1SéquenceL1
5-3-0Fiche domicile2-6-1
5-3-1Fiche visiteur3-4-1
6-4-010 derniers matchs3-6-1
2.35Buts par match 2.00
2.18Buts contre par match 2.00
11.34%Pourcentage en avantage numérique12.50%
87.85%Pourcentage en désavantage numérique87.10%
Meneurs d'équipe
Buts
Carl Grundstrom
6
Passes
Tyler Tucker
10
Points
Tyler Tucker
10
Plus/Moins
Nils Lundkvist
3
Victoires
Louis Domingue
4
Pourcentage d’arrêts
Chris Driedger
0.911

Statistiques d’équipe
Buts pour
34
2.00 GFG
Tirs pour
313
18.41 Avg
Pourcentage en avantage numérique
12.5%
14 GF
Début de zone offensive
39.9%
Buts contre
47
2.76 GAA
Tirs contre
310
18.24 Avg
Pourcentage en désavantage numérique
87.1%%
16 GA
Début de la zone défensive
41.3%
Informations de l'équipe

Directeur généralFarlou Ferland
EntraîneurJohn Stevens
DivisionDivision 1
ConférenceConference 1
Capitaine
Assistant #1
Assistant #2


Informations de l’aréna

Capacité3,000
Assistance0
Billets de saison300


Informations de la formation

Équipe Pro25
Équipe Mineure19
Limite contact 44 / 60
Espoirs23


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
1Alex Turcotte (R)0X100.007471827471575661765958645545456250002212,475,000$
2Carl Grundstrom0XX100.00904690807358735838617265256263715000253750,000$
3Tyson Foerster (R)0XX100.00737276637263646379606264594545635000213863,333$
4Garnet Hathaway0XX100.009770777580619758326068792570727150003121,500,000$
5Greg McKegg0XX100.00717074717067725366494865466565565000312750,000$
6Marco Rossi (R)0X100.006141877764638565745555662545456150002131,744,167$
7Hendrix Lapierre (R)0X100.006965797465677059745361605844446250002031,105,833$
8Tuukka Tieksola (R)0X100.00685988665958595850565660534444605000223843,333$
9Ryan Carpenter0XX100.008144867773548655775957722565656450003221,000,000$
10Ryan Dzingel0XXX100.007470827870505055695348674666665750003121,100,000$
11Blake McLaughlin (R)0X100.00706485636453564450384457424444505000233925,000$
12Tyson Jost0X100.007357858568659358647059757568716850002511,673,333$
13Ronnie Attard0X100.007977837377737856254652654945456150002411,350,000$
14Jacob Bernard-Docker (R)0X100.007777837572697957254447722546466050002331,208,333$
15Samuel Knazko (R)0X100.00767089647061645225503962374444545000213902,500$
16Matt Irwin0X100.00848883737761595725444880256969605000351750,000$
17Lassi Thomson0X100.007570857270717555255046634445455850002211,350,000$
18Tyler Tucker (R)0X100.00849968637865866225504868254646595000233808,333$
Rayé
1Will Cuylle (R)0X100.00787977647978826250556566624444665000213902,500$
2Nils Lundkvist0X99.007142877667707769255250672555556150002331,775,000$
3Vincent Iorio (R)0X100.00827794657773795025464065384444555000203894,167$
4Helge Grans (R)0X100.00807788607768744625364063384444525000213925,000$
MOYENNE D’ÉQUIPE99.9577688371726473574752536741525261500
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
1Chris Driedger0100.00554860845758556059583051525650002923,500,000$
Rayé
1Louis Domingue0100.0056475982595956616160305960585000312750,000$
2Malcolm Subban0100.0052425381555451565554305555535000292850,000$
MOYENNE D’ÉQUIPE100.005446578257575459585730555656500
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire
John Stevens82858681837868CAN5821,500,000$


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
1Tyler Tucker67s (OTT)D170101033210351219690%1035921.140661475011077000%000000.5600101101
2Carl Grundstrom67s (OTT)LW/RW17628-71602592951820.69%229717.531127821012721141.86%4300000.5401000210
3Tyson Foerster67s (OTT)C/RW17358-212014171881916.67%225114.77246780000030071.43%700000.6400000001
4Jacob Bernard-Docker67s (OTT)D17268-222017162061110.00%837422.052461578000187010%000000.4300000011
5Ryan Carpenter67s (OTT)C/RW17358118025412872010.71%029817.550337770003511064.07%16700000.5400000010
6Tyson Jost67s (OTT)C17268-6006402511138.00%236021.191239810110990051.98%35400000.4401000000
7Alex Turcotte67s (OTT)C17527-512018311731229.41%126015.342133440000171055.41%22200000.5400000011
8Matt Irwin67s (OTT)D17347-4261030122241113.64%1537522.082131681000184110%000000.3700011100
9Will Cuylle67s (OTT)LW15426-4160211728102014.29%125517.063259720000261078.57%1400000.4700000001
10Nils Lundkvist67s (OTT)D1005531203814690%321321.390111448000054000%000000.4700000000
11Marco Rossi67s (OTT)C17235-2003181921510.53%218510.91000040000380055.62%16900000.5400000000
12Garnet Hathaway67s (OTT)LW/RW10123-33003223236124.35%221721.800115450001610043.97%14100000.2801000010
13Samuel Knazko67s (OTT)D70330100811210%511216.0100001000016000%000000.5400000010
14Ryan Dzingel67s (OTT)C/LW/RW17033-5295281710670%321912.9100009000000050.00%800000.2700001000
15Tuukka Tieksola67s (OTT)RW17112-440141520695.00%022213.0900003000000054.55%1100000.1800000001
16Ronnie Attard67s (OTT)D17101-10335227831112.50%732118.94101751000046000%000000.0600001000
17Greg McKegg67s (OTT)C/LW171011607443225.00%11156.8200003000140012.50%800000.1700000000
18Hendrix Lapierre67s (OTT)C170110803104230%01015.980001130001170052.73%5500000.2000000000
19Lassi Thomson67s (OTT)D17011-712024132020%427216.05000013000033000%000000.0700000000
20Blake McLaughlin67s (OTT)LW9011000512110%0566.33000112000000060.87%2300000.3500000000
Statistiques d’équipe totales ou en moyenne306346296-53298303403123139720510.86%68487415.93142640115884123107945353.85%122200000.3903114466
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
1Louis Domingue67s (OTT)164920.8422.8987221422650000.6673161010
2Chris Driedger67s (OTT)41100.9111.581520044500000116000
Statistiques d’équipe totales ou en moyenne2051020.8522.691025214631000031717010


Astuces sur les filtres (anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
Nom du joueur Nom de l’équipePOS Âge Date de naissance Recrue Poids Taille Non-échange Disponible pour échange Ballotage forcé Waiver Possible Contrat Type Salaire actuel Salaire restantSalaire moyenSalaire moyen 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 10Lien
Alex Turcotte67s (OTT)C222001-02-25Yes185 Lbs5 ft11NoNoNoNo1Pro & Farm2,475,000$1,948,065$925,000$728,065$0$0$NoLien
Blake McLaughlin67s (OTT)LW232000-02-14Yes173 Lbs6 ft0NoNoNoNo3Pro & Farm925,000$728,065$925,000$728,065$0$0$No925,000$925,000$
Carl Grundstrom67s (OTT)LW/RW251997-12-01No201 Lbs6 ft0NoNoNoNo3Pro & Farm750,000$590,323$750,000$590,323$0$0$No750,000$750,000$Lien
Chris Driedger67s (OTT)G291994-05-18No205 Lbs6 ft4NoNoNoNo2Farm Only3,500,000$2,754,839$3,500,000$2,754,839$0$0$No3,500,000$Lien
Garnet Hathaway67s (OTT)LW/RW311991-11-23No210 Lbs6 ft3NoNoNoNo2Pro & Farm1,500,000$1,180,645$1,500,000$1,180,645$0$0$No1,500,000$Lien
Greg McKegg67s (OTT)C/LW311992-06-17No192 Lbs6 ft0NoNoNoNo2Pro & Farm750,000$590,323$750,000$590,323$0$0$No750,000$Lien
Helge Grans67s (OTT)D212002-05-10Yes206 Lbs6 ft3NoNoNoNo3Pro & Farm925,000$728,065$925,000$728,065$0$0$No925,000$925,000$Lien
Hendrix Lapierre67s (OTT)C202002-09-02Yes179 Lbs6 ft0NoNoNoNo3Pro & Farm1,105,833$870,398$1,105,833$870,398$0$0$No1,105,833$1,105,833$
Jacob Bernard-Docker67s (OTT)D232000-06-30Yes190 Lbs6 ft1NoNoNoNo3Pro & Farm1,208,333$951,075$1,208,333$951,075$0$0$No1,208,333$1,208,333$Lien
Lassi Thomson67s (OTT)D222000-09-24No192 Lbs6 ft0NoNoNoNo1Pro & Farm1,350,000$1,062,581$894,167$703,796$0$0$NoLien
Louis Domingue67s (OTT)G311992-03-06No208 Lbs6 ft3NoNoNoNo2Pro & Farm750,000$590,323$750,000$590,323$0$0$No750,000$Lien
Malcolm Subban67s (OTT)G291993-12-21No215 Lbs6 ft2NoNoNoNo2Pro & Farm850,000$669,032$850,000$669,032$0$0$No850,000$Lien
Marco Rossi67s (OTT)C212001-09-23Yes183 Lbs5 ft9NoNoNoNo3Pro & Farm1,744,167$1,372,828$1,744,167$1,372,828$0$0$No1,744,167$1,744,167$Lien
Matt Irwin67s (OTT)D351987-11-29No207 Lbs6 ft1NoNoNoNo1Pro & Farm750,000$590,323$750,000$590,323$0$0$NoLien
Nils Lundkvist67s (OTT)D232000-07-27No187 Lbs5 ft11NoNoNoNo3Pro & Farm1,775,000$1,397,097$1,775,000$1,397,097$0$0$No1,775,000$1,775,000$Lien
Ronnie Attard67s (OTT)D241999-03-20No205 Lbs6 ft3NoNoNoNo1Pro & Farm1,350,000$1,062,581$1,350,000$1,062,581$0$0$NoLien
Ryan Carpenter67s (OTT)C/RW321991-01-18No200 Lbs6 ft0NoNoNoNo2Pro & Farm1,000,000$787,097$1,000,000$787,097$0$0$No1,000,000$Lien
Ryan Dzingel67s (OTT)C/LW/RW311992-03-09No190 Lbs6 ft0NoNoNoNo2Pro & Farm1,100,000$865,806$1,100,000$865,806$0$0$No1,100,000$Lien
Samuel Knazko67s (OTT)D212002-07-08Yes191 Lbs6 ft0NoNoNoNo3Pro & Farm902,500$710,355$902,500$710,355$0$0$No902,500$902,500$
Tuukka Tieksola67s (OTT)RW222001-06-22Yes160 Lbs5 ft10NoNoNoNo3Pro & Farm843,333$663,785$843,333$663,785$0$0$No843,333$843,333$
Tyler Tucker67s (OTT)D232000-03-01Yes205 Lbs6 ft1NoNoNoNo3Pro & Farm808,333$636,236$808,333$636,236$0$0$No808,333$808,333$Lien
Tyson Foerster67s (OTT)C/RW212002-01-18Yes194 Lbs6 ft2NoNoNoNo3Pro & Farm863,333$679,527$863,333$679,527$0$0$No863,333$863,333$
Tyson Jost67s (OTT)C251998-03-14No187 Lbs5 ft11NoNoNoNo1Pro & Farm1,673,333$1,317,075$1,673,333$1,317,075$0$0$NoLien
Vincent Iorio67s (OTT)D202002-11-14Yes200 Lbs6 ft4NoNoNoNo3Pro & Farm894,167$703,796$894,167$703,796$0$0$No894,167$894,167$
Will Cuylle67s (OTT)LW212002-05-02Yes204 Lbs6 ft3NoNoNoNo3Pro & Farm902,500$710,355$902,500$710,355$0$0$No902,500$902,500$
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2525.04195 Lbs6 ft12.321,227,833$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Garnet HathawayTyson JostCarl Grundstrom35122
2Ryan DzingelRyan CarpenterTyson Foerster30122
3Greg McKeggAlex TurcotteTuukka Tieksola25122
4Blake McLaughlinMarco RossiGarnet Hathaway10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Matt IrwinJacob Bernard-Docker35122
2Tyler TuckerRonnie Attard30122
3Lassi ThomsonSamuel Knazko25122
4Matt IrwinJacob Bernard-Docker10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Garnet HathawayTyson JostCarl Grundstrom60122
2Ryan DzingelRyan CarpenterTyson Foerster40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Matt IrwinJacob Bernard-Docker60122
2Tyler TuckerRonnie Attard40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Garnet HathawayTyson Jost60122
2Carl GrundstromRyan Carpenter40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Matt IrwinJacob Bernard-Docker60122
2Tyler TuckerRonnie Attard40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Garnet Hathaway60122Matt IrwinJacob Bernard-Docker60122
2Tyson Jost40122Tyler TuckerRonnie Attard40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Garnet HathawayTyson Jost60122
2Carl GrundstromRyan Carpenter40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Matt IrwinJacob Bernard-Docker60122
2Tyler TuckerRonnie Attard40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Garnet HathawayTyson JostCarl GrundstromMatt IrwinJacob Bernard-Docker
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Garnet HathawayTyson JostCarl GrundstromMatt IrwinJacob Bernard-Docker
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Hendrix Lapierre, Alex Turcotte, Marco RossiHendrix Lapierre, Alex TurcotteMarco Rossi
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Lassi Thomson, Samuel Knazko, Tyler TuckerLassi ThomsonSamuel Knazko, Tyler Tucker
Tirs de pénalité
Garnet Hathaway, Tyson Jost, Carl Grundstrom, Ryan Carpenter, Alex Turcotte
Gardien
#1 : , #2 : Chris Driedger
Lignes d’attaque personnalisées en prolongation
Garnet Hathaway, Tyson Jost, Carl Grundstrom, Ryan Carpenter, Alex Turcotte, Tyson Foerster, Tyson Foerster, Marco Rossi, Hendrix Lapierre, Ryan Dzingel, Greg McKegg
Lignes de défense personnalisées en prolongation
Matt Irwin, Jacob Bernard-Docker, Tyler Tucker, Ronnie Attard, Lassi Thomson


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
1Bruins30300000610-41010000013-22020000057-200.000611170012813158129869477517706119210.53%28196.43%128848459.50%24350148.50%12722955.46%404271408132214106
2Condors20101000550201010005500000000000020.500581300128131381298694734720481400.00%7185.71%028848459.50%24350148.50%12722955.46%404271408132214106
3Crunch2020000024-21010000012-11010000012-100.000246001281313812986947359273914214.29%9277.78%028848459.50%24350148.50%12722955.46%404271408132214106
4Griffins2020000036-31010000023-11010000013-200.000358001281312412986947531138341119.09%19289.47%028848459.50%24350148.50%12722955.46%404271408132214106
5IceHogs11000000431000000000001100000043121.000471100128131241298694718317209333.33%5180.00%028848459.50%24350148.50%12722955.46%404271408132214106
6Marlies1010000014-31010000014-30000000000000.000123001281311912986947105817800.00%4175.00%028848459.50%24350148.50%12722955.46%404271408132214106
7Phantoms2110000068-22110000068-20000000000020.50061218001281313912986947336364213323.08%17476.47%028848459.50%24350148.50%12722955.46%404271408132214106
8Rocket3100010156-11000010034-12100000122040.6675101500128131571298694741758581616.25%27485.19%028848459.50%24350148.50%12722955.46%404271408132214106
9Thunderbird11000000211000000000001100000021121.00023500128131161298694711326218225.00%80100.00%028848459.50%24350148.50%12722955.46%404271408132214106
Total17410011013447-13916011001929-10834000011518-3120.3533462960012813131312986947310683003401121412.50%1241687.10%128848459.50%24350148.50%12722955.46%404271408132214106
_Since Last GM Reset17410011013447-13916011001929-10834000011518-3120.3533462960012813131312986947310683003401121412.50%1241687.10%128848459.50%24350148.50%12722955.46%404271408132214106
_Vs Conference1127001012032-12614001001221-951300001811-360.27320395900128131211129869471944419921770811.43%851285.88%128848459.50%24350148.50%12722955.46%404271408132214106
_Vs Division1116001011730-1350300100816-861300001914-540.1821732490012813119612986947214492012096868.82%871088.51%128848459.50%24350148.50%12722955.46%404271408132214106

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
1712L13462963133106830034000
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
1741011013447
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
91611001929
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
83400011518
Derniers 10 matchs
WLOTWOTL SOWSOL
360100
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
1121412.50%1241687.10%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
12986947128131
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
28848459.50%24350148.50%12722955.46%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
404271408132214106


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
311Condors367s4BWXSommaire du match
725Marlies467s1BLSommaire du match
93867s0Rocket1ALXXSommaire du match
1050Phantoms367s4BWSommaire du match
125767s3Bruins4ALSommaire du match
1370Bruins367s1BLSommaire du match
168267s1Crunch2ALSommaire du match
1891Phantoms567s2BLSommaire du match
1910367s1Griffins3ALSommaire du match
20113Crunch267s1BLSommaire du match
2212767s4IceHogs3AWSommaire du match
24136Condors267s1BLSommaire du match
2614967s2Rocket1AWSommaire du match
29161Rocket467s3BLXSommaire du match
31177Griffins367s2BLSommaire du match
3218267s2Thunderbird1AWSommaire du match
3319167s2Bruins3ALSommaire du match
35203Eagles-67s-
3721467s-Eagles-
39225Barracuda-67s-
4224167s-Sound Tigers-
43248Moose-67s-
45264Wolf Pack-67s-
4727867s-Crunch-
48286Rocket-67s-
5129967s-Sound Tigers-
52309Thunderbird-67s-
5432367s-Eagles-
56331Marlies-67s-
5734267s-Marlies-
6035167s-Wolves-
61358Monarchs-67s-
6537367s-Heat-
66382Crunch-67s-
68396Gulls-67s-
7141267s-Barracuda-
72419Marlies-67s-
7443167s-Moose-
75440Heat-67s-
78462Monsters-67s-
7946867s-Gulls-
81482Wolf Pack-67s-
8449267s-Monsters-
8650367s-Crunch-
87508Griffins-67s-
8952067s-Bruins-
91530Monsters-67s-
9354467s-Wolf Pack-
94552Bruins-67s-
9656567s-Phantoms-
97573Bruins-67s-
9958567s-Penguins-
101597Condors-67s-
10260867s-Condors-
104618Phantoms-67s-
10662967s-Penguins-
109641Wolves-67s-
11165667s-Stars-
112662Phantoms-67s-
11567667s-Wolf Pack-
116685Crunch-67s-
11769067s-Monsters-
119707Sound Tigers-67s-
12172367s-Monarchs-
122729Sound Tigers-67s-
125749Penguins-67s-
12675367s-IceHogs-
12775867s-Marlies-
130773Penguins-67s-
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
13379267s-Rocket-
134796Stars-67s-
136815Griffins-67s-
13781967s-Phantoms-
14183467s-Stars-
142839IceHogs-67s-
14485167s-IceHogs-
14585267s-Marlies-
146861Condors-67s-
14787067s-Griffins-
15088267s-Rocket-
15289067s-Griffins-
154902Rocket-67s-



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets3515
Assistance00
Assistance PCT0.00%0.00%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
27 0 - 0.00% 0$0$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursSalaire total moyen des joueursSalaire des entraineurs
971,709$ 3,069,582$ 2,868,999$ 0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
0$ 652,366$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
0$ 122 29,481$ 3,596,682$




67s 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

67s 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

67s 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

67s 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

67s 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