Connexion

Bears
GP: 82 | W: 47 | L: 29 | OTL: 6 | P: 100
GF: 311 | GA: 271 | PP%: 20.97% | PK%: 79.47%
DG: JF Langlais | Morale : 50 | Moyenne d’équipe : 57
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Centre de jeu
Minnesota
26-48-8, 60pts
3
FINAL
10 Bears
47-29-6, 100pts
Team Stats
W1StreakW3
12-25-4Home Record28-8-5
14-23-4Away Record19-21-1
3-6-1Last 10 Games7-2-1
3.30Goals Per Game3.79
4.38Goals Against Per Game3.30
24.50%Power Play Percentage20.97%
77.60%Penalty Kill Percentage79.47%
Bears
47-29-6, 100pts
4
FINAL
3 Cabaret Lady Mary Ann
19-56-7, 45pts
Team Stats
W3StreakSOL1
28-8-5Home Record10-28-3
19-21-1Away Record9-28-4
7-2-1Last 10 Games3-6-1
3.79Goals Per Game3.51
3.30Goals Against Per Game5.01
20.97%Power Play Percentage22.42%
79.47%Penalty Kill Percentage70.93%
Meneurs d'équipe
Buts
Philippe Myers
0
Passes
Philippe Myers
1
Points
Philippe Myers
1
Plus/Moins
Philippe Myers
1
Victoires
Kaden Fulcher
47
Pourcentage d’arrêts
Alex Lyon
0.927

Statistiques d’équipe
Buts pour
311
3.79 GFG
Tirs pour
3415
41.65 Avg
Pourcentage en avantage numérique
21.0%
52 GF
Début de zone offensive
42.8%
Buts contre
271
3.30 GAA
Tirs contre
2894
35.29 Avg
Pourcentage en désavantage numérique
79.5%
62 GA
Début de la zone défensive
38.5%
Information d’équipe

Directeur généralJF Langlais
DivisionEst
ConférenceEst
Capitaine
Assistant #1
Assistant #2


Informations de l’aréna

Capacité3,000
Assistance2,207
Billets de saison300


Information formation

Équipe Pro25
Équipe Mineure19
Limite contact 44 / 50
Espoirs18


Historique d'équipe

Saison actuelle47-29-6 (100PTS)
Historique47-29-6 (0.573%)
Apparitions séries éliminatoires
Historique séries éliminatoires (W-L)-


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 moyen
1Joseph Veleno (R)X100.00817987747264576987686770584444050640204894,167$
2Tim SchallerXX100.008078867878687061505362715962630506302951,000,000$
3Nathan BastianX100.00984885627863786238635881254848050630221742,500$
4Boris KatchoukX100.00787682617668686950696567624444050620221742,500$
5Antoine MorandX100.00777387676677746378625966514444050610213778,334$
6John HaydenXX100.00944765738255615535605870636161050610251850,000$
7Nick ShoreX100.006943887671496256785254832451510506002841,300,000$
8Dylan SikuraXX100.00686379616369716450596562624848050590251925,000$
9Byron FroeseXX100.00797489627458595670436269596060050580293850,000$
10Mark Kastelic (R)X100.00817984587964675468505365504444050560213821,667$
11Otto Kivenmaki (R)X100.00474082706059695456514745495050050520204560,000$
12Sam Miletich (R)X100.00555077617034403150203643386060050420234894,167$
13Jakub ZborilX100.00804487667371776325554762755555050620232900,000$
14Mikko LehtonenX100.00774490687168666125574768254646050610264925,000$
15Jaycob MegnaX100.00848582608568744725374167395252050600272969,006$
16Jack Rathbone (R)X100.00656369716353506525615660534444050570213925,000$
17Sean DayX100.00768065538061645325504262404444050560221742,500$
18Evan McEnenyX100.007374796275525544253341643951510505502621,154,888$
Rayé
1Dakota MermisX100.00814283677173556224484871795555050620263655,000$
2Kaedan Korczak (R)X100.00767479597455584625383961374444050540193795,000$
3Layton Ahac (R)X100.00767090617049514625373961374444050530193897,500$
4Igor OzhiganovX100.00583586616944393835354162463532050500272925,000$
MOYENNE D’ÉQUIPE100.0075628265736062554450516549494905058
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
1Kaden Fulcher (R)100.0052435480555852576058304444050560
2Alex Lyon100.0053537575505352575152304646050540
Rayé
1Gilles Senn (R)100.0051556983485151555151334844050540
MOYENNE D’ÉQUIPE100.005250667951545256545431464505055
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire


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
1Tim SchallerBears (Was)LW/RW82465710322321010020642611829810.80%32197124.0412122458208011102697146.85%33300011.04211101848
2Nathan BastianBears (Was)RW77404888197602491463568726711.24%47167721.79810185820201191778238.69%39800001.0579000458
3Antoine MorandBears (Was)C80304171010038212312641959.62%17145318.177132060208000085158.34%196100020.9800000552
4Nick ShoreBears (Was)C8223436678031235321792117.17%37139917.073710371140002362057.92%175600000.9400000243
5Dylan SikuraBears (Was)LW/RW822634601818062842477820510.53%11129915.852136250002494242.11%9500010.9200000423
6Boris KatchoukBears (Was)LW772134551239591129320932116.56%29139218.09000206801181511152.87%8700000.7902001142
7Byron FroeseBears (Was)C/RW82272451186810941552195616112.33%16121114.770005150000276058.06%106100010.8400011324
8Mikko LehtonenBears (Was)D8264450334801079311238795.36%111177921.703811562020222213020.00%000000.5600000013
9John HaydenBears (Was)LW/RW8218304810895317111248841917.26%20148218.0835848193000083339.09%11000000.6501010172
10Jakub ZborilBears (Was)D8254348206201876811229664.46%148196123.9221113462181013239000.00%000000.4911000013
11Joseph VelenoBears (Was)C251625412729152789141359311.35%1258323.3514522651014900058.22%83300011.4026021360
12Dakota MermisBears (Was)D57622282320112619630526.25%83126922.283912391350001145110.00%000000.4400000000
13Jack RathboneBears (Was)D8242428228068547624465.26%64131616.0612310691011107000.00%000000.4300000002
14Jaycob MegnaBears (Was)D82718251812135182377520399.33%104151418.47213291560000154200.00%000000.3300232011
15Sean DayBears (Was)D823121511475145273615208.33%74109313.33000112000050100.00%000000.2700001000
16Mark KastelicBears (Was)C82210129202843479404.26%53884.73000370003750054.69%38400000.6200000000
17Evan McEnenyBears (Was)D31471114140308173923.53%3146314.9400006000039000.00%200000.4800000000
18Philippe MyersWashingtonD1011100131030.00%22424.450111200002000.00%000000.8200000000
19Otto KivenmakiBears (Was)C28000-100026020.00%2541.95000214000000045.24%4200000.0000000000
20Sam MiletichBears (Was)C26000000101000.00%0180.7100006000000066.67%300000.0000000000
Statistiques d’équipe totales ou en moyenne13042845178012427238518701763316986221888.96%8452235517.1447841315011932358451849401355.65%706500060.721230377314241
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
1Kaden FulcherBears (Was)82472760.9073.2346640425127020110.618348201041
2Alex LyonBears (Was)90200.9272.7430700141910000.0000082000
Statistiques d’équipe totales ou en moyenne91472960.9083.2049720426528930110.6183482821041


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
Alex LyonBears (Was)G271992-12-08No201 Lbs6 ft1NoNoNo1Pro & Farm874,125$87,412$0$NoLien
Antoine MorandBears (Was)C211999-02-18No184 Lbs5 ft11NoNoNo3Pro & Farm778,334$77,833$0$No778,334$778,334$Lien
Boris KatchoukBears (Was)LW221998-06-17No206 Lbs6 ft2NoNoNo1Pro & Farm742,500$74,250$0$NoLien
Byron FroeseBears (Was)C/RW291991-03-11No202 Lbs6 ft1NoNoNo3Pro & Farm850,000$85,000$0$No850,000$850,000$Lien
Dakota MermisBears (Was)D261994-01-05No196 Lbs6 ft0NoNoNo3Pro & Farm655,000$65,500$0$No655,000$655,000$Lien
Dylan SikuraBears (Was)LW/RW251995-06-01No170 Lbs5 ft11NoNoNo1Pro & Farm925,000$92,500$0$NoLien
Evan McEnenyBears (Was)D261994-05-22No203 Lbs6 ft2NoNoNo2Pro & Farm1,154,888$115,489$0$No1,154,888$Lien
Gilles SennBears (Was)G241996-03-01Yes191 Lbs6 ft5NoNoNo1Pro & Farm817,500$81,750$0$NoLien
Igor OzhiganovBears (Was)D271992-10-13No210 Lbs6 ft2NoNoNo2Pro & Farm925,000$92,500$0$No925,000$Lien
Jack RathboneBears (Was)D211999-05-20Yes177 Lbs5 ft10NoNoNo3Pro & Farm925,000$92,500$0$No925,000$925,000$Lien
Jakub ZborilBears (Was)D231997-02-21No200 Lbs6 ft0NoNoNo2Pro & Farm900,000$90,000$0$No900,000$Lien
Jaycob MegnaBears (Was)D271992-12-10No221 Lbs6 ft6NoNoNo2Pro & Farm969,006$96,901$0$No969,006$Lien
John HaydenBears (Was)LW/RW251995-02-14No223 Lbs6 ft3NoNoNo1Pro & Farm850,000$85,000$0$NoLien
Joseph VelenoBears (Was)C202000-01-13Yes198 Lbs6 ft1NoNoNo4Pro & Farm894,167$894,167$0$No894,167$894,167$894,167$Lien
Kaden FulcherBears (Was)G221998-09-23Yes201 Lbs6 ft3NoNoNo1Pro & Farm1,200,000$120,000$0$NoLien
Kaedan KorczakBears (Was)D192001-01-29Yes192 Lbs6 ft4NoNoNo3Pro & Farm795,000$79,500$0$No795,000$795,000$Lien
Layton AhacBears (Was)D192001-02-22Yes187 Lbs6 ft2NoNoNo3Pro & Farm897,500$89,750$0$No897,500$897,500$Lien
Mark KastelicBears (Was)C211999-03-10Yes210 Lbs6 ft3NoNoNo3Pro & Farm821,667$82,167$0$No821,667$821,667$Lien
Mikko LehtonenBears (Was)D261994-01-16No196 Lbs6 ft0NoNoNo4Pro & Farm925,000$92,500$0$No925,000$925,000$925,000$Lien
Nathan BastianBears (Was)RW221997-12-06No205 Lbs6 ft4NoNoNo1Pro & Farm742,500$74,250$0$NoLien
Nick ShoreBears (Was)C281992-09-26No194 Lbs6 ft1NoNoNo4Pro & Farm1,300,000$130,000$0$No1,300,000$1,300,000$1,300,000$Lien
Otto KivenmakiBears (Was)C202000-03-24Yes172 Lbs5 ft9NoNoNo4Pro & Farm560,000$56,000$0$No560,000$560,000$560,000$Lien
Sam MiletichBears (Was)C231997-05-04Yes194 Lbs6 ft1NoNoNo4Pro & Farm894,167$89,417$0$No894,167$894,167$894,167$Lien
Sean DayBears (Was)D221998-01-09No218 Lbs6 ft3NoNoNo1Pro & Farm742,500$74,250$0$NoLien
Tim SchallerBears (Was)LW/RW291990-11-16No210 Lbs6 ft2NoNoNo5Pro & Farm1,000,000$100,000$0$No1,000,000$1,000,000$1,000,000$1,000,000$Lien
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2523.76198 Lbs6 ft22.48885,554$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Tim SchallerJoseph VelenoNathan Bastian40122
2Boris KatchoukAntoine MorandJohn Hayden30122
3Dylan SikuraNick ShoreByron Froese20122
4Joseph VelenoMark KastelicTim Schaller10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Jakub ZborilMikko Lehtonen40122
2Jaycob MegnaJack Rathbone30122
3Sean DayEvan McEneny20122
4Jakub ZborilMikko Lehtonen10122
Attaque en svantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Tim SchallerJoseph VelenoNathan Bastian60122
2Boris KatchoukAntoine MorandJohn Hayden40122
Défense en svantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Jakub ZborilMikko Lehtonen60122
2Jaycob MegnaJack Rathbone40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Joseph VelenoTim Schaller60122
2Nathan BastianBoris Katchouk40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Jakub ZborilMikko Lehtonen60122
2Jaycob MegnaJack Rathbone40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Joseph Veleno60122Jakub ZborilMikko Lehtonen60122
2Tim Schaller40122Jaycob MegnaJack Rathbone40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Joseph VelenoTim Schaller60122
2Nathan BastianBoris Katchouk40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Jakub ZborilMikko Lehtonen60122
2Jaycob MegnaJack Rathbone40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Tim SchallerJoseph VelenoNathan BastianJakub ZborilMikko Lehtonen
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Tim SchallerJoseph VelenoNathan BastianJakub ZborilMikko Lehtonen
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Otto Kivenmaki, Sam Miletich, Nick ShoreOtto Kivenmaki, Sam MiletichNick Shore
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Sean Day, Evan McEneny, Jaycob MegnaSean DayEvan McEneny, Jaycob Megna
Tirs de pénalité
Joseph Veleno, Tim Schaller, Nathan Bastian, Boris Katchouk, John Hayden
Gardien
#1 : Kaden Fulcher, #2 : Alex Lyon


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
1Admirals21001000642110000003211000100032141.00069150012210973146811481140110160561914397114.29%70100.00%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
2Baby Hawks2110000079-2110000007431010000005-520.50071320001221097314761148114011016075241841300.00%9277.78%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
3Bruins303000001116-51010000024-220200000912-300.0001120310012210973141351148114011016012233286413430.77%14378.57%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
4Cabaret Lady Mary Ann32000010171251100000064221000010118361.0001729460012210973141941148114011016010631286612433.33%8450.00%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
5Caroline44000000171072200000074322000000106481.000173350001221097314182114811401101601376024841119.09%12191.67%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
6Chiefs2010010046-21000010012-11010000034-110.250481200122109731472114811401101606117123610220.00%5260.00%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
7Chill2010001089-1100000105411010000035-220.500812200012210973148011481140110160742017626233.33%6183.33%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
8Comets2110000069-31010000026-41100000043120.500610160012210973145811481140110160842330566233.33%14471.43%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
9Cougars312000001012-21100000021120200000811-320.333101525001221097314104114811401101609831465813215.38%12191.67%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
10Crunch30300000815-71010000034-120200000511-600.00081422001221097314156114811401101601002326779111.11%13653.85%11738317754.71%1536285953.73%771138855.55%2076144818316011068549
11Heat21100000770110000005321010000024-220.500714210012210973146911481140110160722216604250.00%8187.50%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
12Jayhawks22000000853110000004311100000042241.000816240012210973149211481140110160812119487228.57%6183.33%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
13Las Vegas22000000945110000004221100000052341.00091726001221097314921148114011016081252459200.00%7185.71%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
14Manchots41100020151322010001067-12100001096360.7501523380012210973141571148114011016016350349412325.00%16568.75%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
15Marlies31100001981211000007521000000123-130.50091423001221097314911148114011016011537276111218.18%11281.82%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
16Minnesota22000000183151100000010371100000080841.0001832500112210973141371148114011016057181651100.00%8275.00%11738317754.71%1536285953.73%771138855.55%2076144818316011068549
17Monarchs210000015321000000112-11100000041330.7505813001221097314811148114011016055201240400.00%6183.33%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
18Monsters412000011416-220100001810-22110000066030.3751425390012210973141411148114011016014746379815533.33%16568.75%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
19Monsters2110000045-1110000002021010000025-320.5004812011221097314721148114011016059181859500.00%8275.00%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
20Oceanics20100001810-21000000134-11010000056-110.250816240012210973149711481140110160741825379111.11%10280.00%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
21Oil Kings21100000770110000004221010000035-220.500712190012210973147711481140110160671814394250.00%70100.00%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
22Phantoms430000101486210000107522200000073481.0001425390012210973141521148114011016014053419016318.75%18288.89%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
23Rocket3210000013121220000009631010000046-240.6671324370012210973141471148114011016010527187511327.27%8275.00%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
24Senators31100010810-2210000105321010000037-440.6678142200122109731410011481140110160994031651218.33%12191.67%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
25Sharks21100000651110000004221010000023-120.50061117001221097314771148114011016066194525120.00%20100.00%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
26Sound Tigers403000011119-82010000168-220200000511-610.12511203100122109731414811481140110160157344683500.00%17476.47%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
27Spiders43100000191272110000096322000000106460.7501935541012210973141421148114011016014941257713323.08%9277.78%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
28Stars21100000770110000004221010000035-220.500713200012210973146311481140110160621312376116.67%5180.00%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
29Thunder3300000018992200000013671100000053261.000183149001221097314157114811401101601123327835120.00%11281.82%01738317754.71%1536285953.73%771138855.55%2076144818316011068549
30Wolf Pack43100000176112200000010192110000075260.7501731480212210973141981148114011016012036348311327.27%17288.24%11738317754.71%1536285953.73%771138855.55%2076144818316011068549
Total824029011653112714041248001441591154441162101021152156-41000.610311552863141221097314341511481140110160289487072318742485220.97%3026279.47%31738317754.71%1536285953.73%771138855.55%2076144818316011068549
_Since Last GM Reset824029011653112714041248001441591154441162101021152156-41000.610311552863141221097314341511481140110160289487072318742485220.97%3026279.47%31738317754.71%1536285953.73%771138855.55%2076144818316011068549
_Vs Conference46191601055169148212410600044896920229100101180791550.598169294463121221097314182411481140110160164949940210281443020.83%1723281.40%11738317754.71%1536285953.73%771138855.55%2076144818316011068549

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
82100W331155286334152894870723187414
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
8240291165311271
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
412480144159115
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
4116211021152156
Derniers 10 matchs
WLOTWOTL SOWSOL
720100
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
2485220.97%3026279.47%3
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
114811401101601221097314
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
1738317754.71%1536285953.73%771138855.55%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
2076144818316011068549


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 - 2021-10-122Bears3Chiefs4LSommaire du match
3 - 2021-10-1415Bears2Sound Tigers4LSommaire du match
4 - 2021-10-1523Caroline1Bears3WSommaire du match
7 - 2021-10-1839Stars2Bears4WSommaire du match
9 - 2021-10-2052Bears3Chill5LSommaire du match
11 - 2021-10-2270Bears3Stars5LSommaire du match
13 - 2021-10-2483Monsters0Bears2WSommaire du match
15 - 2021-10-2693Marlies4Bears2LSommaire du match
17 - 2021-10-28109Wolf Pack0Bears5WSommaire du match
19 - 2021-10-30126Bears0Baby Hawks5LSommaire du match
21 - 2021-11-01142Bears2Heat4LSommaire du match
23 - 2021-11-03154Bears3Oil Kings5LSommaire du match
24 - 2021-11-04160Bears4Comets3WSommaire du match
28 - 2021-11-08180Bears2Marlies3LXXSommaire du match
31 - 2021-11-11198Crunch4Bears3LSommaire du match
33 - 2021-11-13218Heat3Bears5WSommaire du match
37 - 2021-11-17238Bears7Cabaret Lady Mary Ann5WSommaire du match
39 - 2021-11-19258Las Vegas2Bears4WSommaire du match
41 - 2021-11-21270Jayhawks3Bears4WSommaire du match
43 - 2021-11-23283Bears4Phantoms1WSommaire du match
45 - 2021-11-25296Rocket4Bears5WSommaire du match
46 - 2021-11-26303Bears5Bruins6LSommaire du match
48 - 2021-11-28316Admirals2Bears3WSommaire du match
50 - 2021-11-30332Bears5Wolf Pack0WSommaire du match
53 - 2021-12-03348Comets6Bears2LSommaire du match
57 - 2021-12-07383Cabaret Lady Mary Ann4Bears6WSommaire du match
59 - 2021-12-09398Thunder5Bears7WSommaire du match
60 - 2021-12-10407Bears5Cougars7LSommaire du match
63 - 2021-12-13433Bears2Sharks3LSommaire du match
64 - 2021-12-14437Bears4Monarchs1WSommaire du match
66 - 2021-12-16451Bears3Admirals2WXSommaire du match
69 - 2021-12-19469Monsters4Bears3LXXSommaire du match
71 - 2021-12-21484Bruins4Bears2LSommaire du match
74 - 2021-12-24509Bears5Thunder3WSommaire du match
76 - 2021-12-26521Bears3Monsters4LSommaire du match
80 - 2021-12-30549Bears5Spiders2WSommaire du match
81 - 2021-12-31559Thunder1Bears6WSommaire du match
83 - 2022-01-02571Bears4Bruins6LSommaire du match
87 - 2022-01-06585Monsters6Bears5LSommaire du match
88 - 2022-01-07598Bears5Caroline3WSommaire du match
91 - 2022-01-10614Sound Tigers4Bears3LSommaire du match
94 - 2022-01-13639Bears5Caroline3WSommaire du match
96 - 2022-01-15653Sharks2Bears4WSommaire du match
98 - 2022-01-17667Senators1Bears2WSommaire du match
99 - 2022-01-18676Bears3Phantoms2WSommaire du match
102 - 2022-01-21696Spiders1Bears6WSommaire du match
104 - 2022-01-23712Caroline3Bears4WSommaire du match
107 - 2022-01-26732Spiders5Bears3LSommaire du match
109 - 2022-01-28743Bears3Sound Tigers7LSommaire du match
118 - 2022-02-06768Bears4Rocket6LSommaire du match
120 - 2022-02-08777Chill4Bears5WXXSommaire du match
122 - 2022-02-10787Bears3Senators7LSommaire du match
124 - 2022-02-12806Manchots5Bears3LSommaire du match
126 - 2022-02-14817Monarchs2Bears1LXXSommaire du match
130 - 2022-02-18850Phantoms3Bears4WSommaire du match
132 - 2022-02-20862Sound Tigers4Bears3LXXSommaire du match
135 - 2022-02-23888Bears2Monsters5LSommaire du match
137 - 2022-02-25904Bears4Jayhawks2WSommaire du match
139 - 2022-02-27918Bears5Las Vegas2WSommaire du match
142 - 2022-03-02935Rocket2Bears4WSommaire du match
144 - 2022-03-04948Bears5Spiders4WSommaire du match
145 - 2022-03-05959Manchots2Bears3WXXSommaire du match
147 - 2022-03-07973Oceanics4Bears3LXXSommaire du match
149 - 2022-03-09992Bears5Oceanics6LSommaire du match
152 - 2022-03-121014Bears8Minnesota0WSommaire du match
155 - 2022-03-151029Phantoms2Bears3WXXSommaire du match
156 - 2022-03-161036Bears2Wolf Pack5LSommaire du match
158 - 2022-03-181050Bears5Manchots4WXXSommaire du match
160 - 2022-03-201066Bears2Crunch6LSommaire du match
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
163 - 2022-03-231087Cougars1Bears2WSommaire du match
165 - 2022-03-251103Baby Hawks4Bears7WSommaire du match
167 - 2022-03-271120Oil Kings2Bears4WSommaire du match
170 - 2022-03-301141Bears3Monsters2WSommaire du match
171 - 2022-03-311147Senators2Bears3WXXSommaire du match
173 - 2022-04-021164Bears4Manchots2WSommaire du match
175 - 2022-04-041181Chiefs2Bears1LXSommaire du match
177 - 2022-04-061194Wolf Pack1Bears5WSommaire du match
179 - 2022-04-081210Bears3Cougars4LSommaire du match
181 - 2022-04-101224Bears3Crunch5LSommaire du match
182 - 2022-04-111231Marlies1Bears5WSommaire du match
184 - 2022-04-131248Minnesota3Bears10WSommaire du match
186 - 2022-04-151265Bears4Cabaret Lady Mary Ann3WXXSommaire du match



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets4020
Assistance63,36927,133
Assistance PCT77.28%66.18%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
0 2207 - 73.58% 75,059$3,077,420$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursSalaire total moyen des joueursSalaire des entraineurs
2,441,770$ 3,018,636$ 3,018,636$ 0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
16,142$ 2,441,770$ 25 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
0$ 0 16,142$ 0$




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