Connexion

Bears
GP: 27 | W: 20 | L: 7 | OTL: 0 | P: 40
GF: 105 | GA: 70 | PP%: 17.72% | PK%: 86.02%
DG: JF Langlais | Morale : 50 | Moyenne d’équipe : 56
Prochains matchs #413 vs Phantoms
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Centre de jeu
Bears
20-7-0, 40pts		5
FINAL
 2 Heat
11-12-2, 24pts
Team Stats
L1StreakW1
10-2-0Home Record6-7-2
10-5-0Away Record5-5-0
7-3-0Last 10 Games6-4-0
3.89Buts par match 3.60
2.59Buts contre par match 3.76
17.72%Pourcentage en avantage numérique19.70%
86.02%Pourcentage en désavantage numérique72.92%
Bears
20-7-0, 40pts		2
FINAL
 3 Oil Kings
14-6-6, 34pts
Team Stats
L1StreakW1
10-2-0Home Record11-2-1
10-5-0Away Record3-4-5
7-3-0Last 10 Games5-1-4
3.89Buts par match 3.54
2.59Buts contre par match 2.73
17.72%Pourcentage en avantage numérique22.09%
86.02%Pourcentage en désavantage numérique89.47%
Bears
20-7-0, 40pts
2022-12-07
 Phantoms
7-17-2, 16pts
Statistiques d’équipe
L1SéquenceL2
10-2-0Fiche domicile5-7-2
10-5-0Fiche visiteur2-10-0
7-3-010 derniers matchs1-7-2
3.89Buts par match 2.65
2.59Buts contre par match 2.65
17.72%Pourcentage en avantage numérique12.05%
86.02%Pourcentage en désavantage numérique80.20%
Seattle
18-6-0, 36pts
2022-12-09
 Bears
20-7-0, 40pts
Statistiques d’équipe
W1SéquenceL1
11-3-0Fiche domicile10-2-0
7-3-0Fiche visiteur10-5-0
7-3-010 derniers matchs7-3-0
4.50Buts par match 3.89
2.29Buts contre par match 3.89
22.95%Pourcentage en avantage numérique17.72%
87.50%Pourcentage en désavantage numérique86.02%
Bears
20-7-0, 40pts
2022-12-11
 Oceanics
14-7-2, 30pts
Statistiques d’équipe
L1SéquenceW2
10-2-0Fiche domicile7-5-0
10-5-0Fiche visiteur7-2-2
7-3-010 derniers matchs6-3-1
3.89Buts par match 4.09
2.59Buts contre par match 4.09
17.72%Pourcentage en avantage numérique26.39%
86.02%Pourcentage en désavantage numérique80.68%
Meneurs d'équipe
Buts
Philip Tomasino
16		 Passes
Philip Tomasino
19
Points
Philip Tomasino
35		 Plus/Moins
Joe Veleno
19
Victoires
Alex Lyon
20		 Pourcentage d’arrêts
Alex Lyon
0.923

Statistiques d’équipe
Buts pour
105
3.89 GFG		 Tirs pour
962
35.63 Avg		 Pourcentage en avantage numérique
17.7%
14 GF		 Début de zone offensive
39.8%
Buts contre
70
2.59 GAA		 Tirs contre
892
33.04 Avg		 Pourcentage en désavantage numérique
86.0%%
13 GA		 Début de la zone défensive
40.5%
Informations de l'équipe

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


Informations de l’aréna

Capacité3,000
Assistance2,163
Billets de saison300


Informations de la formation

Équipe Pro23
Équipe Mineure20
Limite contact 43 / 50
Espoirs14


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
1Philip Tomasino (R)X100.00674295786559787038736662255454050640203894,167$
2Joe VelenoXX100.00844589827565646272606269255252050630213894,167$
3Boris KatchoukX100.00885781727756716136575774255050050610232750,000$
4John HaydenXX100.00849959788558685731545670636464050610266800,000$
5Byron FroeseXX100.00757476627459586480566568625959050600302850,000$
6Michael Dal ColleXX100.00817692667660616150575970565757050600252750,000$
7Tim SchallerXX100.008076886376646756705054705162630505903041,000,000$
8Antoine MorandX100.00726687676662665063474760454444050540222778,334$
9Sam MileticXX100.00797196657154555164504763454444050540243894,167$
10Otto Kivenmaki (R)X100.00454081696055645155474445485050050500213560,000$
11Sean DayX100.00828086668071765525514566434444050610232750,000$
12Alex Vlasic (R)X100.00754495757764435525464973254545050600204916,667$
13Mikko LehtonenX100.00734489677163615825534467244646050590273925,000$
14Dakota MermisX100.00687161677158605425494161395555050570272655,000$
15Jack RathboneX100.00674192716364605825394765254545050570222925,000$
16Kaedan Korczak (R)X100.00767482737456584925434061384444050570202795,000$
17Layton Ahac (R)X100.00767091617047484725393961374444050530202897,500$
18Kim Nousiainen (R)X100.00423599676058754425423842405858050510204859,167$
Rayé
1Ethan Phillips (R)X100.00393099685453684146383637385454050460204650,000$
MOYENNE D’ÉQUIPE100.0071608669715963554250496240515105057
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 moyen
1Alex Lyon100.0059445575616168696868954646050610284800,000$
2Colton Point (R)100.0045465895454545504545454444050510232750,000$
Rayé
1Kaden Fulcher (R)100.0044425380454445494545454444050490232800,000$
2Daniil Chechelev (R)100.0044405076454445494545454444050480204650,000$
MOYENNE D’ÉQUIPE100.004843548249495154515158454505052
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
1Philip TomasinoBears (Was)C27161935180048093268917.20%561422.7717812690001864136.19%80400001.1401000054
2John HaydenBears (Was)LW/RW27151732163315793592256216.30%1149618.4036916710004445237.65%8500111.2901011411
3Joe VelenoBears (Was)C/LW2711193019120405411631879.48%561622.8243718720000843158.33%3600000.9711000212
4Boris KatchoukBears (Was)LW279152412180696410933728.26%654420.1702216580002560038.64%4400000.8811000232
5Tim SchallerBears (Was)C/LW27138216140466098216913.27%743416.0900015000092056.18%46100010.9700000201
6Byron FroeseBears (Was)C/RW27119201040336093247211.83%550418.7002211590001132061.04%59800000.7900000422
7Alex VlasicBears (Was)D27713209140332952162513.46%4054120.042351769000280210.00%000000.7400000210
8Sam MileticBears (Was)C/LW275152066019306826497.35%745316.78011010000350061.40%5700000.8800000021
9Michael Dal ColleBears (Was)LW/RW24712191010027358216648.54%343718.250331454000001048.78%4100000.8700000111
10Jack RathboneBears (Was)D2701616146024233712210.00%2339514.660003800003000.00%000000.8100000010
11Sean DayBears (Was)D27213154355721719152210.53%4563323.47011468000071000.00%000000.4700100011
12Mikko LehtonenBears (Was)D27411151514030294212269.52%3853919.983031563000168010.00%000000.5600000002
13Antoine MorandBears (Was)C2726856020231792411.76%42328.6210111000010060.17%11800000.6900000001
14Kaedan KorczakBears (Was)D27235142606613226119.09%3053419.80000862000062000.00%000000.1900000011
15Layton AhacBears (Was)D2705512604012114110.00%2539114.5000001000026000.00%000000.2600000000
16Dakota MermisBears (Was)D10022100521020.00%4464.610000100006000.00%000000.8700000000
17Otto KivenmakiBears (Was)C27000-500003210.00%1391.4700007000000038.46%1300000.00%00000000
18Ethan PhillipsBears (Was)RW20000200045320.00%131915.9700007000000022.22%2700000.00%00000000
19Kim NousiainenBears (Was)D27000-300042110.00%41696.2600000000115000.00%000000.00%00000000
Statistiques d’équipe totales ou en moyenne4861041832871652042060757496228271010.81%264794516.351428421366850001266719649.12%228400120.7224111171919
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
1Alex LyonBears (Was)2720700.9232.55162423698910110.7504270223
Statistiques d’équipe totales ou en moyenne2720700.9232.55162423698910114270223


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 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 10Link
Alex Lyon (contrat à 1 volet)Bears (Was)G281992-12-08No201 Lbs6 ft1NoNoYes4Pro & Farm800,000$547,368$0$0$No800,000$800,000$800,000$Lien
Alex VlasicBears (Was)D202001-06-05Yes198 Lbs6 ft6NoNoNo4Pro & Farm916,667$627,193$0$0$No916,667$916,667$916,667$Lien
Antoine MorandBears (Was)C221999-02-18No184 Lbs5 ft11NoNoNo2Pro & Farm778,334$532,544$0$0$No778,334$Lien
Boris KatchoukBears (Was)LW231998-06-17No206 Lbs6 ft2NoNoNo2Pro & Farm750,000$513,158$0$0$No750,000$Lien
Byron Froese (contrat à 1 volet)Bears (Was)C/RW301991-03-12No202 Lbs6 ft1NoNoYes2Pro & Farm850,000$581,579$0$0$No850,000$Lien
Colton PointBears (Was)G231998-03-04Yes230 Lbs6 ft5YesNoNo2Pro & Farm750,000$513,158$0$0$No750,000$Lien
Dakota Mermis (contrat à 1 volet)Bears (Was)D271994-01-05No196 Lbs6 ft0NoNoYes2Pro & Farm655,000$448,158$0$0$No655,000$Lien
Daniil ChechelevBears (Was)G202001-02-23Yes187 Lbs6 ft3NoNoNo4Pro & Farm650,000$444,737$0$0$No650,000$650,000$650,000$Lien
Ethan PhillipsBears (Was)RW202001-05-07Yes154 Lbs5 ft9NoNoNo4Pro & Farm650,000$444,737$0$0$No650,000$650,000$650,000$Lien
Jack RathboneBears (Was)D221999-05-20No177 Lbs5 ft10NoNoNo2Pro & Farm925,000$632,895$0$0$No925,000$Lien
Joe VelenoBears (Was)C/LW212000-01-13No206 Lbs6 ft1NoNoNo3Pro & Farm894,167$611,798$0$0$No894,167$894,167$Lien
John Hayden (contrat à 1 volet)Bears (Was)LW/RW261995-02-14No223 Lbs6 ft3NoNoYes6Pro & Farm800,000$547,368$0$0$No800,000$800,000$800,000$800,000$800,000$Lien
Kaden FulcherBears (Was)G231998-09-23Yes201 Lbs6 ft3NoNoNo2Pro & Farm800,000$547,368$0$0$No800,000$Lien
Kaedan KorczakBears (Was)D202001-01-29Yes192 Lbs6 ft4NoNoNo2Pro & Farm795,000$543,947$0$0$No795,000$Lien
Kim NousiainenBears (Was)D202000-11-14Yes170 Lbs5 ft9NoNoNo4Pro & Farm859,167$587,851$0$0$No859,167$859,167$859,167$Lien
Layton AhacBears (Was)D202001-02-22Yes187 Lbs6 ft2NoNoNo2Pro & Farm897,500$614,079$0$0$No897,500$Lien
Michael Dal ColleBears (Was)LW/RW251996-06-20No200 Lbs6 ft3NoNoYes2Pro & Farm750,000$513,158$0$0$No750,000$Lien
Mikko Lehtonen (contrat à 1 volet)Bears (Was)D271994-01-16No196 Lbs6 ft0NoNoYes3Pro & Farm925,000$632,895$25,000$17,105$No925,000$925,000$Lien
Otto KivenmakiBears (Was)C212000-03-24Yes172 Lbs5 ft9NoNoNo3Pro & Farm560,000$383,158$0$0$No560,000$560,000$Lien
Philip TomasinoBears (Was)C202001-07-28Yes179 Lbs6 ft0NoNoNo3Pro & Farm894,167$611,798$0$0$No894,167$894,167$Lien
Sam MileticBears (Was)C/LW241997-05-04No196 Lbs6 ft0NoNoYes3Pro & Farm894,167$611,798$0$0$No894,167$894,167$Lien
Sean DayBears (Was)D231998-01-09No218 Lbs6 ft3NoNoNo2Pro & Farm750,000$513,158$0$0$No750,000$Lien
Tim Schaller (contrat à 1 volet)Bears (Was)C/LW301990-11-16No205 Lbs6 ft2NoNoYes4Pro & Farm1,000,000$684,211$100,000$68,421$No1,000,000$1,000,000$1,000,000$Lien
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2323.26195 Lbs6 ft12.91806,268$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Joe VelenoPhilip TomasinoJohn Hayden40122
2Boris KatchoukByron FroeseMichael Dal Colle30122
3Sam MileticTim SchallerAntoine Morand20122
4Philip TomasinoAntoine MorandJoe Veleno10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Sean DayAlex Vlasic40122
2Mikko LehtonenKaedan Korczak30122
3Jack RathboneLayton Ahac20122
4Kim NousiainenSean Day10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Joe VelenoPhilip TomasinoJohn Hayden60122
2Boris KatchoukByron FroeseMichael Dal Colle40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Sean DayAlex Vlasic60122
2Mikko LehtonenKaedan Korczak40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Philip TomasinoJoe Veleno60122
2John HaydenBoris Katchouk40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Sean DayAlex Vlasic60122
2Mikko LehtonenKaedan Korczak40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Philip Tomasino60122Sean DayAlex Vlasic60122
2Joe Veleno40122Mikko LehtonenKaedan Korczak40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Philip TomasinoJoe Veleno60122
2John HaydenBoris Katchouk40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Sean DayAlex Vlasic60122
2Mikko LehtonenKaedan Korczak40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Joe VelenoPhilip TomasinoJohn HaydenSean DayAlex Vlasic
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Joe VelenoPhilip TomasinoJohn HaydenSean DayAlex Vlasic
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Otto Kivenmaki, Tim Schaller, Sam MileticOtto Kivenmaki, Tim SchallerSam Miletic
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Jack Rathbone, Layton Ahac, Kim NousiainenJack RathboneLayton Ahac, Kim Nousiainen
Tirs de pénalité
Philip Tomasino, Joe Veleno, John Hayden, Boris Katchouk, Michael Dal Colle
Gardien
#1 : Alex Lyon, #2 : Colton Point


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
1Bruins11000000431110000004310000000000021.000471100373730222320345296531134224250.00%2150.00%045991050.44%45792649.35%20644845.98%656450609201359179
2Cabaret Lady Mary Ann11000000624000000000001100000062421.000610160037373025432034529654310624100.00%3166.67%045991050.44%45792649.35%20644845.98%656450609201359179
3Caroline11000000303000000000001100000030321.000358013737302383203452965216217400.00%10100.00%045991050.44%45792649.35%20644845.98%656450609201359179
4Chiefs11000000532000000000001100000053221.000581300373730245320345296527104194125.00%20100.00%045991050.44%45792649.35%20644845.98%656450609201359179
5Chill11000000633000000000001100000063321.000611170037373024732034529653896163133.33%3166.67%045991050.44%45792649.35%20644845.98%656450609201359179
6Comets22000000743110000003211100000042241.0007121900373730262320345296571172166100.00%8187.50%045991050.44%45792649.35%20644845.98%656450609201359179
7Cougars11000000321000000000001100000032121.00035800373730231320345296544111420100.00%70100.00%045991050.44%45792649.35%20644845.98%656450609201359179
8Heat22000000954110000004311100000052341.00091726003737302683203452965642414505120.00%6183.33%045991050.44%45792649.35%20644845.98%656450609201359179
9Jayhawks1010000023-11010000023-10000000000000.0002350037373023432034529653351018400.00%5260.00%045991050.44%45792649.35%20644845.98%656450609201359179
10Las Vegas11000000844110000008440000000000021.0008162400373730249320345296539124295360.00%20100.00%045991050.44%45792649.35%20644845.98%656450609201359179
11Manchots11000000431110000004310000000000021.000461000373730231320345296543161425300.00%70100.00%045991050.44%45792649.35%20644845.98%656450609201359179
12Marlies1010000034-1000000000001010000034-100.00035800373730230320345296533512183133.33%6183.33%045991050.44%45792649.35%20644845.98%656450609201359179
13Monarchs11000000817110000008170000000000021.00081422003737302393203452965215427100.00%20100.00%045991050.44%45792649.35%20644845.98%656450609201359179
14Monsters1010000035-21010000035-20000000000000.0003690037373024232034529653632133133.33%10100.00%045991050.44%45792649.35%20644845.98%656450609201359179
15Oil Kings21100000633110000004041010000023-120.500691501373730280320345296551221235900.00%60100.00%045991050.44%45792649.35%20644845.98%656450609201359179
16Phantoms11000000404110000004040000000000021.00048120137373024232034529652764209111.11%20100.00%045991050.44%45792649.35%20644845.98%656450609201359179
17Rocket10000010431100000104310000000000021.0004590037373023632034529654091024100.00%5180.00%045991050.44%45792649.35%20644845.98%656450609201359179
18Seattle11000000532000000000001100000053221.0005813003737302313203452965348819200.00%4250.00%045991050.44%45792649.35%20644845.98%656450609201359179
19Senators11000000211000000000001100000021121.0002460037373022632034529652115016500.00%000.00%045991050.44%45792649.35%20644845.98%656450609201359179
20Spiders2020000037-4000000000002020000037-400.000369103737302483203452965751935583133.33%11190.91%045991050.44%45792649.35%20644845.98%656450609201359179
21Stars1010000005-5000000000001010000005-500.0000000037373023832034529652717623500.00%30100.00%045991050.44%45792649.35%20644845.98%656450609201359179
22Thunder220000001064110000004221100000064241.000101828003737302693203452965732214483266.67%7185.71%045991050.44%45792649.35%20644845.98%656450609201359179
Total271970001010570351292000105229231510500000534112400.7411051832881337373029623203452965892264206607791417.72%931386.02%045991050.44%45792649.35%20644845.98%656450609201359179
_Since Last GM Reset271970001010570351292000105229231510500000534112400.7411051832881337373029623203452965892264206607791417.72%931386.02%045991050.44%45792649.35%20644845.98%656450609201359179
_Vs Conference11830000044281655000000249156330000020191160.727447912311373730235432034529653621109325034823.53%40587.50%045991050.44%45792649.35%20644845.98%656450609201359179

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
2740L110518328896289226420660713
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
27197001010570
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
129200105229
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
1510500005341
Derniers 10 matchs
WLOTWOTL SOWSOL
730000
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
791417.72%931386.02%0
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
32034529653737302
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
45991050.44%45792649.35%20644845.98%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
656450609201359179


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
6 - 2022-10-125Bruins3Bears4BWSommaire du match
7 - 2022-10-1315Bears3Marlies4ALSommaire du match
9 - 2022-10-1529Rocket3Bears4BWXXSommaire du match
11 - 2022-10-1743Comets2Bears3BWSommaire du match
14 - 2022-10-2065Bears2Senators1AWSommaire du match
16 - 2022-10-2280Monarchs1Bears8BWSommaire du match
18 - 2022-10-2493Bears1Spiders3ALSommaire du match
21 - 2022-10-27117Bears0Stars5ALSommaire du match
23 - 2022-10-29136Bears6Chill3AWSommaire du match
25 - 2022-10-31145Bears3Caroline0AWSommaire du match
26 - 2022-11-01150Las Vegas4Bears8BWSommaire du match
28 - 2022-11-03164Bears3Cougars2AWSommaire du match
30 - 2022-11-05179Jayhawks3Bears2BLSommaire du match
32 - 2022-11-07195Oil Kings0Bears4BWSommaire du match
34 - 2022-11-09208Manchots3Bears4BWSommaire du match
36 - 2022-11-11222Thunder2Bears4BWSommaire du match
38 - 2022-11-13242Bears6Thunder4AWSommaire du match
40 - 2022-11-15253Bears6Cabaret Lady Mary Ann2AWSommaire du match
42 - 2022-11-17269Bears5Chiefs3AWSommaire du match
44 - 2022-11-19279Monsters5Bears3BLSommaire du match
48 - 2022-11-23312Phantoms0Bears4BWSommaire du match
50 - 2022-11-25322Heat3Bears4BWSommaire du match
51 - 2022-11-26336Bears2Spiders4ALSommaire du match
54 - 2022-11-29359Bears4Comets2AWSommaire du match
56 - 2022-12-01374Bears5Seattle3AWSommaire du match
58 - 2022-12-03390Bears5Heat2AWSommaire du match
60 - 2022-12-05400Bears2Oil Kings3ALSommaire du match
62 - 2022-12-07413Bears-Phantoms-
64 - 2022-12-09427Seattle-Bears-
66 - 2022-12-11446Bears-Oceanics-
68 - 2022-12-13463Bears-Baby Hawks-
70 - 2022-12-15471Stars-Bears-
72 - 2022-12-17491Marlies-Bears-
74 - 2022-12-19503Cougars-Bears-
77 - 2022-12-22528Bears-Senators-
78 - 2022-12-23537Oceanics-Bears-
82 - 2022-12-27549Bears-Wolf Pack-
84 - 2022-12-29566Senators-Bears-
86 - 2022-12-31581Rocket-Bears-
89 - 2023-01-03599Crunch-Bears-
91 - 2023-01-05617Bears-Monsters-
92 - 2023-01-06622Chill-Bears-
94 - 2023-01-08639Monsters-Bears-
97 - 2023-01-11659Bears-Phantoms-
100 - 2023-01-14681Phantoms-Bears-
102 - 2023-01-16702Bears-Sound Tigers-
103 - 2023-01-17707Minnesota-Bears-
105 - 2023-01-19726Bears-Jayhawks-
107 - 2023-01-21745Bears-Las Vegas-
110 - 2023-01-24764Bears-Monsters-
112 - 2023-01-26773Manchots-Bears-
115 - 2023-01-29800Bears-Marlies-
117 - 2023-01-31804Bears-Monsters-
128 - 2023-02-11838Bears-Bruins-
129 - 2023-02-12847Sharks-Bears-
131 - 2023-02-14855Caroline-Bears-
133 - 2023-02-16870Cabaret Lady Mary Ann-Bears-
135 - 2023-02-18889Bears-Caroline-
138 - 2023-02-21909Cougars-Bears-
140 - 2023-02-23920Admirals-Bears-
142 - 2023-02-25937Wolf Pack-Bears-
143 - 2023-02-26947Bears-Crunch-
146 - 2023-03-01973Bears-Admirals-
149 - 2023-03-04992Bears-Sharks-
151 - 2023-03-061011Bears-Monarchs-
154 - 2023-03-091029Spiders-Bears-
156 - 2023-03-111048Bears-Sound Tigers-
159 - 2023-03-141067Bears-Wolf Pack-
160 - 2023-03-151076Crunch-Bears-
162 - 2023-03-171093Chiefs-Bears-
164 - 2023-03-191109Bears-Minnesota-
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
166 - 2023-03-211121Monsters-Bears-
168 - 2023-03-231135Baby Hawks-Bears-
170 - 2023-03-251162Bears-Manchots-
174 - 2023-03-291187Sound Tigers-Bears-
175 - 2023-03-301194Bears-Thunder-
178 - 2023-04-021217Wolf Pack-Bears-
182 - 2023-04-061247Bears-Rocket-
184 - 2023-04-081262Cabaret Lady Mary Ann-Bears-
186 - 2023-04-101278Sound Tigers-Bears-
187 - 2023-04-111290Bears-Bruins-
189 - 2023-04-131301Spiders-Bears-



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets4020
Assistance18,0807,873
Assistance PCT75.33%65.61%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
29 2163 - 72.09% 73,388$880,660$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursSalaire total moyen des joueursSalaire des entraineurs
426,199$ 1,351,418$ 1,351,418$ 0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
7,113$ 426,199$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
2,128,262$ 130 7,113$ 924,690$




Bears Leaders statistiques (saison régulière)

# Nom du joueur GP G A P +/- PIM HIT HTT SHT SHT% SB MP AMG PPG PPA PPP PPS PKG PKA PKP PKS GW GT FO% HT P/20 PSG PSS

Bears 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

Bears 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

Bears Leaders statistiques (séries éliminatoires)

# Nom du joueur GP G A P +/- PIM HIT HTT SHT SHT% SB MP AMG PPG PPA PPP PPS PKG PKA PKP PKS GW GT FO% HT P/20 PSG PSS

Bears 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





Site web créé par le SimonT Hockey Simulator (STHS) pour Patrick Pellegrino - English Version of the Website - La base de données a été créé : 2022-12-05 20:01:01