Bears

GP: 49 | W: 26 | L: 16 | OTL: 7 | P: 59
GF: 122 | GA: 95 | PP%: 17.38% | PK%: 87.17%
DG: Mathieu Girard | Morale : 50 | Moyenne d'Équipe : 60
Prochain matchs #762 vs Penguins
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Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Nom du Joueur C L R D CON CK FG DI SK ST EN DU PH FO PA SC DF PS EX LD PO MO OV TA SP
1Kyle CliffordX98.00955565759985727050646672557174163690
2Tanner GlassX98.00895559678179636350606064558476160640
3Jason DickinsonXX98.00635571707167676480626063555050162610
4Anton BlidhX98.00565565757871545650555660556573154590
5Anthony CamaraX100.00755565637867595550555555557274160580
6Paul ThompsonX100.00845569626866606050606060555050143580
7Rich CluneX100.00565558627568685550555556555959163570
8Dennis Yan (R)X100.00565555555657575550555555557575160550
9Brett SutterX100.00565555555758585550555555557475151550
10Reid Duke (R)X100.00565555555555555550555555555050160530
11Andrei Mironov (R)X100.00725561815769576625626265556462160630
12Trevor MurphyX100.00645570655875676525616164555353159610
13Sami Niku (R)X100.00615568755677556125606260556262160610
14Kyle Wood (R)X100.00595559615959795925595959557272162590
15Simon DespresX100.00555555605555555525555555557774160560
16Michael Kapla (R)X100.00555555605555695525555555555555142540
Rayé
MOYENNE D'ÉQUIPE99.5066556265666662594358586055656515759
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
1Vitek Vanecek100.0064788474666670677070556867162680
2Kristers Gudlevskis100.0060788379646463666862555969140650
Rayé
MOYENNE D'ÉQUIPE100.006278847765656767696655646815167
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Bob Hartley61626269885656CAN5756,300,000$


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'ÉquipePOS GP 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
1Kyle CliffordBears (Was)LW492226481313952011332006613311.00%21126425.80813215522510182456156.33%61600020.76020011032
2Jason DickinsonBears (Was)C/LW4992635101401599119371037.56%13108522.16114153322100031902062.29%95200000.6411000041
3Klas DahlbeckWashington CapitalsD268233111495663074284110.81%4555721.468917591170002119110.00%000001.1100001323
4Trevor MurphyBears (Was)D496212710691576527424618.11%5696419.683710461730003193200.00%000000.5600120122
5Tanner GlassBears (Was)LW379172656801437713340816.77%1392124.9028104015600061960147.85%20900010.5601000413
6Andrei MironovBears (Was)D3781624282095538431609.52%4781622.066915681770110190110.00%000000.5900000024
7Kyle WoodBears (Was)D49813211752068314892616.67%4276915.70538311170001110210.00%000000.5500000111
8Brett SutterBears (Was)C371071749554939143225.64%360716.4232591570000223053.31%66400000.5600001113
9Paul ThompsonBears (Was)RW4298178375443063145514.29%654713.0324621133000002141.18%3400000.6201010001
10Sami NikuBears (Was)D3788166475493459173713.56%2770719.116511381290003136300.00%000000.4500000121
11Rich CluneBears (Was)LW4931215811510243714358.11%649410.0914563600001191143.94%13200000.6101001111
12Anton BlidhBears (Was)LW364913-1521057539532564.21%463417.6206617850000740165.52%2900000.4100002012
13Simon DespresBears (Was)D372573480411384325.00%2142411.47000215000047000.00%000000.3300000000
14Anthony CamaraBears (Was)LW3723552401632335316.06%240110.840110130003930051.38%10900000.2500000000
15Reid DukeBears (Was)C3722401201136173811.76%138810.4900004000041052.52%33700000.2100000001
16Michael KaplaBears (Was)D37044-226029124020.00%213649.8500007000057000.00%000000.2201000000
17Dennis YanBears (Was)LW370004140887290.00%31925.200003380000180052.08%4800000.0000000000
Stats d'équipe Total ou en Moyenne68211020031010375355934766109434077310.05%3311114016.344585130428181311229182024855.69%313000030.5617136222025
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
1Juuse SarosWashington Capitals34171250.9151.97201246667770300.6679340314
2Vitek VanecekBears (Was)158410.9241.6082423222900100.00001334210
3Kristers GudlevskisBears (Was)85210.9112.0948841171910201.0003824101
Stats d'équipe Total ou en Moyenne57301870.9171.893326101010512580600.750125558625


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 StatusType Salaire Actuel Cap Salariale Cap Salariale Restant Exclus du Cap Salarial Link
Andrei MironovBears (Was)D221994-07-29Yes198 Lbs6 ft2NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No
Anthony CamaraBears (Was)LW231993-09-03No192 Lbs6 ft0NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Anton BlidhBears (Was)LW211995-03-14No201 Lbs6 ft0NoNoNo2Contrat d'EntréePro & Farm300,000$0$0$No
Brett SutterBears (Was)C291987-06-01No201 Lbs6 ft0NoNoNo3Sans RestrictionPro & Farm300,000$0$0$No
Dennis YanBears (Was)LW191997-04-14Yes183 Lbs6 ft1NoNoNo4Contrat d'EntréePro & Farm500,000$0$0$No
Jason DickinsonBears (Was)C/LW211995-07-04No185 Lbs6 ft1NoNoNo2Contrat d'EntréePro & Farm900,000$0$0$No
Kristers GudlevskisBears (Was)G241992-07-31No190 Lbs6 ft4NoNoNo1Avec RestrictionPro & Farm500,000$0$0$No
Kyle CliffordBears (Was)LW261991-01-13No211 Lbs6 ft2NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No
Kyle WoodBears (Was)D201996-05-04Yes210 Lbs6 ft5NoNoNo3Contrat d'EntréePro & Farm500,000$0$0$No
Michael KaplaBears (Was)D221994-09-19Yes201 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No
Paul ThompsonBears (Was)RW281988-11-29No198 Lbs5 ft11NoNoNo1Sans RestrictionPro & Farm300,000$0$0$No
Reid DukeBears (Was)C201996-01-28Yes192 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No
Rich CluneBears (Was)LW291987-04-24No207 Lbs5 ft10NoNoNo1Sans RestrictionPro & Farm300,000$0$0$No
Sami NikuBears (Was)D201996-10-10Yes179 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No
Simon DespresBears (Was)D251991-07-26No214 Lbs6 ft4NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No
Tanner GlassBears (Was)LW331983-11-28No210 Lbs6 ft1NoNoNo2Sans RestrictionPro & Farm300,000$0$0$No
Trevor MurphyBears (Was)D211995-07-16No172 Lbs5 ft10NoNoNo2Contrat d'EntréePro & Farm300,000$0$0$No
Vitek VanecekBears (Was)G211996-01-09No180 Lbs6 ft1NoNoNo3Contrat d'EntréePro & Farm500,000$0$0$No
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
1823.56196 Lbs6 ft12.56377,778$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Kyle CliffordJason DickinsonPaul Thompson40122
2Tanner GlassBrett SutterAnton Blidh30122
3Anton BlidhReid DukeKyle Clifford20122
4Anthony CamaraTanner GlassJason Dickinson10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Andrei MironovSami Niku40122
2Trevor MurphyKyle Wood30122
3Simon DespresMichael Kapla20122
4Andrei MironovSami Niku10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Kyle CliffordJason DickinsonPaul Thompson60122
2Tanner GlassBrett SutterAnton Blidh40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Andrei MironovSami Niku60122
2Trevor MurphyKyle Wood40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Kyle CliffordTanner Glass60122
2Jason DickinsonAnton Blidh40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Andrei MironovSami Niku60122
2Trevor MurphyKyle Wood40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Kyle Clifford60122Andrei MironovSami Niku60122
2Tanner Glass40122Trevor MurphyKyle Wood40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Kyle CliffordTanner Glass60122
2Jason DickinsonAnton Blidh40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Andrei MironovSami Niku60122
2Trevor MurphyKyle Wood40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Kyle CliffordJason DickinsonPaul ThompsonAndrei MironovSami Niku
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Kyle CliffordJason DickinsonPaul ThompsonAndrei MironovSami Niku
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Rich Clune, Dennis Yan, Anthony CamaraRich Clune, Dennis YanAnthony Camara
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Simon Despres, Michael Kapla, Trevor MurphySimon DespresMichael Kapla, Trevor Murphy
Tirs de Pénalité
Kyle Clifford, Tanner Glass, Jason Dickinson, Anton Blidh, Anthony Camara
Gardien
#1 : Vitek Vanecek, #2 : Kristers Gudlevskis


Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
LigueDomicileVisiteur
# VS Équipe GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff P PCT G A TP SO EG GP1 GP2 GP3 GP4 SHF SH1 SP2 SP3 SP4 SHA SHB Pim Hit PPA PPG PP% PKA PK GA PK% PK GF W OF FO T OF FO OF FO% W DF FO T DF FO DF FO% W NT FO T NT FO NT FO% PZ DF PZ OF PZ NT PC DF PC OF PC NT
1Admirals21100000541110000003121010000023-120.50059140040522923039041339521529404911327.27%20195.00%0802150353.36%726156746.33%38167556.44%12248501162355576294
2Americans2110000034-11010000003-31100000031220.5003580040522924139041339521481740351317.69%19478.95%0802150353.36%726156746.33%38167556.44%12248501162355576294
3Bruins31200000550211000004311010000012-120.333591401405229273390413395216424635812216.67%27196.30%0802150353.36%726156746.33%38167556.44%12248501162355576294
4Checkers440000002021822000000808220000001221081.0002036560340522921633904133952185264010113215.38%180100.00%0802150353.36%726156746.33%38167556.44%12248501162355576294
5Crunch2010010047-31010000024-21000010023-110.250481200405229258390413395213015204116318.75%9366.67%0802150353.36%726156746.33%38167556.44%12248501162355576294
6Devils2110000023-11010000013-21100000010120.5002460140522923639041339521431831421417.14%12283.33%0802150353.36%726156746.33%38167556.44%12248501162355576294
7Griffins22000000624110000003121100000031241.0006121800405229256390413395213911244914428.57%10280.00%0802150353.36%726156746.33%38167556.44%12248501162355576294
8IceHogs11000000202000000000001100000020221.000246014052292253904133952122610126233.33%50100.00%0802150353.36%726156746.33%38167556.44%12248501162355576294
9Monsters2020000006-62020000006-60000000000000.000000004052292353904133952142163135900.00%14471.43%0802150353.36%726156746.33%38167556.44%12248501162355576294
10Penguins953001002019143100000954522001001114-3110.6112036560140522921883904133952123373170173751317.33%701085.71%1802150353.36%726156746.33%38167556.44%12248501162355576294
11Phantoms923002021522-74100020179-251300001813-580.444152237004052292206390413395212146817216765913.85%64985.94%0802150353.36%726156746.33%38167556.44%12248501162355576294
12Rocket1010000012-11010000012-10000000000000.0001120040522922539041339521168622500.00%30100.00%0802150353.36%726156746.33%38167556.44%12248501162355576294
13Senators1010000004-41010000004-40000000000000.000000004052292223904133952132172719900.00%110100.00%0802150353.36%726156746.33%38167556.44%12248501162355576294
14Sound Tigers22000000642110000003211100000032141.0006111700405229242390413395215517283314428.57%13376.92%0802150353.36%726156746.33%38167556.44%12248501162355576294
15Thunderbirds5300110024101422000000101931001100149590.90024446801405229216739041339521106417412520630.00%35488.57%0802150353.36%726156746.33%38167556.44%12248501162355576294
Total49251601502122952726149002016045152311701301625012590.602122218340094052292121039041339521111937781110123055317.38%3434487.17%1802150353.36%726156746.33%38167556.44%12248501162355576294
17Wolf Pack22000000918220000009180000000000041.000917260140522924339041339521381135519333.33%13192.31%0802150353.36%726156746.33%38167556.44%12248501162355576294
_Since Last GM Reset49251601502122952726149002016045152311701301625012590.602122218340094052292121039041339521111937781110123055317.38%3434487.17%1802150353.36%726156746.33%38167556.44%12248501162355576294
_Vs Conference321313004026171-101887002013537-21456002012634-8320.50061107168044052292703390413395217512595776192233515.70%2333385.84%1802150353.36%726156746.33%38167556.44%12248501162355576294
_Vs Division30550020072571516330000037261114220020035314120.20072126198064052292713390413395217102295076021993216.08%2042985.78%1802150353.36%726156746.33%38167556.44%12248501162355576294

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
4959W312221834012101119377811101209
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
492516150212295
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
2614902016045
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
2311713016250
Derniers 10 Matchs
WLOTWOTL SOWSOL
630100
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
3055317.38%3434487.17%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
390413395214052292
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
802150353.36%726156746.33%38167556.44%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
12248501162355576294


Derniers Match Joués
Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
JourMatch Équipe Visiteuse Score Équipe Locale Score ST OT SO RI Lien
4 - 2018-09-0811Crunch4Bears2LSommaire du Match
5 - 2018-09-0926Sound Tigers2Bears3WSommaire du Match
10 - 2018-09-1434Bears3Griffins1WSommaire du Match
11 - 2018-09-1548Bears2Admirals3LSommaire du Match
12 - 2018-09-1662Bears2IceHogs0WSommaire du Match
15 - 2018-09-1966Bears0Penguins2LSommaire du Match
17 - 2018-09-2170Bears6Checkers2WSommaire du Match
18 - 2018-09-2284Bears6Checkers0WSommaire du Match
24 - 2018-09-28109Bears1Penguins4LSommaire du Match
25 - 2018-09-29118Penguins1Bears2WSommaire du Match
26 - 2018-09-30134Phantoms3Bears2LXXSommaire du Match
31 - 2018-10-05145Bears3Americans1WSommaire du Match
32 - 2018-10-06156Americans3Bears0LSommaire du Match
33 - 2018-10-07168Wolf Pack1Bears3WSommaire du Match
39 - 2018-10-13195Thunderbirds1Bears5WSommaire du Match
40 - 2018-10-14209Bears2Crunch3LXSommaire du Match
45 - 2018-10-19223Monsters4Bears0LSommaire du Match
46 - 2018-10-20238Monsters2Bears0LSommaire du Match
52 - 2018-10-26268Bears3Phantoms2WSommaire du Match
53 - 2018-10-27282Bears3Penguins2WSommaire du Match
57 - 2018-10-31300Penguins3Bears0LSommaire du Match
60 - 2018-11-03323Thunderbirds0Bears5WSommaire du Match
61 - 2018-11-04335Devils3Bears1LSommaire du Match
66 - 2018-11-09352Bears2Phantoms3LXXSommaire du Match
67 - 2018-11-10364Wolf Pack0Bears6WSommaire du Match
68 - 2018-11-11379Penguins1Bears4WSommaire du Match
74 - 2018-11-17408Bears1Bruins2LSommaire du Match
75 - 2018-11-18417Bears4Thunderbirds5LXSommaire du Match
78 - 2018-11-21427Bears1Phantoms2LSommaire du Match
80 - 2018-11-23434Penguins0Bears3WSommaire du Match
81 - 2018-11-24456Phantoms2Bears1LXSommaire du Match
87 - 2018-11-30479Bears5Penguins3WSommaire du Match
88 - 2018-12-01488Bruins3Bears2LSommaire du Match
89 - 2018-12-02501Bruins0Bears2WSommaire du Match
95 - 2018-12-08536Griffins1Bears3WSommaire du Match
96 - 2018-12-09549Admirals1Bears3WSommaire du Match
101 - 2018-12-14566Bears4Thunderbirds3WXSommaire du Match
102 - 2018-12-15580Bears1Phantoms3LSommaire du Match
103 - 2018-12-16590Phantoms2Bears1LXSommaire du Match
106 - 2018-12-19598Bears1Devils0WSommaire du Match
109 - 2018-12-22624Bears2Penguins3LXSommaire du Match
110 - 2018-12-23632Bears3Sound Tigers2WSommaire du Match
113 - 2018-12-26649Bears1Phantoms3LSommaire du Match
116 - 2018-12-29672Rocket2Bears1LSommaire du Match
123 - 2019-01-05700Phantoms2Bears3WSommaire du Match
124 - 2019-01-06711Senators4Bears0LSommaire du Match
127 - 2019-01-09718Bears6Thunderbirds1WSommaire du Match
130 - 2019-01-12737Checkers0Bears3WSommaire du Match
131 - 2019-01-13755Checkers0Bears5WSommaire du Match
134 - 2019-01-16762Penguins-Bears-
137 - 2019-01-19781Phantoms-Bears-
138 - 2019-01-20801Bears-Phantoms-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
143 - 2019-01-25822Bears-Penguins-
144 - 2019-01-26833IceHogs-Bears-
145 - 2019-01-27840Bears-Sound Tigers-
148 - 2019-01-30851Bears-Wolf Pack-
150 - 2019-02-01857Bears-Comets-
151 - 2019-02-02870Sound Tigers-Bears-
157 - 2019-02-08901Bears-Bruins-
158 - 2019-02-09912Bears-Bruins-
159 - 2019-02-10923Bears-Wolf Pack-
162 - 2019-02-13935Thunderbirds-Bears-
164 - 2019-02-15945Marlies-Bears-
165 - 2019-02-16956Sound Tigers-Bears-
168 - 2019-02-19972Penguins-Bears-
172 - 2019-02-23996Bears-Monsters-
173 - 2019-02-241009Bears-Monsters-
179 - 2019-03-021040Bruins-Bears-
180 - 2019-03-031054Bears-Sound Tigers-
184 - 2019-03-071072Bears-Marlies-
185 - 2019-03-081073Bears-Senators-
186 - 2019-03-091083Bears-Rocket-
189 - 2019-03-121105Phantoms-Bears-
192 - 2019-03-151122Bears-Wolf Pack-
193 - 2019-03-161129Comets-Bears-
194 - 2019-03-171146Wolf Pack-Bears-



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité de l'Aréna20001000
Prix des Billets3515
Assistance00
Assistance PCT0.00%0.00%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacité de l'ArénaPopularité de l'Équipe
12 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
4,305,978$ 68,000$ 43,940$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 51,851$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 63 32,825$ 2,067,975$




LigueDomicileVisiteur
Année GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff P G A TP SO EG GP1 GP2 GP3 GP4 SHF SH1 SP2 SP3 SP4 SHA SHB Pim Hit PPA PPG PP% PKA PK GA PK% PK GF W OF FO T OF FO OF FO% W DF FO T DF FO DF FO% W NT FO T NT FO NT FO% PZ DF PZ OF PZ NT PC DF PC OF PC NT
134925160150212295272614900201604515231170130162501259122218340094052292121039041339521111937781110123055317.38%3434487.17%1802150353.36%726156746.33%38167556.44%12248501162355576294
Total Saison Régulière4925160150212295272614900201604515231170130162501259122218340094052292121039041339521111937781110123055317.38%3434487.17%1802150353.36%726156746.33%38167556.44%12248501162355576294
Séries
1240400000313-102020000035-22020000008-803580012004915132101112956852129.52%27485.19%0478952.81%6915245.39%325162.75%8860107304723
1240400000313-102020000035-22020000008-803580012004915132101112956852129.52%27485.19%0478952.81%6915245.39%325162.75%8860107304723
Total Séries80800000626-2040400000610-440400000016-16061016002400983026420222581121704249.52%54885.19%09417852.81%13830445.39%6410262.75%177120214619446