Bears

GP: 25 | W: 13 | L: 9 | OTL: 3 | P: 29
GF: 62 | GA: 50 | PP%: 17.33% | PK%: 83.23%
DG: Mathieu Girard | Morale : 49 | Moyenne d'Équipe : 61
Prochain matchs #379 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 CliffordX98.00955565759985727050646672557174155690
2Tanner GlassX98.00895559678179636350606064558476153640
3Jason DickinsonXX98.00635571707167676480626063555050155610
4Alexander Volkov (R)X100.00735578727862695550555555557274153590
5Anthony CamaraX100.00755565637867595550555555557274153580
6Paul ThompsonX100.00845569626866606050606060555050152580
7Rich CluneX100.00565558627568685550555556555959155570
8Dennis Yan (R)X100.00565555555657575550555555557575153550
9Brett SutterX100.00565555555758585550555555557475153550
10Reid Duke (R)X100.00565555555555555550555555555050153530
11Klas DahlbeckX100.00845575738279697225636174557272156700
12Andrei Mironov (R)X100.00725561815769576625626265556462153630
13Trevor MurphyX100.00645570655875676525616164555353155610
14Sami Niku (R)X100.00615568755677556125606260556262153600
15Kyle Wood (R)X100.00595559615959795925595959557272155590
16Simon DespresX100.00555555605555555525555555557774153560
17Michael Kapla (R)X100.00555555605555695525555555555555135540
Rayé
1Anton BlidhX100.00565565757871545650555660556573143590
MOYENNE D'ÉQUIPE99.6767556366686763604258586055656615260
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
1Juuse Saros100.0083788183888885848782557572145800
2Vitek Vanecek100.0064788474666670677070556867155680
Rayé
MOYENNE D'ÉQUIPE100.007478837977777876797655727015074
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
1Klas DahlbeckBears (Was)D258233110495642974273910.81%4453221.298917591080002115110.00%000001.1700001323
2Kyle CliffordBears (Was)LW251311244760976389396814.61%1062825.1546102611400061225157.10%35200020.7602000520
3Jason DickinsonBears (Was)C/LW256101651009526518599.23%854021.63167171080001891061.37%40900000.5911000030
4Tanner GlassBears (Was)LW1367138100552240142615.00%332725.2013411470001740047.06%6800010.7901000303
5Trevor MurphyBears (Was)D252101264204126358295.71%3644517.800222162000168100.00%000000.5400000001
6Kyle WoodBears (Was)D2539121020022162331513.04%2037014.803251753000047100.00%000000.6500000010
7Anton BlidhBears (Was)LW21279-145552426321423.17%136717.5106615600000270058.82%1700000.4900001001
8Rich CluneBears (Was)LW252797206141692312.50%32429.701122110000560147.73%4400000.7401000101
9Paul ThompsonBears (Was)RW25448923523113363512.12%329211.68112955000000138.89%1800000.5501010001
10Andrei MironovBears (Was)D13246014030172510218.00%1226620.501232056000070110.00%000000.4500000002
11Brett SutterBears (Was)C13325700118126425.00%122317.22000253000071050.44%22600000.4500000000
12Sami NikuBears (Was)D1322462351310104520.00%417913.78000519000018100.00%000000.4500000020
13Anthony CamaraBears (Was)LW1310134031011069.09%114010.84000060000370046.55%5800000.1400000000
14Michael KaplaBears (Was)D13011140711000.00%4745.730000100006000.00%000000.2701000000
15Simon DespresBears (Was)D1301112001122110.00%81048.0300001000014000.00%000000.1900000000
16Alexander VolkovBears (Was)RW13000-160335160.00%018714.410003610000100033.33%600000.0001000000
17Dennis YanBears (Was)LW13000420502000.00%2524.05000112000030043.75%1600000.0000000000
18Reid DukeBears (Was)C13000-1802104030.00%013910.7500000000000053.10%11300000.0000000000
Stats d'équipe Total ou en Moyenne3265498152783582044434651016738210.59%160511615.692038582088350001177112555.16%132700030.5918012121012
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 SarosBears (Was)2513930.9151.96146944485620300.6679250203
2Vitek VanecekBears (Was)20001.0000.0042000150000.0000025000
Stats d'équipe Total ou en Moyenne2713930.9171.91151244485770300.66792525203


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 Salaire Année 2 Salaire Année 3 Salaire Année 4 Salaire Année 5 Salaire Année 6 Salaire Année 7 Salaire Année 8 Salaire Année 9 Salaire Année 10 Link
Alexander VolkovBears (Was)RW191997-08-02Yes192 Lbs6 ft1NoNoNo4Contrat d'EntréePro & Farm500,000$0$0$No500,000$500,000$500,000$
Andrei MironovBears (Was)D221994-07-29Yes198 Lbs6 ft2NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No300,000$300,000$300,000$
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$0$No300,000$
Brett SutterBears (Was)C291987-06-01No201 Lbs6 ft0NoNoNo3Sans RestrictionPro & Farm300,000$0$0$No300,000$300,000$
Dennis YanBears (Was)LW191997-04-14Yes183 Lbs6 ft1NoNoNo4Contrat d'EntréePro & Farm500,000$0$0$No500,000$500,000$500,000$
Jason DickinsonBears (Was)C/LW211995-07-04No185 Lbs6 ft1NoNoNo2Contrat d'EntréePro & Farm900,000$0$0$No900,000$
Juuse SarosBears (Was)G211995-04-19No180 Lbs5 ft11NoNoNo2Contrat d'EntréePro & Farm500,000$0$0$No500,000$
Klas DahlbeckBears (Was)D251991-07-06No207 Lbs6 ft3NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Kyle CliffordBears (Was)LW261991-01-13No211 Lbs6 ft2NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No300,000$
Kyle WoodBears (Was)D201996-05-04Yes210 Lbs6 ft5NoNoNo3Contrat d'EntréePro & Farm500,000$0$0$No500,000$500,000$
Michael KaplaBears (Was)D221994-09-19Yes201 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No300,000$300,000$300,000$
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$0$No300,000$300,000$300,000$
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$0$No300,000$300,000$300,000$
Simon DespresBears (Was)D251991-07-26No214 Lbs6 ft4NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No300,000$300,000$
Tanner GlassBears (Was)LW331983-11-28No210 Lbs6 ft1NoNoNo2Sans RestrictionPro & Farm300,000$0$0$No300,000$
Trevor MurphyBears (Was)D211995-07-16No172 Lbs5 ft10NoNoNo2Contrat d'EntréePro & Farm300,000$0$0$No300,000$
Vitek VanecekBears (Was)G211996-01-09No180 Lbs6 ft1NoNoNo3Contrat d'EntréePro & Farm500,000$0$0$No500,000$500,000$
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2023.25196 Lbs6 ft12.60380,000$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Kyle CliffordJason DickinsonAlexander Volkov40122
2Tanner GlassBrett SutterPaul Thompson30122
3Anthony CamaraReid DukeKyle Clifford20122
4Rich CluneTanner GlassJason Dickinson10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Klas DahlbeckAndrei Mironov40122
2Trevor MurphySami Niku30122
3Kyle WoodSimon Despres20122
4Michael KaplaKlas Dahlbeck10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Kyle CliffordJason DickinsonAlexander Volkov60122
2Tanner GlassBrett SutterPaul Thompson40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Klas DahlbeckAndrei Mironov60122
2Trevor MurphySami Niku40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Kyle CliffordTanner Glass60122
2Jason DickinsonAlexander Volkov40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Klas DahlbeckAndrei Mironov60122
2Trevor MurphySami Niku40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Kyle Clifford60122Klas DahlbeckAndrei Mironov60122
2Tanner Glass40122Trevor MurphySami Niku40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Kyle CliffordTanner Glass60122
2Jason DickinsonAlexander Volkov40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Klas DahlbeckAndrei Mironov60122
2Trevor MurphySami Niku40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Kyle CliffordJason DickinsonAlexander VolkovKlas DahlbeckAndrei Mironov
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Kyle CliffordJason DickinsonAlexander VolkovKlas DahlbeckAndrei Mironov
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Dennis Yan, Anthony Camara, Rich CluneDennis Yan, Anthony CamaraRich Clune
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Kyle Wood, Simon Despres, Michael KaplaKyle WoodSimon Despres, Michael Kapla
Tirs de Pénalité
Kyle Clifford, Tanner Glass, Jason Dickinson, Alexander Volkov, Paul Thompson
Gardien
#1 : Juuse Saros, #2 : Vitek Vanecek


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
1Admirals1010000023-1000000000001010000023-100.000235002028141132012101841723316235240.00%8187.50%036174248.65%34082341.31%18335651.40%621435601179291146
2Americans2110000034-11010000003-31100000031220.5003580020281414120121018417481740351317.69%19478.95%036174248.65%34082341.31%18335651.40%621435601179291146
3Checkers220000001221000000000000220000001221041.0001223350120281417920121018417491820618225.00%100100.00%036174248.65%34082341.31%18335651.40%621435601179291146
4Crunch2010010047-31010000024-21000010023-110.250481200202814158201210184173015204116318.75%9366.67%036174248.65%34082341.31%18335651.40%621435601179291146
5Devils1010000013-21010000013-20000000000000.000123002028141152012101841721141822800.00%9277.78%036174248.65%34082341.31%18335651.40%621435601179291146
6Griffins11000000312000000000001100000031221.00036900202814135201210184172148187228.57%4175.00%036174248.65%34082341.31%18335651.40%621435601179291146
7IceHogs11000000202000000000001100000020221.000246012028141252012101841722610126233.33%50100.00%036174248.65%34082341.31%18335651.40%621435601179291146
8Monsters2020000006-62020000006-60000000000000.000000002028141352012101841742163135900.00%14471.43%036174248.65%34082341.31%18335651.40%621435601179291146
9Penguins52300000612-62110000024-23120000048-440.4006111700202814110420121018417140461018942511.90%39782.05%036174248.65%34082341.31%18335651.40%621435601179291146
10Phantoms3100000278-11000000123-12100000155040.6677111800202814182201210184177226466514214.29%13469.23%036174248.65%34082341.31%18335651.40%621435601179291146
11Sound Tigers11000000321110000003210000000000021.0003580020281411520121018417301014187228.57%6183.33%036174248.65%34082341.31%18335651.40%621435601179291146
12Thunderbirds2200000010192200000010190000000000041.0001019290120281415820121018417411438426233.33%180100.00%036174248.65%34082341.31%18335651.40%621435601179291146
Total251390010262501213660000129272127300101332310290.58062114176042028141603201210184175772003975121502617.33%1672883.23%036174248.65%34082341.31%18335651.40%621435601179291146
14Wolf Pack22000000918220000009180000000000041.000917260120281414320121018417381135519333.33%13192.31%036174248.65%34082341.31%18335651.40%621435601179291146
_Since Last GM Reset251390010262501213660000129272127300101332310290.58062114176042028141603201210184175772003975121502617.33%1672883.23%036174248.65%34082341.31%18335651.40%621435601179291146
_Vs Conference1667001023039-91045000011923-4622001011116-5150.469305484012028141352201210184173731382653211051514.29%1032278.64%036174248.65%34082341.31%18335651.40%621435601179291146
_Vs Division16240010038344912000001719-2712001002115650.156386910702202814137320121018417392141265341971414.43%1041981.73%036174248.65%34082341.31%18335651.40%621435601179291146

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
2529W16211417660357720039751204
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
2513901026250
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
136600012927
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
127301013323
Derniers 10 Matchs
WLOTWOTL SOWSOL
440101
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
1502617.33%1672883.23%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
201210184172028141
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
36174248.65%34082341.31%18335651.40%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
621435601179291146


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 - 2018-11-11379Penguins-Bears-
74 - 2018-11-17408Bears-Bruins-
75 - 2018-11-18417Bears-Thunderbirds-
78 - 2018-11-21427Bears-Phantoms-
80 - 2018-11-23434Penguins-Bears-
81 - 2018-11-24456Phantoms-Bears-
87 - 2018-11-30479Bears-Penguins-
88 - 2018-12-01488Bruins-Bears-
89 - 2018-12-02501Bruins-Bears-
95 - 2018-12-08536Griffins-Bears-
96 - 2018-12-09549Admirals-Bears-
101 - 2018-12-14566Bears-Thunderbirds-
102 - 2018-12-15580Bears-Phantoms-
103 - 2018-12-16590Phantoms-Bears-
106 - 2018-12-19598Bears-Devils-
109 - 2018-12-22624Bears-Penguins-
110 - 2018-12-23632Bears-Sound Tigers-
113 - 2018-12-26649Bears-Phantoms-
116 - 2018-12-29672Rocket-Bears-
123 - 2019-01-05700Phantoms-Bears-
124 - 2019-01-06711Senators-Bears-
127 - 2019-01-09718Bears-Thunderbirds-
130 - 2019-01-12737Checkers-Bears-
131 - 2019-01-13755Checkers-Bears-
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
25 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
2,204,104$ 76,000$ 51,190$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 28,342$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 127 32,866$ 4,173,982$




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
132513900102625012136600001292721273001013323102962114176042028141603201210184175772003975121502617.33%1672883.23%036174248.65%34082341.31%18335651.40%621435601179291146
Total Saison Régulière2513900102625012136600001292721273001013323102962114176042028141603201210184175772003975121502617.33%1672883.23%036174248.65%34082341.31%18335651.40%621435601179291146
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