Bears

GP: 74 | W: 29 | L: 40 | OTL: 5 | P: 63
GF: 142 | GA: 189 | PP%: 13.08% | PK%: 83.94%
GM : Jeremy Hiemstra | Morale : 50 | Team Overall : 57
Next Games #1155 vs Phantoms
Your browser screen resolution is too small for this page. Some information are hidden to keep the page readable.

Filter Tips
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
# Player Name 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
1Travis BoydXX100.007066786366848965806561625844446550620
2Nathan WalkerX100.007064836464676961505760615744446250580
3Thomas DiPauliXX100.007167816567666955694758605544445950560
4Jason AkesonX100.007668936568525255505947634544445750550
5Zach SanfordXXX100.006242847376323055425655564244445750530
6Tim McGauley (R)X100.007668946568515250635144624244445450530
7Yannick WeberX100.007543897772597755254648607568695850620
8Nick SeelerX100.007374746676658060255447742546466150620
9Ryan JohnstonX100.007063867063606350255342594044445450560
10Connor Hobbs (R)X100.006769636869677151254741573944445350560
11Tyler LewingtonX100.006269476269788746253640543844444950550
Scratches
1Colby Williams (R)X100.006667626667727948254041563944445150560
TEAM AVERAGE100.00706378676963685442514960464646575057
Filter Tips
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
# Goalie Name CON SK DU EN SZ AG RB SC HS RT PH PS EX LD PO MO OV TA SP
1Marek Langhamer100.00664961727065727571713044446750640
2Vitek Vanecek100.00506480684654505651513044445350530
Scratches
TEAM AVERAGE100.0058577170586061666161304444605059
Coaches Name PH DF OF PD EX LD PO CNT Age Contract Salary
Lindy Ruff54455364496655CAN5411,000,000$


Filter Tips
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
# Player Name Team NamePOS 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
1Yannick WeberBears (WSH)D7482129-216001241028516449.41%72160021.62712196925200001970033.33%300000.3600000323
2Connor HobbsBears (WSH)D7482028-410115160748613259.30%50149920.263366523400001751121.43%1400000.3700201545
3Nick SeelerBears (WSH)D5341923-1257510685707125.71%38115821.86211135518300001351033.33%300000.4000001043
4Nathan WalkerBears (WSH)LW3791120-1016067787241212.50%669918.9032520680000371039.47%3800000.5700000332
5Nic PetanCapitalsC/LW/RW3215419-616033829073116.67%161819.3261725940000321148.68%7600010.6103000511
6Jason AkesonBears (WSH)RW3571017-41204355354620.00%456916.281565590000153043.24%3700000.6000000216
7Ryan JohnstonBears (WSH)D5641014-251007962518197.84%63105518.842133312500001180080.00%500000.2700000241
8Colby WilliamsBears (WSH)D6721214-106601316144884.55%51122518.291232012500021081040.00%2500000.2300000011
9Tyler LewingtonBears (WSH)D7411112-16147151733012688.33%46126417.0810123300011400020.00%1000000.1900111021
10Tim McGauleyBears (WSH)C603811-1943571924710266.38%1484614.120112590000161048.55%51900000.2600010112
11Travis BoydBears (WSH)C/RW173710-4402758266511.54%545526.780223580001501061.50%40000000.4402000032
12Thomas DiPauliBears (WSH)C/LW18145-8602847344102.94%439622.050228580001300048.56%38100000.2501000010
13Marko DanoCapitalsC/RW10123240142017005.88%319919.950000290001231028.57%2100000.3011000010
14Zach SanfordBears (WSH)C/LW/RW32112-1560132931233.23%137811.820001210001110035.71%2800000.1100000001
15Trevor van RiemsdykCapitalsD31120000560016.67%07525.1011241400004000.00%000000.5300000000
Team Total or Average64268141209-152548401069880706952099.63%3581204218.7627437031214190007109811250.51%156000010.3517323212828
Filter Tips
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
# Goalie Name Team NameGP W L OTL PCT GAA MP PIM SO GA SA SAR A EG PS % PSA ST BG S1 S2 S3
1Marek LanghamerBears (WSH)61253240.9042.3236030613914530000.75086101163
2Vitek VanecekBears (WSH)164810.8683.3683940473570100.66731374001
Team Total or Average77294050.8972.5144434618618100100.7271174741164


Filter Tips
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
Player Name Team NamePOS Age Birthday Rookie Weight Height No Trade Available For Trade Force Waivers Contract StatusType Current Salary Salary Cap Salary Cap Remaining Exclude from Salary Cap Link
Colby WilliamsBears (WSH)D241995-01-25Yes191 Lbs5 ft11NoNoNo2ELCPro & Farm500,000$0$0$NoLink
Connor HobbsBears (WSH)D221997-01-04Yes187 Lbs6 ft1NoNoNo3ELCPro & Farm500,000$0$0$NoLink
Jason AkesonBears (WSH)RW281990-06-03No190 Lbs5 ft10NoNoNo1UFAPro & Farm550,000$0$0$NoLink
Marek LanghamerBears (WSH)G241994-07-21No193 Lbs6 ft2NoNoNo1ELCPro & Farm500,000$0$0$NoLink
Nathan WalkerBears (WSH)LW251994-02-06No186 Lbs5 ft8NoNoNo2ELCPro & Farm750,000$0$0$NoLink
Nick SeelerBears (WSH)D251993-06-02No192 Lbs6 ft0NoNoNo2ELCPro & Farm575,000$0$0$NoLink
Ryan JohnstonBears (WSH)D271992-02-14No176 Lbs5 ft10NoNoNo1RFAPro & Farm600,000$0$0$NoLink
Thomas DiPauliBears (WSH)C/LW241994-04-28No187 Lbs5 ft11NoNoNo2ELCPro & Farm650,000$0$0$NoLink
Tim McGauleyBears (WSH)C231995-07-23Yes175 Lbs6 ft0NoNoNo1ELCPro & Farm663,333$0$0$NoLink
Travis BoydBears (WSH)C/RW251993-09-14No191 Lbs5 ft11NoNoNo1ELCPro & Farm500,000$0$0$NoLink
Tyler LewingtonBears (WSH)D241994-12-05No189 Lbs6 ft1NoNoNo1ELCPro & Farm500,000$0$0$NoLink
Vitek VanecekBears (WSH)G231996-01-08No180 Lbs6 ft1NoNoNo2ELCPro & Farm850,000$0$0$NoLink
Yannick WeberBears (WSH)D301988-09-22No200 Lbs5 ft11NoNoNo1UFAPro & Farm650,000$0$0$NoLink
Zach SanfordBears (WSH)C/LW/RW241994-11-09No192 Lbs6 ft4NoNoNo2ELCPro & Farm750,000$0$0$NoLink
Total PlayersAverage AgeAverage WeightAverage HeightAverage ContractAverage Year 1 Salary
1424.86188 Lbs6 ft01.57609,881$



5 vs 5 Forward
Line #Left WingCenterRight WingTime %PHYDFOF
1Nathan WalkerTravis BoydJason Akeson40122
2Zach SanfordThomas DiPauliTim McGauley30122
3Travis BoydTim McGauleyNathan Walker20122
4Jason AkesonThomas DiPauliZach Sanford10122
5 vs 5 Defense
Line #DefenseDefenseTime %PHYDFOF
1Nick SeelerYannick Weber40122
2Ryan JohnstonConnor Hobbs30122
3Tyler LewingtonNick Seeler20122
4Yannick WeberRyan Johnston10122
Power Play Forward
Line #Left WingCenterRight WingTime %PHYDFOF
1Nathan WalkerTravis BoydJason Akeson60122
2Zach SanfordThomas DiPauliTim McGauley40122
Power Play Defense
Line #DefenseDefenseTime %PHYDFOF
1Nick SeelerYannick Weber60122
2Ryan JohnstonConnor Hobbs40122
Penalty Kill 4 Players Forward
Line #CenterWingTime %PHYDFOF
1Travis BoydNathan Walker60122
2Thomas DiPauliJason Akeson40122
Penalty Kill 4 Players Defense
Line #DefenseDefenseTime %PHYDFOF
1Nick SeelerYannick Weber60122
2Ryan JohnstonConnor Hobbs40122
Penalty Kill 3 Players
Line #WingTime %PHYDFOFDefenseDefenseTime %PHYDFOF
1Travis Boyd60122Nick SeelerYannick Weber60122
2Nathan Walker40122Ryan JohnstonConnor Hobbs40122
4 vs 4 Forward
Line #CenterWingTime %PHYDFOF
1Travis BoydNathan Walker60122
2Thomas DiPauliJason Akeson40122
4 vs 4 Defense
Line #DefenseDefenseTime %PHYDFOF
1Nick SeelerYannick Weber60122
2Ryan JohnstonConnor Hobbs40122
Last Minutes Offensive
Left WingCenterRight WingDefenseDefense
Nathan WalkerTravis BoydJason AkesonNick SeelerYannick Weber
Last Minutes Defensive
Left WingCenterRight WingDefenseDefense
Nathan WalkerTravis BoydJason AkesonNick SeelerYannick Weber
Extra Forwards
Normal PowerPlayPenalty Kill
Tim McGauley, Zach Sanford, Thomas DiPauliTim McGauley, Zach SanfordThomas DiPauli
Extra Defensemen
Normal PowerPlayPenalty Kill
Tyler Lewington, Connor Hobbs, Nick SeelerTyler LewingtonConnor Hobbs, Nick Seeler
Penalty Shots
Travis Boyd, Nathan Walker, Thomas DiPauli, Jason Akeson, Tim McGauley
Goalie
#1 : Marek Langhamer, #2 : Vitek Vanecek


Filter Tips
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
OverallHomeVisitor
# VS Team 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
1Admirals11000000202110000002020000000000021.00024601475734515467484443241031314300.00%40100.00%0926188449.15%1061218648.54%486101947.69%172211961871530883437
2Americans2110000024-2000000000002110000024-220.500246014757345284674844432451201433600.00%70100.00%0926188449.15%1061218648.54%486101947.69%172211961871530883437
3Barracuda32100000853110000004222110000043140.6678152300475734544467484443244224364113215.38%180100.00%0926188449.15%1061218648.54%486101947.69%172211961871530883437
4Bruins330000001046220000008351100000021161.000101828014757345614674844432442726501119.09%13192.31%0926188449.15%1061218648.54%486101947.69%172211961871530883437
5Checkers1010000003-3000000000001010000003-300.00000000475734519467484443242591024300.00%40100.00%0926188449.15%1061218648.54%486101947.69%172211961871530883437
6Comets2020000024-2000000000002020000024-200.0002460047573452746748444324521729488112.50%50100.00%0926188449.15%1061218648.54%486101947.69%172211961871530883437
7Condors11000000303110000003030000000000021.000369014757345274674844432420213153133.33%40100.00%0926188449.15%1061218648.54%486101947.69%172211961871530883437
8Crunch32100000972211000005501100000042240.6679132200475734549467484443248220227213215.38%11372.73%0926188449.15%1061218648.54%486101947.69%172211961871530883437
9Devils31100100711-42010010038-51100000043130.5007142100475734556467484443248329407915213.33%18477.78%0926188449.15%1061218648.54%486101947.69%172211961871530883437
10Falcons40400000412-830300000310-71010000012-100.000481200475734558467484443241132234751317.69%16287.50%0926188449.15%1061218648.54%486101947.69%172211961871530883437
11Griffins2110000059-4110000004311010000016-520.50051015004757345414674844432460142442600.00%11281.82%0926188449.15%1061218648.54%486101947.69%172211961871530883437
12Gulls523000001116-532100000911-22020000025-340.400112031004757345994674844432411034267623417.39%13561.54%0926188449.15%1061218648.54%486101947.69%172211961871530883437
13Heat21100000761211000007610000000000020.5007142100475734547467484443244416234417317.65%8362.50%0926188449.15%1061218648.54%486101947.69%172211961871530883437
14IceCaps1010000014-31010000014-30000000000000.00012300475734519467484443243851221300.00%6183.33%0926188449.15%1061218648.54%486101947.69%172211961871530883437
15Icehogs1010000001-1000000000001010000001-100.00000000475734513467484443242151828300.00%9188.89%0926188449.15%1061218648.54%486101947.69%172211961871530883437
16Marlies11000000211000000000001100000021121.0002460047573452146748444324296822500.00%40100.00%1926188449.15%1061218648.54%486101947.69%172211961871530883437
17Monsters3120000056-1211000004311010000013-220.333581300475734590467484443247126267620315.00%11281.82%0926188449.15%1061218648.54%486101947.69%172211961871530883437
18Moose31100001510-52010000128-61100000032130.50058130047573456646748444324892252651400.00%15473.33%0926188449.15%1061218648.54%486101947.69%172211961871530883437
19Penguins713001111216-43120000079-24010011157-260.42912223400475734513346748444324161427613236822.22%37294.59%0926188449.15%1061218648.54%486101947.69%172211961871530883437
20Phantoms4120010067-12020000035-22100010032130.375611170147573457146748444324120263510315213.33%150100.00%0926188449.15%1061218648.54%486101947.69%172211961871530883437
21Pirates1010000034-1000000000001010000034-100.000369004757345214674844432424410204125.00%40100.00%0926188449.15%1061218648.54%486101947.69%172211961871530883437
22Rampage1010000012-11010000012-10000000000000.00012300475734522467484443241362177114.29%110.00%0926188449.15%1061218648.54%486101947.69%172211961871530883437
23Reign32000010844220000005231000001032161.0008142201475734548467484443247117246713215.38%12191.67%0926188449.15%1061218648.54%486101947.69%172211961871530883437
24Senators522010001213-1321000008712010100046-260.600122234004757345714674844432412324529818316.67%25580.00%1926188449.15%1061218648.54%486101947.69%172211961871530883437
25Sound Tigers3030000019-81010000003-32020000016-500.0001230047573456146748444324601136611119.09%15566.67%0926188449.15%1061218648.54%486101947.69%172211961871530883437
26Stars30300000410-61010000012-12020000038-500.00048120047573456546748444324812534621218.33%16475.00%0926188449.15%1061218648.54%486101947.69%172211961871530883437
Total74254002322142189-473816200010184100-1636920022215889-31630.426142261403064757345140646748444324181846975715333214213.08%3305383.94%2926188449.15%1061218648.54%486101947.69%172211961871530883437
28Wild1010000013-21010000013-20000000000000.00012300475734517467484443242341417200.00%6183.33%0926188449.15%1061218648.54%486101947.69%172211961871530883437
29Wolf Pack30201000712-51010000034-12010100048-420.3337132000475734563467484443241082236718225.00%16475.00%0926188449.15%1061218648.54%486101947.69%172211961871530883437
30Wolves1010000025-3000000000001010000025-300.00023500475734524467484443242058309111.11%4250.00%0926188449.15%1061218648.54%486101947.69%172211961871530883437
31Wolves11000000211000000000001100000021121.0002460047573453046748444324322430700.00%20100.00%0926188449.15%1061218648.54%486101947.69%172211961871530883437
_Since Last GM Reset74254002322142189-473816200010184100-1636920022215889-31630.426142261403064757345140646748444324181846975715333214213.08%3305383.94%2926188449.15%1061218648.54%486101947.69%172211961871530883437
_Vs Conference52182502322105132-27291215001016480-1623610022214152-11490.47110518829303475734597046748444324127532652110662233314.80%2353883.83%1926188449.15%1061218648.54%486101947.69%172211961871530883437
_Vs Division24610012113864-261156000002032-121314012111832-14190.3963870108014757345493467484443246281652595461081816.67%1161785.34%0926188449.15%1061218648.54%486101947.69%172211961871530883437

Total For Players
Games PlayedPointsStreakGoalsAssistsPointsShots ForShots AgainstShots BlockedPenalty MinutesHitsEmpty Net GoalsShutouts
7463W114226140314061818469757153306
All Games
GPWLOTWOTL SOWSOLGFGA
7425402322142189
Home Games
GPWLOTWOTL SOWSOLGFGA
381620010184100
Visitor Games
GPWLOTWOTL SOWSOLGFGA
3692022215889
Last 10 Games
WLOTWOTL SOWSOL
630100
Power Play AttempsPower Play GoalsPower Play %Penalty Kill AttempsPenalty Kill Goals AgainstPenalty Kill %Penalty Kill Goals For
3214213.08%3305383.94%2
Shots 1 PeriodShots 2 PeriodShots 3 PeriodShots 4+ PeriodGoals 1 PeriodGoals 2 PeriodGoals 3 PeriodGoals 4+ Period
467484443244757345
Face Offs
Won Offensive ZoneTotal OffensiveWon Offensive %Won Defensif ZoneTotal DefensiveWon Defensive %Won Neutral ZoneTotal NeutralWon Neutral %
926188449.15%1061218648.54%486101947.69%
Puck Time
In Offensive ZoneControl In Offensive ZoneIn Defensive ZoneControl In Defensive ZoneIn Neutral ZoneControl In Neutral Zone
172211961871530883437


Last Played Games
Filter Tips
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
DayGame Visitor Team Score Home Team Score ST OT SO RI Link
1 - 2018-10-025Bears1Penguins2LXXBoxScore
3 - 2018-10-0422Penguins2Bears3WBoxScore
6 - 2018-10-0745Gulls3Bears4WBoxScore
9 - 2018-10-1063Senators1Bears2WBoxScore
12 - 2018-10-1383Bears1Gulls2LBoxScore
14 - 2018-10-1596Penguins3Bears1LBoxScore
16 - 2018-10-17111Bears2Phantoms0WBoxScore
18 - 2018-10-19129Falcons1Bears0LBoxScore
20 - 2018-10-21142Bears1Penguins2LBoxScore
22 - 2018-10-23157Gulls6Bears1LBoxScore
24 - 2018-10-25172Bears1Phantoms2LXBoxScore
25 - 2018-10-26182Bears1Americans0WBoxScore
28 - 2018-10-29200Reign0Bears2WBoxScore
30 - 2018-10-31215Bears0Barracuda1LBoxScore
32 - 2018-11-02229Devils5Bears1LBoxScore
34 - 2018-11-04242Bears3Senators2WXBoxScore
36 - 2018-11-06257Bears2Marlies1WBoxScore
37 - 2018-11-07267Crunch2Bears3WBoxScore
40 - 2018-11-10287Stars2Bears1LBoxScore
42 - 2018-11-12295Bears2Stars4LBoxScore
45 - 2018-11-15322Bears3Pirates4LBoxScore
46 - 2018-11-16330Condors0Bears3WBoxScore
49 - 2018-11-19351Bears4Barracuda2WBoxScore
50 - 2018-11-20363Monsters2Bears4WBoxScore
53 - 2018-11-23380Wild3Bears1LBoxScore
55 - 2018-11-25393Bears0Icehogs1LBoxScore
57 - 2018-11-27408Bears1Griffins6LBoxScore
59 - 2018-11-29422Moose5Bears0LBoxScore
62 - 2018-12-02444Penguins4Bears3LBoxScore
65 - 2018-12-05469Admirals0Bears2WBoxScore
67 - 2018-12-07481Bears1Gulls3LBoxScore
69 - 2018-12-09498Crunch3Bears2LBoxScore
71 - 2018-12-11505Bears3Reign2WXXBoxScore
73 - 2018-12-13525Bears1Senators4LBoxScore
75 - 2018-12-15536Bears1Monsters3LBoxScore
77 - 2018-12-17548Devils3Bears2LXBoxScore
79 - 2018-12-19566Bears0Sound Tigers3LBoxScore
81 - 2018-12-21576Phantoms3Bears2LBoxScore
83 - 2018-12-23591Bears2Bruins1WBoxScore
85 - 2018-12-25602Bears1Americans4LBoxScore
86 - 2018-12-26611Bears4Devils3WBoxScore
87 - 2018-12-27619Bruins3Bears5WBoxScore
90 - 2018-12-30638Bears2Wolves5LBoxScore
92 - 2019-01-01649Moose3Bears2LXXBoxScore
95 - 2019-01-04672Wolf Pack4Bears3LBoxScore
96 - 2019-01-05687Bears1Stars4LBoxScore
98 - 2019-01-07704Barracuda2Bears4WBoxScore
101 - 2019-01-10723Bears0Wolf Pack5LBoxScore
103 - 2019-01-12735IceCaps4Bears1LBoxScore
105 - 2019-01-14754Bears1Sound Tigers3LBoxScore
107 - 2019-01-16765Senators5Bears4LBoxScore
111 - 2019-01-20795Bears3Moose2WBoxScore
112 - 2019-01-21802Rampage2Bears1LBoxScore
115 - 2019-01-24824Phantoms2Bears1LBoxScore
118 - 2019-01-27846Bears1Falcons2LBoxScore
120 - 2019-01-29857Falcons6Bears1LBoxScore
123 - 2019-02-01881Bruins0Bears3WBoxScore
124 - 2019-02-02890Bears0Checkers3LBoxScore
126 - 2019-02-04907Bears4Wolf Pack3WXBoxScore
128 - 2019-02-06922Sound Tigers3Bears0LBoxScore
130 - 2019-02-08943Falcons3Bears2LBoxScore
132 - 2019-02-10953Bears4Crunch2WBoxScore
136 - 2019-02-14975Monsters1Bears0LBoxScore
137 - 2019-02-15984Bears2Penguins1WXXBoxScore
140 - 2019-02-181003Reign2Bears3WBoxScore
142 - 2019-02-201019Bears1Comets2LBoxScore
Trade Deadline --- Trades can’t be done after this day is simulated!
145 - 2019-02-231034Gulls2Bears4WBoxScore
146 - 2019-02-241045Bears2Wolves1WBoxScore
150 - 2019-02-281067Heat1Bears4WBoxScore
153 - 2019-03-031089Senators1Bears2WBoxScore
154 - 2019-03-041096Bears1Penguins2LXBoxScore
157 - 2019-03-071114Bears1Comets2LBoxScore
159 - 2019-03-091127Heat5Bears3LBoxScore
163 - 2019-03-131151Griffins3Bears4WBoxScore
165 - 2019-03-151155Bears-Phantoms-
166 - 2019-03-161160Bears-Devils-



Arena Capacity - Ticket Price Attendance - %
Level 1Level 2
Arena Capacity20001000
Ticket Price3515
Attendance00
Attendance PCT0.00%0.00%

Income
Home Games LeftAverage Attendance - %Average Income per GameYear to Date RevenueArena CapacityTeam Popularity
0 0 - 0.00% 0$0$3000100

Expenses
Year To Date ExpensesPlayers Total SalariesPlayers Total Average SalariesCoaches Salaries
1,863,982$ 853,833$ 853,833$ 0$
Salary Cap Per DaysSalary Cap To DatePlayers In Salary CapPlayers Out of Salary Cap
0$ 883,042$ 0 0

Estimate
Estimated Season RevenueRemaining Season DaysExpenses Per DaysEstimated Season Expenses
0$ 5 10,969$ 54,845$




OverallHomeVisitor
Year 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
201874254002322142189-473816200010184100-1636920022215889-3163142261403064757345140646748444324181846975715333214213.08%3305383.94%2926188449.15%1061218648.54%486101947.69%172211961871530883437
Total Regular Season74254002322142189-473816200010184100-1636920022215889-3163142261403064757345140646748444324181846975715333214213.08%3305383.94%2926188449.15%1061218648.54%486101947.69%172211961871530883437