Please rotate your device to landscape mode for a better experience.
Login

Fighting Pandas
GP: 35 | W: 17 | L: 12 | OTL: 6 | P: 40
GF: 147 | GA: 130 | PP%: 25.71% | PK%: 81.19%
GM : Hunter Jones | Morale : 46 | Team Overall : 60
Next Games #388 vs Ice Bats

Game Center
Marlies
21-12-3, 45pts
4
FINAL
3 Fighting Pandas
17-12-6, 40pts
Team Stats
W2StreakOTL1
9-6-2Home Record7-6-5
12-6-1Home Record10-6-1
7-3-0Last 10 Games3-4-3
4.31Goals Per Game4.20
3.47Goals Against Per Game3.71
26.80%Power Play Percentage25.71%
78.02%Penalty Kill Percentage81.19%
Barons
18-15-3, 39pts
4
FINAL
3 Fighting Pandas
17-12-6, 40pts
Team Stats
W1StreakOTL1
10-6-2Home Record7-6-5
8-9-1Home Record10-6-1
7-3-0Last 10 Games3-4-3
3.50Goals Per Game4.20
3.56Goals Against Per Game3.71
20.55%Power Play Percentage25.71%
91.18%Penalty Kill Percentage81.19%
Fighting Pandas
17-12-6, 40pts
Day 78
Ice Bats
12-16-7, 31pts
Team Stats
OTL1StreakOTW1
7-6-5Home Record5-10-2
10-6-1Away Record7-6-5
3-4-3Last 10 Games3-6-1
4.20Goals Per Game3.91
3.71Goals Against Per Game3.91
25.71%Power Play Percentage18.67%
81.19%Penalty Kill Percentage80.00%
Griffins
20-16-0, 40pts
Day 80
Fighting Pandas
17-12-6, 40pts
Team Stats
W3StreakOTL1
10-8-0Home Record7-6-5
10-8-0Away Record10-6-1
7-3-0Last 10 Games3-4-3
5.75Goals Per Game4.20
5.06Goals Against Per Game4.20
20.00%Power Play Percentage25.71%
72.62%Penalty Kill Percentage81.19%
Fighting Pandas
17-12-6, 40pts
Day 82
Roadrunners
26-8-1, 53pts
Team Stats
OTL1StreakW4
7-6-5Home Record16-2-0
10-6-1Away Record10-6-1
3-4-3Last 10 Games8-2-0
4.20Goals Per Game3.83
3.71Goals Against Per Game3.83
25.71%Power Play Percentage30.12%
81.19%Penalty Kill Percentage86.08%
Team Leaders
Connor BrownGoals
Connor Brown
25
Alec MartinezAssists
Alec Martinez
28
Connor BrownPoints
Connor Brown
45
Keegan KolesarPlus/Minus
Keegan Kolesar
11
Jakub DobesWins
Jakub Dobes
16
Cayden PrimeauSave Percentage
Cayden Primeau
0.897

Team Stats
Goals For
147
4.20 GFG
Shots For
1135
32.43 Avg
Power Play Percentage
25.7%
18 GF
Offensive Zone Start
39.0%
Goals Against
130
3.71 GAA
Shots Against
1119
31.97 Avg
Penalty Kill Percentage
81.2%%
19 GA
Defensive Zone Start
39.2%
Team Info

General ManagerHunter Jones
CoachPatrick Lalime
DivisionAtlantic
ConferenceEastern Conference
CaptainJeff Skinner
Assistant #1Ryker Evans
Assistant #2Keegan Kolesar


Arena Info

Capacity3,000
Attendance0
Season Tickets300


Roster Info

Pro Team25
Farm Team20
Contract Limit45 / 50
Prospects28


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 SPAgeContractSalary
1Leo CarlssonXX100.00595690737378887661707266675258022670202500,000$
2Connor BrownX100.00535494676574927050676876517273067660312500,000$
3Keegan Kolesar (A)X100.00857188687772927550676567506667068660282500,000$
4Mackie Samoskevich (R)XX100.00765590756473837450697060545659066650232500,000$
5Jeff Skinner (C)XX100.006556896971728372576570605177750586503312,000,000$
6Radek FaksaX100.00785979657772826579645472617474064640313500,000$
7Cole KoepkeX100.00816292667469836750616365506565065630271500,000$
8Beck MalenstynX100.00826183637567865861595167506667065610272700,000$
9Alexander HoltzX100.00655690667169706150625359535759058590232500,000$
10Samuel Helenius (R)X100.00846989657463676064585560505758062590233750,000$
11Ivan Ivan (R)XX100.00545589646764626055575857505859031570232500,000$
12Ben JonesX100.00747499606661555250534959506463055550264500,000$
13Alec MartinezX100.00645792657474677150656170568682057660382700,000$
14Ryker Evans (A)X100.00775979677075877250695470565963062660242500,000$
15Sean DurziX100.006166766770756272506660725665680666402711,000,000$
16Declan ChisholmX100.00635691656871806750605367556364062630252500,000$
Scratches
1Arshdeep BainsX100.00645089576560535250505054506261042530242500,000$
2Matej BlümelX100.00515089577350515050505050506061020510254500,000$
3Georgii MerkulovX100.00503983576150535050505050505860020510251500,000$
4Kaedan KorczakX100.00725696627466646950655170656165050630232500,000$
5Samuel BolducX100.00503595588261585050504949556163050540252500,000$
6Ryan JohnsonX100.00504740577050545050505050556062020520241500,000$
TEAM AVERAGE100.0066578664716771635460576254646505160
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 SPAgeContractSalary
1Jakub Dobes (R)100.0072687079746863686665876063019670243750,000$
2Cayden Primeau100.0050656176615453555050596161045570264500,000$
Scratches
1Devon Levi100.0050646071505050505050505656054530231500,000$
TEAM AVERAGE100.005766647562575558555565596003959
Coaches Name PH DF OF PD EX LD PO CNT Age Contract Salary
Patrick Lalime9090957070651CAN515500,000$


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 NamePOSGP 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
1Connor BrownFighting Pandas (OTT)RW352520458203691773710114.12%1368319.5236920540001480348.98%9800021.3222000501
2Mackie SamoskevichFighting Pandas (OTT)C/RW35152742101752760112286913.39%868319.522578550001522048.08%75700101.2322000341
3Keegan KolesarFighting Pandas (OTT)RW35231639112559672126369018.25%976021.7224612450000803049.33%22300001.0302001524
4Alec MartinezFighting Pandas (OTT)D315283311120183250112310.00%2873223.622571651000165000%000000.9000000013
5Radek FaksaFighting Pandas (OTT)C358233110120806274216910.81%462517.86134744000021263.33%73900000.9901000122
6Sean DurziFighting Pandas (OTT)D35326291021563284819366.25%4076121.751451753000074100%000000.7601001222
7Cole KoepkeFighting Pandas (OTT)LW351514297140473892186516.30%566118.91314954000092146.15%5200000.8800000112
8Jeff SkinnerFighting Pandas (OTT)LW/RW35141428-1203147119437411.76%782923.6911212600002960050.72%6900000.6802000330
9Ryker EvansFighting Pandas (OTT)D35224267340111292915276.90%5183023.721231159000075000%000000.6300000013
10Beck MalenstynFighting Pandas (OTT)LW3511102111140554981276813.58%1358816.80000010000233142.86%4900000.7100000021
11Kaedan KorczakFighting Pandas (OTT)D322171982005725246198.33%3364620.22112944000065100%000000.5911000102
12Samuel HeleniusFighting Pandas (OTT)C35108186140446574164513.51%749214.0700000000012056.37%55700000.7300000002
13Declan ChisholmFighting Pandas (OTT)D357916028090313591520.00%5157516.4310146000018100%000000.5600000210
14Alexander HoltzFighting Pandas (OTT)RW3541115210012345821506.90%659717.070000170001270047.95%7300000.5000000010
15Samuel BolducFighting Pandas (OTT)D290772401945170%1847016.2300002000029000%000000.3000000010
16Leo CarlssonFighting Pandas (OTT)C/RW6235-3006282051510.00%214123.630003150000100057.74%16800000.7100000001
17Ivan IvanFighting Pandas (OTT)C/LW13022-300075320%3725.5900000000000038.24%3400000.5500000000
18Ben JonesFighting Pandas (OTT)C29000-400010010%0642.22000020000100036.36%110000000000000
Team Total or Average5601462594059222915759681112931677612.93%2981021618.24183250128568000669216754.17%283000120.79511002232124
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
1Jakub DobesFighting Pandas (OTT)35161240.8803.79191600121101004000350110
2Cayden PrimeauFighting Pandas (OTT)20000.8973.16760043900000013000
Team Total or Average37161240.8813.76199300125104904003513110


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 Country Rookie Weight Height No Trade Available For Trade Acquired By Last Trade Date Force Waivers Waiver Possible Contract Contract Signature Date Force UFA Emergency Recall Type Current Salary Salary Cap Salary Cap Remaining Exclude from Salary Cap Salary Year 2Salary Year 3Salary Year 4Salary Year 5Salary Year 6Salary Year 7Salary Year 8Salary Year 9Salary Year 10Salary Cap Year 2Salary Cap Year 3Salary Cap Year 4Salary Cap Year 5Salary Cap Year 6Salary Cap Year 7Salary Cap Year 8Salary Cap Year 9Salary Cap Year 10No Trade Year 2No Trade Year 3No Trade Year 4No Trade Year 5No Trade Year 6No Trade Year 7No Trade Year 8No Trade Year 9No Trade Year 10Link
Alec MartinezFighting Pandas (OTT)D381987-07-26USANo210 Lbs6 ft1NoNoTrade2025-03-05NoNo22024-08-21FalseFalsePro & Farm700,000$70,000$40,056$No700,000$--------700,000$--------No--------Link / NHL Link
Alexander HoltzFighting Pandas (OTT)RW232002-01-23SWENo198 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Link / NHL Link
Arshdeep BainsFighting Pandas (OTT)LW242001-01-09CANNo184 Lbs6 ft0NoNoFree AgentNoNo22025-07-23FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Link / NHL Link
Beck MalenstynFighting Pandas (OTT)LW271998-02-04CANNo209 Lbs6 ft3NoNoAssign ManuallyNoNo22024-08-21FalseFalsePro & Farm700,000$70,000$40,056$No700,000$--------700,000$--------No--------Link / NHL Link
Ben JonesFighting Pandas (OTT)C261999-02-26CANNo187 Lbs6 ft0NoNoFree AgentNoNo42025-07-25FalseFalsePro & Farm500,000$50,000$28,611$No500,000$500,000$500,000$------500,000$500,000$500,000$------NoNoNo------Link / NHL Link
Cayden PrimeauFighting Pandas (OTT)G261999-08-11USANo205 Lbs6 ft3NoNoFree AgentNoNo42025-07-25FalseFalsePro & Farm500,000$50,000$28,611$No500,000$500,000$500,000$------500,000$500,000$500,000$------NoNoNo------Link / NHL Link
Cole KoepkeFighting Pandas (OTT)LW271998-05-17USANo207 Lbs6 ft1NoNoFree AgentNoNo12024-10-11FalseFalsePro & Farm500,000$50,000$28,611$No---------------------------Link / NHL Link
Connor BrownFighting Pandas (OTT)RW311994-01-14CANNo184 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Link / NHL Link
Declan ChisholmFighting Pandas (OTT)D252000-01-12CANNo190 Lbs6 ft1NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Link / NHL Link
Devon LeviFighting Pandas (OTT)G232001-12-27CANNo192 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm500,000$50,000$28,611$No---------------------------Link / NHL Link
Georgii MerkulovFighting Pandas (OTT)C252000-10-10RUSNo176 Lbs5 ft11NoNoFree AgentNoNo12024-09-09FalseFalsePro & Farm500,000$50,000$28,611$No---------------------------Link / NHL Link
Ivan IvanFighting Pandas (OTT)C/LW232002-08-20CZEYes190 Lbs6 ft0NoNoFree AgentNoNo22025-08-09FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Link / NHL Link
Jakub DobesFighting Pandas (OTT)G242001-05-27CZEYes215 Lbs6 ft4NoNoFree AgentNoNo32025-08-07FalseFalsePro & Farm750,000$75,000$42,917$No750,000$750,000$-------750,000$750,000$-------NoNo-------Link / NHL Link
Jeff SkinnerFighting Pandas (OTT)LW/RW331992-05-16CANNo200 Lbs5 ft11NoNoTrade2024-08-25NoNo1FalseFalsePro & Farm2,000,000$200,000$114,444$No---------------------------Link / NHL Link
Kaedan KorczakFighting Pandas (OTT)D232002-01-18CANNo202 Lbs6 ft3NoNoTrade2025-08-04NoNo2FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Link / NHL Link
Keegan KolesarFighting Pandas (OTT)RW281997-04-08CANNo216 Lbs6 ft2NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Link / NHL Link
Leo CarlssonFighting Pandas (OTT)C/RW202004-12-26SWENo203 Lbs6 ft3NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Link / NHL Link
Mackie SamoskevichFighting Pandas (OTT)C/RW232002-11-15USAYes180 Lbs5 ft11NoNoTrade2025-08-04NoNo22024-08-10FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Link / NHL Link
Matej BlümelFighting Pandas (OTT)RW252000-05-31CZENo205 Lbs6 ft0NoNoFree AgentNoNo42025-07-25FalseFalsePro & Farm500,000$50,000$28,611$No500,000$500,000$500,000$------500,000$500,000$500,000$------NoNoNo------Link
Radek FaksaFighting Pandas (OTT)C311994-01-09CZENo215 Lbs6 ft3NoNoN/ANoNo32024-08-19FalseFalsePro & Farm500,000$50,000$28,611$No500,000$500,000$-------500,000$500,000$-------NoNo-------Link / NHL Link
Ryan JohnsonFighting Pandas (OTT)D242001-07-24USANo195 Lbs6 ft1NoNoFree AgentNoNo12024-09-09FalseFalsePro & Farm500,000$50,000$28,611$No---------------------------Link / NHL Link
Ryker EvansFighting Pandas (OTT)D242001-12-13CANNo195 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Link / NHL Link
Samuel BolducFighting Pandas (OTT)D252000-12-09CANNo224 Lbs6 ft4NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Link / NHL Link
Samuel HeleniusFighting Pandas (OTT)C232002-11-26USAYes201 Lbs6 ft6NoNoFree AgentNoNo32025-08-10FalseFalsePro & Farm750,000$75,000$42,917$No750,000$750,000$-------750,000$750,000$-------NoNo-------Link / NHL Link
Sean DurziFighting Pandas (OTT)D271998-10-21CANNo196 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm1,000,000$100,000$57,222$No---------------------------Link / NHL Link
Total PlayersAverage AgeAverage WeightAverage HeightAverage ContractAverage Year 1 Salary
2525.92199 Lbs6 ft12.12616,000$



5 vs 5 Forward
Line #Left WingCenterRight WingTime %PHYDFOF
1Jeff SkinnerLeo CarlssonAlexander Holtz40122
2Cole KoepkeMackie SamoskevichConnor Brown30122
3Beck MalenstynRadek FaksaKeegan Kolesar20122
4Ivan IvanSamuel HeleniusMackie Samoskevich10122
5 vs 5 Defense
Line #DefenseDefenseTime %PHYDFOF
1Alec MartinezRyker Evans40122
2Sean Durzi30122
3Declan ChisholmAlec Martinez20122
4Ryker EvansSean Durzi10122
Power Play Forward
Line #Left WingCenterRight WingTime %PHYDFOF
1Jeff SkinnerLeo CarlssonAlexander Holtz60122
2Cole KoepkeMackie SamoskevichConnor Brown40122
Power Play Defense
Line #DefenseDefenseTime %PHYDFOF
1Alec MartinezRyker Evans60122
2Sean Durzi40122
Penalty Kill 4 Players Forward
Line #CenterWingTime %PHYDFOF
1Leo CarlssonJeff Skinner60122
2Mackie SamoskevichCole Koepke40122
Penalty Kill 4 Players Defense
Line #DefenseDefenseTime %PHYDFOF
1Alec MartinezRyker Evans60122
2Sean Durzi40122
Penalty Kill 3 Players
Line #WingTime %PHYDFOFDefenseDefenseTime %PHYDFOF
1Leo Carlsson60122Alec MartinezRyker Evans60122
2Mackie Samoskevich40122Sean Durzi40122
4 vs 4 Forward
Line #CenterWingTime %PHYDFOF
1Leo CarlssonJeff Skinner60122
2Mackie SamoskevichCole Koepke40122
4 vs 4 Defense
Line #DefenseDefenseTime %PHYDFOF
1Alec MartinezRyker Evans60122
2Sean Durzi40122
Last Minutes Offensive
Left WingCenterRight WingDefenseDefense
Jeff SkinnerLeo CarlssonAlexander HoltzAlec MartinezRyker Evans
Last Minutes Defensive
Left WingCenterRight WingDefenseDefense
Jeff SkinnerLeo CarlssonConnor BrownAlec MartinezRyker Evans
Extra Forwards
Normal PowerPlayPenalty Kill
Mackie Samoskevich, Radek Faksa, Cole KoepkeMackie Samoskevich, Radek FaksaMackie Samoskevich
Extra Defensemen
Normal PowerPlayPenalty Kill
Sean Durzi, , Declan ChisholmSean DurziSean Durzi,
Penalty Shots
Leo Carlsson, Connor Brown, Keegan Kolesar, Jeff Skinner, Mackie Samoskevich
Goalie
#1 : Jakub Dobes, #2 : Cayden Primeau


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
1Aces1000010023-1000000000001000010023-110.50024600574048627376377371221434212150.00%2150.00%0616111955.05%609112654.09%34762455.61%923663756235445233
2Admirals1010000034-11010000034-10000000000000.00036900574048622376377371222561017000%50100.00%0616111955.05%609112654.09%34762455.61%923663756235445233
3Americiens21100000871110000005321010000034-120.50081523005740486703763773712274286504125.00%3166.67%0616111955.05%609112654.09%34762455.61%923663756235445233
4Barons1000010034-11000010034-10000000000010.5003470057404864337637737122184218200.00%10100.00%0616111955.05%609112654.09%34762455.61%923663756235445233
5Barracuda210001009811000010067-11100000031230.75091726005740486543763773712247168498337.50%4175.00%0616111955.05%609112654.09%34762455.61%923663756235445233
6Broncos422000001513200000000000422000001513240.50015264100574048695376377371221132720833266.67%10280.00%0616111955.05%609112654.09%34762455.61%923663756235445233
7Bruins210010001082110000005411000100054141.0001016260057404867537637737122641120457228.57%9188.89%0616111955.05%609112654.09%34762455.61%923663756235445233
8Butter Knives211000001073110000007251010000035-220.5001017270057404868037637737122671822436116.67%11281.82%0616111955.05%609112654.09%34762455.61%923663756235445233
9Firebirds2100010011742100010011740000000000030.7501120310057404867037637737122791918513133.33%9188.89%0616111955.05%609112654.09%34762455.61%923663756235445233
10Griffins11000000431000000000001100000043121.00048120057404863137637737122388720100.00%20100.00%0616111955.05%609112654.09%34762455.61%923663756235445233
11Lions220000001376110000006331100000074341.0001323360057404868237637737122701819495120.00%7271.43%0616111955.05%609112654.09%34762455.61%923663756235445233
12Lynx22000000945110000007341100000021141.0009162500574048661376377371225412438200.00%10100.00%0616111955.05%609112654.09%34762455.61%923663756235445233
13Marlies403000101220-830300000413-91000001087120.2501222340057404861083763773712211132287410330.00%12375.00%0616111955.05%609112654.09%34762455.61%923663756235445233
14Nordiks201000011214-2201000011214-20000000000010.25012213300574048688376377371221013415454125.00%40100.00%0616111955.05%609112654.09%34762455.61%923663756235445233
15Roadrunners3120000058-3110000003122020000027-520.3335914005740486923763773712276172473500.00%11463.64%0616111955.05%609112654.09%34762455.61%923663756235445233
16Tomahawks1000010034-11000010034-10000000000010.500347005740486323763773712253118222150.00%40100.00%0616111955.05%609112654.09%34762455.61%923663756235445233
17Wombats11000000716000000000001100000071621.000713200057404863137637737122327218400.00%000%0616111955.05%609112654.09%34762455.61%923663756235445233
18Wranglers2110000011831010000036-31100000082620.5001119300057404867437637737122832812462150.00%6183.33%0616111955.05%609112654.09%34762455.61%923663756235445233
Total351512015111471301718760040178753178601110695514400.5711472604070057404861135376377371221119299229762701825.71%1011981.19%0616111955.05%609112654.09%34762455.61%923663756235445233
_Since Last GM Reset351512015111471301718760040178753178601110695514400.5711472604070057404861135376377371221119299229762701825.71%1011981.19%0616111955.05%609112654.09%34762455.61%923663756235445233
_Vs Conference231010011109079111164001004537812460101045423250.5439016025000574048670437637737122695177154492441022.73%711480.28%0616111955.05%609112654.09%34762455.61%923663756235445233
_Vs Division1255010104946374300000282535120101021210140.5834986135005740486394376377371223701018025029724.14%36780.56%0616111955.05%609112654.09%34762455.61%923663756235445233

Total For Players
Games PlayedPointsStreakGoalsAssistsPointsShots ForShots AgainstShots BlockedPenalty MinutesHitsEmpty Net GoalsShutouts
3540OTL11472604071135111929922976200
All Games
GPWLOTWOTL SOWSOLGFGA
3515121511147130
Home Games
GPWLOTWOTL SOWSOLGFGA
187604017875
Visitor Games
GPWLOTWOTL SOWSOLGFGA
178611106955
Last 10 Games
WLOTWOTL SOWSOL
340300
Power Play AttempsPower Play GoalsPower Play %Penalty Kill AttempsPenalty Kill Goals AgainstPenalty Kill %Penalty Kill Goals For
701825.71%1011981.19%0
Shots 1 PeriodShots 2 PeriodShots 3 PeriodShots 4+ PeriodGoals 1 PeriodGoals 2 PeriodGoals 3 PeriodGoals 4+ Period
376377371225740486
Face Offs
Won Offensive ZoneTotal OffensiveWon Offensive %Won Defensif ZoneTotal DefensiveWon Defensive %Won Neutral ZoneTotal NeutralWon Neutral %
616111955.05%609112654.09%34762455.61%
Puck Time
In Offensive ZoneControl In Offensive ZoneIn Defensive ZoneControl In Defensive ZoneIn Neutral ZoneControl In Neutral Zone
923663756235445233


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
27Marlies6Fighting Pandas1LBoxScore
623Fighting Pandas3Americiens4LBoxScore
834Americiens3Fighting Pandas5WBoxScore
938Fighting Pandas2Broncos1WBoxScore
1254Bruins4Fighting Pandas5WBoxScore
1361Fighting Pandas3Broncos6LBoxScore
1674Butter Knives2Fighting Pandas7WBoxScore
1884Fighting Pandas5Bruins4WXBoxScore
2098Lynx3Fighting Pandas7WBoxScore
21105Fighting Pandas0Roadrunners3LBoxScore
25119Roadrunners1Fighting Pandas3WBoxScore
26125Fighting Pandas2Lynx1WBoxScore
29138Fighting Pandas3Broncos5LBoxScore
31147Marlies3Fighting Pandas0LBoxScore
32154Fighting Pandas8Marlies7WXXBoxScore
35169Firebirds2Fighting Pandas7WBoxScore
39184Tomahawks4Fighting Pandas3LXBoxScore
41193Fighting Pandas3Barracuda1WBoxScore
43201Fighting Pandas3Butter Knives5LBoxScore
45212Nordiks7Fighting Pandas6LXXBoxScore
48226Fighting Pandas7Broncos1WBoxScore
49234Nordiks7Fighting Pandas6LBoxScore
51249Barracuda7Fighting Pandas6LXBoxScore
53260Fighting Pandas7Lions4WBoxScore
55271Fighting Pandas8Wranglers2WBoxScore
56278Admirals4Fighting Pandas3LBoxScore
59293Firebirds5Fighting Pandas4LXBoxScore
61305Fighting Pandas2Roadrunners4LBoxScore
63315Wranglers6Fighting Pandas3LBoxScore
66325Fighting Pandas4Griffins3WBoxScore
68337Lions3Fighting Pandas6WBoxScore
70348Fighting Pandas2Aces3LXBoxScore
72358Fighting Pandas7Wombats1WBoxScore
74365Marlies4Fighting Pandas3LBoxScore
76380Barons4Fighting Pandas3LXBoxScore
78388Fighting Pandas-Ice Bats-
80401Griffins-Fighting Pandas-
82412Fighting Pandas-Roadrunners-
84424Canucks-Fighting Pandas-
85431Fighting Pandas-Quacken-
88442Fighting Pandas-Butter Knives-
90451Butter Knives-Fighting Pandas-
92461Fighting Pandas-Barons-
95471Roadrunners-Fighting Pandas-
97482Fighting Pandas-Nordiks-
99492Fighting Pandas-Admirals-
100501Bruins-Fighting Pandas-
102512Fighting Pandas-Lynx-
104523Butter Knives-Fighting Pandas-
107535Fighting Pandas-Admirals-
108544Roadrunners-Fighting Pandas-
110555Fighting Pandas-Wombats-
112567Broncos-Fighting Pandas-
114577Fighting Pandas-Broncos-
116589Bruins-Fighting Pandas-
119598Fighting Pandas-Butter Knives-
120608Americiens-Fighting Pandas-
122621Fighting Pandas-Lynx-
124632Lynx-Fighting Pandas-
126643Fighting Pandas-Americiens-
128652Marlies-Fighting Pandas-
130657Fighting Pandas-Canucks-
132673Quacken-Fighting Pandas-
133677Fighting Pandas-Tomahawks-
135686Fighting Pandas-Americiens-
136692Fighting Pandas-Barons-
138703Americiens-Fighting Pandas-
141719Fighting Pandas-Firebirds-
143725Wombats-Fighting Pandas-
146743Aces-Fighting Pandas-
147748Fighting Pandas-Bruins-
150765Admirals-Fighting Pandas-
154785Aces-Fighting Pandas-
158804Lynx-Fighting Pandas-
159809Fighting Pandas-Marlies-
162825Wombats-Fighting Pandas-
164834Fighting Pandas-Bruins-
167846Fighting Pandas-Marlies-
168854Fighting Pandas-Firebirds-
169858Ice Bats-Fighting Pandas-
173875Broncos-Fighting Pandas-
177892Ice Bats-Fighting Pandas-



Arena Capacity - Ticket Price Attendance - %
Level 1Level 2
Capacity20001000
Ticket Price4020
Attendance00
Attendance PCT0.00%0.00%

Income
Home Games LeftAverage Attendance - %Average Income per GameYear to Date RevenueCapacityTeam Popularity
23 0 - 0.00% 0$0$3000100

Expenses
Year To Date ExpensesPlayers Total SalariesPlayers Total Salaries CapCoaches Salaries
854,913$ 1,540,000$ 1,540,000$ 500,000$0$
Salary Cap Per DaysSalary Cap To DatePlayers In Salary CapPlayers Out of Salary Cap
1,540,000$ 641,020$ 25 0

Estimate
Estimated Season RevenueRemaining Season DaysExpenses Per DaysEstimated Season Expenses
0$ 103 11,333$ 1,167,299$




Fighting Pandas Players Stat Leaders (Regular Season)

# Player Name 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

Fighting Pandas Goalies Stat Leaders (Regular Season)

# Goalie Name GP W L OTL PCT GAA MP PIM SO GA SA SAR A EG PS % PSA

Fighting Pandas Career Team Stats

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

Fighting Pandas Players Stat Leaders (Play-Off)

# Player Name 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

Fighting Pandas Goalies Stat Leaders (Play-Off)

# Goalie Name GP W L OTL PCT GAA MP PIM SO GA SA SAR A EG PS % PSA