Admirals

GP: 8 | W: 3 | L: 3 | OTL: 2 | P: 8
GF: 20 | GA: 23 | PP%: 9.52% | PK%: 86.27%
GM : Danny Rhéaume | Morale : 50 | Team Overall : N/A
Next Games #89 vs Monarchs
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
1Axel Jonsson-FjallbyX100.00766894666858595550485863554444605000
2Deven Sideroff (R)X100.00736395546348485250465163485252555000
3Lukas JasekX100.00746888616871755850516163584444635000
4Nicholas CaamanoX100.00794489637255785444585864254545625000
5Cody Glass (R)XX100.00634194806765617453646459254747665000
6Stefan NoesenXX100.00844577747559696437557161255859685000
7Taylor Raddysh (R)XX100.00838089638078836075536268594444655000
8Ryan MacInnis (R)X100.00714399647154805847605562254444615000
9Jordy Bellerive (R)X100.00726978676974795670495961564444615000
10Nick RitchieX100.00865844758467717529707161256364705000
11Ondrej KaseX100.00594191866874617333756462755960685000
12Sasha Chmelevski (R)XX100.00736884656865666278596263594444645000
13Henri JokiharjuX100.00764386826771846125524875255757635000
14Jacob LarssonX100.00674293777369855725514875256060624700
15Lucas CarlssonX100.00747084657073785425524262404444565000
16Michael Anderson (R)X100.00767189777163674925414161394444545000
17Brogan Rafferty (R)X100.00787292647264666025604565434444595000
18Leon Gawanke (R)X100.00767286637268725425524163394444555000
Scratches
1Manuel Wiederer (R)XX100.00716486616456585265534760454444555000
2Nathan Noel (R)XX100.00646366536349514455384454424444475000
3Riley Sutter (R)X100.00827499637452544850464564434444555000
4Keaton Thompson (R)X100.00736786676762665025444060384545535000
5Noah Dobson (R)X100.00694291807162636925534859254747605000
6Johnathan Kovacevic (R)X100.00787879637855584825384262404444525000
7Mac Hollowell (R)X100.00676279676262665025434257404444525000
TEAM AVERAGE100.0074608668706368574152526341484859500
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
1Adam Werner (R)100.0057597473596151605857304444575000
2Mikhail Berdin100.0059627863606351615857304444585000
Scratches
TEAM AVERAGE100.005861766860625161585730444458500
Coaches Name PH DF OF PD EX LD PO CNT Age Contract Salary
Ryan Huska66707368656082CAN463850,000$


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
1Sasha ChmelevskiAdmirals (ANA)C/RW83362004101021030.00%111914.95011032000000040.00%500001.0000000010
2Stefan NoesenAdmirals (ANA)LW/RW841521001151851222.22%114618.290003280001231053.13%3200010.6801000100
3Taylor RaddyshAdmirals (ANA)C/RW80552261011109030.00%011113.91011330000000045.54%11200000.9000010000
4Leon GawankeAdmirals (ANA)D8055622101351010.00%313216.5700001000016000.00%000000.7500101100
5Ondrej KaseAdmirals (ANA)RW8224-3000181821411.11%018222.851126350001480037.38%10700000.4402000010
6Brogan RaffertyAdmirals (ANA)D804401551693530.00%616720.99011229011034000.00%000000.4800001100
7Henri JokiharjuAdmirals (ANA)D8123-3100261164716.67%518823.52112536000035100.00%000000.3200000001
8Jacob LarssonAdmirals (ANA)D8033-380684020.00%1218723.47011336000035000.00%000000.3200000000
9Lucas CarlssonAdmirals (ANA)D8123-1209621450.00%617521.90101230011038000.00%000000.3400000010
10Cody GlassAdmirals (ANA)C/RW8123-3001161041210.00%115819.750111360000250040.54%14800000.3801000000
11Ryan MacInnisAdmirals (ANA)C830314041160650.00%08811.1000003000000042.86%7700000.6800000002
12Nick RitchieAdmirals (ANA)LW8213-21603691062420.00%116821.111122351011350142.86%700000.3612000000
13Deven SideroffAdmirals (ANA)RW82021004461233.33%1779.6800005000001044.44%900000.5200000010
14Lukas JasekAdmirals (ANA)RW80221405811160.00%0637.980000100000000.00%200000.6300000000
15Michael AndersonAdmirals (ANA)D802251151323030.00%712215.370000300001000.00%000000.3300001000
16Jordy BelleriveAdmirals (ANA)C81121206481512.50%0617.7400000000000068.25%6300000.6500000001
17Axel Jonsson-FjallbyAdmirals (ANA)LW80111141091013230.00%09111.5000000000080060.00%500000.2200200000
18Nicholas CaamanoAdmirals (ANA)RW801121008693110.00%210112.65000000000160050.00%1000000.2000000000
Team Total or Average1442037579154401821521473712813.61%46234616.3048122735012333213145.23%57700010.4916313344
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
1Mikhail BerdinAdmirals (ANA)83320.8652.7645700211560000.429780000
2Adam WernerAdmirals (ANA)10001.0000.0031000100000.000008000
Team Total or Average93320.8732.5848900211660000.429788000


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 Type Current Salary Salary RemainingSalary AverageSalary Ave RemainingSalary 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 10Link
Adam WernerAdmirals (ANA)G231997-01-01Yes200 Lbs6 ft0NoNoNo1Pro & Farm1,000,000$895,161$1,000,000$895,161$0$0$NoLink
Axel Jonsson-FjallbyAdmirals (ANA)LW221998-02-10No185 Lbs6 ft0NoNoNo1Pro & Farm1,000,000$895,161$1,000,000$895,161$0$0$NoLink
Brogan RaffertyAdmirals (ANA)D251995-05-28Yes192 Lbs6 ft2NoNoNo1Pro & Farm1,000,000$895,161$1,000,000$895,161$0$0$NoLink
Cody GlassAdmirals (ANA)C/RW211999-03-31Yes178 Lbs6 ft2NoNoNo3Pro & Farm1,713,333$1,533,709$1,713,333$1,533,709$0$0$No1,713,333$1,713,333$
Deven SideroffAdmirals (ANA)RW231997-04-14Yes171 Lbs5 ft11NoNoNo3Pro & Farm935,833$837,721$935,833$837,721$0$0$No935,833$935,833$
Henri JokiharjuAdmirals (ANA)D211999-06-17No180 Lbs6 ft0NoNoNo3Pro & Farm925,000$828,024$925,000$828,024$0$0$No925,000$925,000$Link
Jacob LarssonAdmirals (ANA)D231997-04-29No195 Lbs6 ft2NoNoNo3Pro & Farm894,166$800,423$894,166$800,423$0$0$No894,166$894,166$Link
Johnathan KovacevicAdmirals (ANA)D221997-07-12Yes207 Lbs6 ft4NoNoNo1Pro & Farm1,000,000$895,161$1,000,000$895,161$0$0$NoLink
Jordy BelleriveAdmirals (ANA)C211999-05-02Yes194 Lbs5 ft11NoNoNo1Pro & Farm1,000,000$895,161$1,000,000$895,161$0$0$NoLink
Keaton ThompsonAdmirals (ANA)D241995-09-14Yes182 Lbs6 ft0NoNoNo3Pro & Farm925,000$828,024$925,000$828,024$0$0$No925,000$925,000$
Leon GawankeAdmirals (ANA)D211999-05-31Yes198 Lbs6 ft1NoNoNo1Pro & Farm1,000,000$895,161$1,000,000$895,161$0$0$NoLink
Lucas CarlssonAdmirals (ANA)D221997-07-05No190 Lbs6 ft0NoNoNo1Pro & Farm1,000,000$895,161$1,000,000$895,161$0$0$NoLink
Lukas JasekAdmirals (ANA)RW221997-08-28No183 Lbs6 ft1NoNoNo1Pro & Farm1,000,000$895,161$1,000,000$895,161$0$0$NoLink
Mac HollowellAdmirals (ANA)D211998-09-26Yes170 Lbs5 ft10NoNoNo3Pro & Farm799,766$715,920$799,766$715,920$0$0$No799,766$799,766$
Manuel WiedererAdmirals (ANA)C/RW231996-11-21Yes170 Lbs6 ft0NoNoNo3Pro & Farm736,667$659,436$736,667$659,436$0$0$No736,667$736,667$
Michael AndersonAdmirals (ANA)D211999-05-25Yes196 Lbs6 ft0NoNoNo1Pro & Farm1,000,000$895,161$1,000,000$895,161$0$0$NoLink
Mikhail BerdinAdmirals (ANA)G221998-02-28No163 Lbs6 ft2NoNoNo1Pro & Farm1,000,000$895,161$1,000,000$895,161$0$0$NoLink
Nathan NoelAdmirals (ANA)C/LW231997-06-21Yes174 Lbs5 ft11NoNoNo3Pro & Farm925,000$828,024$925,000$828,024$0$0$No925,000$925,000$
Nicholas CaamanoAdmirals (ANA)RW211998-10-07No194 Lbs6 ft2NoNoNo1Pro & Farm1,000,000$895,161$1,000,000$895,161$0$0$NoLink
Nick RitchieAdmirals (ANA)LW241995-12-05No234 Lbs6 ft2NoNoNo3Pro & Farm1,498,925$1,341,780$1,498,925$1,341,780$0$0$No1,498,925$1,498,925$Link
Noah DobsonAdmirals (ANA)D202000-01-07Yes183 Lbs6 ft4NoNoNo3Pro & Farm1,431,667$1,281,573$1,431,667$1,281,573$0$0$No1,431,667$1,431,667$
Ondrej KaseAdmirals (ANA)RW241995-11-08No185 Lbs6 ft0NoNoNo3Pro & Farm2,600,000$2,327,419$2,600,000$2,327,419$0$0$No2,600,000$2,600,000$Link
Riley SutterAdmirals (ANA)RW201999-10-24Yes200 Lbs6 ft1NoNoNo3Pro & Farm894,167$800,424$894,167$800,424$0$0$No894,167$894,167$
Ryan MacInnisAdmirals (ANA)C241996-02-13Yes185 Lbs6 ft3NoNoNo3Pro & Farm874,125$782,483$874,125$782,483$0$0$No874,125$874,125$
Sasha ChmelevskiAdmirals (ANA)C/RW211999-06-09Yes187 Lbs6 ft0NoNoNo1Pro & Farm1,000,000$895,161$1,000,000$895,161$0$0$NoLink
Stefan NoesenAdmirals (ANA)LW/RW271993-02-12No205 Lbs6 ft1NoNoNo3Pro & Farm450,000$402,823$450,000$402,823$0$0$No450,000$450,000$Link
Taylor RaddyshAdmirals (ANA)C/RW221998-02-18Yes216 Lbs6 ft3NoNoNo3Pro & Farm894,166$800,423$894,166$800,423$0$0$No894,166$894,166$
Total PlayersAverage AgeAverage WeightAverage HeightAverage ContractAverage Year 1 Salary
2722.33190 Lbs6 ft12.111,055,475$



5 vs 5 Forward
Line #Left WingCenterRight WingTime %PHYDFOF
1Nick RitchieCody GlassOndrej Kase35122
2Stefan NoesenTaylor RaddyshSasha Chmelevski30122
3Axel Jonsson-FjallbyRyan MacInnisNicholas Caamano25122
4Deven SideroffJordy BelleriveLukas Jasek10122
5 vs 5 Defense
Line #DefenseDefenseTime %PHYDFOF
1Jacob LarssonHenri Jokiharju35122
2Brogan RaffertyLucas Carlsson30122
3Michael AndersonLeon Gawanke25122
4Jacob LarssonHenri Jokiharju10122
Power Play Forward
Line #Left WingCenterRight WingTime %PHYDFOF
1Nick RitchieCody GlassOndrej Kase60122
2Stefan NoesenTaylor RaddyshSasha Chmelevski40122
Power Play Defense
Line #DefenseDefenseTime %PHYDFOF
1Jacob LarssonHenri Jokiharju60122
2Brogan RaffertyLucas Carlsson40122
Penalty Kill 4 Players Forward
Line #CenterWingTime %PHYDFOF
1Ondrej KaseNick Ritchie60122
2Stefan NoesenCody Glass40122
Penalty Kill 4 Players Defense
Line #DefenseDefenseTime %PHYDFOF
1Jacob LarssonHenri Jokiharju60122
2Brogan RaffertyLucas Carlsson40122
Penalty Kill 3 Players
Line #WingTime %PHYDFOFDefenseDefenseTime %PHYDFOF
1Ondrej Kase60122Jacob LarssonHenri Jokiharju60122
2Nick Ritchie40122Brogan RaffertyLucas Carlsson40122
4 vs 4 Forward
Line #CenterWingTime %PHYDFOF
1Ondrej KaseNick Ritchie60122
2Stefan NoesenCody Glass40122
4 vs 4 Defense
Line #DefenseDefenseTime %PHYDFOF
1Jacob LarssonHenri Jokiharju60122
2Brogan RaffertyLucas Carlsson40122
Last Minutes Offensive
Left WingCenterRight WingDefenseDefense
Nick RitchieCody GlassOndrej KaseJacob LarssonHenri Jokiharju
Last Minutes Defensive
Left WingCenterRight WingDefenseDefense
Nick RitchieCody GlassOndrej KaseJacob LarssonHenri Jokiharju
Extra Forwards
Normal PowerPlayPenalty Kill
Deven Sideroff, Ryan MacInnis, Nicholas CaamanoDeven Sideroff, Ryan MacInnisNicholas Caamano
Extra Defensemen
Normal PowerPlayPenalty Kill
Michael Anderson, Leon Gawanke, Brogan RaffertyMichael AndersonLeon Gawanke, Brogan Rafferty
Penalty Shots
Ondrej Kase, Nick Ritchie, Stefan Noesen, Cody Glass, Taylor Raddysh
Goalie
#1 : Mikhail Berdin, #2 : Adam Werner


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
1Condors2110000056-1110000003211010000024-220.500591400839129385948850144351900.00%16475.00%110621250.00%9723441.45%5813144.27%1891271996210150
2Crunch1010000003-3000000000001010000003-300.0000000083912638594883581531500.00%50100.00%010621250.00%9723441.45%5813144.27%1891271996210150
3Monarchs11000000523110000005230000000000021.000591400839122385948821412357114.29%6183.33%010621250.00%9723441.45%5813144.27%1891271996210150
4Moose3110000178-11010000023-12100000155030.500714210083914938594885013704917211.76%18288.89%010621250.00%9723441.45%5813144.27%1891271996210150
5Sharks1000000134-11000000134-10000000000010.50035800839121385948810714164125.00%60100.00%010621250.00%9723441.45%5813144.27%1891271996210150
Total833000022023-3421000011311241200001712-580.5002037570083911473859488166461541824249.52%51786.27%110621250.00%9723441.45%5813144.27%1891271996210150
_Since Last GM Reset833000022023-3421000011311241200001712-580.5002037570083911473859488166461541824249.52%51786.27%110621250.00%9723441.45%5813144.27%1891271996210150
_Vs Conference732000022020042100001131123110000179-280.57120375700839112138594881313813915137410.81%46784.78%110621250.00%9723441.45%5813144.27%1891271996210150
_Vs Division732000022020042100001131123110000179-280.57120375700839112138594881313813915137410.81%46784.78%110621250.00%9723441.45%5813144.27%1891271996210150

Total For Players
Games PlayedPointsStreakGoalsAssistsPointsShots ForShots AgainstShots BlockedPenalty MinutesHitsEmpty Net GoalsShutouts
88L22037571471664615418200
All Games
GPWLOTWOTL SOWSOLGFGA
83300022023
Home Games
GPWLOTWOTL SOWSOLGFGA
42100011311
Visitor Games
GPWLOTWOTL SOWSOLGFGA
4120001712
Last 10 Games
WLOTWOTL SOWSOL
330002
Power Play AttempsPower Play GoalsPower Play %Penalty Kill AttempsPenalty Kill Goals AgainstPenalty Kill %Penalty Kill Goals For
4249.52%51786.27%1
Shots 1 PeriodShots 2 PeriodShots 3 PeriodShots 4+ PeriodGoals 1 PeriodGoals 2 PeriodGoals 3 PeriodGoals 4+ Period
38594888391
Face Offs
Won Offensive ZoneTotal OffensiveWon Offensive %Won Defensif ZoneTotal DefensiveWon Defensive %Won Neutral ZoneTotal NeutralWon Neutral %
10621250.00%9723441.45%5813144.27%
Puck Time
In Offensive ZoneControl In Offensive ZoneIn Defensive ZoneControl In Defensive ZoneIn Neutral ZoneControl In Neutral Zone
1891271996210150


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 - 2020-09-2711Admirals0Crunch3LBoxScore
2 - 2020-09-2819Monarchs2Admirals5WBoxScore
4 - 2020-09-3027Sharks4Admirals3LXXBoxScore
5 - 2020-10-0142Condors2Admirals3WBoxScore
6 - 2020-10-0252Admirals3Moose4LXXBoxScore
8 - 2020-10-0462Admirals2Moose1WBoxScore
10 - 2020-10-0668Admirals2Condors4LBoxScore
12 - 2020-10-0879Moose3Admirals2LBoxScore
14 - 2020-10-1089Admirals-Monarchs-
17 - 2020-10-13107Moose-Admirals-
18 - 2020-10-14117Admirals-Flames-
20 - 2020-10-16128Admirals-Stars-
21 - 2020-10-17131Flames-Admirals-
23 - 2020-10-19150Condors-Admirals-
24 - 2020-10-20161Admirals-Marlies-
25 - 2020-10-21169Sharks-Admirals-
26 - 2020-10-22175Admirals-Condors-
28 - 2020-10-24192Admirals-Rampage-
30 - 2020-10-26198Wolf Pack-Admirals-
32 - 2020-10-28210Admirals-Flames-
34 - 2020-10-30222Monsters-Admirals-
36 - 2020-11-01237Rocket-Admirals-
38 - 2020-11-03249Admirals-Marlies-
40 - 2020-11-05253Admirals-IceHogs-
42 - 2020-11-07265Admirals-Flames-
43 - 2020-11-08271Monarchs-Admirals-
45 - 2020-11-10285Marlies-Admirals-
46 - 2020-11-11300Flames-Admirals-
47 - 2020-11-12305Admirals-Moose-
49 - 2020-11-14324Monarchs-Admirals-
51 - 2020-11-16335Condors-Admirals-
52 - 2020-11-17345Admirals-Condors-
53 - 2020-11-18358Admirals-Monarchs-
54 - 2020-11-19367Condors-Admirals-
55 - 2020-11-20378Admirals-Condors-
57 - 2020-11-22390Wolf Pack-Admirals-
58 - 2020-11-23401Admirals-Senators-
59 - 2020-11-24410Senators-Admirals-
60 - 2020-11-25422Bruins-Admirals-
63 - 2020-11-28434Admirals-Griffins-
64 - 2020-11-29446Sound Tigers-Admirals-
65 - 2020-11-30456Admirals-Rocket-
67 - 2020-12-02467Admirals-Sound Tigers-
68 - 2020-12-03475Admirals-Sharks-
69 - 2020-12-04486Phantoms-Admirals-
71 - 2020-12-06497Monarchs-Admirals-
72 - 2020-12-07511Sharks-Admirals-
74 - 2020-12-09525Admirals-Stars-
75 - 2020-12-10534Sharks-Admirals-
77 - 2020-12-12546Admirals-Sharks-
78 - 2020-12-13554Admirals-Soldiers-
79 - 2020-12-14566Soldiers-Admirals-
81 - 2020-12-16582Monarchs-Admirals-
82 - 2020-12-17588Admirals-Moose-
84 - 2020-12-19601Crunch-Admirals-
86 - 2020-12-21611Admirals-Flames-
87 - 2020-12-22625Moose-Admirals-
88 - 2020-12-23636Admirals-Soldiers-
90 - 2020-12-25644Admirals-Monsters-
91 - 2020-12-26654Penguins-Admirals-
92 - 2020-12-27662Admirals-IceHogs-
94 - 2020-12-29677Wolves-Admirals-
95 - 2020-12-30687Flames-Admirals-
96 - 2020-12-31699Admirals-Penguins-
97 - 2021-01-01707Admirals-Phantoms-
99 - 2021-01-03719IceHogs-Admirals-
100 - 2021-01-04731Admirals-Bruins-
102 - 2021-01-06742Flames-Admirals-
103 - 2021-01-07755Griffins-Admirals-
104 - 2021-01-08763Admirals-Marlies-
105 - 2021-01-09777Wolves-Admirals-
Trade Deadline --- Trades can’t be done after this day is simulated!
107 - 2021-01-11787Admirals-Wolf Pack-
108 - 2021-01-12800Rampage-Admirals-
109 - 2021-01-13805Admirals-Sharks-
111 - 2021-01-15821Rampage-Admirals-
112 - 2021-01-16824Admirals-Wolves-
114 - 2021-01-18843Admirals-Monarchs-
116 - 2021-01-20853Stars-Admirals-
117 - 2021-01-21866Admirals-Monarchs-
118 - 2021-01-22873Moose-Admirals-
121 - 2021-01-25890Marlies-Admirals-
122 - 2021-01-26897Admirals-Sharks-



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
37 0 - 0.00% 0$0$3000100

Expenses
Year To Date ExpensesPlayers Total SalariesPlayers Total Average SalariesCoaches Salaries
387,881$ 2,849,782$ 2,849,782$ 0$
Salary Cap Per DaysSalary Cap To DatePlayers In Salary CapPlayers Out of Salary Cap
0$ 298,766$ 0 0

Estimate
Estimated Season RevenueRemaining Season DaysExpenses Per DaysEstimated Season Expenses
0$ 111 29,837$ 3,311,907$




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
2020833000022023-3421000011311241200001712-582037570083911473859488166461541824249.52%51786.27%110621250.00%9723441.45%5813144.27%1891271996210150
Total Regular Season833000022023-3421000011311241200001712-582037570083911473859488166461541824249.52%51786.27%110621250.00%9723441.45%5813144.27%1891271996210150