MILWAUKEE Admirals

GP: 80 | W: 50 | L: 26 | OTL: 4 | P: 104
GF: 268 | GA: 156 | PP%: 20.89% | PK%: 83.83%
GM : Louis Bourgault | Morale : 99 | Team Overall : 62
Next Games #1039 vs STOCKTON Flames
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
1Ryan Hartman0X100.007246667866819571507166656856537682710
2Anthony Cirelli0X100.006242786573708666496563726551516696670
3Pontus Aberg0XX100.006042817271787467306863616453527382670
4Beau Bennett0X100.006043736174668365307058605858547080650
5William Carrier0X100.008942756979725460606261626553576782650
6Brian Flynn0XXX100.005641766069648362506260606061563582630
7Carter Verhaeghe0X100.005543726072647564676561606150504482620
8Daniel Paille0X100.00563781677246575932545767557669782610
9Sergey Tolchinsky0X100.004842746054536060506257605750504482570
10Dalton Smith0X100.006249546078526251505151605150507382570
11Jordan Oesterle0X100.006941927466867271306065686953524282680
12Dylan DeMelo0X100.006943766572758077306557676255534782670
13Viktor Svedberg0X100.007847595099539055305046605351533782610
14Nikita Nikitin0X100.005336806083574449303333725072662382550
15Dylan Olsen0X100.005637796580454451303535695562586382540
16Jakub Kindl0X100.006239666377454648303330654872622380530
Scratches
TEAM AVERAGE100.00634274647463696141575464595855498362
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
1Aaron Dell100.00787882808080817979767554663680690
2Thatcher Demko100.00727082827379747373716950617682650
Scratches
1Samuel Montembeault (R)100.00676872816971706969666650594436610
2Sam Brittain100.00455062776052505150525562516323500
TEAM AVERAGE100.0066677580717169686866665459555561
Coaches Name PH DF OF PD EX LD PO CNT Age Contract Salary
Davis Payne70707070757574CAN484650,000$


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
1Ryan HartmanMILWAUKEE Admirals (NSH)RW634354975379522415034312427512.54%14168126.6939122913300071317158.10%104300041.15230011684
2Pontus AbergMILWAUKEE Admirals (NSH)LW/RW80415192492806210935512322811.55%23206425.81210123116901151648149.71%17500000.8903000497
3William CarrierMILWAUKEE Admirals (NSH)LW8027477443700263110319922308.46%11184123.0251217391620001914262.90%12400000.8001000638
4Jordan OesterleMILWAUKEE Admirals (NSH)D8028457329420103902477215011.34%101194724.35139221122031013208610.00%000100.7501000275
5Carter VerhaegheMILWAUKEE Admirals (NSH)C802844724040271572247818012.50%14133016.644121639171000139265.18%141000011.0811000445
6Beau BennettMILWAUKEE Admirals (NSH)RW80283260352210561032518118211.16%10175521.952242012100031216050.29%85700010.6812011325
7Dylan DeMeloMILWAUKEE Admirals (NSH)D801246583038011686132361159.09%79182622.836101661177000318320100.00%100000.6411000231
8Brian FlynnMILWAUKEE Admirals (NSH)C/LW/RW801737542820052138185661659.19%12143417.944812271820001331260.05%159200000.7501000056
9Daniel PailleMILWAUKEE Admirals (NSH)LW80152338282203362172511308.72%12107813.48123528000111051.09%9200100.7000000100
10Nikita NikitinMILWAUKEE Admirals (NSH)D80328313816022387120524.23%73143117.89257191270001133000.00%000000.4300000022
11Viktor SvedbergMILWAUKEE Admirals (NSH)D8062026428801873346143813.04%71149418.68437171560003137100.00%000000.3500000011
12Jakub KindlMILWAUKEE Admirals (NSH)D8041822354151103130132613.33%61117514.7010129000053030.00%000000.3700010111
13Dylan OlsenMILWAUKEE Admirals (NSH)D804182225400544740252710.00%77118914.86011115000030100.00%000000.3700000001
14Sergey TolchinskyMILWAUKEE Admirals (NSH)LW806121815006388628606.98%26097.6203313430000271059.01%16100000.5900000101
15Dalton SmithMILWAUKEE Admirals (NSH)LW804131719180724010240683.92%12104613.080112140112861067.24%11600000.3200000001
Team Total or Average118326648875450952820138712322603863192610.22%5722190818.5247871344171716123311408481259.20%557100260.69513022404548
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
1Thatcher DemkoMILWAUKEE Admirals (NSH)74492230.9241.80443841213317490200.71414740954
2Aaron DellMILWAUKEE Admirals (NSH)2018020.9431.10120308223880000.0000200312
3Louis DomingueNASHVILLE Predators1713400.9221.70102503293710001.0003170010
4Samuel MontembeaultMILWAUKEE Admirals (NSH)71410.8943.5037700222070000.0000626000
Team Total or Average118813060.9241.76704442320627150200.76517117261276


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
Aaron DellMILWAUKEE Admirals (NSH)G295/4/1989No91 Kg183 CMNoNoNo3UFAPro & Farm750,000$0$0$NoLink
Anthony CirelliMILWAUKEE Admirals (NSH)C217/15/1997No82 Kg183 CMNoNoNo1RFAPro & Farm325,000$0$0$NoLink
Beau BennettMILWAUKEE Admirals (NSH)RW2611/27/1991No89 Kg188 CMNoNoNo1RFAPro & Farm900,000$0$0$NoLink
Brian FlynnMILWAUKEE Admirals (NSH)C/LW/RW307/26/1988No83 Kg185 CMNoNoNo1UFAPro & Farm1,750,000$0$0$NoLink
Carter VerhaegheMILWAUKEE Admirals (NSH)C238/14/1995No86 Kg188 CMNoNoNo1RFAPro & Farm750,000$0$0$NoLink
Dalton SmithMILWAUKEE Admirals (NSH)LW266/30/1992No94 Kg188 CMNoNoNo2RFAPro & Farm750,000$0$0$NoLink
Daniel PailleMILWAUKEE Admirals (NSH)LW344/15/1984No91 Kg185 CMNoNoNo1UFAPro & Farm1,250,000$0$0$No
Dylan DeMeloMILWAUKEE Admirals (NSH)D255/1/1993No89 Kg185 CMNoNoNo2RFAPro & Farm900,000$0$0$NoLink
Dylan OlsenMILWAUKEE Admirals (NSH)D271/3/1991No101 Kg188 CMNoNoNo1RFAPro & Farm750,000$0$0$No
Jakub KindlMILWAUKEE Admirals (NSH)D312/10/1987No90 Kg191 CMNoNoNo1UFAPro & Farm1,500,000$0$0$No
Jordan OesterleMILWAUKEE Admirals (NSH)D266/25/1992No83 Kg183 CMNoNoNo1RFAPro & Farm750,000$0$0$NoLink
Nikita NikitinMILWAUKEE Admirals (NSH)D326/16/1986No99 Kg193 CMNoNoNo1UFAPro & Farm1,500,000$0$0$No
Pontus AbergMILWAUKEE Admirals (NSH)LW/RW249/23/1993No89 Kg180 CMNoNoNo1RFAPro & Farm750,000$0$0$NoLink
Ryan HartmanMILWAUKEE Admirals (NSH)RW239/20/1994No82 Kg183 CMNoNoNo1RFAPro & Farm1,800,000$0$0$NoLink
Sam BrittainMILWAUKEE Admirals (NSH)G265/10/1992No100 Kg191 CMNoNoNo3RFAPro & Farm750,000$0$0$No
Samuel Montembeault (1 Way Contract)MILWAUKEE Admirals (NSH)G2110/30/1996Yes87 Kg191 CMNoNoNo2RFAPro & Farm725,000$725,000$58,468$NoLink
Sergey TolchinskyMILWAUKEE Admirals (NSH)LW232/3/1995No77 Kg173 CMNoNoNo4RFAPro & Farm750,000$0$0$NoLink
Thatcher DemkoMILWAUKEE Admirals (NSH)G2212/8/1995No87 Kg193 CMNoNoNo3RFAPro & Farm750,000$0$0$NoLink
Viktor SvedbergMILWAUKEE Admirals (NSH)D275/24/1991No108 Kg203 CMNoNoNo2RFAPro & Farm750,000$0$0$NoLink
William CarrierMILWAUKEE Admirals (NSH)LW2312/20/1994No96 Kg188 CMNoNoNo3RFAPro & Farm750,000$0$0$NoLink
Total PlayersAverage AgeAverage WeightAverage HeightAverage ContractAverage Year 1 Salary
2025.9590 Kg188 CM1.75945,000$



5 vs 5 Forward
Line #Left WingCenterRight WingTime %PHYDFOF
1Pontus AbergBrian FlynnRyan Hartman31122
2William CarrierCarter VerhaegheBeau Bennett26122
3Daniel PailleRyan HartmanPontus Aberg23122
4Dalton SmithBeau BennettWilliam Carrier20122
5 vs 5 Defense
Line #DefenseDefenseTime %PHYDFOF
1Jordan OesterleDylan DeMelo31122
2Viktor SvedbergNikita Nikitin26122
3Dylan OlsenJakub Kindl23122
4Jordan OesterleDylan DeMelo20122
Power Play Forward
Line #Left WingCenterRight WingTime %PHYDFOF
1Pontus AbergBrian FlynnRyan Hartman55122
2William CarrierCarter VerhaegheBeau Bennett45122
Power Play Defense
Line #DefenseDefenseTime %PHYDFOF
1Jordan OesterleDylan DeMelo55122
2Viktor SvedbergNikita Nikitin45122
Penalty Kill 4 Players Forward
Line #CenterWingTime %PHYDFOF
1Ryan HartmanPontus Aberg55122
2Beau BennettWilliam Carrier45122
Penalty Kill 4 Players Defense
Line #DefenseDefenseTime %PHYDFOF
1Jordan OesterleDylan DeMelo55122
2Viktor SvedbergNikita Nikitin45122
Penalty Kill 3 Players
Line #WingTime %PHYDFOFDefenseDefenseTime %PHYDFOF
1Ryan Hartman55122Jordan OesterleDylan DeMelo55122
2Pontus Aberg45122Viktor SvedbergNikita Nikitin45122
4 vs 4 Forward
Line #CenterWingTime %PHYDFOF
1Ryan HartmanPontus Aberg55122
2Beau BennettWilliam Carrier45122
4 vs 4 Defense
Line #DefenseDefenseTime %PHYDFOF
1Jordan OesterleDylan DeMelo55122
2Viktor SvedbergNikita Nikitin45122
Last Minutes Offensive
Left WingCenterRight WingDefenseDefense
Pontus AbergBrian FlynnRyan HartmanJordan OesterleDylan DeMelo
Last Minutes Defensive
Left WingCenterRight WingDefenseDefense
Pontus AbergBrian FlynnRyan HartmanJordan OesterleDylan DeMelo
Extra Forwards
Normal PowerPlayPenalty Kill
Sergey Tolchinsky, Daniel Paille, Dalton SmithSergey Tolchinsky, Daniel PailleDalton Smith
Extra Defensemen
Normal PowerPlayPenalty Kill
Dylan Olsen, Jakub Kindl, Viktor SvedbergDylan OlsenJakub Kindl, Viktor Svedberg
Penalty Shots
Ryan Hartman, Pontus Aberg, Beau Bennett, William Carrier, Brian Flynn
Goalie
#1 : Thatcher Demko, #2 :
Custom OT Lines Forwards
Ryan Hartman, Pontus Aberg, Beau Bennett, William Carrier, Brian Flynn, Carter Verhaeghe, Carter Verhaeghe, Daniel Paille, Dalton Smith, Sergey Tolchinsky,
Custom OT Lines Defensemen
Jordan Oesterle, Dylan DeMelo, Viktor Svedberg, Nikita Nikitin, Dylan Olsen


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
1BELLEVILLE Senators21100000211110000002021010000001-120.500246011088372845825842915364715644100.00%30100.00%01584267559.21%1140234748.57%633113755.67%222116541657515945492
2BRIDGEPORT Sound Tigers660000004673933000000204163300000026323121.0004682128021088372818182584291536132443293151066.67%15286.67%11584267559.21%1140234748.57%633113755.67%222116541657515945492
3BROOKLYN Wolfpack2200000021219110000001111011000000101941.000214061001088372814982584291536113236100.00%10100.00%01584267559.21%1140234748.57%633113755.67%222116541657515945492
4CHICAGO Wolves623001001518-33110010076131200000812-450.41715284300108837282088258429153614358341061119.09%17288.24%01584267559.21%1140234748.57%633113755.67%222116541657515945492
5CLEVELAND Monsters21000010422100000102111100000021141.000461000108837284782584291536491710296233.33%50100.00%01584267559.21%1140234748.57%633113755.67%222116541657515945492
6COLORADO Eagles6230100014140311010009723120000057-260.500142640001088372814582584291536170444011221314.29%20575.00%01584267559.21%1140234748.57%633113755.67%222116541657515945492
7CORNWALL Aces2200000016313110000009271100000071641.0001632480010883728988258429153626111232000.00%6183.33%01584267559.21%1140234748.57%633113755.67%222116541657515945492
8HERSEY Bears201010003301010000012-11000100021120.5003690010883728518258429153655141834600.00%9188.89%01584267559.21%1140234748.57%633113755.67%222116541657515945492
9HOLLYWOOD Oscar2200000011011110000007071100000040441.0001121320210883728106825842915362112831000.00%40100.00%01584267559.21%1140234748.57%633113755.67%222116541657515945492
10IOWA Wild320010001358110000006242100100073461.00013223500108837289482584291536882024519444.44%11190.91%01584267559.21%1140234748.57%633113755.67%222116541657515945492
11LAVAL Rockets623000101315-2311000109903120000046-260.500131932001088372815482584291536161503610214214.29%18288.89%01584267559.21%1140234748.57%633113755.67%222116541657515945492
12LEHIGH VALLEY Phantoms2110000057-21010000025-31100000032120.5005914001088372848825842915366620124011218.18%6266.67%01584267559.21%1140234748.57%633113755.67%222116541657515945492
13MANITOBA Moose33000000200201100000010010220000001001061.00020375703108837282038258429153643233000.00%10100.00%01584267559.21%1140234748.57%633113755.67%222116541657515945492
14MONT-LAURIER Sommet22000000725110000004041100000032141.000714210110883728868258429153631985911218.18%20100.00%01584267559.21%1140234748.57%633113755.67%222116541657515945492
15PROVIDENCE Bruins30300000710-32020000057-21010000023-100.00071219001088372889825842915369331344811327.27%17664.71%01584267559.21%1140234748.57%633113755.67%222116541657515945492
16PV Sharapovas21100000413110000004041010000001-120.500481201108837285982584291536501014308337.50%70100.00%01584267559.21%1140234748.57%633113755.67%222116541657515945492
17ROCKFORD IceHogs632001001113-23110010068-23210000055070.583112031001088372815282584291536180574510116318.75%18383.33%01584267559.21%1140234748.57%633113755.67%222116541657515945492
18SAN DIEGO Gulls63300000151413120000056-132100000108260.50015284300108837281678258429153617641831422229.09%28582.14%01584267559.21%1140234748.57%633113755.67%222116541657515945492
19STOCKTON Flames1010000001-1000000000001010000001-100.000000001088372819825842915362711620100.00%3166.67%01584267559.21%1140234748.57%633113755.67%222116541657515945492
20SYRACUSE Crunch2010000138-51000000123-11010000015-410.25035800108837286582584291536662116306116.67%60100.00%01584267559.21%1140234748.57%633113755.67%222116541657515945492
21TORONTO Marlies22000000312110000001011100000021141.000369011088372874825842915365912828700.00%40100.00%01584267559.21%1140234748.57%633113755.67%222116541657515945492
22TUSCON Roadrunners22000000743110000003121100000043141.0007121900108837284982584291536561522321218.33%9188.89%01584267559.21%1140234748.57%633113755.67%222116541657515945492
Total804526033212681561123923100122114475694122160210012481431040.65026848875601210883728260582584291536195657653213872254720.89%2353883.83%11584267559.21%1140234748.57%633113755.67%222116541657515945492
24UTICA Comets2110000056-1110000003211010000024-220.500581300108837285182584291536521410304250.00%4175.00%01584267559.21%1140234748.57%633113755.67%222116541657515945492
25VICTORIAVILLE Tigres632001001418-433000000118330200100310-770.58314253900108837281458258429153617541469928414.29%19573.68%01584267559.21%1140234748.57%633113755.67%222116541657515945492
26WILKIES-BARRIE Penguins22000000918110000005141100000040441.000918270110883728120825842915361834254250.00%20100.00%01584267559.21%1140234748.57%633113755.67%222116541657515945492
_Since Last GM Reset804526033212681561123923100122114475694122160210012481431040.65026848875601210883728260582584291536195657653213872254720.89%2353883.83%11584267559.21%1140234748.57%633113755.67%222116541657515945492
_Vs Conference4924190222016310558251190122083542924131001000805129580.592163293456031088372815148258429153612403763488581313022.90%1542782.47%11584267559.21%1140234748.57%633113755.67%222116541657515945492
_Vs Division30181401210876819148601210493118161080000038371420.70087158245031088372894782584291536760223191502851517.65%861681.40%01584267559.21%1140234748.57%633113755.67%222116541657515945492

Total For Players
Games PlayedPointsStreakGoalsAssistsPointsShots ForShots AgainstShots BlockedPenalty MinutesHitsEmpty Net GoalsShutouts
80104W2268488756260519565765321387012
All Games
GPWLOTWOTL SOWSOLGFGA
8045263321268156
Home Games
GPWLOTWOTL SOWSOLGFGA
392310122114475
Visitor Games
GPWLOTWOTL SOWSOLGFGA
412216210012481
Last 10 Games
WLOTWOTL SOWSOL
820000
Power Play AttempsPower Play GoalsPower Play %Penalty Kill AttempsPenalty Kill Goals AgainstPenalty Kill %Penalty Kill Goals For
2254720.89%2353883.83%1
Shots 1 PeriodShots 2 PeriodShots 3 PeriodShots 4+ PeriodGoals 1 PeriodGoals 2 PeriodGoals 3 PeriodGoals 4+ Period
8258429153610883728
Face Offs
Won Offensive ZoneTotal OffensiveWon Offensive %Won Defensif ZoneTotal DefensiveWon Defensive %Won Neutral ZoneTotal NeutralWon Neutral %
1584267559.21%1140234748.57%633113755.67%
Puck Time
In Offensive ZoneControl In Offensive ZoneIn Defensive ZoneControl In Defensive ZoneIn Neutral ZoneControl In Neutral Zone
222116541657515945492


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 - 2019-01-236MILWAUKEE Admirals4CHICAGO Wolves5LBoxScore
2 - 2019-01-2418MILWAUKEE Admirals2ROCKFORD IceHogs1WBoxScore
3 - 2019-01-2525SAN DIEGO Gulls4MILWAUKEE Admirals3LBoxScore
5 - 2019-01-2744VICTORIAVILLE Tigres3MILWAUKEE Admirals4WBoxScore
7 - 2019-01-2960MILWAUKEE Admirals8BRIDGEPORT Sound Tigers1WBoxScore
8 - 2019-01-3064MILWAUKEE Admirals4SAN DIEGO Gulls5LBoxScore
9 - 2019-01-3177BRIDGEPORT Sound Tigers3MILWAUKEE Admirals4WBoxScore
12 - 2019-02-0397ROCKFORD IceHogs1MILWAUKEE Admirals2WBoxScore
13 - 2019-02-04105MILWAUKEE Admirals1COLORADO Eagles3LBoxScore
14 - 2019-02-05123MILWAUKEE Admirals3CHICAGO Wolves1WBoxScore
15 - 2019-02-06129COLORADO Eagles4MILWAUKEE Admirals3LBoxScore
17 - 2019-02-08149LAVAL Rockets5MILWAUKEE Admirals2LBoxScore
18 - 2019-02-09161MILWAUKEE Admirals10BRIDGEPORT Sound Tigers2WBoxScore
20 - 2019-02-11171ROCKFORD IceHogs3MILWAUKEE Admirals2LXBoxScore
21 - 2019-02-12176MILWAUKEE Admirals0LAVAL Rockets3LBoxScore
23 - 2019-02-14198MILWAUKEE Admirals1VICTORIAVILLE Tigres4LBoxScore
24 - 2019-02-15208PROVIDENCE Bruins4MILWAUKEE Admirals3LBoxScore
26 - 2019-02-17226PV Sharapovas0MILWAUKEE Admirals4WBoxScore
27 - 2019-02-18240MILWAUKEE Admirals0PV Sharapovas1LBoxScore
29 - 2019-02-20249BRIDGEPORT Sound Tigers0MILWAUKEE Admirals8WBoxScore
31 - 2019-02-22266VICTORIAVILLE Tigres1MILWAUKEE Admirals2WBoxScore
32 - 2019-02-23275MILWAUKEE Admirals2UTICA Comets4LBoxScore
33 - 2019-02-24292UTICA Comets2MILWAUKEE Admirals3WBoxScore
34 - 2019-02-25304MILWAUKEE Admirals2TORONTO Marlies1WBoxScore
36 - 2019-02-27316MILWAUKEE Admirals3MONT-LAURIER Sommet2WBoxScore
37 - 2019-02-28327MILWAUKEE Admirals1CHICAGO Wolves6LBoxScore
39 - 2019-03-02340CHICAGO Wolves1MILWAUKEE Admirals0LXBoxScore
40 - 2019-03-03354BRIDGEPORT Sound Tigers1MILWAUKEE Admirals8WBoxScore
42 - 2019-03-05368CHICAGO Wolves1MILWAUKEE Admirals5WBoxScore
44 - 2019-03-07383MILWAUKEE Admirals2VICTORIAVILLE Tigres3LXBoxScore
45 - 2019-03-08396MILWAUKEE Admirals0VICTORIAVILLE Tigres3LBoxScore
47 - 2019-03-10409MILWAUKEE Admirals1SYRACUSE Crunch5LBoxScore
48 - 2019-03-11416LEHIGH VALLEY Phantoms5MILWAUKEE Admirals2LBoxScore
49 - 2019-03-12433MILWAUKEE Admirals1ROCKFORD IceHogs3LBoxScore
50 - 2019-03-13442WILKIES-BARRIE Penguins1MILWAUKEE Admirals5WBoxScore
51 - 2019-03-14456MILWAUKEE Admirals6MANITOBA Moose0WBoxScore
53 - 2019-03-16467HOLLYWOOD Oscar0MILWAUKEE Admirals7WBoxScore
55 - 2019-03-18487MILWAUKEE Admirals0BELLEVILLE Senators1LBoxScore
56 - 2019-03-19494COLORADO Eagles1MILWAUKEE Admirals3WBoxScore
57 - 2019-03-20509IOWA Wild2MILWAUKEE Admirals6WBoxScore
59 - 2019-03-22524MILWAUKEE Admirals5IOWA Wild2WBoxScore
60 - 2019-03-23535VICTORIAVILLE Tigres4MILWAUKEE Admirals5WBoxScore
62 - 2019-03-25551MILWAUKEE Admirals2IOWA Wild1WXBoxScore
63 - 2019-03-26563MILWAUKEE Admirals10BROOKLYN Wolfpack1WBoxScore
64 - 2019-03-27572MANITOBA Moose0MILWAUKEE Admirals10WBoxScore
66 - 2019-03-29590SYRACUSE Crunch3MILWAUKEE Admirals2LXXBoxScore
67 - 2019-03-30600MILWAUKEE Admirals2HERSEY Bears1WXBoxScore
69 - 2019-04-01617BELLEVILLE Senators0MILWAUKEE Admirals2WBoxScore
70 - 2019-04-02629BROOKLYN Wolfpack1MILWAUKEE Admirals11WBoxScore
71 - 2019-04-03640MILWAUKEE Admirals2ROCKFORD IceHogs1WBoxScore
73 - 2019-04-05648MILWAUKEE Admirals4WILKIES-BARRIE Penguins0WBoxScore
74 - 2019-04-06667CHICAGO Wolves4MILWAUKEE Admirals2LBoxScore
76 - 2019-04-08680ROCKFORD IceHogs4MILWAUKEE Admirals2LBoxScore
78 - 2019-04-10696MILWAUKEE Admirals4COLORADO Eagles2WBoxScore
79 - 2019-04-11706MILWAUKEE Admirals8BRIDGEPORT Sound Tigers0WBoxScore
80 - 2019-04-12719MONT-LAURIER Sommet0MILWAUKEE Admirals4WBoxScore
82 - 2019-04-14737HERSEY Bears2MILWAUKEE Admirals1LBoxScore
83 - 2019-04-15748MILWAUKEE Admirals4MANITOBA Moose0WBoxScore
84 - 2019-04-16757MILWAUKEE Admirals0STOCKTON Flames1LBoxScore
85 - 2019-04-17773TORONTO Marlies0MILWAUKEE Admirals1WBoxScore
87 - 2019-04-19789LAVAL Rockets3MILWAUKEE Admirals4WXXBoxScore
89 - 2019-04-21805MILWAUKEE Admirals2CLEVELAND Monsters1WBoxScore
90 - 2019-04-22813COLORADO Eagles2MILWAUKEE Admirals3WXBoxScore
92 - 2019-04-24824MILWAUKEE Admirals2PROVIDENCE Bruins3LBoxScore
93 - 2019-04-25833MILWAUKEE Admirals4HOLLYWOOD Oscar0WBoxScore
94 - 2019-04-26847SAN DIEGO Gulls1MILWAUKEE Admirals0LBoxScore
95 - 2019-04-27857MILWAUKEE Admirals4TUSCON Roadrunners3WBoxScore
96 - 2019-04-28867MILWAUKEE Admirals0COLORADO Eagles2LBoxScore
98 - 2019-04-30882CLEVELAND Monsters1MILWAUKEE Admirals2WXXBoxScore
100 - 2019-05-02902LAVAL Rockets1MILWAUKEE Admirals3WBoxScore
101 - 2019-05-03913MILWAUKEE Admirals7CORNWALL Aces1WBoxScore
103 - 2019-05-05926PROVIDENCE Bruins3MILWAUKEE Admirals2LBoxScore
104 - 2019-05-06939MILWAUKEE Admirals3SAN DIEGO Gulls1WBoxScore
106 - 2019-05-08951SAN DIEGO Gulls1MILWAUKEE Admirals2WBoxScore
107 - 2019-05-09964MILWAUKEE Admirals3SAN DIEGO Gulls2WBoxScore
108 - 2019-05-10975MILWAUKEE Admirals3LEHIGH VALLEY Phantoms2WBoxScore
109 - 2019-05-11987CORNWALL Aces2MILWAUKEE Admirals9WBoxScore
111 - 2019-05-13996MILWAUKEE Admirals1LAVAL Rockets2LBoxScore
Trade Deadline --- Trades can’t be done after this day is simulated!
112 - 2019-05-141012TUSCON Roadrunners1MILWAUKEE Admirals3WBoxScore
114 - 2019-05-161026MILWAUKEE Admirals3LAVAL Rockets1WBoxScore
116 - 2019-05-181039STOCKTON Flames-MILWAUKEE Admirals-
118 - 2019-05-201060PROVIDENCE Bruins-MILWAUKEE Admirals-



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
2 0 - 0.00% 0$0$3000100

Expenses
Year To Date ExpensesPlayers Total SalariesPlayers Total Average SalariesCoaches Salaries
2,294,253$ 1,817,500$ 1,817,500$ 0$
Salary Cap Per DaysSalary Cap To DatePlayers In Salary CapPlayers Out of Salary Cap
0$ 1,676,719$ 0 0

Estimate
Estimated Season RevenueRemaining Season DaysExpenses Per DaysEstimated Season Expenses
0$ 6 20,562$ 123,372$




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
Regular Season
287846903200133704-571394310220073342-269390380100060362-30281332643970059422931663567542549565631776149852100.00%7357.14%038096139.54%591231925.49%398147926.91%7094473216494757258
30822790010062812-750412390000042401-359410400010020411-391462124186002625110952297334317470242023207473825.26%12558.33%020268029.71%381237716.03%248144117.21%4212523839491661161
31824432012213131851284121150112116089714123170010015396578831357989211515576794280290097392017189760460816282184621.10%2633885.55%91480260956.73%1062225947.01%705126955.56%224216541708534990517
328045260332126815611239231001221144756941221602100124814310426848875601210883728260582584291536195657653213872254720.89%2353883.83%11584267559.21%1140234748.57%633113755.67%222116541657515945492
Total Regular Season32295206078427761857-1081160509504542419907-4881624511103300357950-5932047761455223112734822619115802225892691270162174404979117447475029518.92%5178483.75%103646692552.65%3174930234.12%1984532637.25%5594400810421203433551431