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Moose
GP: 82 | W: 47 | L: 29 | OTL: 6 | P: 100
GF: 351 | GA: 312 | PP%: 24.48% | PK%: 77.57%
GM : Fabian Zimmermann | Morale : 63 | Team Overall : 57
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Game Center
Reign
39-33-10, 88pts
5
FINAL
2 Moose
47-29-6, 100pts
Team Stats
W4StreakW1
19-15-7Home Record22-15-4
20-18-3Away Record25-14-2
8-1-1Last 10 Games6-4-0
4.13Goals Per Game4.28
4.20Goals Against Per Game3.80
23.48%Power Play Percentage24.48%
77.96%Penalty Kill Percentage77.57%
Canucks
41-33-8, 90pts
3
FINAL
4 Moose
47-29-6, 100pts
Team Stats
L3StreakW1
21-14-6Home Record22-15-4
20-19-2Away Record25-14-2
4-4-2Last 10 Games6-4-0
3.94Goals Per Game4.28
3.95Goals Against Per Game3.80
20.70%Power Play Percentage24.48%
76.09%Penalty Kill Percentage77.57%
Team Leaders
Nick SeelerGoals
Nick Seeler
1
Nick SeelerAssists
Nick Seeler
2
Nick SeelerPoints
Nick Seeler
3
Nick SeelerPlus/Minus
Nick Seeler
0
Collin DeliaWins
Collin Delia
20
Save Percentage
Patrik Rybar
0.884

Team Stats
Goals For
351
4.28 GFG
Shots For
3049
37.18 Avg
Power Play Percentage
24.5%
83 GF
Offensive Zone Start
41.6%
Goals Against
312
3.80 GAA
Shots Against
2509
30.60 Avg
Penalty Kill Percentage
77.6%
85 GA
Defensive Zone Start
37.4%
Team Info

General ManagerFabian Zimmermann
CoachTodd Reirden
DivisionCentral
ConferenceWestern
Captain
Assistant #1Daniel Oregan
Assistant #2


Arena Info

Capacity1,800
Attendance1,502
Season Tickets360


Roster Info

Pro Team42
Farm Team20
Contract Limit62 / 75
Prospects22


Team History

This Season47-29-6 (100PTS)
History358-237-45 (0.559%)
Playoff Appearances4
Playoff Record (W-L)10-16
Stanley Cup0


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 Average
1Matt Moulson36XX100.007874876874545265506258745576776560640391950,000$
2Michael Sgarbossa13X100.006942997967555867866572602545456971630301750,000$
3John-Jason Peterka (R)0XXX100.007469877669636367806466656344446861620203894,167$
4Gerald Mayhew0XX100.008043866258646968345379642557577074620291950,000$
5Liam Foudy (R)0XXX100.0078729280726161627860606657464665616102221,269,167$
6Michael Mersch91X100.007978826378646465506066676344446679610301750,000$
7Remi Elie0X100.008078856578636461505364676147476581600271750,000$
8Glenn Gawdin29XX100.007071676471707264806460625744446356600251762,500$
9Connor Dewar96XX100.007963806566566962566057742547476474600231925,000$
10Jeff Malott0X100.0076777562777174625056656562444465806002611,000,000$
11Daniel Oregan (A)0XX100.0072658868657073627859616358444464815902811,000,000$
12Simon Holmstrom (R)16XX100.008175956375636461506157675444446381590211925,000$
13Brad Hunt77X100.0063419172626759662562486725656561426203411,000,000$
14Dillon Heatherington3X100.008599786386595755253947772544445827610272762,500$
15Colton White6X100.007343906669625955255147712557575970600252762,500$
16Michael Vukojevic (R)0X100.008079816679576050254341643944445456580212925,000$
17Keaton Middleton4X100.008191596191555847253840633844445172570241750,000$
18Joseph Cecconi94X100.007980776380636848254139633744445347570251750,000$
Scratches
1Anthony Richard90XX100.006859896359656761765660635755556239590261750,000$
2Gage Quinney0XX100.0078729262725352627856626759515163205902721,000,000$
3Akil Thomas (R)0XX100.007264896764586054684658615544445920550222910,833$
4Spencer Smallman25X100.007272736372565756505354615144445820550262762,500$
5D.J. Busdeker (R)0X100.006965775965606254505747594544445520540231950,000$
6Andre Lee (R)0XX100.007879756679373549614844624244445220530222925,000$
7Thomas Caron (R)0X100.007779726379464648504446624444445220530221750,000$
8Isaac Johnson0X100.007970996070383553505447644544445620530231750,000$
9Bokondji Imama0X100.006683266683657049504647564544445120530261750,000$
10Maxim Marushev0X100.007266876066525352655248604644445520530231810,000$
11Tyler Boland0XX100.007970996370373547594444624244445220510261750,000$
12Ian McKinnon0X100.005872256272393944553844514244444520470241800,000$
13Cavan Fitzgerald0X100.007771916571586147253641643954545321570261750,000$
14Simon Lundmark (R)0X100.008075917075515247253841633944445320560223925,000$
15Logan Day0X100.007975886375616547253841623944445319560281800,000$
16Brayden Pachal98X100.007073636673667146253740583844445120550231750,000$
17Wyatte Wylie (R)0X100.007570886370596248253941613944445220550232875,000$
18Griffin Luce0X100.007780716580525544253439613744444920550241950,000$
19Jeremy Groleau (R)92X100.007873886173495046253740623844445120540231750,000$
TEAM AVERAGE100.00757081657357595548505264444747584257
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
1Collin Delia100.00524658805555505655543046465346550
2Adam Scheel (R)100.00504455785250515552523044445126530
Scratches
1Ben Meisner100.00505050505050505050505050505021490
2Patrik Rybar100.00505050505050505050505050505019490
3Kai Edmonds (R)100.00505050505050505050505050505015490
TEAM AVERAGE100.0050485362515150525151424848512551
Coaches Name PH DF OF PD EX LD PO CNT Age Contract Salary
Todd Reirden76798474746976USA501850,000$


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
1Michael MerschMoose (WPG)LW8244601042461151441112987621714.77%15138016.83926357927920251155050.65%23100041.51000211227
2Gerald MayhewMoose (WPG)C/RW82465298295201321033299824613.98%6129415.7815163193280000189137.29%11800001.5100000396
3Michael SgarbossaMoose (WPG)C7433629511100271852559219112.94%5115915.67141630672180004212259.12%150700011.6401000675
4Daniel OreganMoose (WPG)C/RW8223446712220471691946214411.86%12106513.0026824880000262059.05%81800001.2600000161
5Brad HuntMoose (WPG)D72755622128051777630589.21%82159222.1141519432200332240000.00%000000.7800000401
6Jeff MalottMoose (WPG)LW82273259-16315111792084916312.98%8108913.286121854250000020150.00%7200001.0800002134
7John-Jason PeterkaMoose (WPG)C/LW/RW68193756-912063153204601539.31%7109816.1681523562160111964156.16%131600001.0200000213
8Liam FoudyMoose (WPG)C/LW/RW75173350322063202187561579.09%16128217.09611175118711272591154.87%135600000.7800000221
9Colton WhiteMoose (WPG)D73143650132408693100406514.00%100157421.5761218582270221252310.00%000100.6300000342
10Connor DewarMoose (WPG)C/LW8224244845001131302183812411.01%34118814.5000014404122386147.20%32200000.8100000023
11Remi ElieMoose (WPG)LW82192241124610105101194561509.79%13104712.7801112112101032146.77%6200000.7800110214
12Simon HolmstromMoose (WPG)LW/RW8213274044406477150381078.67%1197211.860000801131032145.59%6800000.8200000022
13Glenn GawdinMoose (WPG)C/RW65142539322074821404510910.00%794014.47281036198000001051.56%6400000.8300000221
14Matt MoulsonMoose (WPG)LW/RW63122537126066100141411188.51%2199615.81123127211281932046.15%10400000.7401000201
15Anthony RichardMoose (WPG)C/LW49102131880186880154012.50%558311.904610281160001361055.02%55800011.0600000122
16Joseph CecconiMoose (WPG)D70415191383151453829122313.79%66121917.421129700000102010.00%000000.3100111002
17Michael VukojevicMoose (WPG)D60215171469590314712314.26%59115619.2701111890000175010.00%000000.2900001011
18Keaton MiddletonMoose (WPG)D73114151013030209344224192.38%68128217.560005270110103000.00%000000.2300123000
19Dillon HeatheringtonMoose (WPG)D393111410595112323916367.69%5980020.53044121190001108200.00%000000.3500100101
20Logan DayMoose (WPG)D29371091203971671218.75%2548916.89101437000038100.00%000000.4100000010
21Simon LundmarkMoose (WPG)D28549380475173629.41%3656020.01202852101192000.00%000000.3200000010
22Cavan FitzgeraldMoose (WPG)D47628-2260482132111718.75%6380917.231122330000121210.00%000000.2000000001
23Gage QuinneyMoose (WPG)C/LW3125718022534213174.76%42909.370002140112612055.79%28500000.4800000000
24Akil ThomasMoose (WPG)C/RW62130403971328.57%06010.1300000000060042.25%7100000.9900000001
25Nick SeelerJetsD21230205021150.00%23417.161121200005000.00%000001.7500000010
26Richard PanikJetsLW/RW2000040642430.00%02010.3800000000020038.46%1300000.0000000000
Team Total or Average150035163198219389595189019643049900221011.51%7242399115.99831542376572820101222592515471355.25%696500160.8202468424749
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
1Collin DeliaMoose (WPG)3820840.8813.661916801179840000.33333120000
2Adam ScheelMoose (WPG)39181500.8753.8119996212710120000.00003633301
3Patrik RybarMoose (WPG)158310.8843.4567921393370000.0000916100
4Kai EdmondsMoose (WPG)81310.8464.6335000271750010.0000613000
Team Total or Average100472960.8763.76494516331025080010.33338282401


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 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 ScheelMoose (WPG)G2301.05.1999Yes192 Lbs6 ft3NoNoNo1Pro & Farm950,000$6,209$0$0$NoLink / NHL Link
Akil ThomasMoose (WPG)C/RW2201.01.2000Yes171 Lbs6 ft0NoNoNo2Pro & Farm910,833$5,953$0$0$No910,833$Link / NHL Link
Andre LeeMoose (WPG)C/LW2226.07.2000Yes205 Lbs6 ft5NoNoNo2Pro & Farm925,000$6,046$0$0$No925,000$
Anthony RichardMoose (WPG)C/LW2619.12.1996No163 Lbs5 ft10NoNoNo1Pro & Farm750,000$4,902$0$0$NoLink / NHL Link
Ben MeisnerMoose (WPG)G3220.06.1990No176 Lbs5 ft11NoNoNo1Pro & Farm750,000$4,902$0$0$NoLink
Bokondji ImamaMoose (WPG)LW2602.08.1996No221 Lbs6 ft1NoNoNo1Pro & Farm750,000$4,902$0$0$NoLink / NHL Link
Brad Hunt (1 Way Contract)Moose (WPG)D3423.08.1988No177 Lbs5 ft9NoNoYes1Pro & Farm1,000,000$6,536$1,000,000$6,536$NoLink / NHL Link
Brayden PachalMoose (WPG)D2323.08.1999No201 Lbs6 ft0NoNoNo1Pro & Farm750,000$4,902$0$0$NoLink / NHL Link
Cavan FitzgeraldMoose (WPG)D2622.08.1996No190 Lbs6 ft1NoNoNo1Pro & Farm750,000$4,902$0$0$NoLink / NHL Link
Collin DeliaMoose (WPG)G2819.06.1994No208 Lbs6 ft2NoNoNo2Pro & Farm850,000$5,556$0$0$No850,000$Link / NHL Link
Colton WhiteMoose (WPG)D2503.05.1997No185 Lbs6 ft1NoNoNo2Pro & Farm762,500$4,984$0$0$No762,500$Link / NHL Link
Connor DewarMoose (WPG)C/LW2326.06.1999No182 Lbs5 ft10NoNoNo1Pro & Farm925,000$6,046$0$0$NoLink / NHL Link
D.J. BusdekerMoose (WPG)RW2325.09.1999Yes181 Lbs5 ft10NoNoNo1Pro & Farm950,000$6,209$0$0$NoLink / NHL Link
Daniel OreganMoose (WPG)C/RW2830.01.1994No180 Lbs5 ft10NoNoNo1Pro & Farm1,000,000$6,536$0$0$NoLink / NHL Link
Dillon HeatheringtonMoose (WPG)D2708.05.1995No220 Lbs6 ft4NoNoNo2Pro & Farm762,500$4,984$0$0$No762,500$Link / NHL Link
Gage QuinneyMoose (WPG)C/LW2729.07.1995No200 Lbs5 ft11NoNoNo2Pro & Farm1,000,000$6,536$0$0$No1,000,000$Link / NHL Link
Gerald MayhewMoose (WPG)C/RW2931.12.1992No161 Lbs5 ft9NoNoNo1Pro & Farm950,000$6,209$0$0$NoLink / NHL Link
Glenn Gawdin (1 Way Contract)Moose (WPG)C/RW2525.03.1997No191 Lbs6 ft1NoNoYes1Pro & Farm762,500$4,984$762,500$4,984$NoLink / NHL Link
Griffin LuceMoose (WPG)D2410.03.1998No216 Lbs6 ft3NoNoNo1Pro & Farm950,000$6,209$0$0$NoLink / NHL Link
Ian McKinnonMoose (WPG)C2405.03.1998No194 Lbs6 ft2NoNoNo1Pro & Farm800,000$5,229$0$0$NoLink / NHL Link
Isaac JohnsonMoose (WPG)LW2324.01.1999No186 Lbs6 ft2YesNoNo1Pro & Farm750,000$4,902$0$0$NoLink
Jeff MalottMoose (WPG)LW2607.08.1996No201 Lbs6 ft4NoNoNo1Pro & Farm1,000,000$6,536$0$0$NoLink / NHL Link
Jeremy GroleauMoose (WPG)D2325.10.1999Yes193 Lbs6 ft3NoNoNo1Pro & Farm750,000$4,902$0$0$NoLink / NHL Link
John-Jason PeterkaMoose (WPG)C/LW/RW2014.01.2002Yes192 Lbs5 ft11NoNoNo3Pro & Farm894,167$5,844$0$0$No894,167$894,167$
Joseph CecconiMoose (WPG)D2523.05.1997No215 Lbs6 ft3NoNoNo1Pro & Farm750,000$4,902$0$0$NoLink / NHL Link
Kai EdmondsMoose (WPG)G2227.07.2000Yes163 Lbs6 ft2NoNoNo1Pro & Farm950,000$6,209$0$0$NoLink / NHL Link
Keaton MiddletonMoose (WPG)D2410.02.1998No240 Lbs6 ft6NoNoNo1Pro & Farm750,000$4,902$0$0$NoLink / NHL Link
Liam FoudyMoose (WPG)C/LW/RW2204.02.2000Yes193 Lbs6 ft2NoNoNo2Pro & Farm1,269,167$8,295$0$0$No1,269,167$Link / NHL Link
Logan DayMoose (WPG)D2819.09.1994No209 Lbs6 ft1NoNoNo1Pro & Farm800,000$5,229$0$0$NoLink / NHL Link
Matt MoulsonMoose (WPG)LW/RW3901.11.1983No203 Lbs6 ft1NoNoNo1Pro & Farm950,000$6,209$0$0$NoLink / NHL Link
Maxim MarushevMoose (WPG)C2301.01.1999No176 Lbs6 ft1NoNoNo1Pro & Farm810,000$5,294$0$0$NoLink
Michael MerschMoose (WPG)LW3002.10.1992No210 Lbs6 ft2NoNoNo1Pro & Farm750,000$4,902$0$0$NoLink / NHL Link
Michael SgarbossaMoose (WPG)C3025.07.1992No182 Lbs6 ft0NoNoNo1Pro & Farm750,000$4,902$0$0$NoLink / NHL Link
Michael VukojevicMoose (WPG)D2108.06.2001Yes212 Lbs6 ft3NoNoNo2Pro & Farm925,000$6,046$0$0$No925,000$Link / NHL Link
Patrik RybarMoose (WPG)G2909.11.1993No190 Lbs6 ft3NoNoNo1Farm Only750,000$4,902$0$0$NoLink
Remi ElieMoose (WPG)LW2716.04.1995No215 Lbs6 ft1NoNoNo1Pro & Farm750,000$4,902$0$0$NoLink / NHL Link
Simon HolmstromMoose (WPG)LW/RW2124.05.2001Yes202 Lbs6 ft2NoNoNo1Pro & Farm925,000$6,046$0$0$NoLink / NHL Link
Simon LundmarkMoose (WPG)D2208.10.2000Yes201 Lbs6 ft2NoNoNo3Pro & Farm925,000$6,046$0$0$No925,000$925,000$
Spencer SmallmanMoose (WPG)RW2608.09.1996No198 Lbs6 ft1NoNoNo2Pro & Farm762,500$4,984$0$0$No762,500$Link / NHL Link
Thomas CaronMoose (WPG)LW2229.08.2000Yes216 Lbs6 ft2YesNoNo1Pro & Farm750,000$4,902$0$0$NoLink
Tyler BolandMoose (WPG)C/RW2612.09.1996No194 Lbs6 ft0YesNoNo1Pro & Farm750,000$4,902$0$0$NoLink
Wyatte WylieMoose (WPG)D2302.11.1999Yes190 Lbs6 ft0NoNoNo2Pro & Farm875,000$5,719$0$0$No875,000$Link / NHL Link
Total PlayersAverage AgeAverage WeightAverage HeightAverage ContractAverage Year 1 Salary
4225.45195 Lbs6 ft11.33853,194$



5 vs 5 Forward
Line #Left WingCenterRight WingTime %PHYDFOF
1Michael MerschMichael SgarbossaGerald Mayhew27023
2Connor DewarJohn-Jason PeterkaMatt Moulson25023
3Remi ElieLiam FoudyGlenn Gawdin25122
4Jeff MalottDaniel OreganSimon Holmstrom23122
5 vs 5 Defense
Line #DefenseDefenseTime %PHYDFOF
1Dillon HeatheringtonBrad Hunt33032
2Joseph CecconiColton White30131
3Keaton MiddletonMichael Vukojevic27221
4Keaton MiddletonMichael Vukojevic10230
Power Play Forward
Line #Left WingCenterRight WingTime %PHYDFOF
1Michael MerschMichael SgarbossaGerald Mayhew50014
2Jeff MalottJohn-Jason PeterkaGlenn Gawdin50014
Power Play Defense
Line #DefenseDefenseTime %PHYDFOF
1Dillon HeatheringtonBrad Hunt50113
2Colton WhiteMatt Moulson50113
Penalty Kill 4 Players Forward
Line #CenterWingTime %PHYDFOF
1Liam FoudyMatt Moulson50131
2Connor DewarRemi Elie50131
Penalty Kill 4 Players Defense
Line #DefenseDefenseTime %PHYDFOF
1Brad HuntColton White50140
2Michael VukojevicDillon Heatherington50140
Penalty Kill 3 Players
Line #WingTime %PHYDFOFDefenseDefenseTime %PHYDFOF
1Connor Dewar50050Brad HuntColton White50050
2Matt Moulson50050Michael VukojevicDillon Heatherington50050
4 vs 4 Forward
Line #CenterWingTime %PHYDFOF
1Michael SgarbossaGerald Mayhew50023
2John-Jason PeterkaMatt Moulson50023
4 vs 4 Defense
Line #DefenseDefenseTime %PHYDFOF
1Brad HuntDillon Heatherington50131
2Colton WhiteJoseph Cecconi50131
Last Minutes Offensive
Left WingCenterRight WingDefenseDefense
John-Jason PeterkaMichael SgarbossaGerald MayhewBrad HuntColton White
Last Minutes Defensive
Left WingCenterRight WingDefenseDefense
Matt MoulsonConnor DewarSimon HolmstromBrad HuntDillon Heatherington
Extra Forwards
Normal PowerPlayPenalty Kill
Michael Sgarbossa, Matt Moulson, John-Jason PeterkaGerald Mayhew, Michael SgarbossaConnor Dewar
Extra Defensemen
Normal PowerPlayPenalty Kill
Colton White, Keaton Middleton, Michael VukojevicColton WhiteColton White, Keaton Middleton
Penalty Shots
John-Jason Peterka, Michael Mersch, Michael Sgarbossa, Jeff Malott, Gerald Mayhew
Goalie
#1 : Adam Scheel, #2 : Collin Delia
Custom OT Lines Forwards
Matt Moulson, Michael Sgarbossa, John-Jason Peterka, Gerald Mayhew, Michael Mersch, Connor Dewar, Connor Dewar, Jeff Malott, Glenn Gawdin, Daniel Oregan, Remi Elie
Custom OT Lines Defensemen
Brad Hunt, Dillon Heatherington, Colton White, Joseph Cecconi, Keaton Middleton


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
1Admirals42002000191362000200010822200000095481.00019365500142110918159102910529482213039468518316.67%19289.47%11598288555.39%1439259455.47%811145155.89%1980134818876291081543
2Americans20200000813-51010000068-21010000025-300.0008142200142110918961029105294822812232548225.00%16662.50%11598288555.39%1439259455.47%811145155.89%1980134818876291081543
3Barracuda33000000166101100000052322000000114761.000162844001421109188610291052948228630326913430.77%160100.00%11598288555.39%1439259455.47%811145155.89%1980134818876291081543
4Bears21100000111011010000056-11100000064220.500112031001421109185710291052948228522244111327.27%12375.00%11598288555.39%1439259455.47%811145155.89%1980134818876291081543
5Bruins20101000610-41010000016-51000100054120.500612180014211091863102910529482269254248500.00%8362.50%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
6Canucks321000001211121100000101001100000021140.66712233500142110918105102910529482210427425216637.50%15286.67%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
7Checkers2200000015312110000006241100000091841.00015284300142110918781029105294822471522433133.33%10280.00%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
8Comets20200000711-41010000035-21010000046-200.000713201014211091858102910529482276142447800.00%12283.33%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
9Condors3300000017981100000074322000000105561.0001731480014211091813110291052948227326268712433.33%11190.91%11598288555.39%1439259455.47%811145155.89%1980134818876291081543
10Crunch211000005501010000014-31100000041320.5005101500142110918851029105294822311111459111.11%3166.67%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
11Eagles4310000016124220000007342110000099060.75016284400142110918153102910529482210231369221314.29%16287.50%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
12Firebirds21100000761110000006151010000015-420.500714210014211091810110291052948226013162914321.43%8362.50%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
13Griffins21100000880110000006421010000024-220.50081523001421109187410291052948225717305010440.00%10280.00%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
14Gulls210010001293110000006421000100065141.000122032001421109188710291052948226217284312650.00%14564.29%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
15Ice Hogs401021001718-120100100710-320002000108250.62517304700142110918159102910529482210234281042129.52%14564.29%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
16Islanders210001009901000010045-11100000054130.7509152400142110918100102910529482243158486233.33%4325.00%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
17Marlies2110000011101110000007431010000046-220.50011182900142110918851029105294822662314418225.00%70100.00%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
18Monsters21000100550110000002111000010034-130.750581300142110918661029105294822621824517228.57%12191.67%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
19Penguins20200000611-51010000026-41010000045-100.000612180014211091866102910529482267182161500.00%8275.00%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
20Phantoms21000100111101000010056-11100000065130.75011203100142110918601029105294822762023549555.56%9455.56%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
21Reign30201000914-52020000039-61000100065120.33391524001421109189410291052948221093549737228.57%15660.00%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
22Roadrunners4210000114131210000015412110000099050.6251428420114211091812210291052948221332859968562.50%27581.48%11598288555.39%1439259455.47%811145155.89%1980134818876291081543
23Rocket2110000048-41010000005-51100000043120.500471100142110918651029105294822601826434125.00%13284.62%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
24Senators211000008531010000024-21100000061520.50081523001421109188310291052948224410154212325.00%4175.00%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
25Silver Knights32100000161151100000053221100000118340.667162743001421109189810291052948221003022499222.22%11372.73%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
26Stars413000001520-52110000089-120200000711-420.250152641001421109181251029105294822126275510716531.25%22863.64%11598288555.39%1439259455.47%811145155.89%1980134818876291081543
27Thunderbirds4300100017982200000010642100100073481.00017314801142110918168102910529482212131608420420.00%24483.33%11598288555.39%1439259455.47%811145155.89%1980134818876291081543
28Wild42200000171432110000065121100000119240.500173148011421109181681029105294822124393310419421.05%11281.82%21598288555.39%1439259455.47%811145155.89%1980134818876291081543
29Wolfpack211000001082110000005231010000056-120.50010162600142110918851029105294822602216489222.22%8275.00%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
30Wolves21100000981110000007521010000023-120.5009162500142110918731029105294822562222406116.67%10370.00%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
31Wranglers31100100141222110000010731000010045-130.50014243800142110918991029105294822972525601317.69%100100.00%01598288555.39%1439259455.47%811145155.89%1980134818876291081543
Total823929085013513123941201502301167158941191406200184154301000.6103516319821314211091830491029105294822250972491118903398324.48%3798577.57%101598288555.39%1439259455.47%811145155.89%1980134818876291081543
_Since Last GM Reset823929085013513123941201502301167158941191406200184154301000.6103516319821314211091830491029105294822250972491118903398324.48%3798577.57%101598288555.39%1439259455.47%811145155.89%1980134818876291081543
_Vs Conference46221506201202172302312702101101851623108041001018714590.6412023645660214211091816811029105294822141940951310481945226.80%2134777.93%71598288555.39%1439259455.47%811145155.89%1980134818876291081543
_Vs Division28990410011599161454021005345814450200062548270.48211521032503142110918105410291052948228382293176721232621.14%1332878.95%61598288555.39%1439259455.47%811145155.89%1980134818876291081543

Total For Players
Games PlayedPointsStreakGoalsAssistsPointsShots ForShots AgainstShots BlockedPenalty MinutesHitsEmpty Net GoalsShutouts
82100W135163198230492509724911189013
All Games
GPWLOTWOTL SOWSOLGFGA
8239298501351312
Home Games
GPWLOTWOTL SOWSOLGFGA
4120152301167158
Visitor Games
GPWLOTWOTL SOWSOLGFGA
4119146200184154
Last 10 Games
WLOTWOTL SOWSOL
640000
Power Play AttempsPower Play GoalsPower Play %Penalty Kill AttempsPenalty Kill Goals AgainstPenalty Kill %Penalty Kill Goals For
3398324.48%3798577.57%10
Shots 1 PeriodShots 2 PeriodShots 3 PeriodShots 4+ PeriodGoals 1 PeriodGoals 2 PeriodGoals 3 PeriodGoals 4+ Period
1029105294822142110918
Face Offs
Won Offensive ZoneTotal OffensiveWon Offensive %Won Defensif ZoneTotal DefensiveWon Defensive %Won Neutral ZoneTotal NeutralWon Neutral %
1598288555.39%1439259455.47%811145155.89%
Puck Time
In Offensive ZoneControl In Offensive ZoneIn Defensive ZoneControl In Defensive ZoneIn Neutral ZoneControl In Neutral Zone
1980134818876291081543


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 - 2022-12-169Eagles1Moose4WBoxScore
2 - 2022-12-1722Ice Hogs4Moose2LBoxScore
4 - 2022-12-1939Thunderbirds2Moose4WBoxScore
6 - 2022-12-2156Roadrunners4Moose3LXXBoxScore
8 - 2022-12-2375Moose5Admirals2WBoxScore
9 - 2022-12-2487Moose2Stars5LBoxScore
11 - 2022-12-26103Moose7Wild4WBoxScore
13 - 2022-12-28119Wild5Moose4LBoxScore
15 - 2022-12-30135Stars5Moose3LBoxScore
17 - 2023-01-01151Admirals5Moose6WXBoxScore
18 - 2023-01-02168Moose4Roadrunners2WBoxScore
20 - 2023-01-04183Moose4Thunderbirds3WXBoxScore
22 - 2023-01-06197Eagles2Moose3WBoxScore
24 - 2023-01-08214Ice Hogs6Moose5LXBoxScore
25 - 2023-01-09229Moose3Eagles5LBoxScore
27 - 2023-01-11246Moose4Ice Hogs3WXBoxScore
29 - 2023-01-13263Wild0Moose2WBoxScore
31 - 2023-01-15279Moose3Thunderbirds0WBoxScore
32 - 2023-01-16296Moose5Roadrunners7LBoxScore
34 - 2023-01-18311Admirals3Moose4WXBoxScore
36 - 2023-01-20327Stars4Moose5WBoxScore
38 - 2023-01-22342Moose6Ice Hogs5WXBoxScore
40 - 2023-01-24357Moose6Eagles4WBoxScore
41 - 2023-01-25375Thunderbirds4Moose6WBoxScore
43 - 2023-01-27392Roadrunners0Moose2WBoxScore
45 - 2023-01-29407Moose4Admirals3WBoxScore
46 - 2023-01-30423Moose5Stars6LBoxScore
48 - 2023-02-01439Moose4Wild5LBoxScore
50 - 2023-02-03457Crunch4Moose1LBoxScore
51 - 2023-02-04467Marlies4Moose7WBoxScore
54 - 2023-02-07491Checkers2Moose6WBoxScore
56 - 2023-02-09508Penguins6Moose2LBoxScore
58 - 2023-02-11525Wolfpack2Moose5WBoxScore
60 - 2023-02-13542Griffins4Moose6WBoxScore
62 - 2023-02-15559Americans8Moose6LBoxScore
64 - 2023-02-17576Senators4Moose2LBoxScore
66 - 2023-02-19577Monsters1Moose2WBoxScore
68 - 2023-02-21594Bruins6Moose1LBoxScore
71 - 2023-02-24621Moose6Reign5WXBoxScore
73 - 2023-02-26637Moose7Silver Knights3WBoxScore
75 - 2023-02-28653Moose4Wranglers5LXBoxScore
76 - 2023-03-01669Moose6Gulls5WXBoxScore
78 - 2023-03-03685Moose2Canucks1WBoxScore
80 - 2023-03-05701Moose5Barracuda2WBoxScore
82 - 2023-03-07717Moose5Condors2WBoxScore
84 - 2023-03-09733Moose1Firebirds5LBoxScore
85 - 2023-03-10739Rocket5Moose0LBoxScore
87 - 2023-03-12756Bears6Moose5LBoxScore
89 - 2023-03-14773Comets5Moose3LBoxScore
91 - 2023-03-16790Phantoms6Moose5LXBoxScore
92 - 2023-03-17807Wolves5Moose7WBoxScore
94 - 2023-03-19824Islanders5Moose4LXBoxScore
96 - 2023-03-21841Moose4Crunch1WBoxScore
98 - 2023-03-23858Moose4Marlies6LBoxScore
99 - 2023-03-24875Moose9Checkers1WBoxScore
101 - 2023-03-26892Moose4Penguins5LBoxScore
103 - 2023-03-28909Moose5Wolfpack6LBoxScore
105 - 2023-03-30926Moose2Griffins4LBoxScore
107 - 2023-04-01943Moose2Americans5LBoxScore
108 - 2023-04-02960Moose6Senators1WBoxScore
111 - 2023-04-05973Reign4Moose1LBoxScore
113 - 2023-04-07989Silver Knights3Moose5WBoxScore
115 - 2023-04-091005Wranglers6Moose4LBoxScore
116 - 2023-04-101021Gulls4Moose6WBoxScore
118 - 2023-04-121037Canucks7Moose6LBoxScore
120 - 2023-04-141053Barracuda2Moose5WBoxScore
121 - 2023-04-151069Condors4Moose7WBoxScore
Trade Deadline --- Trades can’t be done after this day is simulated!
123 - 2023-04-171085Firebirds1Moose6WBoxScore
125 - 2023-04-191089Moose3Monsters4LXBoxScore
127 - 2023-04-211106Moose5Bruins4WXBoxScore
129 - 2023-04-231123Moose4Rocket3WBoxScore
130 - 2023-04-241140Moose6Bears4WBoxScore
132 - 2023-04-261157Moose4Comets6LBoxScore
134 - 2023-04-281174Moose6Phantoms5WBoxScore
136 - 2023-04-301191Moose2Wolves3LBoxScore
139 - 2023-05-031208Moose5Islanders4WBoxScore
141 - 2023-05-051219Moose6Barracuda2WBoxScore
143 - 2023-05-071236Moose4Silver Knights5LBoxScore
145 - 2023-05-091252Moose5Condors3WBoxScore
147 - 2023-05-111268Wranglers1Moose6WBoxScore
148 - 2023-05-121276Reign5Moose2LBoxScore
149 - 2023-05-131281Canucks3Moose4WBoxScore



Arena Capacity - Ticket Price Attendance - %
Level 1Level 2
Capacity1500300
Ticket Price4020
Attendance49,65511,914
Attendance PCT80.74%96.86%

Income
Home Games LeftAverage Attendance - %Average Income per GameYear to Date RevenueCapacityTeam Popularity
0 1502 - 83.43% 59,681$2,446,928$1800100

Expenses
Year To Date ExpensesPlayers Total SalariesPlayers Total Average SalariesCoaches Salaries
4,098,574$ 3,407,167$ 3,407,167$ 0$
Salary Cap Per DaysSalary Cap To DatePlayers In Salary CapPlayers Out of Salary Cap
22,269$ 3,203,516$ 0 0

Estimate
Estimated Season RevenueRemaining Season DaysExpenses Per DaysEstimated Season Expenses
0$ 1 27,825$ 27,825$




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
376273601282164177-1338171800030817473810180125283103-207616427644008049555117320521564621193856689016253996817.04%3736183.65%11201236950.70%1229249249.32%538107949.86%1791117417996121027514
47638240255219017515381515012329299-738239013209876229719033352316064546519880652636684186652191018793836817.75%3565883.71%21319250352.70%1278240653.12%578109552.79%1830122817765931002503
582323803414227242-1541171901202105107-241151902212122135-138022741564212085746423540816711808232066282119533587621.23%3797281.00%11435266453.87%1373267151.40%701125655.81%2010136618766211068539
68235320562227024822412212023111461113541132003311124137-1392270492762230887410124890847784830245474589618634088821.57%3516382.05%61263265847.52%1307279446.78%626130348.04%1989135019026251075548
782511807330320221994128801220169105644123100611015111635125320565885140118100922984010099191032240265194121113727720.70%4115985.64%61694298956.67%1540280354.94%751132656.64%2036140418616171060535
882323901523298312-14411818011121541431141142100411144169-25782985308283201199581285509659589152649759101520963176921.77%4449379.05%21631295355.23%1567287454.52%774139055.68%1885126019866421081526
98252210450035525010541261202100175118574126902400180132481173556349891201511039731850105410751035241267478919673879725.06%3285882.32%51683308854.50%1456273153.31%789139556.56%2031139318296201079553
10823929085013513123941201502301167158941191406200184154301003516319821314211091830491029105294822250972491118903398324.48%3798577.57%101598288555.39%1439259455.47%811145155.89%1980134818876291081543
Total Regular Season644306237031352114217519372383221631170101410810899151743221431200212111610861022647652175387660511030142784646559206361029691665955947185505302717315384296362621.13%302154981.83%33118242210953.48%111892136552.37%55681029554.08%155531052614921496384754266
Playoff
4734000001519-4422000001012-23120000057-261523380005631920825752205667617231516.13%35585.71%111222948.91%12824252.89%539058.89%159107174548643
6624000001317-43120000067-131200000710-341323360003462010716857182314817721523.81%21385.71%112423951.88%10221248.11%338638.37%156107138458242
7734000002025-5413000001115-432100000910-16203454000758248076631092183989162441022.73%33972.73%016026759.93%15124860.89%7111959.66%177124151539147
9624000002130-9312000001017-7312000001113-242140610006771970715957238496513626311.54%27581.48%011023147.62%12726148.66%6512054.17%14497148488040
Total Playoff261016000006991-221459000003751-141257000003240-8206912018900021222483803002472758431852786471222318.85%1162281.03%250696652.38%50896352.75%22241553.49%638435613201341173