Monsters

GP: 80 | W: 51 | L: 26 | OTL: 3 | P: 105
GF: 527 | GA: 428 | PP%: 64.91% | PK%: 50.99%
GM : Dave Langevin | Morale : 75 | Team Overall : 71
Next Games #805 vs Chiefs
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
1Adrian Kempe (R)X100.00806587898079857762767775706972035740
2Matt NietoXX100.00686495867980797450697889597576069740
3Charles Hudon (R)X100.00896889848180767556777374616871081730
4Nick Ritchie (R)X100.00947680849479807350737374647174081730
5Melker Karlsson (R)X100.00806692887979757070697185537475057730
6Brock McGinn (R)XX100.00896294868080857455707775717074049730
7Tommy WingelsXXX100.00927287848677787066677278547977080720
8Tyler Bertuzzi (R)X100.00806983837980517750807476566868067710
9Nikolay Goldobin (R)X100.00646096907978407450697868626870080690
10Vince Dunn (R)X99.00686794858183827250766768506871080710
11Yannick WeberX97.00786692808178496550656567507774081680
12Adam Clendening (R)X100.00686691857981356450656267506871068660
Scratches
1Joonas DonskoiXX66.76696391877980717850777878787375068740
2Joel WardX100.00737491819277546962687080538681046710
3Andrej SustrX100.00557491799078466750676667507678045670
TEAM AVERAGE97.4776679085837966725571727559737406671
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
1Curtis McElhinney100.0076657588747675757477767275080740
Scratches
TEAM AVERAGE100.007665758874767575747776727508074
Coaches Name PH DF OF PD EX LD PO CNT Age Contract Salary
Glen Gulutzan77827884756799CAN422950,000$


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
1Joonas DonskoiMonsters (FGI)LW/RW7712312224511362015010862521436219.68%57192725.04336497621523141811912262.20%627142990182.541212125612
2Vince DunnMonsters (FGI)D8036118154-341151008932612613811.04%105210526.3229487762169011101360066.67%352115021.4600021378
3Tommy WingelsMonsters (FGI)C/LW/RW8068851531526101429442111323916.15%52145018.1320375731102000037268.48%9019071072.11001106117
4Adam ClendeningMonsters (FGI)D8015779294935615820993887.18%84154919.37122941291060117106000.00%02879001.1900061051
5Joel WardMonsters (FGI)RW52384785248066432397313915.90%2592617.811117282579000005078.18%555332021.8400000523
6Yannick WeberMonsters (FGI)D80196079-1351512187176919510.80%87207125.90161632301680115135200.00%030129000.7600210044
7Charles HudonMonsters (FGI)LW80244165-105935113662177010511.06%29112314.0520221000001264.36%11142843001.1601115324
8Matt NietoMonsters (FGI)LW/RW293625611955124156589123.08%2069023.801392223700225515154.76%423635041.7712001552
9Nikolay GoldobinMonsters (FGI)RW80272350-12473543422166611112.50%3489611.2004406000004167.19%645837011.1201331125
10Melker KarlssonMonsters (FGI)C23142741-82410524759225223.73%1648621.157142111561012191066.79%5361915001.6901011223
11Nick RitchieMonsters (FGI)LW80201939-1249159949139509714.39%2592911.62112120002331054.55%662845100.8412102100
12Andrej SustrMonsters (FGI)D53829372513531369230248.70%45103019.45611171878000165100.00%02352000.7200610003
13Brock McGinnMonsters (FGI)LW/RW2515183332020472384195117.86%2046518.64941317500001211170.00%202414001.4202031030
14Tyler BertuzziMonsters (FGI)LW253811412025213112129.68%1537214.891231101012120069.23%26621000.5911000000
Team Total or Average844446699114523466250110178729901037160414.92%6141602618.99160256416312105356115370540965.46%34546177871341.43412152114514952
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
1Curtis McElhinneyMonsters (FGI)1312100.9332.137900028417207100.91712130131
Team Total or Average1312100.9332.137900028417207100.91712130131


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
Adam ClendeningMonsters (FGI)D261993-03-22 15:26:19Yes190 Lbs6 ft0YesNoNo1ELCPro & Farm650,000$0$0$No
Adrian KempeMonsters (FGI)LW221997-03-22 15:26:19Yes195 Lbs6 ft2NoNoNo2ELCPro & Farm894,000$0$0$No
Andrej SustrMonsters (FGI)D281991-03-23 03:26:19No225 Lbs6 ft8NoNoNo1UFAPro & Farm1,462,500$0$0$No
Brock McGinnMonsters (FGI)LW/RW241995-03-23 03:26:19Yes185 Lbs6 ft0NoNoNo2ELCPro & Farm925,000$0$0$No
Charles HudonMonsters (FGI)LW251994-06-23 17:43:45Yes196 Lbs5 ft10NoNoNo2ELCPro & Farm650,000$0$0$No
Curtis McElhinneyMonsters (FGI)G351984-03-22 09:26:20No200 Lbs6 ft3YesNoNo2UFAPro & Farm785,000$0$0$No
Joel WardMonsters (FGI)RW381981-03-22 15:26:19No226 Lbs6 ft1YesNoNo1UFAPro & Farm965,000$0$0$No
Joonas Donskoi (Out of Payroll)Monsters (FGI)LW/RW261993-03-22 15:26:19No190 Lbs6 ft0NoNoNo2ELCPro & Farm1,900,000$0$0$No
Matt NietoMonsters (FGI)LW/RW261993-03-22 15:26:19No190 Lbs5 ft11NoNoNo1ELCPro & Farm1,000,000$0$0$No
Melker KarlssonMonsters (FGI)C281991-03-23 03:26:19Yes180 Lbs6 ft0NoNoNo1UFAPro & Farm1,500,000$0$0$No
Nick RitchieMonsters (FGI)LW231996-03-22 09:26:19Yes232 Lbs6 ft2NoNoNo1ELCPro & Farm1,627,000$0$0$No
Nikolay GoldobinMonsters (FGI)RW231995-10-07 17:54:06Yes196 Lbs5 ft11NoNoNo2ELCPro & Farm1,137,500$0$0$No
Tommy WingelsMonsters (FGI)C/LW/RW301989-03-22 15:26:19No200 Lbs6 ft0YesNoNo1UFAPro & Farm575,000$0$0$No
Tyler BertuzziMonsters (FGI)LW241995-02-24 07:42:46Yes190 Lbs6 ft0NoNoNo1ELCPro & Farm925,000$0$0$No
Vince DunnMonsters (FGI)D221996-10-29 18:02:11Yes203 Lbs6 ft0NoNoNo1ELCPro & Farm653,333$0$0$No
Yannick WeberMonsters (FGI)D301989-03-22 15:26:19No193 Lbs5 ft11YesNoNo1UFAPro & Farm579,600$0$0$No
Total PlayersAverage AgeAverage WeightAverage HeightAverage ContractAverage Year 1 Salary
1626.88199 Lbs6 ft11.381,014,308$



5 vs 5 Forward
Line #Left WingCenterRight WingTime %PHYDFOF
140122
2Tommy Wingels30122
3Nick RitchieCharles HudonNikolay Goldobin20122
4Charles Hudon10122
5 vs 5 Defense
Line #DefenseDefenseTime %PHYDFOF
1Vince DunnYannick Weber40122
2Adam Clendening30122
320122
4Vince DunnYannick Weber10122
Power Play Forward
Line #Left WingCenterRight WingTime %PHYDFOF
160122
2Tommy Wingels40122
Power Play Defense
Line #DefenseDefenseTime %PHYDFOF
1Vince DunnYannick Weber60122
2Adam Clendening40122
Penalty Kill 4 Players Forward
Line #CenterWingTime %PHYDFOF
160122
2Nick Ritchie40122
Penalty Kill 4 Players Defense
Line #DefenseDefenseTime %PHYDFOF
1Vince DunnYannick Weber60122
2Adam Clendening40122
Penalty Kill 3 Players
Line #WingTime %PHYDFOFDefenseDefenseTime %PHYDFOF
160122Vince DunnYannick Weber60122
240122Adam Clendening40122
4 vs 4 Forward
Line #CenterWingTime %PHYDFOF
160122
2Nick Ritchie40122
4 vs 4 Defense
Line #DefenseDefenseTime %PHYDFOF
1Vince DunnYannick Weber60122
2Adam Clendening40122
Last Minutes Offensive
Left WingCenterRight WingDefenseDefense
Vince DunnYannick Weber
Last Minutes Defensive
Left WingCenterRight WingDefenseDefense
Vince DunnYannick Weber
Extra Forwards
Normal PowerPlayPenalty Kill
, Nikolay Goldobin, , Nikolay Goldobin
Extra Defensemen
Normal PowerPlayPenalty Kill
, Adam Clendening, Vince DunnAdam Clendening, Vince Dunn
Penalty Shots
, , , Nick Ritchie,
Goalie
#1 : , #2 :


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
1Chiefs3300000026161022000000201191100000065161.0002646720014619917861321211108010621892201838111090.91%9544.44%0972162559.82%668118356.47%1064178959.47%1965122515335821318748
2Cobra431000003415192200000016511211000001810860.75034579100146199178619812111080106218108282389191473.68%9366.67%0972162559.82%668118356.47%1064178959.47%1965122515335821318748
3Cyclone440000003623132200000014104220000002213981.00036579300146199178616012111080106218127394877251560.00%9544.44%0972162559.82%668118356.47%1064178959.47%1965122515335821318748
4Destroyers614010002133-12312000001115-4302010001018-840.333213758001461991786184121110801062182045985127261453.85%15566.67%1972162559.82%668118356.47%1064178959.47%1965122515335821318748
5Distraction42100010171432010001077022000000107360.750172643001461991786154121110801062181294612799777.78%6350.00%0972162559.82%668118356.47%1064178959.47%1965122515335821318748
6Dynamos65001000442717320010002116533000000231112121.0004475119001461991786226121110801062181685528144251768.00%13746.15%0972162559.82%668118356.47%1064178959.47%1965122515335821318748
7Falcons422000002022-221100000161422110000048-440.500203757001461991786172121110801062181413742727571.43%161037.50%0972162559.82%668118356.47%1064178959.47%1965122515335821318748
8Intrepides413000002329-6211000001615120200000714-720.2502340630014619917861741211108010621815869405512866.67%10550.00%0972162559.82%668118356.47%1064178959.47%1965122515335821318748
9L'Euphorie43100000332211220000001697211000001713460.750335487001461991786174121110801062181225014779777.78%7357.14%0972162559.82%668118356.47%1064178959.47%1965122515335821318748
10Monkeys 403000101325-1220200000915-620100010410-620.250131932001461991786142121110801062182076047528337.50%6516.67%0972162559.82%668118356.47%1064178959.47%1965122515335821318748
11Patriotes523000002529-4321000001914520200000615-940.40025446910146199178621612111080106218151487998221359.09%17947.06%1972162559.82%668118356.47%1064178959.47%1965122515335821318748
12Prospects404000002234-12202000001418-420200000816-800.00022416310146199178619212111080106218166435288171164.71%11736.36%0972162559.82%668118356.47%1064178959.47%1965122515335821318748
13Senators540001003118132200000018810320001001310390.9003151820014619917862381211108010621810330339114642.86%14564.29%1972162559.82%668118356.47%1064178959.47%1965122515335821318748
14Sharks421000012428-4220000001715220100001713-650.625243761001461991786166121110801062181606133537571.43%9722.22%0972162559.82%668118356.47%1064178959.47%1965122515335821318748
15Sheriefs440000004216262200000020911220000002271581.00042661080014619917861851211108010621888272676141071.43%8275.00%0972162559.82%668118356.47%1064178959.47%1965122515335821318748
Total8047260222152742899402711010103012148740201501211226214121050.65652787113982014619917863356121110801062182655834691148028518564.91%2029950.99%5972162559.82%668118356.47%1064178959.47%1965122515335821318748
17Viking412001002026-6211000001314-120100100712-530.375203151001461991786154121110801062182036528699555.56%14471.43%0972162559.82%668118356.47%1064178959.47%1965122515335821318748
18Warriors43100000352692200000021813211000001418-460.75035609500146199178618912111080106218102263473181161.11%12650.00%1972162559.82%668118356.47%1064178959.47%1965122515335821318748
19Wildcats440000003813252200000022715220000001661081.00038599700146199178616712111080106218122372963231878.26%7442.86%0972162559.82%668118356.47%1064178959.47%1965122515335821318748
20Youngblood3300000023121111000000114722000000128461.0002334570014619917861331211108010621810434205910660.00%10460.00%1972162559.82%668118356.47%1064178959.47%1965122515335821318748
_Since Last GM Reset8047260222152742899402711010103012148740201501211226214121050.65652787113982014619917863356121110801062182655834691148028518564.91%2029950.99%5972162559.82%668118356.47%1064178959.47%1965122515335821318748
_Vs Conference41241302110251219322114601000149111382010701110102108-6550.67125142667710146199178616591211108010621812953744147721569460.26%1115748.65%4972162559.82%668118356.47%1064178959.47%1965122515335821318748
_Vs Division2212702100121107141173010006953161154011005254-2290.65912120732810146199178686412111080106218626192225460875057.47%592655.93%3972162559.82%668118356.47%1064178959.47%1965122515335821318748

Total For Players
Games PlayedPointsStreakGoalsAssistsPointsShots ForShots AgainstShots BlockedPenalty MinutesHitsEmpty Net GoalsShutouts
80105W1527871139833562655834691148020
All Games
GPWLOTWOTL SOWSOLGFGA
8047262221527428
Home Games
GPWLOTWOTL SOWSOLGFGA
4027111010301214
Visitor Games
GPWLOTWOTL SOWSOLGFGA
4020151211226214
Last 10 Games
WLOTWOTL SOWSOL
630001
Power Play AttempsPower Play GoalsPower Play %Penalty Kill AttempsPenalty Kill Goals AgainstPenalty Kill %Penalty Kill Goals For
28518564.91%2029950.99%5
Shots 1 PeriodShots 2 PeriodShots 3 PeriodShots 4+ PeriodGoals 1 PeriodGoals 2 PeriodGoals 3 PeriodGoals 4+ Period
121110801062181461991786
Face Offs
Won Offensive ZoneTotal OffensiveWon Offensive %Won Defensif ZoneTotal DefensiveWon Defensive %Won Neutral ZoneTotal NeutralWon Neutral %
972162559.82%668118356.47%1064178959.47%
Puck Time
In Offensive ZoneControl In Offensive ZoneIn Defensive ZoneControl In Defensive ZoneIn Neutral ZoneControl In Neutral Zone
1965122515335821318748


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 - 2019-05-0610Monsters4Youngblood2WBoxScore
2 - 2019-05-0712Patriotes1Monsters5WBoxScore
4 - 2019-05-0926Monsters5Dynamos3WBoxScore
6 - 2019-05-1132Destroyers1Monsters3WBoxScore
8 - 2019-05-1344Wildcats1Monsters10WBoxScore
10 - 2019-05-1552Monsters5Destroyers7LBoxScore
12 - 2019-05-1761Monsters5Senators2WBoxScore
15 - 2019-05-2078Patriotes5Monsters8WBoxScore
16 - 2019-05-2189Monsters2Monkeys 1WXXBoxScore
18 - 2019-05-2392Dynamos2Monsters5WBoxScore
21 - 2019-05-26109Monsters7Wildcats1WBoxScore
22 - 2019-05-27114Viking5Monsters3LBoxScore
24 - 2019-05-29128Distraction1Monsters2WXXBoxScore
26 - 2019-05-31139Monsters4Destroyers3WXBoxScore
28 - 2019-06-02145Dynamos6Monsters7WXBoxScore
30 - 2019-06-04159Monsters11Cyclone6WBoxScore
33 - 2019-06-07169Monsters1Destroyers8LBoxScore
34 - 2019-06-08173Monsters3Intrepides7LBoxScore
36 - 2019-06-10182Cyclone5Monsters7WBoxScore
38 - 2019-06-12197Viking9Monsters10WBoxScore
40 - 2019-06-14202Monsters2Monkeys 9LBoxScore
42 - 2019-06-16215Sheriefs4Monsters9WBoxScore
45 - 2019-06-19229Cyclone5Monsters7WBoxScore
46 - 2019-06-20232Monsters3Prospects5LBoxScore
48 - 2019-06-22241Monsters8Youngblood6WBoxScore
50 - 2019-06-24258Monsters5Senators4WBoxScore
52 - 2019-06-26263Falcons4Monsters11WBoxScore
54 - 2019-06-28275Sharks8Monsters9WBoxScore
56 - 2019-06-30282Monsters3Falcons2WBoxScore
58 - 2019-07-02296Wildcats6Monsters12WBoxScore
60 - 2019-07-04304Monsters5Prospects11LBoxScore
62 - 2019-07-06312L'Euphorie3Monsters8WBoxScore
64 - 2019-07-08326Sheriefs5Monsters11WBoxScore
66 - 2019-07-10337Monsters11Cyclone7WBoxScore
69 - 2019-07-13348Monsters2Patriotes7LBoxScore
70 - 2019-07-14351Monkeys 9Monsters7LBoxScore
73 - 2019-07-17369Monkeys 6Monsters2LBoxScore
75 - 2019-07-19378Monsters4Intrepides7LBoxScore
76 - 2019-07-20387Monsters3Senators4LXBoxScore
78 - 2019-07-22397Chiefs6Monsters11WBoxScore
79 - 2019-07-23409Monsters2Viking6LBoxScore
81 - 2019-07-25415Monsters4Patriotes8LBoxScore
83 - 2019-07-27425Chiefs5Monsters9WBoxScore
84 - 2019-07-28435Cobra3Monsters13WBoxScore
86 - 2019-07-30449Dynamos8Monsters9WBoxScore
87 - 2019-07-31458Monsters1Falcons6LBoxScore
89 - 2019-08-02466Falcons10Monsters5LBoxScore
91 - 2019-08-04478Monsters6Distraction4WBoxScore
92 - 2019-08-05486Monsters2Sharks7LBoxScore
94 - 2019-08-07495Warriors3Monsters8WBoxScore
96 - 2019-08-09504Monsters9Wildcats5WBoxScore
98 - 2019-08-11516Youngblood4Monsters11WBoxScore
99 - 2019-08-12527Warriors5Monsters13WBoxScore
101 - 2019-08-14536Monsters5Viking6LXBoxScore
103 - 2019-08-16547Cobra2Monsters3WBoxScore
104 - 2019-08-17552Monsters14Sheriefs3WBoxScore
105 - 2019-08-18566Monsters8Sheriefs4WBoxScore
108 - 2019-08-21576L'Euphorie6Monsters8WBoxScore
109 - 2019-08-22584Monsters7Dynamos4WBoxScore
111 - 2019-08-24595Distraction6Monsters5LBoxScore
113 - 2019-08-26605Intrepides6Monsters12WBoxScore
114 - 2019-08-27615Monsters4Distraction3WBoxScore
116 - 2019-08-29625Monsters11Dynamos4WBoxScore
117 - 2019-08-30634Intrepides9Monsters4LBoxScore
119 - 2019-09-01648Monsters4L'Euphorie7LBoxScore
120 - 2019-09-02655Prospects9Monsters7LBoxScore
122 - 2019-09-04669Prospects9Monsters7LBoxScore
123 - 2019-09-05679Senators5Monsters10WBoxScore
Trade Deadline --- Trades can’t be done after this day is simulated!
125 - 2019-09-07694Monsters3Warriors12LBoxScore
126 - 2019-09-08703Patriotes8Monsters6LBoxScore
127 - 2019-09-09708Monsters5Sharks6LXXBoxScore
129 - 2019-09-11720Monsters5Cobra6LBoxScore
130 - 2019-09-12731Destroyers9Monsters4LBoxScore
132 - 2019-09-14743Senators3Monsters8WBoxScore
135 - 2019-09-17753Monsters6Chiefs5WBoxScore
137 - 2019-09-19767Sharks7Monsters8WBoxScore
138 - 2019-09-20769Monsters11Warriors6WBoxScore
139 - 2019-09-21782Monsters13L'Euphorie6WBoxScore
140 - 2019-09-22791Destroyers5Monsters4LBoxScore
142 - 2019-09-24799Monsters13Cobra4WBoxScore
143 - 2019-09-25805Monsters-Chiefs-
145 - 2019-09-27820Youngblood-Monsters-



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
1 0 - 0.00% 0$0$3000100

Expenses
Year To Date ExpensesPlayers Total SalariesPlayers Total Average SalariesCoaches Salaries
2,388,158$ 1,432,893$ 1,180,793$ 0$
Salary Cap Per DaysSalary Cap To DatePlayers In Salary CapPlayers Out of Salary Cap
9,814$ 1,464,164$ 0 0

Estimate
Estimated Season RevenueRemaining Season DaysExpenses Per DaysEstimated Season Expenses
0$ 4 16,321$ 65,284$




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
201880472602221527428994027110101030121487402015012112262141210552787113982014619917863356121110801062182655834691148028518564.91%2029950.99%5972162559.82%668118356.47%1064178959.47%1965122515335821318748
Total Regular Season80472602221527428994027110101030121487402015012112262141210552787113982014619917863356121110801062182655834691148028518564.91%2029950.99%5972162559.82%668118356.47%1064178959.47%1965122515335821318748