Day : 95
WPG - 2
UTA - 4
DAL - 3
OTT - 7
LOS - 4
BUF - 3
SJS - 3
CAR - 2
TOR - 3
TBL - 0
CLB - 4
WSH - 8
VAN - 4
PHI - 2
ANH - 3
NJD - 4
MIN - 2
NYR - 3
FLA - 3
PIT - 2
GRG - 2
SAR - 5
PRO - 6
NOR - 1
HAM - 3
GAT - 2
CTW - 6
SJS - 5
Day : 96
(20-23-1) - L2
(9-33-5) - L2
(34-10-2) - W2
(22-21-4) - L2
(27-16-3) - L2
(10-34-4) - L2
(23-20-4) - W4
(33-6-3) - W2
(19-23-3) - L2
(14-27-1) - L3
(15-23-7) - W3
(24-17-2) - W2
(25-15-7) - OTL1
(25-14-5) - W1
(24-19-3) - L1
(14-26-4) - L1
(25-17-3) - OTL1
(31-8-5) - W5
(24-18-6) - W3
(28-15-2) - W4
(26-12-1) - W1
(26-18-2) - L1
(25-11-5) - W1
(25-19-6) - L2
(26-19-2) - W3
(26-14-3) - W1
(31-6-6) - L1
(31-27-1) - L1
(26-10-6) - W2
(26-19-4) - W2
(31-7-4) - W8
(31-11-4) - L3
(27-12-1) - W2
(27-18-5) - OTL1
(16-26-4) - L1
(16-19-2) - L1
(15-22-4) - W1
(15-23-4) - L1
(16-20-5) - W1
(16-30-0) - W1
(14-26-4) - L1
(14-19-1) - W1

Lake Erie Monsters
GP: 44 | W: 14 | L: 26 | OTL: 4 | P: 32
GF: 162 | GA: 213 | PP%: 22.15% | PK%: 74.07%
GM : Alexandre Brabant | Morale : 50 | Team Overall : N/A
Next Games #716 vs Henderson Silver Knights
Your browser screen resolution is too small for this page. Some information are hidden to keep the page readable.

Game Center
Lake Erie Monsters
14-26-4, 32pts
3
FINAL
4 Joliette Sportif
13-27-4, 30pts
Team Stats
L1StreakL2
8-13-0Home Record7-11-3
6-13-4Home Record6-16-1
2-6-2Last 10 Games2-8-0
3.68Goals Per Game3.25
4.84Goals Against Per Game4.82
22.15%Power Play Percentage22.06%
74.07%Penalty Kill Percentage68.83%
San Antonio Rampage
29-9-4, 62pts
8
FINAL
3 Lake Erie Monsters
14-26-4, 32pts
Team Stats
W4StreakL1
15-5-2Home Record8-13-0
14-4-2Home Record6-13-4
6-1-3Last 10 Games2-6-2
4.62Goals Per Game3.68
3.17Goals Against Per Game4.84
23.33%Power Play Percentage22.15%
81.76%Penalty Kill Percentage74.07%
Lake Erie Monsters
14-26-4, 32pts
Day 96
Henderson Silver Knights
24-19-1, 49pts
Team Stats
L1StreakW1
8-13-0Home Record14-6-1
6-13-4Away Record10-13-0
2-6-2Last 10 Games6-4-0
3.68Goals Per Game4.07
4.84Goals Against Per Game4.07
22.15%Power Play Percentage23.91%
74.07%Penalty Kill Percentage77.12%
San Antonio Rampage
29-9-4, 62pts
Day 97
Lake Erie Monsters
14-26-4, 32pts
Team Stats
W4StreakL1
15-5-2Home Record8-13-0
14-4-2Away Record6-13-4
6-1-3Last 10 Games2-6-2
4.62Goals Per Game3.68
3.17Goals Against Per Game3.68
23.33%Power Play Percentage22.15%
81.76%Penalty Kill Percentage74.07%
Lake Erie Monsters
14-26-4, 32pts
Day 99
Binghamton Senators
19-19-4, 42pts
Team Stats
L1StreakW2
8-13-0Home Record12-8-1
6-13-4Away Record7-11-3
2-6-2Last 10 Games4-5-1
3.68Goals Per Game3.57
4.84Goals Against Per Game3.57
22.15%Power Play Percentage16.06%
74.07%Penalty Kill Percentage78.52%
Team Leaders
Goals
Saku Maenalanen
21
Assists
JC Lipon
25
Points
Saku Maenalanen
43
Plus/Minus
Matej Pekar
3
Wins
Matej Tomek
9
Save Percentage
Matej Tomek
0.852

Team Stats
Goals For
162
3.68 GFG
Shots For
1385
31.48 Avg
Power Play Percentage
22.1%
33 GF
Offensive Zone Start
37.8%
Goals Against
213
4.84 GAA
Shots Against
1381
31.39 Avg
Penalty Kill Percentage
74.1%%
35 GA
Defensive Zone Start
33.3%
Team Info

General ManagerAlexandre Brabant
CoachJeremy Colliton
DivisionGrands Lacs
ConferenceHamel
CaptainJC Lipon
Assistant #1Kyle Palmieri
Assistant #2Tim Erixon


Arena Info

Capacity3,000
Attendance3,000
Season Tickets300


Roster Info

Pro Team24
Farm Team22
Contract Limit46 / 50
Prospects31


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
1Vinni LettieriXX100.008438838078848380727475707381741500N02931,500,000$
2Matej Pekar (R)XX100.0084408080798484797373726771707165000241850,000$
3Adam Mascherin (R)X100.0070358478848079786570716972747345000262900,000$
4Kyle Palmieri (A)XX100.0058329980798079806072736776969870500N03321,600,000$
5Jeremy BraccoXX100.00602597847086808066737065727472550002721,000,000$
6Brendan WarrenX100.0064358377777976756069676668727235000271750,000$
7Keegan IversonXX100.0088457678878378777471716968747535000281900,000$
8Ivan MorozovX100.0076477880798483797476746881737175000241900,000$
9Oskar Bäck (R)X100.0076389980837984807975747175707165000241900,000$
10JC Lipon (C)X100.008442848681858481687575698198961500N03121,800,000$
11Saku MaenalanenXX100.008035868579827482657675677793921500N03021,800,000$
12Ryan TesinkXX100.00914180788079787877737270748790150003111,000,000$
13Sam MileticX100.00683093807983777967737469726767550002721,000,000$
14Christian DjoosX100.007026968074817379507367886779851500N03021,500,000$
15Jérémy DaviesX100.008745798085857980507466876279926500N02831,200,000$
16Xavier Bernard (R)X100.0078438075828182735272628260666555000241750,000$
17Adam ClendeningX100.0090387877807876795276688471969615000323800,000$
18Hunter Drew (R)X100.0092487273888075755069668660666655000263750,000$
19Kyle CapobiancoX100.00763583838181798250757084647373650002721,200,000$
20Juuso VälimäkiX100.00843881848784748050746884697877650002611,500,000$
Scratches
1Tim Erixon (A)X93.667140837975777282507671867199991500N03321,800,000$
2Matthew SpencerX97.0085418070857771715069628860686655000271750,000$
TEAM AVERAGE99.557838847981817879627370757079797500
Filter Tips
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Goalie Name CON SK DU EN SZ AG RB SC HS RT PH PS EX LD PO MO OV TA SPAgeContractSalary Average
1Mitchell Gibson (R)100.008581828282818181818282626165000251800,000$
2Matej Tomek100.0084828385848282838284835958450002721,300,000$
Scratches
TEAM AVERAGE100.00858283848382828282838361605500
Coaches Name PH DF OF PD EX LD PO CNT Age Contract Salary
Jeremy Colliton81888687828470CAN3911,500,000$


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 POSGP 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
1Saku MaenalanenLW/RW44212243-3806427114407718.42%1281118.4378152513200081261032.56%43769021.0603000220
2JC LiponRW44162541-13807039114617814.04%2179418.05311142711200031143348.58%2127016011.0313000323
3Oskar BäckC441619351003339100377316.00%1360013.65000000000170050.13%383654001.1711000310
4Vinni LettieriC/RW44181331-191206452100316118.00%1669515.8054912610112661046.21%422446000.8923000113
5Kyle PalmieriLW/RW44112031-2120232198349311.22%1174416.91156131081013430039.39%33619000.8300000102
6Ivan MorozovC37151631-12120413276344119.74%757415.5387152391000000051.09%460456001.0800000121
7Jeremy BraccoLW/RW4481725-1700192310339687.77%965914.9924615108000001054.29%35737000.7600000100
8Kyle CapobiancoD4471825-6160507863293511.11%4186919.7512312102000191100.00%01244000.5800000002
9Ryan TesinkC/RW4481624-33240108458323569.64%1078217.79213171040000360048.87%266427000.6122000001
10Tim ErixonD4381422-15220618757272414.04%5191421.2628109106000197200.00%01240000.4800000200
11Keegan IversonC/LW4481321-17340673668264711.76%254212.3200000000001051.72%2613111000.7700000020
12Sam MileticLW4461420-100016298025447.50%948110.9500000000000053.85%135112000.8300000012
13Christian DjoosD4471118-27002310155201912.73%4776717.442131039011055110.00%01143000.4700000001
14Juuso VälimäkiD3221618-1730049514928204.08%4979924.9703311101000064000.00%0534000.4500000100
15Adam MascherinLW443811-68025355816255.17%951911.81000010001120035.00%20259000.4200000000
16Jérémy DaviesD3101010-1720053535519220.00%6177825.110227103000168000.00%0033000.2600000000
17Brendan WarrenLW421910212030322719233.70%1249811.8600001000020051.52%33711000.4001000001
18Matej PekarC/LW38369312045113612268.33%113268.59000000001190048.94%94214000.5500000001
19Xavier BernardD33055-162203745291180.00%4051915.7400000000028000.00%0518000.1900000000
20Hunter DrewD11145-3120272416546.25%1418717.0200001000006000.00%017000.5300000000
21Matthew SpencerD102352801418155413.33%1815515.5500001000118000.00%0110000.6400000010
22Adam ClendeningD10011-740242710640.00%919919.9900011500000000.00%039000.1000000000
23J.J. MoserD1000-120120100.00%21717.020000000000000.00%000000.0000000000
24Zach BensonC/LW3000000000000.00%010.580000000000000.00%000000.0000000000
Team Total or Average819161280441-2522680944907140654885211.45%4741323916.1733568918212051232287211448.92%2275661349030.67613000151217
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
1Matej TomekLake Erie Monsters (COL)3091510.8524.99141800118798464200.75042816030
2Mitchell GibsonLake Erie Monsters (COL)2851130.8374.5612492095583305000.75081628000
Team Total or Average58142640.8464.79266820213138176920124444030


Player Name POS Age Birthday Terms Contract Cap % Year 2024Year 2025Year 2026Year 2027Year 2028Year 2029Year 2030
Forward
Adam MascherinLW261998-01-02TW 20.00%900,000$900,000$UFA [Age: 28]
Brendan WarrenLW271997-01-02TW 10.00%750,000$UFA [Age: 28]
Ivan MorozovC242000-01-02TW 10.00%900,000$RFA ( Groupe: 2 ) [Age: 25]
JC LiponRW311993-01-22NT TW 20.00%1,800,000$1,800,000$UFA [Age: 33]
Jeremy BraccoLW/RW271997-01-02TW 20.00%1,000,000$1,000,000$UFA [Age: 29]
Keegan IversonC/LW281996-01-02TW 10.00%900,000$UFA [Age: 29]
Kyle PalmieriLW/RW331991-01-22NT TW 20.00%1,600,000$1,600,000$UFA [Age: 35]
Matej PekarC/LW242000-01-02TW 10.00%850,000$RFA ( Groupe: 2 ) [Age: 25]
Oskar BäckC242000-01-21TW 10.00%900,000$RFA ( Groupe: 2 ) [Age: 25]
Ryan TesinkC/RW311993-01-22TW 10.00%1,000,000$UFA [Age: 32]
Saku MaenalanenLW/RW301994-01-22NT TW 20.00%1,800,000$1,800,000$UFA [Age: 32]
Sam MileticLW271997-01-22TW 20.00%1,000,000$1,000,000$UFA [Age: 29]
Vinni LettieriC/RW291995-01-02NT TW 30.00%1,500,000$1,500,000$1,500,000$UFA [Age: 32]
AVERAGE (13)27.770.00%0$9,600,000$1,500,000$0$0$0$0$
Defenseman
Adam ClendeningD321992-01-22TW 30.00%800,000$800,000$800,000$UFA [Age: 35]
Christian DjoosD301994-01-02NT TW 20.00%1,500,000$1,500,000$UFA [Age: 32]
Hunter DrewD261998-01-22TW 30.00%750,000$750,000$750,000$UFA [Age: 29]
Juuso VälimäkiD261998-01-22TW 10.00%1,500,000$RFA ( Groupe: 2 ) [Age: 27]
Jérémy DaviesD281996-01-02NT TW 30.00%1,200,000$1,200,000$1,200,000$UFA [Age: 31]
Kyle CapobiancoD271997-01-22TW 20.00%1,200,000$1,200,000$UFA [Age: 29]
Matthew SpencerD271997-01-22TW 10.00%750,000$UFA [Age: 28]
Tim ErixonD331991-01-22NT IN TW 20.00%1,800,000$1,800,000$UFA [Age: 35]
Xavier BernardD242000-01-02TW 10.00%750,000$RFA ( Groupe: 2 ) [Age: 25]
AVERAGE (9)28.110.00%0$7,250,000$2,750,000$0$0$0$0$
Goalies
Matej TomekG271997-01-22TW 20.00%1,300,000$1,300,000$UFA [Age: 29]
Mitchell GibsonG251999-01-02TW 10.00%800,000$RFA ( Groupe: 2 ) [Age: 26]

Terms Legends : FV = Force Waiver / NT = No Trade / IN = Injured / TW = Two Way Contract (Can Play Pro + Can Play Farm)

Note: The salary cap amounts for the current year and the % of salary cap are based on a calculation of the simple salary cap calculation (Average Salary or Salary for the year depending on your salary options). If your salary cap is based on complex calculation, the results of this analysis could be incorrect by a small margin. The Current Year Pro Salary Cap is : 89,000,050$.




5 vs 5 Forward
Line # Left Wing Center Right Wing Time % PHY DF OF
1Saku MaenalanenVinni LettieriJC Lipon25122
2Kyle PalmieriRyan TesinkJeremy Bracco25122
3Keegan IversonOskar BäckIvan Morozov25122
4Sam MileticIvan MorozovJC Lipon25122
5 vs 5 Defense
Line # Defense Defense Time % PHY DF OF
1Jérémy DaviesChristian Djoos25122
2Kyle CapobiancoHunter Drew25122
3Adam ClendeningSam Miletic25122
4Juuso VälimäkiChristian Djoos25122
Power Play Forward
Line # Left Wing Center Right Wing Time % PHY DF OF
1Saku MaenalanenVinni LettieriJC Lipon60122
2Kyle PalmieriRyan TesinkJeremy Bracco40122
Power Play Defense
Line # Defense Defense Time % PHY DF OF
1Jérémy DaviesChristian Djoos60122
2Kyle CapobiancoHunter Drew40122
Penalty Kill 4 Players Forward
Line # Center Wing Time % PHY DF OF
1JC LiponSaku Maenalanen60122
2Vinni LettieriRyan Tesink40122
Penalty Kill 4 Players Defense
Line # Defense Defense Time % PHY DF OF
1Jérémy DaviesChristian Djoos60122
2Kyle CapobiancoHunter Drew40122
Penalty Kill 3 Players
Line # Wing Time % PHY DF OF Defense Defense Time % PHY DF OF
1JC Lipon60122Adam ClendeningChristian Djoos60122
2Saku Maenalanen40122Kyle CapobiancoHunter Drew40122
4 vs 4 Forward
Line # Center Wing Time % PHY DF OF
1JC LiponSaku Maenalanen60122
2Vinni LettieriRyan Tesink40122
4 vs 4 Defense
Line # Defense Defense Time % PHY DF OF
1Jérémy DaviesChristian Djoos60122
2Kyle CapobiancoHunter Drew40122
Last Minutes Offensive
Left Wing Center Right Wing Defense Defense
Saku MaenalanenVinni LettieriJC LiponJérémy DaviesChristian Djoos
Last Minutes Defensive
Left Wing Center Right Wing Defense Defense
Saku MaenalanenVinni LettieriJC LiponJérémy DaviesChristian Djoos
Extra Forwards
Normal PowerPlay Penalty Kill
Matej Pekar, Adam Mascherin, Brendan WarrenMatej Pekar, Adam MascherinBrendan Warren
Extra Defensemen
Normal PowerPlay Penalty Kill
Christian Djoos, Kyle Capobianco, Hunter DrewKyle CapobiancoKyle Capobianco, Hunter Drew
Penalty Shots
JC Lipon, Saku Maenalanen, Vinni Lettieri, Ryan Tesink, Oskar Bäck
Goalie
#1 : Matej Tomek, #2 : Mitchell Gibson
Custom OT Lines Forwards
JC Lipon, Saku Maenalanen, Vinni Lettieri, Ryan Tesink, Oskar Bäck, Ivan Morozov, Ivan Morozov, Kyle Palmieri, Keegan Iverson, Sam Miletic, Matej Pekar
Custom OT Lines Defensemen
Juuso Välimäki, Christian Djoos, Kyle Capobianco, Hunter Drew, Jérémy Davies


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
1Binghamton Senators20101000910-120101000910-10000000000020.500913220054475597346843247225611516388225.00%8187.50%044384952.18%35774947.66%31364848.30%1001642987352682342
2Bridgeport Sound Tigers412000101414000000000000412000101414040.50014223600544755912246843247225106462410418316.67%12466.67%044384952.18%35774947.66%31364848.30%1001642987352682342
3Charlotte Checkers10001000431100010004310000000000021.0004711005447559264684324722540101415400.00%7185.71%044384952.18%35774947.66%31364848.30%1001642987352682342
4Chicago Wolves20100100613-71010000028-61000010045-110.250611170054475595046843247225591412576350.00%6350.00%044384952.18%35774947.66%31364848.30%1001642987352682342
5Chicoutimi Saguenéens11000000514000000000001100000051421.0005914005447559324684324722530106234250.00%3166.67%044384952.18%35774947.66%31364848.30%1001642987352682342
6Chisinau Pelicans10000010651100000106510000000000021.0006814005447559414684324722522710174125.00%5260.00%044384952.18%35774947.66%31364848.30%1001642987352682342
7Connecticut Whale20100100712-50000000000020100100712-510.250713200054475597846843247225552312458112.50%6183.33%044384952.18%35774947.66%31364848.30%1001642987352682342
8Danbury Trashers11000000642000000000001100000064221.00061016005447559314684324722526106226233.33%30100.00%144384952.18%35774947.66%31364848.30%1001642987352682342
9Grand Rapids Griffins412001001516-11100000085330200100711-430.3751527420054475591424684324722511747328310440.00%17570.59%044384952.18%35774947.66%31364848.30%1001642987352682342
10Hamilton Bulldogs1010000014-31010000014-30000000000000.00011200544755934468432472253314221200.00%10100.00%044384952.18%35774947.66%31364848.30%1001642987352682342
11Joliette Sportif2010010059-41010000025-31000010034-110.250510150054475595446843247225672412458112.50%6183.33%044384952.18%35774947.66%31364848.30%1001642987352682342
12Manitoba Moose1010000036-3000000000001010000036-300.00035800544755937468432472252482213133.33%110.00%044384952.18%35774947.66%31364848.30%1001642987352682342
13Milwaukee Admirals1010000056-1000000000001010000056-100.0005914005447559334684324722530120145120.00%000%044384952.18%35774947.66%31364848.30%1001642987352682342
14Norfolk Admirals11000000743110000007430000000000021.00071219005447559314684324722525122195240.00%10100.00%044384952.18%35774947.66%31364848.30%1001642987352682342
15Portland Crying Chiwawas2110000068-21010000036-31100000032120.50069150054475595746843247225692112428112.50%6266.67%044384952.18%35774947.66%31364848.30%1001642987352682342
16Providence Bruins20200000613-720200000613-70000000000000.0006101600544755968468432472256317845400.00%40100.00%044384952.18%35774947.66%31364848.30%1001642987352682342
17Roberval Dwarfs522000102627-142100010222111010000046-260.6002645710054475591534684324722517757349112216.67%17382.35%044384952.18%35774947.66%31364848.30%1001642987352682342
18Rochester Americans2020000048-41010000047-31010000001-100.0004711005447559494684324722570248359111.11%4250.00%044384952.18%35774947.66%31364848.30%1001642987352682342
19San Antonio Rampage20200000512-71010000038-51010000024-200.000510150054475596046843247225702012415120.00%6266.67%044384952.18%35774947.66%31364848.30%1001642987352682342
20San Jose Barracuda1010000034-11010000034-10000000000000.000347005447559344684324722536104163133.33%2150.00%044384952.18%35774947.66%31364848.30%1001642987352682342
21Springfield Falcons1010000026-4000000000001010000026-400.00024600544755934468432472253011420300.00%220.00%044384952.18%35774947.66%31364848.30%1001642987352682342
22The Nuuk Vikings302010001116-51010000015-4201010001011-120.3331119300054475599746843247225953320569222.22%10280.00%044384952.18%35774947.66%31364848.30%1001642987352682342
23Trois-Rivières Lions1010000048-4000000000001010000048-400.0004711005447559274684324722537134235240.00%2150.00%044384952.18%35774947.66%31364848.30%1001642987352682342
24Verdun Junior1010000024-21010000024-20000000000000.00024600544755922468432472253991225000%60100.00%044384952.18%35774947.66%31364848.30%1001642987352682342
Total4482603430162213-51214130202083112-29234130141079101-22320.36416227643800544755913854684324722513814672689181493322.15%1353574.07%144384952.18%35774947.66%31364848.30%1001642987352682342
_Since Last GM Reset4482603430162213-51214130202083112-29234130141079101-22320.36416227643800544755913854684324722513814672689181493322.15%1353574.07%144384952.18%35774947.66%31364848.30%1001642987352682342
_Vs Conference244130232095119-241337010205367-141116013004252-10190.3969516325800544755975846843247225767255160493732027.40%811976.54%044384952.18%35774947.66%31364848.30%1001642987352682342
_Vs Division1615012006683-171112010005163-12503002001520-560.188661131790054475594934684324722553316890315521223.08%451077.78%044384952.18%35774947.66%31364848.30%1001642987352682342

Total For Players
Games Played Points Streak Goals Assists Points Shots For Shots Against Shots Blocked Penalty Minutes Hits Empty Net Goals Shutouts
4432L11622764381385138146726891800
All Games
GP W L OTW OTL SOWSOL GF GA
448263430162213
Home Games
GP W L OTW OTL SOWSOL GF GA
21413202083112
Visitor Games
GP W L OTW OTL SOWSOL GF GA
23413141079101
Last 10 Games
W L OTW OTL SOWSOL
260200
Power Play Attemps Power Play Goals Power Play % Penalty Kill Attemps Penalty Kill Goals Against Penalty Kill % Penalty Kill Goals For
1493322.15%1353574.07%1
Shots 1 Period Shots 2 Period Shots 3 Period Shots 4+ Period Goals 1 Period Goals 2 Period Goals 3 Period Goals 4+ Period
468432472255447559
Face Offs
Won Offensive Zone Total Offensive Won Offensive % Won Defensif Zone Total Defensive Won Defensive % Won Neutral Zone Total Neutral Won Neutral %
44384952.18%35774947.66%31364848.30%
Puck Time
In Offensive Zone Control In Offensive Zone In Defensive Zone Control In Defensive Zone In Neutral Zone Control In Neutral Zone
1001642987352682342


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
Day:1
18Lake Erie Monsters6Bridgeport Sound Tigers3WBoxScore
Day:3
323Roberval Dwarfs4Lake Erie Monsters5WXXBoxScore
Day:6
643Binghamton Senators5Lake Erie Monsters6WXBoxScore
Day:8
859Lake Erie Monsters4Connecticut Whale5LXBoxScore
Day:10
1072Lake Erie Monsters1Bridgeport Sound Tigers2LBoxScore
Day:12
1277Lake Erie Monsters3Grand Rapids Griffins4LBoxScore
Day:15
1595Roberval Dwarfs8Lake Erie Monsters3LBoxScore
Day:17
17113Lake Erie Monsters1Grand Rapids Griffins3LBoxScore
Day:18
18125Binghamton Senators5Lake Erie Monsters3LBoxScore
Day:21
21151Hamilton Bulldogs4Lake Erie Monsters1LBoxScore
Day:22
22161Lake Erie Monsters6Bridgeport Sound Tigers5WXXBoxScore
Day:25
25179Lake Erie Monsters3Portland Crying Chiwawas2WBoxScore
Day:26
26191Roberval Dwarfs5Lake Erie Monsters6WBoxScore
Day:28
28214Lake Erie Monsters5Chicoutimi Saguenéens1WBoxScore
Day:29
29223Providence Bruins8Lake Erie Monsters5LBoxScore
Day:32
32243Lake Erie Monsters7The Nuuk Vikings6WXBoxScore
Day:34
34259Chicago Wolves8Lake Erie Monsters2LBoxScore
Day:38
38280Lake Erie Monsters2San Antonio Rampage4LBoxScore
Day:39
39290Lake Erie Monsters5Milwaukee Admirals6LBoxScore
Day:41
41298Chisinau Pelicans5Lake Erie Monsters6WXXBoxScore
Day:44
44322The Nuuk Vikings5Lake Erie Monsters1LBoxScore
Day:45
45341Lake Erie Monsters3The Nuuk Vikings5LBoxScore
Day:47
47357Joliette Sportif5Lake Erie Monsters2LBoxScore
Day:50
50375Lake Erie Monsters4Trois-Rivières Lions8LBoxScore
Day:51
51387Lake Erie Monsters4Roberval Dwarfs6LBoxScore
Day:53
53399Rochester Americans7Lake Erie Monsters4LBoxScore
Day:55
55416Lake Erie Monsters0Rochester Americans1LBoxScore
Day:56
56427Norfolk Admirals4Lake Erie Monsters7WBoxScore
Day:58
58450Lake Erie Monsters2Springfield Falcons6LBoxScore
Day:59
59458San Jose Barracuda4Lake Erie Monsters3LBoxScore
Day:63
63488Roberval Dwarfs4Lake Erie Monsters8WBoxScore
Day:66
66507Lake Erie Monsters4Chicago Wolves5LXBoxScore
Day:68
68521Providence Bruins5Lake Erie Monsters1LBoxScore
Day:71
71546Grand Rapids Griffins5Lake Erie Monsters8WBoxScore
Day:73
73562Lake Erie Monsters1Bridgeport Sound Tigers4LBoxScore
Day:75
75579Lake Erie Monsters3Grand Rapids Griffins4LXBoxScore
Day:76
76587Portland Crying Chiwawas6Lake Erie Monsters3LBoxScore
Day:79
79610Lake Erie Monsters6Danbury Trashers4WBoxScore
Day:81
81616Lake Erie Monsters3Connecticut Whale7LBoxScore
Day:82
82624Verdun Junior4Lake Erie Monsters2LBoxScore
Day:86
86653Charlotte Checkers3Lake Erie Monsters4WXBoxScore
Day:88
88662Lake Erie Monsters3Manitoba Moose6LBoxScore
Day:90
90678Lake Erie Monsters3Joliette Sportif4LXBoxScore
Day:93
93689San Antonio Rampage8Lake Erie Monsters3LBoxScore
Day:96
96716Lake Erie Monsters-Henderson Silver Knights-
Day:97
97722San Antonio Rampage-Lake Erie Monsters-
Day:99
99742Lake Erie Monsters-Binghamton Senators-
Day:101
101757Chicoutimi Saguenéens-Lake Erie Monsters-
Day:104
104779Lake Erie Monsters-Chicoutimi Saguenéens-
Day:106
106789Binghamton Senators-Lake Erie Monsters-
Day:108
108810Lake Erie Monsters-Milwaukee Admirals-
Day:109
109820Quebec Stars-Lake Erie Monsters-
Day:113
113849Blainville-Boisbriand Armada-Lake Erie Monsters-
Day:115
115865Lake Erie Monsters-Wilkes-Barre/Scranton Penguins-
Day:117
117880Lake Erie Monsters-Providence Bruins-
Day:118
118888Trois-Rivières Lions-Lake Erie Monsters-
Day:123
123915Laval Chiefs-Lake Erie Monsters-
Day:125
125930Lake Erie Monsters-Gatineau Olympiques-
Day:127
127945Lake Erie Monsters-Blainville-Boisbriand Armada-
Day:128
128955Milwaukee Admirals-Lake Erie Monsters-
Day:130
130978Lake Erie Monsters-Blainville-Boisbriand Armada-
Day:131
131986Binghamton Senators-Lake Erie Monsters-
Day:134
1341005Lake Erie Monsters-Portland Crying Chiwawas-
Day:135
1351017Chicago Wolves-Lake Erie Monsters-
Day:139
Trade Deadline --- Trades can’t be done after this day is simulated!
1391044Lake Erie Monsters-Albany Devils-
Day:141
1411049Lake Erie Monsters-Verdun Junior-
Day:142
1421058Springfield Falcons-Lake Erie Monsters-
Day:146
1461084Hamilton Bulldogs-Lake Erie Monsters-
Day:148
1481094Lake Erie Monsters-Danbury Trashers-
Day:151
1511118Gatineau Olympiques-Lake Erie Monsters-
Day:152
1521133Lake Erie Monsters-Connecticut Whale-
Day:154
1541148Gatineau Olympiques-Lake Erie Monsters-
Day:156
1561165Lake Erie Monsters-Laval Chiefs-
Day:159
1591182Quebec Stars-Lake Erie Monsters-
Day:163
1631214Houston Aeros-Lake Erie Monsters-
Day:166
1661228Lake Erie Monsters-Hamilton Bulldogs-
Day:168
1681248Henderson Silver Knights-Lake Erie Monsters-
Day:171
1711263Lake Erie Monsters-Quebec Stars-
Day:174
1741281Albany Devils-Lake Erie Monsters-
Day:178
1781307Milwaukee Admirals-Lake Erie Monsters-
Day:181
1811329Portland Crying Chiwawas-Lake Erie Monsters-
Day:183
1831340Lake Erie Monsters-Chisinau Pelicans-



Arena Capacity - Ticket Price Attendance - %
Level 1 Level 2

Income
Home Games Left Average Attendance - % Average Income per Game Year to Date Revenue Capacity Team Popularity

Expenses
Year To Date Expenses Players Total Salaries Players Total Average Salaries Coaches Salaries
Salary Cap Per Days Salary Cap To Date Players In Salary Cap Players Out of Salary Cap

Estimate
Estimated Season Revenue Remaining Season Days Expenses Per Days Estimated Season Expenses




Lake Erie Monsters Players Stat Leaders (Regular Season)

# Player Name GP G A P +/- PIM HIT HTT SHT SHT% SB MP AMG PPG PPA PPP PPS PKG PKA PKP PKS GW GT FO% HT P/20 PSG PSS

Lake Erie Monsters Goalies Stat Leaders (Regular Season)

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

Lake Erie Monsters Career Team Stats

Overall Home Visitor
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

Lake Erie Monsters Players Stat Leaders (Play-Off)

# Player Name GP G A P +/- PIM HIT HTT SHT SHT% SB MP AMG PPG PPA PPP PPS PKG PKA PKP PKS GW GT FO% HT P/20 PSG PSS

Lake Erie Monsters Goalies Stat Leaders (Play-Off)

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