Wolf Pack

GP: 22 | W: 11 | L: 9 | OTL: 2 | P: 24
GF: 69 | GA: 65 | PP%: 46.15% | PK%: 44.23%
GM : Dannick Payment | Morale : 50 | Team Overall : 60
Next Games vs Marlies
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
1Miles WoodXX100.007556709262629268556376622559597250660
2Jake DebruskX100.007553917667688873387974537552527250660
3Nolan Patrick (R)X100.006943878073677473846871604557577050650
4Brett RitchieX100.008960837778579272256059582562636550630
5Lukas SedlakX100.008055877672547560805858752557576550620
6Jordan NolanXXX100.008376727583567557315657642567696250610
7Austin CzarnikX100.006340998058588762567055602550506550600
8Laurent DauphinX100.007267837267717461766057635447476250600
9Mathieu Joseph (R)XX100.007065826665757864506658625544446350600
10Nicholas BaptisteXX100.006542877874568864325662612548486450600
11Kevin RoyX100.006341917161668365265475642546466950600
12Nick SeelerX100.007374746676658060255447742546466150620
13Chris BigrasX100.007143957771646456254747712548485950610
14Kyle Wood (R)X100.008381896881687351254741653944445650600
15Nicolas Meloche (R)X100.007577716277707648253942614044445350570
Scratches
1Cedric PaquetteX100.008556798273577459765759722564676550630
2Vinni Lettieri (R)XX100.007743997265627666386657612546466450600
3Nicolas Roy (R)X100.008078846878747859745954665144446250600
4Spencer Foo (R)X100.007470826870666862505664646144446550590
5William CarrierX100.009661857373536260255556592554546250580
6Blake Speers (R)XX100.007468878268626553664557615444446050570
7Ryan Fitzgerald (R)X100.006661796461676960755362595944446250570
8Brandon Gignac (R)X100.007263936663576047594147594544445350520
9Jakub JerabekX100.007743947769697360255348662550506150620
10Jeremy Lauzon (R)X100.007675786575586245253639603744445150550
TEAM AVERAGE100.00756085737063756047565763385050635060
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
Scratches
1Tyler Parsons (R)100.00455265704144505245463044444750490
TEAM AVERAGE100.0045526570414450524546304444475049
Coaches Name PH DF OF PD EX LD PO CNT Age Contract Salary


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
1Miles WoodWolf Pack (NYR)LW/RW2228315973356435112307025.00%1849022.3077147282022300258.16%983912022.4002001512
2Nolan PatrickWolf Pack (NYR)C221736536255293881235520.99%741318.78111123280000132063.44%6102513002.5712001141
3Jake DebruskWolf Pack (NYR)LW22212243-2004122124237316.94%1146621.203585280221373050.00%82610021.8422000312
4Brett RitchieWolf Pack (NYR)RW2212233583010511642213728.57%1541218.734375280002140127.08%481811001.7001011030
5Jordan NolanWolf Pack (NYR)C/LW/RW22142135127156323121296111.57%1242319.25224527000091133.33%62112011.6500201222
6Nick SeelerWolf Pack (NYR)D22023237612555325514180.00%4362528.43000232000130000.00%0832000.7400140001
7Mathieu JosephWolf Pack (NYR)LW/RW2281422-720291344142418.18%1229213.2811211000001145.45%1137001.5101000010
8Kevin RoyWolf Pack (NYR)LW2212921-540271350112524.00%530113.7000000000010040.00%10107011.3900000101
9Jakub JerabekWolf Pack (NYR)D13213155002521297146.90%2434626.67224518000021000.00%0812000.8700000001
10Lukas SedlakWolf Pack (NYR)C226915-875422540182915.00%1131614.3601105000000061.33%15099010.9500001010
11Chris BigrasWolf Pack (NYR)D22099-47525262515170.00%3252523.89022128000021000.00%0236000.3400010000
12Kyle WoodWolf Pack (NYR)D22246-1037253133187711.11%4246921.33112227000031000.00%0013000.2600113000
13Nicolas MelocheWolf Pack (NYR)D22134-14230291212648.33%1733015.000111400009010.00%0113000.2400222000
14Austin CzarnikWolf Pack (NYR)C22123-60012918585.56%101928.74000000001221047.76%6775000.3100000001
15Nicholas BaptisteWolf Pack (NYR)C/RW22303-40064100630.00%11205.4900000000000050.00%210000.5000000000
16Laurent DauphinWolf Pack (NYR)C22112-10010891511.11%21275.8000009000040055.56%1823000.3100000000
17Cedric PaquetteWolf Pack (NYR)C2101-1404620250.00%12110.6600002000000047.62%2110000.9400000000
Team Total or Average345129220349-1527912554333679222445516.29%263587417.032136573727222472478658.82%1049181195071.19386910121311
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
1Tyler ParsonsWolf Pack (NYR)84210.8675.124450038286175000.000080000
Team Total or Average84210.8675.124450038286175000.000080000


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 CONT StatusType Current Salary Salary Year 2 Salary Year 3 Salary Year 4 Salary Year 5 Salary Year 6 Salary Year 7 Salary Year 8 Salary Year 9 Salary Year 10 Link
Austin CzarnikWolf Pack (NYR)C251992-12-12No160 Lbs5 ft9NoNoNo1RFAPro & Farm925,000$Link
Blake SpeersWolf Pack (NYR)C/RW211997-01-02Yes185 Lbs5 ft11NoNoNo3RFAPro & Farm750,000$750,000$750,000$Link
Brandon GignacWolf Pack (NYR)C211997-11-07Yes173 Lbs5 ft11NoNoNo3RFAPro & Farm700,000$700,000$700,000$Link
Brett RitchieWolf Pack (NYR)RW251993-06-30No215 Lbs6 ft3NoNoNo2RFAPro & Farm1,800,000$1,800,000$Link
Cedric PaquetteWolf Pack (NYR)C251993-08-13No198 Lbs6 ft1NoNoNo1RFAPro & Farm812,500$Link
Chris BigrasWolf Pack (NYR)D231995-02-21No190 Lbs6 ft1NoNoNo1RFAPro & Farm850,000$Link
Jake DebruskWolf Pack (NYR)LW221996-10-16No183 Lbs6 ft0NoNoNo2RFAPro & Farm950,000$950,000$Link
Jakub JerabekWolf Pack (NYR)D271991-05-12No182 Lbs5 ft10NoNoNo4RFAPro & Farm950,000$950,000$950,000$950,000$Link
Jeremy LauzonWolf Pack (NYR)D211997-04-28Yes204 Lbs6 ft1NoNoNo3RFAPro & Farm800,000$800,000$800,000$Link
Jordan NolanWolf Pack (NYR)C/LW/RW281990-06-23No219 Lbs6 ft3NoNoNo4UFAPro & Farm1,000,000$1,000,000$1,000,000$1,000,000$Link
Kevin RoyWolf Pack (NYR)LW251993-05-19No174 Lbs5 ft9NoNoNo2RFAPro & Farm650,000$650,000$Link
Kyle WoodWolf Pack (NYR)D221996-05-03Yes235 Lbs6 ft7NoNoNo2RFAPro & Farm700,000$700,000$Link
Laurent DauphinWolf Pack (NYR)C231995-03-27No180 Lbs6 ft1NoNoNo1RFAPro & Farm850,000$Link
Lukas SedlakWolf Pack (NYR)C251993-02-25No203 Lbs6 ft0NoNoNo2RFAPro & Farm850,000$850,000$Link
Mathieu JosephWolf Pack (NYR)LW/RW211997-02-09Yes173 Lbs6 ft1NoNoNo3RFAPro & Farm600,000$600,000$600,000$Link
Miles WoodWolf Pack (NYR)LW/RW231995-09-13No195 Lbs6 ft2NoNoNo2RFAPro & Farm650,000$650,000$Link
Nicholas BaptisteWolf Pack (NYR)C/RW231995-08-04No206 Lbs6 ft1NoNoNo1RFAPro & Farm750,000$Link
Nick SeelerWolf Pack (NYR)D251993-06-02No192 Lbs6 ft0NoNoNo4RFAPro & Farm1,000,000$1,000,000$1,000,000$1,000,000$Link
Nicolas MelocheWolf Pack (NYR)D211997-07-18Yes204 Lbs6 ft3NoNoNo3RFAPro & Farm850,000$850,000$850,000$Link
Nicolas RoyWolf Pack (NYR)C211997-02-05Yes208 Lbs6 ft4NoNoNo3RFAPro & Farm650,000$650,000$650,000$Link
Nolan PatrickWolf Pack (NYR)C201998-09-19Yes198 Lbs6 ft2NoNoNo3ELCPro & Farm950,000$950,000$950,000$Link
Ryan FitzgeraldWolf Pack (NYR)C241994-10-19Yes172 Lbs5 ft9NoNoNo3RFAPro & Farm600,000$600,000$600,000$Link
Spencer FooWolf Pack (NYR)RW241994-01-01Yes185 Lbs6 ft0NoNoNo2RFAPro & Farm1,800,000$1,800,000$Link
Tyler ParsonsWolf Pack (NYR)G211997-09-18Yes185 Lbs6 ft1NoNoNo3RFAPro & Farm800,000$800,000$800,000$Link
Vinni LettieriWolf Pack (NYR)C/RW231995-02-06Yes195 Lbs5 ft11NoNoNo2RFAPro & Farm950,000$950,000$Link
William CarrierWolf Pack (NYR)LW231994-12-19No212 Lbs6 ft2NoNoNo3RFAPro & Farm950,000$950,000$950,000$Link
Total PlayersAverage AgeAverage WeightAverage HeightAverage ContractAverage Year 1 Salary
2623.15193 Lbs6 ft12.42889,904$



5 vs 5 Forward
Line #Left WingCenterRight WingTime %PHYDFOF
1Miles WoodNolan PatrickBrett Ritchie40122
2Jake DebruskJordan Nolan30122
3Kevin RoyLukas SedlakMathieu Joseph20122
4Miles WoodAustin CzarnikNicholas Baptiste10122
5 vs 5 Defense
Line #DefenseDefenseTime %PHYDFOF
1Nick Seeler40122
2Chris BigrasKyle Wood30122
3Nicolas MelocheNick Seeler20122
4Chris Bigras10122
Power Play Forward
Line #Left WingCenterRight WingTime %PHYDFOF
1Miles WoodNolan PatrickBrett Ritchie60122
2Jake DebruskJordan Nolan40122
Power Play Defense
Line #DefenseDefenseTime %PHYDFOF
1Nick Seeler60122
2Chris BigrasKyle Wood40122
Penalty Kill 4 Players Forward
Line #CenterWingTime %PHYDFOF
1Miles WoodJake Debrusk60122
2Nolan PatrickBrett Ritchie40122
Penalty Kill 4 Players Defense
Line #DefenseDefenseTime %PHYDFOF
1Nick Seeler60122
2Chris BigrasKyle Wood40122
Penalty Kill 3 Players
Line #WingTime %PHYDFOFDefenseDefenseTime %PHYDFOF
1Miles Wood60122Nick Seeler60122
2Jake Debrusk40122Chris BigrasKyle Wood40122
4 vs 4 Forward
Line #CenterWingTime %PHYDFOF
1Miles WoodJake Debrusk60122
2Nolan PatrickBrett Ritchie40122
4 vs 4 Defense
Line #DefenseDefenseTime %PHYDFOF
1Nick Seeler60122
2Chris BigrasKyle Wood40122
Last Minutes Offensive
Left WingCenterRight WingDefenseDefense
Miles WoodNolan PatrickBrett RitchieNick Seeler
Last Minutes Defensive
Left WingCenterRight WingDefenseDefense
Miles WoodNolan PatrickBrett RitchieNick Seeler
Extra Forwards
Normal PowerPlayPenalty Kill
Laurent Dauphin, Lukas Sedlak, Austin CzarnikLaurent Dauphin, Lukas SedlakAustin Czarnik
Extra Defensemen
Normal PowerPlayPenalty Kill
Nicolas Meloche, Kyle Wood, Nick SeelerNicolas MelocheKyle Wood, Nick Seeler
Penalty Shots
Miles Wood, Jake Debrusk, Nolan Patrick, Brett 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
1Bears10100000614-80000000000010100000614-800.000610160030585242922032926516661834203266.67%7442.86%118635352.69%19136951.76%28348957.87%477272460180400209
2Bruins1010000039-6000000000001010000039-600.0003690030585243522032926516371312382150.00%110.00%018635352.69%19136951.76%28348957.87%477272460180400209
3Checkers20200000613-720200000613-70000000000000.0006101600305852462220329265167629852400.00%4175.00%018635352.69%19136951.76%28348957.87%477272460180400209
4Comets11000000862110000008620000000000021.00081422003058524362203292651636102382150.00%10100.00%018635352.69%19136951.76%28348957.87%477272460180400209
5Devils202000001118-700000000000202000001118-700.000111829003058524662203292651694326584375.00%3233.33%018635352.69%19136951.76%28348957.87%477272460180400209
6Falcons11000000963110000009630000000000021.0009162500305852445220329265164415201822100.00%6350.00%018635352.69%19136951.76%28348957.87%477272460180400209
7Griffins210000011614200000000000210000011614230.7501629450030585249322032926516702721548337.50%3233.33%018635352.69%19136951.76%28348957.87%477272460180400209
8IceHogs10100000913-410100000913-40000000000000.0009162500305852443220329265166319401522100.00%660.00%018635352.69%19136951.76%28348957.87%477272460180400209
9Marlies3300000024101411000000103722000000147761.000243963003058524111220329265161044037677114.29%6183.33%018635352.69%19136951.76%28348957.87%477272460180400209
10Penguins10001000651100010006510000000000021.0006111700305852432220329265163032439200.00%2150.00%118635352.69%19136951.76%28348957.87%477272460180400209
11Phantoms1000010023-11000010023-10000000000010.50022400305852436220329265162310023000.00%000.00%018635352.69%19136951.76%28348957.87%477272460180400209
12Pirates202000001419-51010000089-110100000610-400.00014264000305852482220329265167913544211100.00%7528.57%018635352.69%19136951.76%28348957.87%477272460180400209
13Rampage10000010761000000000001000001076121.0007815003058524392203292651648112205360.00%110.00%018635352.69%19136951.76%28348957.87%477272460180400209
14Senators11000000532110000005320000000000021.0005611003058524332203292651632563711100.00%30100.00%018635352.69%19136951.76%28348957.87%477272460180400209
Since Last GM Reset229901111142150-8115401100696541145000117385-12240.54514223737900305852482122032926516911277285574522446.15%522944.23%218635352.69%19136951.76%28348957.87%477272460180400209
16Sound Tigers11000000642110000006420000000000021.0006915003058524442203292651655177345240.00%110.00%018635352.69%19136951.76%28348957.87%477272460180400209
Total229901111142150-8115401100696541145000117385-12240.54514223737900305852482122032926516911277285574522446.15%522944.23%218635352.69%19136951.76%28348957.87%477272460180400209
Vs Conference1558011008398-158330110043403725000004058-18130.4338313722000305852453022032926516596180188410291137.93%341652.94%218635352.69%19136951.76%28348957.87%477272460180400209
Vs Division812011003757-20510011002025-5302000001732-1550.313376097003058524269220329265163441097922618738.89%17947.06%218635352.69%19136951.76%28348957.87%477272460180400209
20Wolves1100000010730000000000011000000107321.0001017270030585243522032926516541512194250.00%110.00%018635352.69%19136951.76%28348957.87%477272460180400209

Total For Players
Games PlayedPointsStreakGoalsAssistsPointsShots ForShots AgainstShots BlockedPenalty MinutesHitsEmpty Net GoalsShutouts
2224L314223737982191127728557400
All Games
GPWLOTWOTL SOWSOLGFGA
22991111142150
Home Games
GPWLOTWOTL SOWSOLGFGA
115411006965
Visitor Games
GPWLOTWOTL SOWSOLGFGA
114500117385
Last 10 Games
WLOTWOTL SOWSOL
640000
Power Play AttempsPower Play GoalsPower Play %Penalty Kill AttempsPenalty Kill Goals AgainstPenalty Kill %Penalty Kill Goals For
522446.15%522944.23%2
Shots 1 PeriodShots 2 PeriodShots 3 PeriodShots 4+ PeriodGoals 1 PeriodGoals 2 PeriodGoals 3 PeriodGoals 4+ Period
220329265163058524
Face Offs
Won Offensive ZoneTotal OffensiveWon Offensive %Won Defensif ZoneTotal DefensiveWon Defensive %Won Neutral ZoneTotal NeutralWon Neutral %
18635352.69%19136951.76%28348957.87%
Puck Time
In Offensive ZoneControl In Offensive ZoneIn Defensive ZoneControl In Defensive ZoneIn Neutral ZoneControl In Neutral Zone
477272460180400209


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
2 - 2018-10-0310Wolf Pack9Marlies4WBoxScore
4 - 2018-10-0525Checkers5Wolf Pack2LBoxScore
6 - 2018-10-0736Phantoms3Wolf Pack2LXBoxScore
9 - 2018-10-1063Senators3Wolf Pack5WBoxScore
11 - 2018-10-1283Wolf Pack8Griffins9LXXBoxScore
12 - 2018-10-1389Wolf Pack6Devils9LBoxScore
15 - 2018-10-16109Sound Tigers4Wolf Pack6WBoxScore
17 - 2018-10-18127Comets6Wolf Pack8WBoxScore
20 - 2018-10-21149Wolf Pack5Devils9LBoxScore
22 - 2018-10-23160Penguins5Wolf Pack6WXBoxScore
24 - 2018-10-25172Wolf Pack3Bruins9LBoxScore
26 - 2018-10-27188Checkers8Wolf Pack4LBoxScore
28 - 2018-10-29206Wolf Pack6Bears14LBoxScore
30 - 2018-10-31221Marlies3Wolf Pack10WBoxScore
32 - 2018-11-02235Wolf Pack5Marlies3WBoxScore
34 - 2018-11-04244Wolf Pack8Griffins5WBoxScore
36 - 2018-11-06255Wolf Pack10Wolves7WBoxScore
37 - 2018-11-07270Falcons6Wolf Pack9WBoxScore
40 - 2018-11-10290Wolf Pack7Rampage6WXXBoxScore
42 - 2018-11-12298IceHogs13Wolf Pack9LBoxScore
45 - 2018-11-15318Wolf Pack6Pirates10LBoxScore
47 - 2018-11-17329Pirates9Wolf Pack8LBoxScore
50 - 2018-11-20348Wolf Pack-Wild-
52 - 2018-11-22360Phantoms-Wolf Pack-
54 - 2018-11-24376Wolf Pack-IceCaps-
56 - 2018-11-26389Wolf Pack-Senators-
57 - 2018-11-27398Bruins-Wolf Pack-
60 - 2018-11-30421Senators-Wolf Pack-
62 - 2018-12-02430Wolf Pack-Moose-
64 - 2018-12-04447Wolf Pack-Bruins-
65 - 2018-12-05457Senators-Wolf Pack-
68 - 2018-12-08478Wolf Pack-Barracuda-
69 - 2018-12-09489Pirates-Wolf Pack-
72 - 2018-12-12512Americans-Wolf Pack-
73 - 2018-12-13521Wolf Pack-Devils-
77 - 2018-12-17545Heat-Wolf Pack-
81 - 2018-12-21569Stars-Wolf Pack-
86 - 2018-12-26596Devils-Wolf Pack-
88 - 2018-12-28615Wolf Pack-Crunch-
89 - 2018-12-29626Checkers-Wolf Pack-
92 - 2019-01-01644Wolf Pack-Admirals-
94 - 2019-01-03657Sound Tigers-Wolf Pack-
96 - 2019-01-05671Wolf Pack-Penguins-
98 - 2019-01-07684Wolf Pack-Condors-
100 - 2019-01-09694IceCaps-Wolf Pack-
102 - 2019-01-11710Wolf Pack-Marlies-
104 - 2019-01-13727Sound Tigers-Wolf Pack-
107 - 2019-01-16744Wolf Pack-Wolves-
109 - 2019-01-18758Marlies-Wolf Pack-
111 - 2019-01-20772Wolf Pack-Sound Tigers-
113 - 2019-01-22787Bears-Wolf Pack-
117 - 2019-01-26812Comets-Wolf Pack-
119 - 2019-01-28829Wolf Pack-Bears-
122 - 2019-01-31846Devils-Wolf Pack-
125 - 2019-02-03872Barracuda-Wolf Pack-
127 - 2019-02-05883Wolf Pack-Checkers-
129 - 2019-02-07900Wolf Pack-Americans-
130 - 2019-02-08908Monsters-Wolf Pack-
132 - 2019-02-10922Wolf Pack-Crunch-
135 - 2019-02-13938Crunch-Wolf Pack-
138 - 2019-02-16964Phantoms-Wolf Pack-
139 - 2019-02-17972Wolf Pack-Penguins-
142 - 2019-02-20996Penguins-Wolf Pack-
Trade Deadline --- Trades can’t be done after this day is simulated!
144 - 2019-02-221007Wolf Pack-Wolves-
147 - 2019-02-251028Griffins-Wolf Pack-
149 - 2019-02-271042Wolf Pack-IceCaps-
151 - 2019-03-011059Moose-Wolf Pack-
152 - 2019-03-021071Wolf Pack-Sound Tigers-
155 - 2019-03-051089Penguins-Wolf Pack-
158 - 2019-03-081107Wolf Pack-Phantoms-
160 - 2019-03-101120Wolf Pack-Gulls-
161 - 2019-03-111123Wolf Pack-Reign-
163 - 2019-03-131134Moose-Wolf Pack-
165 - 2019-03-151151Wolf Pack-Gulls-
167 - 2019-03-171161Americans-Wolf Pack-
168 - 2019-03-181167Wolf Pack-Phantoms-



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
27 0 - 0.00% 0$0$3000100

Expenses
Players Total SalariesPlayers Total Average SalariesCoaches Salaries
2,313,750$ 1,885,000$ 0$
Year To Date ExpensesSalary Cap Per DaysSalary Cap To Date
652,628$ 0$ 647,473$

Estimate
Estimated Season RevenueRemaining Season DaysExpenses Per DaysEstimated Season Expenses
0$ 122 13,531$ 1,650,782$




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
2018229901111142150-8115401100696541145000117385-122414223737900305852482122032926516911277285574522446.15%522944.23%218635352.69%19136951.76%28348957.87%477272460180400209
Total Regular Season229901111142150-8115401100696541145000117385-122414223737900305852482122032926516911277285574522446.15%522944.23%218635352.69%19136951.76%28348957.87%477272460180400209