Login

Wranglers
GP: 69 | W: 24 | L: 36 | OTL: 9 | P: 57
GF: 274 | GA: 340 | PP%: 20.74% | PK%: 73.85%
GM : AOC | Morale : 22 | Team Overall : 60
Next Games #745 vs Nordiks
Your browser screen resolution is too small for this page. Some information are hidden to keep the page readable.

Game Center
Firebirds
27-35-6, 60pts
5
FINAL
6 Wranglers
24-36-9, 57pts
Team Stats
W1StreakL1
14-19-1Home Record11-17-5
13-16-5Home Record13-19-4
3-7-0Last 10 Games4-6-0
4.78Goals Per Game3.97
4.91Goals Against Per Game4.93
18.52%Power Play Percentage20.74%
77.90%Penalty Kill Percentage73.85%
Wranglers
24-36-9, 57pts
5
FINAL
7 Barracuda
29-27-9, 67pts
Team Stats
L1StreakW1
11-17-5Home Record17-13-4
13-19-4Home Record12-14-5
4-6-0Last 10 Games3-3-4
3.97Goals Per Game4.83
4.93Goals Against Per Game5.11
20.74%Power Play Percentage22.07%
73.85%Penalty Kill Percentage70.70%
Nordiks
37-29-2, 76pts
Day 143
Wranglers
24-36-9, 57pts
Team Stats
L1StreakL1
20-12-2Home Record11-17-5
17-17-0Away Record13-19-4
7-3-0Last 10 Games4-6-0
5.66Goals Per Game3.97
5.22Goals Against Per Game3.97
24.83%Power Play Percentage20.74%
70.81%Penalty Kill Percentage73.85%
Wranglers
24-36-9, 57pts
Day 145
Barracuda
29-27-9, 67pts
Team Stats
L1StreakW1
11-17-5Home Record17-13-4
13-19-4Away Record12-14-5
4-6-0Last 10 Games3-3-4
3.97Goals Per Game4.83
4.93Goals Against Per Game4.83
20.74%Power Play Percentage22.07%
73.85%Penalty Kill Percentage70.70%
Wranglers
24-36-9, 57pts
Day 146
Lions
22-38-8, 52pts
Team Stats
L1StreakW1
11-17-5Home Record11-18-4
13-19-4Away Record11-20-4
4-6-0Last 10 Games5-3-2
3.97Goals Per Game3.49
4.93Goals Against Per Game3.49
20.74%Power Play Percentage25.32%
73.85%Penalty Kill Percentage80.30%
Team Leaders
Jakob SilfverbergGoals
Jakob Silfverberg
14
Assists
Parker Wotherspoon
15
Jakob SilfverbergPoints
Jakob Silfverberg
24
John BeecherPlus/Minus
John Beecher
-1

Team Stats
Goals For
274
3.97 GFG
Shots For
2804
40.64 Avg
Power Play Percentage
20.7%
28 GF
Offensive Zone Start
39.7%
Goals Against
340
4.93 GAA
Shots Against
2937
42.57 Avg
Penalty Kill Percentage
73.8%%
34 GA
Defensive Zone Start
40.8%
Team Info

General ManagerAOC
CoachLanny McDonald
DivisionCentral
ConferenceWestern Conference
Captain
Assistant #1
Assistant #2


Arena Info

Capacity3,000
Attendance0
Season Tickets300


Roster Info

Pro Team18
Farm Team21
Contract Limit39 / 50
Prospects21


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
Age
Contract
Salary
1John BeecherX100.00806686687867697475586171506164037640234500,000$
2Jakob SilfverbergX100.00555784707472917050635766507774054640341500,000$
3Kaapo KakkoX100.00625783697473757550606862516062050630244500,000$
4Nick BoninoX100.00555690677171657070595077508482047630361500,000$
5Taylor RaddyshX100.007157846871788565506253695565660476202721,000,000$
6Joshua Roy (R)X100.00605099696968567650636360525659047610213500,000$
7Justin BrazeauX100.00735693648061547250556455516567050600274500,000$
8Brendan LemieuxX100.00796578657760586950575558506866051590281500,000$
9Liam FoudyXX100.00585591626959527050595053506163045560253500,000$
10Jakob PelletierX100.00585491615858526450565055505960021550232500,000$
11Riley TufteX100.00514179578350515050494948516666045520264500,000$
12Brett KulakX100.00695882636968936650615269557473048650314500,000$
13Parker WotherspoonX100.00797180637072656950645276556971051650274500,000$
14Tyson BarrieX100.005959807370736671507051655579760486403321,500,000$
Scratches
1Zack Ostapchuk (R)X100.00585089577450514950505048505658031510213500,000$
2Gavin Brindley (R)X100.00503589576050515050505050505355019500201500,000$
3Ben HuttonX100.00616684647270656650625170557674035630312500,000$
TEAM AVERAGE100.0063568665726565665359546252666704360
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
Age
Contract
Salary
1John Gibson100.0071837877737372737250727373076700313500,000$
Scratches
TEAM AVERAGE100.007183787773737273725072737307670
Coaches Name PH DF OF PD EX LD PO CNT Age Contract Salary
Lanny McDonald7585958070801CAN716500,000$


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 Name
POS
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
1Connor ZaryAnaheim DucksC/LW543648843840601242497018214.46%15134324.886393712401111274155.38%114300001.2503000748
2Jakob SilfverbergWranglers (ANA)RW24141024-1020204689236715.73%944518.57224946000011140.48%4200011.0812000124
3Taylor RaddyshWranglers (ANA)RW2481321-2360433767144911.94%1958024.19123434000061047.81%22800000.7211000312
4Parker WotherspoonWranglers (ANA)D2451520-1780652630111916.67%5361025.4321311400000180037.50%800000.6602000301
5Brendan LemieuxWranglers (ANA)LW2471118-1180561762133911.29%1043618.18123531000031061.76%3400000.8201000221
6Justin BrazeauWranglers (ANA)RW2451015-124030296018418.33%640516.9100005000040153.23%6200000.7401000111
7Tyson BarrieWranglers (ANA)D2421315-226041393220316.25%3860525.2211215380000131050.00%1000000.5001000011
8Joshua RoyWranglers (ANA)RW246713-192012438122717.41%950320.9600018000050053.04%11500000.5200000010
9Brett KulakWranglers (ANA)D244812-2180232933103112.12%4360125.0510116400000160046.67%1500000.4000000113
10Ben HuttonWranglers (ANA)D21358-24002834338159.09%3650824.210111230000012000%000000.3101000110
11Liam FoudyWranglers (ANA)C/RW24246-23201936315296.45%846319.2900003000030048.18%49400000.2600000000
12John BeecherWranglers (ANA)C4134-1208131041010.00%18721.77000310000030058.25%10300000.9200000011
13Zack OstapchukWranglers (ANA)C21112-234072352520.00%142020.0300000000000045.28%31800000.1000000000
14Nick BoninoWranglers (ANA)C2011-200058040%03919.8600014000000054.90%5100000.5000000000
15Riley TufteWranglers (ANA)LW24011-21002488590%540817.0100012000000057.89%3800000.0500000000
Team Total or Average34294150244-19156043650979822560211.78%253745821.8114122611542201112178351.90%266100010.65212000191522
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 Name
GP
W
L
OTL
PCT
GAA
MP
PIM
SO
GA
SA
SAR
A
EG
PS %
PSA
ST
BG
S1
S2
S3
1Calvin PickardAnaheim Ducks111630.8693.7860420382910120.6676110001
2John GibsonWranglers (ANA)10100.9641.9431001280000003000
Team Total or Average121730.8783.6863620393190126113001


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 Name
POS
Age
Birthday
Country
Rookie
Weight
Height
No Trade
Available For Trade
Acquired By
Last Trade Date
Force Waivers
Waiver Possible
Contract
Contract Signature Date
Force UFA
Emergency Recall
Type
Current Salary
Salary Cap
Salary Cap Remaining
Exclude from Salary Cap
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
Salary Cap Year 2
Salary Cap Year 3
Salary Cap Year 4
Salary Cap Year 5
Salary Cap Year 6
Salary Cap Year 7
Salary Cap Year 8
Salary Cap Year 9
Salary Cap Year 10
No Trade Year 2
No Trade Year 3
No Trade Year 4
No Trade Year 5
No Trade Year 6
No Trade Year 7
No Trade Year 8
No Trade Year 9
No Trade Year 10
Link
Ben HuttonWranglers (ANA)D311993-04-20CANNo201 Lbs6 ft3NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$9,429$No500,000$--------500,000$--------No--------NHL Link
Brendan LemieuxWranglers (ANA)LW281996-03-15USANo215 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm500,000$50,000$9,429$No---------------------------NHL Link
Brett KulakWranglers (ANA)D311994-01-06CANNo192 Lbs6 ft2NoNoN/ANoNo42024-08-19FalseFalsePro & Farm500,000$50,000$9,429$No500,000$500,000$500,000$------500,000$500,000$500,000$------NoNoNo------NHL Link
Gavin BrindleyWranglers (ANA)C202004-10-05USAYes175 Lbs5 ft9NoNoTrade2025-01-12NoNo12024-08-09FalseFalsePro & Farm500,000$50,000$9,429$No---------------------------Link / NHL Link
Jakob PelletierWranglers (ANA)LW232001-03-07CANNo170 Lbs5 ft9NoNoTrade2025-01-12NoNo2FalseFalsePro & Farm500,000$50,000$9,429$No500,000$--------500,000$--------No--------NHL Link
Jakob SilfverbergWranglers (ANA)RW341990-10-13SWENo207 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm500,000$50,000$9,429$No---------------------------NHL Link
John BeecherWranglers (ANA)C232001-04-05USANo216 Lbs6 ft3NoNoFree AgentNoNo42024-09-03FalseFalsePro & Farm500,000$50,000$9,429$No500,000$500,000$500,000$------500,000$500,000$500,000$------NoNoNo------NHL Link
John GibsonWranglers (ANA)G311993-07-14USANo210 Lbs6 ft2NoNoN/ANoNo3FalseFalsePro & Farm500,000$50,000$9,429$No500,000$500,000$-------500,000$500,000$-------NoNo-------NHL Link
Joshua RoyWranglers (ANA)RW212003-08-06CANYes192 Lbs6 ft0NoNoN/ANoNo3FalseFalsePro & Farm500,000$50,000$9,429$No500,000$500,000$-------500,000$500,000$-------NoNo-------NHL Link
Justin BrazeauWranglers (ANA)RW271998-02-02CANNo220 Lbs6 ft5NoNoFree AgentNoNo42024-09-05FalseFalsePro & Farm500,000$50,000$9,429$No500,000$500,000$500,000$------500,000$500,000$500,000$------NoNoNo------NHL Link
Kaapo KakkoWranglers (ANA)RW242001-02-13FINNo205 Lbs6 ft2NoNoTrade2025-01-07NoNo42024-08-15FalseFalsePro & Farm500,000$50,000$9,429$No500,000$500,000$500,000$------500,000$500,000$500,000$------NoNoNo------NHL Link
Liam FoudyWranglers (ANA)C/RW252000-02-04CANNo193 Lbs6 ft2NoNoFree AgentNoNo32024-09-08FalseFalsePro & Farm500,000$50,000$9,429$No500,000$500,000$-------500,000$500,000$-------NoNo-------NHL Link
Nick BoninoWranglers (ANA)C361988-04-20USANo198 Lbs6 ft1NoNoFree AgentNoNo12024-09-08FalseFalsePro & Farm500,000$50,000$9,429$No---------------------------NHL Link
Parker WotherspoonWranglers (ANA)D271997-08-24CANNo195 Lbs6 ft1NoNoFree AgentNoNo42024-09-04FalseFalsePro & Farm500,000$50,000$9,429$No500,000$500,000$500,000$------500,000$500,000$500,000$------NoNoNo------
Riley TufteWranglers (ANA)LW261998-04-10USANo230 Lbs6 ft6NoNoFree AgentNoNo42024-09-06FalseFalsePro & Farm500,000$50,000$9,429$No500,000$500,000$500,000$------500,000$500,000$500,000$------NoNoNo------NHL Link
Taylor RaddyshWranglers (ANA)RW271998-02-18CANNo198 Lbs6 ft3NoNoN/ANoNo2FalseFalsePro & Farm1,000,000$100,000$18,857$No1,000,000$--------1,000,000$--------No--------NHL Link
Tyson BarrieWranglers (ANA)D331991-07-26CANNo197 Lbs5 ft11NoNoN/ANoNo2FalseFalsePro & Farm1,500,000$150,000$28,286$No1,500,000$--------1,500,000$--------No--------NHL Link
Zack OstapchukWranglers (ANA)C212003-05-29CANYes205 Lbs6 ft3NoNoN/ANoNo3FalseFalsePro & Farm500,000$50,000$9,429$No500,000$500,000$-------500,000$500,000$-------NoNo-------Link / NHL Link
Total PlayersAverage AgeAverage WeightAverage HeightAverage ContractAverage Year 1 Salary
1827.11201 Lbs6 ft22.67583,333$



5 vs 5 Forward
Line #Left WingCenterRight WingTime %PHYDFOF
1Jakob Silfverberg40122
2Brendan LemieuxTaylor Raddysh35122
3Riley TufteLiam FoudyJoshua Roy15122
4Justin Brazeau10122
5 vs 5 Defense
Line #DefenseDefenseTime %PHYDFOF
1Parker WotherspoonBrett Kulak40122
2Tyson Barrie30122
3Parker WotherspoonBrett Kulak20122
4Tyson Barrie10122
Power Play Forward
Line #Left WingCenterRight WingTime %PHYDFOF
1Jakob Silfverberg60122
2Brendan LemieuxTaylor Raddysh40122
Power Play Defense
Line #DefenseDefenseTime %PHYDFOF
1Parker WotherspoonBrett Kulak60122
2Tyson Barrie40122
Penalty Kill 4 Players Forward
Line #CenterWingTime %PHYDFOF
160122
2Brendan Lemieux40122
Penalty Kill 4 Players Defense
Line #DefenseDefenseTime %PHYDFOF
1Parker WotherspoonBrett Kulak60122
2Tyson Barrie40122
Penalty Kill 3 Players
Line #WingTime %PHYDFOFDefenseDefenseTime %PHYDFOF
160122Parker WotherspoonBrett Kulak60122
240122Tyson Barrie40122
4 vs 4 Forward
Line #CenterWingTime %PHYDFOF
160122
2Brendan Lemieux40122
4 vs 4 Defense
Line #DefenseDefenseTime %PHYDFOF
1Parker WotherspoonBrett Kulak60122
2Tyson Barrie40122
Last Minutes Offensive
Left WingCenterRight WingDefenseDefense
Jakob SilfverbergParker WotherspoonBrett Kulak
Last Minutes Defensive
Left WingCenterRight WingDefenseDefense
Jakob SilfverbergParker WotherspoonBrett Kulak
Extra Forwards
Normal PowerPlayPenalty Kill
Taylor Raddysh, Joshua Roy, Justin BrazeauTaylor Raddysh, Joshua RoyTaylor Raddysh
Extra Defensemen
Normal PowerPlayPenalty Kill
Brett Kulak, Tyson Barrie, Brett KulakBrett Kulak, Tyson Barrie
Penalty Shots
, , Jakob Silfverberg, , Taylor Raddysh
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
Overall
Home
Visitor
#
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
1Aces622002002325-2301002001215-3321000001110160.500234669001128178420290090298529190562611424833.33%12375.00%01178254846.23%1214262146.32%615125049.20%156111091754480848408
2Admirals211000001314-11010000057-21100000087120.500132437001128178412890090298529120378493133.33%40100.00%01178254846.23%1214262146.32%615125049.20%156111091754480848408
3Americiens21000100131211100000042210000100910-130.7501326390011281784116900902985291011710464125.00%5260.00%01178254846.23%1214262146.32%615125049.20%156111091754480848408
4Barons621002011618-230100101710-33200010098170.58316324800112817842069009029852916954321148112.50%15473.33%01178254846.23%1214262146.32%615125049.20%156111091754480848408
5Barracuda503010012531-6302010001215-3201000011316-330.30025497420112817842349009029852926287121198225.00%5180.00%01178254846.23%1214262146.32%615125049.20%156111091754480848408
6Broncos21100000911-2110000006331010000038-520.50091827001128178482900902985295922431400.00%20100.00%01178254846.23%1214262146.32%615125049.20%156111091754480848408
7Bruins21100000770110000005051010000027-520.500712190111281784829009029852996161143200.00%30100.00%11178254846.23%1214262146.32%615125049.20%156111091754480848408
8Butter Knives30300000918-920200000711-41010000027-500.000917261011281784142900902985291395112737114.29%7357.14%01178254846.23%1214262146.32%615125049.20%156111091754480848408
9Canucks633000001619-33210000099031200000710-360.500162844001128178420790090298529152532810415213.33%14471.43%01178254846.23%1214262146.32%615125049.20%156111091754480848408
10Fighting Pandas210001009901000010056-11100000043130.750918270011281784829009029852981201029200.00%5340.00%01178254846.23%1214262146.32%615125049.20%156111091754480848408
11Firebirds2110000089-1110000006511010000024-220.500814220011281784799009029852982232478225.00%10100.00%01178254846.23%1214262146.32%615125049.20%156111091754480848408
12Griffins312000001020-101100000064220200000416-1220.333101929001128178414290090298529188551484400.00%5340.00%01178254846.23%1214262146.32%615125049.20%156111091754480848408
13Ice Bats715010003343-10201010001011-1514000002332-940.2863364970011281784332900902985294431064319213215.38%16662.50%01178254846.23%1214262146.32%615125049.20%156111091754480848408
14Lions21100000910-121100000910-10000000000020.500916251011281784789009029852970266443133.33%2150.00%01178254846.23%1214262146.32%615125049.20%156111091754480848408
15Lynx2010001067-11010000013-21000001054120.5006915001128178447900902985296518231400.00%10100.00%01178254846.23%1214262146.32%615125049.20%156111091754480848408
16Marlies30300000716-91010000017-62020000069-300.00071421001128178411090090298529117456604125.00%30100.00%01178254846.23%1214262146.32%615125049.20%156111091754480848408
17Nordiks32100000201731010000045-1220000001612440.66720365600112817841679009029852915336679200.00%30100.00%01178254846.23%1214262146.32%615125049.20%156111091754480848408
18Punishers623001002124-3312000001113-2311001001011-150.417213859001128178420890090298529195602412117529.41%12283.33%01178254846.23%1214262146.32%615125049.20%156111091754480848408
19Roadrunners1010000048-41010000048-40000000000000.000481200112817845590090298529404626100.00%3166.67%11178254846.23%1214262146.32%615125049.20%156111091754480848408
20Tomahawks211000001110100000000000211000001110120.50011193000112817846690090298529107231044000%5180.00%01178254846.23%1214262146.32%615125049.20%156111091754480848408
21Wombats20200000512-71010000035-21010000027-500.00051015001128178439900902985291082814302150.00%70100.00%01178254846.23%1214262146.32%615125049.20%156111091754480848408
Total69213602712274340-663391702401127149-2236121900311147191-44570.4132745177914111281784280490090298529293783728614801352820.74%1303473.85%21178254846.23%1214262146.32%615125049.20%156111091754480848408
_Since Last GM Reset69213602712274340-663391702401127149-2236121900311147191-44570.4132745177914111281784280490090298529293783728614801352820.74%1303473.85%21178254846.23%1214262146.32%615125049.20%156111091754480848408
_Vs Conference46152202502184217-3321510023018092-1225101200201104125-21410.446184347531301128178418429009029852919295562011015942122.34%892571.91%01178254846.23%1214262146.32%615125049.20%156111091754480848408
_Vs Division23912015028599-141237013014452-81165002014147-6270.5878516124620112817848519009029852979925690458641726.56%431076.74%01178254846.23%1214262146.32%615125049.20%156111091754480848408

Total For Players
Games PlayedPointsStreakGoalsAssistsPointsShots ForShots AgainstShots BlockedPenalty MinutesHitsEmpty Net GoalsShutouts
6957L127451779128042937837286148041
All Games
GPWLOTWOTL SOWSOLGFGA
6921362712274340
Home Games
GPWLOTWOTL SOWSOLGFGA
339172401127149
Visitor Games
GPWLOTWOTL SOWSOLGFGA
3612190311147191
Last 10 Games
WLOTWOTL SOWSOL
261010
Power Play AttempsPower Play GoalsPower Play %Penalty Kill AttempsPenalty Kill Goals AgainstPenalty Kill %Penalty Kill Goals For
1352820.74%1303473.85%2
Shots 1 PeriodShots 2 PeriodShots 3 PeriodShots 4+ PeriodGoals 1 PeriodGoals 2 PeriodGoals 3 PeriodGoals 4+ Period
9009029852911281784
Face Offs
Won Offensive ZoneTotal OffensiveWon Offensive %Won Defensif ZoneTotal DefensiveWon Defensive %Won Neutral ZoneTotal NeutralWon Neutral %
1178254846.23%1214262146.32%615125049.20%
Puck Time
In Offensive ZoneControl In Offensive ZoneIn Defensive ZoneControl In Defensive ZoneIn Neutral ZoneControl In Neutral Zone
156111091754480848408


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
Day
Game
Visitor Team
Score
Home Team
Score
ST
OT
SO
RI
Link
11Punishers2Wranglers4WBoxScore
320Wranglers3Punishers6LBoxScore
422Wranglers3Ice Bats6LBoxScore
737Canucks4Wranglers5WBoxScore
840Wranglers5Ice Bats10LBoxScore
1058Punishers5Wranglers4LBoxScore
1370Aces5Wranglers4LBoxScore
1481Wranglers4Barons3WBoxScore
1792Barons2Wranglers1LBoxScore
19106Wranglers5Aces3WBoxScore
20114Barracuda6Wranglers4LBoxScore
22124Wranglers4Canucks5LBoxScore
24137Ice Bats6Wranglers7WXBoxScore
25144Wranglers9Tomahawks5WBoxScore
28156Wranglers3Marlies5LBoxScore
29159Wranglers7Ice Bats3WBoxScore
30163Wranglers8Barracuda9LXXBoxScore
33176Lions4Wranglers5WBoxScore
36193Wranglers8Admirals7WBoxScore
38198Canucks2Wranglers3WBoxScore
40207Wranglers2Butter Knives7LBoxScore
42218Admirals7Wranglers5LBoxScore
44226Wranglers4Fighting Pandas3WBoxScore
46237Wranglers2Ice Bats6LBoxScore
47242Broncos3Wranglers6WBoxScore
50260Wranglers2Bruins7LBoxScore
51264Roadrunners8Wranglers4LBoxScore
53276Wranglers2Wombats7LBoxScore
54285Butter Knives6Wranglers4LBoxScore
57299Wranglers3Barons4LXBoxScore
59308Canucks3Wranglers1LBoxScore
63328Griffins4Wranglers6WBoxScore
64337Wranglers5Aces3WBoxScore
66349Wranglers9Nordiks7WBoxScore
67352Barons4Wranglers3LXBoxScore
71367Wranglers6Punishers3WBoxScore
72374Bruins0Wranglers5WBoxScore
76391Wranglers3Griffins8LBoxScore
77397Fighting Pandas6Wranglers5LXBoxScore
80413Wranglers7Nordiks5WBoxScore
81418Americiens2Wranglers4WBoxScore
84430Wranglers6Ice Bats7LBoxScore
86440Marlies7Wranglers1LBoxScore
88456Wranglers9Americiens10LXBoxScore
89462Aces7Wranglers6LXBoxScore
92477Wranglers0Canucks4LBoxScore
93484Nordiks5Wranglers4LBoxScore
96503Wranglers1Punishers2LXBoxScore
97507Barons4Wranglers3LXXBoxScore
100523Wranglers3Canucks1WBoxScore
101529Lynx3Wranglers1LBoxScore
104543Wranglers2Firebirds4LBoxScore
105550Butter Knives5Wranglers3LBoxScore
108569Aces3Wranglers2LXBoxScore
110577Wranglers2Tomahawks5LBoxScore
113592Barracuda6Wranglers4LBoxScore
116608Wranglers3Broncos8LBoxScore
117615Wombats5Wranglers3LBoxScore
120631Lions6Wranglers4LBoxScore
122640Wranglers5Lynx4WXXBoxScore
124652Wranglers3Marlies4LBoxScore
125657Ice Bats5Wranglers3LBoxScore
128670Wranglers2Barons1WBoxScore
129679Punishers6Wranglers3LBoxScore
131687Wranglers1Aces4LBoxScore
134701Barracuda3Wranglers4WXBoxScore
137716Wranglers1Griffins8LBoxScore
138722Firebirds5Wranglers6WBoxScore
140729Wranglers5Barracuda7LBoxScore
143745Nordiks-Wranglers-
145752Wranglers-Barracuda-
146764Wranglers-Lions-
147766Wranglers-Roadrunners-
148770Tomahawks-Wranglers-
Trade Deadline --- Trades can’t be done after this day is simulated!
153791Ice Bats-Wranglers-
155801Wranglers-Lions-
156807Wranglers-Roadrunners-
157814Marlies-Wranglers-
161836Griffins-Wranglers-
164856Tomahawks-Wranglers-
168874Lions-Wranglers-
173895Punishers-Wranglers-



Arena Capacity - Ticket Price Attendance - %
Level 1Level 2
Capacity20001000
Ticket Price3515
Attendance00
Attendance PCT0.00%0.00%

Income
Home Games LeftAverage Attendance - %Average Income per GameYear to Date RevenueCapacityTeam Popularity
8 0 - 0.00% 0$0$3000100

Expenses
Year To Date ExpensesPlayers Total SalariesPlayers Total Salaries CapCoaches Salaries
1,246,829$ 1,050,000$ 1,050,000$ 500,000$0$
Salary Cap Per DaysSalary Cap To DatePlayers In Salary CapPlayers Out of Salary Cap
1,050,000$ 841,135$ 18 0

Estimate
Estimated Season RevenueRemaining Season DaysExpenses Per DaysEstimated Season Expenses
0$ 33 8,857$ 292,281$




Wranglers 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

Wranglers Goalies Stat Leaders (Regular Season)

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

Wranglers Career Team Stats

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

Wranglers 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

Wranglers Goalies Stat Leaders (Play-Off)

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