Stars

GP: 77 | W: 36 | L: 35 | OTL: 6 | P: 78
GF: 273 | GA: 298 | PP%: 23.96% | PK%: 76.44%
DG: Julien Desrosiers | Morale : 50 | Moyenne d'Équipe : 49
Prochain matchs #1212 vs Monsters
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Astuces sur les Filtres (Anglais seulement)
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
# Nom du Joueur C L R D CON CK FG DI SK ST EN DU PH FO PA SC DF PS EX LD PO MO OV TA SPÂgeContratSalaire Moyen
1Alexander VolkovXX100.00767182647180865950595767555555050610223864,167$
2Greg McKeggXX100.00755290717055805857586070256364050610271700,000$
3Taylor RaddyshXX100.00838089638078836075536268594444050610212742,500$
4Michael McCarronXX100.00798855658861625671526066594949050590242620,000$
5Liam Hawel (R)X100.00565274697175946165595153545454050590204525,000$
6Andrew PoturalskiX100.00746886596968635468575166485555050570251792,000$
7Hudson ElynuikX100.00747571637561655063474761454444050540212700,000$
8Lucas LessioX100.00473584637343294035364362484036050470261600,000$
9Jesse Gabrielle (R)X100.00364040407135353640363640383230050380222742,500$
10Jake BeanX100.00736983676978836225595163484444050610212925,000$
11Andrew MacDonaldX100.005243846367676547355243764761540505903322,500,000$
12Martin Fehervary (R)X100.00757283727266714725384160394444050570194805,835$
13Louie BelpedioX100.00707065646972834825404060394444050570232925,000$
14Justin FalkX100.00605076597660433835354169475751050540302800,000$
15Connor Hall (R)X100.00394343436337373943393943413230050410212700,000$
16Kenney Morrison (R)X100.00394545456636363945393945423230050410271825,000$
17Stephen Desrocher (R)X100.00323737376931313237323237343230050360231525,000$
Rayé
1Matt Schmalz (R)XX100.00323737377731313237323237343230050360231525,000$
2Chris Leblanc (R)X100.00313737376829293137313137333230050350261560,000$
3Henrik SedinX100.001920202020191919201919202020200502103915,347,152$
4Sam Ruopp (R)X100.00323737376431313237323237343230050360231525,000$
MOYENNE D'ÉQUIPE100.0055536354695355454443435442434105049
Astuces sur les Filtres (Anglais seulement)
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
# Nom du Gardien CON SK DU EN SZ AG RB SC HS RT PH PS EX LD PO MO OV TA SP
1Scott Wedgewood100.0053678176515551585352305454050570
2Mark Visentin100.0044454272403537353333333532050410
Rayé
1Evan Smith (R)100.0033373568333232323232313230050370
MOYENNE D'ÉQUIPE100.004350537241414042393931403905045
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire


Astuces sur les Filtres (Anglais seulement)
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
# Nom du Joueur Nom de l'ÉquipePOSGP G A P +/- PIM PIM5 HIT HTT SHT OSB OSM SHT% SB MP AMG PPG PPA PPP PPS PPM PKG PKA PKP PKS PKM GW GT FO% FOT GA TA EG HT P/20 PSG PSS FW FL FT S1 S2 S3
1Michael McCarronStars (Dal)C/RW773838761479530115534911422610.89%29137317.836511481270001386356.05%15700001.1112010469
2Taylor RaddyshStars (Dal)C/RW56373976936101351103237923111.46%28119521.3491221501330005825159.38%25600101.2715101573
3Liam HawelStars (Dal)C772551761710022222259671899.65%13138217.963912211201015512254.32%186300011.1003000355
4Owen TippettDallasLW/RW4538377518280961282726117813.97%15105523.466101637102000111254347.71%41500021.42050001023
5Greg McKeggStars (Dal)C/LW511653691318044162225641587.11%28114022.37410142711400071284050.43%138000111.2126000432
6Alexander VolkovStars (Dal)LW/RW4526356128240122892287419611.40%1494721.067111857127000014349.30%7100001.2902000673
7Gustav ForslingDallasD60845531742018583127341076.30%135137322.9041216471540003142020.00%000100.7700000125
8Andrew MacDonaldStars (Dal)D64103242-6100408998346410.20%105115117.9947113797011188210.00%000000.7300000021
9Andrew PoturalskiStars (Dal)C77172340-1337568174211621278.06%23109914.281018230000191054.63%134000000.7300000110
10Hudson ElynuikStars (Dal)C77191635-294610155147242581807.85%28119515.5200000000001051.29%31000000.5900011220
11Martin FehervaryStars (Dal)D7752732-5500209499634545.21%77138618.00055271140000119100.00%000000.4600000013
12Jake BeanStars (Dal)D23524291116055235210269.62%4352923.043472760000358200.00%000101.0900000042
13Lucas LessioStars (Dal)LW7781725-1001519037668.89%11115214.9613412910000381028.57%11900000.4300000020
14Louie BelpedioStars (Dal)D7731720-19460181496620484.55%59100013.00022431000142000.00%000000.4000000002
15Justin FalkStars (Dal)D7731619-1634081515925575.08%7790511.7501113000031010.00%000000.4200000001
16Kenney MorrisonStars (Dal)D7735881407112173917.65%365176.7200012000024000.00%000000.3100000000
17Connor HallStars (Dal)D77077318056138460.00%375417.0300000000024000.00%000000.2600000000
18Jesse GabrielleStars (Dal)LW64011-12201347220.00%383213.02000040000240030.00%6000000.0200000000
Stats d'équipe Total ou en Moyenne1178261483744375103018351611272978219249.56%7611878115.9448911394041312112371040331652.32%597100440.79423122364439
Astuces sur les Filtres (Anglais seulement)
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
# Nom du Gardien Nom de l'ÉquipeGP W L OTL PCT GAA MP PIM SO GA SA SAR A EG PS % PSA ST BG S1 S2 S3
1Scott WedgewoodStars (Dal)77363350.9113.6843044026429560100.63219770712
2Mark VisentinStars (Dal)80210.9064.9934920293090000.0002077000
Stats d'équipe Total ou en Moyenne85363560.9103.7846546029332650100.571217777712


Astuces sur les Filtres (Anglais seulement)
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
Nom du Joueur Nom de l'ÉquipePOS Âge Date de Naissance Nouveau Joueur Poids Taille Non-Échange Disponible pour Échange Ballotage Forcé Contrat Type Salaire Actuel Salaire RestantCap Salariale Cap Salariale Restant Exclus du Cap Salarial Salaire Année 2Salaire Année 3Salaire Année 4Salaire Année 5Salaire Année 6Salaire Année 7Salaire Année 8Salaire Année 9Salaire Année 10Link
Alexander VolkovStars (Dal)LW/RW221997-08-02No191 Lbs6 ft1NoNoNo3Pro & Farm864,167$37,168$86,417$3,717$No864,167$864,167$Lien
Andrew MacDonaldStars (Dal)D331986-09-07No204 Lbs6 ft1NoNoNo2Pro & Farm2,500,000$107,526$250,000$10,753$No2,500,000$Lien
Andrew PoturalskiStars (Dal)C251994-01-14No190 Lbs5 ft11NoNoNo1Pro & Farm792,000$34,064$79,200$3,406$NoLien
Chris LeblancStars (Dal)RW261993-09-12Yes195 Lbs6 ft3NoNoNo1Pro & Farm560,000$24,086$56,000$2,409$NoLien
Connor HallStars (Dal)D211998-02-21Yes192 Lbs6 ft4NoNoNo2Pro & Farm700,000$30,107$70,000$3,011$No700,000$Lien
Evan SmithStars (Dal)G221997-02-27Yes181 Lbs6 ft6NoNoNo1Pro & Farm525,000$22,580$52,500$2,258$NoLien
Greg McKeggStars (Dal)C/LW271992-06-17No191 Lbs6 ft0NoNoNo1Pro & Farm700,000$30,107$70,000$3,011$NoLien
Henrik Sedin (Contrat à 1 Volet)Stars (Dal)C391980-09-26No183 Lbs6 ft2NoNoNo1Pro & Farm3,000,000$129,032$5,347,152$229,985$NoLien
Hudson ElynuikStars (Dal)C211997-10-12No194 Lbs6 ft5NoNoNo2Pro & Farm700,000$30,107$70,000$3,011$No700,000$Lien
Jake BeanStars (Dal)D211998-06-09No187 Lbs6 ft1NoNoNo2Pro & Farm925,000$39,784$925,000$39,785$No925,000$Lien
Jesse GabrielleStars (Dal)LW221997-06-17Yes205 Lbs6 ft0NoNoNo2Pro & Farm742,500$31,935$74,250$3,194$No742,500$Lien
Justin FalkStars (Dal)D301988-10-11No223 Lbs6 ft5NoNoNo2Pro & Farm800,000$34,408$80,000$3,441$No800,000$Lien
Kenney MorrisonStars (Dal)D271992-02-13Yes200 Lbs6 ft2NoNoNo1Pro & Farm825,000$35,483$82,500$3,548$NoLien
Liam HawelStars (Dal)C201999-04-18Yes183 Lbs6 ft5NoNoNo4Pro & Farm525,000$22,580$52,500$2,258$No525,000$525,000$525,000$Lien
Louie BelpedioStars (Dal)D231996-05-14No193 Lbs5 ft11NoNoNo2Pro & Farm925,000$39,784$92,500$3,978$No925,000$Lien
Lucas LessioStars (Dal)LW261993-01-23No212 Lbs6 ft1NoNoNo1Pro & Farm600,000$25,806$60,000$2,581$NoLien
Mark VisentinStars (Dal)G271992-08-07No195 Lbs6 ft2NoNoNo1Pro & Farm675,000$29,032$67,500$2,903$NoLien
Martin FehervaryStars (Dal)D191999-10-06Yes194 Lbs6 ft2NoNoNo4Pro & Farm805,835$34,659$80,584$3,466$No805,835$805,835$805,835$Lien
Matt SchmalzStars (Dal)C/RW231996-03-21Yes217 Lbs6 ft6NoNoNo1Pro & Farm525,000$22,580$52,500$2,258$NoLien
Michael McCarronStars (Dal)C/RW241995-03-06No231 Lbs6 ft6NoNoNo2Pro & Farm620,000$26,666$62,000$2,667$No620,000$Lien
Sam RuoppStars (Dal)D231996-06-03Yes195 Lbs6 ft4NoNoNo1Pro & Farm525,000$22,580$52,500$2,258$NoLien
Scott WedgewoodStars (Dal)G271992-08-14No195 Lbs6 ft2NoNoNo4Pro & Farm700,000$30,107$70,000$3,011$No700,000$700,000$700,000$Lien
Stephen DesrocherStars (Dal)D231996-01-26Yes206 Lbs6 ft4NoNoNo1Pro & Farm525,000$22,580$52,500$2,258$NoLien
Taylor RaddyshStars (Dal)C/RW211998-02-18No216 Lbs6 ft3NoNoNo2Pro & Farm742,500$31,935$74,250$3,194$No742,500$Lien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2424.67199 Lbs6 ft31.83866,750$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
140122
2Lucas LessioLiam HawelMichael McCarron30122
3Jesse GabrielleAndrew PoturalskiHudson Elynuik20122
4Hudson Elynuik10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
140122
2Andrew MacDonaldMartin Fehervary30122
3Louie BelpedioJustin Falk20122
4Kenney MorrisonConnor Hall10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
160122
2Lucas LessioLiam HawelMichael McCarron40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
160122
2Andrew MacDonaldMartin Fehervary40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
160122
2Liam Hawel40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
160122
2Andrew MacDonaldMartin Fehervary40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
16012260122
240122Andrew MacDonaldMartin Fehervary40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
160122
2Liam Hawel40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
160122
2Andrew MacDonaldMartin Fehervary40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Andrew Poturalski, Jesse Gabrielle, Michael McCarronAndrew Poturalski, Jesse GabrielleMichael McCarron
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Louie Belpedio, Justin Falk, Kenney MorrisonLouie BelpedioJustin Falk, Kenney Morrison
Tirs de Pénalité
, , , Liam Hawel, Michael McCarron
Gardien
#1 : Scott Wedgewood, #2 : Mark Visentin


Astuces sur les Filtres (Anglais seulement)
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
LigueDomicileVisiteur
# VS Équipe 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
1Admirals21100000660110000004311010000023-120.5006121800949779671955100095953881512518112.50%4175.00%01372274849.93%1422300547.32%695136350.99%175912081903566995486
2Baby Hawks412000011620-420100001710-321100000910-130.37516304600949779614095510009595318056289416425.00%14471.43%01372274849.93%1422300547.32%695136350.99%175912081903566995486
3Bears22000000963110000004311100000053241.0009172600949779668955100095953782332646350.00%11463.64%01372274849.93%1422300547.32%695136350.99%175912081903566995486
4Bruins21100000651110000003121010000034-120.5006111700949779672955100095953551410588225.00%5260.00%01372274849.93%1422300547.32%695136350.99%175912081903566995486
5Cabaret Lady Mary Ann220000001367110000005411100000082641.00013253800949779613195510009595379240483133.33%000.00%01372274849.93%1422300547.32%695136350.99%175912081903566995486
6Caroline21100000862110000006331010000023-120.5008152300949779696955100095953812215515120.00%4250.00%01372274849.93%1422300547.32%695136350.99%175912081903566995486
7Chiefs522001001316-32110000066031100100710-350.500132437009497796155955100095953228753512511218.18%11281.82%01372274849.93%1422300547.32%695136350.99%175912081903566995486
8Chill413000001122-1120200000412-821100000710-320.250112132009497796141955100095953225512710514321.43%10370.00%01372274849.93%1422300547.32%695136350.99%175912081903566995486
9Comets220000001073110000006421100000043141.0001018280094977967095510009595370231435200.00%7185.71%01372274849.93%1422300547.32%695136350.99%175912081903566995486
10Cougars2010000179-21000000145-11010000034-110.25071421009497796649551000959539428195810220.00%7271.43%01372274849.93%1422300547.32%695136350.99%175912081903566995486
11Crunch21100000810-21010000036-31100000054120.500813210094977969895510009595394191475500.00%6266.67%01372274849.93%1422300547.32%695136350.99%175912081903566995486
12Heat330000001486220000008531100000063361.0001426400094977961719551000959531073324835120.00%12191.67%01372274849.93%1422300547.32%695136350.99%175912081903566995486
13Jayhawks3120000089-11010000023-12110000066020.333816240094977961069551000959531383518765120.00%7185.71%01372274849.93%1422300547.32%695136350.99%175912081903566995486
14Las Vegas311000011112-1210000017701010000045-130.5001120310094977961289551000959539531149310330.00%6350.00%01372274849.93%1422300547.32%695136350.99%175912081903566995486
15Manchots210000017701000000134-11100000043130.7507132000949779674955100095953842620654250.00%10280.00%01372274849.93%1422300547.32%695136350.99%175912081903566995486
16Marlies2020000037-41010000023-11010000014-300.0003470094977965495510009595371218493133.33%40100.00%01372274849.93%1422300547.32%695136350.99%175912081903566995486
17Minnesota43100000241212220000001441021100000108260.7502443670094977962639551000959531682622997228.57%11190.91%01372274849.93%1422300547.32%695136350.99%175912081903566995486
18Monarchs20200000516-111010000029-71010000037-400.0005712009497796519551000959531143610522150.00%5260.00%11372274849.93%1422300547.32%695136350.99%175912081903566995486
19Monsters11000000523000000000001100000052321.0005101500949779641955100095953381110243266.67%5180.00%01372274849.93%1422300547.32%695136350.99%175912081903566995486
20Monsters41201000151502010100089-12110000076140.50015243900949779613895510009595317649389014642.86%18666.67%01372274849.93%1422300547.32%695136350.99%175912081903566995486
21Oceanics513000101626-1030300000821-132100001085340.400162642009497796136955100095953233643510817211.76%12191.67%01372274849.93%1422300547.32%695136350.99%175912081903566995486
22Oil Kings3210000012102211000007701100000053240.66712223400949779611395510009595313141187411218.18%80100.00%01372274849.93%1422300547.32%695136350.99%175912081903566995486
23Phantoms20100001610-41010000014-31000000156-110.250612180094977967295510009595395271266600.00%60100.00%01372274849.93%1422300547.32%695136350.99%175912081903566995486
24Rocket2110000056-11010000024-21100000032120.5005101500949779659955100095953832912555240.00%6183.33%01372274849.93%1422300547.32%695136350.99%175912081903566995486
25Senators2010100079-2100010004311010000036-320.5007142100949779672955100095953992010544125.00%5180.00%01372274849.93%1422300547.32%695136350.99%175912081903566995486
26Sharks20200000513-81010000024-21010000039-600.000591400949779676955100095953682812348112.50%6266.67%01372274849.93%1422300547.32%695136350.99%175912081903566995486
27Sound Tigers211000008711010000024-21100000063320.5008142200949779666955100095953892020484125.00%10370.00%01372274849.93%1422300547.32%695136350.99%175912081903566995486
28Spiders21100000440110000002111010000023-120.500471100949779665955100095953601664013323.08%30100.00%01372274849.93%1422300547.32%695136350.99%175912081903566995486
29Thunder2020000027-51010000013-21010000014-300.0002350094977967495510009595357231443300.00%6266.67%01372274849.93%1422300547.32%695136350.99%175912081903566995486
Total77333502115273298-2539151802004131154-2338181700111142144-2780.5062734967690094977962946955100095953326591252619542175223.96%2255376.44%11372274849.93%1422300547.32%695136350.99%175912081903566995486
30Wolf Pack22000000954110000004221100000053241.0009162500949779681955100095953872617375240.00%6350.00%01372274849.93%1422300547.32%695136350.99%175912081903566995486
_Since Last GM Reset77333502115273298-2539151802004131154-2338181700111142144-2780.5062734967690094977962946955100095953326591252619542175223.96%2255376.44%11372274849.93%1422300547.32%695136350.99%175912081903566995486
_Vs Conference41211501103164146182110701003857782011800100796910480.5851643004640094977961732955100095953172449127110561092724.77%1172677.78%01372274849.93%1422300547.32%695136350.99%175912081903566995486
_Vs Division26540100095111-161313010004762-151341000004849-1120.231951682630094977969739551000959531210321185621791924.05%761777.63%01372274849.93%1422300547.32%695136350.99%175912081903566995486

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
7778L527349676929463265912526195400
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
7733352115273298
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
3915182004131154
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
3818170111142144
Derniers 10 Matchs
WLOTWOTL SOWSOL
370000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
2175223.96%2255376.44%1
Tirs en 1e PériodeTirs en 2e PériodeTirs en 3e PériodeTirs en 4e PériodeButs en 1e PériodeButs en 2e PériodeButs en 3e PériodeButs en 4e Période
9551000959539497796
Mises en Jeu
Gagnées en Zone OffensiveTotal en Zone Offensive% Gagnées en Zone Offensive Gagnées en Zone DéfensiveTotal en Zone Défensive% Gagnées en Zone DéfensiveGagnées en Zone NeutreTotal en Zone Neutre% Gagnées en Zone Neutre
1372274849.93%1422300547.32%695136350.99%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
175912081903566995486


Derniers Match Joués
Astuces sur les Filtres (Anglais seulement)
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
JourMatch Équipe Visiteuse Score Équipe Locale Score ST OT SO RI Lien
2 - 2020-10-2310Bruins1Stars3WSommaire du Match
4 - 2020-10-2524Stars4Chiefs2WSommaire du Match
5 - 2020-10-2632Stars3Cougars4LSommaire du Match
7 - 2020-10-2839Stars5Bears3WSommaire du Match
9 - 2020-10-3055Heat3Stars4WSommaire du Match
11 - 2020-11-0170Bears3Stars4WSommaire du Match
13 - 2020-11-0382Stars5Crunch4WSommaire du Match
15 - 2020-11-0594Stars5Monsters2WSommaire du Match
17 - 2020-11-07108Stars4Manchots3WSommaire du Match
18 - 2020-11-08118Stars5Phantoms6LXXSommaire du Match
20 - 2020-11-10132Senators3Stars4WXSommaire du Match
23 - 2020-11-13152Admirals3Stars4WSommaire du Match
25 - 2020-11-15166Manchots4Stars3LXXSommaire du Match
28 - 2020-11-18186Minnesota2Stars7WSommaire du Match
31 - 2020-11-21201Stars3Monsters4LSommaire du Match
32 - 2020-11-22206Rocket4Stars2LSommaire du Match
35 - 2020-11-25230Monsters4Stars5WXSommaire du Match
40 - 2020-11-30264Stars4Oceanics3WXXSommaire du Match
43 - 2020-12-03284Stars6Heat3WSommaire du Match
44 - 2020-12-04291Stars4Comets3WSommaire du Match
46 - 2020-12-06300Stars5Oil Kings3WSommaire du Match
49 - 2020-12-09327Comets4Stars6WSommaire du Match
51 - 2020-12-11342Oceanics5Stars1LSommaire du Match
53 - 2020-12-13359Baby Hawks5Stars4LXXSommaire du Match
55 - 2020-12-15371Las Vegas5Stars4LXXSommaire du Match
56 - 2020-12-16376Stars5Baby Hawks2WSommaire du Match
59 - 2020-12-19402Chiefs2Stars4WSommaire du Match
61 - 2020-12-21416Stars3Minnesota4LSommaire du Match
63 - 2020-12-23431Stars4Oceanics2WSommaire du Match
65 - 2020-12-25445Oceanics9Stars4LSommaire du Match
67 - 2020-12-27461Sound Tigers4Stars2LSommaire du Match
70 - 2020-12-30478Spiders1Stars2WSommaire du Match
73 - 2021-01-02499Las Vegas2Stars3WSommaire du Match
74 - 2021-01-03505Stars5Chill4WSommaire du Match
76 - 2021-01-05523Oil Kings2Stars4WSommaire du Match
79 - 2021-01-08539Stars1Thunder4LSommaire du Match
80 - 2021-01-09548Stars8Cabaret Lady Mary Ann2WSommaire du Match
82 - 2021-01-11566Heat2Stars4WSommaire du Match
88 - 2021-01-17593Monsters5Stars3LSommaire du Match
89 - 2021-01-18609Stars2Jayhawks3LSommaire du Match
92 - 2021-01-21626Chill4Stars2LSommaire du Match
94 - 2021-01-23640Cougars5Stars4LXXSommaire du Match
99 - 2021-01-28677Stars3Monarchs7LSommaire du Match
100 - 2021-01-29687Stars2Admirals3LSommaire du Match
102 - 2021-01-31702Stars3Sharks9LSommaire du Match
105 - 2021-02-03722Stars4Monsters2WSommaire du Match
107 - 2021-02-05737Crunch6Stars3LSommaire du Match
109 - 2021-02-07753Stars7Minnesota4WSommaire du Match
118 - 2021-02-16771Thunder3Stars1LSommaire du Match
120 - 2021-02-18776Marlies3Stars2LSommaire du Match
123 - 2021-02-21798Stars2Spiders3LSommaire du Match
125 - 2021-02-23810Stars5Wolf Pack3WSommaire du Match
126 - 2021-02-24816Stars6Sound Tigers3WSommaire du Match
129 - 2021-02-27842Minnesota2Stars7WSommaire du Match
130 - 2021-02-28852Stars2Chiefs3LXSommaire du Match
133 - 2021-03-03873Caroline3Stars6WSommaire du Match
135 - 2021-03-05881Stars1Marlies4LSommaire du Match
137 - 2021-03-07900Stars3Rocket2WSommaire du Match
138 - 2021-03-08911Stars3Senators6LSommaire du Match
141 - 2021-03-11929Jayhawks3Stars2LSommaire du Match
143 - 2021-03-13944Chiefs4Stars2LSommaire du Match
145 - 2021-03-15960Baby Hawks5Stars3LSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
147 - 2021-03-17974Stars2Caroline3LSommaire du Match
149 - 2021-03-19984Stars3Bruins4LSommaire du Match
151 - 2021-03-211007Stars1Chiefs5LSommaire du Match
154 - 2021-03-241026Oil Kings5Stars3LSommaire du Match
156 - 2021-03-261039Stars2Chill6LSommaire du Match
158 - 2021-03-281051Chill8Stars2LSommaire du Match
161 - 2021-03-311076Wolf Pack2Stars4WSommaire du Match
163 - 2021-04-021090Cabaret Lady Mary Ann4Stars5WSommaire du Match
165 - 2021-04-041106Sharks4Stars2LSommaire du Match
167 - 2021-04-061122Stars4Jayhawks3WSommaire du Match
168 - 2021-04-071130Stars4Las Vegas5LSommaire du Match
171 - 2021-04-101149Phantoms4Stars1LSommaire du Match
173 - 2021-04-121168Oceanics7Stars3LSommaire du Match
175 - 2021-04-141184Monarchs9Stars2LSommaire du Match
178 - 2021-04-171202Stars4Baby Hawks8LSommaire du Match
179 - 2021-04-181212Monsters-Stars-
181 - 2021-04-201227Comets-Stars-
183 - 2021-04-221241Stars-Admirals-
184 - 2021-04-231254Stars-Sharks-
186 - 2021-04-251270Stars-Monarchs-



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité de l'Aréna20001000
Prix des Billets205
Assistance76,89638,103
Assistance PCT98.58%97.70%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacité de l'ArénaPopularité de l'Équipe
2 2949 - 98.29% 48,727$1,900,364$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
1,819,227$ 2,612,700$ 2,612,700$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
14,047$ 1,819,227$ 24 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
97,455$ 8 14,047$ 112,376$




LigueDomicileVisiteur
Année 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