Stars

GP: 4 | W: 4 | L: 0 | OTL: 0 | P: 8
GF: 17 | GA: 8 | PP%: 27.78% | PK%: 87.50%
DG: Pierre-Olivier Lefrançois | Morale : 56 | Moyenne d'Équipe : 63
Prochain matchs #65 vs Griffins
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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 SP
1Roope HintzX100.007438876886777267736665686764657257660
2Peter HollandX100.006338846579949363706261646275687057650
3Dale WeiseXX100.008242807082737666566164656279725157650
4T.J. HensickX100.005435956368787262736556575984745157610
5Nikita JevpalovsX100.006637875679939155575454565569656057600
6Nicholas MerkleyX100.005537876269868061706354565863627857600
7Zack MacEwenX100.006143845984777158625957595565635358600
8Stephen GiontaX100.005935936063726258675956645186763557600
9Nick LappinX100.005936926072846958585657595573674857590
10Isac Lundestrom (R)X100.005435936271746361726253645360628357590
11Jonathan Dahlen (R)X100.005236926157918659635856526063627357590
12Nikita Popugaev (R)X100.007935945596767054565253615461636457590
13Philippe MyersX100.007839836391856861306258634863625457650
14Matt IrwinX100.008254796379695762306856645180722757640
15Kevin GravelX100.005636935989776358306153684972674657630
16Timothy LiljegrenX100.006437896073857958305652615360628357610
17Jake Bean (R)X100.005536916175787160305855574761638557600
18Cam Dineen (R)X100.005335945558918654305352515461637057570
Rayé
1Devante Smith-PellyXX100.008341826379747061566264736573676346640
2Aaron LuchukX100.005035955657787255575453505463626343550
3Dylan McIlrathX100.007540795896856856305553724873677146660
4Chris BreenX100.008439835599878153305450614578705546650
5Mark BarberioX100.005636846277785061306658764877694046640
6Joe HickettsX100.005637885964897058305753684665635346620
MOYENNE D'ÉQUIPE100.00653888617781735950595662547066605462
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
1Josef Korenar100.00797573747877797877797861655357730
2Spencer Martin100.00757270857473757473757467715257720
Rayé
1Martin Ouellette (R)100.00776563697675777675777675814546720
2Felix Sandstrom (R)100.00746462797372747372747363677546690
3Stuart Skinner (R)100.00716563887069717069717061657646680
MOYENNE D'ÉQUIPE100.0075686679747375747375746570605071
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Scott Gordon81788481847863USA5641,000,000$


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 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
1Dale WeiseStars (Dal)LW/RW4336320722251513.64%08521.311123130001140083.33%600001.4111000101
2Isac LundestromStars (Dal)C41563000623154.35%07017.59022513000000062.07%8700001.7100000010
3Roope HintzStars (Dal)LW423504010112041910.00%07819.70022313000080064.44%9000001.2701000010
4Philippe MyersStars (Dal)D414526012773414.29%38521.40022314000010000.00%000001.1700000110
5Nick LappinStars (Dal)RW42133005291622.22%06817.14101413000001075.00%800000.8800000000
6Nicholas MerkleyStars (Dal)RW41230001611239.09%16917.421231140000180055.22%6700000.8600000001
7T.J. HensickStars (Dal)RW42132000352140.00%0379.4800000000001050.00%200001.5800000100
8Jake BeanStars (Dal)D41232002373414.29%06817.0000021200002000.00%000000.8800000000
9Jonathan DahlenStars (Dal)LW41230602283512.50%06716.89112213000000050.00%400000.8900000000
10Peter HollandStars (Dal)C4022200546170.00%04912.4200000000080068.00%2500000.8111000000
11Kevin GravelStars (Dal)D4022220420120.00%56817.2500000000013000.00%000000.5800000000
12Stephen GiontaStars (Dal)C41122200411039.09%0379.37000000000000100.00%200001.0700000000
13Zack MacEwenStars (Dal)C4011075424060.00%05814.7300000000140057.14%700000.3400001000
14Timothy LiljegrenStars (Dal)D4011200312350.00%59022.54000113000015000.00%000000.2200000000
15Cam DineenStars (Dal)D41010000141025.00%04912.4210131400000100.00%000000.4000000000
16Nikita JevpalovsStars (Dal)RW4000000147130.00%1379.48000000000000100.00%300000.0000000000
17Matt IrwinStars (Dal)D4000040701100.00%04912.4600001000011000.00%000000.0000000000
18Nikita PopugaevStars (Dal)LW4000040522020.00%0399.81000000000100100.00%200000.0000000000
Stats d'équipe Total ou en Moyenne72163046233756862149329010.74%15111315.47510152714100021103063.04%30300000.8323001332
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
1Josef KorenarStars (Dal)44000.9051.96245008840001.000240000
Stats d'équipe Total ou en Moyenne44000.9051.96245008840001.000240000


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 StatusType Salaire Actuel Cap Salariale Cap Salariale Restant Exclus du Cap Salarial Link
Aaron LuchukStars (Dal)C221997-04-04No180 Lbs5 ft1NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$NoLien
Cam DineenStars (Dal)D211998-06-19Yes183 Lbs5 ft1NoNoNo4Contrat d'EntréePro & Farm500,000$0$0$NoLien
Chris BreenStars (Dal)D301989-06-29No226 Lbs6 ft7NoNoNo1Sans RestrictionPro & Farm300,000$0$0$NoLien
Dale WeiseStars (Dal)LW/RW301988-08-05No206 Lbs6 ft2NoNoNo2Sans RestrictionPro & Farm936,481$0$0$NoLien
Devante Smith-PellyStars (Dal)LW/RW271992-06-14No223 Lbs6 ft0NoNoNo1Avec RestrictionPro & Farm1,000,000$0$0$NoLien
Dylan McIlrathStars (Dal)D271992-04-20No236 Lbs6 ft5NoNoNo4Avec RestrictionPro & Farm963,777$0$0$NoLien
Felix SandstromStars (Dal)G221997-01-12Yes191 Lbs6 ft2NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$NoLien
Isac LundestromStars (Dal)C191999-11-06Yes187 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm900,000$0$0$NoLien
Jake BeanStars (Dal)D211998-06-09Yes186 Lbs6 ft1NoNoNo4Contrat d'EntréePro & Farm900,000$0$0$NoLien
Joe HickettsStars (Dal)D231996-05-04No180 Lbs5 ft8NoNoNo2Avec RestrictionPro & Farm300,000$0$0$NoLien
Jonathan DahlenStars (Dal)LW211997-12-20Yes176 Lbs5 ft1NoNoNo4Contrat d'EntréePro & Farm900,000$0$0$NoLien
Josef KorenarStars (Dal)G211998-01-31No185 Lbs6 ft1NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$NoLien
Kevin GravelStars (Dal)D271992-03-06No211 Lbs6 ft4NoNoNo1Avec RestrictionPro & Farm973,000$0$0$NoLien
Mark BarberioStars (Dal)D291990-03-23No200 Lbs6 ft1NoNoNo1Sans RestrictionPro & Farm995,999$0$0$NoLien
Martin OuelletteStars (Dal)G271991-12-30Yes160 Lbs6 ft1NoNoNo1Avec RestrictionPro & Farm500,000$0$0$NoLien
Matt IrwinStars (Dal)D311987-11-29No207 Lbs6 ft1NoNoNo1Sans RestrictionPro & Farm1,000,000$0$0$NoLien
Nicholas MerkleyStars (Dal)RW221997-05-23No194 Lbs5 ft10NoNoNo3Contrat d'EntréePro & Farm900,000$0$0$NoLien
Nick LappinStars (Dal)RW261992-11-01No175 Lbs6 ft1NoNoNo2Avec RestrictionPro & Farm300,000$0$0$NoLien
Nikita JevpalovsStars (Dal)RW241994-09-09No210 Lbs6 ft1NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Nikita PopugaevStars (Dal)LW201998-11-20Yes217 Lbs6 ft6NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$NoLien
Peter HollandStars (Dal)C281991-01-14No193 Lbs6 ft2NoNoNo3Sans RestrictionPro & Farm915,775$0$0$NoLien
Philippe MyersStars (Dal)D221997-01-25No210 Lbs6 ft5NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$NoLien
Roope HintzStars (Dal)LW221996-11-17No215 Lbs6 ft3NoNoNo3Contrat d'EntréePro & Farm500,000$0$0$NoLien
Spencer MartinStars (Dal)G241995-06-08No210 Lbs6 ft3NoNoNo1Avec RestrictionPro & Farm500,000$0$0$NoLien
Stephen GiontaStars (Dal)C351983-10-09No177 Lbs5 ft7NoNoNo1Sans RestrictionPro & Farm300,000$0$0$NoLien
Stuart SkinnerStars (Dal)G201998-11-01Yes206 Lbs6 ft4NoNoNo4Contrat d'EntréePro & Farm500,000$0$0$NoLien
T.J. HensickStars (Dal)RW331985-12-10No190 Lbs5 ft10NoNoNo1Sans RestrictionPro & Farm498,888$0$0$NoLien
Timothy LiljegrenStars (Dal)D201999-04-30No192 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm900,000$0$0$NoLien
Zack MacEwenStars (Dal)C221996-07-08No205 Lbs6 ft3NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$NoLien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2924.69198 Lbs6 ft02.59606,342$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Dale WeiseIsac LundestromNick Lappin40122
2Jonathan DahlenRoope HintzZack MacEwen30122
3Nikita PopugaevNicholas MerkleyNikita Jevpalovs20122
4T.J. HensickPeter HollandStephen Gionta10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Timothy LiljegrenPhilippe Myers40122
2Kevin GravelJake Bean30122
3Cam DineenMatt Irwin20122
410122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Dale WeiseIsac LundestromNicholas Merkley60122
2Jonathan DahlenRoope HintzNick Lappin40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Timothy LiljegrenJake Bean60122
2Philippe MyersCam Dineen40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Nicholas MerkleyDale Weise60122
2Roope HintzPeter Holland40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Matt IrwinKevin Gravel60122
2Philippe MyersTimothy Liljegren40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Peter Holland60122Matt IrwinKevin Gravel60122
2Roope Hintz40122Philippe MyersTimothy Liljegren40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Isac LundestromJonathan Dahlen60122
2Zack MacEwenNikita Popugaev40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Timothy LiljegrenJake Bean60122
2Cam DineenPhilippe Myers40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Dale WeisePeter HollandNicholas MerkleyMatt IrwinKevin Gravel
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Roope HintzPeter HollandDale WeiseMatt IrwinKevin Gravel
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Peter Holland, Isac Lundestrom, Roope HintzNikita Popugaev, Nikita JevpalovsZack MacEwen
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Matt Irwin, Kevin Gravel, Timothy LiljegrenMatt IrwinKevin Gravel, Matt Irwin
Tirs de Pénalité
Roope Hintz, Dale Weise, Peter Holland, Jonathan Dahlen, Nicholas Merkley
Gardien
#1 : Josef Korenar, #2 : Spencer Martin


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
1Admirals11000000321110000003210000000000021.00035800943228574149622412245120.00%6183.33%09213468.66%6010060.00%396065.00%1198875254926
2Griffins11000000624110000006240000000000021.000612180094323457414961139155360.00%20100.00%09213468.66%6010060.00%396065.00%1198875254926
3IceHogs11000000413000000000001100000041321.00047110094325657414962144198112.50%20100.00%09213468.66%6010060.00%396065.00%1198875254926
Total430000101789220000009452100001084481.00017304700943215057414968417377218527.78%16287.50%09213468.66%6010060.00%396065.00%1198875254926
5Wild10000010431000000000001000001043121.00046100094323257414963061214000.00%6183.33%09213468.66%6010060.00%396065.00%1198875254926
_Since Last GM Reset430000101789220000009452100001084481.00017304700943215057414968417377218527.78%16287.50%09213468.66%6010060.00%396065.00%1198875254926
_Vs Conference430000101789220000009452100001084481.00017304700943215057414968417377218527.78%16287.50%09213468.66%6010060.00%396065.00%1198875254926

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
48W11730471508417377200
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
4300010178
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
220000094
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
210001084
Derniers 10 Matchs
WLOTWOTL SOWSOL
300010
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
18527.78%16287.50%0
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
57414969432
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
9213468.66%6010060.00%396065.00%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
1198875254926


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
3 - 2019-09-045Griffins2Stars6WSommaire du Match
4 - 2019-09-0519Admirals2Stars3WSommaire du Match
10 - 2019-09-1139Stars4Wild3WXXSommaire du Match
11 - 2019-09-1250Stars4IceHogs1WSommaire du Match
15 - 2019-09-1665Stars-Griffins-
17 - 2019-09-1878Wild-Stars-
18 - 2019-09-1991Wild-Stars-
24 - 2019-09-25113Wolves-Stars-
25 - 2019-09-26127Barracuda-Stars-
31 - 2019-10-02151Stars-Reign-
32 - 2019-10-03166Stars-Condors-
39 - 2019-10-10201Rampage-Stars-
40 - 2019-10-11208Stars-Rampage-
43 - 2019-10-14220Moose-Stars-
45 - 2019-10-16231IceHogs-Stars-
52 - 2019-10-23273Stars-Rampage-
53 - 2019-10-24287Rampage-Stars-
54 - 2019-10-25295Stars-Rampage-
59 - 2019-10-30314Stars-Admirals-
60 - 2019-10-31325Stars-Griffins-
61 - 2019-11-01336Stars-Wolves-
64 - 2019-11-04345Admirals-Stars-
66 - 2019-11-06357Admirals-Stars-
71 - 2019-11-11388Wild-Stars-
73 - 2019-11-13398Stars-Admirals-
74 - 2019-11-14407Stars-IceHogs-
77 - 2019-11-17422Stars-Wolves-
80 - 2019-11-20444Rampage-Stars-
81 - 2019-11-21460Stars-Rampage-
86 - 2019-11-26474Rampage-Stars-
88 - 2019-11-28496Reign-Stars-
89 - 2019-11-29502Stars-Rampage-
94 - 2019-12-04526Moose-Stars-
95 - 2019-12-05541Moose-Stars-
99 - 2019-12-09557Condors-Stars-
101 - 2019-12-11569IceHogs-Stars-
102 - 2019-12-12583Wild-Stars-
106 - 2019-12-16604Stars-Gulls-
108 - 2019-12-18614Stars-Barracuda-
109 - 2019-12-19628Stars-Heat-
111 - 2019-12-21642Stars-Roadrunners-
113 - 2019-12-23651Stars-Roadrunners-
115 - 2019-12-25664Heat-Stars-
116 - 2019-12-26680Gulls-Stars-
121 - 2019-12-31682Griffins-Stars-
127 - 2020-01-06721Stars-Admirals-
130 - 2020-01-09742Stars-Moose-
131 - 2020-01-10750Stars-Moose-
133 - 2020-01-12759Stars-IceHogs-
136 - 2020-01-15777Roadrunners-Stars-
137 - 2020-01-16790Roadrunners-Stars-
140 - 2020-01-19807Stars-Wild-
142 - 2020-01-21814Stars-Wolves-
143 - 2020-01-22820Stars-Griffins-
145 - 2020-01-24844Stars-Wolves-
148 - 2020-01-27853Admirals-Stars-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
150 - 2020-01-29863Wolves-Stars-
151 - 2020-01-30877Wolves-Stars-
154 - 2020-02-02889Stars-Admirals-
155 - 2020-02-03891Stars-Griffins-
158 - 2020-02-06919Stars-Rampage-
159 - 2020-02-07929Rampage-Stars-
162 - 2020-02-10940Stars-Moose-
164 - 2020-02-12951Stars-Moose-
166 - 2020-02-14970Stars-IceHogs-
169 - 2020-02-17982Griffins-Stars-
171 - 2020-02-19990Wolves-Stars-
172 - 2020-02-201004Griffins-Stars-
176 - 2020-02-241024Moose-Stars-
178 - 2020-02-261034Stars-Wild-
179 - 2020-02-271046Stars-Wild-
185 - 2020-03-041080IceHogs-Stars-
186 - 2020-03-051094IceHogs-Stars-
190 - 2020-03-091110Rampage-Stars-
192 - 2020-03-111124Stars-Rampage-
193 - 2020-03-121138Rampage-Stars-



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité de l'Aréna20001000
Prix des Billets3515
Assistance00
Assistance PCT0.00%0.00%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacité de l'ArénaPopularité de l'Équipe
36 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
78,981$ 175,840$ 21,320$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 11,966$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 181 6,061$ 1,097,041$




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
144300001017892200000094521000010844817304700943215057414968417377218527.78%16287.50%09213468.66%6010060.00%396065.00%1198875254926
Total Saison Régulière4300001017892200000094521000010844817304700943215057414968417377218527.78%16287.50%09213468.66%6010060.00%396065.00%1198875254926