Phantoms
GP: 82 | W: 42 | L: 32 | OTL: 8 | P: 92
GF: 232 | GA: 221 | PP%: 12.26% | PK%: 86.68%
DG: Yannick Bernier | Morale : 50 | Moyenne d'Équipe : N/A
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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
1Cole Bardreau (R)XX100.00864582636652625459605968255555635000263700,000$
2Lane PedersonX100.00717170667168696780636763644444675000223777,500$
3Gemel SmithXXX100.006968727268747667805968656556566750002611,000,000$
4Jack KopackaX100.00807298627256565650505765544444605000222910,833$
5Melker KarlssonXX100.006742897865628757406059777566696545002912,000,000$
6Mikkel BoedkerXX100.006743998576546362326059726175776444003011,000,000$
7Maxim LetunovX100.007771916271707265806660665744446550002411,000,000$
8Nathan BastianX100.00777875627877816250586265594444645000223992,500$
9Pontus AbergXX100.007370817670757866506265676254556751002611,000,000$
10Jack Studnicka (R)X100.00716585736575786580616563624444675000213863,333$
11Evan RodriguesXXX100.006541907766597761655964707563636650002611,000,000$
12Valentin ZykovXX100.007845877180625964256558562548486250002511,000,000$
13Alex Formenton (R)X100.00727371767367686650616864654444675000203888,333$
14Mark AltX100.00787781657770764725374262404444535000281650,000$
15Luke GreenX100.00756990606940404225284159394444485000222838,333$
16Mark FriedmanX100.00716781646775815125474159394444545000242825,000$
17Reece Willcox (R)X100.00777092627071784725394061384444535000263675,000$
18Josh Brook (R)X100.00737179697173804725374159394444525000213910,833$
Rayé
1Givani SmithXX100.00857687667654787025555961254545635000222913,333$
2Stelio MattheosX100.00787291557262645550495764544444605000212925,000$
3Lean Bergmann (R)XX100.008946897576516451265855562545456050002111,000,000$
4Danil Yurtaykin (R)XX100.006961897161606255506046594444445650002211,000,000$
5Tyrell GoulbourneXX100.00727078687069764550414458424444525000261775,000$
MOYENNE D'ÉQUIPE100.0075648569716471574654566349494961500
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
1Anton Forsberg100.0055617678555750585352304848554400
2Charlie Lindgren100.0051587375485354585252304646535000
Rayé
1Joseph Woll (R)100.0050648080455250554949304444524300
MOYENNE D'ÉQUIPE100.005261767849545157515130464653460
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Dominique Ducharme73727068696479CAN493850,000$


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'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
1Gemel SmithPhantoms (PHI)C/LW/RW8234367096001011372085716416.35%13149318.211411256435720251758058.85%81900020.9401000957
2Mikkel BoedkerPhantoms (PHI)LW/RW80243458-214063162161449814.91%18169221.15715224033623564023138.70%89400000.69010000345
3Evan RodriguesPhantoms (PHI)C/LW/RW82183856-618043169150599712.00%14147417.98915244533822452583253.28%109600000.7645000243
4Valentin ZykovPhantoms (PHI)LW/RW82242751123808363123318419.51%5120314.684121634274000023031.03%5800000.8500000532
5Pontus AbergPhantoms (PHI)LW/RW7319274606001731141415511413.48%22152520.90512174034623583852246.06%16500000.60411000537
6Melker KarlssonPhantoms (PHI)C/RW5513314432605110392247714.13%28105619.21511162825102231891243.64%102200000.8358000152
7Jack StudnickaPhantoms (PHI)C821427411336034148132369910.61%13116814.261018490002903159.39%95300000.7000000353
8Alex FormentonPhantoms (PHI)LW8215264114831581110162551229.26%7121414.81178221750001855147.31%9300000.6800021355
9Lane PedersonPhantoms (PHI)C8242327138810118574717398.51%3292811.330002270000130155.19%15400000.5800001025
10Nathan BastianPhantoms (PHI)RW829152446820615687245410.34%97429.0500003000001151.72%2900000.6500112104
11Mark AltPhantoms (PHI)D7681119-2129251473539161920.51%52163521.53325183261121321200.00%000000.2300212111
12Maxim LetunovPhantoms (PHI)C8299181412028524693519.57%96057.3802234700011400160.12%32600100.5900000012
13Josh BrookPhantoms (PHI)D72411154995138282382117.39%58155821.64336132990110324000.00%000000.1900001000
14Cole BardreauPhantoms (PHI)C/RW7277144480503267114010.45%85757.990002240000242146.84%7900000.4900000111
15Mark FriedmanPhantoms (PHI)D8231114-21415134283313179.09%53191223.32156183770001370000.00%000100.1500010011
16Reece WillcoxPhantoms (PHI)D8231114410951333924112112.50%49155518.9711272210001254100.00%000000.1800100200
17Lean BergmannPhantoms (PHI)LW/RW5459145500423836121913.89%255010.1900016000001141.38%2900000.5100000100
18Givani SmithPhantoms (PHI)LW/RW19189-22952912188215.56%328414.99134677000010052.94%1700000.6300100010
19Jack KopackaPhantoms (PHI)LW375492607112892517.86%32777.500002100000111141.18%1700000.6500000110
20Luke GreenPhantoms (PHI)D4712310440671811079.09%2671815.290001310000103000.00%000000.0800000001
Stats d'équipe Total ou en Moyenne140522036758797115890158314121628499117313.51%4242217415.785599154354358391221343157361550.83%575100220.531335557384249
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
1Anton ForsbergPhantoms (PHI)80413180.8702.6247020420515800020.62532800332
2Charlie LindgrenPhantoms (PHI)91100.9161.962760091070001.0002282000
Stats d'équipe Total ou en Moyenne89423280.8732.5849790421416870020.647348282332


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 RestantSalaire MoyenSalaire Moyen 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
Alex FormentonPhantoms (PHI)LW201999-09-13Yes190 Lbs6 ft3NoNoNo3Pro & Farm888,333$7,164$888,333$7,164$0$0$No888,333$888,333$
Anton ForsbergPhantoms (PHI)G271992-11-26No192 Lbs6 ft3NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Charlie LindgrenPhantoms (PHI)G261993-12-17No190 Lbs6 ft2NoNoNo1Pro & Farm1,300,000$10,484$1,300,000$10,484$0$0$NoLien
Cole BardreauPhantoms (PHI)C/RW261993-07-22Yes185 Lbs5 ft10NoNoNo3Pro & Farm700,000$5,645$700,000$5,645$0$0$No700,000$700,000$
Danil YurtaykinPhantoms (PHI)LW/RW221997-07-01Yes165 Lbs5 ft11NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Evan RodriguesPhantoms (PHI)C/LW/RW261993-07-28No182 Lbs5 ft11NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Gemel Smith (Contrat à 1 Volet)Phantoms (PHI)C/LW/RW261994-04-16No190 Lbs5 ft10NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$100,000$806$NoLien
Givani SmithPhantoms (PHI)LW/RW221998-02-27No204 Lbs6 ft2NoNoNo2Pro & Farm913,333$7,366$913,333$7,366$0$0$No913,333$Lien
Jack KopackaPhantoms (PHI)LW221998-05-05No192 Lbs6 ft2NoNoNo2Pro & Farm910,833$7,345$910,000$7,339$0$0$No910,833$Lien
Jack StudnickaPhantoms (PHI)C211999-02-17Yes171 Lbs6 ft1NoNoNo3Pro & Farm863,333$6,962$863,333$6,962$0$0$No863,333$863,333$Lien
Joseph WollPhantoms (PHI)G211998-07-12Yes200 Lbs6 ft3NoNoNo3Pro & Farm850,000$6,855$800,000$6,452$0$0$No800,000$800,000$Lien
Josh BrookPhantoms (PHI)D211999-06-17Yes192 Lbs6 ft1NoNoNo3Pro & Farm910,833$7,345$910,833$7,345$0$0$No910,833$910,833$
Lane PedersonPhantoms (PHI)C221997-08-04No192 Lbs6 ft1NoNoNo3Pro & Farm777,500$6,270$690,000$5,565$0$0$No690,000$690,000$Lien
Lean BergmannPhantoms (PHI)LW/RW211998-10-04Yes205 Lbs6 ft2NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Luke GreenPhantoms (PHI)D221998-01-11No188 Lbs6 ft1NoNoNo2Pro & Farm838,333$6,761$850,000$6,855$0$0$No838,333$Lien
Mark AltPhantoms (PHI)D281991-10-17No201 Lbs6 ft4NoNoNo1Pro & Farm650,000$5,242$650,000$5,242$0$0$NoLien
Mark FriedmanPhantoms (PHI)D241995-12-25No185 Lbs5 ft11NoNoNo2Pro & Farm825,000$6,653$825,000$6,653$0$0$No825,000$Lien
Maxim LetunovPhantoms (PHI)C241996-02-19No180 Lbs6 ft4NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Melker KarlssonPhantoms (PHI)C/RW291990-07-18No182 Lbs5 ft10NoNoNo1Pro & Farm2,000,000$16,129$2,000,000$16,129$0$0$NoLien
Mikkel BoedkerPhantoms (PHI)LW/RW301989-12-15No210 Lbs6 ft0NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Nathan BastianPhantoms (PHI)RW221997-12-06No205 Lbs6 ft4NoNoNo3Pro & Farm992,500$8,004$905,000$7,298$0$0$No905,000$905,000$Lien
Pontus AbergPhantoms (PHI)LW/RW261993-09-22No196 Lbs5 ft11NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Reece WillcoxPhantoms (PHI)D261994-05-20Yes183 Lbs6 ft3NoNoNo3Pro & Farm675,000$5,444$675,000$5,444$0$0$No675,000$675,000$
Stelio MattheosPhantoms (PHI)RW211999-06-13No198 Lbs6 ft1NoNoNo2Pro & Farm925,000$7,460$925,000$7,460$0$0$No925,000$Lien
Tyrell GoulbournePhantoms (PHI)LW/RW261994-01-25No195 Lbs5 ft11NoNoNo1Pro & Farm775,000$6,250$775,000$6,250$0$0$NoLien
Valentin ZykovPhantoms (PHI)LW/RW251995-05-14No224 Lbs6 ft1NoNoNo1Pro & Farm1,000,000$8,065$1,000,000$8,065$0$0$NoLien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2624.08192 Lbs6 ft11.81953,654$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Mikkel BoedkerMelker KarlssonPontus Aberg35122
2Gemel SmithEvan RodriguesValentin Zykov30122
3Alex FormentonJack StudnickaCole Bardreau25122
4Jack KopackaLane PedersonNathan Bastian10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Mark FriedmanMark Alt35122
2Reece WillcoxJosh Brook30122
3Luke GreenLane Pederson25122
4Mark FriedmanMark Alt10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Mikkel BoedkerMelker KarlssonPontus Aberg60122
2Gemel SmithEvan RodriguesValentin Zykov40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Mark FriedmanMark Alt60122
2Reece WillcoxJosh Brook40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Mikkel BoedkerPontus Aberg60122
2Melker KarlssonEvan Rodrigues40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Mark FriedmanMark Alt60122
2Reece WillcoxJosh Brook40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Mikkel Boedker60122Mark FriedmanMark Alt60122
2Pontus Aberg40122Reece WillcoxJosh Brook40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Mikkel BoedkerPontus Aberg60122
2Melker KarlssonEvan Rodrigues40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Mark FriedmanMark Alt60122
2Reece WillcoxJosh Brook40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Mikkel BoedkerMelker KarlssonPontus AbergMark FriedmanMark Alt
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Mikkel BoedkerMelker KarlssonPontus AbergMark FriedmanMark Alt
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Maxim Letunov, Jack Studnicka, Alex FormentonMaxim Letunov, Jack StudnickaAlex Formenton
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Luke Green, Reece Willcox, Josh BrookLuke GreenReece Willcox, Josh Brook
Tirs de Pénalité
Mikkel Boedker, Pontus Aberg, Melker Karlsson, Evan Rodrigues, Gemel Smith
Gardien
#1 : Anton Forsberg, #2 : Charlie Lindgren


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
1Admirals21000100532110000004131000010012-130.750581300927162154758955253460271142359111.11%190100.00%01215233052.15%1110228048.68%617118751.98%199413812005611988490
2Bruins30200010612-62010001046-21010000026-420.3336915009271621556589552534607012406225312.00%20385.00%01215233052.15%1110228048.68%617118751.98%199413812005611988490
3Condors210000016601000000123-11100000043130.7506111700927162153858955253460591229385120.00%12283.33%01215233052.15%1110228048.68%617118751.98%199413812005611988490
4Crunch523000001718-111000000541413000001214-240.40017304700927162151285895525346011737659139615.38%29486.21%11215233052.15%1110228048.68%617118751.98%199413812005611988490
5Flames20000110660100000103211000010034-130.75061016009271621537589552534603013164319210.53%7271.43%01215233052.15%1110228048.68%617118751.98%199413812005611988490
6Griffins431000001679211000009632200000071660.750163147019271621577589552534608422599322522.73%25196.00%01215233052.15%1110228048.68%617118751.98%199413812005611988490
7IceHogs2020000037-41010000024-21010000013-200.000369009271621536589552534603611315911218.18%13376.92%11215233052.15%1110228048.68%617118751.98%199413812005611988490
8Marlies32100000963220000007341010000023-140.6679142300927162156758955253460651343551516.67%21385.71%01215233052.15%1110228048.68%617118751.98%199413812005611988490
9Monarchs2010001056-11010000024-21000001032120.500581310927162154658955253460331124458112.50%10190.00%11215233052.15%1110228048.68%617118751.98%199413812005611988490
10Monsters1383000023830865000001221210733000011618-2180.692386510300927162152715895525346021659186238821417.07%841285.71%11215233052.15%1110228048.68%617118751.98%199413812005611988490
11Moose2010000137-41000000134-11010000003-310.25035800927162152858955253460311428341417.14%13192.31%01215233052.15%1110228048.68%617118751.98%199413812005611988490
12Penguins844000001316-3422000007704220000069-380.500132235019271621513958955253460171641181454249.52%53394.34%01215233052.15%1110228048.68%617118751.98%199413812005611988490
13Rampage43100000201193210000014861100000063360.75020355500927162151165895525346010025558630723.33%23578.26%01215233052.15%1110228048.68%617118751.98%199413812005611988490
14Rocket32100000752220000004131010000034-140.66771320019271621554589552534605518344415213.33%16193.75%01215233052.15%1110228048.68%617118751.98%199413812005611988490
15Senators312000001011-11010000023-12110000088020.3331015250092716215655895525346058144662900.00%22672.73%01215233052.15%1110228048.68%617118751.98%199413812005611988490
16Sharks21100000871110000005231010000035-220.500812200092716215435895525346033553381119.09%130100.00%11215233052.15%1110228048.68%617118751.98%199413812005611988490
17Soldiers22000000413110000002021100000021141.000461001927162153958955253460321730331616.25%13192.31%01215233052.15%1110228048.68%617118751.98%199413812005611988490
18Sound Tigers824010102526-1413000001215-3411010101311280.500254166009271621519058955253460235651371923738.11%591083.05%11215233052.15%1110228048.68%617118751.98%199413812005611988490
19Stars21000100761110000005321000010023-130.7507121900927162154758955253460371520321715.88%10190.00%01215233052.15%1110228048.68%617118751.98%199413812005611988490
20Wolf Pack824000111824-641200001811-3412000101013-370.438183149109271621514758955253460155391271643925.13%591181.36%21215233052.15%1110228048.68%617118751.98%199413812005611988490
21Wolves20100010660100000103211010000034-120.500610160092716215285895525346045152941800.00%12191.67%11215233052.15%1110228048.68%617118751.98%199413812005611988490
Total8235320136523222111412113000341251012441141901331107120-13920.56123239462624927162151699589552534601689492121216304735812.26%5337186.68%91215233052.15%1110228048.68%617118751.98%199413812005611988490
_Since Last GM Reset8235320136523222111412113000341251012441141901331107120-13920.56123239462624927162151699589552534601689492121216304735812.26%5337186.68%91215233052.15%1110228048.68%617118751.98%199413812005611988490
_Vs Conference48212000133124128-4231360002262491325814001116279-17520.5421242093331292716215964589552534609372696759042853411.93%3114585.53%41215233052.15%1110228048.68%617118751.98%199413812005611988490
_Vs Division371411001239496-2188400012494541967001114551-6360.486941592531192716215747589552534607772275687392002311.50%2553685.88%41215233052.15%1110228048.68%617118751.98%199413812005611988490

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
8292W4232394626169916894921212163024
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
8235321365232221
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
4121130034125101
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
4114191331107120
Derniers 10 Matchs
WLOTWOTL SOWSOL
820000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
4735812.26%5337186.68%9
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
5895525346092716215
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
1215233052.15%1110228048.68%617118751.98%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
199413812005611988490


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
1 - 2020-09-271Phantoms5Wolf Pack4WXXSommaire du Match
2 - 2020-09-2813Monsters1Phantoms3WSommaire du Match
4 - 2020-09-3030Phantoms3Monsters1WSommaire du Match
5 - 2020-10-0137Monsters4Phantoms3LXXSommaire du Match
6 - 2020-10-0247Phantoms0Monsters1LSommaire du Match
8 - 2020-10-0461Wolf Pack2Phantoms1LSommaire du Match
10 - 2020-10-0675Wolf Pack4Phantoms2LSommaire du Match
12 - 2020-10-0882Phantoms3Monsters4LXXSommaire du Match
15 - 2020-10-1198Bruins2Phantoms3WXXSommaire du Match
16 - 2020-10-12104Phantoms2Monsters4LSommaire du Match
18 - 2020-10-14114Phantoms0Penguins3LSommaire du Match
20 - 2020-10-16129Sound Tigers2Phantoms0LSommaire du Match
22 - 2020-10-18137Monsters4Phantoms6WSommaire du Match
23 - 2020-10-19149Phantoms1Penguins3LSommaire du Match
24 - 2020-10-20156Phantoms3Wolf Pack2WSommaire du Match
25 - 2020-10-21168Rampage5Phantoms4LSommaire du Match
26 - 2020-10-22178Phantoms0Monsters3LSommaire du Match
28 - 2020-10-24190Monsters1Phantoms3WSommaire du Match
31 - 2020-10-27206Senators3Phantoms2LSommaire du Match
32 - 2020-10-28216Phantoms4Monsters2WSommaire du Match
34 - 2020-10-30224Phantoms0Wolf Pack3LSommaire du Match
36 - 2020-11-01235Sound Tigers4Phantoms3LSommaire du Match
38 - 2020-11-03250Rocket0Phantoms2WSommaire du Match
40 - 2020-11-05261Phantoms2Soldiers1WSommaire du Match
42 - 2020-11-07269Marlies2Phantoms5WSommaire du Match
45 - 2020-11-10287Wolf Pack1Phantoms2WSommaire du Match
46 - 2020-11-11301Phantoms3Senators5LSommaire du Match
48 - 2020-11-13310Penguins0Phantoms4WSommaire du Match
50 - 2020-11-15332Penguins1Phantoms0LSommaire du Match
51 - 2020-11-16343Phantoms6Rampage3WSommaire du Match
53 - 2020-11-18353Phantoms2Sound Tigers3LSommaire du Match
54 - 2020-11-19360Rampage1Phantoms4WSommaire du Match
55 - 2020-11-20370Phantoms4Sound Tigers3WSommaire du Match
56 - 2020-11-21379Rocket1Phantoms2WSommaire du Match
58 - 2020-11-23397Phantoms4Sound Tigers3WXXSommaire du Match
59 - 2020-11-24407Monarchs4Phantoms2LSommaire du Match
60 - 2020-11-25418Griffins2Phantoms7WSommaire du Match
62 - 2020-11-27431Phantoms3Flames4LXSommaire du Match
63 - 2020-11-28441Marlies1Phantoms2WSommaire du Match
64 - 2020-11-29449Phantoms4Monsters3WSommaire du Match
66 - 2020-12-01461Griffins4Phantoms2LSommaire du Match
68 - 2020-12-03477Phantoms3Rocket4LSommaire du Match
69 - 2020-12-04486Phantoms1Admirals2LXSommaire du Match
70 - 2020-12-05490Bruins4Phantoms1LSommaire du Match
71 - 2020-12-06506Crunch4Phantoms5WSommaire du Match
73 - 2020-12-08518Phantoms2Wolf Pack4LSommaire du Match
74 - 2020-12-09527Penguins4Phantoms0LSommaire du Match
75 - 2020-12-10538Phantoms3Crunch4LSommaire du Match
77 - 2020-12-12544Phantoms3Sound Tigers2WXSommaire du Match
78 - 2020-12-13556Condors3Phantoms2LXXSommaire du Match
79 - 2020-12-14565Phantoms3Griffins1WSommaire du Match
80 - 2020-12-15577Sharks2Phantoms5WSommaire du Match
82 - 2020-12-17591Penguins2Phantoms3WSommaire du Match
84 - 2020-12-19602Phantoms0Moose3LSommaire du Match
86 - 2020-12-21614Phantoms2Stars3LXSommaire du Match
87 - 2020-12-22624Flames2Phantoms3WXXSommaire du Match
88 - 2020-12-23632Phantoms2Marlies3LSommaire du Match
90 - 2020-12-25642Phantoms3Crunch4LSommaire du Match
91 - 2020-12-26649IceHogs4Phantoms2LSommaire du Match
93 - 2020-12-28666Moose4Phantoms3LXXSommaire du Match
95 - 2020-12-30684Wolves2Phantoms3WXXSommaire du Match
96 - 2020-12-31695Phantoms3Sharks5LSommaire du Match
97 - 2021-01-01707Admirals1Phantoms4WSommaire du Match
98 - 2021-01-02714Phantoms3Wolves4LSommaire du Match
99 - 2021-01-03725Soldiers0Phantoms2WSommaire du Match
101 - 2021-01-05740Phantoms3Crunch4LSommaire du Match
102 - 2021-01-06748Wolf Pack4Phantoms3LXXSommaire du Match
103 - 2021-01-07756Phantoms3Crunch2WSommaire du Match
105 - 2021-01-09769Phantoms5Senators3WSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
106 - 2021-01-10778Rampage2Phantoms6WSommaire du Match
107 - 2021-01-11790Phantoms2Bruins6LSommaire du Match
108 - 2021-01-12796Stars3Phantoms5WSommaire du Match
110 - 2021-01-14814Sound Tigers6Phantoms4LSommaire du Match
112 - 2021-01-16827Phantoms4Condors3WSommaire du Match
114 - 2021-01-18839Sound Tigers3Phantoms5WSommaire du Match
115 - 2021-01-19848Phantoms3Penguins2WSommaire du Match
117 - 2021-01-21860Monsters1Phantoms3WSommaire du Match
118 - 2021-01-22869Phantoms1IceHogs3LSommaire du Match
120 - 2021-01-24883Monsters1Phantoms4WSommaire du Match
121 - 2021-01-25891Phantoms2Penguins1WSommaire du Match
122 - 2021-01-26900Phantoms4Griffins0WSommaire du Match
123 - 2021-01-27902Phantoms3Monarchs2WXXSommaire du Match



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des Billets3515
Assistance00
Assistance PCT0.00%0.00%

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

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
3,213,503$ 2,379,498$ 2,358,082$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 2,370,378$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 1 26,044$ 26,044$




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