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Phantoms
GP: 34 | W: 25 | L: 8 | OTL: 1 | P: 51
GF: 166 | GA: 116 | PP%: 43.30% | PK%: 84.29%
DG: Richard Duguay | Morale : 50 | Moyenne d’équipe : 62
Prochains matchs #572 vs Comets

Centre de jeu
Phantoms
25-8-1, 51pts
8
FINAL
4 Crunch
7-25-2, 16pts
Team Stats
W3SéquenceL4
13-5-1Fiche domicile4-13-1
12-3-0Fiche domicile3-12-1
8-2-0Derniers 10 matchs0-9-1
4.88Buts par match 4.24
3.41Buts contre par match 6.41
43.30%Pourcentage en avantage numérique24.42%
84.29%Pourcentage en désavantage numérique57.81%
Phantoms
25-8-1, 51pts
3
FINAL
1 Wolf Pack
25-12-0, 50pts
Team Stats
W3SéquenceL1
13-5-1Fiche domicile11-7-0
12-3-0Fiche domicile14-5-0
8-2-0Derniers 10 matchs7-3-0
4.88Buts par match 4.41
3.41Buts contre par match 3.16
43.30%Pourcentage en avantage numérique41.96%
84.29%Pourcentage en désavantage numérique70.13%
Comets
19-13-3, 41pts
2025-12-22
Phantoms
25-8-1, 51pts
Statistiques d’équipe
W1SéquenceW3
8-6-1Fiche domicile13-5-1
11-7-2Fiche visiteur12-3-0
6-2-210 derniers matchs8-2-0
4.34Buts par match 4.88
3.63Buts contre par match 4.88
53.25%Pourcentage en avantage numérique43.30%
69.44%Pourcentage en désavantage numérique84.29%
Phantoms
25-8-1, 51pts
2025-12-23
Baby Hawks
25-8-2, 52pts
Statistiques d’équipe
W3SéquenceW1
13-5-1Fiche domicile11-4-1
12-3-0Fiche visiteur14-4-1
8-2-010 derniers matchs7-3-0
4.88Buts par match 5.06
3.41Buts contre par match 5.06
43.30%Pourcentage en avantage numérique31.68%
84.29%Pourcentage en désavantage numérique72.06%
Phantoms
25-8-1, 51pts
2025-12-28
Firebirds
17-12-4, 38pts
Statistiques d’équipe
W3SéquenceL2
13-5-1Fiche domicile6-8-3
12-3-0Fiche visiteur11-4-1
8-2-010 derniers matchs6-4-0
4.88Buts par match 4.00
3.41Buts contre par match 4.00
43.30%Pourcentage en avantage numérique36.36%
84.29%Pourcentage en désavantage numérique71.76%
Meneurs d'équipe
Buts
Frank Nazar
29
Passes
Frank Nazar
37
Points
Frank Nazar
66
Plus/Moins
Kevin Korchinski
19
Victoires
Devon Levi
18
Pourcentage d’arrêts
James Reimer
0.89

Statistiques d’équipe
Buts pour
166
4.88 GFG
Tirs pour
883
25.97 Avg
Pourcentage en avantage numérique
43.3%
42 GF
Début de zone offensive
32.3%
Buts contre
116
3.41 GAA
Tirs contre
896
26.35 Avg
Pourcentage en désavantage numérique
84.3%%
11 GA
Début de la zone défensive
34.4%
Informations de l'équipe

Directeur généralRichard Duguay
DivisionNord-Est
ConférenceEst
Capitaine
Assistant #1
Assistant #2


Informations de l’aréna

Capacité3,000
Assistance2,889
Billets de saison300


Informations de la formation

Équipe Pro21
Équipe Mineure20
Limite contact 41 / 50
Espoirs16


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
1Michael McLeodX100.0074517974807975719266637270736305069N0261650,000$
2Frank Nazar (R)X100.00604076787684687260686867735550050680202950,000$
3Conor Geekie (R)X100.00704076697875666759636564675250050640203886,667$
4Trey Fix-Wolansky (R)X100.00574762715867666746656360675450050620251650,000$
5Tanner LaczynskiX100.0059427267646965644563596263605005061X0271775,000$
6Nick Abruzzese (R)X100.00564174676069676444645855635450050600251850,000$
7Ryder Rolston (R)X100.0064417364666463624259575961515005059X0222895,000$
8Elliot Desnoyers (R)X100.00584271666264636142605355605150050580221825,000$
9Dalibor Dvorský (R)X100.00604167626358576242616055625050050580193886,667$
10Tristan Broz (R)X100.00574264626162616255605955615050050580213925,000$
11Julian Lutz (R)X100.00604168586361595741575454575050050560203923,333$
12Kevin Korchinski (R)X100.00604473686970656840655962655550050630202918,333$
13Dylan CoghlanX100.0054426968666864664061646266605005062N0261650,000$
14Tobias Bjornfot (R)X100.0062417165656361594056536559525005060X0232800,000$
15Topi Niemelä (R)X100.00564271686164626240625563625150050600221856,667$
Rayé
1Caleb JonesX100.0064427568697472654063566963755605065N0272680,000$
MOYENNE D’ÉQUIPE100.0061427167666865644862596164565105061
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 SPÂgeContratSalaire
1Devon Levi (R)100.0077697268737575767674675850050670221925,000$
2Chris Driedger100.00697071676969706868696965580506303031,000,000$
Rayé
1James Reimer100.007085847880807780807878908305073N03612,222,222$
2Vadim Zherenko (R)100.0071636666686871666968585250050620231846,667$
3Cooper Black (R)100.0066595769646466656562545250050590232950,000$
MOYENNE D’ÉQUIPE100.007169707071717271727065635805065
Nom de l’entraîneur 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
1Frank NazarPhantoms (Phi)C3429376671556072146658219.86%2380423.678162417651012621160.00%8053624021.6422100743
2Conor GeekiePhantoms (Phi)C3423264931210514898276223.47%1667019.7248129411019593155.96%3021722001.4602110351
3Ryder RolstonPhantoms (Phi)RW341922417115454985324922.35%968920.29611171363000035147.50%401826011.1900001323
4Elliot DesnoyersPhantoms (Phi)LW342018389100443569163728.99%1567119.76961520600002143350.00%441516001.1300000333
5Nick AbruzzesePhantoms (Phi)C34111627520463882165113.41%1457016.78000121012262048.31%891516000.9500000214
6Dalibor DvorskýPhantoms (Phi)RW34916252155503073224212.33%459117.383478400000101148.28%291619000.8500001103
7Caleb JonesPhantoms (Phi)D305192417115383940171212.50%2880426.812244600112641050.00%2839000.6000100101
8Tanner LaczynskiPhantoms (Phi)C341410248100332963174822.22%1351715.22000010002252154.59%1961518000.9312000042
9Kevin KorchinskiPhantoms (Phi)D34319221922029364215237.14%3591326.88235479033362000%01938000.4800000001
10Dylan CoghlanPhantoms (Phi)D343151886027203412128.82%2471421.021341510111531050.00%21138100.5000000101
11Topi NiemeläPhantoms (Phi)D3411314121517342311114.35%2368520.1714524300004810100.00%1935000.4100001010
12Trey Fix-WolanskyPhantoms (Phi)RW42101245583104920.00%08421.02134315000020050.00%821002.8500001111
13Michael McLeodPhantoms (Phi)C5641022091123121926.09%311923.91022311000190068.94%13225011.6701000102
14Tristan BrozPhantoms (Phi)C34459-314030192771814.81%1252015.32000080000101050.00%34622000.3500000010
15Julian LutzPhantoms (Phi)LW34235-159539291761611.76%647914.111011340110140061.90%21112000.2100100000
16Tobias BjornfotPhantoms (Phi)D34044-91151227141170%1750814.970000150000140050.00%2325000.1600001000
Statistiques d’équipe totales ou en moyenne481151237388651765053851984629049817.85%242934719.433862100865943692448421857.76%1707193356140.8337415232225
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
1Devon LeviPhantoms (Phi)2618510.8693.3314790082624317101.0003259210
2James ReimerPhantoms (Phi)97100.8902.915160125228108000.750497010
3Chris DriedgerPhantoms (Phi)30200.7958.716200944180000016000
Statistiques d’équipe totales ou en moyenne3825810.8713.382058011168964431073432220


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 Pays Recrue Poids Taille Non-échange Disponible pour échange Acquis Par Date de la Dernière Transaction Ballotage forcé Waiver Possible Contrat Date du Signature du Contrat Forcer UFA Rappel d'urgence Type Salaire actuel Plafond salarial Plafond salarial restant Exclus du plafond salarial Salaire année 2Salaire année 3Salaire année 4Salaire année 5Salaire année 6Salaire année 7Salaire année 8Salaire année 9Salaire année 10Plafond salarial année 2Plafond salarial année 3Plafond salarial année 4Plafond salarial année 5Plafond salarial année 6Plafond salarial année 7Plafond salarial année 8Plafond salarial année 9Plafond salarial année 10Non-échange année 2Non-échange année 3Non-échange année 4Non-échange année 5Non-échange année 6Non-échange année 7Non-échange année 8Non-échange année 9Non-échange année 10Lien
Caleb JonesPhantoms (Phi)D271997-06-06USANo194 Lbs6 ft1YesNoFree AgentYesYes22025-09-21FalseFalsePro & Farm680,000$0$0$No680,000$--------680,000$--------No--------Lien
Chris Driedger (contrat à 1 volet)Phantoms (Phi)G301994-05-18CANNo207 Lbs6 ft4NoNoFree AgentYesYes32024-09-23FalseFalsePro & Farm1,000,000$80,000$48,912$No1,000,000$1,000,000$-------1,000,000$1,000,000$-------NoNo-------Lien
Conor GeekiePhantoms (Phi)C202004-05-05CANYes207 Lbs6 ft4NoNoProspectNoNo32025-07-10FalseFalsePro & Farm886,667$0$0$No886,667$886,667$-------886,667$886,667$-------NoNo-------Lien
Cooper BlackPhantoms (Phi)G232001-06-14USAYes194 Lbs6 ft8NoNoDraftNoNo22024-06-25FalseFalsePro & Farm950,000$0$0$No950,000$--------950,000$--------No--------Lien
Dalibor DvorskýPhantoms (Phi)RW192005-06-15SLVYes201 Lbs6 ft1NoNoTrade2025-07-18NoNo32025-07-10FalseFalsePro & Farm886,667$0$0$No886,667$886,667$-------886,667$886,667$-------NoNo-------Lien
Devon LeviPhantoms (Phi)G222001-12-27CANYes183 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm925,000$0$0$No---------------------------Lien
Dylan CoghlanPhantoms (Phi)D261998-02-19CANNo205 Lbs6 ft2YesNoFree AgentYesYes12025-09-21FalseFalsePro & Farm650,000$0$0$No---------600,000$-----------------Lien
Elliot DesnoyersPhantoms (Phi)LW222002-01-21CANYes183 Lbs5 ft11NoNoN/ANoNo1FalseFalsePro & Farm825,000$0$0$No---------------------------Lien
Frank NazarPhantoms (Phi)C202004-01-14USAYes190 Lbs5 ft10NoNoProspectNoNo22024-06-25FalseFalsePro & Farm950,000$0$0$No950,000$--------950,000$--------No--------Lien
James Reimer (contrat à 1 volet)Phantoms (Phi)G361988-03-15CANNo200 Lbs6 ft2YesNoTrade2024-12-12YesYes1FalseFalsePro & Farm2,222,222$1,302,222$796,177$No---------------------------Lien
Julian LutzPhantoms (Phi)LW202004-02-29GERYes194 Lbs6 ft3NoNoProspectNoNo32025-07-10FalseFalsePro & Farm923,333$0$0$No923,333$923,333$-------923,333$923,333$-------NoNo-------Lien
Kevin KorchinskiPhantoms (Phi)D202004-06-21CANYes192 Lbs6 ft3NoNoProspectNoNo22024-06-25FalseFalsePro & Farm918,333$0$0$No918,333$--------918,333$--------No--------Lien
Michael McLeod (contrat à 1 volet)Phantoms (Phi)C261998-02-03CANNo190 Lbs6 ft2YesNoFree AgentYesYes12025-09-14FalseFalsePro & Farm650,000$0$0$No---------------------------Lien
Nick AbruzzesePhantoms (Phi)C251999-06-04USAYes181 Lbs5 ft11NoNoTrade2024-10-17YesYes1FalseFalsePro & Farm850,000$0$0$No---------------------------Lien
Ryder RolstonPhantoms (Phi)RW222001-10-31USAYes201 Lbs6 ft2NoYesProspectNoNo22024-06-25FalseFalsePro & Farm895,000$0$0$No895,000$--------895,000$--------No--------Lien
Tanner LaczynskiPhantoms (Phi)C271997-06-01USANo190 Lbs6 ft1NoYesN/AYesYes1FalseFalsePro & Farm775,000$0$0$No---------------------------Lien
Tobias BjornfotPhantoms (Phi)D232001-04-06SWEYes203 Lbs6 ft0NoYesFree Agent2024-06-23NoNo22024-08-31FalseFalsePro & Farm800,000$0$0$No800,000$--------800,000$--------No--------Lien
Topi NiemeläPhantoms (Phi)D222002-03-25FINYes179 Lbs6 ft0NoNoTrade2024-01-24NoNo1FalseFalsePro & Farm856,667$0$0$No---------------------------Lien
Trey Fix-Wolansky (contrat à 1 volet)Phantoms (Phi)RW251999-05-26CANYes179 Lbs5 ft7NoNoWaiver2025-07-23YesYes12025-09-05FalseFalsePro & Farm650,000$0$0$No---------------------------Lien
Tristan BrozPhantoms (Phi)C212002-10-10USAYes179 Lbs6 ft0NoNoProspectNoNo32025-07-10FalseFalsePro & Farm925,000$0$0$No925,000$925,000$-------925,000$925,000$-------NoNo-------Lien
Vadim ZherenkoPhantoms (Phi)G232001-03-15RUSYes196 Lbs6 ft4NoNoN/ANoNo1FalseFalsePro & Farm846,667$0$0$No---------------------------Lien
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2123.76193 Lbs6 ft11.76907,884$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Elliot DesnoyersMichael McLeodTrey Fix-Wolansky40122
2Julian LutzFrank NazarRyder Rolston30122
3Tanner LaczynskiConor GeekieDalibor Dvorský20122
4Michael McLeodTanner LaczynskiFrank Nazar10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Kevin KorchinskiDylan Coghlan40122
2Topi NiemeläTobias Bjornfot30122
3Nick AbruzzeseTristan Broz20122
4Kevin KorchinskiDylan Coghlan10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Elliot DesnoyersMichael McLeodTrey Fix-Wolansky60122
2Julian LutzFrank NazarRyder Rolston40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Kevin KorchinskiDylan Coghlan60122
2Topi NiemeläTobias Bjornfot40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Michael McLeodFrank Nazar60122
2Conor GeekieTrey Fix-Wolansky40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Kevin KorchinskiDylan Coghlan60122
2Topi NiemeläTobias Bjornfot40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Michael McLeod60122Kevin KorchinskiDylan Coghlan60122
2Frank Nazar40122Topi NiemeläTobias Bjornfot40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Michael McLeodFrank Nazar60122
2Conor GeekieTrey Fix-Wolansky40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Kevin KorchinskiDylan Coghlan60122
2Topi NiemeläTobias Bjornfot40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Elliot DesnoyersMichael McLeodTrey Fix-WolanskyKevin KorchinskiDylan Coghlan
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Elliot DesnoyersMichael McLeodTrey Fix-WolanskyKevin KorchinskiDylan Coghlan
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Nick Abruzzese, Tristan Broz, Dalibor DvorskýNick Abruzzese, Tristan BrozDalibor Dvorský
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Topi Niemelä, Tobias Bjornfot, Kevin KorchinskiTopi NiemeläTobias Bjornfot, Kevin Korchinski
Tirs de pénalité
Michael McLeod, Frank Nazar, Conor Geekie, Trey Fix-Wolansky, Tanner Laczynski
Gardien
#1 : Devon Levi, #2 : Chris Driedger


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
TotalDomicileVisiteur
# 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
1Cabaret Lady Mary Ann33000000198111100000091822000000107361.00019294800445859679226335315137221275510770.00%6266.67%034056160.61%32659754.61%33657758.23%656318716305686367
2Caroline311010001513211000000532201010001010040.66715223700445859683226335315136921105611436.36%5180.00%034056160.61%32659754.61%33657758.23%656318716305686367
3Chiefs20200000613-71010000049-51010000024-200.00068140044585965822633531513441610383133.33%5180.00%034056160.61%32659754.61%33657758.23%656318716305686367
4Crunch220000001156110000003121100000084441.0001116270044585965722633531513492093011218.18%2150.00%034056160.61%32659754.61%33657758.23%656318716305686367
5Firebirds1010000013-21010000013-20000000000000.0001120044585962122633531513244920000%20100.00%034056160.61%32659754.61%33657758.23%656318716305686367
6Heat11000000936110000009360000000000021.0009142300445859625226335315134565193266.67%000%034056160.61%32659754.61%33657758.23%656318716305686367
7Jayhawks220000001266110000006331100000063341.000122133004458596542263353151343154358337.50%20100.00%134056160.61%32659754.61%33657758.23%656318716305686367
8Las Vegas10001000541100010005410000000000021.0005712004458596312263353151322610125240.00%5180.00%034056160.61%32659754.61%33657758.23%656318716305686367
9Manchots20000110990200001109900000000000030.750913220044585963622633531513691714352150.00%8187.50%134056160.61%32659754.61%33657758.23%656318716305686367
10Marlies1010000035-21010000035-20000000000000.0003470044585962722633531513287421100.00%2150.00%034056160.61%32659754.61%33657758.23%656318716305686367
11Minnesota11000000642110000006420000000000021.000610160044585962222633531513226216200.00%10100.00%134056160.61%32659754.61%33657758.23%656318716305686367
12Monsters1010000024-21010000024-20000000000000.0002350044585961522633531513228413100.00%20100.00%134056160.61%32659754.61%33657758.23%656318716305686367
13Oceanics11000000734110000007340000000000021.00071219004458596332263353151341162625200.00%30100.00%034056160.61%32659754.61%33657758.23%656318716305686367
14Oil Kings1010000023-11010000023-10000000000000.00022400445859624226335315132154212150.00%2150.00%034056160.61%32659754.61%33657758.23%656318716305686367
15Rocket2200000013670000000000022000000136741.0001323360044585964122633531513408153410550.00%50100.00%034056160.61%32659754.61%33657758.23%656318716305686367
16Sags11000000422110000004220000000000021.000471100445859618226335315132464194250.00%20100.00%034056160.61%32659754.61%33657758.23%656318716305686367
17Senators220000001028110000006241100000040441.0001014240144585963622633531513391413304250.00%40100.00%034056160.61%32659754.61%33657758.23%656318716305686367
18Sound Tigers21001000752110000005411000100021141.00071118004458596632263353151361199394250.00%20100.00%034056160.61%32659754.61%33657758.23%656318716305686367
19Spiders21100000121201100000042210100000810-220.5001220320044585967722633531513962212296466.67%6183.33%034056160.61%32659754.61%33657758.23%656318716305686367
20Stars10000010431000000000001000001043121.00044800445859628226335315133012615100.00%3166.67%034056160.61%32659754.61%33657758.23%656318716305686367
21Thunder11000000624000000000001100000062421.000681400445859636226335315132289125360.00%20100.00%034056160.61%32659754.61%33657758.23%656318716305686367
22Wolf Pack11000000312000000000001100000031221.00034700445859619226335315131342142150.00%10100.00%034056160.61%32659754.61%33657758.23%656318716305686367
Total3420803120166116501911501110906525159302010765125510.75016625341901445859688322633531513896261208588974243.30%701184.29%434056160.61%32659754.61%33657758.23%656318716305686367
_Since Last GM Reset3420803120166116501911501110906525159302010765125510.75016625341901445859688322633531513896261208588974243.30%701184.29%434056160.61%32659754.61%33657758.23%656318716305686367
_Vs Conference138201110614120851001103827115310100023149210.80861931540144585963452263353151339311393224301550.00%30390.00%134056160.61%32659754.61%33657758.23%656318716305686367
_Vs Division103101000464065200000023185511010002322180.4004670116004458596278226335315133088347173251248.00%22386.36%134056160.61%32659754.61%33657758.23%656318716305686367

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
3451W316625341988389626120858801
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
342083120166116
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
1911511109065
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
159320107651
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
974243.30%701184.29%4
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
226335315134458596
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
34056160.61%32659754.61%33657758.23%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
656318716305686367


Derniers matchs 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 - 2025-10-0912Phantoms5Cabaret Lady Mary Ann3WSommaire du match
5 - 2025-10-1130Phantoms6Caroline5WXSommaire du match
7 - 2025-10-1344Cabaret Lady Mary Ann1Phantoms9WSommaire du match
10 - 2025-10-1666Oceanics3Phantoms7WSommaire du match
12 - 2025-10-1881Minnesota4Phantoms6WSommaire du match
14 - 2025-10-2094Firebirds3Phantoms1LSommaire du match
17 - 2025-10-23112Phantoms4Senators0WSommaire du match
19 - 2025-10-25127Sound Tigers4Phantoms5WSommaire du match
22 - 2025-10-28150Manchots6Phantoms5LXSommaire du match
24 - 2025-10-30170Jayhawks3Phantoms6WSommaire du match
26 - 2025-11-01188Marlies5Phantoms3LSommaire du match
27 - 2025-11-02196Heat3Phantoms9WSommaire du match
29 - 2025-11-04204Phantoms6Rocket4WSommaire du match
31 - 2025-11-06223Phantoms6Jayhawks3WSommaire du match
33 - 2025-11-08232Senators2Phantoms6WSommaire du match
37 - 2025-11-12266Oil Kings3Phantoms2LSommaire du match
39 - 2025-11-14281Phantoms2Chiefs4LSommaire du match
40 - 2025-11-15293Phantoms4Stars3WXXSommaire du match
45 - 2025-11-20324Chiefs9Phantoms4LSommaire du match
47 - 2025-11-22339Spiders2Phantoms4WSommaire du match
49 - 2025-11-24353Phantoms6Thunder2WSommaire du match
51 - 2025-11-26363Phantoms5Cabaret Lady Mary Ann4WSommaire du match
53 - 2025-11-28381Phantoms2Sound Tigers1WXSommaire du match
54 - 2025-11-29395Phantoms8Spiders10LSommaire du match
56 - 2025-12-01407Manchots3Phantoms4WXXSommaire du match
58 - 2025-12-03423Crunch1Phantoms3WSommaire du match
62 - 2025-12-07453Monsters4Phantoms2LSommaire du match
64 - 2025-12-09469Sags2Phantoms4WSommaire du match
66 - 2025-12-11483Las Vegas4Phantoms5WXSommaire du match
68 - 2025-12-13501Caroline3Phantoms5WSommaire du match
69 - 2025-12-14510Phantoms4Caroline5LSommaire du match
71 - 2025-12-16521Phantoms7Rocket2WSommaire du match
73 - 2025-12-18535Phantoms8Crunch4WSommaire du match
75 - 2025-12-20549Phantoms3Wolf Pack1WSommaire du match
77 - 2025-12-22572Comets-Phantoms-
78 - 2025-12-23583Phantoms-Baby Hawks-
83 - 2025-12-28605Phantoms-Firebirds-
85 - 2025-12-30621Phantoms-Comets-
86 - 2025-12-31630Phantoms-Heat-
89 - 2026-01-03647Phantoms-Oil Kings-
92 - 2026-01-06670Admirals-Phantoms-
94 - 2026-01-08686Marlies-Phantoms-
96 - 2026-01-10705Thunder-Phantoms-
98 - 2026-01-12719Thunder-Phantoms-
100 - 2026-01-14736Phantoms-Crunch-
101 - 2026-01-15741Phantoms-Manchots-
103 - 2026-01-17755Wolf Pack-Phantoms-
105 - 2026-01-19775Phantoms-Las Vegas-
107 - 2026-01-21790Phantoms-Roadrunners-
109 - 2026-01-23805Phantoms-Monsters-
112 - 2026-01-26827Sound Tigers-Phantoms-
114 - 2026-01-28840Phantoms-Monsters-
115 - 2026-01-29843Phantoms-Bruins-
117 - 2026-01-31859Monarchs-Phantoms-
120 - 2026-02-03889Bears-Phantoms-
122 - 2026-02-05907Senators-Phantoms-
142 - 2026-02-25911Phantoms-Bears-
143 - 2026-02-26924Phantoms-Wolf Pack-
145 - 2026-02-28935Bruins-Phantoms-
147 - 2026-03-02954Phantoms-Marlies-
150 - 2026-03-05976Roadrunners-Phantoms-
152 - 2026-03-07993Phantoms-Manchots-
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
154 - 2026-03-091008Wolf Pack-Phantoms-
156 - 2026-03-111026Bears-Phantoms-
157 - 2026-03-121035Phantoms-Minnesota-
159 - 2026-03-141052Monsters-Phantoms-
163 - 2026-03-181082Phantoms-Admirals-
164 - 2026-03-191093Phantoms-Monarchs-
166 - 2026-03-211103Phantoms-Sags-
169 - 2026-03-241125Monsters-Phantoms-
171 - 2026-03-261142Baby Hawks-Phantoms-
173 - 2026-03-281163Phantoms-Cougars-
174 - 2026-03-291172Stars-Phantoms-
176 - 2026-03-311184Phantoms-Bears-
178 - 2026-04-021196Cougars-Phantoms-
179 - 2026-04-031205Phantoms-Sound Tigers-
181 - 2026-04-051224Bruins-Phantoms-
183 - 2026-04-071236Phantoms-Spiders-
185 - 2026-04-091250Phantoms-Cougars-
187 - 2026-04-111272Phantoms-Oceanics-
189 - 2026-04-131284Caroline-Phantoms-
190 - 2026-04-141294Rocket-Phantoms-



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets3515
Assistance36,80318,082
Assistance PCT96.85%95.17%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
-16 2889 - 96.29% 98,484$1,871,202$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursPlafond Salariale total des joueursSalaire des entraineurs
573,669$ 1,454,334$ 1,454,334$ 0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
7,535$ 573,669$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
2,166,655$ 118 7,535$ 889,130$




Phantoms Leaders statistiques des joueurs (saison régulière)

# Nom du joueur 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

Phantoms Leaders des statistiques des gardiens (saison régulière)

# Nom du gardien GP W L OTL PCT GAA MP PIM SO GA SA SAR A EG PS % PSA

Phantoms Statistiques de l'Équipe de Carrière

TotalDomicileVisiteur
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

Phantoms Leaders statistiques des joueurs (séries éliminatoires)

# Nom du joueur 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

Phantoms Leaders des statistiques des gardiens (séries éliminatoires)

# Nom du gardien GP W L OTL PCT GAA MP PIM SO GA SA SAR A EG PS % PSA