Phantoms

GP: 4 | W: 3 | L: 0 | OTL: 1 | P: 7
GF: 13 | GA: 8 | PP%: 17.65% | PK%: 80.00%
DG: Kriss Cardenas | Morale : 52 | Moyenne d'Équipe : 63
Prochain matchs #89 vs Penguins
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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
1Tomas JurcoXX100.006238856478817563656261646673677153630
2Colin McDonaldX100.007135935784939156585554585585795053620
3Seth GriffithXXX100.005237896267939161736456586271665553620
4Adam TambelliniXX100.006536925782949356605654585569656053610
5Tomas HykaX100.005235936365786562566461585971665353610
6Filip Zadina (R)X100.005635936473867263565961586359629153610
7Chris ThorburnX100.007737725990726457545553585286764253600
8Cory ConacherXXX100.005039816065787159626157585678704553590
9Marian StudenicX100.005336915770928956595554565561636453590
10Patrick EavesXX100.006035935673725955565753565285754653580
11Scott KosmachukX100.005439825769908556585457535669656453580
12Landon FerraroXXX100.005340785769726856605654575575686456570
13Christian FolinX100.009046796184827359306956754975683654680
14Alex BiegaX100.009037756370756161307556685379714953660
15Oscar FantenbergX100.008143856276817461306759685375683653660
16Griffin ReinhartX100.007336905889949556305452594869658053640
17Andrew CampbellX100.007138845487928953305251574579715153630
18Matt TennysonX100.006637895981786658305753594677694653610
Rayé
1Cameron GaunceX100.006145796281876961306055674777695846640
2Chris CarlisleX100.006436765770777356305452535163645146580
3Brian CooperX100.005337885566777154305650514771665546570
MOYENNE D'ÉQUIPE100.00643885597683765847595559547468565261
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
1Mike McKenna100.00798280777877797877797886902853770
2Peter Budaj100.00757472777473757473757487913453740
Rayé
1Al Montoya100.00706866806968706968706984883046700
MOYENNE D'ÉQUIPE100.0075757378747375747375748690315174
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Mike Vellucci74717256827769USA5341,000,000$


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
1Adam TambelliniPhantoms (Phi)C/LW4224-10012241450.00%18020.091122140000130052.17%6900001.0000000010
2Landon FerraroPhantoms (Phi)C/LW/RW413445503501420.00%04010.1100000000000048.48%3300001.9800100000
3Christian FolinPhantoms (Phi)D412341357744425.00%58822.15101415000013100.00%000000.6800001100
4Alex BiegaPhantoms (Phi)D4033-240942150.00%38421.13011114000016000.00%000000.7100000000
5Oscar FantenbergPhantoms (Phi)D41234805462316.67%19122.76011416000014000.00%000000.6600000001
6Andrew CampbellPhantoms (Phi)D4022420621100.00%26516.270000000003000.00%000000.6100000010
7Cory ConacherPhantoms (Phi)C/LW/RW4202-1406299422.22%06616.70101414000001075.00%400000.6000000001
8Patrick EavesPhantoms (Phi)LW/RW41124000431533.33%04010.130000000000000.00%100000.9900000100
9Colin McDonaldPhantoms (Phi)RW42023808072428.57%08721.980000170000171062.50%800000.4500000010
10Seth GriffithPhantoms (Phi)C/LW/RW4022200445460.00%08521.400111170000140048.86%8800000.4700000000
11Tomas JurcoPhantoms (Phi)LW/RW40222207813650.00%19122.940003160000210051.06%4700000.4400000001
12Filip ZadinaPhantoms (Phi)RW4022-1202115230.00%06616.60011414000000075.00%400000.6000000000
13Griffin ReinhartPhantoms (Phi)D4112-1004221550.00%28421.15011014000016000.00%000000.4700000000
14Tomas HykaPhantoms (Phi)RW40111001310220.00%0256.4500000000000050.00%200000.7800000000
15Matt TennysonPhantoms (Phi)D4011420241010.00%36616.610000000007000.00%000000.3000000000
16Chris ThorburnPhantoms (Phi)RW410140062103510.00%14511.3500000000040050.00%200000.4400000001
17Marian StudenicPhantoms (Phi)RW41011002231033.33%0266.5400000000000163.33%3000000.7600000000
18Scott KosmachukPhantoms (Phi)RW4000100222020.00%1256.45000000000000100.00%100000.0000000000
Stats d'équipe Total ou en Moyenne721324373250108356102407212.75%20116316.163692315600001433152.60%28900000.6400101234
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
1Mike McKennaPhantoms (Phi)43010.9012.00240208810000.000040000
Stats d'équipe Total ou en Moyenne43010.9012.00240208810000.000040000


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
Adam TambelliniPhantoms (Phi)C/LW241994-11-01No191 Lbs6 ft3NoNoNo1Avec RestrictionPro & Farm500,000$0$0$NoLien
Al MontoyaPhantoms (Phi)G341985-02-13No200 Lbs6 ft2NoNoNo1Sans RestrictionPro & Farm750,000$0$0$NoLien
Alex BiegaPhantoms (Phi)D311988-04-04No199 Lbs5 ft10NoNoNo2Sans RestrictionPro & Farm2,500,000$0$0$NoLien
Andrew CampbellPhantoms (Phi)D311988-02-04No205 Lbs6 ft4NoNoNo1Sans RestrictionPro & Farm500,000$0$0$NoLien
Brian CooperPhantoms (Phi)D251993-11-01No182 Lbs5 ft9NoNoNo2Avec RestrictionPro & Farm300,000$0$0$NoLien
Cameron GauncePhantoms (Phi)D291990-03-19No204 Lbs6 ft2NoNoNo1Sans RestrictionPro & Farm1,000,000$0$0$NoLien
Chris CarlislePhantoms (Phi)D241994-12-16No175 Lbs5 ft10NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Chris ThorburnPhantoms (Phi)RW361983-06-03No235 Lbs6 ft3NoNoNo2Sans RestrictionPro & Farm500,000$0$0$NoLien
Christian FolinPhantoms (Phi)D281991-02-09No204 Lbs6 ft3NoNoNo1Sans RestrictionPro & Farm1,850,000$0$0$NoLien
Colin McDonaldPhantoms (Phi)RW341984-09-30No219 Lbs6 ft2NoNoNo2Sans RestrictionPro & Farm500,000$0$0$NoLien
Cory ConacherPhantoms (Phi)C/LW/RW291989-12-14No180 Lbs5 ft8NoNoNo1Sans RestrictionPro & Farm500,000$0$0$NoLien
Filip ZadinaPhantoms (Phi)RW191999-11-27Yes195 Lbs6 ft0NoNoNo1Contrat d'EntréePro & Farm0$0$NoLien
Griffin ReinhartPhantoms (Phi)D251994-01-24No212 Lbs6 ft4NoNoNo2Avec RestrictionPro & Farm300,000$0$0$NoLien
Landon FerraroPhantoms (Phi)C/LW/RW271991-08-08No173 Lbs6 ft0NoNoNo3Avec RestrictionPro & Farm300,000$0$0$NoLien
Marian StudenicPhantoms (Phi)RW201998-10-28No164 Lbs6 ft1NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$NoLien
Matt TennysonPhantoms (Phi)D291990-04-23No205 Lbs6 ft2NoNoNo4Sans RestrictionPro & Farm500,000$0$0$NoLien
Mike McKennaPhantoms (Phi)G361983-04-11No183 Lbs6 ft2NoNoNo1Sans RestrictionPro & Farm500,000$0$0$NoLien
Oscar FantenbergPhantoms (Phi)D271991-10-07No206 Lbs6 ft0NoNoNo3Avec RestrictionPro & Farm300,000$0$0$NoLien
Patrick EavesPhantoms (Phi)LW/RW351984-05-01No203 Lbs5 ft11NoNoNo3Sans RestrictionPro & Farm2,500,000$0$0$NoLien
Peter BudajPhantoms (Phi)G361982-09-18No196 Lbs6 ft1NoNoNo1Sans RestrictionPro & Farm1,920,000$0$0$NoLien
Scott KosmachukPhantoms (Phi)RW251994-01-24No185 Lbs5 ft11NoNoNo3Avec RestrictionPro & Farm300,000$0$0$NoLien
Seth GriffithPhantoms (Phi)C/LW/RW261993-01-04No190 Lbs5 ft9NoNoNo2Avec RestrictionPro & Farm300,000$0$0$NoLien
Tomas HykaPhantoms (Phi)RW261993-03-23No160 Lbs5 ft11NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Tomas JurcoPhantoms (Phi)LW/RW261992-12-28No188 Lbs6 ft2NoNoNo2Avec RestrictionPro & Farm500,000$0$0$NoLien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2428.42194 Lbs6 ft11.88717,500$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Tomas JurcoSeth GriffithColin McDonald40122
2Cory ConacherAdam TambelliniFilip Zadina30122
3Patrick EavesLandon FerraroChris Thorburn20122
4Scott KosmachukMarian StudenicTomas Hyka10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Christian FolinOscar Fantenberg40122
2Alex BiegaGriffin Reinhart30122
3Andrew CampbellMatt Tennyson20122
4Christian FolinOscar Fantenberg10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Tomas JurcoSeth GriffithColin McDonald60122
2Cory ConacherAdam TambelliniFilip Zadina40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Christian FolinOscar Fantenberg60122
2Alex BiegaGriffin Reinhart40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Tomas JurcoColin McDonald60122
2Seth GriffithAdam Tambellini40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Christian FolinOscar Fantenberg60122
2Alex BiegaGriffin Reinhart40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Tomas Jurco60122Christian FolinOscar Fantenberg60122
2Colin McDonald40122Alex BiegaGriffin Reinhart40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Tomas JurcoColin McDonald60122
2Seth GriffithAdam Tambellini40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Christian FolinOscar Fantenberg60122
2Alex BiegaGriffin Reinhart40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Tomas JurcoSeth GriffithColin McDonaldChristian FolinOscar Fantenberg
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Tomas JurcoSeth GriffithColin McDonaldChristian FolinOscar Fantenberg
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Marian Studenic, Scott Kosmachuk, Chris ThorburnMarian Studenic, Scott KosmachukChris Thorburn
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Andrew Campbell, Matt Tennyson, Alex BiegaAndrew CampbellMatt Tennyson, Alex Biega
Tirs de Pénalité
Tomas Jurco, Colin McDonald, Seth Griffith, Adam Tambellini, Filip Zadina
Gardien
#1 : Mike McKenna, #2 : Peter Budaj


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
1Penguins11000000431110000004310000000000021.000471100427022323435127718144125.00%9366.67%07213553.33%509552.63%305752.63%1087782294925
2Sound Tigers11000000312110000003120000000000021.0003690042702232343511462623600.00%70100.00%07213553.33%509552.63%305752.63%1087782294925
3Thunderbirds21000100642110000004131000010023-130.750611170042705832343514078467228.57%4175.00%07213553.33%509552.63%305752.63%1087782294925
Total4300010013853300000011561000010023-170.87513243700427010232343518120528317317.65%20480.00%07213553.33%509552.63%305752.63%1087782294925
_Since Last GM Reset4300010013853300000011561000010023-170.87513243700427010232343518120528317317.65%20480.00%07213553.33%509552.63%305752.63%1087782294925
_Vs Conference22000000743220000007430000000000041.000713200042704432343514113443710110.00%16381.25%07213553.33%509552.63%305752.63%1087782294925
_Vs Division21000000642110000004131000000023-120.500611170042705832343514078467228.57%4175.00%07213553.33%509552.63%305752.63%1087782294925

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
47W11324371028120528300
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
4300100138
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
3300000115
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
100010023
Derniers 10 Matchs
WLOTWOTL SOWSOL
300100
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
17317.65%20480.00%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
32343514270
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
7213553.33%509552.63%305752.63%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
1087782294925


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
4 - 2019-09-0513Sound Tigers1Phantoms3WSommaire du Match
10 - 2019-09-1135Thunderbirds1Phantoms4WSommaire du Match
11 - 2019-09-1252Phantoms2Thunderbirds3LXSommaire du Match
17 - 2019-09-1873Penguins3Phantoms4WSommaire du Match
18 - 2019-09-1989Phantoms-Penguins-
19 - 2019-09-2096Phantoms-Sound Tigers-
22 - 2019-09-23103Phantoms-Wolf Pack-
25 - 2019-09-26124Phantoms-Devils-
26 - 2019-09-27134Phantoms-Bears-
31 - 2019-10-02146Phantoms-Devils-
32 - 2019-10-03162Devils-Phantoms-
38 - 2019-10-09185Thunderbirds-Phantoms-
39 - 2019-10-10198Checkers-Phantoms-
43 - 2019-10-14219Checkers-Phantoms-
45 - 2019-10-16227Phantoms-Bruins-
46 - 2019-10-17244Phantoms-Thunderbirds-
52 - 2019-10-23268Bears-Phantoms-
53 - 2019-10-24281Bruins-Phantoms-
59 - 2019-10-30313Phantoms-Rocket-
60 - 2019-10-31324Phantoms-Senators-
61 - 2019-11-01334Phantoms-Marlies-
64 - 2019-11-04343Monsters-Phantoms-
66 - 2019-11-06352Bears-Phantoms-
67 - 2019-11-07369Monsters-Phantoms-
73 - 2019-11-13394Senators-Phantoms-
74 - 2019-11-14409Phantoms-Penguins-
78 - 2019-11-18427Bears-Phantoms-
80 - 2019-11-20439Crunch-Phantoms-
81 - 2019-11-21456Phantoms-Bears-
85 - 2019-11-25467Phantoms-Penguins-
87 - 2019-11-27478Bruins-Phantoms-
88 - 2019-11-28494Penguins-Phantoms-
94 - 2019-12-04520Phantoms-Bruins-
95 - 2019-12-05533Phantoms-Wolf Pack-
96 - 2019-12-06547Phantoms-Thunderbirds-
101 - 2019-12-11567Devils-Phantoms-
102 - 2019-12-12580Bears-Phantoms-
103 - 2019-12-13590Phantoms-Bears-
106 - 2019-12-16597Thunderbirds-Phantoms-
108 - 2019-12-18609Phantoms-Wolf Pack-
109 - 2019-12-19625Americans-Phantoms-
111 - 2019-12-21638Phantoms-Penguins-
113 - 2019-12-23649Bears-Phantoms-
115 - 2019-12-25660Rocket-Phantoms-
116 - 2019-12-26671Phantoms-Sound Tigers-
122 - 2020-01-01683Phantoms-Sound Tigers-
123 - 2020-01-02700Phantoms-Bears-
129 - 2020-01-08723Phantoms-Comets-
130 - 2020-01-09745Wolf Pack-Phantoms-
131 - 2020-01-10751Wolf Pack-Phantoms-
136 - 2020-01-15771Sound Tigers-Phantoms-
137 - 2020-01-16781Phantoms-Bears-
138 - 2020-01-17801Bears-Phantoms-
143 - 2020-01-22816Phantoms-Checkers-
144 - 2020-01-23829Phantoms-Checkers-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
150 - 2020-01-29856Phantoms-Crunch-
151 - 2020-01-30875Comets-Phantoms-
152 - 2020-01-31883Sound Tigers-Phantoms-
155 - 2020-02-03892Phantoms-Penguins-
157 - 2020-02-05902Checkers-Phantoms-
158 - 2020-02-06913Checkers-Phantoms-
162 - 2020-02-10937Penguins-Phantoms-
164 - 2020-02-12949Bruins-Phantoms-
165 - 2020-02-13963Marlies-Phantoms-
171 - 2020-02-19985Phantoms-Penguins-
172 - 2020-02-20999Penguins-Phantoms-
176 - 2020-02-241021Phantoms-Americans-
178 - 2020-02-261028Phantoms-Monsters-
179 - 2020-02-271041Phantoms-Monsters-
182 - 2020-03-011059Phantoms-Checkers-
183 - 2020-03-021063Phantoms-Checkers-
186 - 2020-03-051092Penguins-Phantoms-
187 - 2020-03-061100Penguins-Phantoms-
189 - 2020-03-081105Phantoms-Bears-
192 - 2020-03-111119Phantoms-Bruins-
193 - 2020-03-121133Wolf Pack-Phantoms-



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
35 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
102,714$ 172,200$ 50,250$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 15,096$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 177 6,042$ 1,069,434$




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
144300010013853300000011561000010023-1713243700427010232343518120528317317.65%20480.00%07213553.33%509552.63%305752.63%1087782294925
Total Saison Régulière4300010013853300000011561000010023-1713243700427010232343518120528317317.65%20480.00%07213553.33%509552.63%305752.63%1087782294925