Phantoms

GP: 45 | W: 26 | L: 15 | OTL: 4 | P: 56
GF: 119 | GA: 85 | PP%: 19.21% | PK%: 85.45%
DG: Kriss Cardenas | Morale : 61 | Moyenne d'Équipe : 63
Prochain matchs #683 vs Sound Tigers
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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
1Colin McDonaldX100.007135935784939156585554585585795072620
2Seth GriffithXXX100.005237896267939161736456586271665572620
3Adam TambelliniXX100.006536925782949356605654585569656073610
4Tomas HykaX100.005235936365786562566461585971665372610
5Filip Zadina (R)X100.005635936473867263565961586359629172610
6Cory ConacherXXX100.005039816065787159626157585678704570600
7Chris ThorburnX100.007737725990726457545553585286764273600
8Scott KosmachukX100.005439825769908556585457535669656473590
9Marian StudenicX100.005336915770928956595554565561636472590
10Patrick EavesXX100.006035935673725955565753565285754672580
11Landon FerraroXXX100.005340785769726856605654575575686465570
12Alex BiegaX100.009037756370756161307556685379714972660
13Oscar FantenbergX100.008143856276817461306759685375683680660
14Griffin ReinhartX100.007336905889949556305452594869658072650
15Cameron GaunceX100.006145796281876961306055674777695837640
16Andrew CampbellX100.007138845487928953305251574579715176630
17Matt TennysonX100.006637895981786658305753594677694672620
18Chris CarlisleX100.006436765770777356305452535163645135580
19Brian CooperX100.005337885566777154305650514771665527560
Rayé
MOYENNE D'ÉQUIPE100.00633885597583765847585558537468566661
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 McKenna100.00798280777877797877797886902872770
2Peter Budaj100.00757472777473757473757487913472740
Rayé
1Al Montoya100.00706866806968706968706984883020700
MOYENNE D'ÉQUIPE100.0075757378747375747375748690315574
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'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
1Oscar FantenbergPhantoms (Phi)D4511182915420555474286714.86%46105723.5081220531710000168110.00%000000.5500202024
2Tomas JurcoPhiladelphia FlyersLW/RW3813152863155289130369410.00%891824.1737103514300041812051.40%46500010.6115001511
3Adam TambelliniPhantoms (Phi)C/LW451314279255886978235816.67%482018.242810191621011443152.60%73200100.6601001213
4Tomas HykaPhantoms (Phi)RW4562026940215410928745.50%478517.44099191210001742047.17%5300000.6613000211
5Seth GriffithPhantoms (Phi)C/LW/RW45719268100361019235617.61%499122.041892417200031492056.55%110000000.5225000220
6Alex BiegaPhantoms (Phi)D45420242860131604624408.70%5295521.23459391580110147100.00%000000.5000000120
7Cory ConacherPhantoms (Phi)C/LW/RW45121123442203654106286011.32%379617.6954931158000043050.00%18000010.5801121112
8Colin McDonaldPhantoms (Phi)RW451111225580923067166116.42%10108224.064592017200002042250.96%15700000.4125000043
9Christian FolinPhiladelphia FlyersD265141946115734235192414.29%2960223.1758132588000086200.00%000000.6300102310
10Filip ZadinaPhantoms (Phi)RW4578155160215410832616.48%668515.231127380000571045.16%6200000.4401000201
11Landon FerraroPhantoms (Phi)C/LW/RW3541014112820163640104010.00%242512.1700000000001050.38%39500000.6600112011
12Matt TennysonPhantoms (Phi)D4511213143203027225144.55%2065114.4800005000071100.00%000000.4000000010
13Andrew CampbellPhantoms (Phi)D4521012145610552024358.33%2563214.06000214000019000.00%000000.3800101121
14Patrick EavesPhantoms (Phi)LW/RW45751211180572951213813.73%660713.50011125000001066.67%2400000.4000000311
15Cameron GauncePhantoms (Phi)D135712314101412213923.81%1326720.564261442000054000.00%000000.9000002031
16Griffin ReinhartPhantoms (Phi)D453912102403329378278.11%4493920.87145211590001167010.00%000000.2600000003
17Chris ThorburnPhantoms (Phi)RW4522442152814247148.33%42265.04000000000220065.00%2000000.3500001001
18Chris CarlislePhantoms (Phi)D13033160911200.00%4896.9200001000011000.00%000000.6700000000
19Brian CooperPhantoms (Phi)D13011020111000.00%7735.680000000000000.00%000000.2700000000
20Scott KosmachukPhantoms (Phi)RW45101229159480312.50%11092.4310135000000050.00%1000000.1811102000
21Marian StudenicPhantoms (Phi)RW45101-1006864316.67%11453.24000016000000157.30%8900000.1411000000
Stats d'équipe Total ou en Moyenne813115209324122617125863788108033275310.65%2931286515.823974113313166011210146622653.33%328700120.508237315222323
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)45261540.9161.83272646839890110.81822450222
Stats d'équipe Total ou en Moyenne45261540.9161.83272646839890110.81822450222


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 Cap 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
Adam TambelliniPhantoms (Phi)C/LW241994-11-01No191 Lbs6 ft3NoNoNo1Pro & Farm500,000$0$0$NoLien
Al MontoyaPhantoms (Phi)G341985-02-13No200 Lbs6 ft2NoNoNo1Pro & Farm750,000$0$0$NoLien
Alex BiegaPhantoms (Phi)D311988-04-04No199 Lbs5 ft10NoNoNo2Pro & Farm2,500,000$0$0$No2,500,000$Lien
Andrew CampbellPhantoms (Phi)D311988-02-04No205 Lbs6 ft4NoNoNo1Pro & Farm500,000$0$0$NoLien
Brian CooperPhantoms (Phi)D251993-11-01No182 Lbs5 ft9NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Cameron GauncePhantoms (Phi)D291990-03-19No204 Lbs6 ft2NoNoNo1Pro & Farm1,000,000$0$0$NoLien
Chris CarlislePhantoms (Phi)D241994-12-16No175 Lbs5 ft10NoNoNo1Pro & Farm300,000$0$0$NoLien
Chris ThorburnPhantoms (Phi)RW361983-06-03No235 Lbs6 ft3NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Colin McDonaldPhantoms (Phi)RW341984-09-30No219 Lbs6 ft2NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Cory ConacherPhantoms (Phi)C/LW/RW291989-12-14No180 Lbs5 ft8NoNoNo1Pro & Farm500,000$0$0$NoLien
Filip ZadinaPhantoms (Phi)RW191999-11-27Yes195 Lbs6 ft0NoNoNo1Pro & Farm0$0$NoLien
Griffin ReinhartPhantoms (Phi)D251994-01-24No212 Lbs6 ft4NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Landon FerraroPhantoms (Phi)C/LW/RW271991-08-08No173 Lbs6 ft0NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Marian StudenicPhantoms (Phi)RW201998-10-28No164 Lbs6 ft1NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Matt TennysonPhantoms (Phi)D291990-04-23No205 Lbs6 ft2NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$500,000$Lien
Mike McKennaPhantoms (Phi)G361983-04-11No183 Lbs6 ft2NoNoNo1Pro & Farm500,000$0$0$NoLien
Oscar FantenbergPhantoms (Phi)D271991-10-07No206 Lbs6 ft0NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Patrick EavesPhantoms (Phi)LW/RW351984-05-01No203 Lbs5 ft11NoNoNo3Pro & Farm2,500,000$0$0$No2,500,000$2,500,000$Lien
Peter BudajPhantoms (Phi)G361982-09-18No196 Lbs6 ft1NoNoNo1Pro & Farm1,920,000$0$0$NoLien
Scott KosmachukPhantoms (Phi)RW251994-01-24No185 Lbs5 ft11NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Seth GriffithPhantoms (Phi)C/LW/RW261993-01-04No190 Lbs5 ft9NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Tomas HykaPhantoms (Phi)RW261993-03-23No160 Lbs5 ft11NoNoNo1Pro & Farm300,000$0$0$NoLien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2228.55194 Lbs6 ft01.91675,909$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Adam TambelliniSeth GriffithColin McDonald40122
2Patrick EavesCory ConacherTomas Hyka30122
3Colin McDonaldLandon FerraroFilip Zadina20122
4Tomas HykaSeth GriffithChris Thorburn10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Alex BiegaOscar Fantenberg40122
2Griffin ReinhartCameron Gaunce30122
3Andrew CampbellMatt Tennyson20122
4Chris CarlisleBrian Cooper10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Adam TambelliniSeth GriffithColin McDonald60122
2Patrick EavesCory ConacherTomas Hyka40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Alex BiegaOscar Fantenberg60122
2Griffin ReinhartCameron Gaunce40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Colin McDonaldSeth Griffith60122
2Tomas HykaFilip Zadina40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Alex BiegaOscar Fantenberg60122
2Griffin ReinhartCameron Gaunce40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Colin McDonald60122Alex BiegaOscar Fantenberg60122
2Seth Griffith40122Griffin ReinhartCameron Gaunce40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Colin McDonaldSeth Griffith60122
2Tomas HykaFilip Zadina40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Alex BiegaOscar Fantenberg60122
2Griffin ReinhartCameron Gaunce40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Adam TambelliniSeth GriffithColin McDonaldAlex BiegaOscar Fantenberg
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Adam TambelliniSeth GriffithColin McDonaldAlex BiegaOscar 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, Chris CarlisleAndrew CampbellMatt Tennyson, Chris Carlisle
Tirs de Pénalité
Colin McDonald, Seth Griffith, Tomas Hyka, Filip Zadina, Adam Tambellini
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
1Americans11000000312110000003120000000000021.000358004027479183523723453518331155240.00%6183.33%0790139856.51%685131452.13%30465446.48%11618231021318547278
2Bears862000002914155320000017116330000001239120.750295483014027479249352372345351935511616237821.62%35682.86%1790139856.51%685131452.13%30465446.48%11618231021318547278
3Bruins4030010038-52010010025-32020000013-210.12536900402747969352372345351002657641218.33%24291.67%0790139856.51%685131452.13%30465446.48%11618231021318547278
4Checkers220000001301322000000130130000000000041.0001324370240274798335237234535281219408225.00%70100.00%0790139856.51%685131452.13%30465446.48%11618231021318547278
5Crunch11000000321110000003210000000000021.000369004027479313523723453529912176116.67%6183.33%0790139856.51%685131452.13%30465446.48%11618231021318547278
6Devils411000116512010000114-32100001051450.62569151140274798335237234535732275792428.33%23386.96%0790139856.51%685131452.13%30465446.48%11618231021318547278
7Marlies11000000505000000000001100000050521.0005914014027479363523723453524481911100.00%30100.00%0790139856.51%685131452.13%30465446.48%11618231021318547278
8Monsters21000100642210001006420000000000030.75069150040274794735237234535341117451000.00%6183.33%0790139856.51%685131452.13%30465446.48%11618231021318547278
9Penguins622000201417-32200000064240200020813-580.66714203401402747911435237234535158428511333618.18%38684.21%0790139856.51%685131452.13%30465446.48%11618231021318547278
10Rocket21100000550110000003211010000023-120.500591400402747948352372345353313223412325.00%9188.89%0790139856.51%685131452.13%30465446.48%11618231021318547278
11Senators2010001034-1100000102111010000013-220.50034700402747933352372345353713183810220.00%9188.89%0790139856.51%685131452.13%30465446.48%11618231021318547278
12Sound Tigers321000001055110000003122110000074340.6671019290040274797735237234535462049511417.14%16287.50%0790139856.51%685131452.13%30465446.48%11618231021318547278
13Thunderbirds623001001214-23120000067-13110010067-150.41712233500402747913435237234535136355112616637.50%22577.27%0790139856.51%685131452.13%30465446.48%11618231021318547278
Total4521150134111985342313600211654223228901130544311560.6221192103291640274791081352372345359902956218632033919.21%2203285.45%1790139856.51%685131452.13%30465446.48%11618231021318547278
15Wolf Pack31101000761000000000003110100076140.6677132000402747959352372345358130616015426.67%16381.25%0790139856.51%685131452.13%30465446.48%11618231021318547278
_Since Last GM Reset4521150134111985342313600211654223228901130544311560.6221192103291640274791081352372345359902956218632033919.21%2203285.45%1790139856.51%685131452.13%30465446.48%11618231021318547278
_Vs Conference331411012418165161684002114032817670103041338410.62181140221134027479762352372345357512284906291612515.53%1732585.55%1790139856.51%685131452.13%30465446.48%11618231021318547278
_Vs Division175301111343409210010119181832010101516-1160.47134629601402747936935237234535377103199313621625.81%791186.08%0790139856.51%685131452.13%30465446.48%11618231021318547278

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
4556W3119210329108199029562186316
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
452115134111985
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
2313602116542
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
228911305443
Derniers 10 Matchs
WLOTWOTL SOWSOL
531001
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
2033919.21%2203285.45%1
Tirs en 1e PériodeTirs en 2e PériodeTirs en 3e PériodeTirs en 4e PériodeButs en 1e PériodeButs en 2e PériodeButs en 3e PériodeButs en 4e Période
352372345354027479
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
790139856.51%685131452.13%30465446.48%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
11618231021318547278


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 - 2019-09-1989Phantoms1Penguins5LSommaire du Match
19 - 2019-09-2096Phantoms2Sound Tigers3LSommaire du Match
22 - 2019-09-23103Phantoms0Wolf Pack1LSommaire du Match
25 - 2019-09-26124Phantoms4Devils1WSommaire du Match
26 - 2019-09-27134Phantoms3Bears1WSommaire du Match
31 - 2019-10-02146Phantoms1Devils0WXXSommaire du Match
32 - 2019-10-03162Devils2Phantoms0LSommaire du Match
38 - 2019-10-09185Thunderbirds2Phantoms1LSommaire du Match
39 - 2019-10-10198Checkers0Phantoms7WSommaire du Match
43 - 2019-10-14219Checkers0Phantoms6WSommaire du Match
45 - 2019-10-16227Phantoms0Bruins1LSommaire du Match
46 - 2019-10-17244Phantoms3Thunderbirds1WSommaire du Match
52 - 2019-10-23268Bears1Phantoms3WSommaire du Match
53 - 2019-10-24281Bruins3Phantoms1LSommaire du Match
59 - 2019-10-30313Phantoms2Rocket3LSommaire du Match
60 - 2019-10-31324Phantoms1Senators3LSommaire du Match
61 - 2019-11-01334Phantoms5Marlies0WSommaire du Match
64 - 2019-11-04343Monsters1Phantoms4WSommaire du Match
66 - 2019-11-06352Bears1Phantoms5WSommaire du Match
67 - 2019-11-07369Monsters3Phantoms2LXSommaire du Match
73 - 2019-11-13394Senators1Phantoms2WXXSommaire du Match
74 - 2019-11-14409Phantoms4Penguins3WXXSommaire du Match
78 - 2019-11-18427Bears3Phantoms2LSommaire du Match
80 - 2019-11-20439Crunch2Phantoms3WSommaire du Match
81 - 2019-11-21456Phantoms4Bears2WSommaire du Match
85 - 2019-11-25467Phantoms1Penguins0WXXSommaire du Match
87 - 2019-11-27478Bruins2Phantoms1LXSommaire du Match
88 - 2019-11-28494Penguins1Phantoms2WSommaire du Match
94 - 2019-12-04520Phantoms1Bruins2LSommaire du Match
95 - 2019-12-05533Phantoms4Wolf Pack3WSommaire du Match
96 - 2019-12-06547Phantoms1Thunderbirds3LSommaire du Match
101 - 2019-12-11567Devils2Phantoms1LXXSommaire du Match
102 - 2019-12-12580Bears3Phantoms2LSommaire du Match
103 - 2019-12-13590Phantoms5Bears0WSommaire du Match
106 - 2019-12-16597Thunderbirds4Phantoms1LSommaire du Match
108 - 2019-12-18609Phantoms3Wolf Pack2WXSommaire du Match
109 - 2019-12-19625Americans1Phantoms3WSommaire du Match
111 - 2019-12-21638Phantoms2Penguins5LSommaire du Match
113 - 2019-12-23649Bears3Phantoms5WSommaire du Match
115 - 2019-12-25660Rocket2Phantoms3WSommaire du Match
116 - 2019-12-26671Phantoms5Sound Tigers1WSommaire du Match
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
15 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
714,359$ 148,700$ 47,250$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 100,988$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 75 5,921$ 444,075$




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
144521150134111985342313600211654223228901130544311561192103291640274791081352372345359902956218632033919.21%2203285.45%1790139856.51%685131452.13%30465446.48%11618231021318547278
Total Saison Régulière4521150134111985342313600211654223228901130544311561192103291640274791081352372345359902956218632033919.21%2203285.45%1790139856.51%685131452.13%30465446.48%11618231021318547278