Phantoms

GP: 3 | W: 3 | L: 0 | OTL: 0 | P: 6
GF: 9 | GA: 5 | PP%: 16.67% | PK%: 88.24%
DG: Kriss Cardenas | Morale : 54 | Moyenne d'Équipe : N/A
Prochain matchs #73 vs Penguins
La résolution de votre navigateur est trop petite pour cette page. Plusieurs informations sont cachées pour garder la page lisible.

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
1Adam TambelliniXX100.0066556664656172605058595555737215500
2Alex FriesenXX100.0059556760666271555055555855707115500
3David BoothXX100.0080557175676970675062666255827315500
4Bryce Van BrabantXX100.0058555862797463555055555755717315600
5Tomas HykaX100.0063558172616968645062626255505015500
6Chris ThorburnXX100.0087555969818068686465617055747615500
7Landon FerraroX100.0068557370706468605059606055535415500
8Tomas JurcoXX100.0076557683748080705864677255777515200
9Paul BissonnetteX100.0059555661858254555055555555626315500
10Iiro PakarinenXX100.0082556579797574675062637055565615500
11Scott KosmachukX100.0060556960686370575056575555505015200
12Mike BlundenX100.0060556968807871605060606055585915500
13Alex BiegaX100.0096557370677767742565617755807513700
14Matt TennysonX100.0069556571788262712560606955707015500
15Clayton StonerX100.0055555560555579552555555555858315200
16Yohann AuvituX100.0070558782647466742565677255535315500
17Oscar Fantenberg (R)X100.0078557975737558752566637355706715500
18Griffin ReinhartX100.0062556879886574622560605955705315200
19Robert BortuzzoX100.0087557781897668802566657955807315500
Rayé
1Brandon MashinterX100.0056555555575859555055555555757414700
2Rene BourqueXX100.0056555555555555555055555555727314700
3Sergey KalininXXX100.0055555555555555555055555555585814700
4Colin McDonaldX100.0056555555575757555055555555747214700
5Marco RoyX100.0072556662696362555055555555505015000
6Adam ComrieX100.0055555560555559552555555555535314700
7Chris CarlisleX100.0055555560555572552555555555535314700
8Andrew CampbellX100.0055555560555573552555555555535314700
9Patrick McNallyX100.0055555560555559552555555555535314700
10Cameron GaunceX100.0055555560555565552555555555727214700
11Jakub KindlX100.0055555860585870582558585855697015000
12Mattias BackmanX100.0055555560555565552555555555535314700
13Brian Cooper (R)X100.0055555560555558552555555555535314700
14Kirill Gotovets (R)X100.0055555560555565552555555555555514700
MOYENNE D'ÉQUIPE100.006455646666656661395858605564631510
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
1Niklas Svedberg100.006870737077697478787455606015500
2Eddie Lack100.007571707680806665727955797715500
Rayé
1Mike McKenna100.006480857865656764696355706914700
2Anthony Stolarz100.006979868768687064696955656614700
MOYENNE D'ÉQUIPE100.00697579787371696872715569681510
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Rick Kowalsky70786682735878CAN463100,000$


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
1Chris ThorburnPhantoms (Phi)LW/RW341542052111436.36%26120.60000080000161150.00%200001.6200000111
2Tomas JurcoPhantoms (Phi)LW/RW3325300671241025.00%16923.00101380000152071.43%3500001.4500000200
3Matt TennysonPhantoms (Phi)D3033320520230.00%35016.930000500004000.00%000001.1800000010
4Adam TambelliniPhantoms (Phi)C/LW3022520000030.00%04515.0400008000010059.52%4200000.8900000000
5Robert BortuzzoPhantoms (Phi)D311204012181712.50%26923.1411278000012000.00%000000.5800000000
6Alex FriesenPhantoms (Phi)C/LW3011140424120.00%13913.00000000000000100.00%200000.5100000000
7David BoothPhantoms (Phi)LW/RW3011-240003160.00%05317.9601119000070050.00%200000.3700000000
8Tomas HykaPhantoms (Phi)RW3011100015010.00%0248.18000000000500100.00%100000.8100000000
9Alex BiegaPhantoms (Phi)D2011020621210.00%14221.040001300009000.00%000000.4800000000
10Oscar FantenbergPhantoms (Phi)D3011080504050.00%35819.4801131000009000.00%000000.3400000000
11Griffin ReinhartPhantoms (Phi)D31014205440025.00%34615.460000000005000.00%000000.4300000000
12Iiro PakarinenPhantoms (Phi)LW/RW3011-2201034380.00%05618.7201129000090057.14%1400000.3600000000
13Mike BlundenPhantoms (Phi)RW3011100216350.00%03913.03000000000000100.00%200000.5100000000
14Bryce Van BrabantPhantoms (Phi)C/LW3000155413110.00%04013.5000000000020063.64%3300000.0000001000
15Clayton StonerPhantoms (Phi)D3000155000100.00%0134.660000000002000.00%000000.0000010000
16Marco RoyPhantoms (Phi)C1000000010000.00%088.9300000000000040.00%500000.0000000000
17Landon FerraroPhantoms (Phi)C3000-200102210.00%04715.8100019000000053.49%4300000.0000000000
18Yohann AuvituPhantoms (Phi)D3000000322140.00%36321.1100018000013000.00%000000.0000000000
19Jakub KindlPhantoms (Phi)D1000000300000.00%01515.380000000001000.00%000000.0000000000
20Paul BissonnettePhantoms (Phi)LW3000100000100.00%0186.300000000000000.00%000000.0000000000
21Scott KosmachukPhantoms (Phi)RW2000200000000.00%021.100000000000000.00%100000.0000000000
Stats d'équipe Total ou en Moyenne5791625214210712969246113.04%1986615.20246199200001193160.99%18200000.5800011321
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
1Eddie LackPhantoms (Phi)33000.9151.67180205590000.000030000
Stats d'équipe Total ou en Moyenne33000.9151.67180205590000.000030000


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 Salaire Année 2 Salaire Année 3 Salaire Année 4 Salaire Année 5 Salaire Année 6 Salaire Année 7 Salaire Année 8 Salaire Année 9 Salaire Année 10 Link
Adam ComriePhantoms (Phi)D261990-07-30No220 Lbs6 ft4NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No300,000$300,000$
Adam TambelliniPhantoms (Phi)C/LW221994-11-01No169 Lbs6 ft2NoNoNo2Contrat d'EntréePro & Farm500,000$0$0$No500,000$
Alex BiegaPhantoms (Phi)D281988-04-03No187 Lbs5 ft10NoNoNo1Sans RestrictionPro & Farm470,000$0$0$No
Alex FriesenPhantoms (Phi)C/LW251991-01-29No185 Lbs5 ft9NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No300,000$
Andrew CampbellPhantoms (Phi)D281988-02-03No206 Lbs6 ft4NoNoNo1Sans RestrictionPro & Farm500,000$0$0$No
Anthony StolarzPhantoms (Phi)G221994-01-19No210 Lbs6 ft6NoNoNo1Contrat d'EntréePro & Farm300,000$0$0$No
Brandon MashinterPhantoms (Phi)LW281988-09-19No212 Lbs6 ft4NoNoNo4Sans RestrictionPro & Farm500,000$0$0$No500,000$500,000$500,000$
Brian CooperPhantoms (Phi)D231993-10-31Yes197 Lbs5 ft10NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No300,000$300,000$
Bryce Van BrabantPhantoms (Phi)C/LW251991-11-12No205 Lbs6 ft2NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No300,000$
Cameron GauncePhantoms (Phi)D261990-03-19No210 Lbs6 ft1NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Chris CarlislePhantoms (Phi)D221994-12-16No174 Lbs5 ft10NoNoNo2Contrat d'EntréePro & Farm300,000$0$0$No300,000$
Chris ThorburnPhantoms (Phi)LW/RW331983-06-03No235 Lbs6 ft3NoNoNo3Sans RestrictionPro & Farm500,000$0$0$No500,000$500,000$
Clayton StonerPhantoms (Phi)D311985-02-19No216 Lbs6 ft4NoNoNo1Sans RestrictionPro & Farm925,000$0$0$No
Colin McDonaldPhantoms (Phi)RW321984-09-30No219 Lbs6 ft2NoNoNo1Sans RestrictionPro & Farm500,000$0$0$No
David BoothPhantoms (Phi)LW/RW321984-11-24No212 Lbs6 ft0NoNoNo1Sans RestrictionPro & Farm500,000$0$0$No
Eddie LackPhantoms (Phi)G291988-01-05No187 Lbs6 ft4NoNoNo1Sans RestrictionPro & Farm1,000,000$0$0$No
Griffin ReinhartPhantoms (Phi)D221994-01-24No217 Lbs6 ft4NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$No300,000$300,000$
Iiro PakarinenPhantoms (Phi)LW/RW251991-08-25No215 Lbs6 ft1NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Jakub KindlPhantoms (Phi)D291987-02-10No199 Lbs6 ft3NoNoNo3Sans RestrictionPro & Farm500,000$0$0$No500,000$500,000$
Kirill GotovetsPhantoms (Phi)D251991-06-25Yes175 Lbs5 ft11NoNoNo1Avec RestrictionPro & Farm500,000$0$0$No
Landon FerraroPhantoms (Phi)C251991-08-08No186 Lbs6 ft0NoNoNo4Avec RestrictionPro & Farm300,000$0$0$No300,000$300,000$300,000$
Marco RoyPhantoms (Phi)C221994-11-05No183 Lbs6 ft0NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$No300,000$300,000$
Matt TennysonPhantoms (Phi)D261990-04-22No205 Lbs6 ft2NoNoNo1Avec RestrictionPro & Farm500,000$0$0$No
Mattias BackmanPhantoms (Phi)D241992-10-02No181 Lbs6 ft2NoNoNo1Avec RestrictionPro & Farm900,000$0$0$No
Mike BlundenPhantoms (Phi)RW301986-12-14No216 Lbs6 ft4NoNoNo1Sans RestrictionPro & Farm500,000$0$0$No
Mike McKennaPhantoms (Phi)G331983-04-10No190 Lbs6 ft2NoNoNo2Sans RestrictionPro & Farm500,000$0$0$No500,000$
Niklas SvedbergPhantoms (Phi)G271989-09-03No176 Lbs6 ft2NoNoNo1Avec RestrictionPro & Farm500,000$0$0$No
Oscar FantenbergPhantoms (Phi)D251991-10-07Yes203 Lbs6 ft0NoNoNo4Avec RestrictionPro & Farm300,000$0$0$No300,000$300,000$300,000$
Patrick McNallyPhantoms (Phi)D251991-12-04No181 Lbs6 ft2NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No300,000$
Paul BissonnettePhantoms (Phi)LW311985-03-10No216 Lbs6 ft2NoNoNo1Sans RestrictionPro & Farm500,000$0$0$No
Rene BourquePhantoms (Phi)LW/RW351981-12-09No214 Lbs6 ft2NoNoNo3Sans RestrictionPro & Farm500,000$0$0$No500,000$500,000$
Robert BortuzzoPhantoms (Phi)D271989-03-17No215 Lbs6 ft4NoNoNo2Avec RestrictionPro & Farm1,000,000$0$0$No1,000,000$
Scott KosmachukPhantoms (Phi)RW221994-01-23No185 Lbs5 ft11NoNoNo1Contrat d'EntréePro & Farm300,000$0$0$No
Sergey KalininPhantoms (Phi)C/LW/RW251991-03-17No190 Lbs6 ft3NoNoNo2Avec RestrictionPro & Farm500,000$0$0$No500,000$
Tomas HykaPhantoms (Phi)RW231993-03-23No168 Lbs5 ft11NoNoNo1Avec RestrictionPro & Farm500,000$0$0$No
Tomas JurcoPhantoms (Phi)LW/RW241992-12-27No203 Lbs5 ft11NoNoNo3Avec RestrictionPro & Farm500,000$0$0$No500,000$500,000$
Yohann AuvituPhantoms (Phi)D271989-07-27No198 Lbs6 ft0NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No300,000$300,000$
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
3726.59199 Lbs6 ft21.95467,432$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Tomas JurcoAdam TambelliniChris Thorburn40122
2Iiro PakarinenLandon FerraroDavid Booth30122
3Alex FriesenBryce Van BrabantMike Blunden20122
4Paul BissonnetteTomas JurcoTomas Hyka10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Robert BortuzzoAlex Biega40122
2Oscar FantenbergYohann Auvitu30122
3Matt TennysonGriffin Reinhart20122
4Clayton StonerRobert Bortuzzo10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Tomas JurcoAdam TambelliniChris Thorburn60122
2Iiro PakarinenLandon FerraroDavid Booth40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Robert BortuzzoAlex Biega60122
2Oscar FantenbergYohann Auvitu40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Tomas JurcoChris Thorburn60122
2Iiro PakarinenDavid Booth40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Robert BortuzzoAlex Biega60122
2Oscar FantenbergYohann Auvitu40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Tomas Jurco60122Robert BortuzzoAlex Biega60122
2Chris Thorburn40122Oscar FantenbergYohann Auvitu40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Tomas JurcoChris Thorburn60122
2Iiro PakarinenDavid Booth40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Robert BortuzzoAlex Biega60122
2Oscar FantenbergYohann Auvitu40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Tomas JurcoAdam TambelliniChris ThorburnRobert BortuzzoAlex Biega
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Tomas JurcoAdam TambelliniChris ThorburnRobert BortuzzoAlex Biega
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Scott Kosmachuk, Mike Blunden, Tomas HykaScott Kosmachuk, Mike BlundenTomas Hyka
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Matt Tennyson, Griffin Reinhart, Clayton StonerMatt TennysonGriffin Reinhart, Clayton Stoner
Tirs de Pénalité
Tomas Jurco, Chris Thorburn, Iiro Pakarinen, David Booth, Mike Blunden
Gardien
#1 : Eddie Lack, #2 : Niklas Svedberg


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
1Sound Tigers11000000211110000002110000000000021.00023500531012241728013117186116.67%6183.33%0447261.11%518063.75%163053.33%916955173318
2Thunderbirds22000000743110000004311100000031241.00071320005310572417280461827536116.67%11190.91%0447261.11%518063.75%163053.33%916955173318
Total33000000954220000006421100000031261.000916250053106924172805919447112216.67%17288.24%0447261.11%518063.75%163053.33%916955173318
_Since Last GM Reset33000000954220000006421100000031261.000916250053106924172805919447112216.67%17288.24%0447261.11%518063.75%163053.33%916955173318
_Vs Conference11000000211110000002110000000000021.00023500531012241728013117186116.67%6183.33%0447261.11%518063.75%163053.33%916955173318
_Vs Division21000000743110000004311000000031220.50071320005310572417280461827536116.67%11190.91%0447261.11%518063.75%163053.33%916955173318

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
36W391625695919447100
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
330000095
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
220000064
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
110000031
Derniers 10 Matchs
WLOTWOTL SOWSOL
300000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
12216.67%17288.24%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
24172805310
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
447261.11%518063.75%163053.33%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
916955173318


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 - 2018-09-0813Sound Tigers1Phantoms2WSommaire du Match
10 - 2018-09-1435Thunderbirds3Phantoms4WSommaire du Match
11 - 2018-09-1552Phantoms3Thunderbirds1WSommaire du Match
17 - 2018-09-2173Penguins-Phantoms-
18 - 2018-09-2289Phantoms-Penguins-
19 - 2018-09-2396Phantoms-Sound Tigers-
22 - 2018-09-26103Phantoms-Wolf Pack-
25 - 2018-09-29124Phantoms-Devils-
26 - 2018-09-30134Phantoms-Bears-
31 - 2018-10-05146Phantoms-Devils-
32 - 2018-10-06162Devils-Phantoms-
38 - 2018-10-12185Thunderbirds-Phantoms-
39 - 2018-10-13198Checkers-Phantoms-
43 - 2018-10-17219Checkers-Phantoms-
45 - 2018-10-19227Phantoms-Bruins-
46 - 2018-10-20244Phantoms-Thunderbirds-
52 - 2018-10-26268Bears-Phantoms-
53 - 2018-10-27281Bruins-Phantoms-
59 - 2018-11-02313Phantoms-Rocket-
60 - 2018-11-03324Phantoms-Senators-
61 - 2018-11-04334Phantoms-Marlies-
64 - 2018-11-07343Monsters-Phantoms-
66 - 2018-11-09352Bears-Phantoms-
67 - 2018-11-10369Monsters-Phantoms-
73 - 2018-11-16394Senators-Phantoms-
74 - 2018-11-17409Phantoms-Penguins-
78 - 2018-11-21427Bears-Phantoms-
80 - 2018-11-23439Crunch-Phantoms-
81 - 2018-11-24456Phantoms-Bears-
85 - 2018-11-28467Phantoms-Penguins-
87 - 2018-11-30478Bruins-Phantoms-
88 - 2018-12-01494Penguins-Phantoms-
94 - 2018-12-07520Phantoms-Bruins-
95 - 2018-12-08533Phantoms-Wolf Pack-
96 - 2018-12-09547Phantoms-Thunderbirds-
101 - 2018-12-14567Devils-Phantoms-
102 - 2018-12-15580Bears-Phantoms-
103 - 2018-12-16590Phantoms-Bears-
106 - 2018-12-19597Thunderbirds-Phantoms-
108 - 2018-12-21609Phantoms-Wolf Pack-
109 - 2018-12-22625Americans-Phantoms-
111 - 2018-12-24638Phantoms-Penguins-
113 - 2018-12-26649Bears-Phantoms-
115 - 2018-12-28660Rocket-Phantoms-
116 - 2018-12-29671Phantoms-Sound Tigers-
122 - 2019-01-04683Phantoms-Sound Tigers-
123 - 2019-01-05700Phantoms-Bears-
129 - 2019-01-11723Phantoms-Comets-
130 - 2019-01-12745Wolf Pack-Phantoms-
131 - 2019-01-13751Wolf Pack-Phantoms-
136 - 2019-01-18771Sound Tigers-Phantoms-
137 - 2019-01-19781Phantoms-Bears-
138 - 2019-01-20801Bears-Phantoms-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
143 - 2019-01-25816Phantoms-Checkers-
144 - 2019-01-26829Phantoms-Checkers-
150 - 2019-02-01856Phantoms-Crunch-
151 - 2019-02-02875Comets-Phantoms-
152 - 2019-02-03883Sound Tigers-Phantoms-
155 - 2019-02-06892Phantoms-Penguins-
157 - 2019-02-08902Checkers-Phantoms-
158 - 2019-02-09913Checkers-Phantoms-
162 - 2019-02-13937Penguins-Phantoms-
164 - 2019-02-15949Bruins-Phantoms-
165 - 2019-02-16963Marlies-Phantoms-
171 - 2019-02-22985Phantoms-Penguins-
172 - 2019-02-23999Penguins-Phantoms-
176 - 2019-02-271021Phantoms-Americans-
178 - 2019-03-011028Phantoms-Monsters-
179 - 2019-03-021041Phantoms-Monsters-
182 - 2019-03-051059Phantoms-Checkers-
183 - 2019-03-061063Phantoms-Checkers-
186 - 2019-03-091092Penguins-Phantoms-
187 - 2019-03-101100Penguins-Phantoms-
189 - 2019-03-121105Phantoms-Bears-
192 - 2019-03-151119Phantoms-Bruins-
193 - 2019-03-161133Wolf 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
36 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
19,655$ 172,950$ 172,500$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 12,436$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 180 1,407$ 253,260$




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
133300000095422000000642110000003126916250053106924172805919447112216.67%17288.24%0447261.11%518063.75%163053.33%916955173318
Total Saison Régulière3300000095422000000642110000003126916250053106924172805919447112216.67%17288.24%0447261.11%518063.75%163053.33%916955173318
Séries
129540000021192431000009815230000012111102139600077702225667762320447193199921010.87%74691.89%118833855.62%16330852.92%8814560.69%2571742207812565
129540000021192431000009815230000012111102139600077702225667762320447193199921010.87%74691.89%118833855.62%16330852.92%8814560.69%2571742207812565
Total Séries18108000004238486200000181621046000002422220427812000141414044411213415246408943863981842010.87%1481291.89%237667655.62%32661652.92%17629060.69%515349441156251131