Phantoms

GP: 24 | W: 18 | L: 5 | OTL: 1 | P: 37
GF: 78 | GA: 44 | PP%: 21.64% | PK%: 90.57%
DG: Kriss Cardenas | Morale : 68 | Moyenne d'Équipe : 60
Prochain matchs #394 vs Senators
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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 JurcoXX98.00765576837480807058646772557775174690
2Chris ThorburnXX100.00875559698180686864656170557476171660
3Iiro PakarinenXX100.00825565797975746750626370555656171650
4David BoothXX100.00805571756769706750626662558273174640
5Tomas HykaX100.00635581726169686450626262555050174610
6Mike BlundenX100.00605569688078716050606060555859178610
7Landon FerraroX100.00685573707064686050596060555354174600
8Adam TambelliniXX100.00665566646561726050585955557372173590
9Bryce Van BrabantXX100.00585558627974635550555557557173171580
10Alex FriesenXX100.00595567606662715550555558557071174570
11Paul BissonnetteX100.00595556618582545550555555556263174570
12Robert BortuzzoX100.00875577818976688025666579558073173740
13Alex BiegaX99.00965573706777677425656177558075156700
14Oscar Fantenberg (R)X100.00785579757375587525666373557067174680
15Matt TennysonX100.00695565717882627125606069557070157660
16Yohann AuvituX100.00705587826474667425656772555353159660
17Griffin ReinhartX100.00625568798865746225606059557053170630
18Clayton StonerX100.00555555605555795525555555558583171580
19Jakub KindlX100.00555558605858705825585858556970137570
Rayé
1Scott KosmachukX100.00605569606863705750565755555050152560
2Brandon MashinterX100.00565555555758595550555555557574126550
3Marco RoyX100.00725566626963625550555555555050129550
4Rene BourqueXX100.00565555555555555550555555557273126540
5Colin McDonaldX100.00565555555757575550555555557472126540
6Sergey KalininXXX100.00555555555555555550555555555858126530
7Cameron GaunceX100.00555555605555655525555555557272139560
8Adam ComrieX100.00555555605555595525555555555353126540
9Chris CarlisleX100.00555555605555725525555555555353126540
10Andrew CampbellX100.00555555605555735525555555555353126540
11Patrick McNallyX100.00555555605555595525555555555353126540
12Mattias BackmanX100.00555555605555655525555555555353126540
13Kirill Gotovets (R)X100.00555555605555655525555555555555126540
14Brian Cooper (R)X100.00555555605555585525555555555353126530
MOYENNE D'ÉQUIPE99.9164556466666566613958586055646315259
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
1Eddie Lack95.0075717076808066657279557977168720
2Niklas Svedberg100.0068707370776974787874556060174700
Rayé
1Anthony Stolarz100.0069798687686870646969556566126690
2Mike McKenna100.0064808578656567646963557069126670
MOYENNE D'ÉQUIPE98.756975797873716968727155696814970
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
1Tomas JurcoPhantoms (Phi)LW/RW241412266275257498236814.29%259624.8775122810700011434152.64%41600000.8704100213
2Chris ThorburnPhantoms (Phi)LW/RW241012227415741165183715.38%451421.4446102111100051351151.22%4100000.8614010321
3Robert BortuzzoPhantoms (Phi)D24713204375432856193712.50%2257523.966713391070000125000.00%000000.7000100211
4Iiro PakarinenPhantoms (Phi)LW/RW24126186635603164174818.75%344718.65549261020004831145.26%9500000.8034001140
5Oscar FantenbergPhantoms (Phi)D2421517620055182713367.41%2253122.14099171030000126000.00%000000.6400000110
6David BoothPhantoms (Phi)LW/RW247815430034216073811.67%245018.77156211030003761050.00%2200000.6701000112
7Alex BiegaPhantoms (Phi)D2331114426057264022197.50%3053423.24268281040001116010.00%000000.5200000103
8Yohann AuvituPhantoms (Phi)D162101262001317309246.67%1533621.052352158000069100.00%000000.7100000000
9Mike BlundenPhantoms (Phi)RW24210124241021235516373.64%228211.78000040000211055.17%2900000.8511101102
10Matt TennysonPhantoms (Phi)D160101022202511138170.00%1529318.34044952000049000.00%000000.6800000020
11Bryce Van BrabantPhantoms (Phi)C/LW246397441031292692723.08%430212.6200000000023052.67%26200000.5900001111
12Adam TambelliniPhantoms (Phi)C/LW240881325513208450.00%135614.850110100000080053.02%38100000.4500100000
13Alex FriesenPhantoms (Phi)C/LW24448612025173082013.33%330612.7700000000000160.00%2000000.5200000110
14Landon FerraroPhantoms (Phi)C24257-360122218101511.11%137815.7923591130000161052.39%41800000.3700000000
15Griffin ReinhartPhantoms (Phi)D191567240241893811.11%1227114.2900005000020000.00%000000.4400000001
16Tomas HykaPhantoms (Phi)RW24235320812253178.00%32068.59000020000462050.00%3800000.4801000000
17Clayton StonerPhantoms (Phi)D2405572151768350.00%625310.5701128000029000.00%000000.3900010000
18Cameron GauncePhantoms (Phi)D51010207120150.00%37314.6700000000113010.00%000000.2700000000
19Marco RoyPhantoms (Phi)C1000000010000.00%088.9300000000000040.00%500000.0000000000
20Jakub KindlPhantoms (Phi)D1000000300000.00%01515.380000000001000.00%000000.0000000000
21Paul BissonnettePhantoms (Phi)LW24000120323120.00%01014.2200000000000083.33%600000.0000000000
22Scott KosmachukPhantoms (Phi)RW16000-120332220.00%0503.1800008000000044.44%3600000.0000000000
Stats d'équipe Total ou en Moyenne43375140215894505055339163919546311.74%150688915.91295483221109600015108615652.18%176900000.62515423141414
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)2218400.9171.65131323364330000.91712222221
2Niklas SvedbergPhantoms (Phi)30110.8772.94143007570000.3333222000
Stats d'équipe Total ou en Moyenne2518510.9121.77145723434900000.800152424221


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 JurcoLandon FerraroChris Thorburn40122
2Iiro PakarinenAdam TambelliniDavid Booth30122
3Alex FriesenBryce Van BrabantTomas Hyka20122
4Paul BissonnetteTomas JurcoMike Blunden10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Robert BortuzzoAlex Biega40122
2Oscar FantenbergMatt Tennyson30122
3Griffin ReinhartClayton Stoner20122
4Robert BortuzzoAlex Biega10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Tomas JurcoLandon FerraroChris Thorburn60122
2Iiro PakarinenAdam TambelliniDavid Booth40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Robert BortuzzoAlex Biega60122
2Oscar FantenbergMatt Tennyson40122
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 FantenbergMatt Tennyson40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Tomas Jurco60122Robert BortuzzoAlex Biega60122
2Chris Thorburn40122Oscar FantenbergMatt Tennyson40122
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 FantenbergMatt Tennyson40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Tomas JurcoLandon FerraroChris ThorburnRobert BortuzzoAlex Biega
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Tomas JurcoLandon FerraroChris ThorburnRobert BortuzzoAlex Biega
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
, Tomas Hyka, Mike Blunden, Tomas HykaMike Blunden
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Griffin Reinhart, Clayton Stoner, Oscar FantenbergGriffin ReinhartClayton Stoner, Oscar Fantenberg
Tirs de Pénalité
Tomas Jurco, Chris Thorburn, Iiro Pakarinen, David Booth, Tomas Hyka
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
1Bears30100020871201000105501000001032140.6678122000233319572198223209248222485713430.77%14285.71%040773855.15%35268951.09%16632650.92%688499491164279152
2Bruins22000000716110000004131100000030341.000714210123331953819822320924259255614428.57%100100.00%040773855.15%35268951.09%16632650.92%688499491164279152
3Checkers22000000835220000008350000000000041.000814220023331958719822320924301236537457.14%18194.44%040773855.15%35268951.09%16632650.92%688499491164279152
4Devils3020000138-51010000014-32010000124-210.16735800233319568198223209246624765318316.67%17382.35%040773855.15%35268951.09%16632650.92%688499491164279152
5Marlies11000000505000000000001100000050521.000581301233319527198223209242041232100.00%60100.00%040773855.15%35268951.09%16632650.92%688499491164279152
6Monsters21100000734211000007340000000000020.5007142101233319551198223209244212493919421.05%17194.12%040773855.15%35268951.09%16632650.92%688499491164279152
7Penguins2110000078-1110000005321010000025-320.5007132000233319540198223209244117515315213.33%13376.92%040773855.15%35268951.09%16632650.92%688499491164279152
8Rocket10000010211000000000001000001021121.000224002333195291982232092420331299111.11%100100.00%040773855.15%35268951.09%16632650.92%688499491164279152
9Senators11000000211000000000001100000021121.000246002333195211982232092428322227114.29%10190.00%040773855.15%35268951.09%16632650.92%688499491164279152
10Sound Tigers22000000633110000002111100000042241.000611170023331953819822320924286294312325.00%12191.67%040773855.15%35268951.09%16632650.92%688499491164279152
11Thunderbirds440000001789220000009632200000082681.0001732490023331951091982232092493336510114214.29%29389.66%040773855.15%35268951.09%16632650.92%688499491164279152
Total2415500031784434128300010412615127200021371819370.77178140218032333195639198223209244901504525531342921.64%1591590.57%040773855.15%35268951.09%16632650.92%688499491164279152
13Wolf Pack11000000615000000000001100000061521.0006111700233319559198223209241558155120.00%30100.00%040773855.15%35268951.09%16632650.92%688499491164279152
_Since Last GM Reset2415500031784434128300010412615127200021371819370.77178140218032333195639198223209244901504525531342921.64%1591590.57%040773855.15%35268951.09%16632650.92%688499491164279152
_Vs Conference16850002146321484300010241778420001122157210.656468413002233319538719822320924327983083381032221.36%961188.54%040773855.15%35268951.09%16632650.92%688499491164279152
_Vs Division94300001331122322000001376621000012041690.500336093022333195224198223209241865215524045817.78%65493.85%040773855.15%35268951.09%16632650.92%688499491164279152

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
2437W17814021863949015045255303
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
2415500317844
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
128300104126
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
127200213718
Derniers 10 Matchs
WLOTWOTL SOWSOL
620020
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
1342921.64%1591590.57%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
198223209242333195
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
40773855.15%35268951.09%16632650.92%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
688499491164279152


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 - 2018-09-2173Penguins3Phantoms5WSommaire du Match
18 - 2018-09-2289Phantoms2Penguins5LSommaire du Match
19 - 2018-09-2396Phantoms4Sound Tigers2WSommaire du Match
22 - 2018-09-26103Phantoms6Wolf Pack1WSommaire du Match
25 - 2018-09-29124Phantoms0Devils1LSommaire du Match
26 - 2018-09-30134Phantoms3Bears2WXXSommaire du Match
31 - 2018-10-05146Phantoms2Devils3LXXSommaire du Match
32 - 2018-10-06162Devils4Phantoms1LSommaire du Match
38 - 2018-10-12185Thunderbirds3Phantoms5WSommaire du Match
39 - 2018-10-13198Checkers1Phantoms4WSommaire du Match
43 - 2018-10-17219Checkers2Phantoms4WSommaire du Match
45 - 2018-10-19227Phantoms3Bruins0WSommaire du Match
46 - 2018-10-20244Phantoms5Thunderbirds1WSommaire du Match
52 - 2018-10-26268Bears3Phantoms2LSommaire du Match
53 - 2018-10-27281Bruins1Phantoms4WSommaire du Match
59 - 2018-11-02313Phantoms2Rocket1WXXSommaire du Match
60 - 2018-11-03324Phantoms2Senators1WSommaire du Match
61 - 2018-11-04334Phantoms5Marlies0WSommaire du Match
64 - 2018-11-07343Monsters3Phantoms2LSommaire du Match
66 - 2018-11-09352Bears2Phantoms3WXXSommaire du Match
67 - 2018-11-10369Monsters0Phantoms5WSommaire du Match
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
26 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
93,774$ 172,950$ 172,500$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 59,229$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 127 1,407$ 178,689$




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
1324155000317844341283000104126151272000213718193778140218032333195639198223209244901504525531342921.64%1591590.57%040773855.15%35268951.09%16632650.92%688499491164279152
Total Saison Régulière24155000317844341283000104126151272000213718193778140218032333195639198223209244901504525531342921.64%1591590.57%040773855.15%35268951.09%16632650.92%688499491164279152
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