Phantoms

GP: 50 | W: 34 | L: 13 | OTL: 3 | P: 71
GF: 148 | GA: 92 | PP%: 19.34% | PK%: 89.83%
DG: Kriss Cardenas | Morale : 76 | Moyenne d'Équipe : 60
Prochain matchs #771 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
1Chris ThorburnXX97.00875559698180686864656170557476184660
2David BoothXX100.00805571756769706750626662558273183650
3Iiro PakarinenXX99.00825565797975746750626370555656181650
4Tomas HykaX100.00635581726169686450626262555050182610
5Mike BlundenX100.00605569688078716050606060555859184610
6Landon FerraroX100.00685573707064686050596060555354180600
7Adam TambelliniXX100.00665566646561726050585955557372173590
8Bryce Van BrabantXX100.00585558627974635550555557557173181580
9Alex FriesenXX100.00595567606662715550555558557071183570
10Paul BissonnetteX100.00595556618582545550555555556263182570
11Scott KosmachukX100.00605569606863705750565755555050139560
12Colin McDonaldX100.00565555555757575550555555557472123540
13Robert BortuzzoX100.00875577818976688025666579558073183740
14Alex BiegaX100.00965573706777677425656177558075127700
15Oscar Fantenberg (R)X100.00785579757375587525666373557067182680
16Yohann AuvituX100.00705587826474667425656772555353174670
17Matt TennysonX100.00695565717882627125606069557070172660
18Clayton StonerX100.00555555605555795525555555558583181580
Rayé
1Brandon MashinterX100.00565555555758595550555555557574120550
2Marco RoyX100.00725566626963625550555555555050120550
3Rene BourqueXX100.00565555555555555550555555557273120540
4Sergey KalininXXX100.00555555555555555550555555555858120530
5Jakub KindlX100.00555558605858705825585858556970140570
6Cameron GaunceX100.00555555605555655525555555557272137560
7Andrew CampbellX100.00555555605555735525555555555353120540
8Mattias BackmanX100.00555555605555655525555555555353120540
9Kirill Gotovets (R)X100.00555555605555655525555555555555120540
10Adam ComrieX100.00555555605555595525555555555353120530
11Patrick McNallyX100.00555555605555595525555555555353120530
12Brian Cooper (R)X100.00555555605555585525555555555353120530
MOYENNE D'ÉQUIPE99.8764556365656565613958586055646415259
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 Lack100.0075717076808066657279557977166720
2Mike McKenna100.0064808578656567646963557069123670
Rayé
1Niklas Svedberg100.0068707370776974787874556060173700
MOYENNE D'ÉQUIPE100.006974767574716969737255706915470
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
1Robert BortuzzoPhantoms (Phi)D50173148672109153106397716.04%50117323.47121729752360110267110.00%000000.8200101223
2Oscar FantenbergPhantoms (Phi)D508324012580855277287410.39%35111322.2732124562300001266100.00%000000.7200000220
3Chris ThorburnPhantoms (Phi)LW/RW50182038121031516349122337714.75%10112222.456111734240101112834360.00%27500000.6815210552
4Iiro PakarinenPhantoms (Phi)LW/RW50201737101001099681283410215.63%999219.86811194823100052103142.47%14600000.7535002352
5David BoothPhantoms (Phi)LW/RW5017183597359060121237314.05%295719.14310134122600041653149.56%11300000.7302100342
6Tomas JurcoPhiladelphia FlyersLW/RW371718357395331071252710213.60%1091824.8188163916501122284153.12%65700000.7605100313
7Yohann AuvituPhantoms (Phi)D42625311342039458828706.82%4089021.1951015661790002210310.00%000000.7000000113
8Matt TennysonPhantoms (Phi)D426222811680713444154413.64%3178018.585813351440000149000.00%000000.7200000124
9Alex BiegaPhantoms (Phi)D3051419738074315426249.26%3769123.064711391380002150110.00%000000.5500000203
10Mike BlundenPhantoms (Phi)RW50511164451540499726645.15%763512.711012220000263058.73%6300000.5012102113
11Bryce Van BrabantPhantoms (Phi)C/LW507815127210445246224215.22%870614.120111350000464051.66%60200000.4211001211
12Tomas HykaPhantoms (Phi)RW506915910018296116539.84%750710.150009650000793046.30%5400000.5901000010
13Landon FerraroPhantoms (Phi)C5041115-4155185335172911.43%176815.38448172390000401050.95%89900000.3900010000
14Adam TambelliniPhantoms (Phi)C/LW42012121631528392513170.00%164315.3203371880001120051.86%67100000.3700100000
15Alex FriesenPhantoms (Phi)C/LW50551011361040394693510.87%563212.65000000000100156.25%11200000.3200101120
16Griffin ReinhartPhiladelphia FlyersD422798500433216101112.50%2253112.66000010000040010.00%000000.3400000002
17Clayton StonerPhantoms (Phi)D5008811415541412470.00%2055911.19011214000079000.00%000000.2901010000
18Cameron GauncePhantoms (Phi)D17112-212016251220.00%31438.4500000000113010.00%000000.2800000000
19Paul BissonnettePhantoms (Phi)LW501127100131111289.09%32985.98000010000310061.11%3600000.1300000010
20Colin McDonaldPhantoms (Phi)RW9000-100110000.00%0222.5000006000000035.71%1400000.0000000000
21Marco RoyPhantoms (Phi)C1000000010000.00%088.9300000000000040.00%500000.0000000000
22Jakub KindlPhantoms (Phi)D18000-31001730010.00%51277.0800002000010000.00%000000.0000000000
23Scott KosmachukPhantoms (Phi)RW25000075343220.00%0893.5700009000000041.03%3900000.0000001000
Stats d'équipe Total ou en Moyenne9051452704151559321001080828122237591411.87%3061431615.82591121714712390123292327311252.06%368600000.58622838262828
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)3728810.9221.59219128587430000.84219376633
2Niklas SvedbergPhantoms (Phi)93210.8682.4741300171290000.3333635000
3Anthony StolarzPhiladelphia Flyers73310.8942.2240600151420100.000070000
4Mike McKennaPhantoms (Phi)10001.0000.001600030000.000009000
Stats d'équipe Total ou en Moyenne54341330.9121.783027289010170100.773225050633


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 ComriePhantoms (Phi)D261990-07-30No220 Lbs6 ft4NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No
Adam TambelliniPhantoms (Phi)C/LW221994-11-01No169 Lbs6 ft2NoNoNo2Contrat d'EntréePro & Farm500,000$0$0$No
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$0$No
Andrew CampbellPhantoms (Phi)D281988-02-03No206 Lbs6 ft4NoNoNo1Sans RestrictionPro & Farm500,000$0$0$No
Brandon MashinterPhantoms (Phi)LW281988-09-19No212 Lbs6 ft4NoNoNo4Sans RestrictionPro & Farm500,000$0$0$No
Brian CooperPhantoms (Phi)D231993-10-31Yes197 Lbs5 ft10NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No
Bryce Van BrabantPhantoms (Phi)C/LW251991-11-12No205 Lbs6 ft2NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No
Cameron GauncePhantoms (Phi)D261990-03-19No210 Lbs6 ft1NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Chris ThorburnPhantoms (Phi)LW/RW331983-06-03No235 Lbs6 ft3NoNoNo3Sans RestrictionPro & Farm500,000$0$0$No
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
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$0$No
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$0$No
Marco RoyPhantoms (Phi)C221994-11-05No183 Lbs6 ft0NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$No
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$0$No
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$0$No
Patrick McNallyPhantoms (Phi)D251991-12-04No181 Lbs6 ft2NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No
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$0$No
Robert BortuzzoPhantoms (Phi)D271989-03-17No215 Lbs6 ft4NoNoNo2Avec RestrictionPro & Farm1,000,000$0$0$No
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$0$No
Tomas HykaPhantoms (Phi)RW231993-03-23No168 Lbs5 ft11NoNoNo1Avec RestrictionPro & Farm500,000$0$0$No
Yohann AuvituPhantoms (Phi)D271989-07-27No198 Lbs6 ft0NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
3327.09199 Lbs6 ft11.91481,667$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Chris ThorburnLandon FerraroIiro Pakarinen40122
2David BoothAdam TambelliniMike Blunden30122
3Alex FriesenBryce Van BrabantTomas Hyka20122
4Paul BissonnetteChris ThorburnScott Kosmachuk10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Robert BortuzzoAlex Biega40122
2Oscar FantenbergYohann Auvitu30122
3Matt TennysonClayton Stoner20122
4Robert BortuzzoAlex Biega10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Chris ThorburnLandon FerraroIiro Pakarinen60122
2David BoothAdam TambelliniMike Blunden40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Robert BortuzzoAlex Biega60122
2Oscar FantenbergYohann Auvitu40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Chris ThorburnIiro Pakarinen60122
2David BoothMike Blunden40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Robert BortuzzoAlex Biega60122
2Oscar FantenbergYohann Auvitu40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Chris Thorburn60122Robert BortuzzoAlex Biega60122
2Iiro Pakarinen40122Oscar FantenbergYohann Auvitu40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Chris ThorburnIiro Pakarinen60122
2David BoothMike Blunden40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Robert BortuzzoAlex Biega60122
2Oscar FantenbergYohann Auvitu40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Chris ThorburnLandon FerraroIiro PakarinenRobert BortuzzoAlex Biega
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Chris ThorburnLandon FerraroIiro PakarinenRobert BortuzzoAlex Biega
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Colin McDonald, Tomas Hyka, Bryce Van BrabantColin McDonald, Tomas HykaBryce Van Brabant
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Matt Tennyson, Clayton Stoner, Oscar FantenbergMatt TennysonClayton Stoner, Oscar Fantenberg
Tirs de Pénalité
Chris Thorburn, Iiro Pakarinen, David Booth, Mike Blunden, Tomas Hyka
Gardien
#1 : Eddie Lack, #2 : Mike McKenna


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
1Americans1010000024-21010000024-20000000000000.000246004660378194024203893320914206233.33%60100.00%0818153053.46%777145053.59%32667748.15%13929961048350589313
2Bears932020202215753100010138540102010972140.778223860004660378214402420389332066517415764914.06%65986.15%0818153053.46%777145053.59%32667748.15%13929961048350589313
3Bruins431000001138220000007162110000042260.750112233024660378894024203893366186110131722.58%260100.00%0818153053.46%777145053.59%32667748.15%13929961048350589313
4Checkers22000000835220000008350000000000041.000814220046603788740242038933301236537457.14%18194.44%0818153053.46%777145053.59%32667748.15%13929961048350589313
5Comets1010000012-1000000000001010000012-100.000123004660378344024203893330914188112.50%7185.71%0818153053.46%777145053.59%32667748.15%13929961048350589313
6Crunch1010000023-11010000023-10000000000000.00024600466037818402420389332151515800.00%4250.00%0818153053.46%777145053.59%32667748.15%13929961048350589313
7Devils4120000159-42110000035-22010000124-230.375591400466037879402420389339226928022522.73%25484.00%0818153053.46%777145053.59%32667748.15%13929961048350589313
8Marlies11000000505000000000001100000050521.000581301466037827402420389332041232100.00%60100.00%0818153053.46%777145053.59%32667748.15%13929961048350589313
9Monsters21100000734211000007340000000000020.5007142101466037851402420389334212493919421.05%17194.12%0818153053.46%777145053.59%32667748.15%13929961048350589313
10Penguins623000011922-32110000089-1412000011113-250.417193453014660378101402420389331344315012934720.59%48785.42%0818153053.46%777145053.59%32667748.15%13929961048350589313
11Rocket21000010422110000002111000001021141.00046100046603784640242038933306474916212.50%180100.00%0818153053.46%777145053.59%32667748.15%13929961048350589313
12Senators2110000045-11010000024-21100000021120.50046100046603784140242038933469364113215.38%17476.47%0818153053.46%777145053.59%32667748.15%13929961048350589313
13Sound Tigers43000100963110000002113200010075270.8759172600466037883402420389337217539624520.83%24291.67%0818153053.46%777145053.59%32667748.15%13929961048350589313
14Thunderbirds66000000251114330000001596330000001028121.00025487301466037815940242038933140479015026519.23%39392.31%0818153053.46%777145053.59%32667748.15%13929961048350589313
Total50291302132148925625177000108353302512602122653926710.710148270418084660378122240242038933101730693410803055919.34%3543689.83%1818153053.46%777145053.59%32667748.15%13929961048350589313
16Wolf Pack550000002442022000000122103300000012210101.0002444680246603781744024203893368249110026623.08%34294.12%1818153053.46%777145053.59%32667748.15%13929961048350589313
_Since Last GM Reset50291302132148925625177000108353302512602122653926710.710148270418084660378122240242038933101730693410803055919.34%3543689.83%1818153053.46%777145053.59%32667748.15%13929961048350589313
_Vs Conference3719110212210370331811600010563620198502112473413490.662103188291064660378850402420389337472197217582414518.67%2603188.08%1818153053.46%777145053.59%32667748.15%13929961048350589313
_Vs Division171030010153282595200000302288510010123617220.647539815104466037839940242038933343982754081011817.82%116992.24%0818153053.46%777145053.59%32667748.15%13929961048350589313

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
5071W214827041812221017306934108008
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
502913213214892
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
2517700108353
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
2512621226539
Derniers 10 Matchs
WLOTWOTL SOWSOL
540100
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
3055919.34%3543689.83%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
402420389334660378
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
818153053.46%777145053.59%32667748.15%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
13929961048350589313


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 - 2018-11-16394Senators4Phantoms2LSommaire du Match
74 - 2018-11-17409Phantoms2Penguins3LXXSommaire du Match
78 - 2018-11-21427Bears1Phantoms2WSommaire du Match
80 - 2018-11-23439Crunch3Phantoms2LSommaire du Match
81 - 2018-11-24456Phantoms2Bears1WXSommaire du Match
85 - 2018-11-28467Phantoms4Penguins0WSommaire du Match
87 - 2018-11-30478Bruins0Phantoms3WSommaire du Match
88 - 2018-12-01494Penguins6Phantoms3LSommaire du Match
94 - 2018-12-07520Phantoms1Bruins2LSommaire du Match
95 - 2018-12-08533Phantoms2Wolf Pack0WSommaire du Match
96 - 2018-12-09547Phantoms2Thunderbirds0WSommaire du Match
101 - 2018-12-14567Devils1Phantoms2WSommaire du Match
102 - 2018-12-15580Bears1Phantoms3WSommaire du Match
103 - 2018-12-16590Phantoms2Bears1WXSommaire du Match
106 - 2018-12-19597Thunderbirds3Phantoms6WSommaire du Match
108 - 2018-12-21609Phantoms4Wolf Pack1WSommaire du Match
109 - 2018-12-22625Americans4Phantoms2LSommaire du Match
111 - 2018-12-24638Phantoms3Penguins5LSommaire du Match
113 - 2018-12-26649Bears1Phantoms3WSommaire du Match
115 - 2018-12-28660Rocket1Phantoms2WSommaire du Match
116 - 2018-12-29671Phantoms1Sound Tigers2LXSommaire du Match
122 - 2019-01-04683Phantoms2Sound Tigers1WSommaire du Match
123 - 2019-01-05700Phantoms2Bears3LSommaire du Match
129 - 2019-01-11723Phantoms1Comets2LSommaire du Match
130 - 2019-01-12745Wolf Pack2Phantoms5WSommaire du Match
131 - 2019-01-13751Wolf Pack0Phantoms7WSommaire du Match
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
13 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
182,151$ 158,950$ 161,500$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 114,607$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 63 1,335$ 84,105$




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
135029130213214892562517700010835330251260212265392671148270418084660378122240242038933101730693410803055919.34%3543689.83%1818153053.46%777145053.59%32667748.15%13929961048350589313
Total Saison Régulière5029130213214892562517700010835330251260212265392671148270418084660378122240242038933101730693410803055919.34%3543689.83%1818153053.46%777145053.59%32667748.15%13929961048350589313
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