Phantoms

GP: 76 | W: 52 | L: 21 | OTL: 3 | P: 107
GF: 227 | GA: 137 | PP%: 16.99% | PK%: 89.03%
DG: Kriss Cardenas | Morale : 89 | Moyenne d'Équipe : 60
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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 ThorburnXX100.00875559698180686864656170557476190660
2Iiro PakarinenXX100.00825565797975746750626370555656189660
3David BoothXX100.00805571756769706750626662558273191650
4Tomas HykaX100.00635581726169686450626262555050190610
5Mike BlundenX100.00605569688078716050606060555859191610
6Landon FerraroX100.00685573707064686050596060555354187600
7Adam TambelliniXX100.00665566646561726050585955557372186590
8Bryce Van BrabantXX100.00585558627974635550555557557173189580
9Paul BissonnetteX100.00595556618582545550555555556263190580
10Alex FriesenXX100.00595567606662715550555558557071187570
11Scott KosmachukX100.00605569606863705750565755555050162560
12Marco RoyX100.00725566626963625550555555555050122550
13Oscar Fantenberg (R)X100.00785579757375587525666373557067190680
14Yohann AuvituX100.00705587826474667425656772555353186670
15Matt TennysonX100.00695565717882627125606069557070185660
16Griffin ReinhartX100.00625568798865746225606059557053189640
17Clayton StonerX100.00555555605555795525555555558583182580
18Jakub KindlX100.00555558605858705825585858556970128570
19Cameron GaunceX100.00555555605555655525555555557272139560
Rayé
1Brandon MashinterX100.00565555555758595550555555557574120550
2Rene BourqueXX100.00565555555555555550555555557273120540
3Colin McDonaldX100.00565555555757575550555555557472120540
4Sergey KalininXXX100.00555555555555555550555555555858120530
5Chris CarlisleX100.00555555605555725525555555555353120540
6Andrew CampbellX100.00555555605555735525555555555353120540
7Mattias BackmanX100.00555555605555655525555555555353120540
8Kirill Gotovets (R)X100.00555555605555655525555555555555120540
9Adam ComrieX100.00555555605555595525555555555353120530
10Patrick McNallyX100.00555555605555595525555555555353120530
11Brian Cooper (R)X100.00555555605555585525555555555353120530
MOYENNE D'ÉQUIPE100.0062556264656465593958585955636215658
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
1Peter Budaj100.0080757771807577727577558787134740
2Niklas Svedberg100.0068707370776974787874556060166700
Rayé
1Anthony Stolarz100.0069798687686870646969556566119690
2Mike McKenna100.0064808578656567646963557069120670
MOYENNE D'ÉQUIPE100.007076807773697270737155717113570
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
1Iiro PakarinenPhantoms (Phi)LW/RW7634296319129151571022215115415.38%12152920.1213142774355224153435347.66%21400000.8236003875
2Oscar FantenbergPhantoms (Phi)D761049592785513489138471137.25%55172722.74526311043580112405100.00%000000.6800001330
3Chris ThorburnPhantoms (Phi)LW/RW7629275628154202481042104912813.81%16173322.819112057366224164325361.65%67800000.6516211983
4Robert BortuzzoPhiladelphia FlyersD53173350379159458114398114.91%52124223.44121830812530110278110.00%000000.8100102223
5Yohann AuvituPhantoms (Phi)D6884048245606978161511104.97%65148921.91519241203010002353310.00%000000.6400000133
6David BoothPhantoms (Phi)LW/RW7219254416895126871754612610.86%4140919.58314175933400072675151.09%18400000.6203100342
7Matt TennysonPhantoms (Phi)D6883543201215123578834849.09%56133319.6151217682510110265000.00%000000.6500001124
8Tomas JurcoPhiladelphia FlyersLW/RW371718357395331071252710213.60%1091824.8188163916501122284153.12%65700000.7605100313
9Tomas HykaPhantoms (Phi)RW761616321825536691273610112.60%885611.2721316990001996049.40%8300000.7501001024
10Alex BiegaPhiladelphia FlyersD43824328560121478536549.41%48100223.3151116582050003215210.00%000000.6400000325
11Mike BlundenPhantoms (Phi)RW76131730126620557414342939.09%8109214.38314141141012936059.34%9100000.5523103124
12Bryce Van BrabantPhantoms (Phi)C/LW761118292410915688677306414.29%12107114.090111460001984153.22%85300000.5411002311
13Adam TambelliniPhantoms (Phi)C/LW68617232249554626319459.52%5105415.51246153040002231053.03%109000000.4400100012
14Landon FerraroPhantoms (Phi)C768142223210328475245510.67%4115415.205510293610000461052.03%135700000.3800110010
15Griffin ReinhartPhantoms (Phi)D68214161777570454624254.35%3697314.3102213680001105010.00%000000.3300001102
16Alex FriesenPhantoms (Phi)C/LW74781519541056507118469.86%691812.42000010000301154.73%14800000.3300101131
17Clayton StonerPhantoms (Phi)D73011112789158116207150.00%2786711.880112210001110000.00%000000.2501021001
18Paul BissonnettePhantoms (Phi)LW763471433517172382113.04%34435.84000020000331057.78%4500000.3200000010
19Cameron GauncePhantoms (Phi)D2924615280377112318.18%1632211.1100014000137110.00%000000.3700000101
20Scott KosmachukPhantoms (Phi)RW50235814107121471514.29%32324.64000010000080040.38%5200000.4300101010
21Seth GriffithPhiladelphia FlyersC/LW/RW5314240091051130.00%08717.521013140000151072.62%8400000.9100000010
22Marco RoyPhantoms (Phi)C80334115455240.00%0526.5700000000000054.84%3100001.1400001000
23Brandon MashinterPhantoms (Phi)LW7000100110000.00%0223.200000200004000.00%100000.0000000000
24Colin McDonaldPhantoms (Phi)RW11000-100110000.00%0262.4200006000000035.71%1400000.0000000000
25Jakub KindlPhantoms (Phi)D19000-31002030010.00%61316.9200002000011000.00%000000.0000000000
Stats d'équipe Total ou en Moyenne13612234106333331409175164412702002604145111.14%4522169315.947814822675436515813563507481553.87%558200000.5872610419404544
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
1Peter BudajPhantoms (Phi)32211100.9091.82187547576270101.0004320323
2Anthony StolarzPhantoms (Phi)104410.8922.2054500201850100.0000910000
3Niklas SvedbergPhantoms (Phi)93210.8682.4741300171290000.3333638000
4Mike McKennaPhantoms (Phi)10001.0000.001600030000.0000011000
Stats d'équipe Total ou en Moyenne52281720.9001.98285147949440200.71474759323


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 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
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$0$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 CarlislePhantoms (Phi)D221994-12-16No174 Lbs5 ft10NoNoNo2Contrat d'EntréePro & 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
Griffin ReinhartPhantoms (Phi)D221994-01-24No217 Lbs6 ft4NoNoNo3Contrat d'EntréePro & Farm300,000$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
Peter BudajPhantoms (Phi)G341982-09-17No192 Lbs6 ft1NoNoNo2Sans RestrictionPro & Farm1,920,000$0$0$No
Rene BourquePhantoms (Phi)LW/RW351981-12-09No214 Lbs6 ft2NoNoNo3Sans RestrictionPro & Farm500,000$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
3426.76199 Lbs6 ft21.97477,794$



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
1Oscar FantenbergYohann Auvitu40122
2Matt TennysonGriffin Reinhart30122
3Clayton StonerCameron Gaunce20122
4Oscar FantenbergYohann Auvitu10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Chris ThorburnLandon FerraroIiro Pakarinen60122
2David BoothAdam TambelliniMike Blunden40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Oscar FantenbergYohann Auvitu60122
2Matt TennysonGriffin Reinhart40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Chris ThorburnIiro Pakarinen60122
2David BoothMike Blunden40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Oscar FantenbergYohann Auvitu60122
2Matt TennysonGriffin Reinhart40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Chris Thorburn60122Oscar FantenbergYohann Auvitu60122
2Iiro Pakarinen40122Matt TennysonGriffin Reinhart40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Chris ThorburnIiro Pakarinen60122
2David BoothMike Blunden40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Oscar FantenbergYohann Auvitu60122
2Matt TennysonGriffin Reinhart40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Chris ThorburnLandon FerraroIiro PakarinenOscar FantenbergYohann Auvitu
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Chris ThorburnLandon FerraroIiro PakarinenOscar FantenbergYohann Auvitu
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
, Tomas Hyka, Bryce Van Brabant, Tomas HykaBryce Van Brabant
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Clayton Stoner, Cameron Gaunce, Matt TennysonClayton StonerCameron Gaunce, Matt Tennyson
Tirs de Pénalité
Chris Thorburn, Iiro Pakarinen, David Booth, Mike Blunden, Tomas Hyka
Gardien
#1 : Peter Budaj, #2 :


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
1Americans2110000034-11010000024-21100000010120.50036901827861950664653672404217264413215.38%120100.00%01301240554.10%1185214155.35%528103451.06%213915321560524896483
2Bears1244020202623363200010141316120201012102160.6672645710082786192966646536724025575209229891112.36%801383.75%01301240554.10%1185214155.35%528103451.06%213915321560524896483
3Bruins65100000176113300000092732100000844100.83317314802827861914866465367240943210113845920.00%35294.29%11301240554.10%1185214155.35%528103451.06%213915321560524896483
4Checkers880000004193244000000186124400000023320161.0004175116038278619321664653672401294214720628932.14%59493.22%11301240554.10%1185214155.35%528103451.06%213915321560524896483
5Comets21100000642110000005231010000012-120.500610160082786196866465367240501643381417.14%14285.71%11301240554.10%1185214155.35%528103451.06%213915321560524896483
6Crunch211000005321010000023-11100000030320.500510150182786195966465367240371242301915.26%14285.71%01301240554.10%1185214155.35%528103451.06%213915321560524896483
7Devils4120000159-42110000035-22010000124-230.375591400827861979664653672409226928022522.73%25484.00%01301240554.10%1185214155.35%528103451.06%213915321560524896483
8Marlies220000001028110000005231100000050541.000101828018278619806646536724045529536233.33%12191.67%11301240554.10%1185214155.35%528103451.06%213915321560524896483
9Monsters412000101073211000007342010001034-140.500101727018278619101664653672408428909132515.63%34585.29%01301240554.10%1185214155.35%528103451.06%213915321560524896483
10Penguins1247000013138-7633000001819-1614000011319-690.3753156870282786192196646536724026777267234681014.71%1011486.14%01301240554.10%1185214155.35%528103451.06%213915321560524896483
11Rocket21000010422110000002111000001021141.00046100082786194666465367240306474916212.50%180100.00%01301240554.10%1185214155.35%528103451.06%213915321560524896483
12Senators2110000045-11010000024-21100000021120.50046100082786194166465367240469364113215.38%17476.47%01301240554.10%1185214155.35%528103451.06%213915321560524896483
13Sound Tigers641001001293321000005413200010075290.75012223401827861913066465367240112309313636719.44%38392.11%01301240554.10%1185214155.35%528103451.06%213915321560524896483
14Thunderbirds66000000251114330000001596330000001028121.00025487301827861915966465367240140479015026519.23%39392.31%01301240554.10%1185214155.35%528103451.06%213915321560524896483
Total76462102142227137903826110001012380433820100213210457471070.70422741063701582786192003664653672401513452141516444597816.99%5385989.03%51301240554.10%1185214155.35%528103451.06%213915321560524896483
16Wolf Pack660000002852333000000163133300000012210121.00028517902827861920666465367240903010312532721.88%40295.00%11301240554.10%1185214155.35%528103451.06%213915321560524896483
_Since Last GM Reset76462102142227137903826110001012380433820100213210457471070.70422741063701582786192003664653672401513452141516444597816.99%5385989.03%51301240554.10%1185214155.35%528103451.06%213915321560524896483
_Vs Conference5427190213213810533271610000107656202711902122624913670.6201382473850982786191279664653672401077319103311043565716.01%3844987.24%21301240554.10%1185214155.35%528103451.06%213915321560524896483
_Vs Division221250011168333511730000037251211520011131823280.63668125193068278619583664653672404341283715051382316.67%1471291.84%21301240554.10%1185214155.35%528103451.06%213915321560524896483

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
76107W22274106372003151345214151644015
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
7646212142227137
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
382611001012380
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
382010213210457
Derniers 10 Matchs
WLOTWOTL SOWSOL
720010
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
4597816.99%5385989.03%5
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
664653672408278619
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
1301240554.10%1185214155.35%528103451.06%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
213915321560524896483


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 Tigers3Phantoms2LSommaire du Match
137 - 2019-01-19781Phantoms2Bears1WSommaire du Match
138 - 2019-01-20801Bears5Phantoms1LSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
143 - 2019-01-25816Phantoms6Checkers0WSommaire du Match
144 - 2019-01-26829Phantoms4Checkers0WSommaire du Match
150 - 2019-02-01856Phantoms3Crunch0WSommaire du Match
151 - 2019-02-02875Comets2Phantoms5WSommaire du Match
152 - 2019-02-03883Sound Tigers0Phantoms1WSommaire du Match
155 - 2019-02-06892Phantoms0Penguins3LSommaire du Match
157 - 2019-02-08902Checkers3Phantoms5WSommaire du Match
158 - 2019-02-09913Checkers0Phantoms5WSommaire du Match
162 - 2019-02-13937Penguins3Phantoms2LSommaire du Match
164 - 2019-02-15949Bruins1Phantoms2WSommaire du Match
165 - 2019-02-16963Marlies2Phantoms5WSommaire du Match
171 - 2019-02-22985Phantoms2Penguins3LSommaire du Match
172 - 2019-02-23999Penguins5Phantoms1LSommaire du Match
176 - 2019-02-271021Phantoms1Americans0WSommaire du Match
178 - 2019-03-011028Phantoms2Monsters1WXXSommaire du Match
179 - 2019-03-021041Phantoms1Monsters3LSommaire du Match
182 - 2019-03-051059Phantoms6Checkers1WSommaire du Match
183 - 2019-03-061063Phantoms7Checkers2WSommaire du Match
186 - 2019-03-091092Penguins2Phantoms3WSommaire du Match
187 - 2019-03-101100Penguins0Phantoms4WSommaire du Match
189 - 2019-03-121105Phantoms1Bears2LSommaire du Match
192 - 2019-03-151119Phantoms4Bruins2WSommaire du Match
193 - 2019-03-161133Wolf Pack1Phantoms4WSommaire du Match



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
0 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
268,189$ 162,450$ 121,500$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 168,164$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 0 1,353$ 0$




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
13764621021422271379038261100010123804338201002132104574710722741063701582786192003664653672401513452141516444597816.99%5385989.03%51301240554.10%1185214155.35%528103451.06%213915321560524896483
Total Saison Régulière764621021422271379038261100010123804338201002132104574710722741063701582786192003664653672401513452141516444597816.99%5385989.03%51301240554.10%1185214155.35%528103451.06%213915321560524896483
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