Capitals

GP: 78 | W: 34 | L: 38 | OTL: 6 | P: 74
GF: 217 | GA: 232 | PP%: 17.95% | PK%: 78.31%
DG: Pierre-Marc Brochu | Morale : 35 | Moyenne d'Équipe : 65
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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
1Marko DanoX100.008142777671737484677572726367747952710
2Jake DeBrusk (R)X100.006742808268819279667669655370528454700
3Curtis McKenzie (R)X100.006147636177728474608973726373705547700
4Derek Ryan (R)X100.006342848360859569857068625670532754680
5Richard Panik (R)X100.007743827576828772577166636367596155680
6Stefan Noesen (R)X100.007243767375818970666867675470527954670
7Ben SmithX100.004321996076606288876865736582842145670
8Chris Mueller (R)X100.00644372607182997870586067588574654660
9Brendan Leipsic (R)X100.006241817861807966707263615265516254650
10Barclay Goodrow (R)X100.007442787178776866756466675571534254650
11Brandon Bollig (R)X100.007048556381569567536260646073554354630
12Dominik Simon (R)X100.006143797062748167656762615164515542630
13Kyle QuinceyX100.007145756978778182256466856192976553740
14Milan JurcinaX100.00369995486677282407189928190992454720
15Mark Barberio (R)X100.006142836575807376307172785967574132680
16Scott Harrington (R)X100.006641826378736068306371765371526817660
17Andreas Borgman (R)X100.008044766075689965306466615266534454640
18Andrew Macwilliam (R)X100.005111876278656868484250705658594726610
Rayé
1Nigel DawesX100.005118996173758159305564476799975920610
2Filip Chytil (R)X100.006041786176687761666261605061508420600
3Warren Foegele (R)X100.005943696072659960505763605063504420600
4Anton Blidh (R)X100.006443706072599956505656605156514920590
5Blake Pietila (R)X100.006242746069619156505558605158515519580
6Joonas Kemppainen (R)X100.004933786869566052805351685759574420570
7Tyler Randell (R)X100.006886716768434953535049695859544520560
8Jake McCabe (R)X100.008042887076848091257365796064705213730
9Petteri Lindbohm (R)X100.006132777079637050254645655074697120610
10Rasmus Andersson (R)X100.006643695078628661306557605057507120600
11Kyle Capobianco (R)X100.005743715074537960306652605052506820570
MOYENNE D'ÉQUIPE100.00634078667470806852646367576962533764
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
1Jonas Gustavsson100.00707782797780838790916199996253810
2Joni Ortio100.00707771757281838483747677736454750
Rayé
MOYENNE D'ÉQUIPE100.0070777777758183868783698886635478
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Adam Oates74847875727176CAN552800,000$


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
1Jake DeBruskCapitals (WAS)LW78344175-154401551232858315311.93%32154219.77616225427402281441244.63%1214715000.9716000455
2Marko DanoCapitals (WAS)RW77313263-155602221472778816711.19%43163421.221210226427920272232250.77%2605525010.7717000534
3Derek RyanCapitals (WAS)C781645612120155179192711258.33%33142818.3151722222340001573153.24%14995419000.8500000233
4Curtis McKenzieCapitals (WAS)LW7323264989151611241646912514.02%39134218.407714142031018932134.48%872722100.7323001052
5Richard PanikCapitals (WAS)RW7816314713151809417839978.99%30134017.19871525217000196250.65%772823000.7001001315
6Ben SmithCapitals (WAS)C75172340112043123175551069.71%2098113.08358863000052155.43%7741220000.8200000231
7Stefan NoesenCapitals (WAS)RW78151328133801518815251739.87%2996212.34000022000012058.97%392716100.5800000124
8Jake McCabeCapitals (WAS)D6932225-1623512514711347532.65%98164123.79257202650114225200.00%02937000.3001010010
9Kyle QuinceyCapitals (WAS)D7842125-1550013616011353573.54%102187524.052810193010114251100.00%03267000.2700000010
10Brendan LeipsicCapitals (WAS)LW78715221114012477136511145.15%17103813.310110240002882042.55%472812000.4200000021
11Milan JurcinaCapitals (WAS)D7841317-1603216812745523.15%109171321.96246121991015191100.00%03647000.2001000011
12Barclay GoodrowCapitals (WAS)RW7851116-5160653810433504.81%125657.25011240000121031.25%162210000.5700000012
13Scott HarringtonCapitals (WAS)D733121510200501077324164.11%85111015.21000122000033010.00%01627000.2700000101
14Chris MuellerCapitals (WAS)C789514-2180698410323518.74%147259.30101170001432052.46%305611000.3900000011
15Mark BarberioCapitals (WAS)D7321012103151001257434342.70%80149920.5413431660112138000.00%02237000.1600100000
16Andreas BorgmanCapitals (WAS)D782683520118794820124.17%82123415.83000132000045000.00%01221000.1300000010
17Brandon BolligCapitals (WAS)LW78336-448087515419235.56%155737.3500000000060147.06%1755000.2100000000
18Andrew MacwilliamCapitals (WAS)D1312312092092511.11%1624018.47000025000020000.00%008000.2500000001
19Dominik SimonCapitals (WAS)C192020407221581013.33%31357.1300000000000054.84%6271000.3000000000
20Joonas KemppainenCapitals (WAS)C21000-200110000.00%0211.04000000000170040.00%500000.0000000000
21Rasmus AnderssonCapitals (WAS)D3000000321110.00%3299.940000000002000.00%011000.0000000000
22Nigel DawesCapitals (WAS)RW24000000320100.00%1321.3400005000000042.11%1901000.0000000000
23Petteri LindbohmCapitals (WAS)D5000100545220.00%17014.120000000000000.00%002000.0000000000
24Filip ChytilCapitals (WAS)C27000-200790310.00%1572.1200004000000065.00%2010000.0000000000
25Warren FoegeleCapitals (WAS)LW27000-220795220.00%2903.35000000000420036.11%3601000.0000000000
Stats d'équipe Total ou en Moyenne1437197331528-85602020151983240382413298.20%8672188615.2349841332462355459431658271152.25%3384467428210.48419112183031
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
1Jonas GustavssonCapitals (WAS)72323260.9162.8142874320124031170450.500167261414
2Joni OrtioCapitals (WAS)82600.9142.994210021244138000.0000672210
Stats d'équipe Total ou en Moyenne80343860.9162.8347094322226471308450.5001678781624


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
Andreas BorgmanCapitals (WAS)D231995-06-18 04:45:15Yes212 Lbs6 ft0NoNoNo3Avec RestrictionPro & Farm925,000$0$0$No
Andrew MacwilliamCapitals (WAS)D291990-03-25 17:11:13Yes216 Lbs6 ft2NoNoNo2Avec RestrictionPro & Farm575,000$0$0$No
Anton BlidhCapitals (WAS)LW241995-03-14 09:14:08Yes202 Lbs6 ft0NoNoNo2Avec RestrictionPro & Farm784,167$0$0$No
Barclay GoodrowCapitals (WAS)RW261993-02-26 11:11:13Yes210 Lbs6 ft2NoNoNo3Avec RestrictionPro & Farm575,000$0$0$No
Ben SmithCapitals (WAS)C301988-07-11 05:11:13No215 Lbs5 ft11NoNoNo1Avec RestrictionPro & Farm600,000$0$0$No
Blake PietilaCapitals (WAS)RW261993-02-20 11:11:13Yes200 Lbs5 ft11NoNoNo1Avec RestrictionPro & Farm750,000$0$0$No
Brandon BolligCapitals (WAS)LW321987-01-31 23:11:13Yes220 Lbs6 ft2NoNoNo2Sans RestrictionPro & Farm575,000$0$0$No
Brendan LeipsicCapitals (WAS)LW241994-05-19 17:11:13Yes180 Lbs5 ft10NoNoNo1Avec RestrictionPro & Farm894,000$0$0$No
Chris MuellerCapitals (WAS)C331986-03-06 17:11:13Yes215 Lbs5 ft11NoNoNo2Sans RestrictionPro & Farm575,000$0$0$No
Curtis McKenzieCapitals (WAS)LW281991-02-22 23:11:13Yes205 Lbs6 ft2NoNoNo3Avec RestrictionPro & Farm1,000,000$0$0$No
Derek RyanCapitals (WAS)C321986-12-29 13:03:29Yes170 Lbs5 ft11NoNoNo2Sans RestrictionPro & Farm600,000$0$0$No
Dominik SimonCapitals (WAS)C241994-08-08 07:29:33Yes178 Lbs5 ft11NoNoNo1Avec RestrictionPro & Farm925,000$0$0$No
Filip ChytilCapitals (WAS)C191999-09-05 13:13:19Yes202 Lbs6 ft2NoNoNo3Contrat d'EntréePro & Farm1,275,000$0$0$No
Jake DeBruskCapitals (WAS)LW221996-10-17 13:20:08Yes188 Lbs6 ft0NoNoNo3Contrat d'EntréePro & Farm1,288,330$0$0$No
Jake McCabeCapitals (WAS)D251993-10-12 11:11:13Yes201 Lbs6 ft0NoNoNo3Avec RestrictionPro & Farm1,250,000$0$0$No
Jonas GustavssonCapitals (WAS)G341984-10-24 05:11:14No202 Lbs6 ft3NoNoNo3Sans RestrictionPro & Farm2,500,000$0$0$No
Joni OrtioCapitals (WAS)G281991-04-16 23:11:14No187 Lbs6 ft1NoNoNo3Avec RestrictionPro & Farm700,000$0$0$No
Joonas KemppainenCapitals (WAS)C311988-04-07 05:11:13Yes216 Lbs6 ft2NoNoNo1Sans RestrictionPro & Farm925,000$0$0$No
Kyle CapobiancoCapitals (WAS)D211997-08-13 13:18:25Yes196 Lbs6 ft1NoNoNo3Contrat d'EntréePro & Farm894,167$0$0$No
Kyle QuinceyCapitals (WAS)D331985-08-12 11:11:13No220 Lbs6 ft2NoNoNo1Sans RestrictionPro & Farm1,000,000$0$0$No
Mark BarberioCapitals (WAS)D291990-03-23 17:11:13Yes207 Lbs6 ft1NoNoNo2Avec RestrictionPro & Farm800,000$0$0$No
Marko DanoCapitals (WAS)RW241994-11-30 17:11:13No188 Lbs5 ft11NoNoNo3Avec RestrictionPro & Farm1,000,000$0$0$No
Milan JurcinaCapitals (WAS)D351983-06-07 23:11:13No221 Lbs6 ft4NoNoNo2Sans RestrictionPro & Farm1,000,000$0$0$No
Nigel DawesCapitals (WAS)RW341985-02-09 11:11:13No203 Lbs5 ft9NoNoNo1Sans RestrictionPro & Farm575,000$0$0$No
Petteri LindbohmCapitals (WAS)D251993-09-23 11:11:13Yes216 Lbs6 ft3NoNoNo3Avec RestrictionPro & Farm575,000$0$0$No
Rasmus AnderssonCapitals (WAS)D221996-10-27 08:36:44Yes216 Lbs6 ft1NoNoNo2Contrat d'EntréePro & Farm755,833$0$0$No
Richard PanikCapitals (WAS)RW281991-02-07 04:14:12Yes210 Lbs6 ft1NoNoNo1Avec RestrictionPro & Farm975,000$0$0$No
Scott HarringtonCapitals (WAS)D261993-03-10 11:11:13Yes207 Lbs6 ft2NoNoNo3Avec RestrictionPro & Farm600,000$0$0$No
Stefan NoesenCapitals (WAS)RW261993-02-12 11:11:13Yes205 Lbs6 ft1NoNoNo3Avec RestrictionPro & Farm700,000$0$0$No
Tyler RandellCapitals (WAS)RW271991-06-15 23:11:13Yes200 Lbs6 ft1NoNoNo1Avec RestrictionPro & Farm600,000$0$0$No
Warren FoegeleCapitals (WAS)LW231996-04-01 13:15:49Yes190 Lbs6 ft2NoNoNo3Avec RestrictionPro & Farm800,000$0$0$No
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
3127.19203 Lbs6 ft12.16870,693$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Curtis McKenzieDerek RyanMarko Dano35122
2Jake DeBruskBen SmithRichard Panik35122
3Brendan LeipsicChris MuellerStefan Noesen25122
4Brandon BolligDominik SimonBarclay Goodrow5122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Kyle QuinceyMilan Jurcina35122
2Andreas BorgmanAndrew Macwilliam35122
3Scott HarringtonMark Barberio30122
4Kyle QuinceyMilan Jurcina0122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Curtis McKenzieDerek RyanMarko Dano60122
2Jake DeBruskBen SmithRichard Panik40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Kyle QuinceyMilan Jurcina60122
2Andreas BorgmanAndrew Macwilliam40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Marko DanoCurtis McKenzie60122
2Jake DeBruskDerek Ryan40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Kyle QuinceyMilan Jurcina60122
2Andreas BorgmanAndrew Macwilliam40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Marko Dano60122Kyle QuinceyMilan Jurcina60122
2Curtis McKenzie40122Andreas BorgmanAndrew Macwilliam40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Marko DanoCurtis McKenzie60122
2Jake DeBruskDerek Ryan40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Kyle QuinceyMilan Jurcina60122
2Andreas BorgmanAndrew Macwilliam40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Curtis McKenzieDerek RyanMarko DanoKyle QuinceyMilan Jurcina
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Curtis McKenzieDerek RyanMarko DanoKyle QuinceyMilan Jurcina
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Stefan Noesen, Chris Mueller, Brendan LeipsicStefan Noesen, Chris MuellerBrendan Leipsic
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Mark Barberio, Scott Harrington, Andreas BorgmanMark BarberioScott Harrington, Andreas Borgman
Tirs de Pénalité
Marko Dano, Curtis McKenzie, Jake DeBrusk, Derek Ryan, Richard Panik
Gardien
#1 : Joni Ortio, #2 : Jonas Gustavsson


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
1Avalanche2110000078-1110000006241010000016-520.50071320005982718708978308333379226488112.50%30100.00%01027199751.43%945188850.05%548108650.46%1717100716787021321668
2Blackhawks2020000047-31010000034-11010000013-200.0004711005982718678978308333371288526116.67%4175.00%01027199751.43%945188850.05%548108650.46%1717100716787021321668
3Blues2010010069-31000010012-11010000057-210.25061117005982718688978308333357303957300.00%7271.43%01027199751.43%945188850.05%548108650.46%1717100716787021321668
4Bruins412000011114-3210000017522020000049-530.37511193001598271812689783083333144473710213430.77%11372.73%01027199751.43%945188850.05%548108650.46%1717100716787021321668
5Canadiens430000011275220000007252100000155070.87512193101598271815089783083333136442410014321.43%12283.33%01027199751.43%945188850.05%548108650.46%1717100716787021321668
6Canucks21000001770110000005411000000123-130.750713200059827187489783083333602014675120.00%7185.71%01027199751.43%945188850.05%548108650.46%1717100716787021321668
7Devils624000001316-330300000610-43210000076140.33313233600598271818189783083333196695017542716.67%25772.00%01027199751.43%945188850.05%548108650.46%1717100716787021321668
8Ducks220000001468110000007161100000075241.00014243800598271872897830833336623125910550.00%6350.00%01027199751.43%945188850.05%548108650.46%1717100716787021321668
9Flames211000005501010000024-21100000031220.50058131059827186989783083333712912517228.57%7271.43%01027199751.43%945188850.05%548108650.46%1717100716787021321668
10Flyers624000001216-43210000086230300000410-640.33312203200598271817389783083333212796816835411.43%34391.18%01027199751.43%945188850.05%548108650.46%1717100716787021321668
11Islanders615000001427-1331200000815-730300000612-620.16714264020598271820489783083333208616016238718.42%301066.67%01027199751.43%945188850.05%548108650.46%1717100716787021321668
12Kings21100000642110000004131010000023-120.5006915005982718648978308333362211249100.00%6183.33%01027199751.43%945188850.05%548108650.46%1717100716787021321668
13Maple Leafs41300000911-2211000007702020000024-220.250918270059827181418978308333315637289710110.00%14285.71%01027199751.43%945188850.05%548108650.46%1717100716787021321668
14Oilers21001000743110000004221000100032141.000791600598271878897830833336017125133100.00%60100.00%01027199751.43%945188850.05%548108650.46%1717100716787021321668
15Penguins6320100020191330000001064302010001013-380.66720315111598271818989783083333193697716927518.52%36975.00%01027199751.43%945188850.05%548108650.46%1717100716787021321668
16Predators2010100056-1100010003211010000024-220.50058130059827185989783083333602216569222.22%8275.00%11027199751.43%945188850.05%548108650.46%1717100716787021321668
17Rangers622010101916330201000711-432000010125780.6671927460059827182048978308333321568751863339.09%35780.00%31027199751.43%945188850.05%548108650.46%1717100716787021321668
18Red Wings4210000111101211000006512100000155050.62511182910598271814789783083333135501212113430.77%7271.43%01027199751.43%945188850.05%548108650.46%1717100716787021321668
19Sabres4130000079-22020000026-42110000053220.25071219005982718122897830833331305026106900.00%13376.92%01027199751.43%945188850.05%548108650.46%1717100716787021321668
20Senateurs4210000112111220000006332010000168-250.6251221330059827181338978308333314247121251417.14%6183.33%01027199751.43%945188850.05%548108650.46%1717100716787021321668
21Sharks2020000047-31010000034-11010000013-200.0004812005982718688978308333358166504125.00%30100.00%01027199751.43%945188850.05%548108650.46%1717100716787021321668
22Stars21100000660110000003211010000034-120.50069150059827186789783083333702514455120.00%7271.43%01027199751.43%945188850.05%548108650.46%1717100716787021321668
Total78293804115217232-153919160210111810993910220201499123-24740.474217364581535982718257489783083333265289563421353125617.95%2956478.31%41027199751.43%945188850.05%548108650.46%1717100716787021321668
24Wild2110000067-11010000035-21100000032120.50061117005982718488978308333371211439300.00%8187.50%01027199751.43%945188850.05%548108650.46%1717100716787021321668
_Since Last GM Reset78293804115217232-153919160210111810993910220201499123-24740.474217364581535982718257489783083333265289563421353125617.95%2956478.31%41027199751.43%945188850.05%548108650.46%1717100716787021321668
_Vs Conference54202702014140156-16271312010017476-227715010136680-14500.463140234374435982718177089783083333186762146915112483915.73%2234978.03%31027199751.43%945188850.05%548108650.46%1717100716787021321668
_Vs Division301017020107894-161568010003948-91549010103946-7260.433781272053159827189518978308333310243463308601752614.86%1603677.50%31027199751.43%945188850.05%548108650.46%1717100716787021321668

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
7874L121736458125742652895634213553
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
7829384115217232
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
3919162101118109
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
391022201499123
Derniers 10 Matchs
WLOTWOTL SOWSOL
630001
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
3125617.95%2956478.31%4
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
897830833335982718
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
1027199751.43%945188850.05%548108650.46%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
1717100716787021321668


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
1 - 2018-09-294Rangers4Capitals0LSommaire du Match
6 - 2018-10-0427Penguins2Capitals3WR3Sommaire du Match
8 - 2018-10-0636Capitals4Rangers3WSommaire du Match
12 - 2018-10-1055Flyers1Capitals2WSommaire du Match
13 - 2018-10-1162Capitals3Islanders5LSommaire du Match
16 - 2018-10-1475Islanders8Capitals1LSommaire du Match
17 - 2018-10-1582Capitals2Flyers3LSommaire du Match
19 - 2018-10-1791Capitals2Devils4LSommaire du Match
21 - 2018-10-19102Capitals5Penguins7LR3Sommaire du Match
24 - 2018-10-22117Devils4Capitals2LSommaire du Match
27 - 2018-10-25132Canadiens2Capitals4WSommaire du Match
30 - 2018-10-28146Maple Leafs3Capitals5WSommaire du Match
33 - 2018-10-31162Capitals1Maple Leafs2LSommaire du Match
35 - 2018-11-02170Red Wings1Capitals4WSommaire du Match
37 - 2018-11-04185Capitals2Canadiens1WSommaire du Match
39 - 2018-11-06196Predators2Capitals3WXSommaire du Match
41 - 2018-11-08205Capitals5Blues7LSommaire du Match
44 - 2018-11-11220Rangers4Capitals5WXSommaire du Match
49 - 2018-11-16241Kings1Capitals4WSommaire du Match
51 - 2018-11-18253Capitals1Avalanche6LSommaire du Match
53 - 2018-11-20263Capitals2Rangers1WXXSommaire du Match
55 - 2018-11-22270Rangers3Capitals2LSommaire du Match
58 - 2018-11-25286Capitals3Canadiens4LXXSommaire du Match
60 - 2018-11-27295Blackhawks4Capitals3LSommaire du Match
63 - 2018-11-30315Devils2Capitals1LSommaire du Match
68 - 2018-12-05334Capitals2Predators4LSommaire du Match
69 - 2018-12-06339Maple Leafs4Capitals2LSommaire du Match
72 - 2018-12-09360Senateurs2Capitals3WSommaire du Match
74 - 2018-12-11371Capitals1Flyers4LSommaire du Match
75 - 2018-12-12379Capitals6Rangers1WSommaire du Match
78 - 2018-12-15388Red Wings4Capitals2LSommaire du Match
80 - 2018-12-17403Capitals3Oilers2WXSommaire du Match
81 - 2018-12-18410Bruins5Capitals4LXXSommaire du Match
84 - 2018-12-21424Capitals2Canucks3LXXSommaire du Match
85 - 2018-12-22435Canucks4Capitals5WSommaire du Match
87 - 2018-12-24449Capitals3Flames1WSommaire du Match
89 - 2018-12-26459Bruins0Capitals3WSommaire du Match
92 - 2018-12-29475Capitals1Flyers3LSommaire du Match
93 - 2018-12-30484Sabres3Capitals1LSommaire du Match
96 - 2019-01-02499Capitals1Blackhawks3LSommaire du Match
97 - 2019-01-03506Avalanche2Capitals6WSommaire du Match
101 - 2019-01-07528Ducks1Capitals7WSommaire du Match
102 - 2019-01-08535Capitals2Kings3LSommaire du Match
105 - 2019-01-11551Capitals1Sharks3LSommaire du Match
106 - 2019-01-12556Flyers4Capitals2LSommaire du Match
109 - 2019-01-15577Blues2Capitals1LXSommaire du Match
113 - 2019-01-19591Capitals1Bruins5LSommaire du Match
114 - 2019-01-20601Islanders5Capitals2LSommaire du Match
119 - 2019-01-25623Stars2Capitals3WSommaire du Match
121 - 2019-01-27635Capitals1Maple Leafs2LSommaire du Match
123 - 2019-01-29640Capitals3Bruins4LSommaire du Match
125 - 2019-01-31651Islanders2Capitals5WSommaire du Match
127 - 2019-02-02662Capitals3Wild2WSommaire du Match
129 - 2019-02-04674Sabres3Capitals1LSommaire du Match
130 - 2019-02-05679Capitals1Sabres2LSommaire du Match
135 - 2019-02-10698Wild5Capitals3LSommaire du Match
138 - 2019-02-13711Capitals2Red Wings1WSommaire du Match
141 - 2019-02-16723Canadiens0Capitals3WSommaire du Match
144 - 2019-02-19737Capitals4Penguins6LR3Sommaire du Match
146 - 2019-02-21745Oilers2Capitals4WSommaire du Match
148 - 2019-02-23759Capitals2Devils1WSommaire du Match
149 - 2019-02-24768Flyers1Capitals4WSommaire du Match
152 - 2019-02-27779Capitals1Penguins0WXR3Sommaire du Match
154 - 2019-03-01784Capitals3Red Wings4LXXSommaire du Match
156 - 2019-03-03797Devils4Capitals3LSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
158 - 2019-03-05810Capitals2Islanders4LSommaire du Match
159 - 2019-03-06819Capitals2Senateurs3LSommaire du Match
161 - 2019-03-08824Flames4Capitals2LSommaire du Match
164 - 2019-03-11841Capitals4Senateurs5LXXSommaire du Match
165 - 2019-03-12846Penguins1Capitals3WR3Sommaire du Match
167 - 2019-03-14859Capitals1Islanders3LSommaire du Match
169 - 2019-03-16869Penguins3Capitals4WR3Sommaire du Match
174 - 2019-03-21890Sharks4Capitals3LSommaire du Match
175 - 2019-03-22893Capitals4Sabres1WSommaire du Match
180 - 2019-03-27914Senateurs1Capitals3WSommaire du Match
181 - 2019-03-28917Capitals7Ducks5WSommaire du Match
182 - 2019-03-29922Capitals3Devils1WSommaire du Match
184 - 2019-03-31934Capitals3Stars4LSommaire du Match



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité de l'Aréna20001000
Prix des Billets4525
Assistance56,76330,837
Assistance PCT72.77%79.07%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacité de l'ArénaPopularité de l'Équipe
0 2246 - 74.87% 77,823$3,035,100$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
3,615,443$ 2,699,150$ 1,574,750$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
14,590$ 2,815,381$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 0 18,914$ 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
1378293804115217232-153919160210111810993910220201499123-2474217364581535982718257489783083333265289563421353125617.95%2956478.31%41027199751.43%945188850.05%548108650.46%1717100716787021321668
Total Saison Régulière78293804115217232-153919160210111810993910220201499123-2474217364581535982718257489783083333265289563421353125617.95%2956478.31%41027199751.43%945188850.05%548108650.46%1717100716787021321668