Flames

GP: 82 | W: 49 | L: 28 | OTL: 5 | P: 103
GF: 246 | GA: 215 | PP%: 17.54% | PK%: 82.83%
DG: Benoit Talbot | Morale : 91 | Moyenne d'Équipe : 67
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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
1Jake Guentzel (R)X100.007035768168929580607581817058576384740
2Jake DeBrusk (R)X100.006335787771859286668376756756578383730
3Jeremy MorinX100.008063696576777976698280667279825388720
4Kevin Fiala (R)X100.006135897770809580637780566977578383710
5Danton Heinen (R)X100.006241867773839580667869665370545782700
6Oscar Lindberg (R)X100.007835716978808678817375685876575684700
7Joel Armia (R)X100.007935797088847069527070735973577883680
8Adrian Kempe (R)X100.006835716881829473806970675473537883680
9Devin Shore (R)X100.005937907175739870717065686670585188670
10Roope Hintz (R)X100.007035717086729269716766686552517283660
11Daniel Carr (R)X100.005940826574737567526665675369553483640
12Henrik Borgstrom (R)X100.005735757081698769736363655652518183640
13Mark PysykX100.007835766576829580307374856168607582730
14Thomas HickeyX100.006537907072818573307876777186846982720
15Thomas Chabot (R)X100.006735797980958377308470746955578383720
16Andrew Campbell (R)X100.009251616478848684494841854564654082710
17Darren Dietz (R)X100.009545826375658767255951725056524676660
18Haydn Fleury (R)X100.006637896180768661306260725264538583650
Rayé
1Alan Quine (R)X100.005435707075565669776869695354514720620
2Andreas Martinsen (R)X100.007635676389586263565756645560553420600
3Kevin Stenlund (R)X100.006135706688525266706161615050506920590
4Brett CarsonX100.006027996280626644244344735099985720630
5Brett Lernout (R)X100.006543725679538953305352715055536220600
6Joel Hanley (R)X100.005235686770545867305656675170643518600
7Dean Kukan (R)X100.006235756278706261306558605259514220600
8Jeremy Lauzon (R)X100.005735705978595859305455595551507220570
9Evan Bouchard (R)X100.006035775782725357305657575057508820570
MOYENNE D'ÉQUIPE100.00673877687873797051666469586559636266
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
1Alexandar Georgiev (R)100.00898296738892939392888153714483830
2Magnus Hellberg (R)100.00768682797784758476707482815283770
Rayé
1Jared Coreau (R)100.00717180867681777676705171653320740
2Spencer Martin100.00675573807270656867725661558420660
MOYENNE D'ÉQUIPE100.0076748380788278807875666768535275
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Paul MacLean91717488998378FRA6131,000,000$


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'ÉquipePOSGP 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 DeBruskFlames (CGY)RW82384482155401791682759818313.82%55176221.491121325330510172894547.31%3346229000.93816000943
2Jake GuentzelFlames (CGY)LW82334578135152221783028614910.93%43177121.6110223256306000102906242.16%4086125010.88516010654
3Danton HeinenFlames (CGY)C82243963452201461942086511011.54%39153418.7191625222590007993346.10%16813928000.8212201254
4Kevin FialaFlames (CGY)RW82233356-62951191113141051887.32%38145517.766814302250004725243.43%994620000.7723001542
5Oscar LindbergFlames (CGY)C8212405245810198197173641166.94%33143517.505172232266000001150.54%16622123000.7211101152
6Jeremy MorinFlames (CGY)LW82232750-4126302141402215712810.41%38160319.5667131922500051751248.15%1623431000.62211312464
7Joel ArmiaFlames (CGY)RW82131831111801347314752898.84%2597511.90000315000003348.33%602012000.6400000222
8Devin Shore Flames (CGY)C82161531142115701461555410210.32%29106312.970000021341164148.73%7083414000.5800300313
9Thomas HickeyFlames (CGY)D8052530110109715712051494.17%126200525.064610213161014313000.00%04757000.3000002011
10Mark PysykFlames (CGY)D8272330269519317810432596.73%105199924.385510223350226285110.00%02864000.3000010132
11Adrian KempeFlames (CGY)LW82121729114801309215052828.00%1798712.04101113000002057.50%403321000.5900000111
12Thomas ChabotFlames (CGY)D7632528134410951339652563.13%84161621.2711251880222182100.00%03944000.3500011112
13Roope HintzFlames (CGY)LW828715-233577499124518.79%106037.36000011011271318.75%161110000.5000010101
14Darren DietzFlames (CGY)D62111121375106673520132.86%5090014.52000111011018000.00%0428000.2700010001
15Andrew CampbellFlames (CGY)D82381117156402451366634434.55%102170320.7710132040002170100.00%0856000.1300125010
16Daniel CarrFlames (CGY)RW825510-511539526219488.06%175476.6800007000001056.25%16119000.3700001012
17Henrik BorgstromFlames (CGY)C82459-412035466220326.45%105797.0700001000092047.58%22797000.3100000000
18Haydn FleuryFlames (CGY)D82279760721045624183.57%62132316.14101228000091100.00%0648000.1400000000
19Joel HanleyFlames (CGY)D2520211802628123416.67%1835314.1300003000017010.00%0212000.1100000001
20Brett LernoutFlames (CGY)D3011000250010.00%13913.040000000006000.00%001000.5100000000
Stats d'équipe Total ou en Moyenne14762343956299385316523992254264991215218.83%9022426116.446010316327027155611522167372447.72%5413515539010.52194910814364035
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
1Alexandar GeorgievFlames (CGY)68392440.9152.584078201752064860400.810426814927
2Magnus HellbergFlames (CGY)1610410.9272.299182035482234010.75081468202
Stats d'équipe Total ou en Moyenne84492850.9182.5249964021025461094410.8005082821129


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 Type Salaire Actuel Salaire RestantCap Salariale Cap Salariale Restant Exclus du Cap Salarial Salaire Année 2Salaire Année 3Salaire Année 4Salaire Année 5Salaire Année 6Salaire Année 7Salaire Année 8Salaire Année 9Salaire Année 10Link
Adrian KempeFlames (CGY)LW231996-09-13 05:44:01Yes201 Lbs6 ft2NoNoNo1Pro & Farm894,167$0$0$No
Alan QuineFlames (CGY)C271993-02-25 18:35:24Yes203 Lbs6 ft0NoNoNo3Pro & Farm575,000$0$0$No575,000$575,000$
Alexandar GeorgievFlames (CGY)G241996-02-10 10:25:08Yes179 Lbs6 ft1NoNoNo2Pro & Farm925,000$0$0$No925,000$
Andreas Martinsen Flames (CGY)RW291990-06-13 18:37:17Yes229 Lbs6 ft3NoNoNo1Pro & Farm575,000$0$0$No
Andrew CampbellFlames (CGY)D321988-02-04 05:11:13Yes212 Lbs6 ft4NoNoNo2Pro & Farm1,250,000$0$0$No1,250,000$
Brett CarsonFlames (CGY)D341985-11-29 11:11:13No220 Lbs6 ft4NoNoNo3Pro & Farm575,000$0$0$No575,000$575,000$
Brett LernoutFlames (CGY)D241995-09-24 23:11:13Yes214 Lbs6 ft4NoNoNo1Pro & Farm575,000$0$0$No
Daniel CarrFlames (CGY)RW281991-10-31 07:15:35Yes194 Lbs6 ft0NoNoNo3Pro & Farm575,000$0$0$No575,000$575,000$
Danton HeinenFlames (CGY)C241995-07-05 09:18:29Yes188 Lbs6 ft1NoNoNo1Pro & Farm872,500$0$0$No
Darren DietzFlames (CGY)D261993-07-16 06:42:37Yes206 Lbs6 ft1NoNoNo3Pro & Farm600,000$0$0$No600,000$600,000$
Dean KukanFlames (CGY)D261993-07-08 06:33:23Yes186 Lbs6 ft2NoNoNo3Pro & Farm725,000$0$0$No725,000$725,000$
Devin Shore Flames (CGY)C251994-07-18 18:38:49Yes205 Lbs6 ft0NoNoNo3Pro & Farm700,000$0$0$No700,000$700,000$
Evan BouchardFlames (CGY)D201999-10-20 02:25:19Yes194 Lbs6 ft3NoNoNo3Pro & Farm925,000$0$0$No925,000$925,000$
Haydn FleuryFlames (CGY)D231996-07-08 04:00:49Yes221 Lbs6 ft3NoNoNo2Pro & Farm1,713,330$0$0$No1,713,330$
Henrik BorgstromFlames (CGY)C221997-08-06 02:20:11Yes190 Lbs6 ft3NoNoNo3Pro & Farm925,000$0$0$No925,000$925,000$
Jake DeBruskFlames (CGY)RW231996-10-17 13:20:08Yes188 Lbs6 ft0NoNoNo2Pro & Farm1,288,330$0$0$No1,288,330$
Jake GuentzelFlames (CGY)LW251994-10-06 05:45:54Yes180 Lbs5 ft11NoNoNo1Pro & Farm734,167$0$0$No
Jared CoreauFlames (CGY)G281991-11-05 05:47:36Yes220 Lbs6 ft6NoNoNo1Pro & Farm612,500$0$0$No
Jeremy LauzonFlames (CGY)D231997-04-28 02:22:22Yes204 Lbs6 ft1NoNoNo3Pro & Farm747,500$0$0$No747,500$747,500$
Jeremy MorinFlames (CGY)LW291991-04-16 23:11:13No201 Lbs6 ft1NoNoNo2Pro & Farm900,000$0$0$No900,000$
Joel ArmiaFlames (CGY)RW261993-05-31 11:11:13Yes210 Lbs6 ft4NoNoNo2Pro & Farm800,000$0$0$No800,000$
Joel HanleyFlames (CGY)D281991-06-08 06:37:23Yes190 Lbs5 ft11NoNoNo2Pro & Farm575,000$0$0$No575,000$
Kevin FialaFlames (CGY)RW231996-07-22 05:11:13Yes193 Lbs5 ft10NoNoNo2Pro & Farm1,000,000$0$0$No1,000,000$
Kevin StenlundFlames (CGY)C231996-09-20 02:17:36Yes210 Lbs6 ft4NoNoNo3Pro & Farm864,166$0$0$No864,166$864,166$
Magnus HellbergFlames (CGY)G291991-04-04 23:11:14Yes192 Lbs6 ft5NoNoNo1Pro & Farm800,000$0$0$No
Mark PysykFlames (CGY)D281992-01-11 05:11:13No200 Lbs6 ft1NoNoNo3Pro & Farm1,750,000$0$0$No1,750,000$1,750,000$
Oscar LindbergFlames (CGY)C281991-10-29 23:11:13Yes202 Lbs6 ft1NoNoNo2Pro & Farm900,000$0$0$No900,000$
Roope HintzFlames (CGY)LW231996-11-17 02:27:43Yes215 Lbs6 ft3NoNoNo3Pro & Farm811,667$0$0$No811,667$811,667$
Spencer MartinFlames (CGY)G241995-06-08 05:53:37No213 Lbs6 ft3NoNoNo1Pro & Farm728,333$0$0$No
Thomas ChabotFlames (CGY)D231997-01-30 05:55:22Yes196 Lbs6 ft2NoNoNo1Pro & Farm863,333$0$0$No
Thomas HickeyFlames (CGY)D311989-02-08 11:11:13No188 Lbs6 ft0NoNoNo3Pro & Farm1,500,000$0$0$No1,500,000$1,500,000$
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
3125.84201 Lbs6 ft22.13880,000$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Jake GuentzelOscar LindbergJake DeBrusk35122
2Jeremy MorinDanton HeinenKevin Fiala35122
3Adrian KempeDevin Shore Joel Armia25122
4Roope HintzHenrik BorgstromDaniel Carr5122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Mark PysykThomas Hickey35122
2Thomas ChabotAndrew Campbell35122
3Darren DietzHaydn Fleury30122
4Mark PysykThomas Hickey0122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Jake GuentzelOscar LindbergJake DeBrusk60122
2Jeremy MorinDanton HeinenKevin Fiala40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Mark PysykThomas Hickey60122
2Thomas ChabotAndrew Campbell40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Jake GuentzelJake DeBrusk60122
2Jeremy MorinKevin Fiala40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Mark PysykThomas Hickey60122
2Thomas ChabotAndrew Campbell40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Jake Guentzel60122Mark PysykThomas Hickey60122
2Jake DeBrusk40122Thomas ChabotAndrew Campbell40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Jake GuentzelJake DeBrusk60122
2Jeremy MorinKevin Fiala40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Mark PysykThomas Hickey60122
2Thomas ChabotAndrew Campbell40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Jake GuentzelOscar LindbergJake DeBruskMark PysykThomas Hickey
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Jake GuentzelOscar LindbergJake DeBruskMark PysykThomas Hickey
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Joel Armia, Adrian Kempe, Devin Shore Joel Armia, Adrian KempeDevin Shore
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Darren Dietz, Haydn Fleury, Thomas ChabotDarren DietzHaydn Fleury, Thomas Chabot
Tirs de Pénalité
Jake Guentzel, Jake DeBrusk, Jeremy Morin, Kevin Fiala, Oscar Lindberg
Gardien
#1 : Magnus Hellberg, #2 : Alexandar Georgiev


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
1Avalanche430000101688210000108622200000082681.000162440005484942013488687487760130543112517317.65%13284.62%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
2Blackhawks401000211315-22010001068-22000001177050.62513183100548494201168868748776013050409813323.08%20575.00%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
3Blues411000209812110000045-12000002053260.75091120005484942013488687487760133542213116318.75%11281.82%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
4Bruins211000006601010000014-31100000052320.500610160054849420698868748776062214666233.33%2150.00%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
5Canadiens21000010743100000102111100000053241.000712190054849420638868748776068258476233.33%40100.00%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
6Canucks603010201617-130200010810-23010101087160.5001627430054849420179886874877601845811316940615.00%39782.05%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
7Capitals321000001064110000005142110000055040.667101727005484942011388687487760902410937114.29%5180.00%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
8Devils21000001752110000004131000000134-130.7507121900548494207288687487760672812628112.50%6183.33%11024212248.26%1006207048.60%553117547.06%1812104317597721431710
9Ducks66000000231211330000001358330000001073121.0002342650054849420176886874877601586414019334617.65%36586.11%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
10Flyers21100000532110000004131010000012-120.50057120054849420608868748776060221250800.00%60100.00%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
11Islanders2110000067-11010000035-21100000032120.5006915005484942067886874877608024449300.00%20100.00%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
12Kings633000002021-1321000001211131200000810-260.500203252005484942019388687487760185737619440512.50%33972.73%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
13Maple Leafs211000008621010000034-11100000052320.5008142200548494207088687487760661910615120.00%5180.00%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
14Oilers6410001021111032000010125732100000963100.83321375800548494201998868748776016561101159431023.26%38586.84%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
15Penguins41300000611-53120000057-21010000014-320.250611170054849420118886874877601295028120800.00%14285.71%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
16Predators411000201183201000104402100001074360.750111324005484942012388687487760127474011611218.18%20385.00%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
17Rangers21000010642100000104311100000021141.000610160054849420628868748776067194477228.57%20100.00%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
18Red Wings2010010036-31000010023-11010000013-210.250358005484942069886874877607516657000.00%30100.00%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
19Sabres22000000954110000005321100000042241.00091423005484942077886874877606324646100.00%30100.00%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
20Senateurs2020000036-31010000013-21010000023-100.00035800548494207488687487760742210754250.00%5260.00%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
21Sharks614010001721-430300000711-4311010001010040.333172542005484942018488687487760179609819134411.76%34682.35%31024212248.26%1006207048.60%553117547.06%1812104317597721431710
22Stars531000011916322000000954311000011011-170.700193251005484942017388687487760156504413120525.00%22481.82%11024212248.26%1006207048.60%553117547.06%1812104317597721431710
Total82352802112424621531411716001701251101541181202054121105161030.6282463956411054849420264988687487760254790285723993426017.54%3325782.83%51024212248.26%1006207048.60%553117547.06%1812104317597721431710
24Wild4120000159-42110000034-12010000125-330.375581310548494201248868748776099373811911218.18%9188.89%01024212248.26%1006207048.60%553117547.06%1812104317597721431710
_Since Last GM Reset82352802112424621531411716001701251101541181202054121105161030.6282463956411054849420264988687487760254790285723993426017.54%3325782.83%51024212248.26%1006207048.60%553117547.06%1812104317597721431710
_Vs Conference55231702010317014624271210000508674122811702053847212730.6641702694391054849420173588687487760164660874316262794917.56%2754982.18%41024212248.26%1006207048.60%553117547.06%1812104317597721431710
_Vs Division3014110203097821515760002052421015750201045405380.633971632600054849420931886874877608713165289061913116.23%1803282.22%31024212248.26%1006207048.60%553117547.06%1812104317597721431710

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
82103W124639564126492547902857239910
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
82352821124246215
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
4117160170125110
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
4118122054121105
Derniers 10 Matchs
WLOTWOTL SOWSOL
720001
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
3426017.54%3325782.83%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
8868748776054849420
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
1024212248.26%1006207048.60%553117547.06%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
1812104317597721431710


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 - 2019-10-065Ducks2Flames5WSommaire du Match
3 - 2019-10-0815Flames3Ducks2WSommaire du Match
6 - 2019-10-1131Flames2Canucks3LSommaire du Match
8 - 2019-10-1343Canucks3Flames2LSommaire du Match
10 - 2019-10-1554Flames3Kings5LR5Sommaire du Match
12 - 2019-10-1765Kings6Flames1LSommaire du Match
15 - 2019-10-2082Sharks2Flames1LR5Sommaire du Match
17 - 2019-10-2291Flames3Stars4LXXSommaire du Match
20 - 2019-10-25103Oilers1Flames5WSommaire du Match
21 - 2019-10-26114Flames6Oilers2WSommaire du Match
24 - 2019-10-29128Flames0Oilers2LSommaire du Match
25 - 2019-10-30138Penguins2Flames1LSommaire du Match
26 - 2019-10-31144Flames2Sharks4LR5Sommaire du Match
31 - 2019-11-05164Blackhawks4Flames1LSommaire du Match
33 - 2019-11-07176Sharks5Flames3LR5Sommaire du Match
37 - 2019-11-11196Avalanche1Flames2WSommaire du Match
39 - 2019-11-13209Flames2Senateurs3LSommaire du Match
41 - 2019-11-15218Flames5Avalanche1WSommaire du Match
42 - 2019-11-16227Capitals1Flames5WSommaire du Match
45 - 2019-11-19243Rangers3Flames4WXXSommaire du Match
48 - 2019-11-22254Flames2Blackhawks3LXXSommaire du Match
51 - 2019-11-25269Flames1Penguins4LSommaire du Match
52 - 2019-11-26276Canucks3Flames4WXXSommaire du Match
55 - 2019-11-29294Canadiens1Flames2WXXSommaire du Match
57 - 2019-12-01305Flames2Rangers1WSommaire du Match
61 - 2019-12-05319Blackhawks4Flames5WXXSommaire du Match
63 - 2019-12-07331Flames5Blackhawks4WXXSommaire du Match
65 - 2019-12-09342Kings2Flames6WR5Sommaire du Match
67 - 2019-12-11355Flames5Sharks4WR5Sommaire du Match
69 - 2019-12-13364Sabres3Flames5WSommaire du Match
71 - 2019-12-15377Flames3Capitals4LSommaire du Match
73 - 2019-12-17386Flames5Maple Leafs2WSommaire du Match
75 - 2019-12-19396Predators2Flames3WXXSommaire du Match
77 - 2019-12-21409Flames4Predators2WSommaire du Match
79 - 2019-12-23417Flames1Flyers2LSommaire du Match
80 - 2019-12-24422Wild3Flames1LSommaire du Match
83 - 2019-12-27438Flames2Stars5LSommaire du Match
84 - 2019-12-28446Bruins4Flames1LSommaire du Match
87 - 2019-12-31462Flames5Stars2WSommaire du Match
89 - 2020-01-02470Flames5Canadiens3WSommaire du Match
90 - 2020-01-03476Kings3Flames5WR5Sommaire du Match
93 - 2020-01-06493Devils1Flames4WSommaire du Match
95 - 2020-01-08500Flames3Ducks2WSommaire du Match
97 - 2020-01-10512Flames5Bruins2WSommaire du Match
98 - 2020-01-11520Penguins2Flames3WSommaire du Match
102 - 2020-01-15538Flames3Blues2WXXSommaire du Match
104 - 2020-01-17549Penguins3Flames1LSommaire du Match
107 - 2020-01-20563Canucks4Flames2LSommaire du Match
109 - 2020-01-22577Flames3Predators2WXXSommaire du Match
111 - 2020-01-24589Wild1Flames2WSommaire du Match
114 - 2020-01-27605Flames4Sabres2WSommaire du Match
116 - 2020-01-29613Flames2Capitals1WSommaire du Match
117 - 2020-01-30622Predators2Flames1LSommaire du Match
121 - 2020-02-03636Ducks2Flames4WSommaire du Match
122 - 2020-02-04647Flames3Sharks2WXR5Sommaire du Match
125 - 2020-02-07659Blues1Flames2WSommaire du Match
129 - 2020-02-11680Flames2Blues1WXXSommaire du Match
130 - 2020-02-12688Sharks4Flames3LR5Sommaire du Match
133 - 2020-02-15704Red Wings3Flames2LXSommaire du Match
135 - 2020-02-17715Flames1Red Wings3LSommaire du Match
137 - 2020-02-19727Flames3Oilers2WSommaire du Match
139 - 2020-02-21736Avalanche5Flames6WXXSommaire du Match
142 - 2020-02-24754Ducks1Flames4WSommaire du Match
143 - 2020-02-25762Flames2Canucks1WXSommaire du Match
147 - 2020-02-29778Senateurs3Flames1LSommaire du Match
151 - 2020-03-04800Islanders5Flames3LSommaire du Match
153 - 2020-03-06811Flames4Ducks3WSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
156 - 2020-03-09825Oilers3Flames4WXXSommaire du Match
157 - 2020-03-10834Flames4Kings1WR5Sommaire du Match
160 - 2020-03-13847Blues4Flames2LSommaire du Match
163 - 2020-03-16863Flames1Wild2LXXSommaire du Match
164 - 2020-03-17872Maple Leafs4Flames3LSommaire du Match
166 - 2020-03-19883Flames3Islanders2WSommaire du Match
169 - 2020-03-22895Flyers1Flames4WSommaire du Match
171 - 2020-03-24901Flames3Devils4LXXSommaire du Match
173 - 2020-03-26912Flames4Canucks3WXXSommaire du Match
175 - 2020-03-28923Oilers1Flames3WSommaire du Match
178 - 2020-03-31938Stars2Flames4WSommaire du Match
180 - 2020-04-02946Flames3Avalanche1WSommaire du Match
181 - 2020-04-03949Flames1Kings4LR5Sommaire du Match
185 - 2020-04-07965Flames1Wild3LSommaire du Match
187 - 2020-04-09974Stars3Flames5WSommaire du Match



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité de l'Aréna20001000
Prix des Billets4525
Assistance58,64638,909
Assistance PCT71.52%94.90%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacité de l'ArénaPopularité de l'Équipe
0 2379 - 79.31% 93,607$3,837,901$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
3,719,561$ 2,728,000$ 1,708,000$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
14,358$ 2,719,588$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 0 19,621$ 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
Saison Régulière
137831230428102312072439131201148100100039181103142131107246223136359412717474162740983900809117255484260021452815720.28%2935979.86%31037206650.19%1012201850.15%551109550.32%1758104616907091333663
1482352802112424621531411716001701251101541181202054121105161032463956411054849420264988687487760254790285723993426017.54%3325782.83%51024212248.26%1006207048.60%553117547.06%1812104317597721431710
Total Saison Régulière1606651063201447742255803028012118225210158036230519625221240165477758123522125158168365389186917741686177510117441457454462311718.78%62511681.44%82061418849.21%2018408849.36%1104227048.63%357020893450148127641374