Comets

GP: 46 | W: 23 | L: 19 | OTL: 4 | P: 50
GF: 118 | GA: 82 | PP%: 16.11% | PK%: 88.61%
DG: Francis Lagace | Morale : 49 | Moyenne d'Équipe : 62
Prochain matchs #685 vs Crunch
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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
1Alexandre GrenierX100.007238845689939155595553595475685357610
2Austin WagnerX100.008340896274677261556264566163625754610
3Steven FogartyX100.006236905785787156625857595571666261600
4Garrett Pilon (R)X100.005835935972949258625753565262636458600
5Joey Anderson (R)X100.006836906170746061596061675862636629600
6John McCarronX100.007337875887746957615756605773675855600
7Justin ScottX100.006439825678939055565354565567646167590
8Tobias LindbergX100.007237895586928854525451575367646061590
9Nolan Stevens (R)X100.006235935580918754625353565465636258590
10Dalton Smith (R)X100.006845655482888253585452565373676653580
11Tyler RandellX100.006341775477827653545251545275685761570
12Chase Pearson (R)X100.006235935478706753615254555363626358560
13Fredrik ClaessonX100.008342826577835863306756725373675658660
14Cale Fleury (R)X100.006636906278918760305956585561637158630
15Dennis Cholowski (R)X100.005937876876826967307165565261638255630
16Philip SamuelssonX100.006436905479939052305351544575685661610
17Erik Brannstrom (R)X100.005437886465877363306258595460628658610
18Mark FriedmanX100.005437895969847358305653554567646261590
Rayé
MOYENNE D'ÉQUIPE100.00663887597884785749585558536765635760
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
1Jared Coreau100.00728078927170727170727175814556730
2Kasimir Kaskisuo100.00757472837473757473757471755166730
3Hunter Miska100.00766563727574767574767567714821700
Rayé
MOYENNE D'ÉQUIPE100.0074737182737274737274737176484872
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Luke Richardson68676563757075CAN5052,500,000$


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
1Garrett PilonComets (Van)C4617203715201675127478813.39%791919.9939122917310151174153.33%84000000.8003000327
2Fredrik ClaessonComets (Van)D46529341198101516910724444.67%50108623.6241216711750000154100.00%000000.6300010221
3John McCarronComets (Van)RW4612213313435604912837999.38%1089119.384711251600112751051.59%12600000.7414010231
4Yannick WeberVancouver CanucksD40423271671580487129335.63%3980320.08448471300110135010.00%000000.6700000132
5Cale FleuryComets (Van)D4652126622033425817438.62%36103422.494812381740001195010.00%000000.5011000011
6Alexandre GrenierComets (Van)RW46141125106159579109338612.84%22118625.8047112116800052203051.94%48900020.4204000300
7Tobias LindbergComets (Van)RW461112236260633780186213.75%887118.9524681020000310158.57%7000000.5311000222
8Dalton SmithComets (Van)LW46811198671578268916508.99%175816.49358251740000313151.16%4300010.5000021210
9Steven FogartyComets (Van)C46106169160257511125819.01%10101622.104263116500021550054.04%79200100.3104000013
10Dennis CholowskiComets (Van)D1641115134081239112010.26%1034321.491342657000162300.00%000000.8700000101
11Erik BrannstromComets (Van)D4611314328027392512244.00%3363113.7300058000019100.00%000000.4411000000
12Nolan StevensComets (Van)C465914910025365921498.47%757212.45000000000681052.08%48000000.4911000041
13Justin ScottComets (Van)C46571222610254646185110.87%24309.3700019000040050.49%40600000.5601200000
14Tyler RandellComets (Van)RW4683119180362353143315.09%346710.1600001000003168.18%2200000.4700000311
15Austin WagnerComets (Van)LW1264108160292638121815.79%226622.241236520001371050.00%2600000.7500000121
16Philip SamuelssonComets (Van)D4609944406916198130.00%2970915.43000325000076000.00%000000.2500000000
17Mark FriedmanComets (Van)D46055-200797120.00%102084.5201113000033000.00%100000.4800000000
18Chase PearsonComets (Van)C46224340861361615.38%32254.900002190000171048.98%4900000.3500000200
19Joey AndersonComets (Van)RW2000-300520040.00%02814.330000000002000.00%000000.0000000000
Stats d'équipe Total ou en Moyenne7601172173341405565084071511793498169.92%2821245416.39346498339160312317144122652.78%334400130.54520241222221
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
1Jared CoreauComets (Van)42231540.9291.60247608669270000.65020420514
2Kasimir KaskisuoComets (Van)20001.0000.0063000210000.0000042000
Stats d'équipe Total ou en Moyenne44231540.9301.56253908669480000.650204242514


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 Cap 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
Alexandre GrenierComets (Van)RW271991-09-05No200 Lbs6 ft5NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Austin WagnerComets (Van)LW221997-06-23No185 Lbs6 ft1NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Cale FleuryComets (Van)D201998-10-19Yes201 Lbs6 ft1NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$500,000$Lien
Chase PearsonComets (Van)C211997-08-23Yes189 Lbs6 ft2NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Dalton SmithComets (Van)LW271992-06-30Yes206 Lbs6 ft2NoNoNo1Pro & Farm300,000$0$0$NoLien
Dennis CholowskiComets (Van)D211998-02-15Yes195 Lbs6 ft1NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$500,000$Lien
Erik BrannstromComets (Van)D191999-09-02Yes173 Lbs5 ft10NoNoNo1Pro & Farm0$0$NoLien
Fredrik ClaessonComets (Van)D261992-11-24No196 Lbs6 ft1NoNoNo4Pro & Farm1,000,000$0$0$No1,000,000$1,000,000$1,000,000$Lien
Garrett PilonComets (Van)C211998-04-13Yes188 Lbs6 ft0NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Hunter MiskaComets (Van)G231995-07-07No175 Lbs6 ft1NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Jared CoreauComets (Van)G271991-11-05No214 Lbs6 ft5NoNoNo2Pro & Farm750,000$0$0$No750,000$Lien
Joey AndersonComets (Van)RW211998-06-19Yes190 Lbs5 ft11NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$500,000$Lien
John McCarronComets (Van)RW271992-04-16No219 Lbs6 ft3NoNoNo1Pro & Farm300,000$0$0$NoLien
Justin ScottComets (Van)C231995-08-13No202 Lbs6 ft1NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Kasimir KaskisuoComets (Van)G251993-10-02No196 Lbs6 ft3NoNoNo1Pro & Farm300,000$0$0$NoLien
Mark FriedmanComets (Van)D231995-12-25No185 Lbs5 ft11NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Nolan StevensComets (Van)C221996-07-22Yes183 Lbs6 ft3NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$500,000$Lien
Philip SamuelssonComets (Van)D271991-07-26No194 Lbs6 ft2NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Steven FogartyComets (Van)C261993-04-19No210 Lbs6 ft3NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Tobias LindbergComets (Van)RW231995-07-22No215 Lbs6 ft3NoNoNo1Pro & Farm300,000$0$0$NoLien
Tyler RandellComets (Van)RW281991-06-15No198 Lbs6 ft1NoNoNo1Pro & Farm300,000$0$0$NoLien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2123.76196 Lbs6 ft22.67378,571$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Austin WagnerGarrett PilonAlexandre Grenier40122
2Dalton SmithSteven FogartyJohn McCarron30122
3Alexandre GrenierJustin ScottJoey Anderson20122
4Austin WagnerNolan StevensTobias Lindberg10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Fredrik ClaessonDennis Cholowski40122
2Cale FleuryPhilip Samuelsson30122
3Erik BrannstromMark Friedman20122
4Fredrik ClaessonDennis Cholowski10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Austin WagnerGarrett PilonAlexandre Grenier60122
2Dalton SmithSteven FogartyJohn McCarron40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Fredrik ClaessonDennis Cholowski60122
2Cale FleuryPhilip Samuelsson40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Alexandre GrenierAustin Wagner60122
2Garrett PilonSteven Fogarty40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Fredrik ClaessonDennis Cholowski60122
2Cale FleuryPhilip Samuelsson40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Alexandre Grenier60122Fredrik ClaessonDennis Cholowski60122
2Austin Wagner40122Cale FleuryPhilip Samuelsson40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Alexandre GrenierAustin Wagner60122
2Garrett PilonSteven Fogarty40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Fredrik ClaessonDennis Cholowski60122
2Cale FleuryPhilip Samuelsson40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Austin WagnerGarrett PilonAlexandre GrenierFredrik ClaessonDennis Cholowski
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Austin WagnerGarrett PilonAlexandre GrenierFredrik ClaessonDennis Cholowski
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Tyler Randell, Chase Pearson, Joey AndersonTyler Randell, Chase PearsonJoey Anderson
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Erik Brannstrom, Mark Friedman, Cale FleuryErik BrannstromMark Friedman, Cale Fleury
Tirs de Pénalité
Alexandre Grenier, Austin Wagner, Garrett Pilon, Steven Fogarty, John McCarron
Gardien
#1 : , #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
1Americans61400001918-92020000015-441200001813-530.250917260038403779035436644436176407412024312.50%30583.33%0737136753.91%681134250.75%34861756.40%11938501036320555287
2Bruins1000000123-11000000123-10000000000010.50023500384037726354366444362231218200.00%5180.00%0737136753.91%681134250.75%34861756.40%11938501036320555287
3Checkers44000000270272200000016016220000001101181.000274976043840377189354366444366122417314428.57%160100.00%1737136753.91%681134250.75%34861756.40%11938501036320555287
4Crunch632010001293431000009542010100034-180.6671223350138403771543543664443615941769839512.82%32390.63%0737136753.91%681134250.75%34861756.40%11938501036320555287
5Devils72400010913-4422000006513020001038-560.4299142300384037712635436644436140429810630310.00%41685.37%0737136753.91%681134250.75%34861756.40%11938501036320555287
6Marlies43000001195141100000071632000001124870.875193554013840377176354366444367327429415320.00%19194.74%0737136753.91%681134250.75%34861756.40%11938501036320555287
7Monsters31200000770110000004222020000035-220.3337142100384037772354366444368628376622418.18%15193.33%0737136753.91%681134250.75%34861756.40%11938501036320555287
8Rocket5220100014113211000007613110100075260.6001427410038403771233543664443611133608029620.69%20385.00%0737136753.91%681134250.75%34861756.40%11938501036320555287
9Senators5230000010733120000045-12110000062440.400101828023840377104354366444367824548320315.00%25484.00%0737136753.91%681134250.75%34861756.40%11938501036320555287
10Sound Tigers11000000312110000003120000000000021.00035800384037719354366444362021220100.00%60100.00%0737136753.91%681134250.75%34861756.40%11938501036320555287
11Thunderbirds2020000014-31010000013-21010000001-100.000123003840377543543664443658112644400.00%13376.92%0737136753.91%681134250.75%34861756.40%11938501036320555287
Total46201902113118823623139000016438262371002112544410500.54311821733508384037711803543664443610362865628472113416.11%2372788.61%1737136753.91%681134250.75%34861756.40%11938501036320555287
13Wolf Pack21000100541110000004221000010012-130.7505101500384037747354366444365213304511327.27%150100.00%0737136753.91%681134250.75%34861756.40%11938501036320555287
_Since Last GM Reset46201902113118823623139000016438262371002112544410500.54311821733508384037711803543664443610362865628472113416.11%2372788.61%1737136753.91%681134250.75%34861756.40%11938501036320555287
_Vs Conference211080100270383284400000321517136401002382315240.5717013020005384037763235436644436479133243411861618.60%981287.76%1737136753.91%681134250.75%34861756.40%11938501036320555287

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
4650L41182173351180103628656284708
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
462019211311882
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
2313900016438
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
2371021125444
Derniers 10 Matchs
WLOTWOTL SOWSOL
360001
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
2113416.11%2372788.61%1
Tirs en 1e PériodeTirs en 2e PériodeTirs en 3e PériodeTirs en 4e PériodeButs en 1e PériodeButs en 2e PériodeButs en 3e PériodeButs en 4e Période
354366444363840377
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
737136753.91%681134250.75%34861756.40%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
11938501036320555287


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
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JourMatch Équipe Visiteuse Score Équipe Locale Score ST OT SO RI Lien
3 - 2019-09-041Marlies1Comets7WSommaire du Match
4 - 2019-09-0512Senators0Comets3WSommaire du Match
10 - 2019-09-1133Checkers0Comets8WSommaire du Match
11 - 2019-09-1245Comets3Marlies0WSommaire du Match
12 - 2019-09-1360Comets7Marlies1WSommaire du Match
15 - 2019-09-1663Americans4Comets1LSommaire du Match
17 - 2019-09-1872Monsters2Comets4WSommaire du Match
18 - 2019-09-1985Comets2Crunch1WXSommaire du Match
22 - 2019-09-23104Checkers0Comets8WSommaire du Match
24 - 2019-09-25111Comets2Americans3LSommaire du Match
31 - 2019-10-02147Comets3Rocket2WXSommaire du Match
32 - 2019-10-03153Comets3Rocket1WSommaire du Match
36 - 2019-10-07176Rocket4Comets3LSommaire du Match
38 - 2019-10-09181Wolf Pack2Comets4WSommaire du Match
39 - 2019-10-10199Comets0Devils3LSommaire du Match
43 - 2019-10-14218Comets5Senators0WSommaire du Match
45 - 2019-10-16224Crunch0Comets3WSommaire du Match
46 - 2019-10-17237Comets1Wolf Pack2LXSommaire du Match
50 - 2019-10-21259Rocket2Comets4WSommaire du Match
52 - 2019-10-23270Comets2Americans3LXXSommaire du Match
53 - 2019-10-24279Senators2Comets1LSommaire du Match
57 - 2019-10-28304Comets1Devils4LSommaire du Match
59 - 2019-10-30309Comets1Senators2LSommaire du Match
60 - 2019-10-31319Comets1Rocket2LSommaire du Match
64 - 2019-11-04344Comets3Americans2WSommaire du Match
66 - 2019-11-06349Thunderbirds3Comets1LSommaire du Match
67 - 2019-11-07370Comets2Devils1WXXSommaire du Match
71 - 2019-11-11383Bruins3Comets2LXXSommaire du Match
73 - 2019-11-13391Devils1Comets0LSommaire du Match
75 - 2019-11-15415Comets5Checkers0WSommaire du Match
77 - 2019-11-17423Comets6Checkers0WSommaire du Match
80 - 2019-11-20440Comets0Thunderbirds1LSommaire du Match
81 - 2019-11-21458Devils1Comets2WSommaire du Match
86 - 2019-11-26472Devils1Comets3WSommaire du Match
87 - 2019-11-27476Comets1Crunch3LSommaire du Match
88 - 2019-11-28490Americans1Comets0LSommaire du Match
92 - 2019-12-02511Crunch1Comets2WSommaire du Match
95 - 2019-12-05531Comets2Monsters3LSommaire du Match
96 - 2019-12-06545Comets1Monsters2LSommaire du Match
99 - 2019-12-09552Comets2Marlies3LXXSommaire du Match
101 - 2019-12-11562Sound Tigers1Comets3WSommaire du Match
102 - 2019-12-12577Crunch1Comets2WSommaire du Match
106 - 2019-12-16596Crunch3Comets2LSommaire du Match
108 - 2019-12-18606Devils2Comets1LSommaire du Match
109 - 2019-12-19621Senators3Comets0LSommaire du Match
115 - 2019-12-25661Comets1Americans5LSommaire du Match
122 - 2020-01-01685Comets-Crunch-
123 - 2020-01-02704Comets-Thunderbirds-
129 - 2020-01-08723Phantoms-Comets-
130 - 2020-01-09740Americans-Comets-
131 - 2020-01-10748Comets-Sound Tigers-
134 - 2020-01-13764Rocket-Comets-
137 - 2020-01-16787Comets-Penguins-
138 - 2020-01-17799Monsters-Comets-
143 - 2020-01-22818Americans-Comets-
144 - 2020-01-23835Comets-Devils-
148 - 2020-01-27852Americans-Comets-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
150 - 2020-01-29857Bears-Comets-
151 - 2020-01-30875Comets-Phantoms-
155 - 2020-02-03890Crunch-Comets-
157 - 2020-02-05897Comets-Crunch-
158 - 2020-02-06910Comets-Rocket-
162 - 2020-02-10938Comets-Americans-
164 - 2020-02-12944Thunderbirds-Comets-
165 - 2020-02-13957Wolf Pack-Comets-
169 - 2020-02-17979Comets-Senators-
171 - 2020-02-19984Rocket-Comets-
172 - 2020-02-20997Comets-Crunch-
178 - 2020-02-261029Marlies-Comets-
179 - 2020-02-271043Comets-Crunch-
185 - 2020-03-041074Marlies-Comets-
186 - 2020-03-051088Comets-Wolf Pack-
187 - 2020-03-061101Comets-Bruins-
192 - 2020-03-111116Penguins-Comets-
193 - 2020-03-121129Comets-Bears-
194 - 2020-03-131147Crunch-Comets-



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
15 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
1,579,990$ 79,500$ 19,180$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 46,470$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 75 13,296$ 997,200$




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
14462019021131188236231390000164382623710021125444105011821733508384037711803543664443610362865628472113416.11%2372788.61%1737136753.91%681134250.75%34861756.40%11938501036320555287
Total Saison Régulière462019021131188236231390000164382623710021125444105011821733508384037711803543664443610362865628472113416.11%2372788.61%1737136753.91%681134250.75%34861756.40%11938501036320555287