Comets

GP: 70 | W: 25 | L: 40 | OTL: 5 | P: 55
GF: 170 | GA: 205 | PP%: 14.65% | PK%: 82.18%
DG: Francis Lagace | Morale : 23 | Moyenne d'Équipe : 61
Prochain matchs #1074 vs Marlies
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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 GrenierX100.007238845689939155595553595475685337610
2Austin WagnerX100.008340896274677261556264566163625731610
3Garrett Pilon (R)X100.005835935972949258625753565262636438600
4Joey Anderson (R)X100.006836906170746061596061675862636613600
5John McCarronX100.007337875887746957615756605773675835600
6Justin ScottX100.006439825678939055565354565567646147590
7Steven FogartyX100.006236905785787156625857595571666241590
8Tobias LindbergX100.007237895586928854525451575367646041590
9Matt BeleskeyX100.006143855776766857585657565479715112580
10Dalton Smith (R)X100.006845655482888253585452565373676627580
11Nolan Stevens (R)X100.006235935580918754625353565465636238580
12Tyler RandellX100.006341775477827653545251545275685741570
13Chase Pearson (R)X100.006235935478706753615254555363626338560
14Cale Fleury (R)X100.006636906278918760305956585561637146630
15Dennis Cholowski (R)X100.005937876876826967307165565261638241630
16Philip SamuelssonX100.006436905479939052305351544575685641610
17Erik Brannstrom (R)X100.005437886465877363306258595460628638610
18Mark FriedmanX100.005437895969847358305653554567646241590
Rayé
MOYENNE D'ÉQUIPE100.00653887587884785751575657536865633660
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 Coreau100.00728078927170727170727175814562730
2Kasimir Kaskisuo100.00757472837473757473757471755172730
Rayé
1Hunter Miska100.00766563727574767574767567714819700
MOYENNE D'ÉQUIPE100.0074737182737274737274737176485172
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
1John McCarronComets (Van)RW70203353-767587871865414910.75%15133619.10101020462390112751050.96%15700010.7914010332
2Garrett PilonComets (Van)C702324471420201101645612014.02%13124617.8149134125920291944153.31%122300000.7503000337
3Fredrik ClaessonVancouver CanucksD58735425127151848313130555.34%68135823.4351520912150001211100.00%000000.6200110321
4Alexandre GrenierComets (Van)RW70172340-8815143122182481319.34%40181425.9248123925400073393050.74%74300020.4404000300
5Steven FogartyComets (Van)C70191534-9220361301825312210.44%22153821.9877144824301122310053.52%128000110.4404000123
6Cale FleuryComets (Van)D6062632334062597522538.00%57133322.224812472130001253010.00%000000.4811000021
7Tobias LindbergComets (Van)RW7012183023206855101248211.88%13109215.6124681020000310154.88%8200000.5511000222
8Dalton SmithComets (Van)LW7092029-13109251415114227816.34%10122117.453912372520000493150.00%7000010.4700041211
9Yannick WeberVancouver CanucksD40423271671580487129335.63%3980320.08448471300110135010.00%000000.6700000132
10Austin WagnerComets (Van)LW3614112534958451116336912.07%1081122.531341413200021333043.75%6400000.6200010131
11Nolan StevensComets (Van)C7081422514032518224689.76%1079311.34000000000681051.27%66700000.5511000041
12Dennis CholowskiComets (Van)D28614206100122858133310.34%2262422.3224641970001127300.00%000000.6400000101
13Justin ScottComets (Van)C7061016-15361042796627759.09%271110.1700019000040051.15%69800000.4501200000
14Philip SamuelssonComets (Van)D7021416-676096323416285.88%52115616.5200010700000139000.00%000000.2800000000
15Erik BrannstromComets (Van)D7011415-2044050503413252.94%4589912.8500069000023100.00%000000.3311000000
16Tyler RandellComets (Van)RW7093127180402561153614.75%45297.57000211000003164.52%3100000.4500000311
17Mark FriedmanComets (Van)D70088-25180202211260.00%204886.9801114000055000.00%100000.3300000000
18Joey AndersonComets (Van)RW26437-2012021335014438.00%236013.88011030000480045.24%4200000.3900000000
19Chase PearsonComets (Van)C70224240981571713.33%42593.700003230000321054.29%7000000.3100000200
Stats d'équipe Total ou en Moyenne1158169310479-608267012271124176150712269.60%4481838115.874683129482227423525215524652.22%512800150.52520371252623
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
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
Matt BeleskeyComets (Van)LW311988-06-07No207 Lbs6 ft0NoNoNo1Pro & Farm500,000$0$0$NoLien
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
2124.00196 Lbs6 ft22.52354,762$



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
140122
2Philip Samuelsson30122
3Erik BrannstromMark Friedman20122
410122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Austin WagnerGarrett PilonAlexandre Grenier60122
2Dalton SmithSteven FogartyJohn McCarron40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
160122
2Philip 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
160122
2Philip Samuelsson40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Alexandre Grenier6012260122
2Austin Wagner40122Philip Samuelsson40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Alexandre GrenierAustin Wagner60122
2Garrett PilonSteven Fogarty40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
160122
2Philip Samuelsson40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Austin WagnerGarrett PilonAlexandre Grenier
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Austin WagnerGarrett PilonAlexandre Grenier
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, Erik BrannstromMark Friedman,
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
1Americans1018000011537-2250500000519-14513000011018-830.150152742006254517163522595630412877114520138410.53%621280.65%11135211053.79%999197950.48%545101353.80%175812471622495848426
2Bears1010000005-51010000005-50000000000000.000000006254517215225956304136171822400.00%8362.50%01135211053.79%999197950.48%545101353.80%175812471622495848426
3Bruins1000000123-11000000123-10000000000010.50023500625451726522595630412231218200.00%5180.00%01135211053.79%999197950.48%545101353.80%175812471622495848426
4Checkers44000000270272200000016016220000001101181.000274976046254517189522595630416122417314428.57%160100.00%11135211053.79%999197950.48%545101353.80%175812471622495848426
5Crunch1137010002037-1753200000118360501000929-2080.364203858016254517276522595630412917611819261711.48%521178.85%01135211053.79%999197950.48%545101353.80%175812471622495848426
6Devils825000101318-54220000065140300010713-660.375132235006254517148522595630411624810612437513.51%45882.22%01135211053.79%999197950.48%545101353.80%175812471622495848426
7Marlies5400000125101522000000136732000001124890.90025477201625451723552259563041103324410821419.05%20290.00%01135211053.79%999197950.48%545101353.80%175812471622495848426
8Monsters41300000711-42110000046-22020000035-220.25071421006254517925225956304111635558827414.81%24483.33%01135211053.79%999197950.48%545101353.80%175812471622495848426
9Penguins1010000015-4000000000001010000015-400.00011200625451723522595630411868206116.67%40100.00%01135211053.79%999197950.48%545101353.80%175812471622495848426
10Phantoms211000007611010000014-31100000062420.500712190062545173452259563041552834279111.11%17288.24%01135211053.79%999197950.48%545101353.80%175812471622495848426
11Rocket824011002329-6413000001118-7411011001211170.438234467006254517219522595630412155211612440820.00%39879.49%01135211053.79%999197950.48%545101353.80%175812471622495848426
12Senators62400000151413120000045-131200000119240.3331528430262545171355225956304111328629924416.67%29679.31%01135211053.79%999197950.48%545101353.80%175812471622495848426
13Sound Tigers2110000035-2110000003121010000004-420.50035800625451741522595630414192836500.00%14378.57%01135211053.79%999197950.48%545101353.80%175812471622495848426
14Thunderbirds40400000415-112020000029-72020000026-400.000471100625451797522595630411222446851000.00%18477.78%01135211053.79%999197950.48%545101353.80%175812471622495848426
Total70224002213170205-35351420000018597-12358200221285108-23550.393170312482086254517176852259563041173648088112783144614.65%3766782.18%21135211053.79%999197950.48%545101353.80%175812471622495848426
16Wolf Pack31100100810-22110000078-11000010012-130.5008152300625451769522595630419429486116425.00%23386.96%01135211053.79%999197950.48%545101353.80%175812471622495848426
_Since Last GM Reset70224002213170205-35351420000018597-12358200221285108-23550.393170312482086254517176852259563041173648088112783144614.65%3766782.18%21135211053.79%999197950.48%545101353.80%175812471622495848426
_Vs Conference311116011029491315510000004752-516660110247398270.43594174268056254517903522595630417882013925911232016.26%1552683.23%21135211053.79%999197950.48%545101353.80%175812471622495848426

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
7055L117031248217681736480881127808
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
7022402213170205
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
35142000018597
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
35820221285108
Derniers 10 Matchs
WLOTWOTL SOWSOL
180100
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
3144614.65%3766782.18%2
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
522595630416254517
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
1135211053.79%999197950.48%545101353.80%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
175812471622495848426


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
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 - 2020-01-01685Comets1Crunch6LSommaire du Match
123 - 2020-01-02704Comets2Thunderbirds5LSommaire du Match
129 - 2020-01-08723Phantoms4Comets1LSommaire du Match
130 - 2020-01-09740Americans4Comets0LSommaire du Match
131 - 2020-01-10748Comets0Sound Tigers4LSommaire du Match
134 - 2020-01-13764Rocket4Comets1LSommaire du Match
137 - 2020-01-16787Comets1Penguins5LSommaire du Match
138 - 2020-01-17799Monsters4Comets0LSommaire du Match
143 - 2020-01-22818Americans6Comets2LSommaire du Match
144 - 2020-01-23835Comets4Devils5LSommaire du Match
148 - 2020-01-27852Americans4Comets2LSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
150 - 2020-01-29857Bears5Comets0LSommaire du Match
151 - 2020-01-30875Comets6Phantoms2WSommaire du Match
155 - 2020-02-03890Crunch3Comets2LSommaire du Match
157 - 2020-02-05897Comets2Crunch7LSommaire du Match
158 - 2020-02-06910Comets5Rocket6LXSommaire du Match
162 - 2020-02-10938Comets2Americans5LSommaire du Match
164 - 2020-02-12944Thunderbirds6Comets1LSommaire du Match
165 - 2020-02-13957Wolf Pack6Comets3LSommaire du Match
169 - 2020-02-17979Comets5Senators7LSommaire du Match
171 - 2020-02-19984Rocket8Comets3LSommaire du Match
172 - 2020-02-20997Comets2Crunch5LSommaire du Match
178 - 2020-02-261029Marlies5Comets6WSommaire du Match
179 - 2020-02-271043Comets1Crunch7LSommaire du Match
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
3 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
2,429,563$ 74,500$ 22,180$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 71,306$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 11 13,271$ 145,981$




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
1470224002213170205-35351420000018597-12358200221285108-2355170312482086254517176852259563041173648088112783144614.65%3766782.18%21135211053.79%999197950.48%545101353.80%175812471622495848426
Total Saison Régulière70224002213170205-35351420000018597-12358200221285108-2355170312482086254517176852259563041173648088112783144614.65%3766782.18%21135211053.79%999197950.48%545101353.80%175812471622495848426