Connexion

Checkers
GP: 0 | W: 0 | L: 0 | OTL: 0 | P: 0
GF: 0 | GA: 0 | PP%: 0.00% | PK%: 0.00%
DG: PKayne | Morale : 40 | Moyenne d’équipe : 59
Prochain matchs #14 vs Islanders
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Checkers
0-0-0, 0pts
2022-09-15
Islanders
0-0-0, 0pts
Statistiques d’équipe
N/ASéquenceN/A
0-0-0Fiche domicile0-0-0
0-0-0Fiche visiteur0-0-0
0-0-010 derniers matchs0-0-0
0.00Buts par match 0.00
0.00Buts contre par match 0.00
0.00%Pourcentage en avantage numérique0.00%
0.00%Pourcentage en désavantage numérique0.00%
Checkers
0-0-0, 0pts
2022-09-17
Americans
0-0-0, 0pts
Statistiques d’équipe
N/ASéquenceN/A
0-0-0Fiche domicile0-0-0
0-0-0Fiche visiteur0-0-0
0-0-010 derniers matchs0-0-0
0.00Buts par match 0.00
0.00Buts contre par match 0.00
0.00%Pourcentage en avantage numérique0.00%
0.00%Pourcentage en désavantage numérique0.00%
Checkers
0-0-0, 0pts
2022-09-19
Bruins
0-0-0, 0pts
Statistiques d’équipe
N/ASéquenceN/A
0-0-0Fiche domicile0-0-0
0-0-0Fiche visiteur0-0-0
0-0-010 derniers matchs0-0-0
0.00Buts par match 0.00
0.00Buts contre par match 0.00
0.00%Pourcentage en avantage numérique0.00%
0.00%Pourcentage en désavantage numérique0.00%
Meneurs d'équipe

Statistiques d’équipe
Buts pour
0
0.00 GFG
Tirs pour
0
0.00 Avg
Pourcentage en avantage numérique
0.0%
0 GF
Début de zone offensive
0.0%
Buts contre
0
0.00 GAA
Tirs contre
0
0.00 Avg
Pourcentage en désavantage numérique
0.0%
0 GA
Début de la zone défensive
0.0%
Information d’équipe

Directeur généralPKayne
EntraîneurPascal Vincent
DivisionAtlantic Division
ConférenceEastern Conference
Capitaine
Assistant #1Danick Martel
Assistant #2


Informations de l’aréna

Capacité3,000
Assistance0
Billets de saison0


Information formation

Équipe Pro28
Équipe Mineure18
Limite contact 46 / 90
Espoirs24


Historique d'équipe

Saison actuelle0-0-0 (0PTS)
Historique96-46-24 (0.578%)
Apparitions séries éliminatoires 2
Historique séries éliminatoires (W-L)32-18
Stanley Cup2


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 SPÂgeContratSalaire moyen
1Alex Chiasson0X100.007047886287787164556567626467750406303221,000,000$
2Martin Frk0X100.00874876608660516940687359676475040620291950,000$
3Kiefer Sherwood0XX100.00724476638260536967697061655967040620273900,000$
4Jansen Harkins0X100.00584688628074725464616063605363040590251900,000$
5Zac Dalpe36X100.00905475578460545950616556636979040590321925,000$
6Taro Hirose0X100.00573677667460566358696161615263040590261900,000$
7Carson Meyer (R)0X100.00914677607960545840626261615164040580251800,000$
8Frederik Gauthier0XXX100.00906077499560495866655861596676040580272800,000$
9Anthony Richard0X100.00504275577960495840646162615968040570251750,000$
10Nate Schnarr0X100.00504175588260415960646262615463040570231900,000$
11Danick Martel (A)0X100.00504475597660485740626162616270040570272850,000$
12Connor Bunnaman0X100.00734777528560535279605762595565040560241800,000$
13Jani Hakanpaa0X100.009850865791787764206361776156690406703041,100,000$
14Mark Borowiecki0X100.009976795885736166206256765858720406603311,000,000$
15Erik Brannstrom0X100.006041876479796965206957715953630406302321,000,000$
16Ville Heinola0X100.006140766278604673207060676052620406002111,250,000$
17Dennis Cholowski0X100.00554480578461417120685966605665040590242850,000$
18Ty Emberson (R)0X100.00504975538260366520615968605161040560222850,000$
Rayé
1Jayden Halbgewachs0X100.00703575657360506140666259615565040580252950,000$
2Cameron Hughes0X100.00504575607960376240686162615464040570252850,000$
3Bokondji Imama0X100.00948075508860485240605859595366040550261800,000$
4Ivan Lodnia (R)0X100.00504475538260165240605762595362040530232850,000$
5Xavier Bernard (R)0X100.00504575518660246420605768595261040560222750,000$
6Josh Brook0X100.00504375528260106220595768595363040550231910,000$
MOYENNE D’ÉQUIPE100.0068487858826349624164616461576704059
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
1Cayden Primeau100.0063747186676764686765666667040670
2Michael Houser100.0056676484606059616157598073040630
Rayé
1Hunter Shepard (R)100.0053605784585756585855567570040600
2Alexei Melnichuk100.0049656284525350535350527068040570
MOYENNE D’ÉQUIPE100.005567648559595760605758737004062
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Pascal Vincent75757575757575CAN508500,000$


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
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


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 restantSalaire moyenSalaire moyen restantPlafond salarial Plafond salarial restant Exclus du plafond salarial Salaire annuel 2Salaire annuel 3Salaire annuel 4Salaire annuel 5Salaire annuel 6Salaire annuel 7Salaire annuel 8Salaire annuel 9Salaire annuel 10Link
Alex ChiassonCheckers (FLA)RW3210/1/1990No208 Lbs6 ft4NoNoNo2Pro & Farm1,000,000$989,418$1,000,000$989,418$0$0$No1,000,000$Lien
Alexei MelnichukCheckers (FLA)G247/1/1998No192 Lbs6 ft1NoNoNo1Pro & Farm925,000$915,212$925,000$915,212$0$0$NoLien
Anthony RichardCheckers (FLA)LW2512/20/1996No186 Lbs5 ft10NoNoNo1Pro & Farm750,000$742,063$750,000$742,063$0$0$NoLien
Bokondji ImamaCheckers (FLA)LW268/3/1996No221 Lbs6 ft1NoNoNo1Pro & Farm800,000$791,534$800,000$791,534$0$0$NoLien
Cameron HughesCheckers (FLA)LW2510/9/1996No181 Lbs5 ft11NoNoNo2Pro & Farm850,000$841,005$850,000$841,005$0$0$No850,000$Lien
Carson MeyerCheckers (FLA)RW258/18/1997Yes184 Lbs5 ft11NoNoNo1Pro & Farm800,000$791,534$800,000$791,534$0$0$NoLien
Cayden PrimeauCheckers (FLA)G238/11/1999No198 Lbs6 ft3NoNoNo1Pro & Farm1,250,000$1,236,772$1,250,000$1,236,772$0$0$NoLien
Connor BunnamanCheckers (FLA)C244/16/1998No207 Lbs6 ft1NoNoNo1Pro & Farm800,000$791,534$800,000$791,534$0$0$NoLien
Danick MartelCheckers (FLA)LW2712/12/1994No176 Lbs5 ft8NoNoNo2Pro & Farm850,000$841,005$850,000$841,005$0$0$No850,000$Lien
Dennis CholowskiCheckers (FLA)D242/15/1998No197 Lbs6 ft2NoNoNo2Pro & Farm850,000$841,005$850,000$841,005$0$0$No850,000$Lien
Erik Brannstrom (contrat à 1 volet)Checkers (FLA)D239/2/1999No185 Lbs5 ft10NoNoNo2Pro & Farm1,000,000$989,418$1,000,000$989,418$0$0$No1,000,000$Lien
Frederik GauthierCheckers (FLA)C/LW/RW274/26/1995No239 Lbs6 ft5NoNoNo2Pro & Farm800,000$791,534$800,000$791,534$0$0$No800,000$Lien
Hunter ShepardCheckers (FLA)G277/1/1995Yes209 Lbs6 ft1NoNoNo0Pro & Farm0$0$NoLien
Ivan LodniaCheckers (FLA)RW238/31/1999Yes195 Lbs6 ft0NoNoNo2Pro & Farm850,000$841,005$850,000$841,005$0$0$No850,000$Lien
Jani Hakanpaa (contrat à 1 volet)Checkers (FLA)D303/31/1992No220 Lbs6 ft6NoNoNo4Pro & Farm1,100,000$1,088,360$1,100,000$1,088,360$0$0$No1,100,000$1,100,000$1,100,000$Lien
Jansen HarkinsCheckers (FLA)C255/23/1997No182 Lbs6 ft1NoNoNo1Pro & Farm900,000$890,476$900,000$890,476$0$0$NoLien
Jayden HalbgewachsCheckers (FLA)LW253/22/1997No160 Lbs5 ft8NoNoNo2Pro & Farm950,000$939,947$950,000$939,947$0$0$No950,000$Lien
Josh BrookCheckers (FLA)D236/17/1999No192 Lbs6 ft1NoNoNo1Pro & Farm910,000$900,370$910,000$900,370$0$0$NoLien
Kiefer SherwoodCheckers (FLA)LW/RW273/31/1995No194 Lbs6 ft0NoNoNo3Pro & Farm900,000$890,476$900,000$890,476$0$0$No900,000$900,000$Lien
Mark BorowieckiCheckers (FLA)D337/12/1989No204 Lbs6 ft2NoNoNo1Pro & Farm1,000,000$989,418$1,000,000$989,418$0$0$NoLien
Martin FrkCheckers (FLA)RW2910/5/1993No210 Lbs6 ft1NoNoNo1Pro & Farm950,000$939,947$950,000$939,947$0$0$NoLien
Michael HouserCheckers (FLA)G307/1/1992No185 Lbs6 ft1NoNoNo1Pro & Farm750,000$742,063$750,000$742,063$0$0$NoLien
Nate SchnarrCheckers (FLA)C232/25/1999No185 Lbs6 ft3NoNoNo1Pro & Farm900,000$890,476$900,000$890,476$0$0$NoLien
Taro HiroseCheckers (FLA)LW266/30/1996No162 Lbs5 ft10NoNoNo1Pro & Farm900,000$890,476$900,000$890,476$0$0$NoLien
Ty EmbersonCheckers (FLA)D225/24/2000Yes194 Lbs6 ft1NoNoNo2Pro & Farm850,000$841,005$850,000$841,005$0$0$No850,000$Lien
Ville HeinolaCheckers (FLA)D213/2/2001No178 Lbs5 ft11NoNoNo1Pro & Farm1,250,000$1,236,772$1,250,000$1,236,772$0$0$NoLien
Xavier BernardCheckers (FLA)D221/6/2000Yes200 Lbs6 ft4NoNoNo2Pro & Farm750,000$742,063$750,000$742,063$0$0$No750,000$Lien
Zac DalpeCheckers (FLA)C3211/1/1989No197 Lbs6 ft2NoNoNo1Pro & Farm925,000$915,212$925,000$915,212$0$0$NoLien
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2825.82194 Lbs6 ft11.50877,143$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Kiefer SherwoodZac DalpeMartin Frk42113
2Taro HiroseJansen HarkinsAlex Chiasson30113
3Danick MartelFrederik GauthierCarson Meyer20122
4Anthony RichardConnor BunnamanNate Schnarr8131
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Erik BrannstromJani Hakanpaa39122
2Ville HeinolaMark Borowiecki37122
3Dennis CholowskiTy Emberson24122
4Mark BorowieckiJani Hakanpaa0122
Attaque en svantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Kiefer SherwoodZac DalpeMartin Frk60005
2Taro HiroseJansen HarkinsAlex Chiasson40005
Défense en svantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Ville HeinolaErik Brannstrom60104
2Dennis CholowskiJani Hakanpaa40104
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Jansen HarkinsAlex Chiasson60140
2Connor BunnamanNate Schnarr40140
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Mark BorowieckiJani Hakanpaa60140
2Erik BrannstromTy Emberson40140
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Jansen Harkins60140Mark BorowieckiJani Hakanpaa60140
2Connor Bunnaman40140Erik BrannstromTy Emberson40140
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Jansen HarkinsAlex Chiasson60122
2Zac DalpeKiefer Sherwood40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Ville HeinolaMark Borowiecki60122
2Erik BrannstromJani Hakanpaa40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Kiefer SherwoodJansen HarkinsMartin FrkVille HeinolaErik Brannstrom
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Jansen HarkinsConnor BunnamanAlex ChiassonMark BorowieckiJani Hakanpaa
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Martin Frk, Kiefer Sherwood, Alex ChiassonTaro Hirose, Martin FrkConnor Bunnaman
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Erik Brannstrom, Jani Hakanpaa, Mark BorowieckiMark BorowieckiDennis Cholowski, Jani Hakanpaa
Tirs de pénalité
Martin Frk, Kiefer Sherwood, Alex Chiasson, Zac Dalpe, Danick Martel
Gardien
#1 : Cayden Primeau, #2 : Michael Houser
Lignes d’attaque personnalisées en prolongation
Jansen Harkins, Martin Frk, Kiefer Sherwood, Alex Chiasson, Zac Dalpe, Taro Hirose, Taro Hirose, Carson Meyer, Frederik Gauthier, Nate Schnarr, Anthony Richard
Lignes de défense personnalisées en prolongation
Erik Brannstrom, Jani Hakanpaa, Ville Heinola, Mark Borowiecki, Dennis Cholowski


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
TotalDomicileVisiteur
# 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
Total00000000000000000000000000000000000.000000000000000000000000.00%000.00%0000.00%000.00%000.00%000000

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
00N/A0000000000
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
000000000
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
000000000
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
000000000
Derniers 10 matchs
WLOTWOTL SOWSOL
000000
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
000.00%000.00%0
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
00000000
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
000.00%000.00%000.00%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
000000


Derniers matchs 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
7 - 2022-09-1514Checkers-Islanders-
9 - 2022-09-1725Checkers-Americans-
11 - 2022-09-1941Checkers-Bruins-
13 - 2022-09-2156Phantoms-Checkers-
15 - 2022-09-2371Crunch-Checkers-
17 - 2022-09-2590Islanders-Checkers-
19 - 2022-09-27103Checkers-IceHogs-
21 - 2022-09-29112Checkers-Phantoms-
23 - 2022-10-01129Senators-Checkers-
26 - 2022-10-04156Checkers-Roadrunners-
28 - 2022-10-06173Checkers-Barracuda-
30 - 2022-10-08189Checkers-Reign-
31 - 2022-10-09192Checkers-Gulls-
34 - 2022-10-12207Wolves-Checkers-
37 - 2022-10-15226Condors-Checkers-
40 - 2022-10-18253Bears-Checkers-
42 - 2022-10-20262Stars-Checkers-
44 - 2022-10-22275Heat-Checkers-
45 - 2022-10-23289Checkers-Monsters-
48 - 2022-10-26305Bruins-Checkers-
51 - 2022-10-29334Thunderbirds-Checkers-
53 - 2022-10-31351Checkers-Condors-
54 - 2022-11-01358Checkers-Heat-
56 - 2022-11-03373Checkers-Canucks-
58 - 2022-11-05389Checkers-Firebirds-
61 - 2022-11-08408Checkers-Moose-
63 - 2022-11-10419Griffins-Checkers-
65 - 2022-11-12436Checkers-Crunch-
66 - 2022-11-13444Firebirds-Checkers-
68 - 2022-11-15455Monsters-Checkers-
70 - 2022-11-17474Penguins-Checkers-
72 - 2022-11-19488Checkers-Comets-
74 - 2022-11-21504Checkers-Bruins-
76 - 2022-11-23518Comets-Checkers-
78 - 2022-11-25538Checkers-Islanders-
84 - 2022-12-01564Rocket-Checkers-
85 - 2022-12-02575Checkers-Wolves-
87 - 2022-12-04591Wolf Pack-Checkers-
89 - 2022-12-06598Roadrunners-Checkers-
92 - 2022-12-09623Checkers-Griffins-
94 - 2022-12-11637Checkers-Stars-
96 - 2022-12-13656Checkers-Eagles-
98 - 2022-12-15673Checkers-Silver Knights-
100 - 2022-12-17684Canucks-Checkers-
102 - 2022-12-19694Checkers-Americans-
103 - 2022-12-20706Checkers-Marlies-
105 - 2022-12-22720Checkers-Rocket-
107 - 2022-12-24734Wild-Checkers-
109 - 2022-12-26751Checkers-Wolf Pack-
110 - 2022-12-27756Checkers-Penguins-
113 - 2022-12-30781Reign-Checkers-
114 - 2022-12-31789Bruins-Checkers-
123 - 2023-01-09808Crunch-Checkers-
126 - 2023-01-12823Barracuda-Checkers-
128 - 2023-01-14839Eagles-Checkers-
130 - 2023-01-16852Checkers-Wild-
131 - 2023-01-17859Checkers-Thunderbirds-
133 - 2023-01-19870Checkers-Bears-
135 - 2023-01-21884Checkers-Admirals-
137 - 2023-01-23902Gulls-Checkers-
141 - 2023-01-27930Americans-Checkers-
145 - 2023-01-31960Checkers-Crunch-
147 - 2023-02-02975Admirals-Checkers-
149 - 2023-02-04993Penguins-Checkers-
152 - 2023-02-071016Silver Knights-Checkers-
155 - 2023-02-101036IceHogs-Checkers-
156 - 2023-02-111047Moose-Checkers-
161 - 2023-02-161081Rocket-Checkers-
163 - 2023-02-181100Comets-Checkers-
165 - 2023-02-201116Checkers-Griffins-
166 - 2023-02-211122Checkers-Phantoms-
168 - 2023-02-231140Marlies-Checkers-
170 - 2023-02-251156Wolf Pack-Checkers-
172 - 2023-02-271170Checkers-Senators-
174 - 2023-03-011186Checkers-Marlies-
175 - 2023-03-021190Checkers-Rocket-
177 - 2023-03-041208Checkers-Monsters-
180 - 2023-03-071229Americans-Checkers-
182 - 2023-03-091245Senators-Checkers-
184 - 2023-03-111262Checkers-Bears-
186 - 2023-03-131279Marlies-Checkers-
189 - 2023-03-161299Wolves-Checkers-



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets3515
Assistance0.00%0.00%
Assistance PCT0.00%0.00%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
41 0 - 0.00%0$0$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursSalaire total moyen des joueursSalaire des entraineurs
23,768$ 2,246,000$ 2,246,000$ 0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
11,884$ 23,768$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
0$ 187 11,884$ 2,222,308$




TotalDomicileVisiteur
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
11823723065562181774141191005322116863041181301234102911110721836758558499959203101101210999528224238387619241153530.43%33972.73%0945185650.92%805154152.24%575107653.44%175997915917831646810
11823723065562181774141191005322116863041181301234102911110721836758558499959203101101210999528224238387619241153530.43%33972.73%0945185650.92%805154152.24%575107653.44%175997915917831646810
Total Saison régulière16474460121010124363548282382001064423217260823626024682041822221443673411701016981981184062022024219819041644846167615238482307030.43%661872.73%01890371250.92%1610308252.24%1150215253.44%351819593182156732921621
Séries éliminatoires
11251690000072502212750000031283139400000412219327212319523142629399929433935889062805159928725.00%170100.00%029663946.32%25353247.56%13231242.31%535303513228468231
11251690000072502212750000031283139400000412219327212319523142629399929433935889062805159928725.00%170100.00%029663946.32%25353247.56%13231242.31%535303513228468231
Total Séries éliminatoires5032180000014410044241410000006256626188000008244386414424639046285258619985886787161618125601021198561425.00%340100.00%0592127846.32%506106447.56%26462442.31%10716071027457936462