Checkers

GP: 76 | W: 3 | L: 73 | OTL: 0 | P: 6
GF: 80 | GA: 462 | PP%: 4.87% | PK%: 73.45%
DG: Sebastien Chando | Morale : 4 | Moyenne d'Équipe : 61
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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
1Alex BelzileX100.005640796170949560686156585475685765620
2Jordan Kyrou (R)X100.005236926369776662726461596562627665610
3Anders BjorkX100.005435925872776256536057595665635569580
4Anthony LouisX100.005336905758949556615753525567646159580
5Kevin RoyX100.005836915763736056635854595371666369570
6Blake SpeersX100.005837875569857954625352555463626338570
7Tyler Steenbergen (R)X100.005136925659928955585553525461636434570
8Tyler Vesel (R)X100.005235955558928854585551535269656065570
9Skyler McKenzie (R)X100.005237875659878155565453525561636434560
10Nic HagueX100.007837885795949556305654624761636465650
11Anton LindholmX100.007835905971766958306052714669655469640
12Henri Jokiharju (R)X100.006938866573837864307356625361638265640
13Adam ClendeningX100.005539816274786661306254664773676569620
14Julius BergmanX100.006536915678857955305452575167646869610
15Kale Clague (R)X100.005438865970908458306053564861637765610
16Stefan Elliott (R)X100.005635946075797059306154575375686265610
17David WarsofskyX100.005239816063908459306352534777695159600
18Lucas JohansenX100.006037885677868055305651544563627369600
19Dylan BlujusX100.006539815582857954305350564869657269600
20Logan PyettX100.006339745771747056306058535466675255590
21James De HaasX100.005634805576767255305652564564655338580
Rayé
MOYENNE D'ÉQUIPE100.00593787587184785743595457526765646060
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
1Calvin Pickard100.00767775797574767574767573776362730
2Jake Paterson100.00626462736160626160626169737169620
Rayé
MOYENNE D'ÉQUIPE100.0069716976686769686769687175676668
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Ian Laperriere73676461686380CAN4551,000,000$


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
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 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
Adam ClendeningCheckers (Car)D261992-10-26No196 Lbs6 ft0NoNoNo1Pro & Farm300,000$0$0$NoLien
Alex BelzileCheckers (Car)C271991-08-31No188 Lbs5 ft11NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Anders BjorkCheckers (Car)LW221996-08-05No190 Lbs6 ft0NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Anthony LouisCheckers (Car)LW241995-02-10No151 Lbs5 ft7NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Anton LindholmCheckers (Car)D241994-11-29No191 Lbs5 ft11NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Blake SpeersCheckers (Car)C221997-01-02No185 Lbs5 ft11NoNoNo3Pro & Farm500,000$0$0$No500,000$500,000$Lien
Calvin PickardCheckers (Car)G271992-04-15No207 Lbs6 ft1NoNoNo4Pro & Farm750,000$0$0$No750,000$750,000$750,000$Lien
David WarsofskyCheckers (Car)D291990-05-30No170 Lbs5 ft9NoNoNo3Pro & Farm500,000$0$0$No500,000$500,000$Lien
Dylan BlujusCheckers (Car)D251994-01-22No191 Lbs6 ft3NoNoNo1Pro & Farm300,000$0$0$NoLien
Henri JokiharjuCheckers (Car)D201999-06-17Yes193 Lbs6 ft0NoNoNo4Pro & Farm900,000$0$0$No900,000$900,000$900,000$Lien
Jake PatersonCheckers (Car)G251994-05-03No176 Lbs6 ft1NoNoNo1Pro & Farm300,000$0$0$NoLien
James De HaasCheckers (Car)D251994-05-03No210 Lbs6 ft4NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Jordan KyrouCheckers (Car)C211998-05-05Yes175 Lbs6 ft0NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$500,000$Lien
Julius BergmanCheckers (Car)D231995-11-02No205 Lbs6 ft1NoNoNo1Pro & Farm500,000$0$0$NoLien
Kale ClagueCheckers (Car)D211998-06-05Yes177 Lbs6 ft0NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$500,000$Lien
Kevin RoyCheckers (Car)LW261993-05-20No170 Lbs5 ft9NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Logan PyettCheckers (Car)D311988-05-26No199 Lbs5 ft10NoNoNo1Pro & Farm300,000$0$0$NoLien
Lucas JohansenCheckers (Car)D211997-11-16No182 Lbs6 ft2NoNoNo3Pro & Farm500,000$0$0$No500,000$500,000$Lien
Nic HagueCheckers (Car)D201998-12-05No215 Lbs6 ft6NoNoNo4Pro & Farm900,000$0$0$No900,000$900,000$900,000$Lien
Skyler McKenzieCheckers (Car)LW211998-01-20Yes154 Lbs5 ft8NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Stefan ElliottCheckers (Car)D281991-01-30Yes190 Lbs6 ft1NoNoNo1Pro & Farm300,000$0$0$NoLien
Tyler SteenbergenCheckers (Car)C211998-01-07Yes188 Lbs5 ft1NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$500,000$Lien
Tyler VeselCheckers (Car)C251994-04-14Yes182 Lbs5 ft1NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2324.09186 Lbs5 ft112.74441,304$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
140122
230122
320122
410122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
140122
230122
320122
410122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
160122
240122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
160122
240122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
160122
240122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
160122
240122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
16012260122
24012240122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
160122
240122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
160122
240122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
, , ,
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
, , ,
Tirs de Pénalité
, , , ,
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
1Americans40400000221-1920200000012-122020000029-700.0002460029262417649052248142025449641100.00%14378.57%0392164323.86%590274621.49%275116923.52%9235832850558789281
2Bears40400000430-2620200000313-1020200000117-1600.00048120029262418449052248142186442661000.00%15380.00%0392164323.86%590274621.49%275116923.52%9235832850558789281
3Bruins80800000347-4440400000024-2440400000323-2000.0003690029262411104905224814388116721372200.00%34682.35%0392164323.86%590274621.49%275116923.52%9235832850558789281
4Comets40400000027-2720200000011-1120200000016-1600.0000000029262416149052248141894137431600.00%14471.43%0392164323.86%590274621.49%275116923.52%9235832850558789281
5Crunch40400000325-2220200000313-1020200000012-1200.0003580029262415549052248141976534711616.25%16568.75%0392164323.86%590274621.49%275116923.52%9235832850558789281
6Devils40400000321-1820200000212-102020000019-800.0003690029262416749052248141605444491200.00%10370.00%0392164323.86%590274621.49%275116923.52%9235832850558789281
7Marlies412010001922-3211000001011-120101000911-240.500193857102926241205490522481420441241021218.33%12466.67%1392164323.86%590274621.49%275116923.52%9235832850558789281
8Monsters40400000332-2920200000215-1320200000117-1600.0003580029262416649052248142297533691218.33%14471.43%0392164323.86%590274621.49%275116923.52%9235832850558789281
9Penguins40400000621-152020000039-620200000312-900.000612180029262417649052248141694736611300.00%16381.25%0392164323.86%590274621.49%275116923.52%9235832850558789281
10Phantoms808000001046-3640400000720-1340400000326-2300.000101929002926241161490522481432278611202328.70%27966.67%0392164323.86%590274621.49%275116923.52%9235832850558789281
11Rocket40400000223-2120200000113-1220200000110-900.00024600292624182490522481419751366013215.38%18288.89%0392164323.86%590274621.49%275116923.52%9235832850558789281
12Senators40400000522-1720200000211-920200000311-800.0005914002926241724905224814223664163700.00%16568.75%0392164323.86%590274621.49%275116923.52%9235832850558789281
13Sound Tigers817000001143-3240400000622-1641300000521-1620.12511223300292624114249052248143361017813214321.43%371754.05%0392164323.86%590274621.49%275116923.52%9235832850558789281
14Thunderbirds40400000225-2320200000213-1120200000012-1200.00024600292624110749052248141825728631100.00%13376.92%0392164323.86%590274621.49%275116923.52%9235832850558789281
Total762730100080462-382381370000042228-186381360100038234-19660.0398015623610292624114974905224814358810156991223226114.87%2907773.45%1392164323.86%590274621.49%275116923.52%9235832850558789281
16Wolf Pack80800000757-5040400000129-2840400000628-2200.000714210029262411334905224814372105841233412.94%34682.35%0392164323.86%590274621.49%275116923.52%9235832850558789281
_Since Last GM Reset762730100080462-382381370000042228-186381360100038234-19660.0398015623610292624114974905224814358810156991223226114.87%2907773.45%1392164323.86%590274621.49%275116923.52%9235832850558789281
_Vs Conference201180100025118-931019000001360-471009010001258-4640.10025507510292624153149052248149742441743326334.76%711677.46%1392164323.86%590274621.49%275116923.52%9235832850558789281

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
766L2180156236149735881015699122310
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
76273100080462
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
38137000042228
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
38136100038234
Derniers 10 Matchs
WLOTWOTL SOWSOL
0100000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
226114.87%2907773.45%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
49052248142926241
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
392164323.86%590274621.49%275116923.52%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
9235832850558789281


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 - 2019-09-043Checkers0Americans6LSommaire du Match
4 - 2019-09-0515Checkers2Americans3LSommaire du Match
10 - 2019-09-1133Checkers0Comets8LSommaire du Match
11 - 2019-09-1246Checkers0Crunch3LSommaire du Match
17 - 2019-09-1870Bears7Checkers0LSommaire du Match
18 - 2019-09-1984Bears6Checkers3LSommaire du Match
22 - 2019-09-23104Checkers0Comets8LSommaire du Match
24 - 2019-09-25110Checkers1Devils5LSommaire du Match
25 - 2019-09-26119Checkers0Crunch9LSommaire du Match
31 - 2019-10-02139Bruins9Checkers0LSommaire du Match
32 - 2019-10-03154Bruins4Checkers0LSommaire du Match
38 - 2019-10-09186Checkers0Devils4LSommaire du Match
39 - 2019-10-10198Checkers0Phantoms7LSommaire du Match
40 - 2019-10-11205Checkers0Sound Tigers8LSommaire du Match
43 - 2019-10-14219Checkers0Phantoms6LSommaire du Match
45 - 2019-10-16228Checkers2Penguins5LSommaire du Match
46 - 2019-10-17243Checkers1Penguins7LSommaire du Match
49 - 2019-10-20253Senators5Checkers1LSommaire du Match
50 - 2019-10-21258Senators6Checkers1LSommaire du Match
53 - 2019-10-24278Rocket5Checkers0LSommaire du Match
54 - 2019-10-25290Rocket8Checkers1LSommaire du Match
59 - 2019-10-30311Checkers0Bruins8LSommaire du Match
60 - 2019-10-31321Checkers2Wolf Pack7LSommaire du Match
61 - 2019-11-01332Checkers0Bruins4LSommaire du Match
66 - 2019-11-06355Checkers0Wolf Pack9LSommaire du Match
67 - 2019-11-07368Checkers0Thunderbirds3LSommaire du Match
72 - 2019-11-12390Wolf Pack8Checkers0LSommaire du Match
74 - 2019-11-14402Wolf Pack8Checkers1LSommaire du Match
75 - 2019-11-15415Comets5Checkers0LSommaire du Match
77 - 2019-11-17423Comets6Checkers0LSommaire du Match
80 - 2019-11-20435Sound Tigers6Checkers1LSommaire du Match
81 - 2019-11-21454Sound Tigers5Checkers3LSommaire du Match
87 - 2019-11-27482Checkers1Rocket7LSommaire du Match
88 - 2019-11-28486Checkers0Rocket3LSommaire du Match
90 - 2019-11-30504Checkers6Marlies5WXSommaire du Match
94 - 2019-12-04519Checkers0Senators6LSommaire du Match
95 - 2019-12-05534Checkers3Senators5LSommaire du Match
96 - 2019-12-06548Checkers3Marlies6LSommaire du Match
101 - 2019-12-11561Penguins5Checkers0LSommaire du Match
102 - 2019-12-12575Penguins4Checkers3LSommaire du Match
105 - 2019-12-15594Sound Tigers6Checkers0LSommaire du Match
106 - 2019-12-16595Sound Tigers5Checkers2LSommaire du Match
109 - 2019-12-19618Marlies5Checkers6WSommaire du Match
110 - 2019-12-20631Marlies6Checkers4LSommaire du Match
115 - 2019-12-25658Checkers2Bruins3LSommaire du Match
116 - 2019-12-26670Checkers2Wolf Pack5LSommaire du Match
122 - 2020-01-01684Bruins6Checkers0LSommaire du Match
123 - 2020-01-02699Bruins5Checkers0LSommaire du Match
126 - 2020-01-05713Monsters9Checkers1LSommaire du Match
127 - 2020-01-06716Monsters6Checkers1LSommaire du Match
130 - 2020-01-09737Checkers1Bears10LSommaire du Match
131 - 2020-01-10755Checkers0Bears7LSommaire du Match
136 - 2020-01-15772Checkers0Thunderbirds9LSommaire du Match
137 - 2020-01-16786Checkers1Bruins8LSommaire du Match
138 - 2020-01-17794Checkers2Sound Tigers1WSommaire du Match
143 - 2020-01-22816Phantoms5Checkers1LSommaire du Match
144 - 2020-01-23829Phantoms5Checkers3LSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
150 - 2020-01-29858Devils6Checkers1LSommaire du Match
151 - 2020-01-30869Devils6Checkers1LSommaire du Match
157 - 2020-02-05902Checkers2Phantoms6LSommaire du Match
158 - 2020-02-06913Checkers1Phantoms7LSommaire du Match
164 - 2020-02-12946Americans5Checkers0LSommaire du Match
165 - 2020-02-13955Americans7Checkers0LSommaire du Match
168 - 2020-02-16973Crunch6Checkers1LSommaire du Match
169 - 2020-02-17978Crunch7Checkers2LSommaire du Match
172 - 2020-02-20995Wolf Pack6Checkers0LSommaire du Match
173 - 2020-02-211008Wolf Pack7Checkers0LSommaire du Match
176 - 2020-02-241018Checkers2Sound Tigers7LSommaire du Match
178 - 2020-02-261033Checkers2Wolf Pack7LSommaire du Match
179 - 2020-02-271039Checkers1Sound Tigers5LSommaire du Match
182 - 2020-03-011059Phantoms6Checkers2LSommaire du Match
183 - 2020-03-021063Phantoms4Checkers1LSommaire du Match
186 - 2020-03-051086Thunderbirds5Checkers1LSommaire du Match
187 - 2020-03-061097Thunderbirds8Checkers1LSommaire du Match
191 - 2020-03-101114Checkers0Monsters10LSommaire du Match
192 - 2020-03-111115Checkers1Monsters7LSommaire du Match



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
0 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
1,103,592$ 101,500$ 22,970$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 103,581$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 0 5,678$ 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
14762730100080462-382381370000042228-186381360100038234-19668015623610292624114974905224814358810156991223226114.87%2907773.45%1392164323.86%590274621.49%275116923.52%9235832850558789281
Total Saison Régulière762730100080462-382381370000042228-186381360100038234-19668015623610292624114974905224814358810156991223226114.87%2907773.45%1392164323.86%590274621.49%275116923.52%9235832850558789281