Checkers

GP: 52 | W: 2 | L: 47 | OTL: 3 | P: 7
GF: 87 | GA: 286 | PP%: 6.61% | PK%: 72.26%
DG: Sebastien Chando | Morale : 18 | Moyenne d'Équipe : 61
Prochain matchs #772 vs Thunderbirds
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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
1Blake ColemanX100.00985577718379897779687389558472163740
2Ales HemskyX100.00655574828280807350626270558485163680
3Anders Bjork (R)X100.00725575787071707250656566557676163660
4Joakim NordstromX100.00855579747167686870636272556364163650
5Jacob JosefsonX100.00645574797167706673606172556566163640
6Tyler MotteX100.00625572747271706859626869555050163630
7Kevin RoyX100.00635566696269686750626365555050163610
8Anthony LouisX100.00575566675454545550555556557470163560
9Tim KennedyX100.00605566606461555550555555556060163550
10Steve OttX100.00555555555555555550555555557374139540
11Erik GustafssonX100.00755593717984709025716776557876163720
12Christian DjoosX100.00715596855775718225706479557979152710
13Anton LindholmX100.00895585687373726925636076557373163690
14Adam ClendeningX100.00625565818078666525626063555757163640
15David WarsofskyX100.00665575685773657325636068555353163620
16Julius BergmanX100.00605566627365686025606057555353163580
17Michael KostkaX100.00585559605959755925595959555758143570
18Kevin KleinX100.00555555605555555525555555557882122560
19Ryan Mantha (R)X100.00555555605555735525555555555555121540
Rayé
1Blake Speers (R)X100.00565555555859595550555555556572120540
2A.J. White (R)X100.00565555555859605550555555555050120530
3James de Haas (R)X100.00555555605555575525555555555555120540
4Dylan BlujusX100.00555555605555595525555555555353128540
5Lucas Johansen (R)X100.00555555605555565525555555555555120530
MOYENNE D'ÉQUIPE100.0065556867656666644160606455646414961
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
1Casey DeSmith100.0073686169737372767874557068143700
2Tom McCollum100.0076696573717176737173557068133690
Rayé
1Jake Paterson (R)100.0052646467596658605758556766120590
MOYENNE D'ÉQUIPE100.006767637068706970696855696713266
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Terry Murray18574751884936CAN6711$


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 GP 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 StatusType Salaire Actuel Cap Salariale Cap Salariale Restant Exclus du Cap Salarial Link
A.J. WhiteCheckers (Car)LW241992-04-19Yes201 Lbs6 ft2NoNoNo4Avec RestrictionPro & Farm300,000$0$0$No
Adam ClendeningCheckers (Car)D241992-10-25No190 Lbs6 ft0NoNoNo1Avec RestrictionPro & Farm900,000$0$0$No
Ales HemskyCheckers (Car)RW331983-08-12No185 Lbs6 ft0NoNoNo3Sans RestrictionPro & Farm500,000$0$0$No
Anders BjorkCheckers (Car)C201996-08-05Yes185 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No
Anthony LouisCheckers (Car)RW211995-02-10No150 Lbs5 ft7NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$No
Anton LindholmCheckers (Car)D221994-11-29No191 Lbs5 ft11NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$No
Blake ColemanCheckers (Car)C251991-11-28No198 Lbs5 ft10NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No
Blake SpeersCheckers (Car)C201997-01-02Yes185 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm500,000$0$0$No
Casey DeSmithCheckers (Car)G251991-08-13No181 Lbs6 ft0NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No
Christian DjoosCheckers (Car)D221994-08-06No159 Lbs5 ft11NoNoNo2Contrat d'EntréePro & Farm300,000$0$0$No
David WarsofskyCheckers (Car)D261990-05-30No170 Lbs5 ft9NoNoNo4Avec RestrictionPro & Farm500,000$0$0$No
Dylan BlujusCheckers (Car)D221994-01-22No191 Lbs6 ft3NoNoNo2Contrat d'EntréePro & Farm300,000$0$0$No
Erik GustafssonCheckers (Car)D281988-12-15No205 Lbs6 ft0NoNoNo2Sans RestrictionPro & Farm1,000,000$0$0$No
Jacob JosefsonCheckers (Car)C251991-03-02No190 Lbs5 ft11NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Jake PatersonCheckers (Car)G221994-05-03Yes176 Lbs6 ft1NoNoNo2Contrat d'EntréePro & Farm300,000$0$0$No
James de HaasCheckers (Car)D221994-05-03Yes209 Lbs6 ft4NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No
Joakim NordstromCheckers (Car)C241992-02-25No189 Lbs6 ft1NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No
Julius BergmanCheckers (Car)D211995-11-02No195 Lbs6 ft1NoNoNo2Contrat d'EntréePro & Farm500,000$0$0$No
Kevin KleinCheckers (Car)D321984-12-13No199 Lbs6 ft1NoNoNo4Sans RestrictionPro & Farm500,000$0$0$No
Kevin RoyCheckers (Car)C231993-05-20No174 Lbs5 ft9NoNoNo3Avec RestrictionPro & Farm500,000$0$0$No
Lucas JohansenCheckers (Car)D181998-11-16Yes179 Lbs6 ft2NoNoNo4Contrat d'EntréePro & Farm500,000$0$0$No
Michael KostkaCheckers (Car)D311985-11-27No210 Lbs6 ft1NoNoNo3Sans RestrictionPro & Farm500,000$0$0$No
Ryan ManthaCheckers (Car)D201996-06-18Yes225 Lbs6 ft5NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No
Steve OttCheckers (Car)LW341982-08-18No189 Lbs6 ft0NoNoNo2Sans RestrictionPro & Farm300,000$0$0$No
Tim KennedyCheckers (Car)LW301986-04-29No175 Lbs5 ft10NoNoNo2Sans RestrictionPro & Farm300,000$0$0$No
Tom McCollumCheckers (Car)G271989-12-06No226 Lbs6 ft2NoNoNo1Avec RestrictionPro & Farm500,000$0$0$No
Tyler MotteCheckers (Car)LW211995-03-10No192 Lbs5 ft9NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$No
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2724.52190 Lbs6 ft02.70414,815$



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
1Americans20200000312-90000000000020200000312-900.00035800333023139419395408373312440800.00%7271.43%0388128230.27%492164629.89%24683129.60%7284701828394569217
2Bears40400000220-1820200000212-102020000008-800.0002460033302318541939540831635030701800.00%13284.62%0388128230.27%492164629.89%24683129.60%7284701828394569217
3Bruins70700000842-3440400000627-2130300000215-1300.00081624003330231159419395408327393481273825.26%23386.96%0388128230.27%492164629.89%24683129.60%7284701828394569217
4Comets40400000823-1520200000511-620200000312-900.000815231033302311144193954083199674472900.00%11281.82%0388128230.27%492164629.89%24683129.60%7284701828394569217
5Crunch20200000211-90000000000020200000211-900.000246003330231394193954083712027291300.00%4175.00%0388128230.27%492164629.89%24683129.60%7284701828394569217
6Devils2020000029-7000000000002020000029-700.00023500333023133419395408379211534600.00%5180.00%0388128230.27%492164629.89%24683129.60%7284701828394569217
7Marlies402011001721-420100100911-220101000810-230.37517345100333023118741939540831876325979333.33%10550.00%0388128230.27%492164629.89%24683129.60%7284701828394569217
8Monsters2010010049-52010010049-50000000000010.2504812003330231314193954083671810407228.57%50100.00%0388128230.27%492164629.89%24683129.60%7284701828394569217
9Penguins40400000729-2220200000116-1520200000613-700.000713200033302317641939540831504433451900.00%14564.29%0388128230.27%492164629.89%24683129.60%7284701828394569217
10Phantoms2020000038-5000000000002020000038-500.000369003330231304193954083872314241815.56%7442.86%0388128230.27%492164629.89%24683129.60%7284701828394569217
11Rocket40400000424-2020200000211-920200000213-1100.00048120033302319441939540831622643692414.17%12466.67%0388128230.27%492164629.89%24683129.60%7284701828394569217
12Senators40400000620-1420200000414-102020000026-400.000611170033302317141939540831544115442100.00%4250.00%0388128230.27%492164629.89%24683129.60%7284701828394569217
13Sound Tigers50500000529-2440400000522-171010000007-700.000510150033302318541939540832174636782713.70%17288.24%0388128230.27%492164629.89%24683129.60%7284701828394569217
14Thunderbirds1010000025-3000000000001010000025-300.0002460033302313041939540835224618600.00%30100.00%0388128230.27%492164629.89%24683129.60%7284701828394569217
Total521470130087286-199240210030044142-98281260100043144-10170.06787168255103330231122541939540832127625410868242166.61%1554372.26%0388128230.27%492164629.89%24683129.60%7284701828394569217
16Wolf Pack513001001424-102010010069-331200000815-730.300142741003330231152419395408319358408119631.58%201050.00%0388128230.27%492164629.89%24683129.60%7284701828394569217
_Since Last GM Reset521470130087286-199240210030044142-98281260100043144-10170.06787168255103330231122541939540832127625410868242166.61%1554372.26%0388128230.27%492164629.89%24683129.60%7284701828394569217
_Vs Conference15013011003485-51605001001633-17908010001852-3430.100346610010333023146441939540836732111422965647.14%431369.77%0388128230.27%492164629.89%24683129.60%7284701828394569217

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
527L3871682551225212762541086810
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
52147130087286
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
24021030044142
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
28126100043144
Derniers 10 Matchs
WLOTWOTL SOWSOL
080200
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
242166.61%1554372.26%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
41939540833330231
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
388128230.27%492164629.89%24683129.60%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
7284701828394569217


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 - 2018-09-073Checkers2Americans5LSommaire du Match
4 - 2018-09-0815Checkers1Americans7LSommaire du Match
10 - 2018-09-1433Checkers2Comets7LSommaire du Match
11 - 2018-09-1546Checkers0Crunch5LSommaire du Match
17 - 2018-09-2170Bears6Checkers2LSommaire du Match
18 - 2018-09-2284Bears6Checkers0LSommaire du Match
22 - 2018-09-26104Checkers1Comets5LSommaire du Match
24 - 2018-09-28110Checkers0Devils3LSommaire du Match
25 - 2018-09-29119Checkers2Crunch6LSommaire du Match
31 - 2018-10-05139Bruins6Checkers3LSommaire du Match
32 - 2018-10-06154Bruins6Checkers1LSommaire du Match
38 - 2018-10-12186Checkers2Devils6LSommaire du Match
39 - 2018-10-13198Checkers1Phantoms4LSommaire du Match
40 - 2018-10-14205Checkers0Sound Tigers7LSommaire du Match
43 - 2018-10-17219Checkers2Phantoms4LSommaire du Match
45 - 2018-10-19228Checkers3Penguins7LSommaire du Match
46 - 2018-10-20243Checkers3Penguins6LSommaire du Match
49 - 2018-10-23253Senators6Checkers2LSommaire du Match
50 - 2018-10-24258Senators8Checkers2LSommaire du Match
53 - 2018-10-27278Rocket6Checkers1LSommaire du Match
54 - 2018-10-28290Rocket5Checkers1LSommaire du Match
59 - 2018-11-02311Checkers0Bruins6LSommaire du Match
60 - 2018-11-03321Checkers3Wolf Pack2WSommaire du Match
61 - 2018-11-04332Checkers2Bruins5LSommaire du Match
66 - 2018-11-09355Checkers3Wolf Pack9LSommaire du Match
67 - 2018-11-10368Checkers2Thunderbirds5LSommaire du Match
72 - 2018-11-15390Wolf Pack5Checkers3LSommaire du Match
74 - 2018-11-17402Wolf Pack4Checkers3LXSommaire du Match
75 - 2018-11-18415Comets5Checkers3LSommaire du Match
77 - 2018-11-20423Comets6Checkers2LSommaire du Match
80 - 2018-11-23435Sound Tigers6Checkers0LSommaire du Match
81 - 2018-11-24454Sound Tigers7Checkers1LSommaire du Match
87 - 2018-11-30482Checkers2Rocket5LSommaire du Match
88 - 2018-12-01486Checkers0Rocket8LSommaire du Match
90 - 2018-12-03504Checkers5Marlies4WXSommaire du Match
94 - 2018-12-07519Checkers2Senators4LSommaire du Match
95 - 2018-12-08534Checkers0Senators2LSommaire du Match
96 - 2018-12-09548Checkers3Marlies6LSommaire du Match
101 - 2018-12-14561Penguins9Checkers0LSommaire du Match
102 - 2018-12-15575Penguins7Checkers1LSommaire du Match
105 - 2018-12-18594Sound Tigers6Checkers3LSommaire du Match
106 - 2018-12-19595Sound Tigers3Checkers1LSommaire du Match
109 - 2018-12-22618Marlies5Checkers4LSommaire du Match
110 - 2018-12-23631Marlies6Checkers5LXSommaire du Match
115 - 2018-12-28658Checkers0Bruins4LSommaire du Match
116 - 2018-12-29670Checkers2Wolf Pack4LSommaire du Match
122 - 2019-01-04684Bruins8Checkers2LSommaire du Match
123 - 2019-01-05699Bruins7Checkers0LSommaire du Match
126 - 2019-01-08713Monsters4Checkers3LXSommaire du Match
127 - 2019-01-09716Monsters5Checkers1LSommaire du Match
130 - 2019-01-12737Checkers0Bears3LSommaire du Match
131 - 2019-01-13755Checkers0Bears5LSommaire du Match
136 - 2019-01-18772Checkers-Thunderbirds-
137 - 2019-01-19786Checkers-Bruins-
138 - 2019-01-20794Checkers-Sound Tigers-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
143 - 2019-01-25816Phantoms-Checkers-
144 - 2019-01-26829Phantoms-Checkers-
150 - 2019-02-01858Devils-Checkers-
151 - 2019-02-02869Devils-Checkers-
157 - 2019-02-08902Checkers-Phantoms-
158 - 2019-02-09913Checkers-Phantoms-
164 - 2019-02-15946Americans-Checkers-
165 - 2019-02-16955Americans-Checkers-
168 - 2019-02-19973Crunch-Checkers-
169 - 2019-02-20978Crunch-Checkers-
172 - 2019-02-23995Wolf Pack-Checkers-
173 - 2019-02-241008Wolf Pack-Checkers-
176 - 2019-02-271018Checkers-Sound Tigers-
178 - 2019-03-011033Checkers-Wolf Pack-
179 - 2019-03-021039Checkers-Sound Tigers-
182 - 2019-03-051059Phantoms-Checkers-
183 - 2019-03-061063Phantoms-Checkers-
186 - 2019-03-091086Thunderbirds-Checkers-
187 - 2019-03-101097Thunderbirds-Checkers-
191 - 2019-03-141114Checkers-Monsters-
192 - 2019-03-151115Checkers-Monsters-



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
14 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
89,812$ 112,000$ 112,320$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 89,812$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 63 577$ 36,351$




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
13521470130087286-199240210030044142-98281260100043144-101787168255103330231122541939540832127625410868242166.61%1554372.26%0388128230.27%492164629.89%24683129.60%7284701828394569217
Total Saison Régulière521470130087286-199240210030044142-98281260100043144-101787168255103330231122541939540832127625410868242166.61%1554372.26%0388128230.27%492164629.89%24683129.60%7284701828394569217