Checkers
GP: 11 | W: 7 | L: 4
GF: 55 | GA: 43 | PP%: 22.97% | PK%: 71.43%
DG: Jean-Francois Chouinard | Morale : 45 | Moyenne d'Équipe : 63
La résolution de votre navigateur est trop petite pour cette page. Plusieurs informations sont cachées pour garder la page lisible.

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
1Michael Latta (R)X100.007138687980678075697674676654484178710242900,000$
2Jonathan Huberdeau (R) (A)X100.007325757474757577748873566649455573710224990,000$
3Aleksander Barkov (R) (C)X100.007550767873737475717873607145426459710204850,000$
4Tobias Rieder (R)X100.0073458072717176777573736569524559717002211,000,000$
5Quinton Howden (R)X100.007136757568687175667466646846454778680232500,000$
6Jerry D'Amigo (R)X100.006534787267776867667067656846463868660243700,000$
7Kerby Rychel (R)X100.008149687374607874636567606645445764660213700,000$
8Jake Virtanen (R)X100.007239696679567768636770515141416678640191500,000$
9Mitchell Heard (R)X100.007232646975566559586968655547473735630233500,000$
10Matt Puempel (R)X100.006438766865636566606167476844445668620224500,000$
11Roberts Lipsbergs (R)X100.006930705871676461726163516444455067600212400,000$
12Michael McCarron (R)X100.007444616277537959616460436142425678590202500,000$
13Aaron Ekblad (R)X100.007446847470767176517466735947418075710191500,000$
14Michal Jordan (R)X100.006624827274776970577370706365593668700252600,000$
15William ColbertX100.007237807066667765547165745878641878700301800,000$
16John Draeger (R)X100.006622757070666756397058805447464565670222500,000$
17Markus Nutivaara (R)X100.005735727360526785328756694744436834660212500,000$
18Niklas Hansson (R)X100.006616777261727057407146825148425376660202400,000$
Rayé
1Kyle Platzer (R)X100.004034676541645172566360567742424919590202400,000$
2Juuso Ikonen (R)X100.005028716558535167575860566042425319580202400,000$
3Anton Zlobin (R)X100.005721636356585661526167405545463919570221400,000$
4Connor Honey (R)X100.006457595267597640554247465642425120520202400,000$
5Lukas Vejdemo (R)X100.004229756152605248595254494842425820520192500,000$
6Dmytro Timashov (R)X100.004724654647455559556440425242425820490192500,000$
7Viktor Baldayev (R)X100.006848676273626864395746684643425419630202400,000$
8Keaton Thompson (R)X100.005636666956556860436854685243425720610202400,000$
9Nelson Nogier (R)X100.006337705863645953336154674141416020600191500,000$
10Jake Walman (R)X100.005832786667665852335738703641416620600191500,000$
MOYENNE D'ÉQUIPE100.00653572676664676555676161584745535063
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
1Garret Sparks100.0072718759647279556380714644487868X0
2Igor Bobkov100.00736366716970756767628048463478680
Rayé
1Marek Langhamer100.00614658626953555856497043435120570
MOYENNE D'ÉQUIPE100.0069607064676570606264744644445964
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Glen Gulutzan54607666334563CAN421350,000$


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
1Jonathan HuberdeauCheckers (Flo)LW11914236100211544151820.45%423020.91471114540000111035.71%14104012.0000000210
2Aleksander BarkovCheckers (Flo)C11517226120221235112014.29%421819.851910754000001146.42%32191002.0100000201
3Michael LattaCheckers (Flo)C11610165140212437133016.22%221219.341346450000411045.60%18293001.5000000100
4Tobias RiederCheckers (Flo)RW11105155401783332330.30%723121.0271813540000172056.25%1672001.3000000021
5Kerby RychelCheckers (Flo)LW1155105601561531033.33%216615.11112245000000040.00%531001.2000000021
6Markus NutivaaraCheckers (Flo)D1136961601024266811.54%1426023.6700046000005000.00%069000.6900000012
7Jerry D'AmigoCheckers (Flo)RW11549520151332111715.63%321219.352028460002440060.00%1032000.8500000100
8Quinton HowdenCheckers (Flo)C11156-140199141097.14%513111.97000010000281047.83%6931000.9100000000
9Michal JordanCheckers (Flo)D110665005129390.00%1020218.4101114400001000.00%046000.5900000000
10William ColbertCheckers (Flo)D11156480152114427.14%1022520.53033344000025000.00%023000.5300000000
11Jake VirtanenCheckers (Flo)RW11516080227203325.00%211410.3801106000000042.86%732011.0500000100
12Aaron EkbladCheckers (Flo)D11145520121920975.00%1928826.22022461000131100.00%0511000.3500000010
13Mitchell HeardCheckers (Flo)C1122400099141914.29%4918.3600000000060135.00%2070000.8700000000
14Niklas HanssonCheckers (Flo)D110331808183340.00%1717816.2000012000043000.00%007000.3400000000
15Michael McCarronCheckers (Flo)RW1111212093127100.00%2928.370000500000000.00%102000.4300000000
16Roberts LipsbergsCheckers (Flo)LW1110116013832833.33%0978.891012500002000.00%212000.2000000000
17Matt PuempelCheckers (Flo)LW11011-160857110.00%21029.27000000000000100.00%313000.2000000000
18John DraegerCheckers (Flo)D110111408166120.00%1817315.7400001000145000.00%0310000.1200000000
Stats d'équipe Total ou en Moyenne198559014554112024922933310118716.52%125322916.311728456553400043047246.15%6507669020.9000000775
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
1Igor BobkovCheckers (Flo)96200.8724.274780034266149100.000092000
2Garret SparksCheckers (Flo)41110.9042.951830099453000.000029000
Stats d'équipe Total ou en Moyenne137310.8813.906610043360202100.00001111000


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 RestantCap 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
Aaron EkbladCheckers (Flo)D191996-01-01Yes180 Lbs6 ft0NoNoNo1Pro & Farm500,000$0$0$No
Aleksander BarkovCheckers (Flo)C201995-01-01Yes185 Lbs6 ft4NoNoNo4Pro & Farm850,000$0$0$No1,750,000$3,000,000$4,250,000$
Anton ZlobinCheckers (Flo)RW221993-01-01Yes180 Lbs6 ft0NoNoNo1Pro & Farm400,000$0$0$No
Connor HoneyCheckers (Flo)RW201995-01-01Yes185 Lbs6 ft1NoNoNo2Pro & Farm400,000$0$0$No500,000$
Dmytro TimashovCheckers (Flo)LW191996-01-01Yes192 Lbs5 ft10NoNoNo2Pro & Farm500,000$0$0$No500,000$
Garret SparksCheckers (Flo)G221993-01-01No207 Lbs6 ft2NoYesNo2Pro & Farm999,999$0$0$No999,999$
Igor BobkovCheckers (Flo)G241991-01-01No235 Lbs6 ft5NoNoNo2Pro & Farm600,000$0$0$No600,000$
Jake VirtanenCheckers (Flo)RW191996-01-01Yes226 Lbs6 ft1NoNoNo1Pro & Farm500,000$0$0$No
Jake WalmanCheckers (Flo)D191996-01-01Yes180 Lbs6 ft0NoNoNo1Pro & Farm500,000$0$0$No
Jerry D'AmigoCheckers (Flo)RW241991-01-01Yes208 Lbs5 ft11NoNoNo3Pro & Farm700,000$0$0$No800,000$850,000$
John DraegerCheckers (Flo)D221993-01-01Yes186 Lbs6 ft1NoNoNo2Pro & Farm500,000$0$0$No500,000$
Jonathan HuberdeauCheckers (Flo)LW221993-01-01Yes188 Lbs6 ft1NoNoNo4Pro & Farm990,000$0$0$No3,000,000$3,000,000$3,500,000$
Juuso IkonenCheckers (Flo)LW201995-01-01Yes185 Lbs5 ft9NoNoNo2Pro & Farm400,000$0$0$No500,000$
Keaton ThompsonCheckers (Flo)D201995-01-01Yes185 Lbs6 ft0NoNoNo2Pro & Farm400,000$0$0$No500,000$
Kerby RychelCheckers (Flo)LW211994-01-01Yes185 Lbs6 ft1NoNoNo3Pro & Farm700,000$0$0$No800,000$900,000$
Kyle PlatzerCheckers (Flo)C201995-01-01Yes185 Lbs5 ft11NoNoNo2Pro & Farm400,000$0$0$No500,000$
Lukas VejdemoCheckers (Flo)C191996-01-01Yes194 Lbs6 ft4NoNoNo2Pro & Farm500,000$0$0$No500,000$
Marek LanghamerCheckers (Flo)G211994-01-01No181 Lbs6 ft1NoNoNo1Pro & Farm300,000$0$0$No
Markus NutivaaraCheckers (Flo)D211994-01-01Yes187 Lbs6 ft1NoNoNo2Pro & Farm500,000$0$0$No500,000$
Matt PuempelCheckers (Flo)LW221993-01-01Yes190 Lbs6 ft0NoNoNo4Pro & Farm500,000$0$0$No550,000$600,000$600,000$
Michael LattaCheckers (Flo)C241991-01-01Yes209 Lbs6 ft0NoNoNo2Pro & Farm900,000$0$0$No1,000,000$
Michael McCarronCheckers (Flo)RW201995-01-01Yes185 Lbs6 ft5NoNoNo2Pro & Farm500,000$0$0$No500,000$
Michal JordanCheckers (Flo)D251990-01-01Yes195 Lbs6 ft1NoNoNo2Pro & Farm600,000$0$0$No600,000$
Mitchell HeardCheckers (Flo)C231992-01-01Yes188 Lbs6 ft0NoNoNo3Pro & Farm500,000$0$0$No500,000$500,000$
Nelson NogierCheckers (Flo)D191996-01-01Yes191 Lbs6 ft2NoNoNo1Pro & Farm500,000$0$0$No
Niklas HanssonCheckers (Flo)D201995-01-01Yes185 Lbs6 ft1NoNoNo2Pro & Farm400,000$0$0$No500,000$
Quinton HowdenCheckers (Flo)C231992-01-01Yes189 Lbs6 ft2NoNoNo2Pro & Farm500,000$0$0$No600,000$
Roberts LipsbergsCheckers (Flo)LW211994-01-01Yes185 Lbs5 ft11NoNoNo2Pro & Farm400,000$0$0$No500,000$
Tobias RiederCheckers (Flo)RW221993-01-01Yes185 Lbs5 ft11NoNoNo1Pro & Farm1,000,000$0$0$No
Viktor BaldayevCheckers (Flo)D201995-01-01Yes185 Lbs6 ft3NoNoNo2Pro & Farm400,000$0$0$No500,000$
William ColbertCheckers (Flo)D301985-01-01No210 Lbs6 ft2NoNoNo1Pro & Farm800,000$0$0$No
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
3121.39192 Lbs6 ft12.03569,032$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Jonathan HuberdeauAleksander BarkovTobias Rieder40023
2Kerby RychelMichael LattaJerry D'Amigo30023
3Matt PuempelQuinton HowdenJake Virtanen20032
4Roberts LipsbergsMitchell HeardMichael McCarron10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Aaron EkbladMarkus Nutivaara40023
2Michal JordanWilliam Colbert30023
3John DraegerNiklas Hansson20122
4Aaron EkbladMarkus Nutivaara10023
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Jonathan HuberdeauAleksander BarkovTobias Rieder60014
2Kerby RychelMichael LattaJerry D'Amigo40014
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Aaron EkbladMarkus Nutivaara60023
2Michal JordanWilliam Colbert40023
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Michael LattaJerry D'Amigo60131
2Quinton HowdenTobias Rieder40131
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1John DraegerNiklas Hansson60140
2William ColbertAaron Ekblad40140
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Michael Latta60131John DraegerNiklas Hansson60140
2Mitchell Heard40131William ColbertAaron Ekblad40140
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Aleksander BarkovJonathan Huberdeau60023
2Michael LattaTobias Rieder40023
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Aaron EkbladMarkus Nutivaara60023
2William ColbertMichal Jordan40023
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Jonathan HuberdeauAleksander BarkovTobias RiederAaron EkbladMarkus Nutivaara
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Kerby RychelMichael LattaTobias RiederJohn DraegerNiklas Hansson
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Jake Virtanen, Jonathan Huberdeau, Aleksander BarkovJake Virtanen, Jonathan HuberdeauMichael Latta
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Niklas Hansson, Markus Nutivaara, Aaron EkbladWilliam ColbertAaron Ekblad, Niklas Hansson
Tirs de Pénalité
Aleksander Barkov, Tobias Rieder, Quinton Howden, Michael Latta, Jonathan Huberdeau
Gardien
#1 : Igor Bobkov, #2 : Garret Sparks


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
1Comets4400000024131122000000125722000000128481.0002441650017211611181191061053127423810430930.00%19573.68%011123148.05%10623844.54%8318145.86%234125226104201104
2Heat7340000031301312000001011-1422000002119260.4293149800017211612151191061053233837414544818.18%371170.27%011123148.05%10623844.54%8318145.86%234125226104201104
Total11740000055431253200000221666420000033276140.63655901450017211613331191061053360125112249741722.97%561671.43%011123148.05%10623844.54%8318145.86%234125226104201104
_Since Last GM Reset11740000055431253200000221666420000033276140.63655901450017211613331191061053360125112249741722.97%561671.43%011123148.05%10623844.54%8318145.86%234125226104201104
_Vs Conference11740000055431253200000221666420000033276140.63655901450017211613331191061053360125112249741722.97%561671.43%011123148.05%10623844.54%8318145.86%234125226104201104
_Vs Division7340000031301312000001011-1422000002119260.4293149800017211612151191061053233837414544818.18%371170.27%011123148.05%10623844.54%8318145.86%234125226104201104

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
1114L1559014533336012511224900
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
117400005543
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
53200002216
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
64200003327
Derniers 10 Matchs
WLOTWOTL SOWSOL
531100
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
741722.97%561671.43%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
11910610531721161
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
11123148.05%10623844.54%8318145.86%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
234125226104201104


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
1 - 2020-10-098Checkers5Comets4WR1Sommaire du Match
2 - 2020-10-1016Checkers7Comets4WSommaire du Match
3 - 2020-10-1124Comets2Checkers5WR1Sommaire du Match
4 - 2020-10-1232Comets3Checkers7WSommaire du Match
8 - 2020-10-1659Checkers5Heat6LXR2Sommaire du Match
9 - 2020-10-1763Checkers7Heat4WSommaire du Match
10 - 2020-10-1867Heat4Checkers3LR2Sommaire du Match
11 - 2020-10-1971Heat4Checkers1LSommaire du Match
12 - 2020-10-2075Checkers6Heat5WXR2Sommaire du Match
13 - 2020-10-2179Heat3Checkers6WSommaire du Match
14 - 2020-10-2283Checkers3Heat4LR2Sommaire du Match



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des Billets3020
Assistance10,0004,395
Assistance PCT100.00%87.90%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacitéPopularité de l'Équipe
35 2879 - 95.97% 81,459$407,295$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
0$ 1,764,000$ 1,689,000$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 0$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 0 0$ 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