Admirals

DG: Jean-François Dos Santos Morale : 82 Moyenne d'Équipe : 47
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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
1Andy Andreoff (R)XX100.00626668707256515074425863483532079550
2Rocco GrimaldiXX100.00503590764261405471466164483532079550
3Ryan ReavesX100.00836169677852624550414854485245079540
4Brock McGinn (R)XX100.00533583725852375035415864483532079530
5Dennis EverbergXX100.00573592667257444636464666483532079530
6Tye McGinnXX100.00584385697258384735454859484338079530
7Will ActonX100.00505073657154344166384466474136080510
8Luke GazdicX100.00666174628049424235384658484336058510
9Joshua Ho-Sang (R)XXX100.00505050505250505050505050503230075490
10Zack Phillips (R)XX100.00505050505350505050505050503230071490
11Jordan Weal (R)XX100.00483590685450363549353565483532080480
12Michael Keranen (R)XXX100.00493595725146353535353556483532047460
13Raphael Bussieres (R)XX100.00454545456745454545454545453230050460
14Remi Elie (R)X100.00454545457045454545454545453230067460
15Connor Chatham (R)X100.00434343437743434343434343433230053450
16Cameron Abney (R)X100.00434343437243434343434343433230026440
17Josh Birkholz (R)X100.00434343436343434343434343433230028440
18Raphael DiazX100.00493588676358634335434365444640079560
19Gustav Olofsson (R)X100.00493595626545353135303263483532079500
20Ryan Collins (R)X100.00454545456845454545454545453230079460
21Jonathan-Ismael Diaby (R)X100.00434343437643434343434343433230079450
22Justin Sefton (R)X100.00434343436943434343434343433230079450
23Drew Olson (R)X100.00404040407040404040404040403230033420
Rayé
1Christophe Lalancette (R)X100.00404040405040404040404040403230020410
2Emil Molin (R)XX100.00404040405040404040404040403230020410
3Philip McRaeX100.00329228446933363335333347473532020400
4Rhett RakhshaniX100.00339030335533473335333333473734020370
5Shawn LalondeX100.00328733336733463335333333473532020380
MOYENNE D'ÉQUIPE100.0048495953654743424441445146353205847
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
1Christopher Gibson100.0045456268464545464565703532063490
2Alexander Pechurski100.0034363668353635363632313532079390
Rayé
MOYENNE D'ÉQUIPE100.004041496841414041414951353207144
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Peter Horachek77667554937673CAN562500,000$


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'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
1Rocco GrimaldiAdmirals (Ana)C/RW80557513040140141823290016.72%22149618.71172946102353224102067359.26%76100121.74130001178
2Andy AndreoffAdmirals (Ana)C/LW8234661003083251501772640012.88%18142817.421019295528201141443065.55%80400011.4012212752
3Brock McGinnAdmirals (Ana)LW/RW8239357427220311332500015.60%2294811.5743722540000253350.00%7200001.5600000713
4Will ActonAdmirals (Ana)C82224971111810261441930011.40%16102412.496131921101000034262.20%111900001.3900101423
5Ryan ReavesAdmirals (Ana)RW82204868814725244691890010.58%20127315.5371320482220002543154.17%9600101.0700014143
6Dennis EverbergAdmirals (Ana)LW/RW8224386210200411042050011.71%16109113.3154925119000043349.02%5100001.1400000222
7Raphael DiazAdmirals (Ana)D711339523328069621130011.50%105162722.9251621673140112229100.00%000000.6400000123
8Tye McGinnAdmirals (Ana)LW/RW82192443-116032961780010.67%1792011.23391222800000453132.53%8300000.9300000033
9Gustav OlofssonAdmirals (Ana)D81132639163805740800016.25%84169720.968715433110001267100.00%000000.4600000012
10Jordan WealAdmirals (Ana)C/RW761117281131522841000011.00%257589.99112160000111152.36%27500000.7400000021
11Justin SeftonAdmirals (Ana)D82514194364013011250020.00%45143117.46314101450110181100.00%000000.2700000001
12Joshua Ho-SangAdmirals (Ana)C/LW/RW678101872754236640012.50%24586.8400000000002044.98%28900000.7900001021
13Zack PhillipsAdmirals (Ana)C/RW56414185160423649008.16%64157.430111100000101049.87%39300000.8711000011
14Jonathan-Ismael DiabyAdmirals (Ana)D82691531861010815170035.29%47141317.2441581450110162100.00%000000.2100010011
15Luke GazdicAdmirals (Ana)LW45761314353817410017.07%82866.370002120000120039.13%2300000.9100100200
16Ryan CollinsAdmirals (Ana)D82191021121151432129003.45%51164320.04123183060000243000.00%000000.1200111000
17Raphael BussieresAdmirals (Ana)LW/RW32246380106130015.38%31544.84000190001380035.29%1700000.7700000000
18Drew OlsonAdmirals (Ana)D29235526045470028.57%1848416.69000125000052000.00%000000.2100000001
19Michael KeranenAdmirals (Ana)C/LW/RW27101-1002360016.67%3612.29101580000340037.50%2400000.3200000000
20Remi ElieAdmirals (Ana)LW52011-440544000.00%41072.06000180000610044.00%2500000.1900000000
21Connor ChathamAdmirals (Ana)RW30000320361000.00%0983.27000160000620059.46%3700000.0000000000
22Shawn LalondeAdmirals (Ana)D9000-422101300000.00%214616.2300006000027000.00%000000.0000200000
Stats d'équipe Total ou en Moyenne13932864877732958361101267125021570013.26%5341896813.62751191944542532268201880341457.53%406900230.8236749353335
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
1Christopher GibsonAdmirals (Ana)73511250.8703.3641752023417940500.0000735111
2Alexander PechurskiAdmirals (Ana)148400.8484.2961620442890001.0005775000
Stats d'équipe Total ou en Moyenne87591650.8673.4847914027820830501.00058080111


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 Ballotage Forcé Contrat StatusType Salaire Actuel Salaire RestantSalaire Année 2 Salaire Année 3 Salaire Année 4 Salaire Année 5 Salaire Année 6 Salaire Année 7 Salaire Année 8 Salaire Année 9 Salaire Année 10 Link
Alexander PechurskiAdmirals (Ana)G251990-06-04No187 Lbs6 ft0NoNo2Avec RestrictionPro & Farm600,000$600,000$Lien
Andy AndreoffAdmirals (Ana)C/LW241991-05-17Yes207 Lbs6 ft1NoNo3Avec RestrictionPro & Farm550,000$550,000$550,000$Lien
Brock McGinnAdmirals (Ana)LW/RW211994-02-02Yes185 Lbs6 ft0NoNo4Contrat d'EntréePro & Farm843,000$843,000$843,000$843,000$Lien
Cameron AbneyAdmirals (Ana)RW241991-05-23Yes205 Lbs6 ft5NoNo4Avec RestrictionPro & Farm700,000$700,000$700,000$700,000$Lien
Christophe LalancetteAdmirals (Ana)RW211994-05-06Yes162 Lbs5 ft11NoNo4Contrat d'EntréePro & Farm650,000$650,000$650,000$650,000$Lien
Christopher GibsonAdmirals (Ana)G221992-12-27No188 Lbs6 ft1NoNo4Avec RestrictionPro & Farm675,000$675,000$675,000$675,000$Lien
Connor ChathamAdmirals (Ana)RW191995-10-30Yes222 Lbs6 ft2NoNo4Contrat d'EntréePro & Farm700,000$700,000$700,000$700,000$Lien
Dennis EverbergAdmirals (Ana)LW/RW231991-12-31No205 Lbs6 ft4NoNo3Avec RestrictionPro & Farm600,000$600,000$600,000$Lien
Drew OlsonAdmirals (Ana)D251990-04-04Yes215 Lbs5 ft11NoNo4Avec RestrictionPro & Farm650,000$650,000$650,000$650,000$Lien
Emil MolinAdmirals (Ana)C/LW221993-02-03Yes170 Lbs6 ft0NoNo4Avec RestrictionPro & Farm625,000$625,000$625,000$625,000$Lien
Gustav OlofssonAdmirals (Ana)D201994-12-01Yes197 Lbs6 ft3NoNo4Contrat d'EntréePro & Farm767,000$767,000$767,000$767,000$Lien
Jonathan-Ismael DiabyAdmirals (Ana)D201994-11-16Yes223 Lbs6 ft5NoNo4Contrat d'EntréePro & Farm637,000$637,000$637,000$637,000$Lien
Jordan WealAdmirals (Ana)C/RW231992-04-15Yes179 Lbs5 ft10NoNo4Avec RestrictionPro & Farm632,000$632,000$632,000$632,000$Lien
Josh BirkholzAdmirals (Ana)RW241991-03-28Yes190 Lbs6 ft1NoNo4Avec RestrictionPro & Farm700,000$700,000$700,000$700,000$Lien
Joshua Ho-SangAdmirals (Ana)C/LW/RW191996-01-22Yes175 Lbs5 ft11NoNo4Contrat d'EntréePro & Farm925,000$925,000$925,000$925,000$Lien
Justin SeftonAdmirals (Ana)D221993-04-14Yes209 Lbs6 ft2NoNo4Avec RestrictionPro & Farm700,000$700,000$700,000$700,000$Lien
Luke GazdicAdmirals (Ana)LW261989-07-25No225 Lbs6 ft4NoNo2Avec RestrictionPro & Farm635,000$635,000$Lien
Michael KeranenAdmirals (Ana)C/LW/RW251990-01-04Yes170 Lbs6 ft1NoNo4Avec RestrictionPro & Farm675,000$675,000$675,000$675,000$Lien
Philip McRaeAdmirals (Ana)C251990-03-15No200 Lbs6 ft2NoNo3Avec RestrictionPro & Farm900,000$900,000$900,000$Lien
Raphael BussieresAdmirals (Ana)LW/RW211993-11-05Yes195 Lbs6 ft1NoNo4Contrat d'EntréePro & Farm818,000$818,000$818,000$818,000$Lien
Raphael DiazAdmirals (Ana)D291986-01-09No197 Lbs5 ft11NoNo1Avec RestrictionPro & Farm700,000$Lien
Remi ElieAdmirals (Ana)LW201995-04-16Yes203 Lbs6 ft0NoNo4Contrat d'EntréePro & Farm767,000$767,000$767,000$767,000$Lien
Rhett RakhshaniAdmirals (Ana)RW271988-03-06No182 Lbs5 ft10NoNo1Avec RestrictionPro & Farm900,000$Lien
Rocco GrimaldiAdmirals (Ana)C/RW221993-02-08No160 Lbs5 ft6NoNo3Avec RestrictionPro & Farm925,000$925,000$925,000$Lien
Ryan CollinsAdmirals (Ana)D191996-05-06Yes202 Lbs6 ft5NoNo4Contrat d'EntréePro & Farm825,000$825,000$825,000$825,000$Lien
Ryan ReavesAdmirals (Ana)RW281987-01-20No224 Lbs6 ft1NoNo3Avec RestrictionPro & Farm750,000$750,000$750,000$Lien
Shawn LalondeAdmirals (Ana)D251990-03-10No204 Lbs6 ft1NoNo1Avec RestrictionPro & Farm525,000$Lien
Tye McGinnAdmirals (Ana)LW/RW251990-07-29No205 Lbs6 ft3NoNo1Avec RestrictionPro & Farm775,000$Lien
Will Acton (Sur la Masse Salariale)Admirals (Ana)C281987-07-16No202 Lbs6 ft3NoNo2Avec RestrictionPro & Farm500,000$500,000$Lien
Zack PhillipsAdmirals (Ana)C/RW221992-10-28Yes175 Lbs6 ft0NoNo4Avec RestrictionPro & Farm833,000$833,000$833,000$833,000$Lien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
3023.20195 Lbs6 ft13.23716,067$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Andy AndreoffRocco GrimaldiRyan Reaves40122
2Tye McGinnWill ActonBrock McGinn30122
3Dennis EverbergZack PhillipsJoshua Ho-Sang20122
4Luke GazdicJordan WealRaphael Bussieres10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Raphael DiazGustav Olofsson40122
2Ryan CollinsJustin Sefton30122
3Jonathan-Ismael DiabyDrew Olson20122
4Raphael DiazGustav Olofsson10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Andy AndreoffRocco GrimaldiRyan Reaves60122
2Tye McGinnWill ActonBrock McGinn40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Raphael DiazGustav Olofsson60122
2Ryan CollinsJustin Sefton40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Rocco GrimaldiAndy Andreoff60122
2Ryan ReavesTye McGinn40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Raphael DiazGustav Olofsson60122
2Ryan CollinsJustin Sefton40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Rocco Grimaldi60122Raphael DiazGustav Olofsson60122
2Andy Andreoff40122Ryan CollinsJustin Sefton40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Rocco GrimaldiAndy Andreoff60122
2Ryan ReavesTye McGinn40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Raphael DiazGustav Olofsson60122
2Ryan CollinsJustin Sefton40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Andy AndreoffRocco GrimaldiRyan ReavesRaphael DiazGustav Olofsson
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Andy AndreoffRocco GrimaldiRyan ReavesRaphael DiazGustav Olofsson
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Remi Elie, Michael Keranen, Connor ChathamRemi Elie, Michael KeranenConnor Chatham
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Jonathan-Ismael Diaby, Drew Olson, Ryan CollinsJonathan-Ismael DiabyDrew Olson, Ryan Collins
Tirs de Pénalité
Rocco Grimaldi, Andy Andreoff, Ryan Reaves, Tye McGinn, Brock McGinn
Gardien
#1 : Christopher Gibson, #2 : Alexander Pechurski


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
Falcons220000001055110000006421100000041341.00010172700164140104794954978101334395144217211.76%7185.71%11613283057.00%1268227155.83%855146058.56%2075142418066151071552
Bruins220000001275110000006421100000063341.00012223400164140104765954978101334411114378225.00%7271.43%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Crunch2200000015510110000009271100000063341.000152540001641401047769549781013346716183012433.33%4250.00%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Heat5410000026188220000001257321000001413180.8002640660016414010471569549781013341094448104301033.33%24483.33%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Phantoms20001100990100010004311000010056-130.750916250016414010478495497810133478262834800.00%12375.00%11613283057.00%1268227155.83%855146058.56%2075142418066151071552
Baby Hawks32100000131212110000089-11100000053240.6671321340016414010478395497810133411817466012541.67%17476.47%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Monsters301002001014-420100100811-31000010023-120.333101727001641401047809549781013348430304715320.00%14285.71%11613283057.00%1268227155.83%855146058.56%2075142418066151071552
Jayhawks311001001314-11100000052320100100812-430.500132134001641401047949549781013348418304816318.75%15286.67%11613283057.00%1268227155.83%855146058.56%2075142418066151071552
Cougars211000009721010000023-11100000074320.50091524001641401047669549781013344517183114321.43%9188.89%11613283057.00%1268227155.83%855146058.56%2075142418066151071552
Oil Kings550000003413213300000023914220000001147101.00034609400164140104722395497810133411432469828932.14%18666.67%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Sound Tigers2110000089-11010000036-31100000053220.50081321001641401047669549781013346215274515320.00%11281.82%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Monarchs522000102222031100010151412110000078-160.60022355700164140104713595497810133415031729524625.00%31680.65%11613283057.00%1268227155.83%855146058.56%2075142418066151071552
Minnesota33000000171071100000062422000000118361.0001729460016414010471389549781013347516306021314.29%15380.00%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Spiders21100000954110000007251010000023-120.50091625001641401047719549781013345621224513430.77%11190.91%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Senators21100000761110000005141010000025-320.500714210016414010475695497810133437516291300.00%8275.00%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Manchots210000101165110000008441000001032141.00011172800164140104767954978101334601539375240.00%12191.67%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Wolf Pack220000001477110000007341100000074341.0001426400016414010471249549781013345415164916425.00%6350.00%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Sharks5410000024131122000000114732100000139480.800244266001641401047157954978101334135598510525624.00%40587.50%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Chiefs321000001293211000008711100000042240.66712223400164140104788954978101334752533678450.00%12375.00%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Thunder220000001174110000005321100000064241.000112233001641401047849549781013345416184214428.57%9188.89%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Marlies2200000013310110000008261100000051441.0001322350016414010471059549781013343620103913323.08%40100.00%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Comets530011002516921000100743320010001812690.900254671001641401047190954978101334149346612033618.18%28389.29%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Cabaret Lady Mary Ann211000009901010000045-11100000054120.5009152400164140104781954978101334541819467114.29%70100.00%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
IceCaps311001001013-3110000005412010010059-430.500101727001641401047969549781013349015394314321.43%16475.00%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Chill42100010191362110000085321000010118360.75019325100164140104715195497810133410233538428828.57%20385.00%11613283057.00%1268227155.83%855146058.56%2075142418066151071552
Rocket2100100014113110000007521000100076141.00014243800164140104777954978101334597363712216.67%11463.64%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Bears22000000945110000004221100000052341.00091625001641401047649549781013343614294512216.67%12191.67%11613283057.00%1268227155.83%855146058.56%2075142418066151071552
Caroline21100000880110000003211010000056-120.50081220001641401047719549781013344316223813215.38%10190.00%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Las Vegas330000002111102200000016791100000054161.0002140610016414010471269549781013346423535116850.00%19384.21%01613283057.00%1268227155.83%855146058.56%2075142418066151071552
Vs Conference37238012301751264918133010109459351910500220816714560.75717530548000164140104713149549781013349942814727322124621.70%2023582.67%51613283057.00%1268227155.83%855146058.56%2075142418066151071552
Since Last GM Reset825416036304142861284129801210220134864125802420194152421260.768414714112800164140104729689549781013342170614977160846211224.24%4097382.15%81613283057.00%1268227155.83%855146058.56%2075142418066151071552
Total825416036304142861284129801210220134864125802420194152421260.768414714112800164140104729689549781013342170614977160846211224.24%4097382.15%81613283057.00%1268227155.83%855146058.56%2075142418066151071552

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
82126L2414714112829682170614977160800
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
8254163630414286
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
412981210220134
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
412582420194152
Derniers 10 Matchs
WLOTWOTL SOWSOL
621100
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
46211224.24%4097382.15%8
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
9549781013341641401047
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
1613283057.00%1268227155.83%855146058.56%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
2075142418066151071552


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
2 - 2016-10-139Admirals5Jayhawks6LXSommaire du Match
4 - 2016-10-1522Admirals3Manchots2WXXSommaire du Match
5 - 2016-10-1630Admirals5Sound Tigers3WSommaire du Match
7 - 2016-10-1838Admirals2Spiders3LSommaire du Match
9 - 2016-10-2052Admirals5Phantoms6LXSommaire du Match
12 - 2016-10-2378Comets1Admirals5WSommaire du Match
14 - 2016-10-2593Admirals1Sharks3LSommaire du Match
15 - 2016-10-2696Las Vegas2Admirals7WSommaire du Match
17 - 2016-10-28112Falcons4Admirals6WSommaire du Match
21 - 2016-11-01138Admirals4Monarchs3WSommaire du Match
22 - 2016-11-02145Manchots4Admirals8WSommaire du Match
24 - 2016-11-04158Chill1Admirals5WSommaire du Match
26 - 2016-11-06179Heat4Admirals6WSommaire du Match
29 - 2016-11-09193Admirals4Falcons1WSommaire du Match
30 - 2016-11-10205Admirals5Caroline6LSommaire du Match
32 - 2016-11-12213Admirals5Las Vegas4WSommaire du Match
35 - 2016-11-15242Oil Kings2Admirals8WSommaire du Match
37 - 2016-11-17251Spiders2Admirals7WSommaire du Match
40 - 2016-11-20275Monarchs1Admirals5WSommaire du Match
42 - 2016-11-22284Sound Tigers6Admirals3LSommaire du Match
45 - 2016-11-25311Baby Hawks2Admirals3WSommaire du Match
46 - 2016-11-26321Admirals8Sharks4WSommaire du Match
49 - 2016-11-29340Rocket5Admirals7WSommaire du Match
51 - 2016-12-01356Admirals3Comets2WSommaire du Match
53 - 2016-12-03365Admirals7Oil Kings1WSommaire du Match
54 - 2016-12-04375Admirals3Heat2WSommaire du Match
57 - 2016-12-07393Caroline2Admirals3WSommaire du Match
59 - 2016-12-09410Sharks1Admirals5WSommaire du Match
61 - 2016-12-11421Senators1Admirals5WSommaire du Match
63 - 2016-12-13435Admirals3Jayhawks6LSommaire du Match
65 - 2016-12-15445Admirals6Bruins3WSommaire du Match
67 - 2016-12-17464Admirals7Cougars4WSommaire du Match
69 - 2016-12-19478Admirals5Marlies1WSommaire du Match
70 - 2016-12-20485Admirals7Rocket6WXSommaire du Match
72 - 2016-12-22499Admirals2Senators5LSommaire du Match
77 - 2016-12-27526Sharks3Admirals6WSommaire du Match
79 - 2016-12-29541Admirals4Heat5LSommaire du Match
80 - 2016-12-30547Admirals6Comets2WSommaire du Match
82 - 2017-01-01561Phantoms3Admirals4WXSommaire du Match
85 - 2017-01-04574Cougars3Admirals2LSommaire du Match
87 - 2017-01-06590Chill4Admirals3LSommaire du Match
89 - 2017-01-08608Minnesota2Admirals6WSommaire du Match
91 - 2017-01-10618Jayhawks2Admirals5WSommaire du Match
93 - 2017-01-12630Admirals2Monsters3LXSommaire du Match
95 - 2017-01-14647Admirals4Chill2WSommaire du Match
96 - 2017-01-15654Chiefs6Admirals4LSommaire du Match
98 - 2017-01-17671Thunder3Admirals5WSommaire du Match
100 - 2017-01-19683Monsters5Admirals3LSommaire du Match
102 - 2017-01-21698Admirals8Minnesota6WSommaire du Match
104 - 2017-01-23708Admirals1IceCaps4LSommaire du Match
106 - 2017-01-25727Oil Kings5Admirals8WSommaire du Match
112 - 2017-01-31744Monsters6Admirals5LXSommaire du Match
115 - 2017-02-03769Admirals5Cabaret Lady Mary Ann4WSommaire du Match
116 - 2017-02-04776Admirals6Thunder4WSommaire du Match
119 - 2017-02-07791Admirals7Wolf Pack4WSommaire du Match
121 - 2017-02-09807Admirals6Crunch3WSommaire du Match
123 - 2017-02-11824Admirals5Bears2WSommaire du Match
126 - 2017-02-14840Admirals3Minnesota2WSommaire du Match
129 - 2017-02-17857Cabaret Lady Mary Ann5Admirals4LSommaire du Match
131 - 2017-02-19875Monarchs7Admirals8WXXSommaire du Match
132 - 2017-02-20881Admirals7Chill6WXXSommaire du Match
134 - 2017-02-22893Bruins4Admirals6WSommaire du Match
137 - 2017-02-25905Admirals3Monarchs5LSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
143 - 2017-03-03947Marlies2Admirals8WSommaire du Match
145 - 2017-03-05967Comets3Admirals2LXSommaire du Match
147 - 2017-03-07981Las Vegas5Admirals9WSommaire du Match
149 - 2017-03-09994Admirals5Baby Hawks3WSommaire du Match
150 - 2017-03-10998Admirals4Chiefs2WSommaire du Match
152 - 2017-03-121014Bears2Admirals4WSommaire du Match
155 - 2017-03-151036Chiefs1Admirals4WSommaire du Match
157 - 2017-03-171052Crunch2Admirals9WSommaire du Match
158 - 2017-03-181062Admirals4Sharks2WSommaire du Match
162 - 2017-03-221087Oil Kings2Admirals7WSommaire du Match
164 - 2017-03-241104IceCaps4Admirals5WSommaire du Match
166 - 2017-03-261121Wolf Pack3Admirals7WSommaire du Match
168 - 2017-03-281135Admirals9Comets8WXSommaire du Match
170 - 2017-03-301144Admirals4IceCaps5LXSommaire du Match
172 - 2017-04-011164Admirals4Oil Kings3WSommaire du Match
173 - 2017-04-021174Admirals7Heat6WSommaire du Match
175 - 2017-04-041184Heat1Admirals6WSommaire du Match
177 - 2017-04-061194Baby Hawks7Admirals5LSommaire du Match
180 - 2017-04-091227Monarchs6Admirals2LSommaire du Match



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité de l'Aréna20001000
Prix des Billets3520
Assistance6182331649
Assistance PCT75.39%77.19%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacité de l'ArénaPopularité de l'Équipe
0 2280 - 75.99% 66,929$2,744,080$3000100

Dépenses
Salaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
2,148,200$ 2,148,200$ 0$
Dépenses Annuelles à Ce JourCap Salarial Par JourCap salarial à ce jour
3,109,954$ 11,592$ 2,604,455$

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 0 14,631$ 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
Saison Régulière
201682541603630414286128412980121022013486412580242019415242108414714112800164140104729689549781013342170614977160846211224.24%4097382.15%81613283057.00%1268227155.83%855146058.56%2075142418066151071552
Total Saison Régulière82541603630414286128412980121022013486412580242019415242108414714112800164140104729689549781013342170614977160846211224.24%4097382.15%81613283057.00%1268227155.83%855146058.56%2075142418066151071552