Heat

GP: 82 | W: 19 | L: 59 | OTL: 4 | P: 42
GF: 253 | GA: 398 | PP%: 17.99% | PK%: 77.61%
DG: Martin Bétit | Morale : 50 | Moyenne d'Équipe : 50
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 Moyen
1Boo NievesX100.00796889767964705966625171485959050620253850,000$
2Oliver Wahlstrom (R)X100.00777581807563655850545864554444050590194925,001$
3Charles HudonXX100.00834995746660646333565756755959050590253750,000$
4Eric CornelXX100.00797393647279865367465567555151050590233850,000$
5Ryan Poehling (R)XX100.00794494776956675733545668254646050590203925,000$
6Mikhail Maltsev (R)XX100.00827793677762645670495866554444050580212650,000$
7Ryan MacInnisX100.00745295647160805450575164254444050570233850,000$
8Rasmus Kupari (R)X100.00756989656955565366435862554444050550194894,167$
9Sergey TolchinskyXX100.00453592754846293235323165423734050440244625,000$
10Jonathan Dahlen (R)X100.00414545455539394145414145433230050410212925,000$
11Aapeli Rasanen (R)X100.00333737376733333337333337353230050360212525,000$
12Ville Heinola (R)X100.00766699806650505025473962374444050570184925,002$
13Nicolas Beaudin (R)X100.00696383666368744825394158394444050550194894,168$
14Julius Honka (R)X100.00483585645558403835393761474236050500233950,000$
15Jacob Moverare (R)X100.00364040406535353640363640383230050390212720,000$
16Anthony Florentino (R)X100.00313737377629293137313137333230050360243560,000$
17Ryan Pilon (R)X100.00323737376831313237323237343230050360221525,000$
Rayé
1Hudson FaschingX100.00817591657675785850536069605252050610242850,000$
2Nikolai Prokhorkin (R)X100.007343957869626265626859597547470506102600$
3Cole Ully (R)XX100.00313737374629293137313137333230050340242650,000$
MOYENNE D'ÉQUIPE100.0061537561675354474545465645424105051
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
1Michael Dipietro (R)100.0059678473586153625756304444050590
2Ilya Sorokin (R)100.0038434060373535353535343230050390
Rayé
1Andrey Makarov100.0039454964383942373550483532050420
2Colton Point (R)100.0034373684343333333333333230050390
MOYENNE D'ÉQUIPE100.004348527042424142404436363405045
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire


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
1Boo NievesHeat (Cal)C75277198-234201353163481082607.76%79174423.25516214918001181572156.72%229000021.1204000352
2Ryan PoehlingHeat (Cal)C/LW75403878-161609521338312425510.44%73163721.83711185918110191620138.10%14700120.9534000442
3Eric CornelHeat (Cal)C/RW75254772-153420111156314962247.96%55159621.297916531940004943061.43%22300000.9023202122
4Oliver WahlstromHeat (Cal)RW75264369-5533151491903601082367.22%57150620.095914591760111705147.65%17000010.9202012413
5Charles HudonHeat (Cal)LW/RW75263965-50360131133337772277.72%34152820.3741014571750002870029.52%10500000.8502000143
6Ryan MacInnisHeat (Cal)C75204363-4612062190210591619.52%54142719.04571234176000022245.91%191900000.8800000233
7Mikhail MaltsevHeat (Cal)C/LW60184159-1220105199248842317.26%39120420.08416205016000091665158.65%152600010.9817000223
8Rasmus KupariHeat (Cal)C75332154-484151292162927719811.30%64123616.4821313400000364054.34%134700000.8711001321
9Sergey TolchinskyHeat (Cal)LW/RW75103242-43009174179411015.59%62118215.770223221012480022.86%10500000.7101000100
10Nicolas BeaudinHeat (Cal)D75102232-336202066412426668.06%178182824.39224391930000137000.00%100000.3500000101
11Jonathan DahlenHeat (Cal)LW756915-5710056409428616.38%11120016.0100005000041043.66%7100000.2511000001
12Aapeli RasanenHeat (Cal)C75145-584401111342625.00%58113115.0800002000000033.78%14800000.0900000000
13Jacob MoverareHeat (Cal)D82235-4254015917268147.69%89145517.7510161380001108000.00%100000.0700000000
14Julius HonkaHeat (Cal)D22145040814249164.17%4552023.65000757000042000.00%000000.1900000001
15Anthony FlorentinoHeat (Cal)D75044-5024091611480.00%50118215.77000249000053000.00%100000.0700000000
16Ryan PilonHeat (Cal)D75044-3430011766590.00%63141418.8501121400001117000.00%100000.0600000000
Stats d'équipe Total ou en Moyenne1139245425670-5714644016741947296085620738.28%10112179719.144284126433189522437129222652.35%805500160.61825215222322
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
1Ilya SorokinHeat (Cal)56113620.8775.3527352024419780000.625165175101
2Michael DipietroHeat (Cal)2481510.9084.13129200899710201.0002240122
Stats d'équipe Total ou en Moyenne80195130.8874.9640282033329490200.667187575223


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
Aapeli RasanenHeat (Cal)C211998-06-01Yes196 Lbs6 ft0NoNoNo2Pro & Farm525,000$52,500$0$No525,000$Lien
Andrey MakarovHeat (Cal)G261993-04-20No178 Lbs6 ft1NoNoNo0Pro & Farm0$0$NoLien
Anthony FlorentinoHeat (Cal)D241995-01-30Yes227 Lbs6 ft1NoNoNo3Pro & Farm560,000$56,000$0$No560,000$560,000$Lien
Boo NievesHeat (Cal)C251994-01-23No212 Lbs6 ft3NoNoNo3Pro & Farm850,000$85,000$0$No850,000$850,000$Lien
Charles HudonHeat (Cal)LW/RW251994-06-23No188 Lbs5 ft10NoNoNo3Pro & Farm750,000$75,000$0$No750,000$750,000$Lien
Cole UllyHeat (Cal)LW/RW241995-02-20Yes164 Lbs5 ft10NoNoNo2Pro & Farm650,000$65,000$0$No650,000$Lien
Colton PointHeat (Cal)G211998-03-07Yes219 Lbs6 ft4NoNoNo2Pro & Farm525,000$52,500$0$No525,000$Lien
Eric CornelHeat (Cal)C/RW231996-04-11No194 Lbs6 ft2NoNoNo3Pro & Farm850,000$85,000$0$No850,000$850,000$Lien
Hudson FaschingHeat (Cal)RW241995-07-28No209 Lbs6 ft2YesNoNo2Pro & Farm850,000$85,000$0$No850,000$Lien
Ilya SorokinHeat (Cal)G241995-08-04Yes167 Lbs6 ft2NoNoNo2Pro & Farm900,000$100,000$0$No1,100,000$Lien
Jacob MoverareHeat (Cal)D211998-08-31Yes198 Lbs6 ft2NoNoNo2Pro & Farm720,000$72,000$0$No720,000$Lien
Jonathan DahlenHeat (Cal)LW211997-12-20Yes180 Lbs5 ft11NoNoNo2Pro & Farm925,000$92,500$0$No925,000$Lien
Julius HonkaHeat (Cal)D231995-12-03Yes180 Lbs5 ft11NoNoNo3Pro & Farm950,000$95,000$0$No950,000$950,000$Lien
Michael DipietroHeat (Cal)G201999-06-09Yes200 Lbs6 ft0NoNoNo3Pro & Farm792,500$79,250$0$No792,500$792,500$Lien
Mikhail MaltsevHeat (Cal)C/LW211998-03-12Yes205 Lbs6 ft3NoNoNo2Pro & Farm650,000$65,000$0$No650,000$Lien
Nicolas BeaudinHeat (Cal)D191999-10-07Yes174 Lbs5 ft11NoNoNo4Pro & Farm894,168$89,417$0$No894,168$894,168$894,168$Lien
Nikolai ProkhorkinHeat (Cal)C261993-09-17Yes183 Lbs6 ft2NoNoNo0Pro & Farm0$0$NoLien
Oliver WahlstromHeat (Cal)RW192000-06-12Yes205 Lbs6 ft1NoNoNo4Pro & Farm925,001$92,500$0$No925,001$925,001$925,001$Lien
Rasmus KupariHeat (Cal)C192000-03-15Yes185 Lbs6 ft1NoNoNo4Pro & Farm894,167$89,417$0$No894,167$894,167$894,167$Lien
Ryan MacInnisHeat (Cal)C231996-02-13No185 Lbs6 ft3NoNoNo3Pro & Farm850,000$85,000$0$No850,000$850,000$Lien
Ryan PilonHeat (Cal)D221996-10-10Yes206 Lbs6 ft2NoNoNo1Pro & Farm525,000$52,500$0$NoLien
Ryan PoehlingHeat (Cal)C/LW201999-01-02Yes183 Lbs6 ft2NoNoNo3Pro & Farm925,000$92,500$0$No925,000$925,000$Lien
Sergey TolchinskyHeat (Cal)LW/RW241995-02-03No170 Lbs5 ft8NoNoNo4Pro & Farm625,000$62,500$0$No625,000$625,000$625,000$Lien
Ville HeinolaHeat (Cal)D182001-03-02Yes181 Lbs5 ft11NoNoNo4Pro & Farm925,002$92,500$0$No925,002$925,002$925,002$Lien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2422.21191 Lbs6 ft12.54710,868$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Ryan PoehlingBoo NievesEric Cornel40122
2Charles HudonRyan MacInnisOliver Wahlstrom30122
3Sergey TolchinskyRasmus KupariJonathan Dahlen20122
4Jonathan DahlenAapeli RasanenBoo Nieves10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Nicolas Beaudin40122
2Jacob MoverareRyan Pilon30122
3Anthony FlorentinoAapeli Rasanen20122
4Nicolas Beaudin10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Ryan PoehlingBoo NievesEric Cornel60122
2Charles HudonRyan MacInnisOliver Wahlstrom40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Nicolas Beaudin60122
2Jacob MoverareRyan Pilon40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Boo NievesRyan Poehling60122
2Eric CornelCharles Hudon40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Nicolas Beaudin60122
2Jacob MoverareRyan Pilon40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Boo Nieves60122Nicolas Beaudin60122
2Ryan Poehling40122Jacob MoverareRyan Pilon40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Boo NievesRyan Poehling60122
2Eric CornelCharles Hudon40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Nicolas Beaudin60122
2Jacob MoverareRyan Pilon40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Ryan PoehlingBoo NievesEric CornelNicolas Beaudin
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Ryan PoehlingBoo NievesEric CornelNicolas Beaudin
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Rasmus Kupari, Sergey Tolchinsky, Oliver WahlstromRasmus Kupari, Sergey TolchinskyOliver Wahlstrom
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Anthony Florentino, Jacob Moverare, Ryan PilonAnthony FlorentinoJacob Moverare, Ryan Pilon
Tirs de Pénalité
Boo Nieves, Ryan Poehling, Eric Cornel, Charles Hudon, Oliver Wahlstrom
Gardien
#1 : , #2 : Ilya Sorokin


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
1Admirals403000101116-52010001068-22020000058-320.2501118290010182687154101010031009331516116829222.22%7271.43%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
2Baby Hawks30300000716-920200000512-71010000024-200.000711180010182687891010100310093314137315712216.67%12375.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
3Bears20200000512-71010000037-41010000025-300.000581300101826875410101003100933983422448225.00%9366.67%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
4Bruins22000000862110000005411100000032141.000814220010182687691010100310093381298296116.67%30100.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
5Cabaret Lady Mary Ann220000001486110000007341100000075241.00014233700101826871521010100310093384194562150.00%2150.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
6Caroline2020000039-61010000015-41010000024-200.00035800101826876010101003100933108261050300.00%40100.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
7Chiefs3210000011101211000007701100000043140.6671119300010182687113101010031009331403012611317.69%5180.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
8Chill30300000719-121010000036-320200000413-900.00071219001018268789101010031009331412222597228.57%10460.00%11397284449.12%1444296748.67%717144649.59%1771121721386001038488
9Comets413000001523-820200000711-421100000812-420.25015284300101826871481010100310093319050206514321.43%4175.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
10Cougars2020000059-41010000013-21010000046-200.000581300101826876110101003100933932194311218.18%220.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
11Crunch2010001079-21010000014-31000001065120.50071118001018268783101010031009339834163811218.18%80100.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
12Jayhawks404000001322-920200000912-320200000410-600.000132033001018268715610101003100933190582480800.00%12191.67%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
13Las Vegas41300000818-102020000039-62110000059-420.250815230010182687119101010031009331904726728112.50%110100.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
14Manchots20200000612-61010000034-11010000038-500.0006111700101826877510101003100933922112398112.50%6266.67%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
15Marlies2010000149-51010000015-41000000134-110.25045900101826877110101003100933822414618112.50%6183.33%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
16Minnesota302000011622-61010000057-2201000011115-410.167162541001018268715810101003100933154602267200.00%9455.56%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
17Monarchs413000001623-720200000713-621100000910-120.250162945001018268720710101003100933219621410710110.00%7357.14%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
18Monsters211000008801010000056-11100000032120.50081321001018268773101010031009337430642600.00%30100.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
19Monsters311001001112-11010000014-321000100108230.5001120310010182687110101010031009331515020717228.57%10370.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
20Oceanics30200100715-820200000512-71000010023-110.167713200010182687661010100310093313953226411327.27%11190.91%11397284449.12%1444296748.67%717144649.59%1771121721386001038488
21Oil Kings505000001132-2130300000717-1020200000415-1100.000111930001018268714810101003100933270954210013215.38%16475.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
22Phantoms2020000048-41010000046-21010000002-200.0004812001018268754101010031009331323614528337.50%20100.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
23Rocket20200000311-81010000015-41010000026-400.000358001018268772101010031009331012121427114.29%60100.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
24Senators2110000059-4110000003211010000027-520.500571200101826876710101003100933933516476350.00%8362.50%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
25Sharks404000001322-920200000510-520200000812-400.00013233600101826871611010100310093317850169311218.18%7185.71%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
26Sound Tigers21100000880110000005411010000034-120.500814220010182687801010100310093356178414250.00%4175.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
27Spiders2110000046-2110000003121010000015-420.50047110010182687681010100310093380361040200.00%4175.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
28Stars30300000814-61010000036-32020000058-300.000813210010182687107101010031009331713810711218.33%5180.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
29Thunder2110000067-1110000005321010000014-320.5006111700101826878210101003100933592314506116.67%6183.33%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
Total82175900222253398-1454183200010126197-714192700212127201-74420.25625343068300101826873037101010031009333820114548517522394317.99%2014577.61%21397284449.12%1444296748.67%717144649.59%1771121721386001038488
30Wolf Pack22000000936110000005141100000042241.000915240010182687911010100310093364264296116.67%2150.00%01397284449.12%1444296748.67%717144649.59%1771121721386001038488
_Since Last GM Reset82175900222253398-1454183200010126197-714192700212127201-74420.25625343068300101826873037101010031009333820114548517522394317.99%2014577.61%21397284449.12%1444296748.67%717144649.59%1771121721386001038488
_Vs Conference4273200111132215-83212190000058105-47215130011174110-36180.214132222354001018268715761010100310093320815862678731231814.63%1062180.19%01397284449.12%1444296748.67%717144649.59%1771121721386001038488

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
8242L4253430683303738201145485175200
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
8217590222253398
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
418320010126197
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
419270212127201
Derniers 10 Matchs
WLOTWOTL SOWSOL
370000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
2394317.99%2014577.61%2
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
1010100310093310182687
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
1397284449.12%1444296748.67%717144649.59%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
1771121721386001038488


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 - 2020-10-2311Heat5Monsters6LXSommaire du Match
4 - 2020-10-2529Comets4Heat1LSommaire du Match
7 - 2020-10-2842Monarchs7Heat5LSommaire du Match
9 - 2020-10-3055Heat3Stars4LSommaire du Match
11 - 2020-11-0174Heat0Las Vegas5LSommaire du Match
12 - 2020-11-0277Heat5Sharks7LSommaire du Match
14 - 2020-11-0488Phantoms6Heat4LSommaire du Match
16 - 2020-11-06103Cougars3Heat1LSommaire du Match
18 - 2020-11-08122Heat7Monarchs5WSommaire du Match
19 - 2020-11-09128Heat2Admirals3LSommaire du Match
21 - 2020-11-11142Bears7Heat3LSommaire du Match
23 - 2020-11-13153Cabaret Lady Mary Ann3Heat7WSommaire du Match
25 - 2020-11-15169Heat2Oceanics3LXSommaire du Match
28 - 2020-11-18183Heat2Caroline4LSommaire du Match
30 - 2020-11-20194Heat2Chill6LSommaire du Match
32 - 2020-11-22212Heat3Monsters2WSommaire du Match
33 - 2020-11-23218Heat2Bears5LSommaire du Match
35 - 2020-11-25231Jayhawks6Heat4LSommaire du Match
37 - 2020-11-27245Spiders1Heat3WSommaire du Match
39 - 2020-11-29261Chiefs4Heat3LSommaire du Match
43 - 2020-12-03284Stars6Heat3LSommaire du Match
46 - 2020-12-06301Heat2Jayhawks4LSommaire du Match
47 - 2020-12-07315Heat5Las Vegas4WSommaire du Match
49 - 2020-12-09328Monsters4Heat1LSommaire du Match
51 - 2020-12-11338Heat4Chiefs3WSommaire du Match
53 - 2020-12-13349Heat0Phantoms2LSommaire du Match
55 - 2020-12-15368Heat3Manchots8LSommaire du Match
57 - 2020-12-17377Heat6Crunch5WXXSommaire du Match
60 - 2020-12-20405Senators2Heat3WSommaire du Match
65 - 2020-12-25446Crunch4Heat1LSommaire du Match
67 - 2020-12-27462Monarchs6Heat2LSommaire du Match
69 - 2020-12-29471Heat5Monsters2WSommaire du Match
70 - 2020-12-30480Heat2Jayhawks6LSommaire du Match
72 - 2021-01-01494Marlies5Heat1LSommaire du Match
74 - 2021-01-03504Caroline5Heat1LSommaire du Match
77 - 2021-01-06531Manchots4Heat3LSommaire du Match
79 - 2021-01-08545Rocket5Heat1LSommaire du Match
82 - 2021-01-11566Heat2Stars4LSommaire du Match
83 - 2021-01-12570Heat5Minnesota8LSommaire du Match
87 - 2021-01-16590Heat2Oil Kings7LSommaire du Match
89 - 2021-01-18611Comets7Heat6LSommaire du Match
91 - 2021-01-20623Baby Hawks7Heat3LSommaire du Match
93 - 2021-01-22635Wolf Pack1Heat5WSommaire du Match
96 - 2021-01-25656Heat6Minnesota7LXXSommaire du Match
98 - 2021-01-27672Heat2Baby Hawks4LSommaire du Match
100 - 2021-01-29685Minnesota7Heat5LSommaire du Match
102 - 2021-01-31701Oil Kings4Heat1LSommaire du Match
104 - 2021-02-02709Heat2Rocket6LSommaire du Match
107 - 2021-02-05728Heat3Marlies4LXXSommaire du Match
109 - 2021-02-07746Heat2Senators7LSommaire du Match
119 - 2021-02-17775Chiefs3Heat4WSommaire du Match
120 - 2021-02-18780Heat2Oil Kings8LSommaire du Match
123 - 2021-02-21803Oil Kings7Heat3LSommaire du Match
126 - 2021-02-24823Sharks5Heat2LSommaire du Match
128 - 2021-02-26836Chill6Heat3LSommaire du Match
130 - 2021-02-28853Heat4Comets3WSommaire du Match
132 - 2021-03-02865Heat3Sharks5LSommaire du Match
134 - 2021-03-04878Heat2Monarchs5LSommaire du Match
135 - 2021-03-05890Heat3Admirals5LSommaire du Match
137 - 2021-03-07903Baby Hawks5Heat2LSommaire du Match
139 - 2021-03-09916Admirals5Heat2LSommaire du Match
143 - 2021-03-13945Bruins4Heat5WSommaire du Match
145 - 2021-03-15963Heat4Cougars6LSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
147 - 2021-03-17968Heat3Bruins2WSommaire du Match
149 - 2021-03-19991Heat2Chill7LSommaire du Match
151 - 2021-03-211001Heat1Thunder4LSommaire du Match
152 - 2021-03-221012Heat7Cabaret Lady Mary Ann5WSommaire du Match
155 - 2021-03-251030Monsters6Heat5LSommaire du Match
157 - 2021-03-271046Jayhawks6Heat5LSommaire du Match
159 - 2021-03-291061Las Vegas5Heat2LSommaire du Match
163 - 2021-04-021091Sound Tigers4Heat5WSommaire du Match
165 - 2021-04-041108Oceanics6Heat1LSommaire du Match
167 - 2021-04-061119Heat4Wolf Pack2WSommaire du Match
168 - 2021-04-071124Heat3Sound Tigers4LSommaire du Match
170 - 2021-04-091139Heat1Spiders5LSommaire du Match
172 - 2021-04-111162Thunder3Heat5WSommaire du Match
174 - 2021-04-131173Sharks5Heat3LSommaire du Match
176 - 2021-04-151187Admirals3Heat4WXXSommaire du Match
178 - 2021-04-171204Heat4Comets9LSommaire du Match
182 - 2021-04-211236Oceanics6Heat4LSommaire du Match
184 - 2021-04-231252Las Vegas4Heat1LSommaire du Match
186 - 2021-04-251269Oil Kings6Heat3LSommaire du Match



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité de l'Aréna20001000
Prix des Billets3515
Assistance78,62439,416
Assistance PCT95.88%96.14%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacité de l'ArénaPopularité de l'Équipe
0 2879 - 95.97% 81,539$3,343,080$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
1,614,767$ 1,706,084$ 1,716,084$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
9,226$ 1,624,797$ 22 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 0 9,172$ 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