Marlies

GP: 82 | W: 61 | L: 16 | OTL: 5 | P: 127
GF: 315 | GA: 211 | PP%: 21.77% | PK%: 85.00%
DG: Patrick Pellegrino | Morale : 50 | Moyenne d'Équipe : 59
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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 SPÂgeContratSalaire Moyen
1Austin CzarnikXXX100.006456858058636468676466666762630506302611,500,000$
2Pontus AbergXX100.007261847670727863436265626254550506202621,250,000$
3Matt ReadXX100.007359886466707862455260715969700506103313,125,000$
4Victor RaskXXX100.006843957275596463676360573667670506102612,183,546$
5Nicholas Merkley (R)XX100.00736885796867696278625863554444050610221895,000$
6Timothy GettingerX100.00878591648574785850506169584444050600213770,000$
7Valentin ZykovXX100.007547907180615963256357612548480505902421,225,000$
8Brian GibbonsXX100.006641917061577056275356685364650505803132,200,000$
9Shane Bowers (R)X100.00767090667062636075585864554444050580204925,002$
10David Gustafsson (R)X100.00594199807145575164505769254646050570193817,500$
11Marian GaborikXX100.004535816769595856405160614983740505703713,166,667$
12Sven AndrighettoXX100.00473588695858695535525866444639050550261825,000$
13Nicolas Hague (R)X100.00834676788269846625604856254747050630204791,668$
14Chad RuhwedelX100.008447907269665461304847732559600506302911,425,000$
15Kevin ConnautonX100.0078667880766664602554466544646505063X02931,500,000$
16Scott HarringtonX100.007743887076637358255247672560610506202622,300,000$
17Alex Petrovic (R)X100.008381866281606351254740653844440505802721,600,000$
18Alexey MarchenkoX100.00484385586958524335463982454439050560273700,000$
Rayé
1Adam CracknellXXX100.00493594667352333358333363475449050470341750,000$
2Spencer FooX100.00453593666250313649324061463532050450252925,000$
3Jason GarrisonX100.005635825972553540353445714765570505403413,625,001$
4Lucas JohansenX100.00776990606757514825403961374444050540212925,000$
MOYENNE D'ÉQUIPE100.0068528870716161554351526644545305058
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
1Alexandar Georgiev100.0067726966757169678167685555050670
2Max Lagace100.0063637975647061686967304444050640
Rayé
1Anthony Stolarz100.0063617688666555656261304545050620
2Daniil Tarasov G (R)100.0051817372405053534548495454050540
MOYENNE D'ÉQUIPE100.006169747561646063646144505005062
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
1Pontus AbergMarlies (Tor)LW/RW82404989253201571563689926510.87%22193623.62910195522621382187142.95%15600010.92390001133
2Victor RaskMarlies (Tor)C/LW/RW8226527823160762042385916310.92%12161719.73613194420803331645256.09%218400000.9635000326
3Matt ReadMarlies (Tor)LW/RW82324375226220118113355982119.01%24159419.44511167120910171063041.41%12800000.9426121364
4Valentin ZykovMarlies (Tor)LW/RW82333568212801281032966818611.15%12145817.79681449210000008328.30%10600000.9300000733
5Kevin ConnautonMarlies (Tor)D8218496719620153106143449712.59%146195823.8872128612251011179320.00%000100.6800000515
6Nicholas MerkleyMarlies (Tor)C/RW8226386420440123164273851939.52%26159119.40213155522801151042357.66%27400000.8027000138
7Timothy GettingerMarlies (Tor)LW8228336131555257882727320210.29%11129515.79581344144000004151.41%31900010.9401001671
8Chad RuhwedelMarlies (Tor)D821438523056019811217256948.14%102178521.778311772040113151510.00%000000.5800000431
9David GustafssonMarlies (Tor)C82193251344014183227551558.37%14120414.691013240000193253.89%131000000.8526000234
10Scott HarringtonMarlies (Tor)D828384629280846911240887.14%90151618.49156321740001145310.00%000000.6100000133
11Shane BowersMarlies (Tor)C82152742243515122189192431387.81%14129215.7600000000000058.51%165100000.6500012244
12Marian GaborikMarlies (Tor)LW/RW821922413100455169399311.24%3115514.09000020000115051.65%9100000.7100000122
13Nicolas HagueMarlies (Tor)D764323611620184576624396.06%75151219.903691996000165100.00%000000.4800000040
14Alex PetrovicMarlies (Tor)D82618243136101693958225310.34%93153018.66325191410001177100.00%000000.3100020020
15Alexey MarchenkoMarlies (Tor)D704192315009424313469.30%78105415.06000129011073110.00%000000.4400000020
16Austin CzarnikMarlies (Tor)C/LW/RW5571522-620266710131886.93%75199.45371028940003712055.38%74400000.8513000211
17Brian GibbonsMarlies (Tor)C/LW82871520022478719639.20%95927.230000500001051137.02%18100000.5100000000
18Trevor DaleyTorontoD15211131012019234414304.55%2335723.810112236000037000.00%000100.7311000000
19Matt IrwinTorontoD12044101202917154140.00%1121017.550000000000000.00%000000.3800000000
20Adam CracknellMarlies (Tor)C/LW/RW21011-300044170.00%31808.59000140000170053.85%1300000.1100000000
21Lucas JohansenMarlies (Tor)D28011440513010.00%7923.320000000001000.00%000000.2200000000
22Jason GarrisonMarlies (Tor)D5000000010000.00%010.390000000000000.00%000000.0000000000
23Sven AndrighettoMarlies (Tor)LW/RW47000000000000.00%040.100000000000000.00%000000.0000000000
Stats d'équipe Total ou en Moyenne14773095648733835505018971840323888722269.54%7822446316.565910816758122684711331650541854.52%715700220.711438154484945
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
1Alexandar GeorgievMarlies (Tor)76551640.9242.5044256418424200120.83330766846
2Max LagaceMarlies (Tor)126010.9252.5055200233060000.7508676000
Stats d'équipe Total ou en Moyenne88611650.9242.5049786420727260120.816388282846


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
Adam CracknellMarlies (Tor)C/LW/RW341985-07-15No209 Lbs6 ft3NoNoNo1Pro & Farm750,000$75,000$0$NoLien
Alex PetrovicMarlies (Tor)D271992-03-03Yes216 Lbs6 ft4NoNoNo2Pro & Farm1,600,000$160,000$0$No1,600,000$Lien
Alexandar GeorgievMarlies (Tor)G231996-02-10No176 Lbs6 ft1NoNoNo1Pro & Farm792,500$79,250$0$NoLien
Alexey MarchenkoMarlies (Tor)D271992-01-02No210 Lbs6 ft3NoNoNo3Pro & Farm700,000$70,000$0$No700,000$700,000$Lien
Anthony StolarzMarlies (Tor)G251994-01-20No210 Lbs6 ft6NoNoNo3Pro & Farm900,000$90,000$0$No900,000$900,000$Lien
Austin CzarnikMarlies (Tor)C/LW/RW261992-12-12No160 Lbs5 ft9NoNoNo1Pro & Farm1,500,000$150,000$0$NoLien
Brian GibbonsMarlies (Tor)C/LW311988-02-05No175 Lbs5 ft8NoNoNo3Pro & Farm2,000,000$220,000$0$No2,000,000$2,000,000$Lien
Chad RuhwedelMarlies (Tor)D291990-05-07No191 Lbs5 ft11NoNoNo1Pro & Farm1,425,000$142,500$0$NoLien
Daniil Tarasov GMarlies (Tor)G201999-03-27Yes185 Lbs6 ft5NoNoNo4Pro & Farm825,000$82,500$0$No825,000$825,000$825,000$Lien
David GustafssonMarlies (Tor)C192000-04-11Yes194 Lbs6 ft1NoNoNo3Pro & Farm817,500$81,750$0$No817,500$817,500$Lien
Jason GarrisonMarlies (Tor)D341984-11-13No218 Lbs6 ft1NoNoNo1Pro & Farm3,000,001$362,500$0$NoLien
Kevin ConnautonMarlies (Tor)D291990-02-23No205 Lbs6 ft2NoYesNo3Pro & Farm1,500,000$150,000$0$No1,500,000$1,500,000$Lien
Lucas JohansenMarlies (Tor)D211997-11-16No176 Lbs6 ft2NoNoNo2Pro & Farm925,000$92,500$0$No925,000$Lien
Marian Gaborik (Contrat à 1 Volet)Marlies (Tor)LW/RW371982-02-14No200 Lbs6 ft1NoNoNo1Pro & Farm2,500,000$3,166,667$0$NoLien
Matt ReadMarlies (Tor)LW/RW331986-06-13No185 Lbs5 ft10NoNoNo1Pro & Farm1,500,000$312,500$0$NoLien
Max LagaceMarlies (Tor)G261993-01-11No190 Lbs6 ft2NoNoNo4Pro & Farm950,000$95,000$0$No950,000$950,000$950,000$Lien
Nicholas MerkleyMarlies (Tor)C/RW221997-05-23Yes194 Lbs5 ft10NoNoNo1Pro & Farm895,000$89,500$0$NoLien
Nicolas HagueMarlies (Tor)D201998-12-05Yes214 Lbs6 ft6NoNoNo4Pro & Farm791,668$79,167$0$No791,668$791,668$791,668$Lien
Pontus AbergMarlies (Tor)LW/RW261993-09-22No196 Lbs5 ft11NoNoNo2Pro & Farm1,250,000$125,000$0$No1,250,000$Lien
Scott HarringtonMarlies (Tor)D261993-03-10No207 Lbs6 ft2NoNoNo2Pro & Farm2,300,000$230,000$0$No2,300,000$Lien
Shane BowersMarlies (Tor)C201999-07-30Yes187 Lbs6 ft2NoNoNo4Pro & Farm925,002$92,500$0$No925,002$925,002$925,002$Lien
Spencer FooMarlies (Tor)RW251994-05-19No190 Lbs6 ft0NoNoNo2Pro & Farm925,000$92,500$0$No925,000$Lien
Sven AndrighettoMarlies (Tor)LW/RW261993-03-21No188 Lbs5 ft10NoNoNo1Pro & Farm825,000$82,500$0$NoLien
Timothy GettingerMarlies (Tor)LW211998-04-14No220 Lbs6 ft6NoNoNo3Pro & Farm770,000$77,000$0$No770,000$770,000$Lien
Valentin ZykovMarlies (Tor)LW/RW241995-05-14No224 Lbs6 ft1NoNoNo2Pro & Farm1,225,000$122,500$0$No1,225,000$Lien
Victor RaskMarlies (Tor)C/LW/RW261993-03-01No200 Lbs6 ft2NoNoNo1Pro & Farm2,183,546$218,355$0$NoLien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2626.04197 Lbs6 ft12.151,299,047$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Pontus AbergShane BowersNicholas Merkley40122
2Matt ReadVictor RaskValentin Zykov30122
3Timothy GettingerDavid GustafssonMarian Gaborik20122
4Brian GibbonsShane BowersPontus Aberg10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Kevin ConnautonNicolas Hague40122
2Alexey MarchenkoChad Ruhwedel30122
3Scott HarringtonAlex Petrovic20122
4Kevin ConnautonChad Ruhwedel10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Pontus AbergTimothy GettingerNicholas Merkley60122
2Matt ReadVictor RaskValentin Zykov40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Kevin ConnautonAlex Petrovic60122
2Scott HarringtonChad Ruhwedel40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Victor RaskPontus Aberg60122
2Nicholas MerkleyMatt Read40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Kevin ConnautonScott Harrington60122
2Alex PetrovicChad Ruhwedel40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Nicholas Merkley60122Kevin ConnautonScott Harrington60122
2Pontus Aberg40122Alex PetrovicChad Ruhwedel40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Brian GibbonsPontus Aberg60122
2Nicholas MerkleyMatt Read40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Kevin ConnautonAlex Petrovic60122
2Scott HarringtonChad Ruhwedel40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Pontus AbergMatt ReadNicholas MerkleyKevin ConnautonChad Ruhwedel
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Pontus AbergMatt ReadNicholas MerkleyKevin ConnautonChad Ruhwedel
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
David Gustafsson, Timothy Gettinger, Brian GibbonsDavid Gustafsson, Timothy GettingerBrian Gibbons
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Scott Harrington, Alex Petrovic, Kevin ConnautonScott HarringtonAlex Petrovic, Kevin Connauton
Tirs de Pénalité
David Gustafsson, Pontus Aberg, Nicholas Merkley, Matt Read, Victor Rask
Gardien
#1 : Alexandar Georgiev, #2 : Max Lagace


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
1Admirals2010001045-1100000102111010000024-220.500459001111148016741031110610337853234566233.33%20100.00%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
2Baby Hawks21100000541110000005231010000002-220.5005914101111148016591031110610337861198409111.11%4175.00%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
3Bears3120000079-21010000035-22110000044020.33371421001111148016103103111061033789234334519421.05%14192.86%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
4Bruins4310000015105220000009542110000065160.7501528430011111480161581031110610337810824248517211.76%12375.00%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
5Cabaret Lady Mary Ann4400000029121722000000156922000000146881.00029507900111114801630210311106103378130292412111654.55%12283.33%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
6Caroline320000101394210000108621100000053261.0001322350011111480169010311106103378103303254800.00%11281.82%11614296454.45%1560287554.26%728131855.24%2110147017975851072562
7Chiefs21100000440110000003031010000014-320.50046100111111480165610311106103378501113569333.33%40100.00%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
8Chill2010001078-11010000024-21000001054120.50071118001111148016681031110610337883186617228.57%3166.67%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
9Comets210010001064110000004131000100065141.0001016260011111480168810311106103378852718449333.33%9366.67%11614296454.45%1560287554.26%728131855.24%2110147017975851072562
10Cougars42200000811-3220000004222020000049-540.500816241011111480161261031110610337814040248616318.75%12191.67%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
11Crunch440000001495220000006332200000086281.0001426400011111480161631031110610337815958241228225.00%100100.00%21614296454.45%1560287554.26%728131855.24%2110147017975851072562
12Heat21000010945100000104311100000051441.000916250011111480168210311106103378712018486116.67%8187.50%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
13Jayhawks210000101284110000006331000001065141.0001220320011111480169710311106103378902418525240.00%8362.50%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
14Las Vegas20000110660100000104311000010023-130.750610160011111480166810311106103378611314491119.09%6266.67%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
15Manchots31200000911-21010000013-22110000088020.3339162500111114801610910311106103378111202273400.00%11372.73%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
16Minnesota2200000011110110000004041100000071641.00011213201111114801610510311106103378592010434250.00%50100.00%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
17Monarchs200010018801000000134-11000100054130.750813210011111480167610311106103378782912565240.00%60100.00%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
18Monsters321000001495211000009721100000052340.667142438001111148016145103111061033788421237210330.00%9366.67%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
19Monsters210000016601000000112-11100000054130.75061218001111148016531031110610337891242038300.00%10190.00%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
20Oceanics210001007701000010034-11100000043130.750714210011111480166610311106103378721612478112.50%6183.33%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
21Oil Kings21100000770110000003211010000045-120.500713200011111480166610311106103378661921387114.29%8187.50%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
22Phantoms32000010954110000003212100001063361.00091423011111148016106103111061033789824204814428.57%8187.50%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
23Rocket430001001688220000008352100010085370.8751629450011111480161701031110610337813438349019421.05%12191.67%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
24Senators42200000131212200000092720200000410-640.500132336001111148016132103111061033781354131891417.14%12283.33%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
25Sharks2110000057-21010000025-31100000032120.50059140011111480166610311106103378682210505120.00%50100.00%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
26Sound Tigers3300000014410220000009271100000052361.0001427410111111480161121031110610337886331757500.00%60100.00%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
27Spiders330000001266220000009451100000032161.0001220320011111480161181031110610337810232148313323.08%7271.43%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
28Stars22000000734110000004131100000032141.0007111800111114801671103111061033785415659400.00%3166.67%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
29Thunder430010001587210010008352200000075281.00015284300111114801613910311106103378114342067500.00%100100.00%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
Total825116033723152111044128501142156896741231102230159122371270.774315558873241111148016321110311106103378272878454618952715921.77%2403685.00%41614296454.45%1560287554.26%728131855.24%2110147017975851072562
30Wolf Pack330000001941511000000514220000001431161.000193554001111148016143103111061033789026146610550.00%70100.00%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
_Since Last GM Reset825116033723152111044128501142156896741231102230159122371270.774315558873241111148016321110311106103378272878454618952715921.77%2403685.00%41614296454.45%1560287554.26%728131855.24%2110147017975851072562
_Vs Conference432511021311581134521125011117752252213601020816120620.72115828143902111114801616151031110610337813743972629551423021.13%1181785.59%01614296454.45%1560287554.26%728131855.24%2110147017975851072562
_Vs Division285301111110704014220010159243514310101051465160.286110200310101111148016119010311106103378920264181660901820.00%80988.75%21614296454.45%1560287554.26%728131855.24%2110147017975851072562

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
82127W231555887332112728784546189524
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
8251163372315211
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
41285114215689
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
4123112230159122
Derniers 10 Matchs
WLOTWOTL SOWSOL
730000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
2715921.77%2403685.00%4
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
103111061033781111148016
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
1614296454.45%1560287554.26%728131855.24%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
2110147017975851072562


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 - 2020-10-221Senators1Marlies4WSommaire du Match
3 - 2020-10-2416Marlies5Monsters2WSommaire du Match
4 - 2020-10-2519Rocket1Marlies3WSommaire du Match
6 - 2020-10-2734Chiefs0Marlies3WSommaire du Match
9 - 2020-10-3047Thunder2Marlies3WXSommaire du Match
11 - 2020-11-0167Marlies0Cougars3LSommaire du Match
14 - 2020-11-0485Minnesota0Marlies4WSommaire du Match
15 - 2020-11-0593Marlies4Bears2WSommaire du Match
18 - 2020-11-08116Bruins2Marlies5WSommaire du Match
20 - 2020-11-10129Monsters2Marlies5WSommaire du Match
21 - 2020-11-11133Marlies1Bruins4LSommaire du Match
24 - 2020-11-14156Sharks5Marlies2LSommaire du Match
25 - 2020-11-15163Marlies5Rocket1WSommaire du Match
28 - 2020-11-18180Bears5Marlies3LSommaire du Match
32 - 2020-11-22210Marlies4Phantoms3WXXSommaire du Match
35 - 2020-11-25228Monarchs4Marlies3LXXSommaire du Match
37 - 2020-11-27237Las Vegas3Marlies4WXXSommaire du Match
39 - 2020-11-29254Phantoms2Marlies3WSommaire du Match
40 - 2020-11-30267Marlies0Baby Hawks2LSommaire du Match
43 - 2020-12-03282Marlies5Sound Tigers2WSommaire du Match
45 - 2020-12-05294Bruins3Marlies4WSommaire du Match
46 - 2020-12-06308Marlies1Manchots4LSommaire du Match
49 - 2020-12-09329Marlies2Las Vegas3LXSommaire du Match
51 - 2020-12-11343Marlies6Jayhawks5WXXSommaire du Match
53 - 2020-12-13351Marlies5Monsters4WSommaire du Match
57 - 2020-12-17379Marlies4Cougars6LSommaire du Match
59 - 2020-12-19396Marlies5Crunch4WSommaire du Match
60 - 2020-12-20406Crunch1Marlies3WSommaire du Match
63 - 2020-12-23428Marlies2Phantoms0WSommaire du Match
64 - 2020-12-24434Monsters2Marlies1LXXSommaire du Match
67 - 2020-12-27454Marlies1Chiefs4LSommaire du Match
70 - 2020-12-30481Marlies6Comets5WXSommaire du Match
72 - 2021-01-01494Marlies5Heat1WSommaire du Match
74 - 2021-01-03506Marlies4Oil Kings5LSommaire du Match
77 - 2021-01-06525Crunch2Marlies3WSommaire du Match
80 - 2021-01-09550Marlies8Wolf Pack1WSommaire du Match
81 - 2021-01-10557Cougars1Marlies2WSommaire du Match
83 - 2021-01-12569Caroline3Marlies4WSommaire du Match
87 - 2021-01-16583Marlies3Spiders2WSommaire du Match
88 - 2021-01-17594Wolf Pack1Marlies5WSommaire du Match
91 - 2021-01-20616Marlies7Minnesota1WSommaire du Match
93 - 2021-01-22633Marlies4Oceanics3WSommaire du Match
95 - 2021-01-24646Sound Tigers0Marlies3WSommaire du Match
97 - 2021-01-26659Oil Kings2Marlies3WSommaire du Match
99 - 2021-01-28675Oceanics4Marlies3LXSommaire du Match
103 - 2021-02-01707Marlies6Cabaret Lady Mary Ann3WSommaire du Match
105 - 2021-02-03715Spiders3Marlies5WSommaire du Match
107 - 2021-02-05728Heat3Marlies4WXXSommaire du Match
109 - 2021-02-07747Baby Hawks2Marlies5WSommaire du Match
118 - 2021-02-16770Marlies5Chill4WXXSommaire du Match
120 - 2021-02-18776Marlies3Stars2WSommaire du Match
123 - 2021-02-21796Senators1Marlies5WSommaire du Match
125 - 2021-02-23809Cabaret Lady Mary Ann2Marlies7WSommaire du Match
127 - 2021-02-25825Marlies6Wolf Pack2WSommaire du Match
129 - 2021-02-27839Admirals1Marlies2WXXSommaire du Match
130 - 2021-02-28846Marlies3Rocket4LXSommaire du Match
133 - 2021-03-03867Jayhawks3Marlies6WSommaire du Match
135 - 2021-03-05881Stars1Marlies4WSommaire du Match
137 - 2021-03-07901Marlies3Senators6LSommaire du Match
138 - 2021-03-08913Marlies3Crunch2WSommaire du Match
140 - 2021-03-10921Marlies7Manchots4WSommaire du Match
142 - 2021-03-12933Manchots3Marlies1LSommaire du Match
144 - 2021-03-14951Caroline3Marlies4WXXSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
147 - 2021-03-17970Marlies3Thunder2WSommaire du Match
149 - 2021-03-19987Marlies8Cabaret Lady Mary Ann3WSommaire du Match
151 - 2021-03-211003Comets1Marlies4WSommaire du Match
154 - 2021-03-241028Marlies3Sharks2WSommaire du Match
156 - 2021-03-261041Marlies5Monarchs4WXSommaire du Match
157 - 2021-03-271048Marlies2Admirals4LSommaire du Match
161 - 2021-03-311070Thunder1Marlies5WSommaire du Match
163 - 2021-04-021083Chill4Marlies2LSommaire du Match
165 - 2021-04-041101Marlies5Bruins1WSommaire du Match
168 - 2021-04-071123Spiders1Marlies4WSommaire du Match
170 - 2021-04-091137Sound Tigers2Marlies6WSommaire du Match
172 - 2021-04-111155Monsters5Marlies4LSommaire du Match
174 - 2021-04-131170Cabaret Lady Mary Ann4Marlies8WSommaire du Match
176 - 2021-04-151185Marlies4Thunder3WSommaire du Match
177 - 2021-04-161195Marlies5Caroline3WSommaire du Match
179 - 2021-04-181209Marlies1Senators4LSommaire du Match
182 - 2021-04-211231Marlies0Bears2LSommaire du Match
184 - 2021-04-231242Cougars1Marlies2WSommaire du Match
186 - 2021-04-251262Rocket2Marlies5WSommaire du Match



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité de l'Aréna40002000
Prix des Billets3515
Assistance157,72978,158
Assistance PCT96.18%95.31%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacité de l'ArénaPopularité de l'Équipe
0 5753 - 95.89% 163,241$6,692,885$6000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
3,488,410$ 3,127,522$ 3,372,522$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
18,132$ 3,734,690$ 26 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 0 16,815$ 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