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Toronto Marlies
GP: 10 | W: 6 | L: 4
GF: 25 | GA: 23 | PP%: 28.57% | PK%: 81.82%
DG: Dale Lautner | Morale : 62 | Moyenne d’équipe : 61
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Centre de jeu
Toronto Marlies
6-4-0, 12pts
3
FINAL
5 Chicago Wolves
16-6-0, 32pts
Team Stats
L3SéquenceW2
3-2-0Fiche domicile10-2-0
3-2-0Fiche domicile6-4-0
6-4-0Derniers 10 matchs9-0-1
2.50Buts par match 3.41
2.30Buts contre par match 2.64
28.57%Pourcentage en avantage numérique18.18%
81.82%Pourcentage en désavantage numérique71.88%
Chicago Wolves
16-6-0, 32pts
2
FINAL
1 Toronto Marlies
6-4-0, 12pts
Team Stats
W2SéquenceL3
10-2-0Fiche domicile3-2-0
6-4-0Fiche domicile3-2-0
9-0-1Derniers 10 matchs6-4-0
3.41Buts par match 2.50
2.64Buts contre par match 2.30
18.18%Pourcentage en avantage numérique28.57%
71.88%Pourcentage en désavantage numérique81.82%
Meneurs d'équipe
Eric RobinsonButs
Eric Robinson
5
Eric RobinsonPasses
Eric Robinson
6
Eric RobinsonPoints
Eric Robinson
11
Plus/Moins
Danila Klimovich
3
Pavel FrancouzVictoires
Pavel Francouz
6
Alex StalockPourcentage d’arrêts
Alex Stalock
0.923

Statistiques d’équipe
Buts pour
25
2.50 GFG
Tirs pour
248
24.80 Avg
Pourcentage en avantage numérique
28.6%
6 GF
Début de zone offensive
38.9%
Buts contre
23
2.30 GAA
Tirs contre
271
27.10 Avg
Pourcentage en désavantage numérique
81.8%%
2 GA
Début de la zone défensive
36.5%
Informations de l'équipe

Directeur généralDale Lautner
DivisionNorth Division
ConférenceWestern Conference
Capitaine
Assistant #1Ryan Murray
Assistant #2Ridly Greig


Informations de l’aréna

Capacité3,000
Assistance0
Billets de saison1,650


Informations de la formation

Équipe Pro35
Équipe Mineure18
Limite contact 53 / 65
Espoirs35


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
1Eric Robinson0X100.008043917582848579477371757574640787102821,600,000$
2Taylor Raddysh0XX100.00764791768385778154757670785750077710252775,000$
3Mason Appleton0XX100.006643787481867775437266717269590846802731,150,000$
4Alex Formenton0X100.00666775886792957355556964555567083670242775,000$
5Austin Czarnik0X100.00604375746578736765626162677363081630312775,000$
6Dominic Toninato0X100.00664374706871736661626266666353084620291925,000$
7Ridly Greig (R) (A)0XX100.00654469717478606758666263675150083620211775,000$
8Cedric Paquette0XX100.009051678264728760554750635570700846103011,400,000$
9Ben Jones (R)0X100.00655262716266656661646067665250086610241775,000$
10Matej Blumel (R)0X100.00624071686462626442626061645150084580231775,000$
11Jamieson Rees (R)0X100.00565358705862626359635654635050084570222839,167$
12Danila Klimovich (R)0X100.00674664686757566241565862635050082570204886,667$
13Ozzy Wiesblatt (R)0X100.00614066686260605941565560615050080560211775,000$
14Ryan Suzuki (R)0X100.00574568686261606157595654615050080560223863,333$
15John Beecher (R)0X100.00675263626961605951575359595050039560225925,000$
16Ben Harpur0X100.007058726891838069406461706679690826802811,150,000$
17Ryan Murray (A)0X100.00584377718283766740636066668474083660303775,000$
18Cale Fleury0X100.00674771717978676640625967656154084640251775,000$
19Mark Pysyk0X100.00584372656875766440595568618572084630311850,000$
20Alec Regula0X100.00665158676961606340615567625050077590232866,667$
21Fredrik Claesson0X100.00523595826065645540405060407367080590311775,000$
22David Farrance (R)0X100.00594470696364636140615363615250080580242925,000$
23Matthew Robertson (R)0X100.00625366666862626340595464615050080580222797,500$
24Ville Pokka0X100.00613575716351624950535255407358083560291775,000$
25Jesse Blacker0X100.00533575746459685350505452407358039560321775,000$
Rayé
1Joachim Blichfeld (R)0XX100.00593575785466665450515351506048019550253933,333$
2Chase Lang (R)0XX100.00565555555758585550555555556568019540271775,000$
3Zac Rinaldo0XX100.00613575736066675145505051466048019540331775,000$
4Filip Sandberg0XX100.00454545454545454545354545454545019420291775,000$
5Jesse Graham0X100.00504545454645474525454545255454020450291775,000$
MOYENNE D’ÉQUIPE100.0063467070676867624858576159615606959
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 SPÂgeContratSalaire
1Pavel Francouz0100.00837675737881818282797969620767503322,000,000$
2Alex Stalock0100.0074797767767673757675758376081730362775,000$
Rayé
1Ilya Konovalov (R)0100.0075757579846771676670487172072700252842,500$
2Marek Langhamer0100.0075757582787068677068466668025700291900,000$
3Philippe Desrosiers0100.0061595764595554535459575450025550283775,000$
MOYENNE D’ÉQUIPE100.007473727375706969707061696605669
Nom de l’entraîneur 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
1Eric RobinsonToronto Marlies (TOR)LW105611-300161538101413.16%622122.15123318000051030.77%13137000.9900000111
2Mason AppletonToronto Marlies (TOR)C/RW104610-34022262961813.79%724724.80101118000071145.49%23364100.8100000101
3Taylor RaddyshToronto Marlies (TOR)LW/RW10369-300181223102013.04%221521.59123518000001054.55%11148000.8300000110
4Austin CzarnikToronto Marlies (TOR)C1034735581828102110.71%518918.90112313000030057.14%119710000.7400010200
5Ben HarpurToronto Marlies (TOR)D1016710092017225.88%1727127.15123120000010100%0108000.5200000010
6Ridly GreigToronto Marlies (TOR)C/LW102353751219284207.14%419119.16011113000041070.00%1076000.5200001010
7Danila KlimovichToronto Marlies (TOR)RW10314395151093633.33%318218.24101113000000050.00%863000.4400001000
8Cale FleuryToronto Marlies (TOR)D10044-200358230%919219.2701111200007000%045000.4200000000
9Jamieson ReesToronto Marlies (TOR)C10213-1751316124616.67%316316.3200000000001044.26%6133000.3700001010
10Ben JonesToronto Marlies (TOR)C10112-2951218177125.88%513913.9200000000000035.00%6022000.2900100001
11Mark PysykToronto Marlies (TOR)D10022-3209126420%824124.1201142000009000%013000.1700000000
12Dominic ToninatoToronto Marlies (TOR)C101010002820250.00%0414.1900000000000050.00%400000.4800000000
13Ryan MurrayToronto Marlies (TOR)D1001120071314210%1022422.4100001200017000%047000.0900000000
14Cedric PaquetteToronto Marlies (TOR)C/LW10000-100313210%1313.1300000000050033.33%30000000000000
15Ville PokkaToronto Marlies (TOR)D10000-120461110%114414.460000000001000%02200000000000
16Alec RegulaToronto Marlies (TOR)D10000000000000%060.620000000000000%00000000000000
17Alex FormentonToronto Marlies (TOR)LW10000000011110%0161.680000000009000%00000000000000
18David FarranceToronto Marlies (TOR)D10000000000000%010.150000000000000%00000000000000
19Fredrik ClaessonToronto Marlies (TOR)D10000-100191100%613913.990000000000000%01000000000000
20Matej BlumelToronto Marlies (TOR)LW10000-12017911440%316416.4200000000000028.57%712100000000000
21Ozzy WiesblattToronto Marlies (TOR)RW10000-100000000%070.800000000000000%00000000000000
Statistiques d’équipe totales ou en moyenne210254166-1047251712182487313410.08%90303414.4561016201640001726146.88%5299269100.4300113553
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
1Pavel FrancouzToronto Marlies (TOR)106400.9182.1857802212571200100100111
2Alex StalockToronto Marlies (TOR)10000.9232.00300011390000010000
Statistiques d’équipe totales ou en moyenne116400.9192.1760802222701290101010111


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 Recrue Poids Taille Non-échange Disponible pour échange Acquis Par Date de la Dernière Transaction Ballotage forcé Waiver Possible Contrat Date du Signature du Contrat Type Salaire actuel Plafond salarial Non Activé Plafond salarial restant Exclus du plafond salarial Salaire annuel 2Salaire annuel 3Salaire annuel 4Salaire annuel 5Salaire annuel 6Salaire annuel 7Salaire annuel 8Salaire annuel 9Salaire annuel 10Non-échange Année 2Non-échange Année 3Non-échange Année 4Non-échange Année 5Non-échange Année 6Non-échange Année 7Non-échange Année 8Non-échange Année 9Non-échange Année 10Lien
Alec RegulaToronto Marlies (TOR)D238/6/2000No207 Lbs6 ft4NoNoN/ANoNo2Pro & Farm866,667$0$0$No866,667$--------No--------
Alex FormentonToronto Marlies (TOR)LW249/13/1999No195 Lbs6 ft3NoNoN/ANoNo2Pro & Farm775,000$0$0$No775,000$--------No--------Lien
Alex StalockToronto Marlies (TOR)G367/28/1987No170 Lbs5 ft11NoNoN/ANoNo2Pro & Farm775,000$0$0$No775,000$--------No--------Lien NHL
Austin CzarnikToronto Marlies (TOR)C3112/12/1992No170 Lbs5 ft9NoNoN/ANoNo2Pro & Farm775,000$0$0$No775,000$--------No--------
Ben HarpurToronto Marlies (TOR)D281/12/1995No231 Lbs6 ft6NoNoN/ANoNo1Pro & Farm1,150,000$0$0$No------------------Lien NHL
Ben JonesToronto Marlies (TOR)C242/26/1999Yes187 Lbs6 ft0NoNoN/ANoNo1Pro & Farm775,000$0$0$No------------------
Cale FleuryToronto Marlies (TOR)D2511/19/1998No204 Lbs6 ft1NoNoN/ANoNo1Pro & Farm775,000$0$0$No------------------Lien NHL
Cedric PaquetteToronto Marlies (TOR)C/LW308/13/1993No205 Lbs6 ft0NoNoN/ANoNo1Pro & Farm1,400,000$0$0$No------------------Lien
Chase LangToronto Marlies (TOR)C/RW279/13/1996Yes187 Lbs6 ft1NoNoN/ANoNo1Pro & Farm775,000$0$0$No------------------
Danila KlimovichToronto Marlies (TOR)RW201/9/2003Yes203 Lbs6 ft2NoNoN/ANoNo4Pro & Farm886,667$0$0$No886,667$886,667$886,667$------NoNoNo------
David FarranceToronto Marlies (TOR)D246/23/1999Yes192 Lbs6 ft0NoNoN/ANoNo2Pro & Farm925,000$0$0$No925,000$--------No--------
Dominic ToninatoToronto Marlies (TOR)C293/9/1994No192 Lbs6 ft2NoNoN/ANoNo1Pro & Farm925,000$0$0$No------------------
Eric RobinsonToronto Marlies (TOR)LW286/14/1995No205 Lbs6 ft2NoNoN/ANoNo2Pro & Farm1,600,000$0$0$No1,600,000$--------No--------Lien NHL
Filip SandbergToronto Marlies (TOR)C/RW297/23/1994 11:23:08 AMNo190 Lbs5 ft9NoNoN/ANoNo1Pro & Farm775,000$0$0$No------------------Lien
Fredrik ClaessonToronto Marlies (TOR)D3111/24/1992No196 Lbs6 ft1NoNoN/ANoNo1Pro & Farm775,000$0$0$No------------------Lien
Ilya KonovalovToronto Marlies (TOR)G257/13/1998Yes194 Lbs6 ft0NoNoN/ANoNo2Pro & Farm842,500$0$0$No842,500$--------No--------Lien
Jamieson ReesToronto Marlies (TOR)C222/26/2001Yes172 Lbs5 ft11NoNoN/ANoNo2Pro & Farm839,167$0$0$No839,167$--------No--------
Jesse BlackerToronto Marlies (TOR)D324/19/1991No190 Lbs6 ft1NoNoN/ANoNo1Pro & Farm775,000$0$0$No------------------Lien
Jesse GrahamToronto Marlies (TOR)D295/13/1994No184 Lbs6 ft0NoNoN/ANoNo1Pro & Farm775,000$0$0$No------------------Lien
Joachim BlichfeldToronto Marlies (TOR)LW/RW257/17/1998Yes180 Lbs6 ft2NoNoN/ANoNo3Pro & Farm933,333$0$0$No933,333$933,333$-------NoNo-------Lien
John BeecherToronto Marlies (TOR)C224/5/2001Yes209 Lbs6 ft3NoNoN/ANoNo5Pro & Farm925,000$0$0$No925,000$925,000$925,000$925,000$-----NoNoNoNo-----
Marek LanghamerToronto Marlies (TOR)G297/22/1994No193 Lbs6 ft2NoNoN/ANoNo1Pro & Farm900,000$0$0$No------------------Lien
Mark PysykToronto Marlies (TOR)D311/11/1992No198 Lbs6 ft1NoNoN/ANoNo1Pro & Farm850,000$0$0$No------------------
Mason AppletonToronto Marlies (TOR)C/RW271/15/1996No197 Lbs6 ft3NoNoN/ANoNo3Pro & Farm1,150,000$0$0$No1,150,000$1,150,000$-------NoNo-------Lien NHL
Matej BlumelToronto Marlies (TOR)LW235/31/2000Yes198 Lbs6 ft0NoNoN/ANoNo1Pro & Farm775,000$0$0$No------------------
Matthew RobertsonToronto Marlies (TOR)D223/9/2001Yes201 Lbs6 ft4NoNoN/ANoNo2Pro & Farm797,500$0$0$No797,500$--------No--------
Ozzy WiesblattToronto Marlies (TOR)RW213/9/2002Yes183 Lbs5 ft10NoNoN/ANoNo1Pro & Farm775,000$0$0$No------------------
Pavel FrancouzToronto Marlies (TOR)G336/3/1990No179 Lbs6 ft0NoNoN/ANoNo2Pro & Farm2,000,000$0$0$No2,000,000$--------No--------Lien NHL
Philippe DesrosiersToronto Marlies (TOR)G288/16/1995No201 Lbs6 ft2NoNoN/ANoNo3Pro & Farm775,000$0$0$No775,000$775,000$-------NoNo-------
Ridly GreigToronto Marlies (TOR)C/LW218/8/2002Yes183 Lbs6 ft0NoNoN/ANoNo1Pro & Farm775,000$0$0$No------------------
Ryan MurrayToronto Marlies (TOR)D309/27/1993No206 Lbs6 ft1NoNoN/ANoNo3Pro & Farm775,000$0$0$No775,000$775,000$-------NoNo-------Lien NHL
Ryan SuzukiToronto Marlies (TOR)C225/28/2001Yes176 Lbs6 ft0NoNoN/ANoNo3Pro & Farm863,333$0$0$No863,333$863,333$-------NoNo-------
Taylor RaddyshToronto Marlies (TOR)LW/RW252/18/1998No198 Lbs6 ft3NoNoN/ANoNo2Pro & Farm775,000$0$0$No775,000$--------No--------Lien NHL
Ville PokkaToronto Marlies (TOR)D296/3/1994No214 Lbs5 ft10NoNoN/ANoNo1Pro & Farm775,000$0$0$No------------------Lien
Zac RinaldoToronto Marlies (TOR)C/LW336/15/1990No192 Lbs5 ft10NoNoN/ANoNo1Pro & Farm775,000$0$0$No------------------Lien
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
3526.80194 Lbs6 ft11.80908,690$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Eric RobinsonMason AppletonTaylor Raddysh40122
2Ridly GreigAustin CzarnikDanila Klimovich30122
3Matej BlumelBen JonesJamieson Rees20122
4Matej BlumelJamieson ReesMason Appleton10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Mark PysykBen Harpur40122
2Ryan MurrayCale Fleury30122
3Ville PokkaFredrik Claesson20122
4Ryan MurrayBen Harpur10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Eric RobinsonMason AppletonTaylor Raddysh60122
2Ridly GreigAustin CzarnikDanila Klimovich40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Mark PysykBen Harpur60122
2Ryan MurrayCale Fleury40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Mason AppletonAlex Formenton60122
2Austin CzarnikEric Robinson40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Mark PysykBen Harpur60122
2Ryan MurrayCale Fleury40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Mason Appleton60122Mark PysykBen Harpur60122
2Austin Czarnik40122Ryan MurrayCale Fleury40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Mason AppletonEric Robinson60122
2Ridly GreigTaylor Raddysh40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Mark PysykBen Harpur60122
2Ryan MurrayCale Fleury40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Eric RobinsonMason AppletonTaylor RaddyshRyan MurrayBen Harpur
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Eric RobinsonMason AppletonTaylor RaddyshRyan MurrayBen Harpur
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Ridly Greig, Dominic Toninato, Cedric PaquetteRidly Greig, Dominic ToninatoRidly Greig
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Mark Pysyk, Ryan Murray, Ville PokkaMark PysykMark Pysyk, Ben Harpur
Tirs de pénalité
Mason Appleton, Taylor Raddysh, Alex Formenton, Eric Robinson, Ridly Greig
Gardien
#1 : Pavel Francouz, #2 : Alex Stalock
Lignes d’attaque personnalisées en prolongation
Mason Appleton, Taylor Raddysh, Alex Formenton, Austin Czarnik, Ridly Greig, Dominic Toninato, Dominic Toninato, Cedric Paquette, Ben Jones, Matej Blumel, Danila Klimovich
Lignes de défense personnalisées en prolongation
Ville Pokka, Ben Harpur, Ryan Murray, Cale Fleury, Mark Pysyk


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
TotalDomicileVisiteur
# 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
1Chicago Wolves624000001518-33120000076131200000812-440.333152742102911314661100751218255369910110.00%8275.00%09420645.63%9519349.22%5913045.38%18989202102218110
2Rockford IceHogs440000001055220000005322200000052381.00010142402291131026110075128935117211545.45%30100.00%09420645.63%9519349.22%5913045.38%18989202102218110
Total10640000025232532000001293532000001314-1120.6002541661229113248611007512271904717121628.57%11281.82%09420645.63%9519349.22%5913045.38%18989202102218110
_Since Last GM Reset10640000025232532000001293532000001314-1120.6002541661229113248611007512271904717121628.57%11281.82%09420645.63%9519349.22%5913045.38%18989202102218110
_Vs Conference10640000025232532000001293532000001314-1120.6002541661229113248611007512271904717121628.57%11281.82%09420645.63%9519349.22%5913045.38%18989202102218110

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
1012L3254166248271904717112
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
106400002523
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
5320000129
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
53200001314
Derniers 10 matchs
WLOTWOTL SOWSOL
640000
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
21628.57%11281.82%0
Tirs en 1e périodeTirs en 2e périodeTirs en 3e périodeTirs en 4e périodeButs en 1e périodeButs en 2e périodeButs en 3e périodeButs en 4e période
61100751229113
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
9420645.63%9519349.22%5913045.38%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
18989202102218110


Derniers matchs 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
17Rockford IceHogs0Toronto Marlies1BWSommaire du match
215Rockford IceHogs3Toronto Marlies4BWXSommaire du match
323Toronto Marlies2Rockford IceHogs0AWSommaire du match
431Toronto Marlies3Rockford IceHogs2AWXSommaire du match
860Toronto Marlies3Chicago Wolves2AWXSommaire du match
964Toronto Marlies2Chicago Wolves5ALSommaire du match
1068Chicago Wolves1Toronto Marlies5BWSommaire du match
1172Chicago Wolves3Toronto Marlies1BLSommaire du match
1276Toronto Marlies3Chicago Wolves5ALSommaire du match
1380Chicago Wolves2Toronto Marlies1BLSommaire du match



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets4020
Assistance00
Assistance PCT0.00%0.00%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
36 0 - 0.00% 0$0$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursSalaire total moyen des joueursSalaire des entraineurs
0$ 3,180,417$ 2,911,250$ 0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
0$ 0$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
0$ 0 0$ 0$




Toronto Marlies Leaders statistiques des joueurs (saison régulière)

# Nom du joueur GP G A P +/- PIM HIT HTT SHT SHT% SB MP AMG PPG PPA PPP PPS PKG PKA PKP PKS GW GT FO% HT P/20 PSG PSS

Toronto Marlies Leaders des statistiques des gardiens (saison régulière)

# Nom du gardien GP W L OTL PCT GAA MP PIM SO GA SA SAR A EG PS % PSA

Toronto Marlies Statistiques de l'Équipe de Carrière

TotalDomicileVisiteur
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

Toronto Marlies Leaders statistiques des joueurs (séries éliminatoires)

# Nom du joueur GP G A P +/- PIM HIT HTT SHT SHT% SB MP AMG PPG PPA PPP PPS PKG PKA PKP PKS GW GT FO% HT P/20 PSG PSS

Toronto Marlies Leaders des statistiques des gardiens (séries éliminatoires)

# Nom du gardien GP W L OTL PCT GAA MP PIM SO GA SA SAR A EG PS % PSA