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Marlies
GP: 80 | W: 36 | L: 36 | OTL: 8 | P: 80
GF: 305 | GA: 328 | PP%: 19.94% | PK%: 78.33%
DG: Pascal Landry | Morale : 28 | Moyenne d’équipe : 65

Centre de jeu
Phantoms
40-31-9, 89pts
2
FINAL
3 Marlies
36-36-8, 80pts
Team Stats
W1SéquenceL1
21-15-4Fiche domicile21-13-6
19-16-5Fiche domicile15-23-2
5-4-1Derniers 10 matchs5-4-1
4.16Buts par match 3.81
3.93Buts contre par match 4.10
24.62%Pourcentage en avantage numérique19.94%
77.88%Pourcentage en désavantage numérique78.33%
Marlies
36-36-8, 80pts
2
FINAL
7 Senators
42-29-9, 93pts
Team Stats
L1SéquenceW4
21-13-6Fiche domicile22-17-1
15-23-2Fiche domicile20-12-8
5-4-1Derniers 10 matchs5-4-1
3.81Buts par match 4.10
4.10Buts contre par match 4.05
19.94%Pourcentage en avantage numérique25.50%
78.33%Pourcentage en désavantage numérique78.70%
Meneurs d'équipe
Buts
Aleksi Saarela
44
Passes
Pierre-Olivier Joseph
72
Points
Aleksi Saarela
94
Plus/Moins
Mathieu Olivier
11
Victoires
Logan Thompson
18
Pourcentage d’arrêts
Maxime Lagace
0.875

Statistiques d’équipe
Buts pour
305
3.81 GFG
Tirs pour
2469
30.86 Avg
Pourcentage en avantage numérique
19.9%
62 GF
Début de zone offensive
36.0%
Buts contre
328
4.10 GAA
Tirs contre
2542
31.78 Avg
Pourcentage en désavantage numérique
78.3%%
65 GA
Début de la zone défensive
36.9%
Informations de l'équipe

Directeur généralPascal Landry
EntraîneurGuy Boucher
DivisionFritz-Kraatz
ConférenceRobert-Lebel
Capitaine
Assistant #1
Assistant #2


Informations de l’aréna

Capacité3,000
Assistance2,731
Billets de saison300


Informations de la formation

Équipe Pro32
Équipe Mineure18
Limite contact 50 / 250
Espoirs0


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
1Aleksi Saarela (R)X100.007525808472767778727377677960505649730221900,000$
2Tyler Benson (R)X100.008143757782658176637677746156466041730211900,000$
3Martin Necas (R)X100.006734817562747675849079576349446848720201950,000$
4Mathieu Olivier (R)X100.008043787881657473697574696753495339720221900,000$
5Alex Formenton (R)X100.007423866887697372697282616150447148700202950,000$
6Joel Farabee (R)X100.005737808557726587746870648843428131700191500,000$
7Kole Sherwood (R)X100.008441737372638073627373686047464735690221900,000$
8Lias Andersson (R)X100.006237837073797069637767716249476035680212750,000$
9Matthew Highmore (R)X100.007321826671717473767370567148504526670232500,000$
10Isac Lundestrom (R)X100.006034726272637264707377586743456848650201500,000$
11Otto Koivula (R)X100.005834696173706372716172537044445135640211750,000$
12Arthur Kaliyev (R)X100.006541726858636758686666526640407838610182500,000$
13Pierre-Olivier Joseph (R)X100.006028839069787891458767745857447231740202975,000$
14Lucas Carlsson (R)X100.007538887474747369506960755655475540710221900,000$
15Rasmus Dahlin (R)X100.005935828961637288438267725244427948700191500,000$
16Keaton ThompsonX100.006639757665647669537664755955513925690241600,000$
17Dennis Gilbert (R)X100.007438787161686757487355774348483649680231500,000$
18Jacob Bernard-Docker (R)X100.005943666166665764446156654741416719610191500,000$
Rayé
1Hugh McGing (R)X100.007444746980577369547467606045465920660211500,000$
2Jakob Forsbacka-Karlsson (R)X100.007141687059635958645671595846463820620231550,000$
3Samuel Fagemo (R)X100.005426756466606268545875496141427120610192500,000$
4Jonathan Davidsson (R)X100.006122736264616063617257465844484620590222650,000$
5Vasili Podkolzin (R)X100.007634556675567160565661554740407019590182500,000$
6Jan Jenik (R)X100.006533646877576659546255514941416420580191500,000$
7Jonathan Gruden (R)X100.006446595864516660456259485041416318560191500,000$
8Blake Murray (R)X100.005921585059575057524956355640406920510182500,000$
9Dmitri Samorukov (R)X94.026524847264637452396953774843435818660202500,000$
10Xavier Bernard (R)X100.005430636750435468326745593741416419560191500,000$
MOYENNE D’ÉQUIPE99.79673474716865696958706662594745603165
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
1Maxime Lagace100.00707069757276797573706957543768720261900,000$
2Logan Thompson100.00826077827869617372608851486068700222800,000$
Rayé
1Veini Vehvilainen100.00737476605672726158807349495420650221500,000$
2Matthew Thiessen100.00593951656447545755465341416120540191500,000$
MOYENNE D’ÉQUIPE100.0071616871686667676564715048534465
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Guy Boucher73787278387865CAN444500,000$


Astuces sur les filtres (anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Nom du joueur Nom de l’équipePOSGP G A P +/- PIM PIM5 HIT HTT SHT OSB OSM SHT% SB MP AMG PPG PPA PPP PPS PPM PKG PKA PKP PKS PKM GW GT FO% FOT GA TA EG HT P/20 PSG PSS FW FL FT S1 S2 S3
1Aleksi SaarelaMarlies (TOR)C74445094-65001421802566216117.19%52168822.821114252725300062214049.74%25133726011.11050001022
2Martin NecasMarlies (TOR)RW80434487-8480153692506615017.20%31159119.89121123402611015653158.39%1375223011.0913000872
3Pierre-Olivier JosephMarlies (TOR)D74137285842093153258931205.04%108201027.1781725472640338219010%06963100.8501000451
4Tyler BensonMarlies (TOR)LW69314576-36801311081806410117.22%35139420.214121613201202102004346.08%1023520021.0914000541
5Joel FarabeeMarlies (TOR)LW77344074046084842488214413.71%30130616.974812311591011236152.34%1285921001.13120001211
6Mathieu OlivierMarlies (TOR)RW8030417111375140882075913614.49%30140817.6169152121822461103052.17%694222101.0111001127
7Rasmus DahlinMarlies (TOR)D80114657-53408213617477816.32%101190023.756121824238022719920100.00%13462000.6000000221
8Alex FormentonMarlies (TOR)LW80222042-1260116611315210516.79%27118914.87426711901121074137.74%531717000.7101000121
9Lucas CarlssonMarlies (TOR)D7862935860881639127346.59%105188124.12369152540222233100%01967000.3700000001
10Matthew HighmoreMarlies (TOR)C68131831-9160716767323619.40%1990513.322354881011321053.28%51895000.6800000002
11Lias AnderssonMarlies (TOR)C6491827760879176205311.84%1897315.2224681320000511147.16%7041312000.5500000021
12Kole SherwoodMarlies (TOR)RW74131326-1644011163112516911.61%2684411.420000150112170241.94%311715000.6201000211
13Keaton ThompsonMarlies (TOR)D7312324-9635941006031231.67%83144819.8401121730001158000%02142000.3300000010
14Isac LundestromMarlies (TOR)C80111021-19220656371264715.49%1983010.39000050003551050.00%3561715000.5100000011
15Otto KoivulaMarlies (TOR)LW72119205220443476193414.47%155067.04000016000032058.82%3456000.7900000010
16Dmitri SamorukovMarlies (TOR)D6901111-182028772819140%5888412.82011019000048000%0524000.2500000001
17Arthur KaliyevMarlies (TOR)RW773811-1026062294212307.14%106198.0400002000000043.75%16310000.3600000010
18Dennis GilbertMarlies (TOR)D8001010-1239564875318200%59112814.11000035011060000%0538000.1801001000
19Jakob Forsbacka-KarlssonMarlies (TOR)C42336010032152781511.11%52515.9900001000011049.44%8936000.4800000110
20Hugh McGingMarlies (TOR)LW37516-9100402242192611.90%113178.5800001000041054.55%1164000.3800000101
21Jacob Bernard-DockerMarlies (TOR)D33011-1180102212270%1434710.5400000000013000%004000.0600000000
22Xavier BernardMarlies (TOR)D12011-440100010%2473.950000000003000%011000.4200000000
23Jonathan DavidssonMarlies (TOR)RW25011400756250%5903.62000010001110040.00%1001000.2200000000
24Vasili PodkolzinMarlies (TOR)LW18000-200002100%0261.450000000003000%00000000000000
25Samuel FagemoMarlies (TOR)LW17000-100110110%0321.92000010000180050.00%61000000000000
Statistiques d’équipe totales ou en moyenne1533303514817-10161915174617182469843141312.27%8632362815.4162100162239246771219551864341049.90%4778470504240.69419002363434
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
1Logan ThompsonMarlies (TOR)50182230.8734.022686211801414742530.71474436110
2Maxime LagaceMarlies (TOR)41181450.8753.922145001401123534220.636113644111
Statistiques d’équipe totales ou en moyenne91363680.8743.974831213202537127675188080221


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 Pays 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 Forcer UFA Rappel d'urgence Type Salaire actuel Salaire restantPlafond salarial Plafond salarial restant Exclus du plafond salarial Salaire année 2Salaire année 3Salaire année 4Salaire année 5Salaire année 6Salaire année 7Salaire année 8Salaire année 9Salaire année 10Plafond salarial année 2Plafond salarial année 3Plafond salarial année 4Plafond salarial année 5Plafond salarial année 6Plafond salarial année 7Plafond salarial année 8Plafond salarial année 9Plafond salarial année 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
Aleksi SaarelaMarlies (TOR)C221997-01-01FINYes200 Lbs5 ft10NoNoN/ANoNo1FalseFalsePro & Farm900,000$0$0$No---------------------------
Alex FormentonMarlies (TOR)LW201999-01-01CANYes195 Lbs6 ft3NoNoFree AgentNoNo22024-09-24FalseFalsePro & Farm950,000$0$0$No950,000$--------950,000$--------No--------
Arthur KaliyevMarlies (TOR)RW182001-01-01UZBYes209 Lbs6 ft2NoNoAssign ManuallyNoNo22024-07-10FalseFalsePro & Farm500,000$0$0$No500,000$--------500,000$--------No--------Lien
Blake MurrayMarlies (TOR)C182001-01-01CANYes190 Lbs6 ft2NoNoAssign ManuallyNoNo22024-07-10FalseFalsePro & Farm500,000$0$0$No500,000$--------500,000$--------No--------Lien
Dennis GilbertMarlies (TOR)D231996-01-01USAYes216 Lbs6 ft2NoNoFree AgentNoNo12024-09-24FalseFalsePro & Farm500,000$0$0$No---------------------------
Dmitri Samorukov (sur la masse salariale)Marlies (TOR)D201999-01-01RUSYes188 Lbs6 ft3NoNoFree AgentNoNo22024-09-24FalseFalsePro & Farm500,000$0$0$Yes500,000$--------500,000$--------No--------
Hugh McGingMarlies (TOR)LW211998-01-01USAYes176 Lbs5 ft8NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Isac LundestromMarlies (TOR)C201999-01-01SWEYes193 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Jacob Bernard-DockerMarlies (TOR)D192000-01-01CANYes190 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Jakob Forsbacka-KarlssonMarlies (TOR)C231996-01-01SWEYes184 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm550,000$0$0$No---------------------------
Jan JenikMarlies (TOR)RW192000-01-01CZEYes185 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Joel FarabeeMarlies (TOR)LW192000-01-01USAYes180 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Jonathan DavidssonMarlies (TOR)RW221997-01-01SWEYes181 Lbs5 ft11NoNoN/ANoNo2FalseFalsePro & Farm650,000$0$0$No650,000$--------650,000$--------No--------
Jonathan GrudenMarlies (TOR)C192000-01-01USAYes172 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Keaton ThompsonMarlies (TOR)D241995-01-01USANo185 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm600,000$0$0$No---------------------------
Kole SherwoodMarlies (TOR)RW221997-01-01USAYes212 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm900,000$0$0$No---------------------------
Lias AnderssonMarlies (TOR)C211998-01-01SWEYes185 Lbs6 ft1NoNoFree Agent2024-08-01NoNo22024-09-24FalseFalsePro & Farm750,000$0$0$No850,000$--------750,000$--------No--------
Logan ThompsonMarlies (TOR)G221997-01-01CANNo201 Lbs6 ft4NoNoFree AgentNoNo22024-09-24FalseFalsePro & Farm800,000$0$0$No900,000$--------800,000$--------No--------
Lucas CarlssonMarlies (TOR)D221997-01-01SWEYes190 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm900,000$0$0$No---------------------------
Martin NecasMarlies (TOR)RW201999-01-01CZEYes189 Lbs6 ft2NoNoFree AgentNoNo12024-09-24FalseFalsePro & Farm950,000$0$0$No---------------------------
Mathieu OlivierMarlies (TOR)RW221997-01-01CANYes209 Lbs6 ft2NoNoN/ANoNo1FalseFalsePro & Farm900,000$0$0$No---------------------------
Matthew HighmoreMarlies (TOR)C231996-01-01CANYes188 Lbs5 ft11NoNoN/ANoNo2FalseFalsePro & Farm500,000$0$0$No500,000$--------500,000$--------No--------
Matthew ThiessenMarlies (TOR)G192000-01-01CANNo208 Lbs6 ft2NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Maxime LagaceMarlies (TOR)G261993-01-01CANNo190 Lbs6 ft0NoNoFree AgentNoNo12024-09-24FalseFalsePro & Farm900,000$0$0$No---------------------------
Otto KoivulaMarlies (TOR)LW211998-01-01FINYes220 Lbs6 ft4NoNoN/ANoNo1FalseFalsePro & Farm750,000$0$0$No---------------------------
Pierre-Olivier JosephMarlies (TOR)D201999-01-01CANYes185 Lbs6 ft2NoNoFree AgentNoNo22024-09-24FalseFalsePro & Farm975,000$0$0$No1,750,000$--------975,000$--------No--------
Rasmus DahlinMarlies (TOR)D192000-01-01SWEYes202 Lbs6 ft3NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Samuel FagemoMarlies (TOR)LW192000-01-01SWEYes201 Lbs6 ft0NoNoAssign ManuallyNoNo22024-07-10FalseFalsePro & Farm500,000$0$0$No500,000$--------500,000$--------No--------Lien
Tyler BensonMarlies (TOR)LW211998-01-01CANYes190 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm900,000$0$0$No---------------------------
Vasili PodkolzinMarlies (TOR)LW182001-01-01RUSYes190 Lbs6 ft1NoNoAssign ManuallyNoNo22024-07-10FalseFalsePro & Farm500,000$0$0$No500,000$--------500,000$--------No--------Lien
Veini VehvilainenMarlies (TOR)G221997-01-01FINNo174 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Xavier BernardMarlies (TOR)D192000-01-01CANYes200 Lbs6 ft4NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
3220.72193 Lbs6 ft11.34652,344$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Tyler BensonAleksi SaarelaMartin Necas40122
2Joel FarabeeLias AnderssonMathieu Olivier30122
3Alex FormentonMatthew HighmoreKole Sherwood20122
4Otto KoivulaIsac LundestromArthur Kaliyev10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Pierre-Olivier JosephRasmus Dahlin40122
2Lucas CarlssonKeaton Thompson30122
3Dennis GilbertJacob Bernard-Docker20122
4Pierre-Olivier JosephRasmus Dahlin10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Joel FarabeeAleksi SaarelaMartin Necas60122
2Tyler BensonLias AnderssonMathieu Olivier40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Pierre-Olivier JosephRasmus Dahlin60122
2Lucas CarlssonKeaton Thompson40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Aleksi SaarelaTyler Benson60122
2Lias AnderssonAlex Formenton40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Pierre-Olivier JosephLucas Carlsson60122
2Rasmus DahlinKeaton Thompson40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Aleksi Saarela60122Pierre-Olivier JosephLucas Carlsson60122
2Lias Andersson40122Rasmus DahlinKeaton Thompson40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Aleksi SaarelaTyler Benson60122
2Lias AnderssonMathieu Olivier40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Pierre-Olivier JosephRasmus Dahlin60122
2Lucas CarlssonKeaton Thompson40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Joel FarabeeAleksi SaarelaMartin NecasPierre-Olivier JosephRasmus Dahlin
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Tyler BensonAleksi SaarelaMathieu OlivierPierre-Olivier JosephRasmus Dahlin
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Alex Formenton, Joel Farabee, Kole SherwoodAlex Formenton, Joel FarabeeAlex Formenton
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Keaton Thompson, Dennis Gilbert, Jacob Bernard-DockerKeaton ThompsonKeaton Thompson, Dennis Gilbert
Tirs de pénalité
Tyler Benson, Aleksi Saarela, Martin Necas, Mathieu Olivier, Alex Formenton
Gardien
#1 : Logan Thompson, #2 : Maxime Lagace
Lignes d’attaque personnalisées en prolongation
Tyler Benson, Aleksi Saarela, Martin Necas, Mathieu Olivier, Alex Formenton, Joel Farabee, Kole Sherwood, Lias Andersson, Matthew Highmore, Isac Lundestrom
Lignes de défense personnalisées en prolongation
Pierre-Olivier Joseph, Lucas Carlsson, Rasmus Dahlin, Keaton Thompson, Dennis Gilbert


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
1Admirals31200000511-620200000310-71100000021120.333581300961069781167928278283888282675900.00%13284.62%0832171048.65%887174950.71%665128651.71%173396716677361442734
2Americans30201000910-1100010004312020000057-220.33391524009610697810479282782838842118581200.00%9366.67%0832171048.65%887174950.71%665128651.71%173396716677361442734
3Barracuda42100100181441000010034-1321000001510550.62518304800961069781007928278283813447318915213.33%13376.92%0832171048.65%887174950.71%665128651.71%173396716677361442734
4Bears21000001541110000003121000000123-130.7505101500961069785779282782838511414444125.00%70100.00%0832171048.65%887174950.71%665128651.71%173396716677361442734
5Canucks20200000712-51010000036-31010000046-200.000781500961069786979282782838621710557114.29%5420.00%0832171048.65%887174950.71%665128651.71%173396716677361442734
6Checkers21100000871110000007521010000012-120.5008152300961069785979282782838712212527228.57%6183.33%0832171048.65%887174950.71%665128651.71%173396716677361442734
7Comets210010001192110000005411000100065141.0001121320096106978657928278283879231641200.00%8275.00%0832171048.65%887174950.71%665128651.71%173396716677361442734
8Condors311000101415-12100001013941010000016-540.667142337009610697891792827828389444316511327.27%10280.00%0832171048.65%887174950.71%665128651.71%173396716677361442734
9Crunch31101000141401100000053220101000911-240.667142337009610697889792827828381042120611317.69%100100.00%1832171048.65%887174950.71%665128651.71%173396716677361442734
10Eagles20100001811-31000000145-11010000046-210.2508152310961069786679282782838652718417342.86%9277.78%1832171048.65%887174950.71%665128651.71%173396716677361442734
11Firebirds30200010913-420100010810-21010000013-220.333913221096106978887928278283810338187810330.00%8362.50%1832171048.65%887174950.71%665128651.71%173396716677361442734
12Griffins31200000916-71010000049-52110000057-220.33391726009610697884792827828388130205310330.00%10280.00%0832171048.65%887174950.71%665128651.71%173396716677361442734
13Gulls33000000166102200000010371100000063361.0001628440096106978927928278283810521225614428.57%11190.91%1832171048.65%887174950.71%665128651.71%173396716677361442734
14Icehogs312000001113-22110000067-11010000056-120.33311172800961069789679282782838984324749222.22%12375.00%1832171048.65%887174950.71%665128651.71%173396716677361442734
15Islander2020000039-61010000027-51010000012-100.00034700961069786579282782838632512468225.00%6350.00%0832171048.65%887174950.71%665128651.71%173396716677361442734
16Little Stars513010002127-620101000770312000001420-640.40021375800961069781617928278283817844468715640.00%24579.17%0832171048.65%887174950.71%665128651.71%173396716677361442734
17Moose422000001712531200000101001100000072540.50017264310961069781157928278283812051389619421.05%18383.33%2832171048.65%887174950.71%665128651.71%173396716677361442734
18Penguins2110000056-1110000002111010000035-220.500591400961069785379282782838652518534125.00%9277.78%0832171048.65%887174950.71%665128651.71%173396716677361442734
19Phantoms6320010020191320001001055312000001014-470.583203252019610697818079282782838177566012928310.71%29679.31%0832171048.65%887174950.71%665128651.71%173396716677361442734
20Punishers22000000972110000004311100000054141.0009142300961069787879282782838501516418225.00%8187.50%0832171048.65%887174950.71%665128651.71%173396716677361442734
21Rockets614000012226-43110000112111303000001015-530.250223961109610697818679282782838179673915234514.71%17570.59%0832171048.65%887174950.71%665128651.71%173396716677361442734
22Senators413000001218-61010000023-1312000001015-520.25012203200961069781287928278283813157288816212.50%14471.43%0832171048.65%887174950.71%665128651.71%173396716677361442734
23Thunderbirds32000100161332200000012841000010045-150.833162844009610697884792827828389940188115533.33%9277.78%0832171048.65%887174950.71%665128651.71%173396716677361442734
24Wolfpack210001009811000010056-11100000042230.75091625009610697870792827828386719163310220.00%9188.89%0832171048.65%887174950.71%665128651.71%173396716677361442734
25Wranglers632001002728-1311001001517-2321000001211170.58327467310961069781737928278283819468509824520.83%26580.77%0832171048.65%887174950.71%665128651.71%173396716677361442734
Total80303604523305328-2340171302422159157240132302101146171-25800.5003055148195196106978246979282782838254286362117463116219.94%3006578.33%7832171048.65%887174950.71%665128651.71%173396716677361442734
_Since Last GM Reset80303604523305328-2340171302422159157240132302101146171-25800.5003055148195196106978246979282782838254286362117463116219.94%3006578.33%7832171048.65%887174950.71%665128651.71%173396716677361442734
_Vs Conference48192301311180188-824990131192911241014000008897-9460.4791803014813196106978146579282782838148553338710332013316.42%1823978.57%4832171048.65%887174950.71%665128651.71%173396716677361442734
_Vs Division1878002016973-49420020137334936000003240-8170.47269117186219610697853979282782838550191149379861315.12%721677.78%0832171048.65%887174950.71%665128651.71%173396716677361442734

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
8080L130551481924692542863621174651
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
8030364523305328
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
4017132422159157
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
4013232101146171
Derniers 10 matchs
WLOTWOTL SOWSOL
440110
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
3116219.94%3006578.33%7
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
7928278283896106978
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
832171048.65%887174950.71%665128651.71%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
173396716677361442734


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
12Marlies3Little Stars8LSommaire du match
522Moose5Marlies3LSommaire du match
732Wranglers7Marlies6LXR1Sommaire du match
942Marlies4Senators3WSommaire du match
1362Rockets2Marlies6WR1Sommaire du match
1571Marlies3Rockets4LSommaire du match
1886Phantoms0Marlies5WR1Sommaire du match
1994Marlies1Barracuda5LSommaire du match
21104Marlies5Wranglers3WR1Sommaire du match
22113Marlies6Phantoms2WSommaire du match
25128Moose2Marlies6WSommaire du match
28144Wranglers4Marlies5WR1Sommaire du match
30157Marlies4Griffins2WSommaire du match
32165Marlies2Phantoms7LR1Sommaire du match
34177Icehogs5Marlies3LSommaire du match
37192Marlies5Crunch4WXSommaire du match
39203Gulls2Marlies7WSommaire du match
41209Marlies6Comets5WXSommaire du match
46229Griffins9Marlies4LSommaire du match
49247Americans3Marlies4WXSommaire du match
51257Marlies1Firebirds3LSommaire du match
54275Barracuda4Marlies3LXSommaire du match
57288Marlies5Punishers4WSommaire du match
59300Islander7Marlies2LSommaire du match
63321Thunderbirds4Marlies5WSommaire du match
65329Marlies1Checkers2LSommaire du match
67342Marlies2Phantoms5LR1Sommaire du match
70356Moose3Marlies1LSommaire du match
72369Marlies2Bears3LXXSommaire du match
73380Admirals6Marlies1LSommaire du match
78399Wranglers6Marlies4LR1Sommaire du match
80413Marlies4Wolfpack2WSommaire du match
82421Marlies1Griffins5LSommaire du match
83430Eagles5Marlies4LXXSommaire du match
86444Marlies5Icehogs6LSommaire du match
89456Admirals4Marlies2LSommaire du match
93479Condors4Marlies7WSommaire du match
95488Marlies4Wranglers3WR1Sommaire du match
98503Penguins1Marlies2WSommaire du match
103525Canucks6Marlies3LSommaire du match
105533Marlies3Wranglers5LR1Sommaire du match
108550Checkers5Marlies7WSommaire du match
110565Marlies4Thunderbirds5LXSommaire du match
112573Marlies3Rockets4LR1Sommaire du match
114581Wolfpack6Marlies5LXSommaire du match
116595Marlies4Rockets7LR1Sommaire du match
118603Marlies2Americans3LSommaire du match
119613Senators3Marlies2LSommaire du match
123628Marlies6Gulls3WSommaire du match
125639Bears1Marlies3WSommaire du match
129661Comets4Marlies5WSommaire du match
131674Marlies1Islander2LSommaire du match
132684Marlies1Condors6LSommaire du match
134692Rockets5Marlies3LR1Sommaire du match
137709Marlies4Eagles6LSommaire du match
138719Rockets4Marlies3LXXR1Sommaire du match
142739Crunch3Marlies5WSommaire du match
143745Marlies6Little Stars5WSommaire du match
146763Condors5Marlies6WXXSommaire du match
148773Marlies4Crunch7LSommaire du match
151791Gulls1Marlies3WSommaire du match
153802Marlies3Penguins5LSommaire du match
156814Icehogs2Marlies3WSommaire du match
158825Marlies8Barracuda1WSommaire du match
160833Marlies3Americans4LSommaire du match
162847Punishers3Marlies4WSommaire du match
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
166869Little Stars4Marlies3LSommaire du match
168879Marlies5Little Stars7LSommaire du match
171893Little Stars3Marlies4WXSommaire du match
172899Marlies6Barracuda4WSommaire du match
174908Marlies2Admirals1WSommaire du match
175917Marlies4Canucks6LSommaire du match
177927Marlies7Moose2WSommaire du match
178935Thunderbirds4Marlies7WSommaire du match
183959Firebirds6Marlies3LSommaire du match
187977Firebirds4Marlies5WXXSommaire du match
192999Phantoms3Marlies2LXR1Sommaire du match
1931005Marlies4Senators5LSommaire du match
1961021Phantoms2Marlies3WR1Sommaire du match
2001036Marlies2Senators7LSommaire du match



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets7040
Assistance72,78936,446
Assistance PCT90.99%91.12%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
0 2731 - 91.03% 172,018$6,880,724$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursPlafond Salariale total des joueursSalaire des entraineurs
2,550,768$ 2,087,500$ 2,087,500$ 500,000$0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
10,087$ 2,027,302$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
0$ 0 12,809$ 0$




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

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

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

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

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