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Connexion

Senators
GP: 80 | W: 42 | L: 29 | OTL: 9 | P: 93
GF: 328 | GA: 324 | PP%: 25.50% | PK%: 78.70%
DG: Didier Theodore | Morale : 42 | Moyenne d’équipe : 65

Centre de jeu
Senators
42-29-9, 93pts
5
FINAL
4 Wranglers
42-33-5, 89pts
Team Stats
W4SéquenceW1
22-17-1Fiche domicile21-17-2
20-12-8Fiche domicile21-16-3
5-4-1Derniers 10 matchs4-6-0
4.10Buts par match 4.20
4.05Buts contre par match 4.16
25.50%Pourcentage en avantage numérique21.80%
78.70%Pourcentage en désavantage numérique79.82%
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
Radek Faksa
56
Passes
Radek Faksa
74
Points
Radek Faksa
130
Plus/Moins
Radek Faksa
7
Victoires
David Rittich
24
Pourcentage d’arrêts
David Rittich
0.878

Statistiques d’équipe
Buts pour
328
4.10 GFG
Tirs pour
2769
34.61 Avg
Pourcentage en avantage numérique
25.5%
90 GF
Début de zone offensive
38.5%
Buts contre
324
4.05 GAA
Tirs contre
2550
31.88 Avg
Pourcentage en désavantage numérique
78.7%%
46 GA
Début de la zone défensive
34.2%
Informations de l'équipe

Directeur généralDidier Theodore
EntraîneurMario Lemieux
DivisionJohn-Ahearne
ConférenceRobert-Lebel
CapitaineRadek Faksa
Assistant #1Carl Grundstrom
Assistant #2C.J Suess


Informations de l’aréna

Capacité3,000
Assistance2,817
Billets de saison300


Informations de la formation

Équipe Pro30
Équipe Mineure20
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
1Radek Faksa (C)X100.007345838177737478797376657369573675740253950,000$
2Brendan Perlini (R)X100.007322828078767976687187627658494623730231850,000$
3C.J Suess (A)X100.007931787581716774657278676854544142710251850,000$
4Marc Michaelis (R)X100.007739767168776471687775726754514657710241750,000$
5Chase De Leo (R)X100.007130827474767368747977586652494749700231900,000$
6Kiefer SherwoodX100.007043756781646675746588617049473870700241900,000$
7Carl Grundstrom (R) (A)X100.007252877171767070696971667350495962690221900,000$
8Vitaly Abramov (R)X100.005635797672676573606877537146465462670211500,000$
9Arttu Ruotsalainen (R)X100.005237736962726768575984576847474539650221600,000$
10Cliff Pu (R)X100.005726725464655565586470406743434132590211500,000$
11Bryce Kindopp (R)X100.006133655966566160605766596143436058590204500,000$
12Akil Thomas (R)X100.007346595668496557466160485641415848560191500,000$
13Zac LeslieX100.007947737972767475526969827176533622740252800,000$
14Artem ZubX100.007055848077747980517262766472543864740242975,000$
15Corey ShuenemanX100.007647837264807071527660796159534640720242750,000$
16Jesse GrahamX100.005445758070656974527164725451493158670251800,000$
17Jonas Siegenthaler (R)X100.006326856668607361387054805248474368670223600,000$
18Mason GeertsenX100.007245786467597968546257706252513667660241500,000$
19Martin Fehervary (R)X100.006140727559556580438556624845456252650201500,000$
20Scott Perunovich (R)X100.005836767256597375407454735246456041650211500,000$
Rayé
1Anton Karlsson (R)X100.006526697865685876607065687347474123660232800,000$
2Adam Mascherin (R)X100.005441667354605871636267577546466112630212500,000$
3Tyler Angle (R)X100.005444586452676161595652455942436320550192500,000$
4Ivan Morozov (R)X100.005338615761505958545560474841415819540191500,000$
5Gustav Forsling (R)X100.007134766358706163446670605746464347630231500,000$
6Alec Regula (R)X100.006142635755616066465155674441416028590191500,000$
7Jett Woo (R)X100.005523636145526164336841574043415219550192500,000$
MOYENNE D’ÉQUIPE100.00653874696666677056676663625047484466
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
1David Rittich100.006865856362828459558474595652816902721,500,000$
2Justus Annunen100.00786271737863596867487942416969650191500,000$
Rayé
1Olivier Rodrigue100.00437054555457595970574741416220570193500,000$
MOYENNE D’ÉQUIPE100.0063667064656767626463674746615764
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Mario Lemieux58697272515555CAN5511,000,000$


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
1Radek FaksaSenators (Ott)C7456741307261010715533410019516.77%41166722.531735526327600071687454.52%25316233231.56381019142
2Chase De LeoSenators (Ott)C81334477-62401181102136011915.49%27147018.15715222624003351392349.75%10014227011.0501000253
3C.J SuessSenators (Ott)LW64274976-2300129862577615210.51%29135221.141223354923800051215240.95%1055030001.1226000355
4Kiefer SherwoodSenators (Ott)RW803837753500115892135010317.84%30164320.55191534493020000293155.04%1293528020.9114000606
5Marc MichaelisSenators (Ott)LW77274471-4380139932245312812.05%50136817.7781018291940001933161.18%854428011.0404000334
6Artem ZubSenators (Ott)D79104757-1414010013318591635.41%127206226.1071320343270111191100%06265000.5500000111
7Carl GrundstromSenators (Ott)RW80263056-77510091140539118.57%18134916.8791019162330001224152.38%842913000.8302010024
8Vitaly AbramovSenators (Ott)LW80302656-48057582186212113.76%2198612.331454542025334238.71%313511111.1400000423
9Jesse GrahamSenators (Ott)D8033538-194404412511264432.68%81162420.30044132430114138200%04252000.4700000000
10Jonas SiegenthalerSenators (Ott)D8032831-3120631035529235.45%86146418.3124651810113113200%0347100.4200000110
11Christoph BertschySenateursC17101727780283277233512.99%532819.330334551013351249.57%347143011.6412000411
12Corey ShuenemanSenators (Ott)D6142125-1712084977238265.56%78144323.66358202060111151000%02633000.3500000011
13Arttu RuotsalainenSenators (Ott)C70131225-373555270114326211.40%2285212.182353400000120144.70%4811315000.5900000003
14Zac LeslieSenators (Ott)D3531821-824061586929294.35%6290425.85281017148000263000%01934000.4600000011
15Brendan PerliniSenators (Ott)LW1481119-320251264265212.50%1131122.2426817540001300060.71%28123001.2202000320
16Anton KarlssonSenators (Ott)LW498816-2460343211449597.02%2152010.620000120003370146.67%152211000.6100000001
17Martin FehervarySenators (Ott)D6741216-1014022394422169.09%3367510.0900005000021000%0918000.4700000011
18Bryce KindoppSenators (Ott)RW757916-5180523151163613.73%115707.6100002000001223.08%13710000.5600000011
19Akil ThomasSenators (Ott)C706713-83001003532102118.75%136559.360000110000141132.58%26726000.4000000000
20Mason GeertsenSenators (Ott)D7411011319546654518302.22%4093912.69123154000245000%0529000.2301001000
21Scott PerunovichSenators (Ott)D762810-722030594128194.88%4787611.5300014000132000%0321000.2300000000
22Adam MascherinSenators (Ott)LW22448-420151636182111.11%51808.1900011000001114.29%791000.8900000100
23Cliff PuSenators (Ott)RW49246-221404122347275.88%1155811.40000010000001063.64%1142000.2100000000
24Tyler AngleSenators (Ott)C332243601715102720.00%32557.7300000000000043.56%10101000.3100000001
25Gustav ForslingSenators (Ott)D65033-740132116750%254787.3600006000021000%029000.1300000000
26Jett WooSenators (Ott)D29022-120240000%71364.700000000002000%001000.2900000000
27Alec RegulaSenators (Ott)D50011-416012124440%182484.970000000003000%008000.0800000000
28Ivan MorozovSenators (Ott)C1000000000000%011.320000000001000%00000000000000
29Jesse PuljujarviSenateursRW1000-200114130%12525.2700016000030062.50%81000000000000
Statistiques d’équipe totales ou en moyenne1633327563890-19548725160716642778968149011.77%9232495015.289216025235329133710451527382250.69%5244552539490.71730112374038
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
1David RittichSenators (Ott)50241760.8783.802682001701396649710.600154526321
2Justus AnnunenSenators (Ott)41181240.8773.982219001471194616410.400103739401
3Olivier RodrigueSenators (Ott)10001.0000330001814010009000
Statistiques d’équipe totales ou en moyenne924229100.8783.8549350031726081279113258274722


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
Adam MascherinSenators (Ott)LW211998-01-01CANYes205 Lbs5 ft10NoNoAssign ManuallyNoNo22024-07-10FalseFalsePro & Farm500,000$0$0$No500,000$--------500,000$--------No--------Lien
Akil ThomasSenators (Ott)C192000-01-01CANYes195 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Alec RegulaSenators (Ott)D192000-01-01USAYes208 Lbs6 ft4NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Anton KarlssonSenators (Ott)LW231996-01-01SWEYes194 Lbs5 ft11NoNoN/ANoNo2FalseFalsePro & Farm800,000$0$0$No800,000$--------600,000$--------No--------
Artem ZubSenators (Ott)D241995-01-01RUSNo198 Lbs6 ft2NoNoTrade2024-08-06NoNo2FalseFalsePro & Farm975,000$0$0$No975,000$--------975,000$--------No--------
Arttu RuotsalainenSenators (Ott)C221997-01-01FINYes181 Lbs5 ft8NoNoN/ANoNo1FalseFalsePro & Farm600,000$0$0$No---------------------------
Brendan PerliniSenators (Ott)LW231996-01-01GBRYes211 Lbs6 ft3NoNoTrade2025-04-14NoNo1FalseFalsePro & Farm850,000$0$0$No---------------------------
Bryce KindoppSenators (Ott)RW201999-01-01CANYes185 Lbs6 ft1NoNoFree AgentNoNo42024-08-21FalseFalsePro & Farm500,000$0$0$No500,000$500,000$500,000$------500,000$500,000$500,000$------NoNoNo------
C.J SuessSenators (Ott)LW251994-01-01USANo190 Lbs5 ft11NoNoN/ANoNo1FalseFalsePro & Farm850,000$0$0$No---------------------------
Carl GrundstromSenators (Ott)RW221997-01-01SWEYes194 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm900,000$0$0$No---------------------------
Chase De LeoSenators (Ott)C231996-01-01USAYes179 Lbs5 ft9NoNoTrade2024-12-29NoNo1FalseFalsePro & Farm900,000$0$0$No---------------------------
Cliff PuSenators (Ott)RW211998-01-01CANYes185 Lbs6 ft2NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Corey ShuenemanSenators (Ott)D241995-01-01USANo196 Lbs6 ft0NoNoFree AgentNoNo22025-02-02FalseFalsePro & Farm750,000$0$0$No750,000$--------750,000$--------No--------
David RittichSenators (Ott)G271992-01-01CZENo206 Lbs6 ft3NoNoN/ANoNo2FalseFalsePro & Farm1,500,000$0$0$No2,500,000$--------950,000$--------No--------
Gustav ForslingSenators (Ott)D231996-01-01SWEYes186 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Ivan MorozovSenators (Ott)C192000-01-01RUSYes178 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Jesse GrahamSenators (Ott)D251994-01-01CANNo170 Lbs5 ft11NoNoTrade2024-08-08NoNo1FalseFalsePro & Farm800,000$0$0$No---------------------------
Jett WooSenators (Ott)D192000-01-01CANYes190 Lbs6 ft0NoNoAssign ManuallyNoNo22024-07-10FalseFalsePro & Farm500,000$0$0$No500,000$--------500,000$--------No--------Lien
Jonas SiegenthalerSenators (Ott)D221997-01-01CHEYes210 Lbs6 ft3NoNoFree AgentNoNo32024-08-21FalseFalsePro & Farm600,000$0$0$No600,000$600,000$-------600,000$600,000$-------NoNo-------
Justus AnnunenSenators (Ott)G192000-01-01FINNo210 Lbs6 ft4NoNoTrade2024-12-28NoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Kiefer SherwoodSenators (Ott)RW241995-01-01USANo194 Lbs6 ft0NoNoTrade2024-09-07NoNo1FalseFalsePro & Farm900,000$0$0$No---------------------------
Marc MichaelisSenators (Ott)LW241995-01-01DEUYes187 Lbs5 ft11NoNoN/ANoNo1FalseFalsePro & Farm750,000$0$0$No---------------------------
Martin FehervarySenators (Ott)D201999-01-01SVKYes199 Lbs6 ft2NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Mason GeertsenSenators (Ott)D241995-01-01CANNo185 Lbs6 ft4NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Olivier RodrigueSenators (Ott)G192000-01-01CANNo158 Lbs6 ft1NoNoN/ANoNo3FalseFalsePro & Farm500,000$0$0$No500,000$500,000$-------500,000$500,000$-------NoNo-------
Radek FaksaSenators (Ott)C251994-01-01CZENo200 Lbs6 ft2NoNoFree Agent2024-08-06NoNo32024-08-21FalseFalsePro & Farm950,000$0$0$No950,000$950,000$-------950,000$950,000$-------NoNo-------
Scott PerunovichSenators (Ott)D211998-01-01USAYes175 Lbs5 ft10NoNoTrade2025-04-14NoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Tyler AngleSenators (Ott)C192000-01-01CANYes165 Lbs5 ft10NoNoAssign ManuallyNoNo22024-07-10FalseFalsePro & Farm500,000$0$0$No500,000$--------500,000$--------No--------Lien
Vitaly AbramovSenators (Ott)LW211998-01-01RUSYes181 Lbs5 ft10NoNoN/ANoNo1FalseFalsePro & Farm500,000$0$0$No---------------------------
Zac LeslieSenators (Ott)D251994-01-01CANNo185 Lbs6 ft0NoNoFree AgentNoNo22024-10-18FalseFalsePro & Farm800,000$0$0$No800,000$--------800,000$--------No--------
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
3022.07190 Lbs6 ft01.57680,833$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Brendan PerliniRadek FaksaKiefer Sherwood40122
2C.J SuessChase De LeoCarl Grundstrom30122
3Marc MichaelisArttu RuotsalainenCliff Pu20122
4Vitaly AbramovAkil ThomasBryce Kindopp10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Zac LeslieArtem Zub40122
2Corey ShuenemanJesse Graham30122
3Jonas SiegenthalerMason Geertsen20122
4Martin FehervaryScott Perunovich10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Brendan PerliniRadek FaksaKiefer Sherwood60122
2C.J SuessChase De LeoCarl Grundstrom40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Zac LeslieArtem Zub60122
2Corey ShuenemanJesse Graham40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Radek FaksaBrendan Perlini60122
2Chase De LeoC.J Suess40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Zac LeslieArtem Zub60122
2Corey ShuenemanJesse Graham40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Radek Faksa60122Zac LeslieArtem Zub60122
2Chase De Leo40122Corey ShuenemanJesse Graham40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Radek FaksaBrendan Perlini60122
2Chase De LeoC.J Suess40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Zac LeslieArtem Zub60122
2Corey ShuenemanJesse Graham40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Brendan PerliniRadek FaksaKiefer SherwoodZac LeslieArtem Zub
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Brendan PerliniRadek FaksaKiefer SherwoodZac LeslieArtem Zub
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Kiefer Sherwood, Chase De Leo, Carl GrundstromKiefer Sherwood, Chase De LeoKiefer Sherwood
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Mason Geertsen, Martin Fehervary, Scott PerunovichMason GeertsenMason Geertsen, Martin Fehervary
Tirs de pénalité
Radek Faksa, Brendan Perlini, C.J Suess, Marc Michaelis, Kiefer Sherwood
Gardien
#1 : Justus Annunen, #2 : David Rittich
Lignes d’attaque personnalisées en prolongation
Radek Faksa, Brendan Perlini, C.J Suess, Marc Michaelis, Kiefer Sherwood, Chase De Leo, Carl Grundstrom, Vitaly Abramov, Arttu Ruotsalainen, Cliff Pu
Lignes de défense personnalisées en prolongation
Zac Leslie, Artem Zub, Corey Shueneman, Jesse Graham, Jonas Siegenthaler


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
1Admirals64200000312653210000016124321000001514180.66731508100109951171221594995084851182664812430930.00%19384.21%1993190752.07%873169551.50%680134950.41%174299916767321438727
2Americans613000022228-630200001916-7311000011312140.333223557101099511712199949950848512116622101381026.32%11463.64%0993190752.07%873169551.50%680134950.41%174299916767321438727
3Barracuda3110100011101211000007701000100043140.6671118290010995117121009499508485110437166611545.45%80100.00%0993190752.07%873169551.50%680134950.41%174299916767321438727
4Bears22000000642110000002111100000043141.00061117001099511712689499508485159251047400.00%5260.00%0993190752.07%873169551.50%680134950.41%174299916767321438727
5Canucks311001001112-11100000031220100100811-330.5001118290010995117121079499508485110034245413430.77%7271.43%0993190752.07%873169551.50%680134950.41%174299916767321438727
6Checkers22000000945110000004131100000053241.000911200010995117126594995084851642412376233.33%6183.33%0993190752.07%873169551.50%680134950.41%174299916767321438727
7Comets2110000010821010000057-21100000051420.5001016260010995117126994995084851653318335360.00%9277.78%0993190752.07%873169551.50%680134950.41%174299916767321438727
8Condors31200000914-51100000064220200000310-720.333914230010995117121169499508485110021205316425.00%10280.00%0993190752.07%873169551.50%680134950.41%174299916767321438727
9Crunch21100000810-2110000003121010000059-420.500814220010995117127194995084851612614395120.00%7185.71%0993190752.07%873169551.50%680134950.41%174299916767321438727
10Eagles210000011183110000007341000000145-130.7501120310010995117127094995084851613463812325.00%30100.00%0993190752.07%873169551.50%680134950.41%174299916767321438727
11Firebirds20001001770100010002111000000156-130.75071219001099511712709499508485179286473133.33%3166.67%0993190752.07%873169551.50%680134950.41%174299916767321438727
12Griffins31200000121202020000079-21100000053220.33312213300109951171211894995084851984766013215.38%30100.00%0993190752.07%873169551.50%680134950.41%174299916767321438727
13Gulls513010001519-42020000058-3311010001011-140.4001524390010995117121759499508485116147149018316.67%7185.71%0993190752.07%873169551.50%680134950.41%174299916767321438727
14Icehogs612000122628-2312000001213-1300000121415-160.500264268101099511712207949950848512127140131401230.00%21861.90%0993190752.07%873169551.50%680134950.41%174299916767321438727
15Islander21001000743100010003211100000042241.00071320001099511712619499508485139148391417.14%40100.00%0993190752.07%873169551.50%680134950.41%174299916767321438727
16Little Stars31100010161152100001013671010000035-240.667162642101099511712110949950848519324257111218.18%10370.00%0993190752.07%873169551.50%680134950.41%174299916767321438727
17Marlies431000001812632100000151051100000032160.7501832500010995117121319499508485112835327714428.57%16287.50%0993190752.07%873169551.50%680134950.41%174299916767321438727
18Moose3110000113130110000006422010000179-230.500132235001099511712101949950848519433246916318.75%12375.00%1993190752.07%873169551.50%680134950.41%174299916767321438727
19Penguins21100000810-21010000025-31100000065120.50081119001099511712789499508485149231443700.00%7271.43%0993190752.07%873169551.50%680134950.41%174299916767321438727
20Phantoms413000001117-61010000034-131200000813-520.250112233001099511712137949950848511433928737228.57%14192.86%1993190752.07%873169551.50%680134950.41%174299916767321438727
21Punishers20001100990100010005411000010045-130.750916250010995117127094995084851592223910110.00%10100.00%0993190752.07%873169551.50%680134950.41%174299916767321438727
22Rockets321000001613321100000911-21100000072540.667162945001099511712115949950848519438105110440.00%5260.00%0993190752.07%873169551.50%680134950.41%174299916767321438727
23Thunderbirds5410000024231321000001214-222000000129380.80024366000109951171215594995084851142373410829827.59%17382.35%0993190752.07%873169551.50%680134950.41%174299916767321438727
24Wolfpack20200000614-81010000047-31010000027-500.000611170010995117126794995084851621912346233.33%6183.33%0993190752.07%873169551.50%680134950.41%174299916767321438727
25Wranglers330000001284110000003122200000097261.00012203200109951171294949950848519022235615426.67%5260.00%0993190752.07%873169551.50%680134950.41%174299916767321438727
Total803529052273283244401817030111631521140171202216165172-7930.581328544872301099511712276994995084851255086546815803539025.50%2164678.70%3993190752.07%873169551.50%680134950.41%174299916767321438727
_Since Last GM Reset803529052273283244401817030111631521140171202216165172-7930.581328544872301099511712276994995084851255086546815803539025.50%2164678.70%3993190752.07%873169551.50%680134950.41%174299916767321438727
_Vs Conference49202102015196200-4241013000019899-1251080201498101-3510.52019632952520109951171217089499508485116175222839512286227.19%1312878.63%3993190752.07%873169551.50%680134950.41%174299916767321438727
_Vs Division1867000147982-3935000013741-49320001342411180.50079127206201099511712621949950848516052031103561083128.70%511570.59%1993190752.07%873169551.50%680134950.41%174299916767321438727

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
8093W432854487227692550865468158030
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
8035295227328324
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
4018173011163152
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
4017122216165172
Derniers 10 matchs
WLOTWOTL SOWSOL
540001
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
3539025.50%2164678.70%3
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
949950848511099511712
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
993190752.07%873169551.50%680134950.41%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
174299916767321438727


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
13Admirals3Senators7WR1Sommaire du match
415Senators3Phantoms2WSommaire du match
625Senators6Admirals4WR1Sommaire du match
942Marlies4Senators3LSommaire du match
1157Senators6Americans2WR1Sommaire du match
1570Americans6Senators2LSommaire du match
1887Thunderbirds5Senators6WSommaire du match
2097Senators4Icehogs5LXXR1Sommaire du match
22110Senators3Americans5LR1Sommaire du match
24124Admirals6Senators4LSommaire du match
26134Senators2Gulls1WXSommaire du match
28143Icehogs5Senators3LR1Sommaire du match
30155Senators8Admirals7WSommaire du match
32169Senators6Icehogs7LXXR1Sommaire du match
34176Thunderbirds6Senators1LSommaire du match
38197Little Stars1Senators7WSommaire du match
40207Senators3Canucks5LSommaire du match
44224Little Stars5Senators6WXXSommaire du match
48240Senators6Penguins5WSommaire du match
50252Checkers1Senators4WSommaire du match
52267Senators4Eagles5LXXSommaire du match
55277Wolfpack7Senators4LSommaire du match
59296Gulls3Senators2LSommaire du match
61307Senators6Gulls4WSommaire du match
63320Crunch1Senators3WSommaire du match
65331Senators4Barracuda3WXSommaire du match
68348Admirals3Senators5WR1Sommaire du match
71366Senators4Americans5LXXR1Sommaire du match
72374Eagles3Senators7WSommaire du match
75384Senators5Checkers3WSommaire du match
77395Senators4Punishers5LXSommaire du match
79405Punishers4Senators5WXSommaire du match
81416Senators7Rockets2WSommaire du match
83428Islander2Senators3WXSommaire du match
87450Senators2Gulls6LSommaire du match
89459Phantoms4Senators3LSommaire du match
92474Senators5Griffins3WSommaire du match
94481Canucks1Senators3WSommaire du match
96496Senators4Icehogs3WXXR1Sommaire du match
100510Icehogs6Senators5LSommaire du match
105530Rockets6Senators7WSommaire du match
108548Senators4Bears3WSommaire du match
109558Griffins6Senators5LSommaire du match
113578Thunderbirds3Senators5WSommaire du match
115588Senators1Admirals3LR1Sommaire du match
118604Comets7Senators5LSommaire du match
119613Senators3Marlies2WSommaire du match
124631Firebirds1Senators2WXSommaire du match
126644Senators6Thunderbirds4WSommaire du match
128656Rockets5Senators2LSommaire du match
129663Senators3Little Stars5LSommaire du match
132681Wranglers1Senators3WSommaire du match
134690Senators5Canucks6LXSommaire du match
136707Barracuda2Senators4WSommaire du match
138720Senators4Islander2WSommaire du match
140728Senators2Wolfpack7LSommaire du match
142737Moose4Senators6WSommaire du match
145760Icehogs2Senators4WR1Sommaire du match
149783Griffins3Senators2LSommaire du match
152793Senators5Firebirds6LXXSommaire du match
154809Condors4Senators6WSommaire du match
156817Senators5Comets1WSommaire du match
158827Senators3Phantoms7LSommaire du match
160837Gulls5Senators3LSommaire du match
161842Senators2Condors5LSommaire du match
163853Senators5Crunch9LSommaire du match
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
166870Americans5Senators4LXXR1Sommaire du match
168878Senators2Phantoms4LSommaire du match
171892Senators6Thunderbirds5WSommaire du match
172897Penguins5Senators2LSommaire du match
173905Senators4Moose5LXXSommaire du match
176922Barracuda5Senators3LSommaire du match
180945Bears1Senators2WSommaire du match
181951Senators3Moose4LSommaire du match
185969Americans5Senators3LR1Sommaire du match
187978Senators1Condors5LSommaire du match
189987Senators4Wranglers3WSommaire du match
1931005Marlies4Senators5WSommaire du match
1961023Senators5Wranglers4WSommaire du match
2001036Marlies2Senators7WSommaire du match



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets7040
Assistance75,36837,303
Assistance PCT94.21%93.26%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
0 2817 - 93.89% 177,657$7,106,276$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursPlafond Salariale total des joueursSalaire des entraineurs
2,869,821$ 2,042,500$ 2,042,500$ 1,000,000$0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
10,111$ 1,860,742$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
0$ 0 15,062$ 0$




Senators 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

Senators 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

Senators 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

Senators 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

Senators 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