Senators

GP: 44 | W: 26 | L: 14 | OTL: 4 | P: 56
GF: 120 | GA: 92 | PP%: 16.91% | PK%: 80.70%
DG: Marc-André Bilodeau Lamontagne | Morale : 59 | Moyenne d'Équipe : 60
Prochain matchs #656 vs Marlies
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Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Nom du Joueur C L R D CON CK FG DI SK ST EN DU PH FO PA SC DF PS EX LD PO MO OV TA SP
1Andrew PoturalskiX100.005137886466949363756658576169656055630
2Adam CracknellX100.007036915985786658625958605484743970610
3A.J. GreerX100.006540806185766860615960615565637570610
4Matt ReadXX100.005636905968827058545759625682733970600
5Justin BaileyX100.006536905989766858555756605467646970600
6Boris Katchouk (R)X100.006538845880949556525354575561637770600
7Glenn Gawdin (R)X100.006139825976928958666054575663626363600
8Joshua Ho-SangXX100.005236916069856959626157585365638062590
9Gabriel FontaineX100.006436925678949354585553565263626370590
10Carsen Twarynski (R)X100.006538855680939155585453565463626366590
11Martin Kaut (R)X100.005937875776928956585554565560628348590
12Logan O'ConnorX100.005336925869777156545857595465635326580
13Joakim RyanX100.006443886369735762307056675271664768630
14Cody GoloubefX100.006439836077796659305754594678706166620
15Dakota MermisX100.005536905973817058305753574669755170600
16Jordan SchmaltzX100.005836915978766458306153594771667770600
17Andreas EnglundX100.005940795881847157305453594565637557600
18Tim ErixonX100.006538865480797353305452554875686756600
19Kyle CumiskeyX100.005336915868827657306051544682735270600
20Matt TaorminaX100.005437895568807454305552534682735228580
21Michael Prapavessis (R)X100.006236925476787253305451544665636224580
Rayé
1Vladislav KamenevX100.006138855779645057735758625865637420570
2Brad Morrison (R)X100.005336925669898355615453575263626320570
3Max McCormickX100.006539825970746658545257565473675020570
4Kole Lind (R)X100.005736905673908455595552545361636420570
5Grayson DowningX100.006242735473756953565451525073675820550
MOYENNE D'ÉQUIPE100.00603887587582755750575558526966625259
Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Nom du Gardien CON SK DU EN SZ AG RB SC HS RT PH PS EX LD PO MO OV TA SP
1Chris Driedger100.00777573877675777675777669735165740
2Evan Fitzpatrick (R)100.00706664856968706968706961657023670
Rayé
MOYENNE D'ÉQUIPE100.0074716986737274737274736569614471
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Benoit Groulx71727574797473CAN5151,000,000$


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
1Andrew PoturalskiSenators (Ott)RW371518331112033861343710511.19%1095425.8047112516100001285257.46%35500000.6916000442
2Glenn GawdinSenators (Ott)C44924336335427412530667.20%283218.922111324167000012160.00%82000000.7911010311
3Joakim RyanSenators (Ott)D44428328662067546722365.97%4099022.5131114471790000121000.00%000000.6500121223
4Adam CracknellSenators (Ott)RW4416122802206843128409012.50%294221.437411281650114943157.43%14800100.5904000213
5Justin BaileySenators (Ott)RW44814221326040488624619.30%369015.691123420000212057.14%6300000.6401000042
6Joshua Ho-SangSenators (Ott)C/RW361011211420134871187214.08%554015.02000130001513055.90%44900010.7800000122
7Matt ReadSenators (Ott)C/RW448132188032919235668.70%887319.86246211821012792054.67%92000000.4812000022
8Cody GoloubefSenators (Ott)D44912219575683261194714.75%3790720.636410351620000114120.00%000000.4600000123
9Taylor FedunOttawa SenatorsD35714216180274049143314.29%3379222.65369311420002107200.00%000000.5301000023
10A.J. GreerSenators (Ott)LW44416207340104528127944.94%595621.732682118200041431059.68%12400000.4215000210
11Jordan SchmaltzSenators (Ott)D4461319620039226123339.84%4485419.4324629130011099000.00%100000.4400000211
12Boris KatchoukSenators (Ott)LW44612185300583310835635.56%489720.39246341670001331051.11%4500000.4001000011
13Carsen TwarynskiSenators (Ott)LW35971613120272857132915.79%846813.3700000000051068.42%1900000.6800000212
14Kyle CumiskeySenators (Ott)D444913122005317178623.53%2361514.0010123000052100.00%000000.4200000111
15Dakota MermisSenators (Ott)D44110111520038323415222.94%3369915.900221245000038000.00%000000.3100000010
16Gabriel FontaineSenators (Ott)C44213-110028353111266.45%63447.840001100002210054.08%29400000.1700000100
17Logan O'ConnorSenators (Ott)RW4022100136190.00%14611.6800000000000033.33%300000.8600000000
18Andreas EnglundSenators (Ott)D12011-1155472130.00%213511.3301103000080025.00%400000.1500001000
19Vladislav KamenevSenators (Ott)C1000000000000.00%000.270000000000000.00%000000.0000000000
20Matt TaorminaSenators (Ott)D3000000010010.00%1134.660000200000000.00%000000.0000000000
21Tim ErixonSenators (Ott)D12000-340812000.00%813211.000000100009000.00%100000.0000000000
Stats d'équipe Total ou en Moyenne7031182173351294093575074712123738629.74%2751269118.053565100314175612316113424656.75%324600110.53421132212626
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
1Pheonix CopleyOttawa Senators30181020.9111.90174103556210000.75012300222
2Chris DriedgerSenators (Ott)178420.9052.0687523303170100.77891444201
Stats d'équipe Total ou en Moyenne47261440.9091.95261626859380100.762214444423


Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
Nom du Joueur Nom de l'ÉquipePOS Âge Date de Naissance Nouveau Joueur Poids Taille Non-Échange Disponible pour Échange Ballotage Forcé Contrat Type Salaire Actuel Cap Salariale Cap Salariale Restant Exclus du Cap Salarial Salaire Année 2Salaire Année 3Salaire Année 4Salaire Année 5Salaire Année 6Salaire Année 7Salaire Année 8Salaire Année 9Salaire Année 10Link
A.J. GreerSenators (Ott)LW221996-12-14No210 Lbs6 ft3NoNoNo3Pro & Farm500,000$0$0$No500,000$500,000$Lien
Adam CracknellSenators (Ott)RW331985-07-15No209 Lbs6 ft3NoNoNo1Pro & Farm500,000$0$0$NoLien
Andreas EnglundSenators (Ott)D231996-01-21No189 Lbs6 ft3NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Andrew PoturalskiSenators (Ott)RW251994-01-14No180 Lbs5 ft10NoNoNo1Pro & Farm300,000$0$0$NoLien
Boris KatchoukSenators (Ott)LW211998-06-18Yes199 Lbs6 ft2NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$500,000$Lien
Brad MorrisonSenators (Ott)C221997-01-04Yes171 Lbs6 ft0NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Carsen TwarynskiSenators (Ott)LW211997-11-24Yes198 Lbs6 ft2NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Chris DriedgerSenators (Ott)G251994-05-18No205 Lbs6 ft4NoNoNo1Pro & Farm500,000$0$0$NoLien
Cody GoloubefSenators (Ott)D291989-11-30No200 Lbs6 ft1NoNoNo1Pro & Farm500,000$0$0$NoLien
Dakota MermisSenators (Ott)D251994-01-05No195 Lbs6 ft0NoNoNo1Pro & Farm300,000$0$0$NoLien
Evan FitzpatrickSenators (Ott)G211998-01-28Yes206 Lbs6 ft3NoNoNo1Pro & Farm0$0$NoLien
Gabriel FontaineSenators (Ott)C221997-04-30No201 Lbs6 ft1NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Glenn GawdinSenators (Ott)C221997-03-25Yes191 Lbs6 ft1NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Grayson DowningSenators (Ott)C271992-04-18No195 Lbs6 ft0NoNoNo1Pro & Farm300,000$0$0$NoLien
Joakim RyanSenators (Ott)D261993-06-17No185 Lbs5 ft11NoNoNo1Pro & Farm500,000$0$0$NoLien
Jordan SchmaltzSenators (Ott)D251993-10-08No190 Lbs6 ft2NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Joshua Ho-SangSenators (Ott)C/RW231996-01-22No173 Lbs6 ft0NoNoNo2Pro & Farm900,000$0$0$No900,000$Lien
Justin BaileySenators (Ott)RW241995-07-01No214 Lbs6 ft4NoNoNo1Pro & Farm300,000$0$0$NoLien
Kole LindSenators (Ott)RW201998-10-16Yes178 Lbs6 ft1NoNoNo4Pro & Farm900,000$0$0$No900,000$900,000$Lien
Kyle CumiskeySenators (Ott)D321986-12-02No180 Lbs5 ft11NoNoNo1Pro & Farm300,000$0$0$NoLien
Logan O'ConnorSenators (Ott)RW221996-08-14No175 Lbs6 ft0NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Martin KautSenators (Ott)RW191999-10-02Yes180 Lbs6 ft2NoNoNo4Pro & Farm900,000$0$0$No900,000$900,000$900,000$Lien
Matt ReadSenators (Ott)C/RW331986-06-14No188 Lbs5 ft10NoNoNo2Pro & Farm2,150,000$0$0$No2,150,000$Lien
Matt TaorminaSenators (Ott)D321986-10-20No189 Lbs5 ft10NoNoNo1Pro & Farm500,000$0$0$NoLien
Max McCormickSenators (Ott)LW271992-05-01No188 Lbs5 ft11NoNoNo1Pro & Farm500,000$0$0$NoLien
Michael PrapavessisSenators (Ott)D281991-06-01Yes193 Lbs6 ft1NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Tim ErixonSenators (Ott)D281991-02-24No200 Lbs6 ft2NoNoNo1Pro & Farm500,000$0$0$NoLien
Vladislav KamenevSenators (Ott)C221996-08-12No194 Lbs6 ft2NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2824.96192 Lbs6 ft12.21483,929$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1A.J. GreerMatt ReadAndrew Poturalski40122
2Boris KatchoukGlenn GawdinAdam Cracknell30122
3Carsen TwarynskiJoshua Ho-SangJustin Bailey20122
4Andrew PoturalskiGabriel Fontaine10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Joakim Ryan40122
2Cody GoloubefJordan Schmaltz30122
3Dakota MermisKyle Cumiskey20122
4Joakim Ryan10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1A.J. GreerMatt ReadAndrew Poturalski60122
2Boris KatchoukGlenn GawdinAdam Cracknell40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Joakim Ryan60122
2Cody GoloubefJordan Schmaltz40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Andrew PoturalskiA.J. Greer60122
2Adam CracknellMatt Read40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Joakim Ryan60122
2Cody GoloubefJordan Schmaltz40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Andrew Poturalski60122Joakim Ryan60122
2A.J. Greer40122Cody GoloubefJordan Schmaltz40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Andrew PoturalskiA.J. Greer60122
2Adam CracknellMatt Read40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Joakim Ryan60122
2Cody GoloubefJordan Schmaltz40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
A.J. GreerMatt ReadAndrew PoturalskiJoakim Ryan
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
A.J. GreerMatt ReadAndrew PoturalskiJoakim Ryan
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
, Justin Bailey, Joshua Ho-Sang, Justin BaileyJoshua Ho-Sang
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Dakota Mermis, Kyle Cumiskey, Cody GoloubefDakota MermisKyle Cumiskey, Cody Goloubef
Tirs de Pénalité
Andrew Poturalski, A.J. Greer, Adam Cracknell, Matt Read, Boris Katchouk
Gardien
#1 : , #2 : Chris Driedger


Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
LigueDomicileVisiteur
# VS Équipe GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff P PCT G A TP SO EG GP1 GP2 GP3 GP4 SHF SH1 SP2 SP3 SP4 SHA SHB Pim Hit PPA PPG PP% PKA PK GA PK% PK GF W OF FO T OF FO OF FO% W DF FO T DF FO DF FO% W NT FO T NT FO NT FO% PZ DF PZ OF PZ NT PC DF PC OF PC NT
1Americans211000003301010000002-21100000031220.50036900443539429383395401283610192610110.00%70100.00%0765130358.71%686123855.41%34559857.69%1213885927301526280
2Bruins11000000413110000004130000000000021.000471100443539418383395401283086163266.67%30100.00%0765130358.71%686123855.41%34559857.69%1213885927301526280
3Checkers440000002251722000000113822000000112981.000224062014435394223383395401287220337616531.25%70100.00%0765130358.71%686123855.41%34559857.69%1213885927301526280
4Comets53200000710-32110000026-43210000054160.60071219014435394783833954012810427448825416.00%20385.00%0765130358.71%686123855.41%34559857.69%1213885927301526280
5Crunch3120000079-2000000000003120000079-220.3337121900443539462383395401288117454916212.50%16287.50%0765130358.71%686123855.41%34559857.69%1213885927301526280
6Devils4120000148-43110000134-11010000014-330.37547110144353948138339540128882433732627.69%13376.92%0765130358.71%686123855.41%34559857.69%1213885927301526280
7Griffins440000001165220000004132200000075281.000112233014435394101383395401288320227315533.33%10280.00%0765130358.71%686123855.41%34559857.69%1213885927301526280
8Marlies550000003252733000000193162200000013211101.0003259910144353942153833954012810126329818633.33%15286.67%1765130358.71%686123855.41%34559857.69%1213885927301526280
9Monsters31100001513-81000000145-12110000018-730.500571201443539472383395401287720415020315.00%18666.67%0765130358.71%686123855.41%34559857.69%1213885927301526280
10Moose21000100550000000000002100010055030.750510150044353947838339540128381425388225.00%10370.00%0765130358.71%686123855.41%34559857.69%1213885927301526280
11Phantoms21000001431110000003121000000112-130.75048120044353943738339540128331720339111.11%10280.00%0765130358.71%686123855.41%34559857.69%1213885927301526280
12Rocket714000201416-22000002064251400000812-460.4291422360044353941583833954012816955751063326.06%33778.79%0765130358.71%686123855.41%34559857.69%1213885927301526280
13Sound Tigers1010000014-31010000014-30000000000000.00011200443539416383395401282781215200.00%5180.00%0765130358.71%686123855.41%34559857.69%1213885927301526280
Total4424140012312092282011500022583820241390010162548560.6361202143340644353941187383395401289642714157502073516.91%1713380.70%1765130358.71%686123855.41%34559857.69%1213885927301526280
15Wolf Pack1010000014-31010000014-30000000000000.000112004435394193833954012825589600.00%4250.00%0765130358.71%686123855.41%34559857.69%1213885927301526280
_Since Last GM Reset4424140012312092282011500022583820241390010162548560.6361202143340644353941187383395401289642714157502073516.91%1713380.70%1765130358.71%686123855.41%34559857.69%1213885927301526280
_Vs Conference1767001033147-16833000021619-3934001011528-13160.47131538402443539438338339540128399113190283901213.33%791975.95%0765130358.71%686123855.41%34559857.69%1213885927301526280
_Vs Division151200000373709000000023212612000001416-220.06737641010344353944483833954012832294147256791113.92%571475.44%0765130358.71%686123855.41%34559857.69%1213885927301526280

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
4456W1120214334118796427141575006
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
442414012312092
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
2011500225838
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
2413901016254
Derniers 10 Matchs
WLOTWOTL SOWSOL
530002
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
2073516.91%1713380.70%1
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
383395401284435394
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
765130358.71%686123855.41%34559857.69%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
1213885927301526280


Derniers Match Joués
Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
JourMatch Équipe Visiteuse Score Équipe Locale Score ST OT SO RI Lien
4 - 2019-09-0512Senators0Comets3LSommaire du Match
10 - 2019-09-1138Senators2Moose1WSommaire du Match
11 - 2019-09-1249Senators3Moose4LXSommaire du Match
15 - 2019-09-1664Devils0Senators1WSommaire du Match
17 - 2019-09-1871Wolf Pack4Senators1LSommaire du Match
18 - 2019-09-1986Americans2Senators0LSommaire du Match
22 - 2019-09-23105Devils2Senators1LSommaire du Match
24 - 2019-09-25108Griffins1Senators2WSommaire du Match
25 - 2019-09-26121Griffins0Senators2WSommaire du Match
29 - 2019-09-30137Senators1Rocket2LSommaire du Match
31 - 2019-10-02140Senators1Crunch2LSommaire du Match
32 - 2019-10-03157Senators5Crunch3WSommaire du Match
38 - 2019-10-09183Marlies2Senators7WSommaire du Match
39 - 2019-10-10196Marlies0Senators4WSommaire du Match
43 - 2019-10-14218Comets5Senators0LSommaire du Match
45 - 2019-10-16225Marlies1Senators8WSommaire du Match
46 - 2019-10-17236Sound Tigers4Senators1LSommaire du Match
49 - 2019-10-20253Senators5Checkers1WSommaire du Match
50 - 2019-10-21258Senators6Checkers1WSommaire du Match
53 - 2019-10-24279Senators2Comets1WSommaire du Match
57 - 2019-10-28305Senators4Rocket3WSommaire du Match
59 - 2019-10-30309Comets1Senators2WSommaire du Match
60 - 2019-10-31324Phantoms1Senators3WSommaire du Match
66 - 2019-11-06351Rocket2Senators3WXXSommaire du Match
67 - 2019-11-07365Bruins1Senators4WSommaire du Match
68 - 2019-11-08378Senators5Marlies1WSommaire du Match
71 - 2019-11-11387Senators0Rocket2LSommaire du Match
73 - 2019-11-13394Senators1Phantoms2LXXSommaire du Match
74 - 2019-11-14410Senators1Devils4LSommaire du Match
78 - 2019-11-18430Senators3Americans1WSommaire du Match
80 - 2019-11-20436Senators1Monsters0WSommaire du Match
81 - 2019-11-21449Senators0Monsters8LSommaire du Match
85 - 2019-11-25464Senators8Marlies1WSommaire du Match
86 - 2019-11-26473Rocket2Senators3WXXSommaire du Match
88 - 2019-11-28491Devils2Senators1LXXSommaire du Match
92 - 2019-12-02513Senators3Rocket4LSommaire du Match
94 - 2019-12-04519Checkers0Senators6WSommaire du Match
95 - 2019-12-05534Checkers3Senators5WSommaire du Match
99 - 2019-12-09553Monsters5Senators4LXXSommaire du Match
101 - 2019-12-11564Senators3Griffins2WSommaire du Match
102 - 2019-12-12579Senators4Griffins3WSommaire du Match
106 - 2019-12-16599Senators0Rocket1LSommaire du Match
108 - 2019-12-18605Senators1Crunch4LSommaire du Match
109 - 2019-12-19621Senators3Comets0WSommaire du Match
115 - 2019-12-25656Marlies-Senators-
116 - 2019-12-26674Marlies-Senators-
123 - 2020-01-02705Senators-Devils-
124 - 2020-01-03711Senators-Bears-
126 - 2020-01-05714Senators-Marlies-
129 - 2020-01-08727Senators-Rocket-
130 - 2020-01-09741Rocket-Senators-
131 - 2020-01-10753Senators-Marlies-
134 - 2020-01-13765Crunch-Senators-
136 - 2020-01-15769Monsters-Senators-
137 - 2020-01-16783Monsters-Senators-
139 - 2020-01-18804Rocket-Senators-
143 - 2020-01-22823Senators-Devils-
144 - 2020-01-23834Senators-Americans-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
150 - 2020-01-29860Moose-Senators-
151 - 2020-01-30872Moose-Senators-
156 - 2020-02-04896Senators-Monsters-
157 - 2020-02-05898Senators-Monsters-
160 - 2020-02-08931Senators-Marlies-
164 - 2020-02-12947Rocket-Senators-
165 - 2020-02-13959Rocket-Senators-
169 - 2020-02-17979Comets-Senators-
171 - 2020-02-19986Senators-Bruins-
172 - 2020-02-201002Senators-Sound Tigers-
176 - 2020-02-241019Crunch-Senators-
179 - 2020-02-271042Senators-Wolf Pack-
183 - 2020-03-021064Crunch-Senators-
185 - 2020-03-041073Bears-Senators-
186 - 2020-03-051090Monsters-Senators-
190 - 2020-03-091108Marlies-Senators-
193 - 2020-03-121128Senators-Marlies-
194 - 2020-03-131142Americans-Senators-



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité de l'Aréna20001000
Prix des Billets3515
Assistance00
Assistance PCT0.00%0.00%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacité de l'ArénaPopularité de l'Équipe
18 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
669,414$ 135,500$ 68,798$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 81,769$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 80 5,853$ 468,240$




LigueDomicileVisiteur
Année GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff P G A TP SO EG GP1 GP2 GP3 GP4 SHF SH1 SP2 SP3 SP4 SHA SHB Pim Hit PPA PPG PP% PKA PK GA PK% PK GF W OF FO T OF FO OF FO% W DF FO T DF FO DF FO% W NT FO T NT FO NT FO% PZ DF PZ OF PZ NT PC DF PC OF PC NT
144424140012312092282011500022583820241390010162548561202143340644353941187383395401289642714157502073516.91%1713380.70%1765130358.71%686123855.41%34559857.69%1213885927301526280
Total Saison Régulière4424140012312092282011500022583820241390010162548561202143340644353941187383395401289642714157502073516.91%1713380.70%1765130358.71%686123855.41%34559857.69%1213885927301526280