Senators

GP: 76 | W: 46 | L: 19 | OTL: 11 | P: 103
GF: 234 | GA: 165 | PP%: 20.00% | PK%: 83.82%
DG: Marc-André Bilodeau Lamontagne | Morale : 68 | Moyenne d'Équipe : 60
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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
1Adam CracknellX100.007036915985786658625958605484743982610
2A.J. GreerX100.006540806185766860615960615565637584610
3Glenn Gawdin (R)X100.006139825976928958666054575663626377610
4Joshua Ho-SangXX100.005236916069856959626157585365638079600
5Gabriel FontaineX100.006436925678949354585553565263626382600
6Matt ReadXX100.005636905968827058545759625682733982600
7Justin BaileyX100.006536905989766858555756605467646982600
8Boris Katchouk (R)X100.006538845880949556525354575561637782600
9Carsen Twarynski (R)X100.006538855680939155585453565463626381590
10Martin Kaut (R)X100.005937875776928956585554565560628362590
11Logan O'ConnorX100.005336925869777156545857595465635333580
12Joakim RyanX100.006443886369735762307056675271664779630
13Cody GoloubefX100.006439836077796659305754594678706181620
14Jordan SchmaltzX100.005836915978766458306153594771667782610
15Andreas EnglundX100.005940795881847157305453594565637572610
16Dakota MermisX100.005536905973817058305753574669755182600
17Tim ErixonX100.006538865480797353305452554875686770600
18Kyle CumiskeyX100.005336915868827657306051544682735282600
19Urho Vaakanainen (R)X100.005535936374736262305754564660628561590
20Michael Prapavessis (R)X100.006236925476787253305451544665636237580
Rayé
1Vladislav KamenevX100.006138855779645057735758625865637420570
2Brad Morrison (R)X100.005336925669898355615453575263626320570
3Max McCormickX100.006539825970746658545257565473675020570
4Kole Lind (R)X100.005736905673908455595552545361636420570
5Grayson DowningX100.006242735473756953565451525073675820550
6Matt TaorminaX100.005437895568807454305552534682735229580
MOYENNE D'ÉQUIPE100.00603887587681735748575458516966636259
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 Driedger100.00777573877675777675777669735181740
2Evan Fitzpatrick (R)100.00706664856968706968706961657037670
Rayé
MOYENNE D'ÉQUIPE100.0074716986737274737274736569615971
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
1Joakim RyanSenators (Ott)D769576616114401049613741756.57%74169522.31822301013210110218400.00%000000.7800143364
2Justin BaileySenators (Ott)RW762335583538092841804814012.78%7131017.247916211590003787058.02%16200000.8904000456
3Glenn GawdinSenators (Ott)C7620365674810741291854311710.81%6143718.9161521403020001582159.42%147600000.7812020423
4A.J. GreerSenators (Ott)LW762230527535162822145817410.28%11163821.56711185831520292615156.84%19000010.63110100542
5Matt ReadSenators (Ott)C/RW761537522514055146178631298.43%12145319.1341418423201013842054.55%158200010.7214000224
6Adam CracknellSenators (Ott)RW76232447448013093242701599.50%7168722.20109194629602252015159.35%43300100.5609000324
7Chris KunitzOttawa SenatorsLW511829473137558421423910712.68%174709.2300000000003066.67%3300002.0001010425
8Boris KatchoukSenators (Ott)LW7619284723560108591865411910.22%9151519.9471219593070001573045.57%7900010.6201000061
9Cody GoloubefSenators (Ott)D761626421995512060115307613.91%61160921.1881119673030110209140.00%000000.5200000324
10Jordan SchmaltzSenators (Ott)D7611294014500844294366211.70%69147819.4561218522530221193000.00%100000.5400000213
11Joshua Ho-SangSenators (Ott)C/RW6820183818120221081474112113.61%12101214.90000140002605357.18%86400010.7500000326
12Andrew PoturalskiOttawa SenatorsRW4116213712120351001604411510.00%11105525.7358133018100001405257.18%38300000.7017000442
13Dakota MermisSenators (Ott)D76622282753571557827417.69%50130617.19268391550000127100.00%000000.4300001120
14Carsen TwarynskiSenators (Ott)LW67111223152806460100267711.00%1191513.6601100000071061.90%4200000.5000000212
15Kyle CumiskeySenators (Ott)D766162222280642229101120.69%3585811.3010123000055100.00%000000.5100000112
16Taylor FedunOttawa SenatorsD35714216180274049143314.29%3379222.65369311420002107200.00%000000.5301000023
17Nicolas DeslauriersOttawa SenatorsLW/RW246101614410683442113914.29%430012.5115613490111390148.89%9000001.0701020121
18Urho VaakanainenSenators (Ott)D192810922040144119394.88%1939820.962573348000011000.00%000000.5000000010
19Martin KautSenators (Ott)RW28549-26015325210349.62%240014.3200015000000138.10%2100000.4500000000
20Andreas EnglundSenators (Ott)D40088-64220262194120.00%1848212.07011030000530025.00%400000.3300112000
21Gabriel FontaineSenators (Ott)C76246-210043525620503.57%85256.920002100003740053.07%47300000.2300000100
22Tim ErixonSenators (Ott)D40033-722029813230.00%1746711.6900002000028000.00%100000.1300000000
23Matt TaorminaSenators (Ott)D2311210001103100.00%2351.531011600004000.00%000001.1300000000
24Logan O'ConnorSenators (Ott)RW120220201782100.00%1736.0900000000000033.33%300000.5500000000
25Michael PrapavessisSenators (Ott)D280114602564290.00%51806.440000000000000.00%000000.1100000000
26Vladislav KamenevSenators (Ott)C1000000000000.00%000.270000000000000.00%000000.0000000000
Stats d'équipe Total ou en Moyenne1389258475733279858100151713932462714175510.48%5012310416.637814722563931943710312076471456.55%583700140.634403106434752
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
1Chris DriedgerSenators (Ott)4928990.9022.082829259810050600.704274648521
2Pheonix CopleyOttawa Senators30181020.9111.90174103556210000.75012300222
Stats d'équipe Total ou en Moyenne794619110.9062.0145712815316260600.718397648743


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
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
Urho VaakanainenSenators (Ott)D201999-01-01Yes185 Lbs6 ft1NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$500,000$Lien
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
2824.79192 Lbs6 ft12.32491,071$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1A.J. GreerGlenn GawdinAdam Cracknell40122
2Boris KatchoukMatt ReadJustin Bailey30122
3Carsen TwarynskiJoshua Ho-SangMartin Kaut20122
4Adam CracknellGabriel Fontaine10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Joakim RyanCody Goloubef40122
2Jordan SchmaltzDakota Mermis30122
3Andreas EnglundTim Erixon20122
4Kyle CumiskeyMichael Prapavessis10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1A.J. GreerGlenn GawdinAdam Cracknell60122
2Boris KatchoukMatt ReadJustin Bailey40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Joakim RyanCody Goloubef60122
2Jordan SchmaltzDakota Mermis40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Adam CracknellA.J. Greer60122
2Justin BaileyGlenn Gawdin40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Joakim RyanCody Goloubef60122
2Jordan SchmaltzDakota Mermis40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Adam Cracknell60122Joakim RyanCody Goloubef60122
2A.J. Greer40122Jordan SchmaltzDakota Mermis40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Adam CracknellA.J. Greer60122
2Justin BaileyGlenn Gawdin40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Joakim RyanCody Goloubef60122
2Jordan SchmaltzDakota Mermis40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
A.J. GreerGlenn GawdinAdam CracknellJoakim RyanCody Goloubef
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
A.J. GreerGlenn GawdinAdam CracknellJoakim RyanCody Goloubef
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Martin Kaut, Joshua Ho-Sang, Gabriel FontaineMartin Kaut, Joshua Ho-SangGabriel Fontaine
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
, Andreas Englund, Tim ErixonAndreas Englund, Tim Erixon
Tirs de Pénalité
Adam Cracknell, A.J. Greer, Justin Bailey, Glenn Gawdin, Matt Read
Gardien
#1 : Chris Driedger, #2 : Evan Fitzpatrick


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
1Americans4220000068-22110000023-12110000045-140.5006121800856282771726716742656422435819315.79%19289.47%01384238957.93%1182209156.53%624109057.25%212415541591516906484
2Bears2010100067-11010000035-21000100032120.500691500856282765726716742655616415111327.27%10190.00%01384238957.93%1182209156.53%624109057.25%212415541591516906484
3Bruins21100000541110000004131010000013-220.50059140085628273572671674265611411354250.00%30100.00%01384238957.93%1182209156.53%624109057.25%212415541591516906484
4Checkers440000002251722000000113822000000112981.000224062018562827223726716742657220337616531.25%70100.00%01384238957.93%1182209156.53%624109057.25%212415541591516906484
5Comets642000001415-132100000911-23210000054180.66714253901856282711372671674265135365211729620.69%24483.33%01384238957.93%1182209156.53%624109057.25%212415541591516906484
6Crunch622011001415-1310011007613120000079-270.58314253900856282714172671674265163447510727311.11%25388.00%01384238957.93%1182209156.53%624109057.25%212415541591516906484
7Devils62200002913-43110000134-13110000169-360.500915240185628271377267167426513035451034249.52%19478.95%01384238957.93%1182209156.53%624109057.25%212415541591516906484
8Griffins440000001165220000004132200000075281.000112233018562827101726716742658320227315533.33%10280.00%01384238957.93%1182209156.53%624109057.25%212415541591516906484
9Marlies121200000072145866000000385336600000034925241.00072136208028562827570726716742652134974216591830.51%36586.11%31384238957.93%1182209156.53%624109057.25%212415541591516906484
10Monsters832010021925-6401010021113-243100000812-4100.62519335201856282720472671674265193509413849816.33%39879.49%01384238957.93%1182209156.53%624109057.25%212415541591516906484
11Moose430001001961322000000141132100010055070.875193554018562827153726716742656319478420840.00%20385.00%01384238957.93%1182209156.53%624109057.25%212415541591516906484
12Phantoms21000001431110000003121000000112-130.75048120085628273772671674265331720339111.11%10280.00%01384238957.93%1182209156.53%624109057.25%212415541591516906484
13Rocket1234001222931-26100012217170624000001214-2130.542295079008562827285726716742652818814720558813.79%631182.54%01384238957.93%1182209156.53%624109057.25%212415541591516906484
14Sound Tigers2020000027-51010000014-31010000013-200.000235008562827387267167426556143130400.00%12375.00%01384238957.93%1182209156.53%624109057.25%212415541591516906484
Total7641190332823416569382070222512879493821120110310686201030.678234425659088562827221272671674265165245876113523757520.00%3095083.82%31384238957.93%1182209156.53%624109057.25%212415541591516906484
16Wolf Pack2010000126-41010000014-31000000112-110.2502350085628273972671674265491426261317.69%12283.33%01384238957.93%1182209156.53%624109057.25%212415541591516906484
_Since Last GM Reset7641190332823416569382070222512879493821120110310686201030.678234425659088562827221272671674265165245876113523757520.00%3095083.82%31384238957.93%1182209156.53%624109057.25%212415541591516906484
_Vs Conference341211032068086-6176502103473981766011033347-14380.55980140220038562827849726716742658042233906071793016.76%1502682.67%01384238957.93%1182209156.53%624109057.25%212415541591516906484
_Vs Division2623021006466-21311011003334-11312010003132-190.17364111175038562827743726716742655891662904571442215.28%1092081.65%01384238957.93%1182209156.53%624109057.25%212415541591516906484

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
76103W323442565922121652458761135208
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
7641193328234165
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
38207222512879
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
382112110310686
Derniers 10 Matchs
WLOTWOTL SOWSOL
332101
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
3757520.00%3095083.82%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
726716742658562827
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
1384238957.93%1182209156.53%624109057.25%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
212415541591516906484


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 - 2019-12-25656Marlies1Senators9WSommaire du Match
116 - 2019-12-26674Marlies1Senators7WSommaire du Match
123 - 2020-01-02705Senators1Devils2LXXSommaire du Match
124 - 2020-01-03711Senators3Bears2WXSommaire du Match
126 - 2020-01-05714Senators8Marlies1WSommaire du Match
129 - 2020-01-08727Senators4Rocket2WSommaire du Match
130 - 2020-01-09741Rocket1Senators2WSommaire du Match
131 - 2020-01-10753Senators4Marlies2WSommaire du Match
134 - 2020-01-13765Crunch1Senators2WSommaire du Match
136 - 2020-01-15769Monsters3Senators2LXXSommaire du Match
137 - 2020-01-16783Monsters4Senators3LSommaire du Match
139 - 2020-01-18804Rocket4Senators3LXXSommaire du Match
143 - 2020-01-22823Senators4Devils3WSommaire du Match
144 - 2020-01-23834Senators1Americans4LSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
150 - 2020-01-29860Moose1Senators6WSommaire du Match
151 - 2020-01-30872Moose0Senators8WSommaire du Match
156 - 2020-02-04896Senators3Monsters2WSommaire du Match
157 - 2020-02-05898Senators4Monsters2WSommaire du Match
160 - 2020-02-08931Senators4Marlies1WSommaire du Match
164 - 2020-02-12947Rocket4Senators3LXXSommaire du Match
165 - 2020-02-13959Rocket4Senators3LXSommaire du Match
169 - 2020-02-17979Comets5Senators7WSommaire du Match
171 - 2020-02-19986Senators1Bruins3LSommaire du Match
172 - 2020-02-201002Senators1Sound Tigers3LSommaire du Match
176 - 2020-02-241019Crunch2Senators3WXSommaire du Match
179 - 2020-02-271042Senators1Wolf Pack2LXXSommaire du Match
183 - 2020-03-021064Crunch3Senators2LXSommaire du Match
185 - 2020-03-041073Bears5Senators3LSommaire du Match
186 - 2020-03-051090Monsters1Senators2WXSommaire du Match
190 - 2020-03-091108Marlies0Senators3WSommaire du Match
193 - 2020-03-121128Senators5Marlies3WSommaire du Match
194 - 2020-03-131142Americans1Senators2WSommaire du Match



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
0 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
1,138,084$ 137,500$ 61,020$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 138,092$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 0 5,863$ 0$




LigueDomicileVisiteur
Année GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff P G A TP SO EG GP1 GP2 GP3 GP4 SHF SH1 SP2 SP3 SP4 SHA SHB Pim Hit PPA PPG PP% PKA PK GA PK% PK GF W OF FO T OF FO OF FO% W DF FO T DF FO DF FO% W NT FO T NT FO NT FO% PZ DF PZ OF PZ NT PC DF PC OF PC NT
14764119033282341656938207022251287949382112011031068620103234425659088562827221272671674265165245876113523757520.00%3095083.82%31384238957.93%1182209156.53%624109057.25%212415541591516906484
Total Saison Régulière764119033282341656938207022251287949382112011031068620103234425659088562827221272671674265165245876113523757520.00%3095083.82%31384238957.93%1182209156.53%624109057.25%212415541591516906484