Senators

GP: 29 | W: 17 | L: 10 | OTL: 2 | P: 36
GF: 75 | GA: 56 | PP%: 17.56% | PK%: 82.61%
DG: Marc-André Bilodeau Lamontagne | Morale : 56 | Moyenne d'Équipe : 61
Prochain matchs #430 vs Americans
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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 PoturalskiX100.005137886466949363756658576169656048630
2Adam CracknellX100.007036915985786658625958605484743963610
3A.J. GreerX100.006540806185766860615960615565637563610
4Matt ReadXX100.005636905968827058545759625682733963600
5Justin BaileyX100.006536905989766858555756605467646963600
6Boris Katchouk (R)X100.006538845880949556525354575561637763600
7Glenn Gawdin (R)X100.006139825976928958666054575663626356600
8Joshua Ho-SangXX100.005236916069856959626157585365638055590
9Gabriel FontaineX100.006436925678949354585553565263626363590
10Carsen Twarynski (R)X100.006538855680939155585453565463626359590
11Martin Kaut (R)X100.005937875776928956585554565560628344590
12Joakim RyanX100.006443886369735762307056675271664763630
13Taylor FedunX100.005437896278756961306657665579714463630
14Cody GoloubefX100.006439836077796659305754594678706159610
15Dakota MermisX100.005536905973817058305753574669755163600
16Jordan SchmaltzX100.005836915978766458306153594771667763600
17Andreas EnglundX100.005940795881847157305453594565637554600
18Tim ErixonX100.006538865480797353305452554875686753600
19Kyle CumiskeyX100.005336915868827657306051544682735263600
Rayé
1Vladislav KamenevX100.006138855779645057735758625865637424580
2Logan O'ConnorX100.005336925869777156545857595465635331580
3Brad Morrison (R)X100.005336925669898355615453575263626321570
4Max McCormickX100.006539825970746658545257565473675021570
5Kole Lind (R)X100.005736905673908455595552545361636421570
6Grayson DowningX100.006242735473756953565451525073675821550
7Matt TaorminaX100.005437895568807454305552534682735228580
8Michael Prapavessis (R)X100.006236925476787253305451544665636224580
MOYENNE D'ÉQUIPE100.00603887587582745749585558527066624959
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
1Pheonix Copley100.00797876867877797877797873774248760
2Chris Driedger100.00777573877675777675777669735163740
Rayé
1Evan Fitzpatrick (R)100.00706664856968706968706961657021670
MOYENNE D'ÉQUIPE100.0075737186747375747375746872544472
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'ÉquipePOS GP 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
1Glenn GawdinSenators (Ott)C2961925729528458418447.14%255419.120101013108000012060.24%50800000.9011010311
2Adam CracknellSenators (Ott)RW291210221160462975195216.00%264322.18628121060113721156.90%11600100.6802000213
3Taylor FedunSenators (Ott)D29513184140223039123212.82%3365422.5836925114000294200.00%000000.5501000013
4Joakim RyanSenators (Ott)D29314174401036364515206.67%2264122.11371035112000086000.00%000000.5300110122
5Andrew PoturalskiSenators (Ott)RW227916410020527820518.97%856825.8513413900000722161.88%22300000.5613000220
6Cody GoloubefSenators (Ott)D2996159455372245102920.00%2358820.3061724104000076120.00%000000.5100000122
7Boris KatchoukSenators (Ott)LW296814620040206719378.96%160520.88224221080001251050.00%3800000.4601000011
8Jordan SchmaltzSenators (Ott)D2921012512026143713205.41%3154918.950441773011065000.00%100000.4400000101
9Justin BaileySenators (Ott)RW295712820030305221399.62%149317.011123420000212057.14%5600000.4901000031
10Joshua Ho-SangSenators (Ott)C/RW21471192062636134011.11%331415.00000130001311055.12%25400000.7000000011
11Matt ReadSenators (Ott)C/RW29381122020615321485.66%858820.30033141111011571056.15%61800000.3712000010
12Carsen TwarynskiSenators (Ott)LW20651188018223671716.67%526813.4500000000051066.67%1200000.8200000112
13Kyle CumiskeySenators (Ott)D293696803111105330.00%1537813.0710112000030100.00%000000.4700000110
14A.J. GreerSenators (Ott)LW29178024065304515482.22%364322.19033101120004961058.14%8600000.2502000000
15Dakota MermisSenators (Ott)D29167814023212011185.00%2146916.190111039000035000.00%000000.3000000000
16Gabriel FontaineSenators (Ott)C29112-1801926246174.17%22729.380001100002210051.79%22400000.1500000100
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 Moyenne4727413921377291254804877562275289.79%192856518.1523446720111491231481216457.09%214400100.50313121131717
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 CopleySenators (Ott)29171020.9111.93168103546060000.75012290222
2Chris DriedgerSenators (Ott)30000.9660.8670001290000.0000029000
Stats d'équipe Total ou en Moyenne32171020.9131.89175103556350000.750122929222


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 StatusType Salaire Actuel Cap Salariale Cap Salariale Restant Exclus du Cap Salarial Link
A.J. GreerSenators (Ott)LW221996-12-14No210 Lbs6 ft3NoNoNo3Contrat d'EntréePro & Farm500,000$0$0$NoLien
Adam CracknellSenators (Ott)RW331985-07-15No209 Lbs6 ft3NoNoNo1Sans RestrictionPro & Farm500,000$0$0$NoLien
Andreas EnglundSenators (Ott)D231996-01-21No189 Lbs6 ft3NoNoNo2Avec RestrictionPro & Farm300,000$0$0$NoLien
Andrew PoturalskiSenators (Ott)RW251994-01-14No180 Lbs5 ft10NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Boris KatchoukSenators (Ott)LW211998-06-18Yes199 Lbs6 ft2NoNoNo4Contrat d'EntréePro & Farm500,000$0$0$NoLien
Brad MorrisonSenators (Ott)C221997-01-04Yes171 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$NoLien
Carsen TwarynskiSenators (Ott)LW211997-11-24Yes198 Lbs6 ft2NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$NoLien
Chris DriedgerSenators (Ott)G251994-05-18No205 Lbs6 ft4NoNoNo1Avec RestrictionPro & Farm500,000$0$0$NoLien
Cody GoloubefSenators (Ott)D291989-11-30No200 Lbs6 ft1NoNoNo1Sans RestrictionPro & Farm500,000$0$0$NoLien
Dakota MermisSenators (Ott)D251994-01-05No195 Lbs6 ft0NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Evan FitzpatrickSenators (Ott)G211998-01-28Yes206 Lbs6 ft3NoNoNo1Contrat d'EntréePro & Farm0$0$NoLien
Gabriel FontaineSenators (Ott)C221997-04-30No201 Lbs6 ft1NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$NoLien
Glenn GawdinSenators (Ott)C221997-03-25Yes191 Lbs6 ft1NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$NoLien
Grayson DowningSenators (Ott)C271992-04-18No195 Lbs6 ft0NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Joakim RyanSenators (Ott)D261993-06-17No185 Lbs5 ft11NoNoNo1Avec RestrictionPro & Farm500,000$0$0$NoLien
Jordan SchmaltzSenators (Ott)D251993-10-08No190 Lbs6 ft2NoNoNo2Avec RestrictionPro & Farm300,000$0$0$NoLien
Joshua Ho-SangSenators (Ott)C/RW231996-01-22No173 Lbs6 ft0NoNoNo2Avec RestrictionPro & Farm900,000$0$0$NoLien
Justin BaileySenators (Ott)RW241995-07-01No214 Lbs6 ft4NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Kole LindSenators (Ott)RW201998-10-16Yes178 Lbs6 ft1NoNoNo4Contrat d'EntréePro & Farm900,000$0$0$NoLien
Kyle CumiskeySenators (Ott)D321986-12-02No180 Lbs5 ft11NoNoNo1Sans RestrictionPro & Farm300,000$0$0$NoLien
Logan O'ConnorSenators (Ott)RW221996-08-14No175 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$NoLien
Martin KautSenators (Ott)RW191999-10-02Yes180 Lbs6 ft2NoNoNo4Contrat d'EntréePro & Farm900,000$0$0$NoLien
Matt ReadSenators (Ott)C/RW331986-06-14No188 Lbs5 ft10NoNoNo2Sans RestrictionPro & Farm2,150,000$0$0$NoLien
Matt TaorminaSenators (Ott)D321986-10-20No189 Lbs5 ft10NoNoNo1Sans RestrictionPro & Farm500,000$0$0$NoLien
Max McCormickSenators (Ott)LW271992-05-01No188 Lbs5 ft11NoNoNo1Avec RestrictionPro & Farm500,000$0$0$NoLien
Michael PrapavessisSenators (Ott)D281991-06-01Yes193 Lbs6 ft1NoNoNo4Sans RestrictionPro & Farm300,000$0$0$NoLien
Pheonix CopleySenators (Ott)G271992-01-18No200 Lbs6 ft4NoNoNo3Avec RestrictionPro & Farm1,000,000$0$0$NoLien
Taylor FedunSenators (Ott)D311988-06-04No201 Lbs6 ft1NoNoNo2Sans RestrictionPro & Farm500,000$0$0$NoLien
Tim ErixonSenators (Ott)D281991-02-24No200 Lbs6 ft2NoNoNo1Sans RestrictionPro & Farm500,000$0$0$NoLien
Vladislav KamenevSenators (Ott)C221996-08-12No194 Lbs6 ft2NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$NoLien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
3025.23193 Lbs6 ft12.23501,667$



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 RyanTaylor Fedun40122
2Cody GoloubefJordan Schmaltz30122
3Dakota MermisKyle Cumiskey20122
4Joakim RyanTaylor Fedun10122
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 RyanTaylor Fedun60122
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 RyanTaylor Fedun60122
2Cody GoloubefJordan Schmaltz40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Andrew Poturalski60122Joakim RyanTaylor Fedun60122
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 RyanTaylor Fedun60122
2Cody GoloubefJordan Schmaltz40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
A.J. GreerMatt ReadAndrew PoturalskiJoakim RyanTaylor Fedun
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
A.J. GreerMatt ReadAndrew PoturalskiJoakim RyanTaylor Fedun
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 : Pheonix Copley, #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
1Americans1010000002-21010000002-20000000000000.0000000032182436246237245142181012300.00%50100.00%049583659.21%47083156.56%21338155.91%799581605201347185
2Bruins11000000413110000004130000000000021.000471100321824318246237245143086163266.67%30100.00%049583659.21%47083156.56%21338155.91%799581605201347185
3Checkers2200000011290000000000022000000112941.000112031003218243952462372451436929278225.00%50100.00%049583659.21%47083156.56%21338155.91%799581605201347185
4Comets42200000410-62110000026-42110000024-240.500471100321824366246237245148224347722313.64%15380.00%049583659.21%47083156.56%21338155.91%799581605201347185
5Crunch21100000651000000000002110000065120.500610160032182433724623724514571235258112.50%11281.82%049583659.21%47083156.56%21338155.91%799581605201347185
6Devils3120000036-3211000002201010000014-320.3333690132182435624623724514742127562129.52%10280.00%049583659.21%47083156.56%21338155.91%799581605201347185
7Griffins22000000413220000004130000000000041.0004812013218243502462372451438910358337.50%40100.00%049583659.21%47083156.56%21338155.91%799581605201347185
8Marlies440000002442033000000193161100000051481.000244569013218243175246237245148124268016637.50%12191.67%149583659.21%47083156.56%21338155.91%799581605201347185
9Moose21000100550000000000002100010055030.750510150032182437824623724514381425388225.00%10370.00%049583659.21%47083156.56%21338155.91%799581605201347185
10Phantoms21000001431110000003121000000112-130.75048120032182433724623724514331720339111.11%10280.00%049583659.21%47083156.56%21338155.91%799581605201347185
11Rocket4120001089-1100000103213120000057-240.500813210032182437724623724514932949541715.88%21480.95%049583659.21%47083156.56%21338155.91%799581605201347185
12Sound Tigers1010000014-31010000014-30000000000000.00011200321824316246237245142781215200.00%5180.00%049583659.21%47083156.56%21338155.91%799581605201347185
Total2916100011175561915950001039261314750010136306360.62175136211033218243730246237245146351882914771312317.56%1152082.61%149583659.21%47083156.56%21338155.91%799581605201347185
14Wolf Pack1010000014-31010000014-30000000000000.000112003218243192462372451425589600.00%4250.00%049583659.21%47083156.56%21338155.91%799581605201347185
_Since Last GM Reset2916100011175561915950001039261314750010136306360.62175136211033218243730246237245146351882914771312317.56%1152082.61%149583659.21%47083156.56%21338155.91%799581605201347185
_Vs Conference1255001012428-4633000001112-1622001011316-3120.500244367013218243261246237245142848513319257814.04%531277.36%049583659.21%47083156.56%21338155.91%799581605201347185
_Vs Division911000002019150000000711-441100000138520.11120365601321824322324623724514195609614046510.87%34779.41%049583659.21%47083156.56%21338155.91%799581605201347185

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
2936L17513621173063518829147703
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
29161001117556
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
159500103926
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
147501013630
Derniers 10 Matchs
WLOTWOTL SOWSOL
620011
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
1312317.56%1152082.61%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
246237245143218243
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
49583659.21%47083156.56%21338155.91%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
799581605201347185


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 - 2019-11-18430Senators-Americans-
80 - 2019-11-20436Senators-Monsters-
81 - 2019-11-21449Senators-Monsters-
85 - 2019-11-25464Senators-Marlies-
86 - 2019-11-26473Rocket-Senators-
88 - 2019-11-28491Devils-Senators-
92 - 2019-12-02513Senators-Rocket-
94 - 2019-12-04519Checkers-Senators-
95 - 2019-12-05534Checkers-Senators-
99 - 2019-12-09553Monsters-Senators-
101 - 2019-12-11564Senators-Griffins-
102 - 2019-12-12579Senators-Griffins-
106 - 2019-12-16599Senators-Rocket-
108 - 2019-12-18605Senators-Crunch-
109 - 2019-12-19621Senators-Comets-
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
23 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
440,579$ 150,500$ 71,798$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 53,975$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 119 5,930$ 705,670$




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
1429161000111755619159500010392613147500101363063675136211033218243730246237245146351882914771312317.56%1152082.61%149583659.21%47083156.56%21338155.91%799581605201347185
Total Saison Régulière29161000111755619159500010392613147500101363063675136211033218243730246237245146351882914771312317.56%1152082.61%149583659.21%47083156.56%21338155.91%799581605201347185