Banshees

GP: 18 | W: 9 | L: 5 | T: 4 | P: 22
GF: 63 | GA: 55 | PP%: 17.65% | PK%: 77.92%
DG: Louis-Philippe DesHaies | Morale : 55 | Moyenne d'Équipe : 65
Prochain matchs #127 vs Wolves
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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
1Dallas Drake (R)X100.007051717473747273677369886628297659680
2Andrei Kovalenko (R)X99.007255667075706975677979726832358150680
3Alexander SemakX100.007060646771737281728278697035384762680
4Wes WalzX100.005844757370727274667478767240408148680
5Greg GilbertX100.005943836572717170636665796461682460660
6Ryan Walter (C)X100.00554668627372716762666482638078160660
7Steve LeachX100.007458686374717274667275737142435341660
8Jody HullX100.006448797174707171677172746846407451660
9Garry ValkX100.006955686375757665627166816435356260650
10Brian RolstonX100.005444737174676671657168756428289750640
11Mariusz Czerkawski (R)X100.005644726973676774657271636630339560630
12Murray Baron (A)X100.007461646278767660567156875143446160670
13Jason WoolleyX100.006650757474737470647063806036407660670
14Bill Berg (R) (A)X100.006347836376707273587270795843466060660
15Chris JosephX100.007050766578656664617048804547526860650
16Peter AholaX100.006956626774676766637361765833336960630
17Philippe Boucher (R)X100.006450716477646464586752774827309857620
Rayé
1Terry YakeX100.006549807072717367646764756362646835660
2Jose CharbonneauX100.007157626573687066647069676651475248640
3Jeff DanielsX100.005541806775687069657262736048516841640
4Craig ConroyX95.506350697175707172667260735826328947640
5Mike SillingerX100.006148746874656565636966716428288935620
6Craig Darby (R)X100.006348826872606261576360605836369632580
7Todd Warriner (R)X100.006047706670646459576655655228309632580
8Lyndon ByersX100.007274315467646443424845744353593851540
9Neil WilkinsonX100.007661676773697161586442814045535935650
MOYENNE D'ÉQUIPE99.79655171677469706862706475604143685164
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
1Jim Hrivnak100.00717480777676818681747245416855720
2Craig Billington100.00757672707476798480767651465555710
Rayé
1Fred Brathwaite (R)100.00687475657370687073716943408432650
MOYENNE D'ÉQUIPE100.0071757671747476807874724642694769
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
John Tortorella78778177748490USA36295,000$


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
1Jason WoolleyBanshees (PHI)D183161956015282282413.64%2344924.9819101283000158210.00%000000.8400000110
2Alexander SemakBanshees (PHI)C18891732810373555124714.55%034919.391562180000072152.49%46100000.9700002230
3Murray BaronBanshees (PHI)D1841115022048112091720.00%2442923.841451575000053100.00%000000.7000000201
4Andrei KovalenkoBanshees (PHI)RW187613012054235493712.96%133818.802241379000021035.48%3100010.7700000310
5Wes WalzBanshees (PHI)C176713310013484273614.29%030217.8016711680001330050.28%36200000.8600000010
6Peter AholaBanshees (PHI)D183811126047131913715.79%1235919.993141176000024000.00%000000.6100000102
7Mariusz CzerkawskiBanshees (PHI)RW185611-120181629132117.24%027715.414041173000000076.19%2100000.7900000110
8Brian RolstonBanshees (PHI)LW18471102072839132910.26%229816.57134663000000127.78%1800000.7400000100
9Bill BergBanshees (PHI)D18011110201416244220.00%2334619.27011137500009000.00%000000.6300000001
10Dallas DrakeBanshees (PHI)LW1845924032344918528.16%838721.5100010810000661157.14%2100000.4600000111
11Greg GilbertBanshees (PHI)LW18538-12051529102017.24%420611.48202290000121053.33%1500000.7700000002
12Ryan WalterBanshees (PHI)C184484009361961421.05%525514.211011160001680051.56%32000000.6300000011
13Garry ValkBanshees (PHI)LW1843754021102561016.00%119310.76000001015420156.52%2300000.7200000101
14Philippe BoucherBanshees (PHI)D1706656015123330.00%1728116.5301102011048000.00%000000.4300000000
15Chris JosephBanshees (PHI)D1823542602510130615.38%2533018.36101314011049000.00%000000.3000000010
16Steve LeachBanshees (PHI)RW9224-111568116418.18%09210.3200001000000140.00%500000.8600001001
17Craig ConroyBanshees (PHI)C17134-180927293193.45%019011.21000290001100047.72%19700000.4200000000
18Jeff DanielsBanshees (PHI)LW51122001230233.33%0377.520000000000100.00%200001.0600000010
19Jose CharbonneauBanshees (PHI)RW90112201614030.00%110011.1700018000000028.57%700000.2000000000
20Lyndon ByersBanshees (PHI)RW90112601120000.00%0667.4300000000000066.67%300000.3000000000
21Jody HullBanshees (PHI)RW4011000255040.00%1348.600000000000000.00%100000.5800000000
22Mike SillingerBanshees (PHI)C1000000030100.00%01111.2800000000010050.00%1200000.0000000000
23Terry YakeBanshees (PHI)C1000000012000.00%01010.2300000000000041.67%1200000.0000000000
24Neil WilkinsonBanshees (PHI)D1000-100210100.00%11111.880000000000000.00%000000.0000000000
Stats d'équipe Total ou en Moyenne32463114177331791540738549614237712.70%148536216.5518325013282012394909650.63%151100010.6600003131110
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
1Jim HrivnakBanshees (PHI)125230.8703.0566800342610100.0000117000
2Craig BillingtonBanshees (PHI)84310.8542.9143301211440100.0000711000
Stats d'équipe Total ou en Moyenne209540.8643.00110201554050200.00001818000


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
Alexander SemakBanshees (PHI)C281992-02-09 9:08:37 PMNo185 Lbs5 ft10NoNoNo1Pro & Farm475,000$47,500$37,371$No
Andrei KovalenkoBanshees (PHI)RW231997-02-09 9:08:37 PMYes210 Lbs5 ft10NoNoNo2Pro & Farm620,000$62,000$48,779$No620,000$
Bill BergBanshees (PHI)D261994-02-09 9:08:37 PMYes205 Lbs6 ft1NoNoNo2Pro & Farm385,000$38,500$30,290$No385,000$
Brian RolstonBanshees (PHI)LW202000-02-09 9:08:37 PMNo200 Lbs6 ft2NoNoNo2Pro & Farm500,000$50,000$39,338$No500,000$
Chris JosephBanshees (PHI)D251995-02-09 9:08:37 PMNo210 Lbs6 ft2NoNoNo1Pro & Farm310,000$31,000$24,390$No
Craig BillingtonBanshees (PHI)G271993-02-09 9:08:37 PMNo170 Lbs5 ft10NoNoNo3Pro & Farm780,000$78,000$61,368$No780,000$780,000$
Craig ConroyBanshees (PHI)C221998-02-09 9:08:37 PMNo198 Lbs6 ft2NoNoNo4Pro & Farm700,000$70,000$55,074$No700,000$700,000$700,000$
Craig DarbyBanshees (PHI)C211999-02-09 9:08:37 PMYes200 Lbs6 ft3NoNoNo2Pro & Farm225,000$22,500$17,702$No225,000$
Dallas DrakeBanshees (PHI)LW241996-02-09 9:08:37 PMYes185 Lbs6 ft1NoNoNo1Pro & Farm450,000$45,000$35,404$No
Fred BrathwaiteBanshees (PHI)G221998-08-11 10:33:53 AMYes185 Lbs5 ft7NoNoNo3Pro & Farm250,000$25,000$19,669$No250,000$250,000$
Garry ValkBanshees (PHI)LW261994-02-09 9:08:37 PMNo205 Lbs6 ft1NoNoNo1Pro & Farm500,000$50,000$39,338$No
Greg GilbertBanshees (PHI)LW311989-02-09 9:08:37 PMNo195 Lbs6 ft1NoNoNo2Pro & Farm550,000$55,000$43,272$No550,000$
Jason WoolleyBanshees (PHI)D241996-02-09 9:08:37 PMNo188 Lbs6 ft1NoNoNo2Pro & Farm700,000$70,000$55,074$No700,000$
Jeff DanielsBanshees (PHI)LW251995-02-09 9:08:37 PMNo200 Lbs6 ft1NoNoNo1Pro & Farm250,000$25,000$19,669$No
Jim HrivnakBanshees (PHI)G251995-02-09 9:08:37 PMNo185 Lbs6 ft2NoNoNo2Pro & Farm900,000$90,000$70,809$No900,000$
Jody HullBanshees (PHI)RW241996-02-09 9:08:37 PMNo195 Lbs6 ft2NoNoNo3Pro & Farm535,000$53,500$42,092$No535,000$535,000$
Jose CharbonneauBanshees (PHI)RW271993-02-09 9:08:37 PMNo195 Lbs6 ft0NoNoNo1Pro & Farm100,000$10,000$7,868$No
Lyndon ByersBanshees (PHI)RW291991-02-09 9:08:37 PMNo190 Lbs6 ft1NoNoNo1Pro & Farm100,000$10,000$7,868$No
Mariusz CzerkawskiBanshees (PHI)RW211999-02-09 9:08:37 PMYes200 Lbs6 ft0NoNoNo2Pro & Farm400,000$40,000$31,471$No400,000$
Mike SillingerBanshees (PHI)C221998-02-09 9:08:37 PMNo200 Lbs6 ft0NoNoNo2Pro & Farm320,000$32,000$25,176$No320,000$
Murray BaronBanshees (PHI)D261994-02-09 9:08:37 PMNo218 Lbs6 ft3NoNoNo3Pro & Farm635,000$63,500$49,960$No635,000$635,000$
Neil WilkinsonBanshees (PHI)D261994-02-09 9:08:37 PMNo180 Lbs6 ft3NoNoNo1Pro & Farm525,000$52,500$41,305$No
Peter AholaBanshees (PHI)D251995-02-09 9:08:37 PMNo205 Lbs6 ft3NoNoNo3Pro & Farm500,000$50,000$39,338$No500,000$500,000$
Philippe BoucherBanshees (PHI)D202000-02-09 9:08:37 PMYes212 Lbs6 ft3NoNoNo2Pro & Farm250,000$25,000$19,669$No250,000$
Ryan WalterBanshees (PHI)C351985-02-09 9:08:37 PMNo195 Lbs6 ft0NoNoNo2Pro & Farm325,000$32,500$25,570$No325,000$
Steve LeachBanshees (PHI)RW271993-02-09 9:08:37 PMNo200 Lbs5 ft11NoNoNo2Pro & Farm525,000$52,500$41,305$No525,000$
Terry YakeBanshees (PHI)C251995-02-09 9:08:37 PMNo185 Lbs5 ft11NoNoNo1Pro & Farm440,000$44,000$34,618$No
Todd WarrinerBanshees (PHI)LW192001-02-09 9:08:37 PMYes182 Lbs6 ft1NoNoNo4Pro & Farm325,000$32,500$25,570$No325,000$325,000$325,000$
Wes WalzBanshees (PHI)C231997-02-09 9:08:37 PMNo180 Lbs5 ft10NoNoNo1Pro & Farm300,000$30,000$23,603$No
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2924.76195 Lbs6 ft11.97443,966$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Brian RolstonAlexander SemakAndrei Kovalenko35023
2Dallas DrakeWes WalzSteve Leach30023
3Greg GilbertMariusz Czerkawski20023
4Garry ValkRyan WalterJody Hull15023
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Peter AholaJason Woolley35032
2Murray BaronBill Berg30032
3Chris JosephPhilippe Boucher20032
4Murray BaronJason Woolley15032
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Dallas DrakeAlexander SemakAndrei Kovalenko60014
2Brian RolstonWes WalzMariusz Czerkawski40014
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Peter AholaJason Woolley60131
2Murray BaronBill Berg40131
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Ryan WalterDallas Drake60041
2Wes WalzGarry Valk40041
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Murray BaronJason Woolley60140
2Chris JosephPhilippe Boucher40140
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Ryan Walter60050Chris JosephJason Woolley60122
2Dallas Drake40050Murray BaronPhilippe Boucher40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Alexander SemakDallas Drake60122
2Wes WalzAndrei Kovalenko40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Chris JosephJason Woolley60122
2Murray BaronBill Berg40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Brian RolstonAlexander SemakAndrei KovalenkoPeter AholaJason Woolley
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Dallas DrakeRyan WalterAndrei KovalenkoMurray BaronJason Woolley
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Andrei Kovalenko, Ryan Walter, Garry ValkAndrei Kovalenko, Ryan WalterGarry Valk
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Chris Joseph, Peter Ahola, Murray BaronChris JosephPeter Ahola, Murray Baron
Tirs de Pénalité
Alexander Semak, Dallas Drake, Andrei Kovalenko, Mariusz Czerkawski, Steve Leach
Gardien
#1 : Jim Hrivnak, #2 : Craig Billington


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
1Ailes Rouges21010000752000000000002101000075230.750712190022182125316915815217451312401218.33%6183.33%031562950.08%28856051.43%16232250.31%441296423141242121
2As1010000034-11010000034-10000000000000.000369002218212351691581521723810238225.00%4175.00%031562950.08%28856051.43%16232250.31%441296423141242121
3Canadiens210010001284110000006331000100065141.00012233500221821280169158152173816164613430.77%8450.00%131562950.08%28856051.43%16232250.31%441296423141242121
4Chiefs2011000049-5000000000002011000049-510.2504610002218212591691581521752172040600.00%9366.67%031562950.08%28856051.43%16232250.31%441296423141242121
5Citadelles3201000014104110000005232101000098150.83314243800221821264169158152178827478619421.05%18288.89%031562950.08%28856051.43%16232250.31%441296423141242121
6Croque-Morts11000000321110000003210000000000021.000369002218212241691581521719114304250.00%20100.00%031562950.08%28856051.43%16232250.31%441296423141242121
7Isotopes41111000121023111000010911000100021150.62512233500221821211216915815217813646752627.69%18383.33%031562950.08%28856051.43%16232250.31%441296423141242121
8Pacifiques de la route1010000023-1000000000001010000023-100.00023500221821226169158152172058245120.00%4250.00%031562950.08%28856051.43%16232250.31%441296423141242121
9Snipers21100000642211000006420000000000020.500611170122182124316915815217391516439222.22%8187.50%031562950.08%28856051.43%16232250.31%441296423141242121
Total187542000635589531000033249922320003031-1220.61163114177012218212496169158152174051481794071021817.65%771777.92%131562950.08%28856051.43%16232250.31%441296423141242121
_Since Last GM Reset1811502000635589531000033249962-120003031-1260.72263114177012218212496169158152174051481794071021817.65%771777.92%131562950.08%28856051.43%16232250.31%441296423141242121
_Vs Conference117202000423755311000021147641-120002123-2180.81842761180022182123151691581521725996129247641015.63%531277.36%131562950.08%28856051.43%16232250.31%441296423141242121
_Vs Division75101000262064211000015114330-110001192120.85726477300221821217616915815217169639316145613.33%36586.11%031562950.08%28856051.43%16232250.31%441296423141242121

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
1822W16311417749640514817940701
Tous les Matchs
GPWLOTWOTL TGFGA
18752046355
Matchs locaux
GPWLOTWOTL TGFGA
9530013324
Matchs Éxtérieurs
GPWLOTWOTL TGFGA
9222033031
Derniers 10 Matchs
WLOTWOTL T
43003
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
1021817.65%771777.92%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
169158152172218212
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
31562950.08%28856051.43%16232250.31%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
441296423141242121


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
1 - 2020-09-212Banshees5Citadelles5TXR2Sommaire du Match
2 - 2020-09-228Banshees2Isotopes1WXSommaire du Match
3 - 2020-09-2314Isotopes3Banshees2LSommaire du Match
5 - 2020-09-2522Isotopes2Banshees4WSommaire du Match
6 - 2020-09-2629Banshees6Canadiens5WXSommaire du Match
7 - 2020-09-2732Banshees4Citadelles3WR2Sommaire du Match
8 - 2020-09-2839Citadelles2Banshees5WSommaire du Match
10 - 2020-09-3052As4Banshees3LSommaire du Match
11 - 2020-10-0158Banshees3Ailes Rouges3TXSommaire du Match
13 - 2020-10-0362Banshees1Chiefs6LSommaire du Match
15 - 2020-10-0569Snipers0Banshees3WSommaire du Match
17 - 2020-10-0779Banshees4Ailes Rouges2WSommaire du Match
19 - 2020-10-0984Snipers4Banshees3LSommaire du Match
21 - 2020-10-1193Canadiens3Banshees6WSommaire du Match
23 - 2020-10-1399Banshees3Chiefs3TXSommaire du Match
26 - 2020-10-16107Isotopes4Banshees4TXSommaire du Match
27 - 2020-10-17113Banshees2Pacifiques de la route3LSommaire du Match
29 - 2020-10-19120Croque-Morts2Banshees3WSommaire du Match
31 - 2020-10-21127Banshees-Wolves-
33 - 2020-10-23133As-Banshees-
35 - 2020-10-25139Banshees-Snipers-
37 - 2020-10-27147Harvard-Banshees-
39 - 2020-10-29156Ailes Rouges-Banshees-
42 - 2020-11-01164Banshees-Isotopes-
43 - 2020-11-02170Banshees-Citadelles-
44 - 2020-11-03175Banshees-Chiefs-
46 - 2020-11-05181Pacifiques de la route-Banshees-
47 - 2020-11-06191As-Banshees-
49 - 2020-11-08197Banshees-Pacifiques de la route-
50 - 2020-11-09204Ailes Rouges-Banshees-
52 - 2020-11-11211Banshees-Canadiens-
53 - 2020-11-12218Banshees-As-
55 - 2020-11-14223Spoonman's-Banshees-
56 - 2020-11-15230Banshees-Isotopes-
58 - 2020-11-17235Isotopes-Banshees-
59 - 2020-11-18242Banshees-Riverman-
61 - 2020-11-20252Chiefs-Banshees-
62 - 2020-11-21256Banshees-Spoonman's-
64 - 2020-11-23265Spoonman's-Banshees-
66 - 2020-11-25274Snipers-Banshees-
67 - 2020-11-26283Spoonman's-Banshees-
68 - 2020-11-27284Banshees-Citadelles-
71 - 2020-11-30298Isotopes-Banshees-
72 - 2020-12-01302Banshees-Croque-Morts-
73 - 2020-12-02307Banshees-Snipers-
74 - 2020-12-03316Snipers-Banshees-
75 - 2020-12-04318Banshees-Isotopes-
77 - 2020-12-06330Wolves-Banshees-
78 - 2020-12-07334Banshees-Harvard-
79 - 2020-12-08342Banshees-Wolves-
80 - 2020-12-09347Wolves-Banshees-
82 - 2020-12-11356Banshees-Canadiens-
84 - 2020-12-13361Citadelles-Banshees-
86 - 2020-12-15370Banshees-Croque-Morts-
88 - 2020-12-17376Banshees-As-
90 - 2020-12-19380Harvard-Banshees-
92 - 2020-12-21389Citadelles-Banshees-
94 - 2020-12-23398Banshees-Wolves-
96 - 2020-12-25403Riverman-Banshees-
98 - 2020-12-27412Banshees-Isotopes-
100 - 2020-12-29417Riverman-Banshees-
101 - 2020-12-30425Banshees-Canadiens-
103 - 2021-01-01431Croque-Morts-Banshees-
105 - 2021-01-03440Banshees-Riverman-
106 - 2021-01-04444Banshees-Chiefs-
108 - 2021-01-06449Citadelles-Banshees-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
110 - 2021-01-08458Chiefs-Banshees-
112 - 2021-01-10469Banshees-Pacifiques de la route-
113 - 2021-01-11472Pacifiques de la route-Banshees-
115 - 2021-01-13484Harvard-Banshees-
118 - 2021-01-16496Wolves-Banshees-
119 - 2021-01-17503Banshees-Snipers-
120 - 2021-01-18506Banshees-Spoonman's-
121 - 2021-01-19512Ailes Rouges-Banshees-
122 - 2021-01-20520Banshees-Citadelles-
123 - 2021-01-21527Riverman-Banshees-
125 - 2021-01-23537Riverman-Banshees-
127 - 2021-01-25542Banshees-Harvard-
129 - 2021-01-27553Isotopes-Banshees-
130 - 2021-01-28558Banshees-Harvard-
133 - 2021-01-31566Canadiens-Banshees-
134 - 2021-02-01572Banshees-Croque-Morts-



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
32 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
284,738$ 1,287,500$ 1,287,500$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
1,287,500$ 284,738$ 29 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 107 10,165$ 1,087,655$




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
1993187542000635589531000033249922320003031-12263114177012218212496169158152174051481794071021817.65%771777.92%131562950.08%28856051.43%16232250.31%441296423141242121
Total Saison Régulière187542000635589531000033249922320003031-12263114177012218212496169158152174051481794071021817.65%771777.92%131562950.08%28856051.43%16232250.31%441296423141242121