Spoonman's

GP: 17 | W: 11 | L: 5 | T: 1 | P: 23
GF: 45 | GA: 42 | PP%: 19.59% | PK%: 82.93%
DG: Louis-Philippe Fraser | Morale : 59 | Moyenne d'Équipe : 65
Prochain matchs #123 vs Isotopes
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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
1Joel OttoX100.007663596179787968667264896362571763680
2Dave SnuggerudX100.006448816772737475657774716649535463680
3Dave ReidX100.006244856276697272587271866245544055670
4Bob SweeneyX100.008072536375737467627170786657604064670
5Mike McPheeX100.006245816176767568677167696666651063660
6Mike EaglesX100.007055666571707267626760835849533363650
7Scott Pellerin (R)X100.006652697173697070657363755844478161650
8Dave BarrX100.005543805771707163616965776681811063650
9Jamie BakerX100.006245756573727269647361805845505463650
10Derek LaxdalX100.008272546476707061566370756851515461640
11Glen Murray (R)X100.006452656476616270617071696634359640630
12Todd Marchant (R)X100.007863736173777463646864716436468052630
13Luke RichardsonX100.008778496979767761586654875143407541680
14Dan KeczmerX100.007053697072747473627565785551596850680
15Ric NattressX100.006847796476787758556144844354652563670
16Cam RussellX100.008066526976666664596652785034347553640
17Rich PilonX100.008778506276687053546551834932396863640
18Kevin HallerX100.007662616876717363566244764234348261630
Rayé
1Mike DonnellyX100.006748736468777874677572706854553357670
2Petri SkrikoX100.006447817368747566627075627165722444660
3Joe Sacco (R)X100.006452677371646665616964726234347533630
4Scott ThorntonX100.006760626476666965617165696129328233620
5Kirk Maltby (R)X100.005746786974646664626760745733299533610
6Turner Stevenson (R)X100.007160566480686859576661695828289633600
7Rob PearsonX100.006861627072666857546061696227279033590
8Chris Tamer (R)X100.006661606578687255566350774832328237620
9Sean O'DonnellX100.007364536281626465576346714232329046610
MOYENNE D'ÉQUIPE100.00705766657570716560686276594447605264
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
1Danny Lorenz100.00687581787872807676747047557663710
2Rick Tabaracci (R)100.00697876747574817878747328277553680
Rayé
1Peter Sidorkiewicz100.00757070697172708777727338406143670
MOYENNE D'ÉQUIPE100.0071747674757377807773723841715369
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Bob Johnson71797472948755USA63295,000$


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
1Dave SnuggerudSpoonman's (HAR)RW17971662032753133316.98%133119.5033613790001253146.05%7600000.9700000401
2Joel OttoSpoonman's (HAR)C1731114612049484013377.50%735620.96279167810141052054.93%47700000.7900000112
3Ric NattressSpoonman's (HAR)D17110118120101813897.69%3640423.821231073000089000.00%000000.5400000002
4Bob SweeneySpoonman's (HAR)C176410231564364182214.63%231418.5223515680000183050.13%38500000.6400001101
5Dan KeczmerSpoonman's (HAR)D17279-81952039287267.14%2440824.032241874011088100.00%000000.4400010101
6Dave ReidSpoonman's (HAR)LW105383003735112014.29%216516.502241138000091066.67%600000.9700000111
7Todd MarchantSpoonman's (HAR)LW1235811001514345168.82%121918.332351160000000062.50%1600000.7300000020
8Mike DonnellySpoonman's (HAR)LW1143722010112841614.29%017616.0732515450000191028.57%1400000.7900000010
9Kevin HallerSpoonman's (HAR)D162575801414203710.00%1229218.271231655000041000.00%000000.4800000010
10Cam RussellSpoonman's (HAR)D17156-5400472523434.35%2135420.861121770000072000.00%000000.3400000010
11Mike KeaneWhalersRW8156320711124108.33%215219.060445390000420046.15%1300000.7900000001
12Glen MurraySpoonman's (HAR)RW6055280724050.00%18414.16022125000000033.33%600001.1800000000
13Mike McPheeSpoonman's (HAR)LW17055210059244130.00%118110.70000111000000055.00%2000000.5500000000
14Jamie BakerSpoonman's (HAR)C17404218012331931621.05%319011.21000020000380147.49%21900000.4200000000
15Luke RichardsonSpoonman's (HAR)D111232335391212238.33%1021319.42011515000048000.00%000000.2800100100
16Derek LaxdalSpoonman's (HAR)RW16213-53603410196910.53%222414.00000000002320033.33%900000.2700000000
17Rich PilonSpoonman's (HAR)D17022630052115280.00%1225615.08000014000011000.00%000000.1600000000
18Mike EaglesSpoonman's (HAR)C17011-660103012590.00%11599.36000000001330053.25%15400000.1300000000
19Dave BarrSpoonman's (HAR)RW17101-1404915586.67%01106.5100028000010060.00%1000000.1800000000
20Petri SkrikoSpoonman's (HAR)LW5011100013130.00%0357.1000023000040060.00%500000.5600000000
21Paul RanheimWhalersLW1000000024310.00%02323.33000250000700100.00%200000.0000000000
22Sean O'DonnellSpoonman's (HAR)D5000100720000.00%57915.8300001000024000.00%000000.0000000000
23Chris TamerSpoonman's (HAR)D1000000210000.00%01616.520000000006000.00%000000.0000000000
24Scott PellerinSpoonman's (HAR)LW16000-62078111150.00%21247.77000000000450040.00%500000.0000000000
25Doug CrossmanWhalersD1000100303110.00%22222.770003400005000.00%000000.0000000000
Stats d'équipe Total ou en Moyenne306458212722285154243804581132909.83%147490016.01193453163776112877411251.45%141700000.5200111979
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
1Danny LorenzSpoonman's (HAR)1610510.9052.5596623414300200.0000161321
2Rick TabaracciSpoonman's (HAR)11000.9501.0060001200000.0000111000
Stats d'équipe Total ou en Moyenne1711510.9072.46102623424500200.00001712321


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
Bob SweeneySpoonman's (HAR)C291991-02-09 9:08:37 PMNo200 Lbs6 ft3NoNoNo2Pro & Farm620,000$62,000$48,779$No620,000$
Cam RussellSpoonman's (HAR)D241996-02-09 9:08:37 PMNo200 Lbs6 ft4NoNoNo4Pro & Farm550,000$55,000$43,272$No550,000$550,000$550,000$
Chris TamerSpoonman's (HAR)D231997-02-09 9:08:37 PMYes215 Lbs6 ft1NoNoNo3Pro & Farm375,000$37,500$29,504$No375,000$375,000$
Dan KeczmerSpoonman's (HAR)D251995-02-09 9:08:37 PMNo180 Lbs6 ft1NoNoNo2Pro & Farm285,000$28,500$22,423$No285,000$
Danny LorenzSpoonman's (HAR)G241996-02-09 9:08:37 PMNo185 Lbs5 ft10NoNoNo3Pro & Farm746,000$74,600$58,693$No746,000$746,000$
Dave BarrSpoonman's (HAR)RW331987-02-09 9:08:37 PMNo195 Lbs6 ft1NoNoNo2Pro & Farm500,000$50,000$39,338$No500,000$
Dave ReidSpoonman's (HAR)LW291991-02-09 9:08:37 PMNo205 Lbs6 ft0NoNoNo1Pro & Farm425,000$42,500$33,438$No
Dave SnuggerudSpoonman's (HAR)RW271993-02-09 9:08:37 PMNo190 Lbs6 ft0NoNoNo2Pro & Farm850,000$85,000$66,875$No850,000$
Derek LaxdalSpoonman's (HAR)RW271993-02-09 9:08:37 PMNo210 Lbs6 ft1NoNoNo1Pro & Farm520,000$52,000$40,912$No
Glen MurraySpoonman's (HAR)RW211999-02-09 9:08:37 PMYes221 Lbs6 ft2NoNoNo2Pro & Farm185,000$18,500$14,555$No185,000$
Jamie BakerSpoonman's (HAR)C271993-02-09 9:08:37 PMNo190 Lbs6 ft1NoNoNo4Pro & Farm700,000$70,000$55,074$No700,000$700,000$700,000$
Joe SaccoSpoonman's (HAR)LW241996-02-09 9:08:37 PMYes180 Lbs6 ft1NoNoNo3Pro & Farm250,000$25,000$19,669$No250,000$250,000$
Joel OttoSpoonman's (HAR)C321988-02-09 9:08:37 PMNo220 Lbs6 ft4NoNoNo2Pro & Farm720,000$72,000$56,647$No720,000$
Kevin HallerSpoonman's (HAR)D231997-02-09 9:08:37 PMNo195 Lbs6 ft2NoNoNo3Pro & Farm400,000$40,000$31,471$No400,000$400,000$
Kirk MaltbySpoonman's (HAR)LW211999-02-09 9:08:37 PMYes200 Lbs6 ft0NoNoNo3Pro & Farm395,000$39,500$31,077$No395,000$395,000$
Luke RichardsonSpoonman's (HAR)D241996-02-09 9:08:37 PMNo210 Lbs6 ft4NoNoNo1Pro & Farm650,000$65,000$51,140$No
Mike DonnellySpoonman's (HAR)LW301990-02-09 9:08:37 PMNo170 Lbs5 ft11NoNoNo3Pro & Farm680,000$68,000$53,500$No680,000$680,000$
Mike EaglesSpoonman's (HAR)C301990-02-09 9:08:37 PMNo190 Lbs5 ft10NoNoNo1Pro & Farm485,000$48,500$38,158$No
Mike McPheeSpoonman's (HAR)LW331987-02-09 9:08:37 PMNo205 Lbs6 ft2NoNoNo1Pro & Farm640,000$64,000$50,353$No
Peter SidorkiewiczSpoonman's (HAR)G261994-02-09 9:08:37 PMNo165 Lbs5 ft9NoNoNo1Pro & Farm650,000$65,000$51,140$No
Petri SkrikoSpoonman's (HAR)LW311989-02-09 9:08:37 PMNo170 Lbs5 ft10NoNoNo2Pro & Farm885,000$88,500$69,629$No885,000$
Ric NattressSpoonman's (HAR)D311989-02-09 9:08:37 PMNo210 Lbs6 ft3NoNoNo2Pro & Farm405,000$40,500$31,864$No405,000$
Rich PilonSpoonman's (HAR)D251995-02-09 9:08:37 PMNo216 Lbs6 ft0NoNoNo1Pro & Farm285,000$28,500$22,423$No
Rick TabaracciSpoonman's (HAR)G241996-02-09 9:08:37 PMYes190 Lbs5 ft11NoNoNo1Pro & Farm475,000$47,500$37,371$No
Rob PearsonSpoonman's (HAR)RW221998-02-09 9:08:37 PMNo185 Lbs6 ft1NoNoNo2Pro & Farm200,000$20,000$15,735$No200,000$
Scott PellerinSpoonman's (HAR)LW231997-02-09 9:08:37 PMYes190 Lbs5 ft11NoNoNo2Pro & Farm350,000$35,000$27,537$No350,000$
Scott ThorntonSpoonman's (HAR)C231997-02-09 9:08:37 PMNo206 Lbs6 ft3NoNoNo2Pro & Farm225,000$22,500$17,702$No225,000$
Sean O'DonnellSpoonman's (HAR)D221998-02-09 9:08:37 PMNo230 Lbs6 ft3NoNoNo3Pro & Farm350,000$35,000$27,537$No350,000$350,000$
Todd MarchantSpoonman's (HAR)LW211999-08-11 9:10:52 AMYes175 Lbs5 ft10NoNoNo3Pro & Farm100,000$10,000$7,868$No250,000$250,000$
Turner StevensonSpoonman's (HAR)RW211999-02-09 9:08:37 PMYes226 Lbs6 ft3NoNoNo3Pro & Farm240,000$24,000$18,882$No240,000$240,000$
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
3025.83197 Lbs6 ft12.17471,367$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Todd MarchantBob SweeneyDave Snuggerud35023
2Dave ReidJoel OttoDerek Laxdal30023
3Mike McPheeJamie BakerGlen Murray20122
4Scott PellerinMike EaglesDerek Laxdal15122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Cam RussellDan Keczmer35023
2Ric NattressRich Pilon30023
3Luke RichardsonKevin Haller20122
4Ric NattressDan Keczmer15122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Todd MarchantJoel OttoDave Snuggerud60023
2Dave ReidBob SweeneyGlen Murray40023
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Cam RussellDan Keczmer60122
2Ric NattressKevin Haller40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Joel OttoDerek Laxdal60122
2Jamie BakerScott Pellerin40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Ric NattressDan Keczmer60122
2Luke RichardsonCam Russell40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Bob Sweeney60122Ric NattressDan Keczmer60122
2Mike Eagles40122Luke RichardsonCam Russell40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Bob SweeneyTodd Marchant60122
2Joel OttoDave Reid40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Ric NattressDan Keczmer60122
2Rich PilonCam Russell40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Dave ReidJoel OttoDave SnuggerudKevin HallerDan Keczmer
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Scott PellerinJoel OttoDave SnuggerudRich PilonKevin Haller
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Mike McPhee, Jamie Baker, Mike EaglesMike McPhee, Jamie BakerMike Eagles
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Rich Pilon, Kevin Haller, Ric NattressRich PilonKevin Haller, Ric Nattress
Tirs de Pénalité
Dave Snuggerud, Joel Otto, Bob Sweeney, Todd Marchant, Derek Laxdal
Gardien
#1 : Danny Lorenz, #2 : Rick Tabaracci


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
1Canadiens31101000910-12110000068-21000100032140.6679172600161116273113169172490281007420420.00%36586.11%028557949.22%31160251.66%13323656.36%387244409139230116
2Chiefs22000000413110000001011100000031241.0004711011611162601131691724427265612325.00%130100.00%028557949.22%31160251.66%13323656.36%387244409139230116
3Citadelles420110001385200110005412200000084470.8751323360016111621141131691724107393010418633.33%15380.00%028557949.22%31160251.66%13323656.36%387244409139230116
4Harvard22000000532110000001011100000043141.00058130116111625511316917244321245215213.33%12191.67%128557949.22%31160251.66%13323656.36%387244409139230116
5Isotopes211000004401010000034-11100000010120.5004711011611162431131691724611338548112.50%18194.44%028557949.22%31160251.66%13323656.36%387244409139230116
6Pacifiques de la route11000000211110000002110000000000021.0002460016111623711316917242051519600.00%4175.00%028557949.22%31160251.66%13323656.36%387244409139230116
7Riverman30300000815-71010000045-120200000410-600.000816240016111627611316917248734546518316.67%251060.00%028557949.22%31160251.66%13323656.36%387244409139230116
Total1795120004542394311000222208520100023203230.67645821270316111624581131691724450147287424971919.59%1232182.93%128557949.22%31160251.66%13323656.36%387244409139230116
_Since Last GM Reset1710502000454239431100022220862-1100023203240.70645821270316111624581131691724450147287424971919.59%1232182.93%128557949.22%31160251.66%13323656.36%387244409139230116
_Vs Conference139202000352697321100016160660-1100019109220.8463562970316111623451131691724343108218340731621.92%941089.36%128557949.22%31160251.66%13323656.36%387244409139230116
_Vs Division751010001814443100000880320010001064120.85718325002161116218811316917241755615018247919.15%61690.16%128557949.22%31160251.66%13323656.36%387244409139230116

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
1723T1458212745845014728742403
Tous les Matchs
GPWLOTWOTL TGFGA
17952014542
Matchs locaux
GPWLOTWOTL TGFGA
9431012222
Matchs Éxtérieurs
GPWLOTWOTL TGFGA
8521002320
Derniers 10 Matchs
WLOTWOTL T
44101
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
971919.59%1232182.93%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
11316917241611162
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
28557949.22%31160251.66%13323656.36%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
387244409139230116


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 - 2020-09-211Canadiens6Spoonman's3LSommaire du Match
2 - 2020-09-2211Spoonman's3Canadiens2WXSommaire du Match
3 - 2020-09-2315Spoonman's3Chiefs1WSommaire du Match
5 - 2020-09-2524Harvard0Spoonman's1WR3Sommaire du Match
7 - 2020-09-2733Canadiens2Spoonman's3WSommaire du Match
9 - 2020-09-2940Spoonman's4Harvard3WR3Sommaire du Match
10 - 2020-09-3046Pacifiques de la route1Spoonman's2WSommaire du Match
11 - 2020-10-0156Chiefs0Spoonman's1WSommaire du Match
12 - 2020-10-0260Spoonman's2Riverman5LSommaire du Match
14 - 2020-10-0467Spoonman's1Isotopes0WSommaire du Match
16 - 2020-10-0674Isotopes4Spoonman's3LSommaire du Match
18 - 2020-10-0881Spoonman's5Citadelles3WSommaire du Match
20 - 2020-10-1088Spoonman's2Riverman5LSommaire du Match
22 - 2020-10-1294Citadelles1Spoonman's2WXSommaire du Match
24 - 2020-10-14102Riverman5Spoonman's4LSommaire du Match
26 - 2020-10-16109Spoonman's3Citadelles1WSommaire du Match
28 - 2020-10-18116Citadelles3Spoonman's3TXSommaire du Match
30 - 2020-10-20123Spoonman's-Isotopes-
32 - 2020-10-22130Riverman-Spoonman's-
34 - 2020-10-24136Spoonman's-Riverman-
36 - 2020-10-26143Spoonman's-Citadelles-
38 - 2020-10-28151Isotopes-Spoonman's-
40 - 2020-10-30158Spoonman's-Riverman-
42 - 2020-11-01165Croque-Morts-Spoonman's-
43 - 2020-11-02171Spoonman's-Ailes Rouges-
46 - 2020-11-05179Ailes Rouges-Spoonman's-
47 - 2020-11-06187Wolves-Spoonman's-
48 - 2020-11-07193Spoonman's-Croque-Morts-
49 - 2020-11-08200Isotopes-Spoonman's-
52 - 2020-11-11207Spoonman's-Ailes Rouges-
53 - 2020-11-12215Wolves-Spoonman's-
55 - 2020-11-14223Spoonman's-Banshees-
56 - 2020-11-15227Spoonman's-Snipers-
57 - 2020-11-16234Citadelles-Spoonman's-
58 - 2020-11-17240Spoonman's-Canadiens-
61 - 2020-11-20248Canadiens-Spoonman's-
62 - 2020-11-21256Banshees-Spoonman's-
64 - 2020-11-23265Spoonman's-Banshees-
65 - 2020-11-24269Spoonman's-Wolves-
66 - 2020-11-25275Canadiens-Spoonman's-
67 - 2020-11-26283Spoonman's-Banshees-
70 - 2020-11-29291Ailes Rouges-Spoonman's-
71 - 2020-11-30299Snipers-Spoonman's-
74 - 2020-12-03311Harvard-Spoonman's-
75 - 2020-12-04321As-Spoonman's-
78 - 2020-12-07336Chiefs-Spoonman's-
79 - 2020-12-08338Spoonman's-Ailes Rouges-
80 - 2020-12-09349Spoonman's-Ailes Rouges-
82 - 2020-12-11353As-Spoonman's-
84 - 2020-12-13364Spoonman's-Snipers-
86 - 2020-12-15366Chiefs-Spoonman's-
88 - 2020-12-17374Spoonman's-Chiefs-
90 - 2020-12-19381Snipers-Spoonman's-
92 - 2020-12-21387Spoonman's-Canadiens-
94 - 2020-12-23395Harvard-Spoonman's-
96 - 2020-12-25402Spoonman's-Croque-Morts-
98 - 2020-12-27408Pacifiques de la route-Spoonman's-
100 - 2020-12-29420Croque-Morts-Spoonman's-
101 - 2020-12-30426Spoonman's-Chiefs-
103 - 2021-01-01432Spoonman's-As-
104 - 2021-01-02436Ailes Rouges-Spoonman's-
106 - 2021-01-04446Spoonman's-Harvard-
108 - 2021-01-06450As-Spoonman's-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
110 - 2021-01-08461Spoonman's-Harvard-
111 - 2021-01-09464Harvard-Spoonman's-
113 - 2021-01-11475Chiefs-Spoonman's-
115 - 2021-01-13482Spoonman's-Pacifiques de la route-
116 - 2021-01-14489Canadiens-Spoonman's-
118 - 2021-01-16497Spoonman's-Canadiens-
119 - 2021-01-17504Spoonman's-Harvard-
120 - 2021-01-18506Banshees-Spoonman's-
121 - 2021-01-19513Spoonman's-Chiefs-
122 - 2021-01-20519Pacifiques de la route-Spoonman's-
123 - 2021-01-21525Spoonman's-Chiefs-
124 - 2021-01-22530Spoonman's-Croque-Morts-
125 - 2021-01-23536Spoonman's-Canadiens-
126 - 2021-01-24539Wolves-Spoonman's-
127 - 2021-01-25547Spoonman's-Wolves-
129 - 2021-01-27552Snipers-Spoonman's-
132 - 2021-01-30562Pacifiques de la route-Spoonman's-
133 - 2021-01-31564Spoonman's-Pacifiques de la route-
134 - 2021-02-01569Spoonman's-As-



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
319,774$ 1,414,100$ 1,424,100$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
1,414,100$ 319,774$ 30 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 107 11,096$ 1,187,272$




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
199317951200045423943110002222085201000232032345821270316111624581131691724450147287424971919.59%1232182.93%128557949.22%31160251.66%13323656.36%387244409139230116
Total Saison Régulière17951200045423943110002222085201000232032345821270316111624581131691724450147287424971919.59%1232182.93%128557949.22%31160251.66%13323656.36%387244409139230116