Croque-Morts

GP: 17 | W: 10 | L: 7 | T: 0 | P: 20
GF: 62 | GA: 57 | PP%: 21.59% | PK%: 77.91%
DG: Pascal Beaulieu | Morale : 53 | Moyenne d'Équipe : 65
Prochain matchs #126 vs Canadiens
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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
1Dixon Ward (R)X100.006752676675707076677874726840406857670
2Luciano BorsatoX100.007152716767707078697677637246515457670
3Ray Whitney (R)X100.005541817769717078687774726830309657670
4Tony HrkacX100.006044826968717373687669756645475454660
5Dave McLlwainX100.006146777073757371657470716439485957660
6Corey MillenX100.005849696971727468667366766454593957660
7Tom ChorskeX100.006344836177717272677670726847465343660
8Tom FitzgeraldX100.006647846974717271647166826441426857660
9Bryan Smolinski (R) (A)X100.005541786977717175677573686832288957650
10Brian Savage (R)X100.006650687073666771637469766435378857650
11Blair AytchenumX100.006544886877697072627068796232287557650
12Brian Holzinger (R)X100.006250707271636462607065746226289057620
13Dana MurzynX100.008071546675847170627358785149525357680
14Trent YawneyX100.007869555975707062607052835062674857670
15Bob Halkidis (A)X100.008272546575757551485648764660585357650
16Richard Smehlik (R)X100.006850716479727367617159815429248257640
17Scott Lachance (R)X100.005744796677707266616857785434349657630
18Eric Cairns (R)X100.007062525980545558495938733128289754580
Rayé
1Dave LowryX100.006856646674787865616867746566724838660
2Patrik JuhlinX100.005340817272666869667468736434287533640
3Dan Kesa (R)X100.006358646376586057545656715429298833570
4Mike KennedyX100.005651695967464652515652495028289533510
5Jeff FinleyX100.007157656377697062586857825343446233650
6Dean Malkoc (R)X100.008374406878727257515554785135427533640
7Ivan DroppaX100.005844737073686867596355725230269633610
MOYENNE D'ÉQUIPE100.00655270677469696761696274594041725064
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
1Parris Duffus100.00686881787777798581716428338237690
2Jocelyn Thibault (R)100.00737070677775777680747135409952680
Rayé
1Byron Dafoe (R)100.00737877747773788479767335318238700
MOYENNE D'ÉQUIPE100.0071727673777578828074693335884269
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Al Arbour80798474999858CAN62295,000$


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
1Dixon WardCroque-Morts (ANA)RW17991811180492548174718.75%435520.8935817740000380051.38%10900101.0100000220
2Ray WhitneyCroque-Morts (ANA)LW1751217114013346715447.46%535721.0204415740001371066.67%1800000.9500000211
3Tony HrkacCroque-Morts (ANA)C17671332093442112714.29%426715.7634718720002472250.17%29300000.9700000101
4Scott LachanceCroque-Morts (ANA)D171121314032014267.14%924414.400333500006100.00%000001.0600000011
5Luciano BorsatoCroque-Morts (ANA)LW1784121120281740142920.00%224714.5651619720002263075.00%4000100.9700000111
6Bryan SmolinskiCroque-Morts (ANA)C175611106072529103417.24%027216.04000438000001146.40%34700010.8100000111
7Dave McLlwainCroque-Morts (ANA)RW172810-84016233417345.88%230317.830449730000191050.00%2200000.6600000001
8Dana MurzynCroque-Morts (ANA)D173710120036202742011.11%2741424.363472077000062000.00%000000.4800000011
9Richard SmehlikCroque-Morts (ANA)D17371036061624101812.50%2136021.182241872011157000.00%000000.5600000010
10Blair AytchenumCroque-Morts (ANA)RW17549080592672219.23%41569.21000001011201050.00%2400001.1500000001
11Bob HalkidisCroque-Morts (ANA)D17178280437196125.26%1736621.570001072000062000.00%000000.4400000000
12Corey MillenCroque-Morts (ANA)C17268-81201259327206.25%325515.060441138000070052.08%28800000.6300000000
13Trent YawneyCroque-Morts (ANA)D1726811201825217129.52%2240824.022351674000061000.00%000000.3900000011
14Brian SavageCroque-Morts (ANA)LW17527-710024154372111.63%324614.47000020000310038.10%2100000.5700000100
15Brian HolzingerCroque-Morts (ANA)C1706608072413990.00%41659.73000000000300046.15%19500000.7300000000
16Tom FitzgeraldCroque-Morts (ANA)RW17156120815269123.85%11508.8400014000000063.16%1900000.8000000000
17Dave LowryCroque-Morts (ANA)LW922406010581625.00%0798.8211213000050050.00%400001.0100000000
18Eric CairnsCroque-Morts (ANA)D17033041529106220.00%1525114.7700001000020000.00%000000.2400010000
19Tom ChorskeCroque-Morts (ANA)LW820212043831125.00%0597.4900000000000050.00%200000.6700000000
Stats d'équipe Total ou en Moyenne3066211317523185532738652715838611.76%143496216.22193554162760112753610350.07%138200210.7100010899
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
1Parris DuffusCroque-Morts (ANA)64100.8833.1031001161370000.000051100
2Byron DafoeCroque-Morts (ANA)83500.8373.7243500271660000.000083000
3Jocelyn ThibaultCroque-Morts (ANA)73100.8653.0327700141040000.0000413000
Stats d'équipe Total ou en Moyenne2110700.8603.34102301574070000.00001717100


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
Blair AytchenumCroque-Morts (ANA)RW241996-02-09 9:08:37 PMNo210 Lbs6 ft2NoNoNo1Pro & Farm440,000$44,000$34,294$No
Bob HalkidisCroque-Morts (ANA)D271993-02-09 9:08:37 PMNo200 Lbs6 ft1NoNoNo1Pro & Farm250,000$25,000$19,485$No
Brian HolzingerCroque-Morts (ANA)C221998-02-09 9:08:37 PMYes190 Lbs5 ft11NoNoNo2Pro & Farm220,000$22,000$17,147$No220,000$
Brian SavageCroque-Morts (ANA)LW221998-02-09 9:08:37 PMYes192 Lbs6 ft2NoNoNo2Pro & Farm400,000$40,000$31,176$No400,000$
Bryan SmolinskiCroque-Morts (ANA)C221998-02-09 9:08:37 PMYes209 Lbs6 ft1NoNoNo1Pro & Farm500,000$50,000$38,971$No
Byron DafoeCroque-Morts (ANA)G231997-02-09 9:08:37 PMYes190 Lbs5 ft11NoNoNo1Pro & Farm300,000$30,000$23,382$No
Corey MillenCroque-Morts (ANA)C291991-02-09 9:08:37 PMNo184 Lbs6 ft0NoNoNo2Pro & Farm580,000$58,000$45,206$No580,000$
Dan KesaCroque-Morts (ANA)RW221998-02-09 9:08:37 PMYes208 Lbs6 ft0NoNoNo2Pro & Farm230,000$23,000$17,926$No230,000$
Dana MurzynCroque-Morts (ANA)D271993-02-09 9:08:37 PMNo200 Lbs6 ft2NoNoNo2Pro & Farm515,000$51,500$40,140$No515,000$
Dave LowryCroque-Morts (ANA)LW281992-02-09 9:08:37 PMNo200 Lbs6 ft1NoNoNo1Pro & Farm550,000$55,000$42,868$No
Dave McLlwainCroque-Morts (ANA)RW261994-02-09 9:08:37 PMNo185 Lbs6 ft1NoNoNo2Pro & Farm455,000$45,500$35,463$No455,000$
Dean MalkocCroque-Morts (ANA)D241996-02-09 9:08:37 PMYes210 Lbs6 ft3NoNoNo3Pro & Farm500,000$50,000$38,971$No500,000$500,000$
Dixon WardCroque-Morts (ANA)RW251995-02-09 9:08:37 PMYes200 Lbs6 ft0NoNoNo3Pro & Farm450,000$45,000$35,074$No450,000$450,000$
Eric CairnsCroque-Morts (ANA)D192001-02-09 9:08:37 PMYes220 Lbs6 ft5NoNoNo3Pro & Farm400,000$40,000$31,176$No400,000$400,000$
Ivan DroppaCroque-Morts (ANA)D211999-02-09 9:08:37 PMNo185 Lbs6 ft2NoNoNo3Pro & Farm365,000$36,500$28,449$No365,000$365,000$
Jeff FinleyCroque-Morts (ANA)D261994-02-09 9:08:37 PMNo205 Lbs6 ft2NoNoNo2Pro & Farm475,000$47,500$37,022$No475,000$
Jocelyn ThibaultCroque-Morts (ANA)G192001-08-11 10:49:31 AMYes170 Lbs5 ft11NoNoNo3Pro & Farm250,000$25,000$19,485$No250,000$250,000$
Luciano BorsatoCroque-Morts (ANA)LW271993-02-09 9:08:37 PMNo165 Lbs5 ft10NoNoNo1Pro & Farm450,000$45,000$35,074$No
Mike KennedyCroque-Morts (ANA)C211999-02-09 9:08:37 PMNo200 Lbs6 ft0NoNoNo3Pro & Farm145,000$14,500$11,301$No145,000$145,000$
Parris DuffusCroque-Morts (ANA)G231997-02-09 9:08:37 PMNo192 Lbs6 ft2NoNoNo1Pro & Farm300,000$30,000$23,382$No
Patrik JuhlinCroque-Morts (ANA)RW241996-02-09 9:08:37 PMNo194 Lbs6 ft1NoNoNo1Pro & Farm150,000$15,000$11,691$No
Ray WhitneyCroque-Morts (ANA)LW211999-02-09 9:08:37 PMYes175 Lbs5 ft10NoNoNo2Pro & Farm450,000$45,000$35,074$No450,000$
Richard SmehlikCroque-Morts (ANA)D231997-02-09 9:08:37 PMYes222 Lbs6 ft3NoNoNo1Pro & Farm510,000$51,000$39,750$No
Scott LachanceCroque-Morts (ANA)D211999-02-09 9:08:37 PMYes209 Lbs6 ft1NoNoNo3Pro & Farm480,000$48,000$37,412$No480,000$480,000$
Tom ChorskeCroque-Morts (ANA)LW271993-02-09 9:08:37 PMNo212 Lbs6 ft1NoNoNo1Pro & Farm625,000$62,500$48,713$No
Tom FitzgeraldCroque-Morts (ANA)RW251995-02-09 9:08:37 PMNo191 Lbs6 ft0NoNoNo2Pro & Farm525,000$52,500$40,919$No525,000$
Tony HrkacCroque-Morts (ANA)C271993-02-09 9:08:37 PMNo170 Lbs5 ft11NoNoNo3Pro & Farm700,000$70,000$54,559$No700,000$700,000$
Trent YawneyCroque-Morts (ANA)D281992-02-09 9:08:37 PMNo195 Lbs6 ft3NoNoNo3Pro & Farm627,000$62,700$48,869$No627,000$627,000$
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2824.04196 Lbs6 ft11.96422,929$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Ray WhitneyBryan SmolinskiDixon Ward35122
2Brian SavageCorey MillenDave McLlwain30122
3Luciano BorsatoTony HrkacTom Fitzgerald20122
4Tom ChorskeBrian HolzingerBlair Aytchenum15122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Dana MurzynTrent Yawney35122
2Bob HalkidisRichard Smehlik30122
3Scott LachanceEric Cairns20122
4Dana MurzynTrent Yawney15122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Ray WhitneyBryan SmolinskiDixon Ward60122
2Luciano BorsatoTony HrkacDave McLlwain40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Dana MurzynTrent Yawney60122
2Bob HalkidisRichard Smehlik40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Brian HolzingerBrian Savage60122
2Tony HrkacDave McLlwain40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Dana MurzynTrent Yawney60122
2Bob HalkidisRichard Smehlik40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Corey Millen60122Dana MurzynTrent Yawney60122
2Brian Holzinger40122Bob HalkidisRichard Smehlik40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Bryan SmolinskiRay Whitney60122
2Brian HolzingerLuciano Borsato40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Dana MurzynTrent Yawney60122
2Bob HalkidisRichard Smehlik40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Ray WhitneyTony HrkacDixon WardDana MurzynTrent Yawney
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Ray WhitneyTony HrkacDixon WardDana MurzynTrent Yawney
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Dixon Ward, Tom Fitzgerald, Blair AytchenumRay Whitney, Tom FitzgeraldBlair Aytchenum
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Scott Lachance, Eric Cairns, Bob HalkidisScott LachanceEric Cairns, Bob Halkidis
Tirs de Pénalité
Dixon Ward, Ray Whitney, Luciano Borsato, Tony Hrkac, Tom Fitzgerald
Gardien
#1 : Jocelyn Thibault, #2 : Parris Duffus


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 Rouges43100000201463210000012931100000085360.7502039590115252111431771641824103353266301033.33%16475.00%029057850.17%26251950.48%14028549.12%408277390135226112
2As4310000015141220000008532110000079-260.750152439001525211116177164182410137507817529.41%23482.61%129057850.17%26251950.48%14028549.12%408277390135226112
3Banshees1010000023-1000000000001010000023-100.000246001525211191771641824246821200.00%4250.00%029057850.17%26251950.48%14028549.12%408277390135226112
4Chiefs21100000862211000008620000000000020.50081523001525211581771641824531826505120.00%12283.33%029057850.17%26251950.48%14028549.12%408277390135226112
5Pacifiques de la route1010000014-31010000014-30000000000000.00012300152521120177164182420102013500.00%10370.00%029057850.17%26251950.48%14028549.12%408277390135226112
Total179701000625759630000033276834010002930-1200.588621131750115252115271771641824407143185327881921.59%861977.91%129057850.17%26251950.48%14028549.12%408277390135226112
7Wolves522010001616011000000431412010001213-160.600162945001525211171177164182410637499929310.34%21480.95%029057850.17%26251950.48%14028549.12%408277390135226112
_Since Last GM Reset179701000625759630000033276834010002930-1200.588621131750115252115271771641824407143185327881921.59%861977.91%129057850.17%26251950.48%14028549.12%408277390135226112
_Vs Conference1485010005248475200000252147330100027270180.64352941460115252114501771641824330119151256811822.22%701578.57%129057850.17%26251950.48%14028549.12%408277390135226112
_Vs Division1384010005144765100000241777330100027270180.69251921430115252114301771641824310109131243761823.68%601280.00%129057850.17%26251950.48%14028549.12%408277390135226112

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
1720L36211317552740714318532701
Tous les Matchs
GPWLOTWOTL TGFGA
17971006257
Matchs locaux
GPWLOTWOTL TGFGA
9630003327
Matchs Éxtérieurs
GPWLOTWOTL TGFGA
8341002930
Derniers 10 Matchs
WLOTWOTL T
54100
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
881921.59%861977.91%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
17716418241525211
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
29057850.17%26251950.48%14028549.12%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
408277390135226112


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 - 2020-09-213As3Croque-Morts5WSommaire du Match
2 - 2020-09-2212Croque-Morts2As6LSommaire du Match
4 - 2020-09-2418Chiefs4Croque-Morts3LSommaire du Match
6 - 2020-09-2626Croque-Morts8Ailes Rouges5WSommaire du Match
7 - 2020-09-2735Ailes Rouges0Croque-Morts3WSommaire du Match
9 - 2020-09-2945As2Croque-Morts3WSommaire du Match
10 - 2020-09-3048Croque-Morts3Wolves5LSommaire du Match
11 - 2020-10-0155Croque-Morts5As3WSommaire du Match
13 - 2020-10-0363Croque-Morts3Wolves2WXSommaire du Match
15 - 2020-10-0568Wolves3Croque-Morts4WSommaire du Match
17 - 2020-10-0776Croque-Morts5Wolves3WSommaire du Match
19 - 2020-10-0982Pacifiques de la route4Croque-Morts1LSommaire du Match
21 - 2020-10-1189Chiefs2Croque-Morts5WSommaire du Match
23 - 2020-10-13100Ailes Rouges3Croque-Morts5WSommaire du Match
25 - 2020-10-15103Croque-Morts1Wolves3LSommaire du Match
27 - 2020-10-17114Ailes Rouges6Croque-Morts4LSommaire du Match
29 - 2020-10-19120Croque-Morts2Banshees3LSommaire du Match
31 - 2020-10-21126Canadiens-Croque-Morts-
33 - 2020-10-23135Pacifiques de la route-Croque-Morts-
35 - 2020-10-25142Croque-Morts-Chiefs-
37 - 2020-10-27148Croque-Morts-Ailes Rouges-
39 - 2020-10-29155Croque-Morts-Canadiens-
41 - 2020-10-31162As-Croque-Morts-
42 - 2020-11-01165Croque-Morts-Spoonman's-
44 - 2020-11-03174Croque-Morts-Pacifiques de la route-
46 - 2020-11-05180Riverman-Croque-Morts-
47 - 2020-11-06186Croque-Morts-Isotopes-
48 - 2020-11-07193Spoonman's-Croque-Morts-
50 - 2020-11-09202Chiefs-Croque-Morts-
52 - 2020-11-11213Riverman-Croque-Morts-
53 - 2020-11-12219Croque-Morts-Ailes Rouges-
55 - 2020-11-14224Croque-Morts-Chiefs-
57 - 2020-11-16232Wolves-Croque-Morts-
58 - 2020-11-17237Croque-Morts-Harvard-
59 - 2020-11-18245Canadiens-Croque-Morts-
61 - 2020-11-20254Pacifiques de la route-Croque-Morts-
62 - 2020-11-21259Croque-Morts-Canadiens-
65 - 2020-11-24266Croque-Morts-Harvard-
66 - 2020-11-25273Harvard-Croque-Morts-
67 - 2020-11-26281Wolves-Croque-Morts-
68 - 2020-11-27289Croque-Morts-Harvard-
70 - 2020-11-29294Croque-Morts-Riverman-
72 - 2020-12-01302Banshees-Croque-Morts-
73 - 2020-12-02309Croque-Morts-Citadelles-
74 - 2020-12-03312Croque-Morts-Wolves-
75 - 2020-12-04319Harvard-Croque-Morts-
77 - 2020-12-06329Snipers-Croque-Morts-
78 - 2020-12-07335Croque-Morts-As-
79 - 2020-12-08340As-Croque-Morts-
80 - 2020-12-09351Croque-Morts-Pacifiques de la route-
82 - 2020-12-11355Chiefs-Croque-Morts-
84 - 2020-12-13359Croque-Morts-Wolves-
86 - 2020-12-15370Banshees-Croque-Morts-
88 - 2020-12-17377Croque-Morts-Snipers-
90 - 2020-12-19385Riverman-Croque-Morts-
92 - 2020-12-21390Croque-Morts-As-
94 - 2020-12-23397Croque-Morts-Riverman-
96 - 2020-12-25402Spoonman's-Croque-Morts-
98 - 2020-12-27413Snipers-Croque-Morts-
100 - 2020-12-29420Croque-Morts-Spoonman's-
101 - 2020-12-30427Ailes Rouges-Croque-Morts-
103 - 2021-01-01431Croque-Morts-Banshees-
105 - 2021-01-03441Citadelles-Croque-Morts-
106 - 2021-01-04447Croque-Morts-Ailes Rouges-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
109 - 2021-01-07455Croque-Morts-Canadiens-
110 - 2021-01-08457Isotopes-Croque-Morts-
112 - 2021-01-10466Isotopes-Croque-Morts-
113 - 2021-01-11473Croque-Morts-As-
115 - 2021-01-13479As-Croque-Morts-
116 - 2021-01-14490Croque-Morts-Pacifiques de la route-
117 - 2021-01-15494Croque-Morts-Isotopes-
118 - 2021-01-16499Citadelles-Croque-Morts-
119 - 2021-01-17505Croque-Morts-Citadelles-
120 - 2021-01-18511Croque-Morts-Snipers-
121 - 2021-01-19517Wolves-Croque-Morts-
122 - 2021-01-20523Croque-Morts-Riverman-
124 - 2021-01-22530Spoonman's-Croque-Morts-
125 - 2021-01-23534Croque-Morts-Citadelles-
126 - 2021-01-24540Croque-Morts-Snipers-
127 - 2021-01-25546Isotopes-Croque-Morts-
130 - 2021-01-28557Isotopes-Croque-Morts-
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
281,775$ 1,184,200$ 1,184,200$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
1,184,200$ 281,775$ 28 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 106 9,406$ 997,036$




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
1993179701000625759630000033276834010002930-120621131750115252115271771641824407143185327881921.59%861977.91%129057850.17%26251950.48%14028549.12%408277390135226112
Total Saison Régulière179701000625759630000033276834010002930-120621131750115252115271771641824407143185327881921.59%861977.91%129057850.17%26251950.48%14028549.12%408277390135226112