Harvard
GP: 82 | W: 29 | L: 48 | T: 3 | P: 63
GF: 264 | GA: 301 | PP%: 19.29% | PK%: 76.72%
DG: Marcel | Morale : 31 | Moyenne d'Équipe : 64
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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 SPÂgeContratSalaire
1Ron SutterX99.008270576871797972687568746655663338680312554,500$
2Jim SandlakX100.007868506381777769626864776161675534670282400,000$
3Brent FedykX99.006146787072747473647374706848446140670272575,000$
4Mikko MakelaX99.005741826575676767616971696759684653650292250,000$
5Josef BeranekX100.005339817272707275687573617138407452650252470,000$
6Ronnie SternX100.008376386574747662636563796247416052640273429,000$
7Craig JohnsonX100.005948677272676867657368666432328942630232225,000$
8Jozef StumpelX100.005948686778687070657367666431339721630222500,000$
9Niklas AnderssonX100.004937827569656872647267606232328921630233386,000$
10Reid SimpsonX100.006960546777687166656658795538387550630251480,000$
11Stu GrimsonX100.009890305981747660586345794352524720620291395,000$
12Pat PeakeX100.005544797170616466656861676041419620610213220,000$
13Jeff NortonX100.006551676575697064627352765041354829630292225,000$
14Janne Laukkanen (R)X100.005745787172666768627258725335288243620241280,000$
15Ryan McGill (R)X100.008265556674646455546442784035357636620251387,000$
16Todd ReirdenX100.006347726480686864606852745019289033610233359,000$
17Cory Cross (R)X100.006865446281676755525845764336419020610233380,000$
18Jan Vopat (R)X100.005644796274596057586541733931339819580213300,000$
Rayé
1Matthew Barnaby (R)X100.008276456674656654536054755228339820590212350,000$
MOYENNE D'ÉQUIPE99.84675663677569706562685972564041743463
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
1Vincent Riendeau100.00717883808071758477777352525411720
2Wendell Young100.00658674727068728073697173763321710
Rayé
1Milan Hnilicka100.00677475737168758175696727319720660
MOYENNE D'ÉQUIPE100.0068797775746974827572705153611770
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Michel Therrien71707072738388CAN31295,000$


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
1Ron SutterHarvard (BOS)C75244165-23148202842132124614611.32%18176623.551223355531402263094151.90%234100000.7400112645
2J. J. DaigneaultBruinsD73134659-3434051103118316911.02%97175724.08112132773211232303010.00%000000.6700000221
3Jason AllisonBruinsC77243357-9220681772036214911.82%17142118.45710174721910152102156.34%126200000.8000000161
4Brent FedykHarvard (BOS)RW76223153-2314047842004713411.00%7144018.95817256833300031365148.70%15400010.7400000313
5Janne LaukkanenHarvard (BOS)D7913385113205997115357511.30%78163220.6691221833080000259110.00%000000.6200000212
6Dimitri YushkevichBruinsD70133750-211395174112114327111.40%94157022.4391120732571123210200.00%000000.6400010143
7Tim TaylorBruinsC47132841111002914314746868.84%14105422.43311144718831472202153.29%80500000.7800000212
8Mikko MakelaHarvard (BOS)LW81172441-1860970139349812.23%7120614.8981018292501121362049.02%10200000.6800000023
9Reid SimpsonHarvard (BOS)LW791622381056107867118437913.56%497312.330221159000040042.00%5000000.7800110103
10Ronnie SternHarvard (BOS)RW82122335-11122019478109317111.01%9101612.4005571280110321049.52%10500000.6900004132
11Josef BeranekHarvard (BOS)C82161733-10601079113267714.16%184910.371129620000402052.91%66900000.7800000111
12Jeff NortonHarvard (BOS)D7642731-1780133676425616.25%67155420.4531013412970110199100.00%000000.4000000001
13Jim SandlakHarvard (BOS)RW65161430-166016961106368215.09%8104416.07257382520110916145.90%12200000.5700000231
14Craig JohnsonHarvard (BOS)LW82101424-5100186811119549.01%47909.640225561122941048.54%10300000.6100000000
15Ryan McGillHarvard (BOS)D6351520010801793733122715.15%60103616.461231284000089010.00%000000.3900000111
16Todd ReirdenHarvard (BOS)D5641115920038382171019.05%4489415.98000423000091000.00%000000.3400000001
17Niklas AnderssonHarvard (BOS)LW5121214-7004385017394.00%461912.151349122000100045.00%10000000.4500000000
18Cory CrossHarvard (BOS)D31145-55007514101610.00%2546915.15000117000029000.00%000000.2100000000
19Jan VopatHarvard (BOS)D14314-321517551360.00%721215.1600015000032000.00%000000.3800000000
20Jozef StumpelHarvard (BOS)C53033200234213180.00%43005.67000180002480046.86%27100000.2000000000
21Matthew BarnabyHarvard (BOS)RW31011-32604816132130.00%32989.6300002000000043.75%6400000.0700000000
22Pat PeakeHarvard (BOS)C131010001350420.00%0493.7700000000030043.10%5800000.4100000000
23Stu GrimsonHarvard (BOS)LW12000-140840100.00%1484.0800008000080044.00%2500000.0000000000
Stats d'équipe Total ou en Moyenne1368229442671-13296260169516082027557137211.30%5732200816.097514522061833248111932245429852.14%623100010.6100236232930
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
1Vincent RiendeauHarvard (BOS)68234110.8583.9734902223116310100.0000685320
2Wendell YoungHarvard (BOS)296810.8882.98120700605350200.00001448200
3Milan HnilickaHarvard (BOS)60110.9022.6222900101020000.0000029000
Stats d'équipe Total ou en Moyenne103295030.8673.6749282230122680300.00008282520


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
Brent FedykHarvard (BOS)RW271994-02-09 9:08:37 PMNo186 Lbs6 ft1NoNoNo2Pro & Farm575,000$57,500$423$No575,000$
Cory CrossHarvard (BOS)D231998-02-09 9:08:37 PMYes219 Lbs6 ft5NoNoNo3Pro & Farm380,000$38,000$279$No380,000$380,000$
Craig JohnsonHarvard (BOS)LW231998-02-09 9:08:37 PMNo197 Lbs6 ft2NoNoNo2Pro & Farm225,000$22,500$165$No225,000$
Jan VopatHarvard (BOS)D212000-02-09 9:08:37 PMYes207 Lbs6 ft0NoNoNo3Pro & Farm300,000$30,000$221$No300,000$300,000$
Janne LaukkanenHarvard (BOS)D241997-02-09 9:08:37 PMYes180 Lbs6 ft0NoNoNo1Pro & Farm280,000$28,000$206$No
Jeff NortonHarvard (BOS)D291992-02-09 9:08:37 PMNo195 Lbs6 ft2NoNoNo2Pro & Farm225,000$22,500$165$No225,000$
Jim SandlakHarvard (BOS)RW281993-02-09 9:08:37 PMNo219 Lbs6 ft4NoNoNo2Pro & Farm400,000$40,000$294$No400,000$
Josef BeranekHarvard (BOS)C251996-02-09 9:08:37 PMNo195 Lbs6 ft2NoNoNo2Pro & Farm470,000$47,000$346$No470,000$
Jozef StumpelHarvard (BOS)C221999-02-09 9:08:37 PMNo216 Lbs6 ft3NoNoNo2Pro & Farm500,000$50,000$368$No500,000$
Matthew BarnabyHarvard (BOS)RW212000-02-09 9:08:37 PMYes195 Lbs6 ft1NoNoNo2Pro & Farm350,000$35,000$257$No350,000$
Mikko MakelaHarvard (BOS)LW291992-02-09 9:08:37 PMNo200 Lbs6 ft2NoNoNo2Pro & Farm250,000$25,000$184$No250,000$
Milan HnilickaHarvard (BOS)G221999-02-09 9:08:37 PMNo180 Lbs6 ft0NoNoNo2Pro & Farm270,000$27,000$199$No270,000$
Niklas AnderssonHarvard (BOS)LW231998-02-09 9:08:37 PMNo175 Lbs5 ft9NoNoNo3Pro & Farm386,000$38,600$284$No386,000$386,000$
Pat PeakeHarvard (BOS)C212000-02-09 9:08:37 PMNo190 Lbs6 ft1NoNoNo3Pro & Farm220,000$22,000$162$No220,000$220,000$
Reid SimpsonHarvard (BOS)LW251996-02-09 9:08:37 PMNo210 Lbs6 ft1NoNoNo1Pro & Farm480,000$48,000$353$No
Ron SutterHarvard (BOS)C311990-02-09 9:08:37 PMNo180 Lbs6 ft0NoNoNo2Pro & Farm554,500$55,450$408$No554,500$
Ronnie SternHarvard (BOS)RW271994-02-09 9:08:37 PMNo195 Lbs6 ft0NoNoNo3Pro & Farm429,000$42,900$315$No429,000$429,000$
Ryan McGillHarvard (BOS)D251996-02-09 9:08:37 PMYes197 Lbs6 ft2NoNoNo1Pro & Farm387,000$38,700$285$No
Stu GrimsonHarvard (BOS)LW291992-02-09 9:08:37 PMNo230 Lbs6 ft5NoNoNo1Pro & Farm395,000$39,500$290$No
Todd ReirdenHarvard (BOS)D231998-02-09 9:08:37 PMNo220 Lbs6 ft5NoNoNo3Pro & Farm359,000$35,900$264$No359,000$359,000$
Vincent RiendeauHarvard (BOS)G281993-02-09 9:08:37 PMNo181 Lbs5 ft10NoNoNo3Pro & Farm985,000$98,500$724$No985,000$985,000$
Wendell YoungHarvard (BOS)G311990-02-09 9:08:37 PMNo182 Lbs5 ft9NoNoNo2Pro & Farm750,000$75,000$551$No750,000$
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2225.32198 Lbs6 ft12.14416,841$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Mikko MakelaRon SutterBrent Fedyk35122
2Reid SimpsonJosef BeranekJim Sandlak30122
3Craig JohnsonJozef StumpelRonnie Stern20122
4Niklas AnderssonPat PeakeRon Sutter15122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Jeff NortonJanne Laukkanen35122
2Ryan McGillCory Cross30122
3Todd ReirdenJan Vopat20122
4Jeff NortonJanne Laukkanen15122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Mikko MakelaRon SutterBrent Fedyk60122
2Reid SimpsonJosef BeranekJim Sandlak40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Jeff NortonJanne Laukkanen60122
2Ryan McGillCory Cross40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Ron SutterBrent Fedyk60122
2Jim SandlakMikko Makela40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Jeff NortonJanne Laukkanen60122
2Ryan McGillCory Cross40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Ron Sutter60122Jeff NortonJanne Laukkanen60122
2Brent Fedyk40122Ryan McGillCory Cross40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Ron SutterBrent Fedyk60122
2Jim SandlakMikko Makela40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Jeff NortonJanne Laukkanen60122
2Ryan McGillCory Cross40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Mikko MakelaRon SutterBrent FedykJeff NortonJanne Laukkanen
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Mikko MakelaRon SutterBrent FedykJeff NortonJanne Laukkanen
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Stu Grimson, Ronnie Stern, Craig JohnsonStu Grimson, Ronnie SternCraig Johnson
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Todd Reirden, Jan Vopat, Ryan McGillTodd ReirdenJan Vopat, Ryan McGill
Tirs de Pénalité
Ron Sutter, Brent Fedyk, Jim Sandlak, Mikko Makela, Josef Beranek
Gardien
#1 : Vincent Riendeau, #2 : Wendell Young


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 Rouges624000001422-820200000411-7422000001011-140.33314264000105768121747497277909172338310837616.22%32778.13%11517284553.32%1432276951.72%749139953.54%1954130619166421070530
2As624000001618-230300000715-83210000093640.33316284401105768121527497277909179378313937718.92%34682.35%01517284553.32%1432276951.72%749139953.54%1954130619166421070530
3Banshees624000001925-631200000610-4312000001315-240.3331936550010576812146749727790918242841282129.52%31970.97%01517284553.32%1432276951.72%749139953.54%1954130619166421070530
4Canadiens86200000332310422000001315-24400000020812120.7503358910010576812263749727790919357109187711216.90%48981.25%11517284553.32%1432276951.72%749139953.54%1954130619166421070530
5Chiefs9350100028253513010001716142200000119280.444285280011057681225774972779092337110316340922.50%451077.78%21517284553.32%1432276951.72%749139953.54%1954130619166421070530
6Citadelles615000002128-720200000611-5413000001517-220.16721375800105768121607497277909186576610835411.43%31680.65%11517284553.32%1432276951.72%749139953.54%1954130619166421070530
7Croque-Morts522100001819-132010000141042020000049-550.50018325000105768121277497277909137514311122418.18%18572.22%01517284553.32%1432276951.72%749139953.54%1954130619166421070530
8Isotopes513001001419-530200100913-42110000056-130.30014264000105768121387497277909121425010023417.39%23482.61%01517284553.32%1432276951.72%749139953.54%1954130619166421070530
9Pacifiques de la route514000001621-5312000001210220200000411-720.20016294500105768121257497277909178446710215533.33%301066.67%11517284553.32%1432276951.72%749139953.54%1954130619166421070530
10Riverman623010002423121100000990412010001514160.500244569001057681219374972779091414359148341235.29%19573.68%01517284553.32%1432276951.72%749139953.54%1954130619166421070530
11Snipers732101002526-14111010015141321000001012-280.57125467100105768121727497277909196548112623730.43%35780.00%11517284553.32%1432276951.72%749139953.54%1954130619166421070530
12Spoonman's817000001930-11413000001217-540400000713-620.125193453001057681220274972779092295411821465913.85%561475.00%11517284553.32%1432276951.72%749139953.54%1954130619166421070530
13Wolves513100001722-531200000910-120110000812-430.3001731480010576812166749727790912135409628621.43%19668.42%01517284553.32%1432276951.72%749139953.54%1954130619166421070530
Total82274832200264301-3741112521200133161-2841162311000131140-9630.384264480744021057681222757497277909226862098617304518719.29%4219876.72%81517284553.32%1432276951.72%749139953.54%1954130619166421070530
_Since Last GM Reset82304802200264301-3741112521200133161-28411923-21000131140-9660.402264480744021057681222757497277909226862098617304518719.29%4219876.72%81517284553.32%1432276951.72%749139953.54%1954130619166421070530
_Vs Conference42142601100134150-1621514011006382-19219120000071683310.36913424337701105768121166749727790911443235309002554015.69%2345277.78%51517284553.32%1432276951.72%749139953.54%1954130619166421070530
_Vs Division25101401000807821348010004248-612660000038308220.44080144224011057681272274972779096551823305641763017.05%1493377.85%41517284553.32%1432276951.72%749139953.54%1954130619166421070530

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
8263L326448074422752268620986173002
Tous les Matchs
GPWLOTWOTL TGFGA
822748223264301
Matchs locaux
GPWLOTWOTL TGFGA
411125122133161
Matchs Éxtérieurs
GPWLOTWOTL TGFGA
411623101131140
Derniers 10 Matchs
WLOTWOTL T
37000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
4518719.29%4219876.72%8
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
749727790910576812
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
1517284553.32%1432276951.72%749139953.54%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
1954130619166421070530


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 - 2020-09-216Snipers5Harvard5TXSommaire du Match
2 - 2020-09-229Harvard7Riverman3WSommaire du Match
4 - 2020-09-2417Harvard3Citadelles6LSommaire du Match
5 - 2020-09-2524Harvard0Spoonman's1LSommaire du Match
6 - 2020-09-2628Chiefs2Harvard3WXSommaire du Match
7 - 2020-09-2734Harvard2Chiefs0WSommaire du Match
9 - 2020-09-2940Spoonman's4Harvard3LSommaire du Match
10 - 2020-09-3051Harvard3Ailes Rouges1WSommaire du Match
11 - 2020-10-0153Canadiens3Harvard1LSommaire du Match
13 - 2020-10-0361Harvard9Canadiens2WSommaire du Match
15 - 2020-10-0570Chiefs5Harvard4LSommaire du Match
17 - 2020-10-0778Harvard2Snipers7LSommaire du Match
19 - 2020-10-0983As5Harvard4LSommaire du Match
21 - 2020-10-1192Harvard3Ailes Rouges4LSommaire du Match
23 - 2020-10-1397Pacifiques de la route4Harvard2LSommaire du Match
25 - 2020-10-15104Harvard6Canadiens3WSommaire du Match
27 - 2020-10-17111Wolves4Harvard1LSommaire du Match
29 - 2020-10-19119Harvard3Canadiens2WSommaire du Match
31 - 2020-10-21125Harvard1Ailes Rouges5LSommaire du Match
33 - 2020-10-23131Wolves4Harvard3LSommaire du Match
35 - 2020-10-25140Ailes Rouges5Harvard1LSommaire du Match
37 - 2020-10-27147Harvard5Banshees6LSommaire du Match
39 - 2020-10-29152Harvard2Pacifiques de la route5LSommaire du Match
41 - 2020-10-31159Canadiens3Harvard5WSommaire du Match
42 - 2020-11-01168Pacifiques de la route2Harvard8WSommaire du Match
44 - 2020-11-03176Wolves2Harvard5WSommaire du Match
46 - 2020-11-05182Harvard3Snipers2WSommaire du Match
47 - 2020-11-06189Chiefs6Harvard3LSommaire du Match
49 - 2020-11-08198Harvard1Chiefs2LSommaire du Match
50 - 2020-11-09203Harvard6Wolves6TXSommaire du Match
52 - 2020-11-11209Chiefs1Harvard6WSommaire du Match
53 - 2020-11-12217Snipers1Harvard5WSommaire du Match
55 - 2020-11-14226Harvard2Wolves6LSommaire du Match
57 - 2020-11-16233Harvard1As2LSommaire du Match
58 - 2020-11-17237Croque-Morts3Harvard5WSommaire du Match
59 - 2020-11-18243Harvard2Isotopes4LSommaire du Match
61 - 2020-11-20250Citadelles6Harvard4LSommaire du Match
63 - 2020-11-22261Harvard2Chiefs3LSommaire du Match
65 - 2020-11-24266Croque-Morts3Harvard5WSommaire du Match
66 - 2020-11-25273Harvard2Croque-Morts4LSommaire du Match
67 - 2020-11-26280Riverman7Harvard3LSommaire du Match
68 - 2020-11-27289Croque-Morts4Harvard4TXSommaire du Match
71 - 2020-11-30297Harvard8Citadelles4WSommaire du Match
72 - 2020-12-01303As3Harvard1LSommaire du Match
73 - 2020-12-02306Harvard4As0WSommaire du Match
74 - 2020-12-03311Harvard3Spoonman's4LSommaire du Match
75 - 2020-12-04319Harvard2Croque-Morts5LSommaire du Match
76 - 2020-12-05325Isotopes5Harvard3LSommaire du Match
78 - 2020-12-07334Banshees5Harvard2LSommaire du Match
79 - 2020-12-08344Harvard5Snipers3WSommaire du Match
80 - 2020-12-09345Harvard2Riverman5LSommaire du Match
82 - 2020-12-11352Isotopes5Harvard4LSommaire du Match
84 - 2020-12-13362Canadiens3Harvard4WSommaire du Match
86 - 2020-12-15367Harvard2Citadelles3LSommaire du Match
88 - 2020-12-17375Isotopes3Harvard2LXSommaire du Match
90 - 2020-12-19380Harvard4Banshees3WSommaire du Match
92 - 2020-12-21391Riverman2Harvard6WSommaire du Match
94 - 2020-12-23395Harvard1Spoonman's2LSommaire du Match
96 - 2020-12-25404As7Harvard2LSommaire du Match
98 - 2020-12-27409Harvard2Riverman3LSommaire du Match
100 - 2020-12-29416Harvard2Pacifiques de la route6LSommaire du Match
101 - 2020-12-30421Snipers4Harvard3LXSommaire du Match
103 - 2021-01-01434Chiefs2Harvard1LSommaire du Match
104 - 2021-01-02439Harvard2Canadiens1WSommaire du Match
106 - 2021-01-04446Spoonman's3Harvard4WSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
109 - 2021-01-07454Harvard6Chiefs4WSommaire du Match
110 - 2021-01-08461Spoonman's4Harvard3LSommaire du Match
111 - 2021-01-09464Harvard3Spoonman's6LSommaire du Match
113 - 2021-01-11471Harvard4Riverman3WXSommaire du Match
114 - 2021-01-12477Ailes Rouges6Harvard3LSommaire du Match
115 - 2021-01-13484Harvard4Banshees6LSommaire du Match
116 - 2021-01-14487Harvard3Ailes Rouges1WSommaire du Match
117 - 2021-01-15492Pacifiques de la route4Harvard2LSommaire du Match
119 - 2021-01-17504Spoonman's6Harvard2LSommaire du Match
121 - 2021-01-19516Snipers4Harvard2LSommaire du Match
122 - 2021-01-20521Harvard3Isotopes2WSommaire du Match
123 - 2021-01-21528Harvard4As1WSommaire du Match
124 - 2021-01-22532Citadelles5Harvard2LSommaire du Match
127 - 2021-01-25542Banshees1Harvard2WSommaire du Match
128 - 2021-01-26549Harvard2Citadelles4LSommaire du Match
130 - 2021-01-28558Banshees4Harvard2LSommaire du Match
135 - 2021-02-02573Canadiens6Harvard3LSommaire du Match



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des Billets3515
Assistance00
Assistance PCT0.00%0.00%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacitéPopularité de l'Équipe
0 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
1,161,610$ 917,050$ 917,050$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
917,050$ 1,161,610$ 22 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 1 7,442$ 7,442$




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