Harvard

GP: 54 | W: 19 | L: 32 | T: 3 | P: 41
GF: 185 | GA: 198 | PP%: 20.42% | PK%: 77.78%
DG: Marcel | Morale : 40 | Moyenne d'Équipe : 64
Prochain matchs #375 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 SPÂgeContratSalaire
1Ron SutterX98.008270576871797972687568746655663350680302554,500$
2Tim TaylorX99.006448727472727675667570836432357442680241450,000$
3Jim SandlakX99.007868506381777769626864776161675536670272400,000$
4Jason Allison (R)X100.006658757666776978797573707735359853670193250,000$
5Mikko MakelaX100.005741826575676767616971696759684654650282250,000$
6Josef BeranekX100.005339817272707275687573617138407453650242470,000$
7Ronnie SternX100.008376386574747662636563796247416055640263429,000$
8Craig JohnsonX100.005948677272676867657368666432328943630222225,000$
9Jozef StumpelX100.005948686778687070657367666431339722630212500,000$
10Niklas AnderssonX100.004937827569656872647267606232328921630223386,000$
11Reid SimpsonX100.006960546777687166656658795538387551630241480,000$
12Matthew Barnaby (R)X100.008276456674656654536054755228339821590202350,000$
13J. J. DaigneaultX100.006446816971777676677767736057575451680271595,000$
14Dimitri YushkevichX100.007765626975686874677666806240378944670222500,000$
15Janne Laukkanen (R)X100.005745787172666768627258725335288244620231280,000$
16Ryan McGill (R)X100.008265556674646455546442784035357649620241387,000$
17Todd ReirdenX100.006347726480686864606852745019289035610223359,000$
18Cory Cross (R)X100.006865446281676755525845764336419024610223380,000$
Rayé
1Pat PeakeX100.005544797170616466656861676041419621610203220,000$
2Jeff NortonX95.696551676575697064627352765041354831630282225,000$
3Jan Vopat (R)X100.005644796274596057586541733931339820580203300,000$
MOYENNE D'ÉQUIPE99.60665466687469706763706173583941773964
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 Riendeau99.00717883808071758477777352525413720
2Wendell Young100.00658674727068728073697173763341710
Rayé
1Milan Hnilicka100.00677475737168758175696727319720660
MOYENNE D'ÉQUIPE99.6768797775746974827572705153612570
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
1Jason AllisonHarvard (BOS)C54212748-9180491211473810014.29%1497418.0579163415510151272058.36%79500000.9800000160
2Ron SutterHarvard (BOS)C50142539-131002016813114627819.59%10117123.43710173220102251953053.96%147700000.6700112224
3J. J. DaigneaultHarvard (BOS)D46112839-18240366874183714.86%64111524.2591120491971231189010.00%000000.7000000211
4Tim TaylorHarvard (BOS)C37112738111002010312633648.73%1483122.49311144314721361682153.40%60300000.9100000212
5Janne LaukkanenHarvard (BOS)D5172431-8220316361244411.48%54102520.107815471850000150100.00%000000.6000000111
6Brent FedykBruinsRW48151631-121403144115288213.04%487218.184913372000001434047.19%8900010.7100000203
7Dimitri YushkevichHarvard (BOS)D436243007351017560214810.00%4694021.88369371331123122100.00%000000.6400010122
8Ronnie SternHarvard (BOS)RW5481927-358101264674214110.81%666712.360557121011091054.90%5100000.8100002121
9Reid SimpsonHarvard (BOS)LW5110142464810624278265512.82%463512.45011628000040046.88%3200000.7600110001
10Mikko MakelaHarvard (BOS)LW5381523-9204399024648.89%673413.86224101171121240048.00%5000000.6300000000
11Jeff NortonHarvard (BOS)D4831821-1040071423816337.89%3895719.95279241730000104100.00%000000.4400000001
12Craig JohnsonHarvard (BOS)LW5481119060104173163610.96%35259.740225421122741051.35%7400000.7200000000
13Jim SandlakHarvard (BOS)RW3771219-2280743550194614.00%554214.67055181220110551048.39%6200000.7000000121
14Josef BeranekHarvard (BOS)C5410717-92094574144713.51%05349.890002210000242053.00%38300000.6400000011
15Ryan McGillHarvard (BOS)D41581347201152324102020.83%4372417.66123865000072010.00%000000.3600000110
16Niklas AnderssonHarvard (BOS)LW2311011-1004222913203.45%429612.89134635000100039.13%2300000.7400000000
17Todd ReirdenHarvard (BOS)D29281012401622123416.67%2647316.34000214000038000.00%000000.4200000001
18Cory CrossHarvard (BOS)D23134-4360651181212.50%1433914.7700005000020000.00%000000.2400000000
19Jan VopatHarvard (BOS)D13213-417516541350.00%719915.3300014000032000.00%000000.3000000000
20Matthew BarnabyHarvard (BOS)RW23011320036129190.00%224210.5500001000000045.90%6100000.0800000000
21Stu GrimsonBruinsLW2000000110000.00%042.490000000000000.00%000000.0000000000
22Jozef StumpelHarvard (BOS)C25000-100185060.00%0652.61000150002200046.43%5600000.0000000000
23Pat PeakeHarvard (BOS)C5000000100000.00%081.6600000000010050.00%400000.0000000000
Stats d'équipe Total ou en Moyenne864150298448-67594501047999129735484211.57%3641388416.07469113736919837101727148319353.94%376000010.6500234141919
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)45152710.8553.9823082215310580100.0000450310
2Wendell YoungHarvard (BOS)214510.8783.0181800413360100.0000943000
3Milan HnilickaHarvard (BOS)30010.9172.03118004480000.0000011000
Stats d'équipe Total ou en Moyenne69193230.8633.6632452219814420200.00005454310


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
Cory CrossHarvard (BOS)D221998-02-09 9:08:37 PMYes219 Lbs6 ft5NoNoNo3Pro & Farm380,000$38,000$13,971$No380,000$380,000$
Craig JohnsonHarvard (BOS)LW221998-02-09 9:08:37 PMNo197 Lbs6 ft2NoNoNo2Pro & Farm225,000$22,500$8,272$No225,000$
Dimitri YushkevichHarvard (BOS)D221998-02-09 9:08:37 PMNo203 Lbs6 ft0NoNoNo2Pro & Farm500,000$50,000$18,382$No500,000$
J. J. DaigneaultHarvard (BOS)D271993-02-09 9:08:37 PMNo180 Lbs5 ft11NoNoNo1Pro & Farm595,000$59,500$21,875$No
Jan VopatHarvard (BOS)D202000-02-09 9:08:37 PMYes207 Lbs6 ft0NoNoNo3Pro & Farm300,000$30,000$11,029$No300,000$300,000$
Janne LaukkanenHarvard (BOS)D231997-02-09 9:08:37 PMYes180 Lbs6 ft0NoNoNo1Pro & Farm280,000$28,000$10,294$No
Jason AllisonHarvard (BOS)C192001-08-11 9:56:05 AMYes205 Lbs6 ft3NoNoNo3Pro & Farm250,000$25,000$9,191$No250,000$250,000$
Jeff NortonHarvard (BOS)D281992-02-09 9:08:37 PMNo195 Lbs6 ft2NoNoNo2Pro & Farm225,000$22,500$8,272$No225,000$
Jim SandlakHarvard (BOS)RW271993-02-09 9:08:37 PMNo219 Lbs6 ft4NoNoNo2Pro & Farm400,000$40,000$14,706$No400,000$
Josef BeranekHarvard (BOS)C241996-02-09 9:08:37 PMNo195 Lbs6 ft2NoNoNo2Pro & Farm470,000$47,000$17,279$No470,000$
Jozef StumpelHarvard (BOS)C211999-02-09 9:08:37 PMNo216 Lbs6 ft3NoNoNo2Pro & Farm500,000$50,000$18,382$No500,000$
Matthew BarnabyHarvard (BOS)RW202000-02-09 9:08:37 PMYes195 Lbs6 ft1NoNoNo2Pro & Farm350,000$35,000$12,868$No350,000$
Mikko MakelaHarvard (BOS)LW281992-02-09 9:08:37 PMNo200 Lbs6 ft2NoNoNo2Pro & Farm250,000$25,000$9,191$No250,000$
Milan HnilickaHarvard (BOS)G211999-02-09 9:08:37 PMNo180 Lbs6 ft0NoNoNo2Pro & Farm270,000$27,000$9,926$No270,000$
Niklas AnderssonHarvard (BOS)LW221998-02-09 9:08:37 PMNo175 Lbs5 ft9NoNoNo3Pro & Farm386,000$38,600$14,191$No386,000$386,000$
Pat PeakeHarvard (BOS)C202000-02-09 9:08:37 PMNo190 Lbs6 ft1NoNoNo3Pro & Farm220,000$22,000$8,088$No220,000$220,000$
Reid SimpsonHarvard (BOS)LW241996-02-09 9:08:37 PMNo210 Lbs6 ft1NoNoNo1Pro & Farm480,000$48,000$17,647$No
Ron SutterHarvard (BOS)C301990-02-09 9:08:37 PMNo180 Lbs6 ft0NoNoNo2Pro & Farm554,500$55,450$20,386$No554,500$
Ronnie SternHarvard (BOS)RW261994-02-09 9:08:37 PMNo195 Lbs6 ft0NoNoNo3Pro & Farm429,000$42,900$15,772$No429,000$429,000$
Ryan McGillHarvard (BOS)D241996-02-09 9:08:37 PMYes197 Lbs6 ft2NoNoNo1Pro & Farm387,000$38,700$14,228$No
Tim TaylorHarvard (BOS)C241996-02-09 9:08:37 PMNo185 Lbs6 ft1NoNoNo1Pro & Farm450,000$45,000$16,544$No
Todd ReirdenHarvard (BOS)D221998-02-09 9:08:37 PMNo220 Lbs6 ft5NoNoNo3Pro & Farm359,000$35,900$13,199$No359,000$359,000$
Vincent RiendeauHarvard (BOS)G271993-02-09 9:08:37 PMNo181 Lbs5 ft10NoNoNo3Pro & Farm985,000$98,500$36,213$No985,000$985,000$
Wendell YoungHarvard (BOS)G301990-02-09 9:08:37 PMNo182 Lbs5 ft9NoNoNo2Pro & Farm750,000$75,000$27,574$No750,000$
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2423.88196 Lbs6 ft12.13416,479$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Mikko MakelaRon SutterJim Sandlak35122
2Niklas AnderssonTim TaylorRonnie Stern30122
3Reid SimpsonJason AllisonMatthew Barnaby20122
4Craig JohnsonJosef BeranekRon Sutter15122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1J. J. DaigneaultDimitri Yushkevich35122
2Janne LaukkanenRyan McGill30122
3Todd ReirdenCory Cross20122
4J. J. DaigneaultDimitri Yushkevich15122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Mikko MakelaRon SutterJim Sandlak60122
2Niklas AnderssonTim TaylorRonnie Stern40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1J. J. DaigneaultDimitri Yushkevich60122
2Janne LaukkanenRyan McGill40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Ron SutterTim Taylor60122
2Jason AllisonJim Sandlak40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1J. J. DaigneaultDimitri Yushkevich60122
2Janne LaukkanenRyan McGill40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Ron Sutter60122J. J. DaigneaultDimitri Yushkevich60122
2Tim Taylor40122Janne LaukkanenRyan McGill40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Ron SutterTim Taylor60122
2Jason AllisonJim Sandlak40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1J. J. DaigneaultDimitri Yushkevich60122
2Janne LaukkanenRyan McGill40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Mikko MakelaRon SutterJim SandlakJ. J. DaigneaultDimitri Yushkevich
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Mikko MakelaRon SutterJim SandlakJ. J. DaigneaultDimitri Yushkevich
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Jozef Stumpel, Josef Beranek, Reid SimpsonJozef Stumpel, Josef BeranekReid Simpson
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Todd Reirden, Cory Cross, Janne LaukkanenTodd ReirdenCory Cross, Janne Laukkanen
Tirs de Pénalité
Ron Sutter, Tim Taylor, Jason Allison, Jim Sandlak, Mikko Makela
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 Rouges41300000815-71010000015-431200000710-320.2508152300795352110850949153871192255652428.33%21671.43%11053189955.45%922172053.60%50292354.39%12998751253417703350
2As41300000101002020000058-32110000052320.250101727017953521101509491538710519518926519.23%20480.00%01053189955.45%922172053.60%50292354.39%12998751253417703350
3Banshees20200000711-41010000025-31010000056-100.0007142100795352148509491538765182239500.00%11554.55%01053189955.45%922172053.60%50292354.39%12998751253417703350
4Canadiens651000002816123210000010913300000018711100.83328507800795352119250949153871554277135451022.22%34585.29%11053189955.45%922172053.60%50292354.39%12998751253417703350
5Chiefs724010002119241201000161423120000055060.4292138590179535212035094915387173568112133618.18%35780.00%21053189955.45%922172053.60%50292354.39%12998751253417703350
6Citadelles413000001719-21010000046-2312000001313020.250172946007953521113509491538712639507221314.29%24579.17%11053189955.45%922172053.60%50292354.39%12998751253417703350
7Croque-Morts522100001819-132010000141042020000049-550.5001832500079535211275094915387137514311122418.18%18572.22%01053189955.45%922172053.60%50292354.39%12998751253417703350
8Isotopes30300000914-520200000710-31010000024-200.000917260079535218450949153876530385816318.75%17476.47%01053189955.45%922172053.60%50292354.39%12998751253417703350
9Pacifiques de la route31200000121112110000010641010000025-320.333122133007953521835094915387972731499333.33%13469.23%11053189955.45%922172053.60%50292354.39%12998751253417703350
10Riverman312000001215-31010000037-42110000098120.33312233500795352111850949153875719357117741.18%8362.50%01053189955.45%922172053.60%50292354.39%12998751253417703350
11Snipers5311000020182210100001064321000001012-270.7002037570079535211245094915387141345710016637.50%23195.65%11053189955.45%922172053.60%50292354.39%12998751253417703350
12Spoonman's3030000069-31010000034-12020000035-200.000612180079535217850949153878119387622313.64%18383.33%01053189955.45%922172053.60%50292354.39%12998751253417703350
Total54183231000185198-13268152100094100-6281017100009198-7410.38018533652102795352115455094915387144241161810822845820.42%2615877.78%71053189955.45%922172053.60%50292354.39%12998751253417703350
13Wolves513100001722-531200000910-120110000812-430.300173148007953521166509491538712135409628621.43%19668.42%01053189955.45%922172053.60%50292354.39%12998751253417703350
_Since Last GM Reset54213201000185198-13268152100094100-6281317-200009198-7440.40718533652102795352115455094915387144241161810822845820.42%2615877.78%71053189955.45%922172053.60%50292354.39%12998751253417703350
_Vs Conference2581601000888801238010004248-613580000046406180.3608816024801795352171850949153876652043065011422517.61%1392979.14%41053189955.45%922172053.60%50292354.39%12998751253417703350
_Vs Division16780100055441183401000292728440000026179160.5005510015501795352147350949153874091171963321001919.00%871582.76%31053189955.45%922172053.60%50292354.39%12998751253417703350

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
5441L118533652115451442411618108202
Tous les Matchs
GPWLOTWOTL TGFGA
541832103185198
Matchs locaux
GPWLOTWOTL TGFGA
2681510294100
Matchs Éxtérieurs
GPWLOTWOTL TGFGA
2810170019198
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
2845820.42%2615877.78%7
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
50949153877953521
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
1053189955.45%922172053.60%50292354.39%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
12998751253417703350


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 - 2020-12-17375Isotopes-Harvard-
90 - 2020-12-19380Harvard-Banshees-
92 - 2020-12-21391Riverman-Harvard-
94 - 2020-12-23395Harvard-Spoonman's-
96 - 2020-12-25404As-Harvard-
98 - 2020-12-27409Harvard-Riverman-
100 - 2020-12-29416Harvard-Pacifiques de la route-
101 - 2020-12-30421Snipers-Harvard-
103 - 2021-01-01434Chiefs-Harvard-
104 - 2021-01-02439Harvard-Canadiens-
106 - 2021-01-04446Spoonman's-Harvard-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
109 - 2021-01-07454Harvard-Chiefs-
110 - 2021-01-08461Spoonman's-Harvard-
111 - 2021-01-09464Harvard-Spoonman's-
113 - 2021-01-11471Harvard-Riverman-
114 - 2021-01-12477Ailes Rouges-Harvard-
115 - 2021-01-13484Harvard-Banshees-
116 - 2021-01-14487Harvard-Ailes Rouges-
117 - 2021-01-15492Pacifiques de la route-Harvard-
119 - 2021-01-17504Spoonman's-Harvard-
121 - 2021-01-19516Snipers-Harvard-
122 - 2021-01-20521Harvard-Isotopes-
123 - 2021-01-21528Harvard-As-
124 - 2021-01-22532Citadelles-Harvard-
127 - 2021-01-25542Banshees-Harvard-
128 - 2021-01-26549Harvard-Citadelles-
130 - 2021-01-28558Banshees-Harvard-
135 - 2021-02-02573Canadiens-Harvard-



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
15 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
751,292$ 999,550$ 999,550$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
999,550$ 751,292$ 24 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 50 8,048$ 402,400$




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