Crunch

GP: 73 | W: 45 | L: 23 | OTL: 5 | P: 95
GF: 225 | GA: 138 | PP%: 16.67% | PK%: 88.83%
DG: Olivier Gagné | Morale : 77 | Moyenne d'Équipe : 62
Prochain matchs #1117 vs Rocket
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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
1Nick PaulX100.007337896292746961656060636167646683630
2Kevin PorterX100.005838866072918759706254565782795284620
3Lane PedersonX100.005837876172939060645859576063626386610
4Francis PerronX100.005237896268928960686257565965636485610
5Mario KempeX100.007938886071706759725658645979715085610
6Nathan BastianX100.007249816087836959525664585963627481610
7Matt PuempelX100.005836906278807060585961575871667785610
8Reid BoucherX100.005435936369766962696161596371665985610
9Austin CzarnikXX100.005136936163696759636558576073703785600
10Chase De LeoX100.005135945965847158636057615567645878600
11Colin GreeningX100.006835945482928853565351565282735284600
12Henrik SamuelssonX100.007339835487837753595351585369657769590
13Mason Shaw (R)X100.005037875766959656635852535461636470590
14Spencer WatsonX100.005135935556726853565451565365636240550
15Kevin CzuczmanX100.006739825682928954305651574575685785630
16Duncan SiemensX100.007141765385888252305451594871667785620
17Brandon CrawleyX100.006545665678898354305552594563626385610
18Joni Tuulola (R)X100.006736915583908454305451564565636285610
19Blake HillmanX100.006136905476908553305251534665636885600
Rayé
MOYENNE D'ÉQUIPE100.00623887587584795754575558546966628061
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
1Adin Hill100.00808179937978807978807965696784770
2Oscar Dansk100.00787775827776787776787769735185750
Rayé
1Kaden Fulcher100.00646563776362646362646361654920630
MOYENNE D'ÉQUIPE100.0074747284737274737274736569566372
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Mike Van Ryn60706565595586CAN3951,000,000$


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
1Jesse PuljujarviTampa Bay LightningRW5721396036471582831986513710.61%11101117.742810391730005953043.95%15700011.1911012347
2Nick PaulCrunch (Tam)LW622429532146090731695813114.20%12119019.2159144120600051466055.56%7200020.8900000443
3Reid BoucherCrunch (Tam)C7321315224100201271814913211.60%8112815.46691542211000002057.69%122900000.9200000424
4Francis PerronCrunch (Tam)LW7311344520602373138351107.97%7118116.18412164025121382243156.66%41300000.7601000332
5Lane PedersonCrunch (Tam)C68152843192005110416152999.32%10123818.2239123123511292411053.12%126500100.6904000254
6Mario KempeCrunch (Tam)RW731921402456011476143408413.29%1193212.7821312450002303067.39%4600010.8600000543
7Duncan SiemensCrunch (Tam)D739253435136101254663174714.29%86172223.604711262711011263010.00%000010.3900101132
8Joni TuulolaCrunch (Tam)D738253316595783969184311.59%67152220.8551015382230110243110.00%000000.4300001202
9Brandon CrawleyCrunch (Tam)D73527323410915141525515239.09%41124717.081341466000083110.00%000000.5100002014
10Brendan PerliniTampa Bay LightningLW/RW3710203072157152134291107.46%677921.0748122514501131703243.55%41100000.7724001125
11Nathan BastianCrunch (Tam)RW731713302412525148721503410811.33%12101913.97022964000076249.45%9100010.5901005440
12Kevin CzuczmanCrunch (Tam)D7332629197715109477523564.00%78171323.473811372680112279200.00%000000.3400102011
13Matt PuempelCrunch (Tam)LW73151429101201763125398612.00%1285911.771239490001104247.17%5300000.6700000231
14Kevin PorterCrunch (Tam)C66820282325543929938948.08%3111816.9533615810000362157.43%54500000.5000100021
15Austin CzarnikCrunch (Tam)C/RW7310172784087810730619.35%678110.71000080001413253.14%63600000.6900000022
16Juuso RiikolaTampa Bay LightningD50720271144074578922527.87%57116023.216410601960001181000.00%000000.4700000140
17Blake HillmanCrunch (Tam)D60211132918033171461514.29%3693615.600111110110116000.00%000000.2800000021
18Ryan CallahanTampa Bay LightningRW2536944010132492312.50%61415.680225170001250065.57%6100001.2700000110
19Zach WhitecloudTampa Bay LightningD1509952752364790.00%733922.66011250000027000.00%000000.5300010100
20Tyler GraovacTampa Bay LightningC134373100262040142310.00%429422.6323513460002671061.44%23600000.4801000100
21Dmitrij JaskinTampa Bay LightningRW114261200251534121311.76%321619.721238460000201055.26%3800000.5501000110
22Maxime LajoieTampa Bay LightningD111452005161611126.25%1527424.921121045011245010.00%000000.3600000000
23Byron FroeseTampa Bay LightningC9134055430468212.17%122024.450118361013490066.67%29100000.3600010000
24Mason ShawCrunch (Tam)LW580444203874120.00%11282.2200000000000044.44%900000.6200000001
25Henrik SamuelssonCrunch (Tam)LW5410114011324150.00%0450.8500000000000066.67%300000.4400000000
26Chase De LeoCrunch (Tam)C65000-2202812170.00%21482.28000120001260056.84%9500000.0000000000
27Spencer WatsonCrunch (Tam)RW28000000000000.00%020.100000000000000.00%000000.0000000000
28Colin GreeningCrunch (Tam)C66000-26011313270.00%11642.48000426000000051.43%3500000.0000000000
Stats d'équipe Total ou en Moyenne1485219431650376895105134712732168642151610.10%5032152014.495310615949027795712472436421455.10%568600160.603133314364843
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
1Adin HillCrunch (Tam)73452350.9191.8143656913216290120.66715730742
2Oscar DanskCrunch (Tam)10000.9581.2847001240000.0000073000
Stats d'équipe Total ou en Moyenne74452350.9201.8144126913316530120.667157373742


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
Adin HillCrunch (Tam)G231996-05-11No202 Lbs6 ft6NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Austin CzarnikCrunch (Tam)C/RW261992-12-12No170 Lbs5 ft9NoNoNo1Pro & Farm300,000$0$0$NoLien
Blake HillmanCrunch (Tam)D231996-01-26No193 Lbs6 ft1NoNoNo1Pro & Farm0$0$NoLien
Brandon CrawleyCrunch (Tam)D221997-02-02No204 Lbs6 ft1NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Chase De LeoCrunch (Tam)C231995-10-25No179 Lbs5 ft9NoNoNo1Pro & Farm500,000$0$0$NoLien
Colin GreeningCrunch (Tam)C331986-03-09No210 Lbs6 ft2NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Duncan SiemensCrunch (Tam)D251993-09-07No210 Lbs6 ft3NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Francis PerronCrunch (Tam)LW231996-04-18No166 Lbs6 ft0NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Henrik SamuelssonCrunch (Tam)LW251994-02-07No219 Lbs6 ft3NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Joni TuulolaCrunch (Tam)D231996-01-01Yes198 Lbs6 ft3NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Kaden FulcherCrunch (Tam)G201998-09-23No182 Lbs6 ft2NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Kevin CzuczmanCrunch (Tam)D281991-01-09No206 Lbs6 ft2NoNoNo1Pro & Farm300,000$0$0$NoLien
Kevin PorterCrunch (Tam)C331986-03-12No190 Lbs6 ft0NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Lane PedersonCrunch (Tam)C211997-08-04No190 Lbs6 ft0NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Mario KempeCrunch (Tam)RW301988-09-19No185 Lbs6 ft0NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Mason ShawCrunch (Tam)LW201998-11-03Yes182 Lbs5 ft9NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$500,000$Lien
Matt PuempelCrunch (Tam)LW261993-01-24No205 Lbs6 ft1NoNoNo1Pro & Farm300,000$0$0$NoLien
Nathan BastianCrunch (Tam)RW211997-12-06No205 Lbs6 ft4NoNoNo3Pro & Farm500,000$0$0$No500,000$500,000$Lien
Nick PaulCrunch (Tam)LW241995-03-20No230 Lbs6 ft4NoNoNo1Pro & Farm300,000$0$0$NoLien
Oscar DanskCrunch (Tam)G251994-02-28No195 Lbs6 ft3NoNoNo3Pro & Farm500,000$0$0$No500,000$500,000$Lien
Reid BoucherCrunch (Tam)C251993-09-08No195 Lbs5 ft10NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Spencer WatsonCrunch (Tam)RW231996-04-25No170 Lbs5 ft1NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2224.64195 Lbs6 ft12.50331,818$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Nathan BastianLane PedersonKevin Porter40122
2Nick PaulReid BoucherMario Kempe30122
3Francis PerronKevin PorterMario Kempe20122
4Matt PuempelAustin CzarnikNathan Bastian10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Brandon CrawleyDuncan Siemens40122
2Kevin CzuczmanJoni Tuulola30122
3Brandon CrawleyBlake Hillman20122
4Kevin CzuczmanDuncan Siemens10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Kevin PorterLane PedersonFrancis Perron60122
2Nick PaulReid BoucherNathan Bastian40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Brandon CrawleyDuncan Siemens60122
2Kevin CzuczmanJoni Tuulola40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Francis PerronLane Pederson60122
2Kevin PorterNick Paul40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Brandon CrawleyDuncan Siemens60122
2Kevin CzuczmanJoni Tuulola40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Nick Paul60122Brandon CrawleyDuncan Siemens60122
2Lane Pederson40122Kevin CzuczmanJoni Tuulola40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Francis PerronLane Pederson60122
2Kevin PorterNick Paul40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Blake HillmanDuncan Siemens60122
2Kevin CzuczmanJoni Tuulola40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Nick PaulLane PedersonKevin PorterKevin CzuczmanDuncan Siemens
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Nick PaulLane PedersonKevin PorterKevin CzuczmanDuncan Siemens
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Nick Paul, Colin Greening, Francis PerronNick Paul, Colin GreeningFrancis Perron
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Brandon Crawley, Blake Hillman, Kevin CzuczmanBrandon CrawleyBlake Hillman, Kevin Czuczman
Tirs de Pénalité
Nathan Bastian, Lane Pederson, Kevin Porter, Nick Paul, Francis Perron
Gardien
#1 : Adin Hill, #2 : Oscar Dansk


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
1Americans11440101127252531000101293613010011516-1130.591274572028773596272730676723302858613422837821.62%63985.71%01338236856.50%1179220053.59%586104755.97%192313761627506868453
2Bears2020000013-21010000001-11010000012-100.0001230087735965273067672330441029376116.67%11281.82%01338236856.50%1179220053.59%586104755.97%192313761627506868453
3Bruins22000000413110000002021100000021141.0004711018773596477306767233056143336900.00%130100.00%01338236856.50%1179220053.59%586104755.97%192313761627506868453
4Checkers44000000253222200000012012220000001331081.000254671028773596197730676723305518348416531.25%16193.75%01338236856.50%1179220053.59%586104755.97%192313761627506868453
5Comets117300100372017650001002992052300000811-3150.68237701070087735962917306767233027692136204521121.15%61788.52%11338236856.50%1179220053.59%586104755.97%192313761627506868453
6Devils1043000122622452100011141135220000112111120.6002650761187735962507306767233024077178149591322.03%66887.88%01338236856.50%1179220053.59%586104755.97%192313761627506868453
7Marlies660000004393433000000244203300000019514121.000437611902877359632073067672330108346412523417.39%22290.91%31338236856.50%1179220053.59%586104755.97%192313761627506868453
8Monsters41300000911-2211000006512020000036-320.25091827008773596112730676723307827417330310.00%15473.33%01338236856.50%1179220053.59%586104755.97%192313761627506868453
9Penguins22000000523110000002111100000031241.000510150087735963973067672330428123513215.38%6183.33%01338236856.50%1179220053.59%586104755.97%192313761627506868453
10Phantoms2020000036-31010000013-21010000023-100.0003580087735965873067672330541116461218.33%8187.50%01338236856.50%1179220053.59%586104755.97%192313761627506868453
11Rocket532000009632020000014-33300000082660.600917260187735961137306767233010527651011700.00%210100.00%11338236856.50%1179220053.59%586104755.97%192313761627506868453
12Senators6220110015141321000009723010110067-170.58315304510877359616373067672330141418611625312.00%27388.89%01338236856.50%1179220053.59%586104755.97%192313761627506868453
13Sound Tigers20100010440100000103211010000012-120.50045900877359658730676723303792046300.00%9277.78%01338236856.50%1179220053.59%586104755.97%192313761627506868453
14Thunderbirds430010001165220000006332100100053281.00011213200877359611373067672330842547761516.67%200100.00%01338236856.50%1179220053.59%586104755.97%192313761627506868453
Total733923032332251388736229001311236360371714031021027527950.651225412637298773596214573067672330165549993013903305516.67%3674188.83%51338236856.50%1179220053.59%586104755.97%192313761627506868453
16Wolf Pack211000006601010000024-21100000042220.5006101600877359660730676723305020353413323.08%9188.89%01338236856.50%1179220053.59%586104755.97%192313761627506868453
_Since Last GM Reset733923032332251388736229001311236360371714031021027527950.651225412637298773596214573067672330165549993013903305516.67%3674188.83%51338236856.50%1179220053.59%586104755.97%192313761627506868453
_Vs Conference3212140112273694167600021393451658011013435-1330.51673137210228773596839730676723307422174505721702615.29%1642286.59%01338236856.50%1179220053.59%586104755.97%192313761627506868453
_Vs Division28440110079572214320000040271314120110039309110.19679146225138773596826730676723306001803655041522818.42%1402085.71%01338236856.50%1179220053.59%586104755.97%192313761627506868453

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
7395W122541263721451655499930139029
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
7339233233225138
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
36229013112363
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
371714310210275
Derniers 10 Matchs
WLOTWOTL SOWSOL
612100
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
3305516.67%3674188.83%5
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
730676723308773596
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
1338236856.50%1179220053.59%586104755.97%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
192313761627506868453


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
4 - 2019-09-0511Crunch1Bears2LSommaire du Match
8 - 2019-09-0930Crunch1Americans3LSommaire du Match
11 - 2019-09-1246Checkers0Crunch3WSommaire du Match
17 - 2019-09-1875Crunch1Devils3LSommaire du Match
18 - 2019-09-1985Comets2Crunch1LXSommaire du Match
24 - 2019-09-25107Crunch5Marlies0WSommaire du Match
25 - 2019-09-26119Checkers0Crunch9WSommaire du Match
31 - 2019-10-02140Senators1Crunch2WSommaire du Match
32 - 2019-10-03157Senators5Crunch3LSommaire du Match
38 - 2019-10-09187Crunch2Americans3LXXSommaire du Match
40 - 2019-10-11209Bears1Crunch0LSommaire du Match
43 - 2019-10-14217Devils4Crunch2LSommaire du Match
45 - 2019-10-16224Crunch0Comets3LSommaire du Match
46 - 2019-10-17239Rocket3Crunch1LSommaire du Match
50 - 2019-10-21260Crunch3Devils2WSommaire du Match
52 - 2019-10-23265Devils2Crunch3WXXSommaire du Match
53 - 2019-10-24283Crunch2Thunderbirds1WSommaire du Match
59 - 2019-10-30308Crunch1Monsters3LSommaire du Match
60 - 2019-10-31318Crunch2Monsters3LSommaire du Match
66 - 2019-11-06350Devils2Crunch4WSommaire du Match
67 - 2019-11-07366Americans2Crunch3WSommaire du Match
71 - 2019-11-11384Crunch6Marlies4WSommaire du Match
73 - 2019-11-13396Crunch2Rocket1WSommaire du Match
74 - 2019-11-14401Crunch3Rocket0WSommaire du Match
78 - 2019-11-18429Crunch3Thunderbirds2WXSommaire du Match
80 - 2019-11-20439Crunch2Phantoms3LSommaire du Match
81 - 2019-11-21457Penguins1Crunch2WSommaire du Match
85 - 2019-11-25468Crunch2Devils3LXXSommaire du Match
87 - 2019-11-27476Comets1Crunch3WSommaire du Match
88 - 2019-11-28489Thunderbirds1Crunch3WSommaire du Match
92 - 2019-12-02511Crunch1Comets2LSommaire du Match
94 - 2019-12-04518Sound Tigers2Crunch3WXXSommaire du Match
95 - 2019-12-05535Marlies3Crunch7WSommaire du Match
99 - 2019-12-09554Crunch5Devils0WSommaire du Match
101 - 2019-12-11563Monsters2Crunch5WSommaire du Match
102 - 2019-12-12577Crunch1Comets2LSommaire du Match
106 - 2019-12-16596Crunch3Comets2WSommaire du Match
108 - 2019-12-18605Senators1Crunch4WSommaire du Match
109 - 2019-12-19626Crunch1Devils3LSommaire du Match
111 - 2019-12-21636Devils1Crunch4WSommaire du Match
115 - 2019-12-25655Thunderbirds2Crunch3WSommaire du Match
116 - 2019-12-26673Americans5Crunch2LSommaire du Match
122 - 2020-01-01685Comets1Crunch6WSommaire du Match
123 - 2020-01-02702Monsters3Crunch1LSommaire du Match
127 - 2020-01-06719Crunch6Americans2WSommaire du Match
129 - 2020-01-08726Crunch4Wolf Pack2WSommaire du Match
131 - 2020-01-10752Crunch2Bruins1WSommaire du Match
134 - 2020-01-13765Crunch1Senators2LSommaire du Match
136 - 2020-01-15774Crunch1Americans3LSommaire du Match
137 - 2020-01-16782Americans2Crunch3WXXSommaire du Match
139 - 2020-01-18805Crunch8Marlies1WSommaire du Match
141 - 2020-01-20809Rocket1Crunch0LSommaire du Match
143 - 2020-01-22817Marlies0Crunch8WSommaire du Match
144 - 2020-01-23832Marlies1Crunch9WSommaire du Match
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
150 - 2020-01-29856Phantoms3Crunch1LSommaire du Match
151 - 2020-01-30871Americans0Crunch3WSommaire du Match
152 - 2020-01-31884Crunch1Americans2LSommaire du Match
155 - 2020-02-03890Crunch3Comets2WSommaire du Match
157 - 2020-02-05897Comets2Crunch7WSommaire du Match
158 - 2020-02-06915Crunch3Penguins1WSommaire du Match
162 - 2020-02-10939Crunch3Rocket1WSommaire du Match
164 - 2020-02-12943Wolf Pack4Crunch2LSommaire du Match
165 - 2020-02-13958Devils2Crunch1LXXSommaire du Match
168 - 2020-02-16973Crunch6Checkers1WSommaire du Match
169 - 2020-02-17978Crunch7Checkers2WSommaire du Match
172 - 2020-02-20997Comets2Crunch5WSommaire du Match
173 - 2020-02-211013Crunch1Sound Tigers2LSommaire du Match
176 - 2020-02-241019Crunch2Senators3LXSommaire du Match
178 - 2020-02-261027Bruins0Crunch2WSommaire du Match
179 - 2020-02-271043Comets1Crunch7WSommaire du Match
183 - 2020-03-021064Crunch3Senators2WXSommaire du Match
185 - 2020-03-041077Crunch4Americans3WXSommaire du Match
186 - 2020-03-051089Americans0Crunch1WSommaire du Match
192 - 2020-03-111117Rocket-Crunch-
193 - 2020-03-121130Americans-Crunch-
194 - 2020-03-131147Crunch-Comets-



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
2 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
1,058,027$ 73,000$ 28,870$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 99,273$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 8 5,531$ 44,248$




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
1473392303233225138873622900131123636037171403102102752795225412637298773596214573067672330165549993013903305516.67%3674188.83%51338236856.50%1179220053.59%586104755.97%192313761627506868453
Total Saison Régulière73392303233225138873622900131123636037171403102102752795225412637298773596214573067672330165549993013903305516.67%3674188.83%51338236856.50%1179220053.59%586104755.97%192313761627506868453