Crunch

GP: 22 | W: 10 | L: 10 | OTL: 2 | P: 22
GF: 55 | GA: 49 | PP%: 15.74% | PK%: 85.83%
DG: Stéphane Lacasse | Morale : 46 | Moyenne d'Équipe : 62
Prochain matchs #396 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
1Jared McCannXX100.006644827374779272837168747065687953690
2Brendan PerliniXX100.006939836786698166546270586965638448640
3Nicolas DeslauriersXX100.009645726582666164586163715975685349640
4Jesse PuljujarviX100.007341876887696367536266587061638948630
5Nick PaulX100.007337896292746961656060636167646644620
6Kevin PorterX100.005838866072918759706254565782795245620
7Lane PedersonX100.005837876172939060645859576063626359610
8Francis PerronX100.005237896268928960686257565965636452610
9Mario KempeX100.007938886071706759725658645979715052610
10Nathan BastianX100.007249816087836959525664585963627453610
11Matt PuempelX100.005836906278807060585961575871667752610
12Reid BoucherX100.005435936369766962696161596371665952610
13Austin CzarnikXX100.005136936163696759636558576073703752590
14Colin GreeningX100.006835945482928853565351565282735245590
15Kevin CzuczmanX100.006739825682928954305651574575685752630
16Juuso RiikolaX100.007336916372756962306558625470675345630
17Duncan SiemensX100.007141765385888252305451594871667752620
18Brandon CrawleyX100.006545665678898354305552594563626352610
19Joni Tuulola (R)X100.006736915583908454305451564565636252610
20Blake HillmanX100.006136905476908553305251534665636857590
Rayé
1Chase De LeoX100.005135945965847158636057615567645844590
2Henrik SamuelssonX100.007339835487837753595351585369657735580
3Mason Shaw (R)X100.005037875766959656635852535461636436580
4Spencer WatsonX100.005135935556726853565451565365636228550
MOYENNE D'ÉQUIPE100.00653986607682785954595759566966644861
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 Hill100.00808179937978807978807965696752770
2Oscar Dansk100.00787775827776787776787769735152740
Rayé
1Kaden Fulcher100.00646563776362646362646361654928630
MOYENNE D'ÉQUIPE100.0074747284737274737274736569564471
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'ÉquipePOS GP 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 PuljujarviCrunch (Tam)RW2572027199531388331588.43%540216.0902211580001231053.85%5200001.3400001122
2Nicolas DeslauriersCrunch (Tam)LW/RW2269150375632940103515.00%425911.7815613450001330150.00%8600001.1601010121
3Lane PedersonCrunch (Tam)C172810100317264217.69%219911.72134640000000056.85%24800001.0000000001
4Duncan SiemensCrunch (Tam)D221910105204113204185.00%3349222.401341289000083000.00%000000.4100000001
5Brendan PerliniCrunch (Tam)LW/RW9369200682262213.64%120522.802465370001491045.76%11800000.8811000003
6Mario KempeCrunch (Tam)RW22369216031204911346.12%332614.8321312450002300062.50%1600000.5500000011
7Reid BoucherCrunch (Tam)C2254924093238133113.16%228813.09000543000002058.19%28700000.6200000100
8Zach WhitecloudTampa Bay LightningD1509952752364790.00%733922.66011250000027000.00%000000.5300010100
9Kevin CzuczmanCrunch (Tam)D22178-21401317233144.35%3350422.911341190000099100.00%000000.3200000000
10Juuso RiikolaCrunch (Tam)D15358412022142491412.50%1634222.813141666000158000.00%000000.4700000120
11Jared McCannCrunch (Tam)C/LW12347117521232381213.04%417714.820223380001341056.48%21600000.7901001110
12Francis PerronCrunch (Tam)LW221670003284112292.44%338617.5702211570001550051.61%9300000.3601000011
13Nathan BastianCrunch (Tam)RW22527029533194592711.11%123110.51000221000071151.92%5200010.6100001100
14Matt PuempelCrunch (Tam)LW222573005154013345.00%125511.61022847000020062.50%1600000.5500000010
15Tyler GraovacTampa Bay LightningC134373100262040142310.00%429422.6323513460002671061.44%23600000.4801000100
16Dmitrij JaskinTampa Bay LightningRW114261200251534121311.76%321619.721238460000201055.26%3800000.5501000110
17Brandon CrawleyCrunch (Tam)D221563300331418365.56%831714.44000213000021000.00%000000.3800000001
18Joni TuulolaCrunch (Tam)D22156-4100161214557.14%2039117.80112541000068000.00%000000.3100000000
19Maxime LajoieTampa Bay LightningD111452005161611126.25%1527424.921121045011245010.00%000000.3600000000
20Byron FroeseTampa Bay LightningC9134055430468212.17%122024.450118361013490066.67%29100000.3600010000
21Mason ShawCrunch (Tam)LW110444203874120.00%112811.6800000000000044.44%900000.6200000001
22Nick PaulCrunch (Tam)LW11213160881861811.11%218616.970005380000261050.00%800000.3200000010
23Austin CzarnikCrunch (Tam)C/RW22011-14011020790.00%11305.9500003000070060.00%6500000.1500000000
24Kevin PorterCrunch (Tam)C15011-16061354100.00%0976.5000000000000059.74%7700000.2100000000
25Blake HillmanCrunch (Tam)D91012406421350.00%413314.8500000000017000.00%000000.1500000010
26Henrik SamuelssonCrunch (Tam)LW710114011324150.00%0456.4400000000000066.67%300000.4400000000
27Chase De LeoCrunch (Tam)C18000-1201811050.00%11126.27000010001200056.99%9300000.0000000000
28Colin GreeningCrunch (Tam)C15000000100000.00%060.450000400000000.00%200000.0000000000
Stats d'équipe Total ou en Moyenne4655812918757320304504407112194968.16%175696614.9816375316810101121685010357.73%200600010.541603391312
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)22101020.9092.08132823465030020.8005220121
Stats d'équipe Total ou en Moyenne22101020.9092.08132823465030020.8005220121


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 StatusType Salaire Actuel Cap Salariale Cap Salariale Restant Exclus du Cap Salarial Link
Adin HillCrunch (Tam)G231996-05-11No202 Lbs6 ft6NoNoNo2Avec RestrictionPro & Farm500,000$0$0$NoLien
Austin CzarnikCrunch (Tam)C/RW261992-12-12No170 Lbs5 ft9NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Blake HillmanCrunch (Tam)D231996-01-26No193 Lbs6 ft1NoNoNo1Avec RestrictionPro & Farm0$0$NoLien
Brandon CrawleyCrunch (Tam)D221997-02-02No204 Lbs6 ft1NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$NoLien
Brendan PerliniCrunch (Tam)LW/RW231996-04-27No211 Lbs6 ft3NoNoNo2Avec RestrictionPro & Farm900,000$0$0$NoLien
Chase De LeoCrunch (Tam)C231995-10-25No179 Lbs5 ft9NoNoNo1Avec RestrictionPro & Farm500,000$0$0$NoLien
Colin GreeningCrunch (Tam)C331986-03-09No210 Lbs6 ft2NoNoNo2Sans RestrictionPro & Farm300,000$0$0$NoLien
Duncan SiemensCrunch (Tam)D251993-09-07No210 Lbs6 ft3NoNoNo4Avec RestrictionPro & Farm300,000$0$0$NoLien
Francis PerronCrunch (Tam)LW231996-04-18No166 Lbs6 ft0NoNoNo2Avec RestrictionPro & Farm300,000$0$0$NoLien
Henrik SamuelssonCrunch (Tam)LW251994-02-07No219 Lbs6 ft3NoNoNo2Avec RestrictionPro & Farm300,000$0$0$NoLien
Jared McCannCrunch (Tam)C/LW231996-05-31No185 Lbs6 ft1NoNoNo3Avec RestrictionPro & Farm1,990,000$0$0$NoLien
Jesse PuljujarviCrunch (Tam)RW211998-05-07No201 Lbs6 ft4NoNoNo2Contrat d'EntréePro & Farm900,000$0$0$NoLien
Joni TuulolaCrunch (Tam)D231996-01-01Yes198 Lbs6 ft3NoNoNo4Avec RestrictionPro & Farm300,000$0$0$NoLien
Juuso RiikolaCrunch (Tam)D251993-11-09No189 Lbs6 ft0NoNoNo4Avec RestrictionPro & Farm300,000$0$0$NoLien
Kaden FulcherCrunch (Tam)G201998-09-23No182 Lbs6 ft2NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$NoLien
Kevin CzuczmanCrunch (Tam)D281991-01-09No206 Lbs6 ft2NoNoNo1Sans RestrictionPro & Farm300,000$0$0$NoLien
Kevin PorterCrunch (Tam)C331986-03-12No190 Lbs6 ft0NoNoNo3Sans RestrictionPro & Farm300,000$0$0$NoLien
Lane PedersonCrunch (Tam)C211997-08-04No190 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$NoLien
Mario KempeCrunch (Tam)RW301988-09-19No185 Lbs6 ft0NoNoNo4Sans RestrictionPro & Farm300,000$0$0$NoLien
Mason ShawCrunch (Tam)LW201998-11-03Yes182 Lbs5 ft9NoNoNo4Contrat d'EntréePro & Farm500,000$0$0$NoLien
Matt PuempelCrunch (Tam)LW261993-01-24No205 Lbs6 ft1NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Nathan BastianCrunch (Tam)RW211997-12-06No205 Lbs6 ft4NoNoNo3Contrat d'EntréePro & Farm500,000$0$0$NoLien
Nick PaulCrunch (Tam)LW241995-03-20No230 Lbs6 ft4NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Nicolas DeslauriersCrunch (Tam)LW/RW281991-02-22No221 Lbs6 ft1NoNoNo2Sans RestrictionPro & Farm903,386$0$0$NoLien
Oscar DanskCrunch (Tam)G251994-02-28No195 Lbs6 ft3NoNoNo3Avec RestrictionPro & Farm500,000$0$0$NoLien
Reid BoucherCrunch (Tam)C251993-09-08No195 Lbs5 ft10NoNoNo2Avec RestrictionPro & Farm300,000$0$0$NoLien
Spencer WatsonCrunch (Tam)RW231996-04-25No170 Lbs5 ft1NoNoNo3Avec RestrictionPro & Farm300,000$0$0$NoLien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2724.52196 Lbs6 ft12.52455,311$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Brendan PerliniLane PedersonNicolas Deslauriers40122
2Nick PaulReid BoucherJesse Puljujarvi30122
3Francis PerronKevin PorterMario Kempe20122
4Matt PuempelAustin CzarnikNathan Bastian10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Juuso RiikolaDuncan Siemens40122
2Kevin CzuczmanJoni Tuulola30122
3Brandon CrawleyBlake Hillman20122
4Juuso RiikolaDuncan Siemens10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Brendan PerliniLane PedersonNicolas Deslauriers60122
2Nick PaulReid BoucherJesse Puljujarvi40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Juuso RiikolaDuncan Siemens60122
2Kevin CzuczmanJoni Tuulola40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Brendan PerliniNicolas Deslauriers60122
2Jesse PuljujarviNick Paul40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Juuso RiikolaDuncan Siemens60122
2Kevin CzuczmanJoni Tuulola40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Brendan Perlini60122Juuso RiikolaDuncan Siemens60122
2Nicolas Deslauriers40122Kevin CzuczmanJoni Tuulola40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Brendan PerliniNicolas Deslauriers60122
2Jesse PuljujarviNick Paul40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Juuso RiikolaDuncan Siemens60122
2Kevin CzuczmanJoni Tuulola40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Brendan PerliniLane PedersonNicolas DeslauriersJuuso RiikolaDuncan Siemens
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Brendan PerliniLane PedersonNicolas DeslauriersJuuso RiikolaDuncan Siemens
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Brendan Perlini, Colin Greening, Francis PerronNicolas Deslauriers, Colin GreeningFrancis Perron
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Brandon Crawley, Blake Hillman, Kevin CzuczmanBrandon CrawleyBlake Hillman, Kevin Czuczman
Tirs de Pénalité
Brendan Perlini, Nicolas Deslauriers, Jesse Puljujarvi, 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
1Americans3110000168-2110000003212010000136-330.50069150022191317423221619198528466416425.00%20575.00%043573459.26%36666355.20%20132362.23%594425474152261139
2Bears2020000013-21010000001-11010000012-100.000123002219131522322161919441029376116.67%11281.82%043573459.26%36666355.20%20132362.23%594425474152261139
3Checkers220000001201222000000120120000000000041.000122335022219131112232216191928883610330.00%40100.00%043573459.26%36666355.20%20132362.23%594425474152261139
4Comets2010010015-41000010012-11010000003-310.25012300221913150232216191969273739900.00%16193.75%043573459.26%36666355.20%20132362.23%594425474152261139
5Devils5220001013130311000109812110000045-160.600132437102219131109232216191910948888426519.23%37586.49%043573459.26%36666355.20%20132362.23%594425474152261139
6Marlies2200000011470000000000022000000114741.00011193001221913194232216191940111642500.00%8187.50%143573459.26%36666355.20%20132362.23%594425474152261139
7Monsters2020000036-3000000000002020000036-300.0003690022191315623221619194012243818211.11%10370.00%043573459.26%36666355.20%20132362.23%594425474152261139
8Rocket1010000013-21010000013-20000000000000.0001230022191312523221619192251220400.00%50100.00%043573459.26%36666355.20%20132362.23%594425474152261139
9Senators2110000056-12110000056-10000000000020.500510151022191315723221619193716163611218.18%8187.50%043573459.26%36666355.20%20132362.23%594425474152261139
10Thunderbirds11000000211000000000001100000021121.0002460022191311423221619193141818300.00%80100.00%043573459.26%36666355.20%20132362.23%594425474152261139
Total229100011155496115400110312291146000012427-3220.5005510115623221913164323221619195051692944141081715.74%1271885.83%143573459.26%36666355.20%20132362.23%594425474152261139
_Since Last GM Reset229100011155496115400110312291146000012427-3220.5005510115623221913164323221619195051692944141081715.74%1271885.83%143573459.26%36666355.20%20132362.23%594425474152261139
_Vs Conference1137000102228-6623000101415-151400000813-580.364224264202219131274232216191923086157195611016.39%661183.33%043573459.26%36666355.20%20132362.23%594425474152261139
_Vs Division11130000029227612000002191250100000813-520.091295584122219131329232216191922178149195601118.33%621083.87%043573459.26%36666355.20%20132362.23%594425474152261139

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
2222W35510115664350516929441423
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
2291001115549
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
115401103122
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
114600012427
Derniers 10 Matchs
WLOTWOTL SOWSOL
540010
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
1081715.74%1271885.83%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
23221619192219131
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
43573459.26%36666355.20%20132362.23%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
594425474152261139


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 - 2019-11-13396Crunch-Rocket-
74 - 2019-11-14401Crunch-Rocket-
78 - 2019-11-18429Crunch-Thunderbirds-
80 - 2019-11-20439Crunch-Phantoms-
81 - 2019-11-21457Penguins-Crunch-
85 - 2019-11-25468Crunch-Devils-
87 - 2019-11-27476Comets-Crunch-
88 - 2019-11-28489Thunderbirds-Crunch-
92 - 2019-12-02511Crunch-Comets-
94 - 2019-12-04518Sound Tigers-Crunch-
95 - 2019-12-05535Marlies-Crunch-
99 - 2019-12-09554Crunch-Devils-
101 - 2019-12-11563Monsters-Crunch-
102 - 2019-12-12577Crunch-Comets-
106 - 2019-12-16596Crunch-Comets-
108 - 2019-12-18605Senators-Crunch-
109 - 2019-12-19626Crunch-Devils-
111 - 2019-12-21636Devils-Crunch-
115 - 2019-12-25655Thunderbirds-Crunch-
116 - 2019-12-26673Americans-Crunch-
122 - 2020-01-01685Comets-Crunch-
123 - 2020-01-02702Monsters-Crunch-
127 - 2020-01-06719Crunch-Americans-
129 - 2020-01-08726Crunch-Wolf Pack-
131 - 2020-01-10752Crunch-Bruins-
134 - 2020-01-13765Crunch-Senators-
136 - 2020-01-15774Crunch-Americans-
137 - 2020-01-16782Americans-Crunch-
139 - 2020-01-18805Crunch-Marlies-
141 - 2020-01-20809Rocket-Crunch-
143 - 2020-01-22817Marlies-Crunch-
144 - 2020-01-23832Marlies-Crunch-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
150 - 2020-01-29856Phantoms-Crunch-
151 - 2020-01-30871Americans-Crunch-
152 - 2020-01-31884Crunch-Americans-
155 - 2020-02-03890Crunch-Comets-
157 - 2020-02-05897Comets-Crunch-
158 - 2020-02-06915Crunch-Penguins-
162 - 2020-02-10939Crunch-Rocket-
164 - 2020-02-12943Wolf Pack-Crunch-
165 - 2020-02-13958Devils-Crunch-
168 - 2020-02-16973Crunch-Checkers-
169 - 2020-02-17978Crunch-Checkers-
172 - 2020-02-20997Comets-Crunch-
173 - 2020-02-211013Crunch-Sound Tigers-
176 - 2020-02-241019Crunch-Senators-
178 - 2020-02-261027Bruins-Crunch-
179 - 2020-02-271043Comets-Crunch-
183 - 2020-03-021064Crunch-Senators-
185 - 2020-03-041077Crunch-Americans-
186 - 2020-03-051089Americans-Crunch-
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
27 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
412,573$ 122,934$ 28,870$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 41,469$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 122 5,788$ 706,136$




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
14229100011155496115400110312291146000012427-3225510115623221913164323221619195051692944141081715.74%1271885.83%143573459.26%36666355.20%20132362.23%594425474152261139
Total Saison Régulière229100011155496115400110312291146000012427-3225510115623221913164323221619195051692944141081715.74%1271885.83%143573459.26%36666355.20%20132362.23%594425474152261139