Crunch

GP: 22 | W: 15 | L: 5 | OTL: 2 | P: 32
GF: 73 | GA: 54 | PP%: 15.83% | PK%: 86.36%
DG: Stéphane Lacasse | Morale : 61 | Moyenne d'Équipe : 59
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
1Reid BoucherXX100.00715573827871777350636566557472167670
2Nikolay KuleminXX100.00775581807968646650616167558482167660
3Austin CzarnikX100.00635580807070737064636462557272167650
4Freddie HamiltonX100.00605566707770666360616061557268164620
5Kevin PorterX100.00665566727066696050606060556464167610
6Matt PuempelX100.00605566727972606150606058555454167600
7Francis Perron (R)X100.00615566606360755750565756557574167580
8Chase De LeoX100.00615562626360725550555555557273167570
9Henrik SamuelssonX100.00595574627975545550555555555050167560
10Borna RendulicX100.00565555555859595550555555557075164550
11Colin GreeningX100.00555555555555555550555555557271161540
12Nathan Bastian (R)XX100.00565555555555555550555555555050159530
13Jason GarrisonX100.00645565789075846525626064557477167680
14Duncan SiemensX100.00735560798071676925616170555353165660
15Jeff SchultzX100.00595559615959795925595959558381170600
16Mat BodieX100.00625566615865696025606056555353168570
17Ryan GravesX100.00555556605656775625565656555353167560
18Nate GueninX100.00555555605555655525555555556566167550
19Lukas Bengtsson (R)X100.00555556605656575625565656555353167540
20Nick EbertX100.00555555605555645525555555555353161540
Rayé
1Tyler GraovacX100.00765563757873606150606060555050134600
2Michael ZalewskiX100.00565555555555555550555555555050128520
3Ryan MaloneX100.00565555555555555550555555555050128520
4Spencer Watson (R)XX100.00565555555555555550555555555050128520
5Sam HenleyX100.00565555555555555550555555555050128520
6Kevin CzuczmanX100.00555555605555695525555555555353134540
7Brandon Crawley (R)X100.00555555605555565525555555555555134540
MOYENNE D'ÉQUIPE100.0060556264656265594258585855616115758
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 Hill100.0060686380656565636763556062156640
2Jamie Phillips100.0060696664666665636763556062161630
Rayé
1Ondrej Pavelec100.0070727176707069656969558078119690
MOYENNE D'ÉQUIPE100.006370677367676664686555676714565
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Steve Spott60486266636461CAN475100,000$


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
1Austin CzarnikCrunch (Tam)C2291322840174155223516.36%946221.01426179202211275058.97%39000000.9500000121
2Reid BoucherCrunch (Tam)LW/RW2210112110240455086255611.63%452123.72235148710191151153.57%28000010.8001000132
3Matt PuempelCrunch (Tam)LW22812206140232958153313.79%338517.5436913910000162057.14%2100001.0400000033
4Jason GarrisonCrunch (Tam)D22810181024029203672022.22%1551523.4521324981122113100.00%000000.7000000123
5Nikolay KuleminCrunch (Tam)LW/RW22612186260514748113412.50%139017.7405513940222740047.83%9200000.9201000202
6Freddie HamiltonCrunch (Tam)C22512179255295046183910.87%244220.1425713901122712056.10%38500000.7700010111
7Duncan SiemensCrunch (Tam)D11491311235331723102017.39%2224522.301561144101456100.00%000001.0600001310
8Kevin PorterCrunch (Tam)C2248123300264435143411.43%528212.8301108000001149.19%24600000.8500000101
9Jeff SchultzCrunch (Tam)D223691129540152561212.00%1943019.562131985000099000.00%000000.4200100101
10Francis PerronCrunch (Tam)LW22358316023151971915.79%133415.21000020000550044.29%7000000.4800000001
11Mat BodieCrunch (Tam)D221678355241615586.67%1642119.150331287000094000.00%000000.3300001000
12Nathan BastianCrunch (Tam)C/RW19336111521101931515.79%223612.4500003000001038.46%1300000.5100001000
13Borna RendulicCrunch (Tam)RW22325536103313154920.00%235216.02202883000000056.52%2300000.2800001010
14Nick EbertCrunch (Tam)D200550802371230.00%121758.800000000002000.00%000000.5700000000
15Chase De LeoCrunch (Tam)C22134220101883712.50%11205.48000000000150048.91%9200000.6600000000
16Ryan GravesCrunch (Tam)D20044115517106120.00%111919.6000039000026000.00%000000.4200001000
17Colin GreeningCrunch (Tam)LW202243005691422.22%0934.6500000000011077.78%900000.8600000100
18Paul PostmaTampa Bay LightningD11044-521516816190.00%920518.680221252000033000.00%000000.3900010000
19Lukas BengtssonCrunch (Tam)D22123018012982112.50%1622110.0801113000025000.00%000000.2700000000
20Nate GueninCrunch (Tam)D22123624028872114.29%1324311.0900002000042000.00%000000.2500000000
21Henrik SamuelssonCrunch (Tam)C221013203760216.67%1622.86101120000000063.64%2200000.3200000000
22Kevin CzuczmanCrunch (Tam)D2000000000000.00%010.710000100000000.00%000000.0000000000
Stats d'équipe Total ou en Moyenne433731312041013874550844054115936313.49%164633814.6419355416196146102097215254.05%164300010.6402125121315
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)2013520.8962.45115302474500000.0002202212
2Ondrej PavelecCrunch (Tam)22000.9142.48121005580000.000020000
3Jamie PhillipsCrunch (Tam)20000.9621.0756001260000.0000020000
Stats d'équipe Total ou en Moyenne2415520.9012.39133102535340000.00022222212


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 Salaire Année 2 Salaire Année 3 Salaire Année 4 Salaire Année 5 Salaire Année 6 Salaire Année 7 Salaire Année 8 Salaire Année 9 Salaire Année 10 Link
Adin HillCrunch (Tam)G201996-05-11No198 Lbs6 ft4NoNoNo3Contrat d'EntréePro & Farm500,000$0$0$No500,000$500,000$
Austin CzarnikCrunch (Tam)C241992-12-12No160 Lbs5 ft9NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No300,000$
Borna RendulicCrunch (Tam)RW241992-03-24No200 Lbs6 ft2NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Brandon CrawleyCrunch (Tam)D191997-02-02Yes205 Lbs6 ft1NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No300,000$300,000$300,000$
Chase De LeoCrunch (Tam)C211995-10-25No178 Lbs5 ft9NoNoNo2Contrat d'EntréePro & Farm500,000$0$0$No500,000$
Colin GreeningCrunch (Tam)LW301986-03-09No210 Lbs6 ft3NoNoNo1Sans RestrictionPro & Farm300,000$0$0$No
Duncan SiemensCrunch (Tam)D231993-09-07No205 Lbs6 ft3NoNoNo1Avec RestrictionPro & Farm900,000$0$0$No
Francis PerronCrunch (Tam)LW201996-04-18Yes166 Lbs6 ft0NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$No300,000$300,000$
Freddie HamiltonCrunch (Tam)C251992-01-01No195 Lbs6 ft1NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No300,000$300,000$
Henrik SamuelssonCrunch (Tam)C221994-02-07No210 Lbs6 ft3NoNoNo1Contrat d'EntréePro & Farm900,000$0$0$No
Jamie PhillipsCrunch (Tam)G231993-03-24No170 Lbs6 ft1NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No300,000$300,000$
Jason GarrisonCrunch (Tam)D321984-11-13No222 Lbs6 ft3NoNoNo1Sans RestrictionPro & Farm2,500,000$0$0$No
Jeff SchultzCrunch (Tam)D301986-02-25No230 Lbs6 ft6NoNoNo1Sans RestrictionPro & Farm300,000$0$0$No
Kevin CzuczmanCrunch (Tam)D261991-01-09No209 Lbs6 ft2NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Kevin PorterCrunch (Tam)C301986-03-12No194 Lbs5 ft11NoNoNo1Sans RestrictionPro & Farm300,000$0$0$No
Lukas BengtssonCrunch (Tam)D221994-04-13Yes172 Lbs5 ft11NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$No300,000$300,000$
Mat BodieCrunch (Tam)D261990-06-03No175 Lbs6 ft0NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Matt PuempelCrunch (Tam)LW231993-01-23No205 Lbs6 ft1NoNoNo1Avec RestrictionPro & Farm500,000$0$0$No
Michael ZalewskiCrunch (Tam)LW241992-08-18No205 Lbs6 ft2NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Nate GueninCrunch (Tam)D341982-12-09No207 Lbs6 ft3NoNoNo2Sans RestrictionPro & Farm450,000$0$0$No450,000$
Nathan BastianCrunch (Tam)C/RW191997-12-06Yes205 Lbs6 ft4NoNoNo4Contrat d'EntréePro & Farm500,000$0$0$No500,000$500,000$500,000$
Nick EbertCrunch (Tam)D221994-05-10No203 Lbs6 ft0NoNoNo1Contrat d'EntréePro & Farm500,000$0$0$No
Nikolay KuleminCrunch (Tam)LW/RW301986-07-13No225 Lbs6 ft1NoNoNo2Sans RestrictionPro & Farm2,500,000$0$0$No2,500,000$
Ondrej PavelecCrunch (Tam)G291987-08-30No218 Lbs6 ft3NoNoNo2Sans RestrictionPro & Farm500,000$0$0$No500,000$
Reid BoucherCrunch (Tam)LW/RW231993-09-07No190 Lbs5 ft10NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No300,000$300,000$
Ryan GravesCrunch (Tam)D211995-05-21No220 Lbs6 ft4NoNoNo1Contrat d'EntréePro & Farm865,000$0$0$No
Ryan MaloneCrunch (Tam)LW371979-11-30No225 Lbs6 ft4NoNoNo1Sans RestrictionPro & Farm500,000$0$0$No
Sam HenleyCrunch (Tam)LW231993-07-24No209 Lbs6 ft4NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No300,000$
Spencer WatsonCrunch (Tam)LW/RW201996-04-26Yes170 Lbs5 ft10NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No300,000$300,000$300,000$
Tyler GraovacCrunch (Tam)C231993-04-26No200 Lbs6 ft5NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
3024.83199 Lbs6 ft21.90557,167$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Reid BoucherAustin CzarnikNikolay Kulemin40113
2Matt PuempelFreddie HamiltonBorna Rendulic30113
3Francis PerronKevin PorterNathan Bastian20122
4Colin GreeningChase De LeoReid Boucher10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Jason GarrisonDuncan Siemens40122
2Jeff SchultzMat Bodie30122
3Ryan GravesNate Guenin20122
4Nick EbertLukas Bengtsson10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Reid BoucherAustin CzarnikNikolay Kulemin60122
2Matt PuempelFreddie HamiltonBorna Rendulic40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Jason GarrisonDuncan Siemens60122
2Jeff SchultzMat Bodie40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Reid BoucherNikolay Kulemin60122
2Austin CzarnikFreddie Hamilton40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Jason GarrisonDuncan Siemens60122
2Jeff SchultzMat Bodie40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Reid Boucher60122Jason GarrisonDuncan Siemens60122
2Nikolay Kulemin40122Jeff SchultzMat Bodie40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Reid BoucherNikolay Kulemin60122
2Austin CzarnikFreddie Hamilton40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Jason GarrisonDuncan Siemens60122
2Jeff SchultzMat Bodie40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Reid BoucherAustin CzarnikNikolay KuleminJason GarrisonDuncan Siemens
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Reid BoucherAustin CzarnikNikolay KuleminJason GarrisonDuncan Siemens
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Henrik Samuelsson, Kevin Porter, Francis PerronHenrik Samuelsson, Kevin PorterFrancis Perron
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Ryan Graves, Nate Guenin, Nick EbertRyan GravesNate Guenin, Nick Ebert
Tirs de Pénalité
Reid Boucher, Nikolay Kulemin, Austin Czarnik, Freddie Hamilton, Kevin Porter
Gardien
#1 : Adin Hill, #2 : Jamie Phillips


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
1Americans330000001174110000005412200000063361.00011203101312515277168186179105817306824520.83%15286.67%036365455.50%34565852.43%18032355.73%570401495157266139
2Bears21001000743100010003211100000042241.000714210031251523016818617910582534359333.33%16381.25%136365455.50%34565852.43%18032355.73%570401495157266139
3Checkers2200000011292200000011290000000000041.000111829013125152711681861791039245564125.00%130100.00%136365455.50%34565852.43%18032355.73%570401495157266139
4Comets22000000963110000005321100000043141.0009162500312515263168186179103517434117211.76%14285.71%036365455.50%34565852.43%18032355.73%570401495157266139
5Devils51300001717-103020000139-62110000048-430.3007132000312515270168186179101585512111220210.00%41880.49%036365455.50%34565852.43%18032355.73%570401495157266139
6Marlies2200000011380000000000022000000113841.0001121320031251529316818617910381228655240.00%13192.31%036365455.50%34565852.43%18032355.73%570401495157266139
7Monsters21001000752000000000002100100075241.0007121900312515233168186179105812263613215.38%13284.62%036365455.50%34565852.43%18032355.73%570401495157266139
8Rocket1000010012-11000010012-10000000000010.50011200312515220168186179102861223500.00%60100.00%036365455.50%34565852.43%18032355.73%570401495157266139
9Senators2020000046-22020000046-20000000000000.0004711003125152431681861791039928481500.00%13284.62%136365455.50%34565852.43%18032355.73%570401495157266139
10Thunderbirds11000000523000000000001100000052321.0005914003125152411681861791023920248225.00%10190.00%136365455.50%34565852.43%18032355.73%570401495157266139
Total221350210173541911440110132284119101000412615320.72773131204023125152541168186179105341643875081201915.83%1542186.36%436365455.50%34565852.43%18032355.73%570401495157266139
_Since Last GM Reset320000011587210000017701100000081750.83315274200312515291168186179106831597612433.33%18383.33%036365455.50%34565852.43%18032355.73%570401495157266139
_Vs Conference1135020012532-7604010011017-75310100015150110.5002546710031251521761681861791031310120923157712.28%831581.93%236365455.50%34565852.43%18032355.73%570401495157266139
_Vs Division111201000322846020100017134510000001515040.182325789013125152204168186179103139422623946817.39%831384.34%236365455.50%34565852.43%18032355.73%570401495157266139

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
2232W27313120454153416438750802
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
2213521017354
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
114411013228
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
119110004126
Derniers 10 Matchs
WLOTWOTL SOWSOL
521101
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
1201915.83%1542186.36%4
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
168186179103125152
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
36365455.50%34565852.43%18032355.73%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
570401495157266139


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 - 2018-09-0811Crunch4Bears2WSommaire du Match
8 - 2018-09-1230Crunch2Americans0WSommaire du Match
11 - 2018-09-1546Checkers0Crunch5WSommaire du Match
17 - 2018-09-2175Crunch4Devils3WSommaire du Match
18 - 2018-09-2285Comets3Crunch5WSommaire du Match
24 - 2018-09-28107Crunch3Marlies2WSommaire du Match
25 - 2018-09-29119Checkers2Crunch6WSommaire du Match
31 - 2018-10-05140Senators4Crunch3LSommaire du Match
32 - 2018-10-06157Senators2Crunch1LSommaire du Match
38 - 2018-10-12187Crunch4Americans3WSommaire du Match
40 - 2018-10-14209Bears2Crunch3WXSommaire du Match
43 - 2018-10-17217Devils4Crunch0LSommaire du Match
45 - 2018-10-19224Crunch4Comets3WSommaire du Match
46 - 2018-10-20239Rocket2Crunch1LXSommaire du Match
50 - 2018-10-24260Crunch0Devils5LSommaire du Match
52 - 2018-10-26265Devils2Crunch1LSommaire du Match
53 - 2018-10-27283Crunch5Thunderbirds2WSommaire du Match
59 - 2018-11-02308Crunch4Monsters3WXSommaire du Match
60 - 2018-11-03318Crunch3Monsters2WSommaire du Match
66 - 2018-11-09350Devils3Crunch2LXXSommaire du Match
67 - 2018-11-10366Americans4Crunch5WSommaire du Match
71 - 2018-11-14384Crunch8Marlies1WSommaire du Match
73 - 2018-11-16396Crunch-Rocket-
74 - 2018-11-17401Crunch-Rocket-
78 - 2018-11-21429Crunch-Thunderbirds-
80 - 2018-11-23439Crunch-Phantoms-
81 - 2018-11-24457Penguins-Crunch-
85 - 2018-11-28468Crunch-Devils-
87 - 2018-11-30476Comets-Crunch-
88 - 2018-12-01489Thunderbirds-Crunch-
92 - 2018-12-05511Crunch-Comets-
94 - 2018-12-07518Sound Tigers-Crunch-
95 - 2018-12-08535Marlies-Crunch-
99 - 2018-12-12554Crunch-Devils-
101 - 2018-12-14563Monsters-Crunch-
102 - 2018-12-15577Crunch-Comets-
106 - 2018-12-19596Crunch-Comets-
108 - 2018-12-21605Senators-Crunch-
109 - 2018-12-22626Crunch-Devils-
111 - 2018-12-24636Devils-Crunch-
115 - 2018-12-28655Thunderbirds-Crunch-
116 - 2018-12-29673Americans-Crunch-
122 - 2019-01-04685Comets-Crunch-
123 - 2019-01-05702Monsters-Crunch-
127 - 2019-01-09719Crunch-Americans-
129 - 2019-01-11726Crunch-Wolf Pack-
131 - 2019-01-13752Crunch-Bruins-
134 - 2019-01-16765Crunch-Senators-
136 - 2019-01-18774Crunch-Americans-
137 - 2019-01-19782Americans-Crunch-
139 - 2019-01-21805Crunch-Marlies-
141 - 2019-01-23809Rocket-Crunch-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
143 - 2019-01-25817Marlies-Crunch-
144 - 2019-01-26832Marlies-Crunch-
150 - 2019-02-01856Phantoms-Crunch-
151 - 2019-02-02871Americans-Crunch-
152 - 2019-02-03884Crunch-Americans-
155 - 2019-02-06890Crunch-Comets-
157 - 2019-02-08897Comets-Crunch-
158 - 2019-02-09915Crunch-Penguins-
162 - 2019-02-13939Crunch-Rocket-
164 - 2019-02-15943Wolf Pack-Crunch-
165 - 2019-02-16958Devils-Crunch-
168 - 2019-02-19973Crunch-Checkers-
169 - 2019-02-20978Crunch-Checkers-
172 - 2019-02-23997Comets-Crunch-
173 - 2019-02-241013Crunch-Sound Tigers-
176 - 2019-02-271019Crunch-Senators-
178 - 2019-03-011027Bruins-Crunch-
179 - 2019-03-021043Comets-Crunch-
183 - 2019-03-061064Crunch-Senators-
185 - 2019-03-081077Crunch-Americans-
186 - 2019-03-091089Americans-Crunch-
192 - 2019-03-151117Rocket-Crunch-
193 - 2019-03-161130Americans-Crunch-
194 - 2019-03-171147Crunch-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
96,893$ 167,150$ 184,783$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 59,764$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 122 1,377$ 167,994$




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
132213502101735419114401101322841191010004126153273131204023125152541168186179105341643875081201915.83%1542186.36%436365455.50%34565852.43%18032355.73%570401495157266139
Total Saison Régulière2213502101735419114401101322841191010004126153273131204023125152541168186179105341643875081201915.83%1542186.36%436365455.50%34565852.43%18032355.73%570401495157266139
Séries
1251400000515-102110000055030300000010-10251015011130812626281145358913016212.50%38781.58%07513256.82%9217054.12%356553.85%10063138376329
1251400000515-102110000055030300000010-10251015011130812626281145358913016212.50%38781.58%07513256.82%9217054.12%356553.85%10063138376329
Total Séries1028000001030-20422000001010060600000020-20410203002226016252525622907017826032412.50%761481.58%015026456.82%18434054.12%7013053.85%2011272777412658