Marlies

GP: 30 | W: 25 | L: 3 | OTL: 2 | P: 52
GF: 185 | GA: 94 | PP%: 46.75% | PK%: 68.91%
DG: Marcel Fournier | Morale : 71 | Moyenne d'Équipe : 65
Prochain matchs #389 vs Icedogs
La résolution de votre navigateur est trop petite pour cette page. Plusieurs informations sont cachées pour garder la page lisible.

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
1Charles Hudon (C)X99.007642788563819079696161686753545770700
2Nick CousinsX99.007642787565808667684964737357566478680
3Brendan LemieuxX99.006456436176728963526363606550507880670
4Colin WilsonXXX99.006341837384828069825559606872617078670
5Zack KassianX99.007847627287789469585458656865607275670
6Dylan Strome (A)X99.005643747478739079735955646651518979660
7Tomas JurcoX100.006144747373758471535064696558547375660
8Luke KuninXX100.006444748170718479744656646451518475650
9Brendan GaunceX100.006941817381806861754559697054527878640
10Colin White (A)X100.005944727470669172635557606251518278630
11Anton LindholmX99.008041817766747669304850756252535465690
12Mirco MuellerX99.006041876581755165305050796153548265670
13Eric GrybaX100.007445775786617458304850775861565336660
14Gustav OlofssonX100.006241816276736872305650746252517174660
15Brian LashoffX100.007242725084489954305453665355533646620
16Rinat ValievX100.006743735184527658305756625750506672610
17Ryan ManthaX100.006841775089617054305553605350504468600
Rayé
1Anton SlepyshevX100.007141837982776875525058636954526220650
2Dominic TurgeonX100.006442756176609958665758685850506820620
3Michael McCarronX100.007151536193639158544550605652518174590
4Christian JarosX100.006544695184557557305854605550505720600
MOYENNE D'ÉQUIPE99.62684474677869816651535666625453686365
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
1Charlie Lindgren100.00778397747981807882767451644278750
2Ville Husso100.00726976847275747373716950614478700
Rayé
MOYENNE D'ÉQUIPE100.0075768779767877767874725163437873
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Willie Desjardins75715578765548CAN614325,000$


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
1Charles HudonMarlies (TOR)LW24284775211804332165529216.97%958024.1813274022881124582172.73%444713032.5800000824
2Colton SceviourMaple LeafsC26264470212754664167528315.57%1866525.587313822951129794471.86%7892829022.1100001463
3Nick CousinsMarlies (TOR)C3020345420120475692285821.74%1755318.4612122422760005302169.96%5261521021.9500000244
4Zack KassianMarlies (TOR)RW30252954154757430120325720.83%1458219.4116183437103000002355.88%343719021.8600010334
5Colin WilsonMarlies (TOR)C/LW/RW3025194420203622116245621.55%1453617.88129213481000085265.12%432415021.6400000322
6Brendan LemieuxMarlies (TOR)LW30162339177515462063224225.40%1152717.59691511750002272344.00%25137001.4800200111
7Gustav OlofssonMarlies (TOR)D30321242712036326024265.00%3782027.34381110120011492000.00%01142000.5900000001
8Tomas JurcoMarlies (TOR)LW30814229155401168285811.76%1543614.550113100002282061.11%361621001.0100010230
9Luke KuninMarlies (TOR)C/RW30137208115311483215115.66%1641313.79000080001221068.60%1721913000.9700010104
10Dylan StromeMarlies (TOR)C30412161211529165117317.84%2649016.36167538000050065.35%101733000.6500001001
11Rinat ValievMarlies (TOR)D264111515220291833211812.12%2762424.00369883000269100.00%0537000.4800000010
12Brendan GaunceMarlies (TOR)C301910840242019795.26%1442914.320000190112341064.41%59420000.4701000000
13Ryan ManthaMarlies (TOR)D30281014001921231368.70%2656418.81101260011257000.00%0224000.3500000000
14Anton LindholmMarlies (TOR)D22574001352040.00%25527.912132900002100.00%026002.5100000002
15Eric GrybaMarlies (TOR)D11145140010617675.88%1626323.93000335011027000.00%0016000.3800000000
16Colin WhiteMarlies (TOR)C301342809788412.50%62869.56000030000000100.00%3019000.2800000000
17Brian LashoffMarlies (TOR)D1112346020442425.00%1424121.95112126000029000.00%0113000.2500000000
18Michael McCarronMarlies (TOR)RW2812332203616227104.55%1030210.8100003000011020.00%10211000.2000000000
19Mirco MuellerMarlies (TOR)D21124001140125.00%45025.231121900012000.00%002000.7900000000
Stats d'équipe Total ou en Moyenne46018229547723829240577393112036661316.25%296842518.327813020818395026834579241469.16%18422333610111.1301232242226
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
1Charlie LindgrenMarlies (TOR)2118120.9012.9712334261616282310.00002010111
2Ville HussoMarlies (TOR)107200.8993.385682132316180000.00001020120
Stats d'équipe Total ou en Moyenne3125320.9003.1018026393932462310.00003030231


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
Anton LindholmMarlies (TOR)D221994-11-29No191 Lbs5 ft11NoNoNo1Contrat d'EntréePro & Farm750,000$0$0$NoLien
Anton SlepyshevMarlies (TOR)LW231994-05-13No221 Lbs6 ft2NoNoNo1Contrat d'EntréePro & Farm750,000$0$0$NoLien
Brendan GaunceMarlies (TOR)C231994-03-25No217 Lbs6 ft2NoNoNo1Contrat d'EntréePro & Farm750,000$0$0$NoLien
Brendan LemieuxMarlies (TOR)LW211996-03-15No210 Lbs6 ft1NoNoNo1Contrat d'EntréePro & Farm839,167$0$0$NoLien
Brian LashoffMarlies (TOR)D271990-07-16No219 Lbs6 ft3NoNoNo1Avec RestrictionPro & Farm750,000$0$0$NoLien
Charles HudonMarlies (TOR)LW231994-06-23No188 Lbs5 ft10NoNoNo1Contrat d'EntréePro & Farm750,000$0$0$NoLien
Charlie LindgrenMarlies (TOR)G231993-12-18No182 Lbs6 ft1NoNoNo3Contrat d'EntréePro & Farm750,000$0$0$No750,000$750,000$Lien
Christian JarosMarlies (TOR)D211996-04-02No201 Lbs6 ft3NoNoNo2Contrat d'EntréePro & Farm755,000$0$0$No755,000$Lien
Colin WhiteMarlies (TOR)C201997-01-30No183 Lbs6 ft0NoNoNo1Contrat d'EntréePro & Farm925,000$0$0$NoLien
Colin WilsonMarlies (TOR)C/LW/RW271989-10-20No221 Lbs6 ft1NoNoNo3Avec RestrictionPro & Farm800,000$0$0$No800,000$800,000$Lien
Dominic TurgeonMarlies (TOR)C211996-02-25No200 Lbs6 ft2NoNoNo1Contrat d'EntréePro & Farm750,000$0$0$NoLien
Dylan StromeMarlies (TOR)C201997-03-07No200 Lbs6 ft3NoNoNo2Contrat d'EntréePro & Farm863,333$0$0$No863,333$Lien
Eric GrybaMarlies (TOR)D291988-04-14No222 Lbs6 ft4NoNoNo1Sans RestrictionPro & Farm950,000$0$0$NoLien
Gustav OlofssonMarlies (TOR)D221994-12-01No196 Lbs6 ft3NoNoNo1Contrat d'EntréePro & Farm750,000$0$0$NoLien
Luke KuninMarlies (TOR)C/RW191997-12-04No193 Lbs6 ft0NoNoNo2Contrat d'EntréePro & Farm925,000$0$0$No925,000$Lien
Michael McCarronMarlies (TOR)RW221995-03-07No230 Lbs6 ft6NoNoNo1Contrat d'EntréePro & Farm874,125$0$0$NoLien
Mirco MuellerMarlies (TOR)D221995-03-21No210 Lbs6 ft3NoNoNo1Contrat d'EntréePro & Farm850,000$0$0$NoLien
Nick CousinsMarlies (TOR)C241993-07-20No185 Lbs5 ft11NoNoNo1Contrat d'EntréePro & Farm1,000,000$0$0$NoLien
Rinat ValievMarlies (TOR)D221995-05-11No215 Lbs6 ft3NoNoNo1Contrat d'EntréePro & Farm750,000$0$0$NoLien
Ryan ManthaMarlies (TOR)D211996-06-18No225 Lbs6 ft5NoNoNo1Contrat d'EntréePro & Farm750,000$0$0$NoLien
Tomas JurcoMarlies (TOR)LW241992-12-28No188 Lbs6 ft2NoNoNo1Contrat d'EntréePro & Farm750,000$0$0$NoLien
Ville HussoMarlies (TOR)G221995-02-06No205 Lbs6 ft3NoNoNo1Contrat d'EntréePro & Farm750,000$0$0$NoLien
Zack KassianMarlies (TOR)RW261991-01-24No209 Lbs6 ft3NoNoNo2Contrat d'EntréePro & Farm1,950,000$0$0$No1,950,000$Lien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2322.78205 Lbs6 ft21.35857,897$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Charles HudonNick CousinsColin Wilson40122
2Brendan LemieuxDylan StromeZack Kassian30122
3Tomas JurcoLuke KuninBrendan Gaunce20122
4Charles HudonBrendan GaunceNick Cousins10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Anton LindholmMirco Mueller40122
2Eric GrybaGustav Olofsson30122
3Brian LashoffRinat Valiev20122
4Ryan ManthaAnton Lindholm10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Charles HudonNick CousinsColin Wilson60122
2Brendan LemieuxDylan StromeZack Kassian40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Anton LindholmMirco Mueller60122
2Eric GrybaGustav Olofsson40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Charles HudonNick Cousins60122
2Colin WilsonZack Kassian40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Anton LindholmMirco Mueller60122
2Eric GrybaGustav Olofsson40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Charles Hudon60122Anton LindholmMirco Mueller60122
2Nick Cousins40122Eric GrybaGustav Olofsson40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Charles HudonNick Cousins60122
2Colin WilsonZack Kassian40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Anton LindholmMirco Mueller60122
2Eric GrybaGustav Olofsson40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Charles HudonNick CousinsColin WilsonAnton LindholmMirco Mueller
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Charles HudonNick CousinsColin WilsonAnton LindholmMirco Mueller
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Colin White, Tomas Jurco, Luke KuninColin White, Tomas JurcoLuke Kunin
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Brian Lashoff, Rinat Valiev, Ryan ManthaBrian LashoffRinat Valiev, Ryan Mantha
Tirs de Pénalité
Charles Hudon, Nick Cousins, Colin Wilson, Zack Kassian, Brendan Lemieux
Gardien
#1 : Charlie Lindgren, #2 : Ville Husso


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
1Admirals11000000835110000008350000000000021.0008132100348466137295442392238141528100.00%5260.00%146467368.95%46767069.70%37355667.09%683372592233525317
2Barracuda220000001569110000008261100000074341.0001525400034846617929544239224214143411872.73%6266.67%046467368.95%46767069.70%37355667.09%683372592233525317
3Bears210010001239100010004311100000080841.000121830013484661602954423922621821375360.00%8187.50%046467368.95%46767069.70%37355667.09%683372592233525317
4Blacknight22000000144101100000010461100000040441.000142337013484661782954423922339174818950.00%10100.00%046467368.95%46767069.70%37355667.09%683372592233525317
5Bruins33000000191091100000053222000000147761.000192948003484661122295442392210441376810440.00%16568.75%046467368.95%46767069.70%37355667.09%683372592233525317
6Condors1010000035-21010000035-20000000000000.000358103484661332954423922421014234250.00%7185.71%046467368.95%46767069.70%37355667.09%683372592233525317
7Crunch22000000166101100000011381100000053241.0001624400034846618229544239226522294211763.64%9277.78%146467368.95%46767069.70%37355667.09%683372592233525317
8Gulls11000000826110000008260000000000021.00081220003484661322954423922171722312541.67%110.00%046467368.95%46767069.70%37355667.09%683372592233525317
9Heat321000001293110000004222110000087140.66712203200348466112529544239227921206021733.33%10460.00%046467368.95%46767069.70%37355667.09%683372592233525317
10Monsters11000000505000000000001100000050521.00058130134846613529544239222178186350.00%50100.00%046467368.95%46767069.70%37355667.09%683372592233525317
11Moose1100000012481100000012480000000000021.00012203200348466132295442392232168158787.50%4325.00%046467368.95%46767069.70%37355667.09%683372592233525317
12Rocket33000000229132200000015781100000072561.00022355700348466112129544239228117325318633.33%14285.71%146467368.95%46767069.70%37355667.09%683372592233525317
13Senateurs53100100161332110000067-132000100106470.700162945003484661203295442392218656469624729.17%18761.11%046467368.95%46767069.70%37355667.09%683372592233525317
14Stars11000000853110000008530000000000021.00081422003484661272954423922551329205480.00%7271.43%046467368.95%46767069.70%37355667.09%683372592233525317
15Thunderbird21000100151501000010078-11100000087130.7501526410034846616529544239227630164015746.67%8537.50%046467368.95%46767069.70%37355667.09%683372592233525317
Total30243012001859491161220110010958511412100100763640520.867185301486133484661113129544239229333053086051697946.75%1193768.91%346467368.95%46767069.70%37355667.09%683372592233525317
_Since Last GM Reset30243012001859491161220110010958511412100100763640520.867185301486133484661113129544239229333053086051697946.75%1193768.91%346467368.95%46767069.70%37355667.09%683372592233525317
_Vs Conference1814101200105564985101100483117109000100572532320.8891051692740234846616882954423922595191189354893741.57%782271.79%246467368.95%46767069.70%37355667.09%683372592233525317
_Vs Division15121002008853357510010044281687000100442519260.867881432310034846615932954423922512166160299783139.74%652167.69%246467368.95%46767069.70%37355667.09%683372592233525317

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
3052W7185301486113193330530860513
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
30243120018594
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
16122110010958
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
1412101007636
Derniers 10 Matchs
WLOTWOTL SOWSOL
901000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
1697946.75%1193768.91%3
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
29544239223484661
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
46467368.95%46767069.70%37355667.09%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
683372592233525317


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
3 - 2018-10-1110Senateurs4Marlies2LSommaire du Match
5 - 2018-10-1318Marlies7Rocket2WSommaire du Match
8 - 2018-10-1631Marlies3Senateurs1WSommaire du Match
11 - 2018-10-1938Marlies6Bruins5WSommaire du Match
13 - 2018-10-2144Crunch3Marlies11WSommaire du Match
18 - 2018-10-2662Rocket2Marlies4WSommaire du Match
19 - 2018-10-2767Marlies5Crunch3WSommaire du Match
22 - 2018-10-3083Marlies8Thunderbird7WSommaire du Match
24 - 2018-11-0190Bruins3Marlies5WSommaire du Match
27 - 2018-11-04106Marlies8Bears0WSommaire du Match
29 - 2018-11-06116Thunderbird8Marlies7LXSommaire du Match
30 - 2018-11-07124Marlies5Senateurs2WSommaire du Match
33 - 2018-11-10139Rocket5Marlies11WSommaire du Match
36 - 2018-11-13151Marlies4Heat6LSommaire du Match
38 - 2018-11-15161Admirals3Marlies8WSommaire du Match
40 - 2018-11-17172Marlies5Monsters0WSommaire du Match
44 - 2018-11-21185Marlies8Bruins2WSommaire du Match
46 - 2018-11-23191Heat2Marlies4WSommaire du Match
50 - 2018-11-27210Condors5Marlies3LSommaire du Match
53 - 2018-11-30220Marlies2Senateurs3LXSommaire du Match
57 - 2018-12-04233Senateurs3Marlies4WSommaire du Match
59 - 2018-12-06240Marlies7Barracuda4WSommaire du Match
65 - 2018-12-12260Bears3Marlies4WXSommaire du Match
69 - 2018-12-16278Stars5Marlies8WSommaire du Match
74 - 2018-12-21301Gulls2Marlies8WSommaire du Match
77 - 2018-12-24314Marlies4Heat1WSommaire du Match
79 - 2018-12-26323Blacknight4Marlies10WSommaire du Match
83 - 2018-12-30344Barracuda2Marlies8WSommaire du Match
87 - 2019-01-03364Moose4Marlies12WSommaire du Match
90 - 2019-01-06377Marlies4Blacknight0WSommaire du Match
92 - 2019-01-08389Icedogs-Marlies-
98 - 2019-01-14411Wolves-Marlies-
100 - 2019-01-16421Marlies-Wolves-
103 - 2019-01-19434Reigh-Marlies-
107 - 2019-01-23447Marlies-Gulls-
109 - 2019-01-25457Marlies-Crunch-
110 - 2019-01-26462Bears-Marlies-
113 - 2019-01-29476Marlies-Phantoms-
115 - 2019-01-31483Phantoms-Marlies-
117 - 2019-02-02493Marlies-Moose-
119 - 2019-02-04501Marlies-Rampages-
123 - 2019-02-08513Devils-Marlies-
128 - 2019-02-13528Marlies-Devils-
129 - 2019-02-14538Phantoms-Marlies-
132 - 2019-02-17555Marlies-Thunderbird-
133 - 2019-02-18559Crunch-Marlies-
137 - 2019-02-22578Sound Tigers-Marlies-
143 - 2019-02-28602Heat-Marlies-
147 - 2019-03-04620Marlies-Icedogs-
148 - 2019-03-05626Rampages-Marlies-
151 - 2019-03-08641Marlies-Rocket-
152 - 2019-03-09650Rocket-Marlies-
156 - 2019-03-13669Marlies-Rocket-
157 - 2019-03-14675Admirals-Marlies-
161 - 2019-03-18686Marlies-Phantoms-
163 - 2019-03-20696Marlies-Devils-
164 - 2019-03-21700Bruins-Marlies-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
168 - 2019-03-25721Monsters-Marlies-
170 - 2019-03-27727Marlies-Monsters-
175 - 2019-04-01745Marlies-Thunderbird-
176 - 2019-04-02748Senateurs-Marlies-
183 - 2019-04-09771Devils-Marlies-
187 - 2019-04-13793Bruins-Marlies-
189 - 2019-04-15799Marlies-Sound Tigers-
191 - 2019-04-17807Marlies-Penguins-
194 - 2019-04-20819Sound Tigers-Marlies-
196 - 2019-04-22828Marlies-Bruins-
200 - 2019-04-26841Marlies-Crunch-
201 - 2019-04-27845Penguins-Marlies-
206 - 2019-05-02866Monsters-Marlies-
208 - 2019-05-04876Marlies-Condors-
210 - 2019-05-06884Marlies-Reigh-
212 - 2019-05-08890Marlies-Bears-
213 - 2019-05-09892Marlies-Stars-
214 - 2019-05-10897Marlies-Admirals-
215 - 2019-05-11904Thunderbird-Marlies-
218 - 2019-05-14918Marlies-Penguins-
220 - 2019-05-16927Penguins-Marlies-
225 - 2019-05-21948Thunderbird-Marlies-
227 - 2019-05-23953Marlies-Sound Tigers-
233 - 2019-05-29971Crunch-Marlies-
234 - 2019-05-30976Marlies-Admirals-



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
25 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
855,991$ 1,973,162$ 1,960,750$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 732,598$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 147 9,697$ 1,425,459$




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
23024301200185949116122011001095851141210010076364052185301486133484661113129544239229333053086051697946.75%1193768.91%346467368.95%46767069.70%37355667.09%683372592233525317
Total Saison Régulière3024301200185949116122011001095851141210010076364052185301486133484661113129544239229333053086051697946.75%1193768.91%346467368.95%46767069.70%37355667.09%683372592233525317