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Moose

GP: 50 | W: 17 | L: 32 | OTL: 1 | P: 35
GF: 170 | GA: 287 | PP%: 17.78% | PK%: 73.81%
DG: | Morale : 50 | Moyenne d'Équipe : 61
Prochain matchs #781 vs Eagles
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Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Nom du Joueur C L R D CON CK FG DI SK ST EN DU PH FO PA SC DF PS EX LD PO MO OV TA SPÂgeContratSalaire
1Paul CareyX100.00624588637776746067616264638078050640312700,000$
2Brad MaloneX100.00665183628479766164605963627971050630301700,000$
3Austin CzarnikX100.005641886563777664626363596575680506302611,250,000$
4Brendan GaunceX100.00664887638474736264616062637166050630251700,000$
5Hudson FaschingX100.00694681608487895855575961586965050620242737,500$
6Luke JohnsonX100.00563987616876726274605963627166050620252700,000$
7Ross ColtonX100.00645384597688895861595756596764050610231925,000$
8JC LiponX100.00566769607187885855575756607367050600261700,000$
9Nick MoutreyX100.00745087568885835450525358566965050600241650,000$
10Carter BancksX100.00534986556987885351545456557977050590301650,000$
11Mario FerraroX100.008539786469838163307056655263610506402131,137,500$
12Joe MorrowX100.00664584637477756230605866537568050630261650,000$
13Reece WillcoxX100.00714789578790885630555358457166050610251675,000$
14William BorgenX100.00645186598385755830575660536764050610222925,000$
15Reece ScarlettX100.00544587577185845630555453467367050590261700,000$
Rayé
1Ryan OlsenX100.00595483587587905762555659577166050600251650,000$
2Samuel KurkerX100.00664588558169705450535356527166050590251650,000$
MOYENNE D'ÉQUIPE100.0064488460778281595158576057726705061
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
1Garret Sparks100.0075737184747375747375747377050660
2Tom McCollum100.0067706884666567666567667985050620
Rayé
1Adam Wilcox100.0065767472646365646365647581050600
2Filip Larsson100.0061565479605961605961606367050570
MOYENNE D'ÉQUIPE100.006769678066656766656766737805061
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire


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
1Luke JohnsonMoose (WIN)C503615517120691162175117916.59%7378615.7210134000044260.04%54800051.3000000413
2Mario FerraroMoose (WIN)D2582028112801034042212519.05%3658423.381451348000146200.00%000000.9600000232
3Paul CareyMoose (WIN)C25919289802298126361057.14%760524.2322412491016662060.93%77300000.9212000220
4Brad MaloneMoose (WIN)C25141327171605877123346911.38%458823.540224391013600261.20%54900010.9212000330
5Hudson FaschingMoose (WIN)RW251113248155422794316311.70%544017.622461651000001243.24%3700001.0900100113
6Austin CzarnikMoose (WIN)C25913221280378811929737.56%644917.98000000003262057.06%52400000.9801000103
7Joe MorrowMoose (WIN)D25714211240204455192712.73%2559523.832241848000156000.00%000000.7100000101
8Nick MoutreyMoose (WIN)LW25613199155482457164810.53%344917.97044551000092053.33%3000000.8500001000
9William BorgenMoose (WIN)D255141914100352946153910.87%3552621.041451042022053000.00%000000.7200000002
10JC LiponMoose (WIN)RW25991815115331765126013.85%341416.58112639000000040.74%2700000.8700001000
11Brendan GaunceMoose (WIN)C25512171610036538925825.62%654621.870339390001250057.65%17000000.6201000200
12Reece WillcoxMoose (WIN)D255111614120541839132512.82%2552621.063031142011154010.00%000000.6100000011
13Carter BancksMoose (WIN)RW25411151311527284214359.52%544817.94000100111282147.83%4600000.6700010110
14Reece ScarlettMoose (WIN)D2521315136014141071220.00%3640216.0900000000012010.00%000000.7500000020
15Ross ColtonMoose (WIN)C253811121403718204715.00%3239315.7300000000000066.67%600000.5600000010
Stats d'équipe Total ou en Moyenne40013319833118218020635691114432784911.63%301775719.391326391084612461744515959.11%271000060.8526112171515
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
1Garret SparksMoose (WIN)25141010.9052.94148823737680000.7147250200
2Tom McCollumMoose (WIN)10001.0000.001800070000.0000025000
Stats d'équipe Total ou en Moyenne26141010.9062.91150723737750000.71472525200


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
Adam WilcoxMoose (WIN)G261992-11-26No189 Lbs6 ft0NoNoNo1Pro & Farm650,000$0$0$NoLien / Lien NHL
Austin CzarnikMoose (WIN)C261992-12-12No170 Lbs5 ft9NoNoNo1Pro & Farm1,250,000$0$0$NoLien / Lien NHL
Brad MaloneMoose (WIN)C301989-05-20No217 Lbs6 ft2NoNoNo1Pro & Farm700,000$0$0$NoLien / Lien NHL
Brendan GaunceMoose (WIN)C251994-03-25No217 Lbs6 ft2NoNoNo1Pro & Farm700,000$0$0$NoLien / Lien NHL
Carter BancksMoose (WIN)RW301989-08-09No181 Lbs5 ft11NoNoNo1Pro & Farm650,000$0$0$NoLien / Lien NHL
Filip LarssonMoose (WIN)G211998-08-17No194 Lbs6 ft2NoNoNo3Pro & Farm1,100,000$0$0$No1,100,000$1,100,000$Lien
Garret SparksMoose (WIN)G261993-06-28No201 Lbs6 ft3NoNoNo1Pro & Farm750,000$0$0$NoLien / Lien NHL
Hudson FaschingMoose (WIN)RW241995-07-28No204 Lbs6 ft3NoNoNo2Pro & Farm737,500$0$0$No737,500$Lien / Lien NHL
JC LiponMoose (WIN)RW261993-07-10No183 Lbs6 ft0NoNoNo1Pro & Farm700,000$0$0$NoLien / Lien NHL
Joe MorrowMoose (WIN)D261992-12-09No196 Lbs6 ft0NoNoNo1Pro & Farm650,000$0$0$NoLien / Lien NHL
Luke JohnsonMoose (WIN)C251994-09-19No177 Lbs5 ft11NoNoNo2Pro & Farm700,000$0$0$No700,000$Lien / Lien NHL
Mario FerraroMoose (WIN)D211998-09-17No185 Lbs5 ft11NoNoNo3Pro & Farm1,137,500$0$0$No1,137,500$1,137,500$Lien
Nick MoutreyMoose (WIN)LW241995-06-24No222 Lbs6 ft3NoNoNo1Pro & Farm650,000$0$0$NoLien / Lien NHL
Paul CareyMoose (WIN)C311988-09-24No200 Lbs6 ft1NoNoNo2Pro & Farm700,000$0$0$No700,000$Lien / Lien NHL
Reece ScarlettMoose (WIN)D261993-03-31No168 Lbs6 ft1NoNoNo1Pro & Farm700,000$0$0$NoLien
Reece WillcoxMoose (WIN)D251994-03-20No205 Lbs6 ft4NoNoNo1Pro & Farm675,000$0$0$NoLien / Lien NHL
Ross ColtonMoose (WIN)C231996-09-11No209 Lbs6 ft0NoNoNo1Pro & Farm925,000$0$0$NoLien / Lien NHL
Ryan OlsenMoose (WIN)C251994-03-25No187 Lbs6 ft1NoNoNo1Pro & Farm650,000$0$0$NoLien / Lien NHL
Samuel KurkerMoose (WIN)RW251994-04-08No202 Lbs6 ft2NoNoNo1Pro & Farm650,000$0$0$NoLien / Lien NHL
Tom McCollumMoose (WIN)G291989-12-07No220 Lbs6 ft2NoNoNo1Pro & Farm650,000$0$0$NoLien / Lien NHL
William BorgenMoose (WIN)D221996-12-19No198 Lbs6 ft3NoNoNo2Pro & Farm925,000$0$0$No925,000$Lien / Lien NHL
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2125.52196 Lbs6 ft11.38773,810$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Nick MoutreyPaul CareyHudson Fasching30122
2Brendan GaunceBrad MaloneJC Lipon30122
3Luke JohnsonAustin CzarnikCarter Bancks25122
4Paul CareyBrendan GaunceBrad Malone15122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Mario FerraroJoe Morrow30122
2William BorgenReece Willcox30122
3Reece ScarlettRoss Colton25122
4Mario FerraroJoe Morrow15122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Nick MoutreyPaul CareyHudson Fasching60122
2Brendan GaunceBrad MaloneJC Lipon40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Mario FerraroJoe Morrow60122
2William BorgenReece Willcox40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Paul CareyBrad Malone60122
2Austin CzarnikBrendan Gaunce40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Mario FerraroJoe Morrow60122
2William BorgenReece Willcox40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Paul Carey60122Mario FerraroJoe Morrow60122
2Brad Malone40122William BorgenReece Willcox40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Paul CareyBrad Malone60122
2Austin CzarnikBrendan Gaunce40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Mario FerraroJoe Morrow60122
2William BorgenReece Willcox40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Nick MoutreyPaul CareyHudson FaschingMario FerraroJoe Morrow
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Nick MoutreyPaul CareyHudson FaschingMario FerraroJoe Morrow
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Luke Johnson, Ross Colton, Carter BancksLuke Johnson, Ross ColtonCarter Bancks
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Reece Scarlett, William Borgen, Reece WillcoxReece ScarlettWilliam Borgen, Reece Willcox
Tirs de Pénalité
Paul Carey, Brad Malone, Austin Czarnik, Brendan Gaunce, Hudson Fasching
Gardien
#1 : Garret Sparks, #2 : Tom McCollum


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
1Admirals20200000321-180000000000020200000321-1800.000358007248492446126345881618861442100.00%20100.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
2Americans523000002125-41010000018-7422000002017340.4002138590072484922356126345881625673261065240.00%13469.23%0780160148.72%849195243.49%40193043.12%9746851444342591269
3Barracuda20200000515-1020200000515-100000000000000.0005712007248492476126345881615836036100.00%000.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
4Bears211000009721010000037-41100000060620.500918270172484921036126345881688382544125.00%10100.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
5Bruins10000010321100000103210000000000021.00034700724849231612634588162799192150.00%20100.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
6Checkers210000016601000000123-11100000043130.75061016007248492756126345881670218483133.33%30100.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
7Comets2110000015961100000010191010000058-320.500152843007248492153612634588169127868200.00%40100.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
8Condors22000000197121100000011741100000080841.000193655017248492161612634588169629456300.00%20100.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
9Crunch1010000009-91010000009-90000000000000.00000000724849221612634588169026418200.00%220.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
10Eagles21100000411-71100000021110100000210-820.50048120072484925461263458816102301042200.00%4175.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
11Griffins11000000202110000002020000000000021.0002350172484923661263458816259822300.00%20100.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
12Gulls20200000613-710100000410-61010000023-100.000611170072484927161263458816114291047400.00%5180.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
13Heat20200000220-180000000000020200000220-1800.000246007248492596126345881617344437200.00%20100.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
14IceHogs10100000010-1010100000010-100000000000000.00000000724849217612634588168125012100.00%000.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
15Marlies20200000517-121010000057-210100000010-1000.0005813007248492616126345881612431639100.00%330.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
16Monsters10100000010-1010100000010-100000000000000.00000000724849220612634588167419012100.00%000.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
17Penguins2200000017512110000009361100000082641.0001730470072484929961263458816612418632150.00%8275.00%1780160148.72%849195243.49%40193043.12%9746851444342591269
18Phantoms10100000211-90000000000010100000211-900.000246007248492186126345881610834414100.00%2150.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
19Rampage20200000315-121010000027-51010000018-700.0003690072484925061263458816175602341000.00%000.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
20Reign1010000013-2000000000001010000013-200.0001120072484922661263458816318629400.00%3166.67%0780160148.72%849195243.49%40193043.12%9746851444342591269
21Roadrunners10100000111-100000000000010100000111-1000.00012300724849219612634588168124013000.00%000.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
22Rocket11000000523110000005230000000000021.000591400724849246612634588162062205120.00%10100.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
23Senators20200000614-81010000034-110100000310-700.000611170072484926261263458816118282314250.00%10100.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
24Sound Tigers11000000431110000004310000000000021.0004812007248492376126345881628811173133.33%3233.33%0780160148.72%849195243.49%40193043.12%9746851444342591269
25Stars20200000614-81010000029-71010000045-100.000611170072484926761263458816125298345120.00%3166.67%0780160148.72%849195243.49%40193043.12%9746851444342591269
26Thunderbirds1010000045-1000000000001010000045-100.000471100724849230612634588162766182150.00%30100.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
Total50163200011170287-117258150001184134-50258170000086153-67350.35017030747703724849218446126345881627687992061073901617.78%842273.81%2780160148.72%849195243.49%40193043.12%9746851444342591269
27Wild31200000111012020000068-21100000052320.33311203100724849294612634588161173010729333.33%50100.00%0780160148.72%849195243.49%40193043.12%9746851444342591269
28Wolf Pack21100000611-51010000017-61100000054120.50061016007248492696126345881691252850500.00%7357.14%0780160148.72%849195243.49%40193043.12%9746851444342591269
29Wolves11000000413110000004130000000000021.0004812007248492396126345881629106203133.33%3166.67%1780160148.72%849195243.49%40193043.12%9746851444342591269
_Since Last GM Reset50163200011170287-117258150001184134-50258170000086153-67350.35017030747703724849218446126345881627687992061073901617.78%842273.81%2780160148.72%849195243.49%40193043.12%9746851444342591269
_Vs Conference256190000067161-941349000003878-4012210000002983-54120.240671221890272484928046126345881615694437250849510.20%31583.87%1780160148.72%849195243.49%40193043.12%9746851444342591269
_Vs Division1338000005379-266250000034340713000001945-2660.231539715001724849257561263458816773207383061915.26%19384.21%1780160148.72%849195243.49%40193043.12%9746851444342591269

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
5035L117030747718442768799206107303
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
5016320011170287
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
25815001184134
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
25817000086153
Derniers 10 Matchs
WLOTWOTL SOWSOL
540001
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
901617.78%842273.81%2
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
612634588167248492
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
780160148.72%849195243.49%40193043.12%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
9746851444342591269


Derniers Match Joués
Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
JourMatch Équipe Visiteuse Score Équipe Locale Score ST OT SO RI Lien
1 - 2020-10-056Barracuda9Moose2LSommaire du Match
2 - 2020-10-069Moose6Americans3WSommaire du Match
6 - 2020-10-1038Moose6Americans4WSommaire du Match
7 - 2020-10-1148Moose2Admirals9LSommaire du Match
9 - 2020-10-1362Stars9Moose2LSommaire du Match
12 - 2020-10-1679Moose5Comets8LSommaire du Match
13 - 2020-10-1792Condors7Moose11WSommaire du Match
15 - 2020-10-19111IceHogs10Moose0LSommaire du Match
18 - 2020-10-22129Moose2Heat11LSommaire du Match
20 - 2020-10-24143Barracuda6Moose3LSommaire du Match
23 - 2020-10-27162Moose1Admirals12LSommaire du Match
24 - 2020-10-28173Gulls10Moose4LSommaire du Match
27 - 2020-10-31195Wild5Moose4LSommaire du Match
28 - 2020-11-01200Moose2Eagles10LSommaire du Match
32 - 2020-11-05228Bears7Moose3LSommaire du Match
33 - 2020-11-06239Moose1Rampage8LSommaire du Match
36 - 2020-11-09258Rampage7Moose2LSommaire du Match
38 - 2020-11-11272Moose3Senators10LSommaire du Match
40 - 2020-11-13289Americans8Moose1LSommaire du Match
42 - 2020-11-15300Moose0Marlies10LSommaire du Match
44 - 2020-11-17314Moose0Heat9LSommaire du Match
46 - 2020-11-19330Crunch9Moose0LSommaire du Match
48 - 2020-11-21349Moose2Phantoms11LSommaire du Match
49 - 2020-11-22357Moose1Roadrunners11LSommaire du Match
51 - 2020-11-24369Monsters10Moose0LSommaire du Match
54 - 2020-11-27390Bruins2Moose3WXXSommaire du Match
57 - 2020-11-30414Senators4Moose3LSommaire du Match
58 - 2020-12-01429Moose4Checkers3WSommaire du Match
60 - 2020-12-03444Moose1Reign3LSommaire du Match
62 - 2020-12-05456Eagles1Moose2WSommaire du Match
66 - 2020-12-09478Comets1Moose10WSommaire du Match
69 - 2020-12-12500Marlies7Moose5LSommaire du Match
71 - 2020-12-14516Moose5Wild2WSommaire du Match
72 - 2020-12-15529Sound Tigers3Moose4WSommaire du Match
75 - 2020-12-18546Moose4Stars5LSommaire du Match
78 - 2020-12-21566Penguins3Moose9WSommaire du Match
80 - 2020-12-23581Moose4Americans5LSommaire du Match
82 - 2020-12-25591Moose8Condors0WSommaire du Match
83 - 2020-12-26603Rocket2Moose5WSommaire du Match
86 - 2020-12-29626Wild3Moose2LSommaire du Match
88 - 2020-12-31637Moose2Gulls3LSommaire du Match
90 - 2021-01-02653Moose6Bears0WSommaire du Match
91 - 2021-01-03666Griffins0Moose2WSommaire du Match
94 - 2021-01-06685Moose4Thunderbirds5LSommaire du Match
96 - 2021-01-08696Wolf Pack7Moose1LSommaire du Match
98 - 2021-01-10708Moose5Wolf Pack4WSommaire du Match
100 - 2021-01-12727Checkers3Moose2LXXSommaire du Match
102 - 2021-01-14741Moose8Penguins2WSommaire du Match
104 - 2021-01-16756Wolves1Moose4WSommaire du Match
106 - 2021-01-18772Moose4Americans5LSommaire du Match
108 - 2021-01-20781Moose-Eagles-
110 - 2021-01-22792Condors-Moose-
111 - 2021-01-23807Moose-Admirals-
114 - 2021-01-26823Admirals-Moose-
117 - 2021-01-29845Moose-Devils-
119 - 2021-01-31856Moose-Barracuda-
120 - 2021-02-01862IceHogs-Moose-
123 - 2021-02-04884Bears-Moose-
126 - 2021-02-07905Moose-Roadrunners-
128 - 2021-02-09916Eagles-Moose-
130 - 2021-02-11931Moose-Griffins-
132 - 2021-02-13947Devils-Moose-
134 - 2021-02-15959Moose-Rocket-
136 - 2021-02-17977Moose-Monsters-
138 - 2021-02-19985Gulls-Moose-
140 - 2021-02-211004Moose-Crunch-
141 - 2021-02-221015Thunderbirds-Moose-
143 - 2021-02-241032Moose-IceHogs-
145 - 2021-02-261041Moose-Sound Tigers-
147 - 2021-02-281052Phantoms-Moose-
150 - 2021-03-031078Heat-Moose-
152 - 2021-03-051096Moose-Bruins-
154 - 2021-03-071106Barracuda-Moose-
157 - 2021-03-101130Moose-Rampage-
158 - 2021-03-111142Reign-Moose-
161 - 2021-03-141163Reign-Moose-
164 - 2021-03-171180Moose-Comets-
165 - 2021-03-181181Moose-Wolves-
168 - 2021-03-211203Roadrunners-Moose-
174 - 2021-03-271232Stars-Moose-
175 - 2021-03-281241Moose-Wolves-
179 - 2021-04-011260Stars-Moose-



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
16 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
974,023$ 1,625,000$ 447,500$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 974,023$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 76 8,929$ 678,604$




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
Saison Régulière
282432304345307239684126901221165112534117140312414212715863075368431311010485142944947103394054228068784414433908922.82%3185582.70%81606293454.74%1304250152.14%739135254.66%2079145618536071050532
382126001342236411-1754182901111121208-874143100231115203-88242363555913010173571030201061984952443728100271515033695615.18%2869267.83%01269286444.31%1122260443.09%619142643.41%180913022205584945434
482116300062185442-2574182700042102207-105413360002083235-1522218533251711775844102247762735722514238122497316734595712.42%38511270.91%0817237034.47%960317930.20%404134729.99%14039072594639962411
582175701412241388-1474192601311119184-654183100101122204-82342414026432110165734256187884182624307683745519702205123.18%2096767.94%11115259542.97%822222936.88%600139942.89%196314051989545996494
682333501535314318-441161800322158169-114117170121315614976631455787112136103716337110831090117350351296246621832314419.05%2014975.62%31390295347.07%1329304743.62%633139545.38%1937137720195711009499
78287100102197513-3164163300101106250-144412380000191263-172161973245211073646002667864918880124397139239122141702615.29%1816663.54%0987268936.71%943280833.58%536147436.36%166711622295553968445
882126700120257596-3394183200100128283-1554143500020129313-1842425742568210113826033328115210751083244870138026019381603421.25%1064656.60%0993265537.40%952283633.57%590159536.99%163711812359536934421
882126700120257596-3394183200100128283-1554143500020129313-1842425742568210113826033328115210751083244870138026019381603421.25%1064656.60%0993265537.40%952283633.57%590159536.99%163711812359536934421
950163200011170287-117258150001184134-50258170000086153-673517030747703724849218446126345881627687992061073901617.78%842273.81%2780160148.72%849195243.49%40193043.12%9746851444342591269
Total Saison Régulière7061644750718231921643790-162635397221031211911111830-71935367254046121010531960-90733121643663582711108966795595225310851183858247299337399663457015935224940718.10%187655570.42%1499502331642.67%92332399238.48%51121251340.85%151111065919121491883923931
Séries
2514000001120-92020000029-731200000911-2211213200623015452465601595460882328.70%22863.64%08615954.09%7114947.65%397750.65%11077127356230
Total Séries514000001120-92020000029-731200000911-2211213200623015452465601595460882328.70%22863.64%08615954.09%7114947.65%397750.65%11077127356230