Connexion

Moose
GP: 49 | W: 21 | L: 22 | OTL: 6 | P: 48
GF: 157 | GA: 167 | PP%: 19.02% | PK%: 76.35%
DG: Eric Pilon | Morale : 40 | Moyenne d’équipe : 64
Prochains matchs #802 vs Roadrunners
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Centre de jeu
Moose
21-22-6, 48pts
3
FINAL
1 Phantoms
12-32-5, 29pts
Team Stats
W2SéquenceW1
11-11-3Fiche domicile6-14-5
10-11-3Fiche domicile6-18-0
4-4-2Derniers 10 matchs3-6-1
3.20Buts par match 2.49
3.41Buts contre par match 4.02
19.02%Pourcentage en avantage numérique15.94%
76.35%Pourcentage en désavantage numérique73.33%
Wolf Pack
23-26-2, 48pts
0
FINAL
3 Moose
21-22-6, 48pts
Team Stats
L1SéquenceW2
12-11-1Fiche domicile11-11-3
11-15-1Fiche domicile10-11-3
6-4-0Derniers 10 matchs4-4-2
2.98Buts par match 3.20
3.25Buts contre par match 3.41
17.65%Pourcentage en avantage numérique19.02%
85.37%Pourcentage en désavantage numérique76.35%
Moose
21-22-6, 48pts
2023-12-11
Roadrunners
19-24-5, 43pts
Statistiques d’équipe
W2SéquenceL5
11-11-3Fiche domicile11-12-2
10-11-3Fiche visiteur8-12-3
4-4-210 derniers matchs1-8-1
3.20Buts par match 3.19
3.41Buts contre par match 3.19
19.02%Pourcentage en avantage numérique26.95%
76.35%Pourcentage en désavantage numérique79.04%
Moose
21-22-6, 48pts
2023-12-12
Iowa Wild
24-21-4, 52pts
Statistiques d’équipe
W2SéquenceW2
11-11-3Fiche domicile13-10-2
10-11-3Fiche visiteur11-11-2
4-4-210 derniers matchs6-3-1
3.20Buts par match 3.20
3.41Buts contre par match 3.20
19.02%Pourcentage en avantage numérique20.25%
76.35%Pourcentage en désavantage numérique82.98%
BruinsF
17-25-7, 41pts
2023-12-14
Moose
21-22-6, 48pts
Statistiques d’équipe
W1SéquenceW2
11-12-2Fiche domicile11-11-3
6-13-5Fiche visiteur10-11-3
3-5-210 derniers matchs4-4-2
3.02Buts par match 3.20
3.65Buts contre par match 3.20
19.84%Pourcentage en avantage numérique19.02%
76.26%Pourcentage en désavantage numérique76.35%
Meneurs d'équipe
Buts
Pavel Kraskovsky
16
Passes
Andrei Mironov
32
Points
Andrei Mironov
47
Plus/Moins
Andrei Mironov
19
Victoires
Spencer Martin
15
Pourcentage d’arrêts
Dylan Wells
0.914

Statistiques d’équipe
Buts pour
157
3.20 GFG
Tirs pour
1593
32.51 Avg
Pourcentage en avantage numérique
19.0%
31 GF
Début de zone offensive
41.4%
Buts contre
167
3.41 GAA
Tirs contre
1380
28.16 Avg
Pourcentage en désavantage numérique
76.4%%
35 GA
Début de la zone défensive
38.5%
Informations de l'équipe

Directeur généralEric Pilon
EntraîneurRick Tocchet
DivisionDivision Sud-Est
ConférenceConference 1
CapitainePeter Holland
Assistant #1Mike Reilly
Assistant #2Andrei Mironov


Informations de l’aréna

Capacité3,000
Assistance2,886
Billets de saison300


Informations de la formation

Équipe Pro28
Équipe Mineure20
Limite contact 48 / 55
Espoirs40


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
1Yegor ChinakhovX100.0064459275698083735972727070595072426602221,700,000$
2Valentin ZykovX100.0062459071737277696872706866635558526502821,500,000$
3Ryan LombergX98.0072607475687690716869717069595059526502831,500,000$
4Mason Shaw (R)X98.007070827267728471767272726650456056650253900,000$
5Peter Holland (C)X96.0059469470737371717168697266696347556503211,300,000$
6Pavel KraskovskyX100.007566697270737470737369706657526034650271750,000$
7Riley SheahanX100.006045716874707069756765716377725020640321900,000$
8Joakim NygardX100.0063459172676777676669696669655348526403021,200,000$
9Anthony RichardX100.007246707267747271727072666356536046640261750,000$
10Nick SorensenX100.006246837272617069626867666269565643630291800,000$
11Lias Andersson (R)X100.006445857269707070707170656055456052630253750,000$
12Jeremy BraccoX100.006245907268667167696865666256506034630261750,000$
13Nils Lundkvist (R)X98.006745867269768473457270736455457052660233900,000$
14Mike Reilly (A)X100.0066458470727387664570677160785751526603011,500,000$
15Andrei Mironov (A)X99.0067458071727276734569627654655655526502911,200,000$
16Anton LindholmX100.0064449571706977704568657363524559526402921,100,000$
17Brendan GuhleX100.006544867270697164456465735452486052630262750,000$
Rayé
1Phil VaroneX100.0064449070657170676366696766595047196303321,000,000$
2Stefan MatteauX100.0080616768756576647466677062695352196302931,000,000$
3Julius NattinenX100.007045817270727068747066705954496022630261750,000$
4Vladimir TkachyovX100.005945847266667167597067636558515728620281700,000$
5Greg MckeggX100.005539906868687267666762736461565420620311900,000$
6Alexander PetrovicX67.4669468569757081714569657363625250426503111,200,000$
7James GreenwayX100.007149787274686858456060675749486020610252750,000$
8Chad KrysX100.006651887068696959456361695750486020610252750,000$
MOYENNE D’ÉQUIPE98.24664883717070756861696770636052574064
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 SPÂgeContratSalaire
1Spencer Martin100.008486798078788076767584597057396502811,200,000$
2Dylan Wells (R)97.00838580797878797779837848506044640253750,000$
Rayé
1Felix Sandstrom100.00858875797875807575747953646022640262750,000$
MOYENNE D’ÉQUIPE99.0084867879787780767777805361593564
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Rick Tocchet86847061999949CAN585100,000$


Astuces sur les filtres (anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Nom du joueur Nom de l’équipePOSGP G A P +/- PIM PIM5 HIT HTT SHT OSB OSM SHT% SB MP AMG PPG PPA PPP PPS PPM PKG PKA PKP PKS PKM GW GT FO% FOT GA TA EG HT P/20 PSG PSS FW FL FT S1 S2 S3
1Andrei MironovMoose (Win)D49153247193006443100245815.00%57109522.358412581530000111010%000000.8600000165
2Yegor ChinakhovMoose (Win)RW49133245-3140276717552957.43%1393219.032911431500000113245.83%9600000.9717000314
3Pavel KraskovskyMoose (Win)C4616193563758973114278114.04%678117.004610231141011651152.71%92200000.9011010321
4Nils LundkvistMoose (Win)D49529346220435210127484.95%63112723.02358571540110121000%000000.6001000011
5Mason ShawMoose (Win)C49132033-65630669713731879.49%1496519.712792813101131072151.43%129500000.6804105412
6Brendan GuhleMoose (Win)D4942226-1020050244910448.16%3691618.7107720101000031000%000000.5700000020
7Anthony RichardMoose (Win)LW36141125121205523102285913.73%564617.96246201030000123228.95%3800010.7701000120
8Mike ReillyMoose (Win)D4932124-542058325614405.36%5298920.1924629129000029000%000000.4900000002
9Joakim NygardMoose (Win)RW4913102316092382205715.85%361912.6500014000002045.00%4000000.7417000021
10Peter HollandMoose (Win)C4981422675166410628857.55%875715.45022114812341272052.76%95900000.5802100221
11Valentin ZykovMoose (Win)RW4912921-5403639109257411.01%1177215.76459221330000105153.70%5400000.5414000013
12Ryan LombergMoose (Win)LW49108182180653987347811.49%970114.3100053400021020143.48%6900100.5107000220
13Lias AnderssonMoose (Win)LW498816-1460303411527666.96%679716.28314271150000110043.86%5700000.4000000000
14Alexander PetrovicMoose (Win)D4951116-1260744237183813.51%5494119.210006370111107100%000100.3401000121
15Anton LindholmMoose (Win)D493912-11280413629141810.34%4688118.0010155011089000%000000.2701000000
16Vladimir TkachyovMoose (Win)LW26358-712020829103510.34%129711.46000052022140037.50%1600000.5400000010
17Nick SorensenMoose (Win)RW404483201514497418.16%13458.6300013000000045.45%2200000.4600000000
18Jeremy BraccoMoose (Win)LW333583409253911227.69%134510.46022224000000036.36%2200000.4600000100
19Riley SheahanMoose (Win)C24246-36092721689.52%22279.4900003000040056.64%25600000.5300000000
20Julius NattinenMoose (Win)C2724608021403210366.25%633512.41000000000211156.18%35600000.3600000011
21Phil VaroneMoose (Win)LW12011-4407824390%11149.5601100000010014.29%700000.1711000000
22Stefan MatteauMoose (Win)C1000000000000%011.480000000000000%00000000000000
Statistiques d’équipe totales ou en moyenne882156278434-1136440804810159342610799.79%3951459416.553157883581456461013983201051.91%420900210.59537215182622
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
1Spencer MartinMoose (Win)34151230.8803.39173622988140200.765172920100
2Dylan WellsMoose (Win)165520.9142.2782102313610000.714141219200
3Felix SandstromMoose (Win)91510.8444.6241600322050000.6005810000
Statistiques d’équipe totales ou en moyenne59212260.8833.252974241611380020364949300


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 Recrue Poids Taille Non-échange Disponible pour échange Acquis Par Date de la Dernière Transaction Ballotage forcé Waiver Possible Contrat Date du Signature du Contrat Type Salaire actuel Plafond salarial Non Activé Plafond salarial restant Exclus du plafond salarial Salaire annuel 2Salaire annuel 3Salaire annuel 4Salaire annuel 5Salaire annuel 6Salaire annuel 7Salaire annuel 8Salaire annuel 9Salaire annuel 10Non-échange Année 2Non-échange Année 3Non-échange Année 4Non-échange Année 5Non-échange Année 6Non-échange Année 7Non-échange Année 8Non-échange Année 9Non-échange Année 10Lien
Alexander Petrovic (sur la masse salariale)Moose (Win)D311992-03-03No210 Lbs6 ft4NoNoN/ANoNo1Pro & Farm1,200,000$0$0$Yes------------------Lien
Andrei MironovMoose (Win)D291994-07-29No194 Lbs6 ft3NoNoN/ANoNo1Pro & Farm1,200,000$0$0$No------------------Lien
Anthony RichardMoose (Win)LW261996-12-20No176 Lbs5 ft10NoNoN/ANoNo1Pro & Farm750,000$0$0$No------------------Lien
Anton LindholmMoose (Win)D291994-11-29No191 Lbs5 ft11NoNoN/ANoNo2Pro & Farm1,100,000$0$0$No1,100,000$--------No--------Lien
Brendan GuhleMoose (Win)D261997-07-29No197 Lbs6 ft2NoNoN/ANoNo2Pro & Farm750,000$0$0$No750,000$--------No--------Lien
Chad KrysMoose (Win)D251998-04-10No185 Lbs6 ft0NoNoN/ANoNo2Pro & Farm750,000$0$0$No750,000$--------No--------Lien
Dylan WellsMoose (Win)G251998-01-03Yes190 Lbs6 ft2NoNoN/ANoNo3Pro & Farm750,000$0$0$No750,000$750,000$-------NoNo-------Lien
Felix SandstromMoose (Win)G261997-01-12No191 Lbs6 ft2NoNoN/ANoNo2Pro & Farm750,000$0$0$No750,000$--------No--------Lien
Greg MckeggMoose (Win)RW311992-06-12No191 Lbs6 ft0NoNoN/ANoNo1Pro & Farm900,000$0$0$No------------------Lien
James GreenwayMoose (Win)D251998-04-27No211 Lbs6 ft5NoNoN/ANoNo2Pro & Farm750,000$0$0$No750,000$--------No--------Lien
Jeremy BraccoMoose (Win)LW261997-03-17No185 Lbs5 ft11NoNoN/ANoNo1Pro & Farm750,000$0$0$No------------------Lien
Joakim NygardMoose (Win)RW301993-01-08No179 Lbs6 ft1NoNoN/ANoNo2Pro & Farm1,200,000$0$0$No1,200,000$--------No--------Lien
Julius NattinenMoose (Win)C261997-01-14No192 Lbs6 ft2NoNoN/ANoNo1Pro & Farm750,000$0$0$No------------------Lien
Lias AnderssonMoose (Win)LW251998-10-13Yes185 Lbs6 ft1NoNoN/ANoNo3Pro & Farm750,000$0$0$No750,000$750,000$-------NoNo-------Lien
Mason ShawMoose (Win)C251998-11-03Yes184 Lbs5 ft10NoNoN/ANoNo3Pro & Farm900,000$0$0$No900,000$900,000$-------NoNo-------Lien
Mike ReillyMoose (Win)D301993-07-13No195 Lbs6 ft2NoNoN/ANoNo1Pro & Farm1,500,000$0$0$No------------------Lien
Nick SorensenMoose (Win)RW291994-10-23No182 Lbs6 ft1NoNoN/ANoNo1Pro & Farm800,000$0$0$No------------------Lien
Nils LundkvistMoose (Win)D232000-07-27Yes190 Lbs5 ft11NoNoN/ANoNo3Pro & Farm900,000$0$0$No900,000$900,000$-------NoNo-------Lien
Pavel KraskovskyMoose (Win)C271996-09-11No185 Lbs6 ft4NoNoN/ANoNo1Pro & Farm750,000$0$0$No------------------Lien
Peter HollandMoose (Win)C321991-01-14No201 Lbs6 ft2NoNoN/ANoNo1Pro & Farm1,300,000$0$0$No------------------Lien
Phil VaroneMoose (Win)LW331990-12-04No169 Lbs5 ft8NoNoN/ANoNo2Pro & Farm1,000,000$0$0$No1,000,000$--------No--------Lien
Riley SheahanMoose (Win)C321991-12-07No214 Lbs6 ft2NoNoN/ANoNo1Pro & Farm900,000$0$0$No------------------Lien
Ryan LombergMoose (Win)LW281994-12-09No187 Lbs5 ft9NoNoN/ANoNo3Pro & Farm1,500,000$0$0$No1,500,000$1,500,000$-------NoNo-------Lien
Spencer MartinMoose (Win)G281995-06-08No205 Lbs6 ft3NoNoN/ANoNo1Pro & Farm1,200,000$0$0$No------------------Lien
Stefan MatteauMoose (Win)C291994-02-23No220 Lbs6 ft2NoNoN/ANoNo3Pro & Farm1,000,000$0$0$No1,000,000$1,000,000$-------NoNo-------Lien
Valentin ZykovMoose (Win)RW281995-05-15No220 Lbs6 ft0NoNoN/ANoNo2Pro & Farm1,500,000$0$0$No1,500,000$--------No--------Lien
Vladimir TkachyovMoose (Win)LW281995-10-05No141 Lbs5 ft9NoNoN/ANoNo1Pro & Farm700,000$0$0$No------------------Lien
Yegor ChinakhovMoose (Win)RW222001-02-01No178 Lbs6 ft0NoNoN/ANoNo2Pro & Farm1,700,000$0$0$No1,700,000$--------No--------Lien
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2827.64191 Lbs6 ft11.751,000,000$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Anthony RichardMason ShawYegor Chinakhov30122
2Ryan LombergPeter HollandValentin Zykov30122
3Lias AnderssonPavel KraskovskyJoakim Nygard25122
4Jeremy BraccoRiley SheahanNick Sorensen15122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Nils LundkvistMike Reilly35041
2Brendan GuhleAndrei Mironov35041
3Anton Lindholm30041
4Nils LundkvistMike Reilly0122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Ryan LombergMason ShawYegor Chinakhov50122
2Anthony RichardPeter HollandValentin Zykov50122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Nils LundkvistMike Reilly50122
2Andrei Mironov50122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Mason ShawRyan Lomberg50122
2Peter HollandAnthony Richard50122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Nils LundkvistMike Reilly50122
2Andrei Mironov50122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Mason Shaw50122Nils LundkvistMike Reilly50122
2Peter Holland50122Andrei Mironov50122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Mason ShawRyan Lomberg50122
2Peter HollandAnthony Richard50122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Nils LundkvistMike Reilly50122
2Andrei Mironov50122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Ryan LombergMason ShawYegor ChinakhovNils LundkvistMike Reilly
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Ryan LombergMason ShawYegor ChinakhovNils LundkvistMike Reilly
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Peter Holland, Pavel Kraskovsky, Joakim NygardPeter Holland, Pavel KraskovskyPeter Holland
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Andrei Mironov, Anton Lindholm, Brendan GuhleAndrei MironovAndrei Mironov, Anton Lindholm
Tirs de pénalité
Yegor Chinakhov, Valentin Zykov, Ryan Lomberg, Mason Shaw, Peter Holland
Gardien
#1 : Dylan Wells, #2 : Spencer Martin


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
TotalDomicileVisiteur
# 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
1Admirals11000000101000000000001100000010121.00012301545248725509503551672610413200.00%20100.00%0902174151.81%834162251.42%44984653.07%12098341120363650329
2Americans11000000422110000004220000000000021.00048120054524873550950355167149216700.00%10100.00%1902174151.81%834162251.42%44984653.07%12098341120363650329
3Bears20200000510-520200000510-50000000000000.000591400545248753509503551675598376116.67%4175.00%0902174151.81%834162251.42%44984653.07%12098341120363650329
4BruinsF22000000826000000000002200000082641.000815230054524876450950355167431016306116.67%8187.50%0902174151.81%834162251.42%44984653.07%12098341120363650329
5Canucks1010000024-21010000024-20000000000000.000246005452487325095035516722116106116.67%30100.00%0902174151.81%834162251.42%44984653.07%12098341120363650329
6Checkers30300000514-91010000025-32020000039-600.0005101500545248795509503551678920127010110.00%6350.00%0902174151.81%834162251.42%44984653.07%12098341120363650329
7Comets2020000046-2000000000002020000046-200.00046100054524877050950355167392121299111.11%8275.00%0902174151.81%834162251.42%44984653.07%12098341120363650329
8Condors1010000015-4000000000001010000015-400.00012300545248728509503551672210411400.00%20100.00%0902174151.81%834162251.42%44984653.07%12098341120363650329
9Crunch22000000862220000008620000000000041.000813210054524875350950355167561931326233.33%12466.67%1902174151.81%834162251.42%44984653.07%12098341120363650329
10Eagles1010000056-11010000056-10000000000000.00058130054524873350950355167339419200.00%2150.00%0902174151.81%834162251.42%44984653.07%12098341120363650329
11Firebirds42100001151322110000054121000001109150.625152641005452487141509503551671413851849222.22%17382.35%0902174151.81%834162251.42%44984653.07%12098341120363650329
12Griffins11000000642110000006420000000000021.00061117005452487395095035516720761210440.00%2150.00%0902174151.81%834162251.42%44984653.07%12098341120363650329
13Gulls11000000734110000007340000000000021.0007132000545248732509503551672558173133.33%3233.33%1902174151.81%834162251.42%44984653.07%12098341120363650329
14Icehogs10001000431000000000001000100043121.000461000545248741509503551673728133133.33%40100.00%0902174151.81%834162251.42%44984653.07%12098341120363650329
15Islanders20000011101001000000145-11000001065130.75010172700545248775509503551676312173733100.00%6266.67%0902174151.81%834162251.42%44984653.07%12098341120363650329
16Marlies311000011115-420100001410-61100000075230.500111930005452487113509503551679419214812216.67%8275.00%0902174151.81%834162251.42%44984653.07%12098341120363650329
17PenguinsF3210000012102220000009631010000034-140.667122133005452487985095035516710537344110220.00%14564.29%0902174151.81%834162251.42%44984653.07%12098341120363650329
18Phantoms22000000615110000003031100000031241.000611170154524875750950355167571312293133.33%60100.00%0902174151.81%834162251.42%44984653.07%12098341120363650329
19Reign1000000156-1000000000001000000156-110.500581300545248731509503551673712619400.00%30100.00%0902174151.81%834162251.42%44984653.07%12098341120363650329
20Rocket3030000059-41010000024-22020000035-200.000591400545248787509503551677225174610220.00%5180.00%0902174151.81%834162251.42%44984653.07%12098341120363650329
21SenatorsF2010100045-11010000002-21000100043120.5004812005452487575095035516755191122500.00%3166.67%0902174151.81%834162251.42%44984653.07%12098341120363650329
22StarsF1010000056-1000000000001010000056-100.00059140054524873650950355167381013144250.00%20100.00%0902174151.81%834162251.42%44984653.07%12098341120363650329
23Thunderbirds1010000035-2000000000001010000035-200.0003580054524873050950355167281222611100.00%10100.00%0902174151.81%834162251.42%44984653.07%12098341120363650329
24Wolf Pack330000001037220000005051100000053261.000101727025452487895095035516770252451800.00%120100.00%1902174151.81%834162251.42%44984653.07%12098341120363650329
25Wolves30100002611-52010000148-41000000123-120.3336111700545248710350950355167871718489111.11%9544.44%0902174151.81%834162251.42%44984653.07%12098341120363650329
26Wranglers2020000058-31010000024-21010000034-100.0005101500545248776509503551675214103011218.18%5180.00%0902174151.81%834162251.42%44984653.07%12098341120363650329
Total49182202016157167-10251111000037783-624711020138084-4480.49015727843504545248715935095035516713803953668041633119.02%1483576.35%4902174151.81%834162251.42%44984653.07%12098341120363650329
_Since Last GM Reset49182202016157167-10251111000037783-624711020138084-4480.49015727843504545248715935095035516713803953668041633119.02%1483576.35%4902174151.81%834162251.42%44984653.07%12098341120363650329
_Vs Conference37151501015113117-42098000035562-717670101258553390.52711320031303545248711905095035516710402932956201131916.81%1193074.79%3902174151.81%834162251.42%44984653.07%12098341120363650329

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
4948W21572784351593138039536680404
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
4918222016157167
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
25111100037783
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
2471120138084
Derniers 10 matchs
WLOTWOTL SOWSOL
440002
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
1633119.02%1483576.35%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
509503551675452487
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
902174151.81%834162251.42%44984653.07%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
12098341120363650329


Derniers matchs 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
2 - 2023-08-269PenguinsF3Moose5BWSommaire du match
5 - 2023-08-2933Moose6Islanders5AWXXSommaire du match
6 - 2023-08-3046Islanders5Moose4BLXXSommaire du match
9 - 2023-09-0268Firebirds1Moose4BWSommaire du match
11 - 2023-09-0482Moose3Firebirds4ALXXSommaire du match
12 - 2023-09-0593Moose2Rocket3ALSommaire du match
15 - 2023-09-08109Wolves4Moose1BLSommaire du match
17 - 2023-09-10130Moose2Checkers5ALSommaire du match
19 - 2023-09-12141Crunch4Moose5BWSommaire du match
21 - 2023-09-14161Checkers5Moose2BLSommaire du match
23 - 2023-09-16172Moose7Marlies5AWSommaire du match
25 - 2023-09-18187Moose5Wolf Pack3AWSommaire du match
27 - 2023-09-20206Rocket4Moose2BLSommaire du match
31 - 2023-09-24230Marlies6Moose1BLSommaire du match
33 - 2023-09-26242Moose4BruinsF1AWSommaire du match
35 - 2023-09-28258Bears4Moose2BLSommaire du match
37 - 2023-09-30273Moose4Icehogs3AWXSommaire du match
39 - 2023-10-02287Moose3PenguinsF4ALSommaire du match
40 - 2023-10-03294Moose3Wranglers4ALSommaire du match
42 - 2023-10-05307Gulls3Moose7BWSommaire du match
44 - 2023-10-07322Moose7Firebirds5AWSommaire du match
46 - 2023-10-09339Americans2Moose4BWSommaire du match
48 - 2023-10-11358Moose1Condors5ALSommaire du match
50 - 2023-10-13373Bears6Moose3BLSommaire du match
52 - 2023-10-15386Moose3Thunderbirds5ALSommaire du match
54 - 2023-10-17401PenguinsF3Moose4BWSommaire du match
57 - 2023-10-20421Moose4BruinsF1AWSommaire du match
59 - 2023-10-22438Marlies4Moose3BLXXSommaire du match
62 - 2023-10-25459SenatorsF2Moose0BLSommaire du match
67 - 2023-10-30489Moose1Checkers4ALSommaire du match
68 - 2023-10-31498Phantoms0Moose3BWSommaire du match
71 - 2023-11-03519Eagles6Moose5BLSommaire du match
73 - 2023-11-05528Moose1Comets2ALSommaire du match
76 - 2023-11-08550Crunch2Moose3BWSommaire du match
77 - 2023-11-09563Moose2Wolves3ALXXSommaire du match
80 - 2023-11-12581Moose5StarsF6ALSommaire du match
81 - 2023-11-13591Griffins4Moose6BWSommaire du match
84 - 2023-11-16615Moose4SenatorsF3AWXSommaire du match
85 - 2023-11-17623Firebirds3Moose1BLSommaire du match
88 - 2023-11-20645Moose3Comets4ALSommaire du match
90 - 2023-11-22656Wolf Pack0Moose2BWSommaire du match
92 - 2023-11-24670Moose1Admirals0AWSommaire du match
94 - 2023-11-26686Wranglers4Moose2BLSommaire du match
96 - 2023-11-28706Moose5Reign6ALXXSommaire du match
97 - 2023-11-29718Wolves4Moose3BLXXSommaire du match
101 - 2023-12-03743Canucks4Moose2BLSommaire du match
103 - 2023-12-05760Moose1Rocket2ALSommaire du match
105 - 2023-12-07775Moose3Phantoms1AWSommaire du match
106 - 2023-12-08782Wolf Pack0Moose3BWSommaire du match
109 - 2023-12-11802Moose-Roadrunners-
110 - 2023-12-12811Moose-Iowa Wild-
112 - 2023-12-14822BruinsF-Moose-
114 - 2023-12-16845Moose-Iowa Wild-
116 - 2023-12-18854Islanders-Moose-
119 - 2023-12-21871Moose-SenatorsF-
120 - 2023-12-22884Wranglers-Moose-
123 - 2023-12-25908Monsters-Moose-
125 - 2023-12-27924Moose-PenguinsF-
127 - 2023-12-29937BruinsF-Moose-
129 - 2023-12-31949Moose-Wolves-
132 - 2024-01-03970Americans-Moose-
134 - 2024-01-05982Moose-Americans-
136 - 2024-01-071003Comets-Moose-
138 - 2024-01-091018Moose-Canucks-
141 - 2024-01-121034Moose-Marlies-
142 - 2024-01-131044Rocket-Moose-
145 - 2024-01-161068Barracuda-Moose-
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
148 - 2024-01-191085Moose-Islanders-
150 - 2024-01-211099Moose-Wolf Pack-
152 - 2024-01-231108Moose-Americans-
153 - 2024-01-241117Phantoms-Moose-
156 - 2024-01-271141SenatorsF-Moose-
159 - 2024-01-301159Moose-Phantoms-
160 - 2024-01-311170Silver Knights-Moose-
163 - 2024-02-031195Checkers-Moose-
164 - 2024-02-041200Moose-Bears-
168 - 2024-02-081227Barracuda-Moose-
169 - 2024-02-091234Moose-Bears-
173 - 2024-02-131259Comets-Moose-
177 - 2024-02-171277Moose-Crunch-
179 - 2024-02-191293StarsF-Moose-
180 - 2024-02-201300Moose-Crunch-



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets3015
Assistance48,33223,808
Assistance PCT96.66%95.23%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
16 2886 - 96.19% 104,811$2,620,268$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursSalaire total moyen des joueursSalaire des entraineurs
1,662,395$ 2,680,000$ 2,680,000$ 0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
0$ 1,604,182$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
1,676,972$ 76 15,275$ 1,160,900$




Moose Leaders statistiques des joueurs (saison régulière)

# Nom du joueur GP G A P +/- PIM HIT HTT SHT SHT% SB MP AMG PPG PPA PPP PPS PKG PKA PKP PKS GW GT FO% HT P/20 PSG PSS

Moose Leaders des statistiques des gardiens (saison régulière)

# Nom du gardien GP W L OTL PCT GAA MP PIM SO GA SA SAR A EG PS % PSA

Moose Statistiques de l'Équipe de Carrière

TotalDomicileVisiteur
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

Moose Leaders statistiques des joueurs (séries éliminatoires)

# Nom du joueur GP G A P +/- PIM HIT HTT SHT SHT% SB MP AMG PPG PPA PPP PPS PKG PKA PKP PKS GW GT FO% HT P/20 PSG PSS

Moose Leaders des statistiques des gardiens (séries éliminatoires)

# Nom du gardien GP W L OTL PCT GAA MP PIM SO GA SA SAR A EG PS % PSA