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Connexion

Sags
GP: 33 | W: 20 | L: 9 | OTL: 4 | P: 44
GF: 146 | GA: 120 | PP%: 28.21% | PK%: 63.89%
DG: Nick Gagnon | Morale : 50 | Moyenne d’équipe : 56
Prochains matchs #527 vs Heat

Centre de jeu
Sags
20-9-4, 44pts
5
FINAL
6 Marlies
22-8-1, 45pts
Team Stats
W1SéquenceW6
12-3-2Fiche domicile15-3-1
8-6-2Fiche domicile7-5-0
5-4-1Derniers 10 matchs7-3-0
4.42Buts par match 5.42
3.64Buts contre par match 4.06
28.21%Pourcentage en avantage numérique37.00%
63.89%Pourcentage en désavantage numérique67.53%
Sags
20-9-4, 44pts
9
FINAL
4 Manchots
18-10-2, 38pts
Team Stats
W1SéquenceL1
12-3-2Fiche domicile9-6-0
8-6-2Fiche domicile9-4-2
5-4-1Derniers 10 matchs3-6-1
4.42Buts par match 4.27
3.64Buts contre par match 3.63
28.21%Pourcentage en avantage numérique26.53%
63.89%Pourcentage en désavantage numérique67.65%
Heat
15-16-2, 32pts
2025-12-16
Sags
20-9-4, 44pts
Statistiques d’équipe
W3SéquenceW1
7-6-1Fiche domicile12-3-2
8-10-1Fiche visiteur8-6-2
5-4-110 derniers matchs5-4-1
5.45Buts par match 4.42
5.52Buts contre par match 4.42
49.38%Pourcentage en avantage numérique28.21%
65.31%Pourcentage en désavantage numérique63.89%
Stars
24-6-3, 51pts
2025-12-18
Sags
20-9-4, 44pts
Statistiques d’équipe
W1SéquenceW1
12-3-1Fiche domicile12-3-2
12-3-2Fiche visiteur8-6-2
9-0-110 derniers matchs5-4-1
5.09Buts par match 4.42
3.24Buts contre par match 4.42
68.09%Pourcentage en avantage numérique28.21%
75.29%Pourcentage en désavantage numérique63.89%
Firebirds
15-10-4, 34pts
2025-12-20
Sags
20-9-4, 44pts
Statistiques d’équipe
W2SéquenceW1
4-8-3Fiche domicile12-3-2
11-2-1Fiche visiteur8-6-2
6-4-010 derniers matchs5-4-1
4.03Buts par match 4.42
3.52Buts contre par match 4.42
37.08%Pourcentage en avantage numérique28.21%
69.44%Pourcentage en désavantage numérique63.89%
Meneurs d'équipe
Buts
Matthew Phillips
29
Passes
Matthew Phillips
30
Points
Matthew Phillips
59
Plus/Moins
Matthew Phillips
20
Victoires
Aleksei Kolosov
19
Pourcentage d’arrêts
Aleksei Kolosov
0.875

Statistiques d’équipe
Buts pour
146
4.42 GFG
Tirs pour
811
24.58 Avg
Pourcentage en avantage numérique
28.2%
22 GF
Début de zone offensive
32.2%
Buts contre
120
3.64 GAA
Tirs contre
857
25.97 Avg
Pourcentage en désavantage numérique
63.9%%
26 GA
Début de la zone défensive
35.7%
Informations de l'équipe

Directeur généralNick Gagnon
DivisionAtlantique
ConférenceEst
Capitaine
Assistant #1
Assistant #2


Informations de l’aréna

Capacité3,000
Assistance1,939
Billets de saison300


Informations de la formation

Équipe Pro25
Équipe Mineure21
Limite contact 46 / 50
Espoirs12


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
1Matthew PhillipsX100.00574077755672696744656167686452050640262660,000$
2Ben Meyers (R)XX100.00624175686973676662636261656050050630251912,500$
3Rory Kerins (R)X100.00614173706364626462626260655150050610221846,667$
4Zayde Wisdom (R)X100.00634368646464626057575558605150050580221797,500$
5Ty Tullio (R)X100.00594367656263626042575355595150050570221833,333$
6Michal Teply (R)X100.00554273636560595942565252595150050560232620,000$
7Philippe Daoust (R)X100.00564166586059575654565253555150050550223600,000$
8Carter Savoie (R)X100.00564170656258565640525156585050050550221925,000$
9James Stefan (R)X100.00554068625961595740535453575050050550213870,000$
10Cameron Berg (R)X100.00404040404040404040404040404040050410223620,000$
11Oliver KylingtonX100.0057427674738376684064636768847305068N0273635,000$
12Jack Thompson (R)X100.00574078747476666940656469695550050660221828,333$
13Jack Matier (R)X100.00654170636760575640535264575050050590212801,667$
14Jack Peart (R)X100.00604264626261605840545461585050050580213925,000$
15Anttoni Honka (R)X100.00504068676052535638534863585050050570241700,000$
16Calle Odelius (R)X100.00554167616161605940575453585050050570203815,000$
17Dyllan Gill (R)X100.00594067626159575740545559585050050570203870,000$
Rayé
1Tucker Robertson (R)X100.00604171636361585741525354585050050560212870,000$
2Eetu Liukas (R)X100.00604366596663615942525351575050050550222867,500$
3Jett Woo (R)X100.00655656676565646440615568625250050620241700,000$
4Gannon Laroque (R)X100.00574057575852515240515254535050050540212836,667$
5Oscar Plandowski (R)X100.00373737373737373737373737373737050380213620,000$
MOYENNE D’ÉQUIPE100.0057426663616159584455545758525005057
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
1Aleksei Kolosov (R)100.0072676672706864646969655250050620222925,000$
2Chase Clark (R)100.0037373737373737373737373737050350213620,000$
Rayé
1Dryden McKay100.0059555356555252505255535550050500261560,000$
MOYENNE D’ÉQUIPE100.005653525554525150535452484605049
Nom de l’entraîneur 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
1Matthew PhillipsSags (San)RW31293059201004359134527521.64%1973323.65761315540001543351.28%1562415031.6123000844
2Rory KerinsSags (San)C312130511180405689347323.60%664920.973694490002311155.93%4473227011.5722000352
3Ben MeyersSags (San)C/LW181612281280272164223725.00%840122.2824611360001314060.00%410912021.4023000310
4Oliver KylingtonSags (San)D31423279181029487221325.56%4281226.22235768000260110%02842000.6600011120
5Michal TeplySags (San)LW318172519155423231172225.81%863720.572357540000181247.62%42213000.7800001105
6Zayde WisdomSags (San)RW3111142563210593465232416.92%1359319.151347460001211050.00%3269000.8401011133
7Jack ThompsonSags (San)D312212368019406729372.99%3075124.26088967000058000%02039000.6100000101
8Ty TullioSags (San)RW3112820-11810302565132918.46%1348015.5200015000180161.11%181310000.8301002122
9Carter SavoieSags (San)LW3197168155343041112621.95%1356518.25224546000080242.86%2178000.5700100210
10Philippe DaoustSags (San)C318614340313134162023.53%647715.4200005000062153.75%160109100.5900000112
11Tucker RobertsonSags (San)C1346102551915149428.57%423017.75101117000002146.81%14157000.8700001010
12Jack PeartSags (San)D3119101319517342413154.17%1759119.08101137000130000%1223000.3400100000
13Cameron BergSags (San)C3127942810303391522.22%933710.88000000000160050.56%8926000.5300001000
14Jack MatierSags (San)D3117811952032218144.76%2259519.22000138000034100%0221000.2700001002
15James StefanSags (San)RW18538200241728102017.86%428115.6400001000000155.56%9611000.5700000021
16Eetu LiukasSags (San)LW6224-5005463833.33%18314.000000000000000%214000.9500000100
17Anttoni HonkaSags (San)D31123104081740325.00%1341913.530000100008100%1116000.1400000001
18Calle OdeliusSags (San)D3103313135982420%540413.050000000003000%0310000.1500001000
19Dyllan GillSags (San)D31011-120873110%72307.4200002000040033.33%318000.0900000000
20Oscar PlandowskiSags (San)D13000-500000000%0896.910000000000000%00200000000000
Statistiques d’équipe totales ou en moyenne5331362083441372167049454377328744717.59%240936917.58213556695340009397171354.50%1532174292160.73610229232223
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
1Aleksei KolosovSags (San)3119730.8753.2217690095761384010.60010310000
2Chase ClarkSags (San)40110.7185.69116001139261000031000
Statistiques d’équipe totales ou en moyenne3519840.8683.3718860010680041011103131000


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 Pays 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 Forcer UFA Rappel d'urgence Type Salaire actuel Plafond salarial Plafond salarial restant Exclus du plafond salarial Salaire année 2Salaire année 3Salaire année 4Salaire année 5Salaire année 6Salaire année 7Salaire année 8Salaire année 9Salaire année 10Plafond salarial année 2Plafond salarial année 3Plafond salarial année 4Plafond salarial année 5Plafond salarial année 6Plafond salarial année 7Plafond salarial année 8Plafond salarial année 9Plafond salarial année 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
Aleksei KolosovSags (San)G222002-01-04BLRYes185 Lbs6 ft1NoNoTrade2025-07-16NoNo22024-06-25FalseFalsePro & Farm925,000$0$0$No925,000$--------925,000$--------No--------Lien
Anttoni HonkaSags (San)D242000-10-05FINYes179 Lbs5 ft10NoNoN/ANoYes1FalseFalsePro & Farm700,000$0$0$No---------------------------Lien
Ben MeyersSags (San)C/LW251998-11-15USAYes194 Lbs5 ft11NoNoTrade2024-01-20YesYes1FalseFalsePro & Farm912,500$0$0$No---------------------------Lien
Calle OdeliusSags (San)D202004-05-30SWEYes183 Lbs6 ft0NoNoProspectNoNo32025-07-10FalseFalsePro & Farm815,000$0$0$No815,000$815,000$-------815,000$815,000$-------NoNo-------Lien
Cameron BergSags (San)C222002-01-29USAYes205 Lbs6 ft0NoNoProspectNoNo32025-07-10FalseFalsePro & Farm620,000$0$0$No620,000$620,000$-------620,000$620,000$-------NoNo-------Lien
Carter SavoieSags (San)LW222002-01-23CANYes185 Lbs5 ft10NoNoN/ANoNo1FalseFalsePro & Farm925,000$0$0$No---------------------------Lien
Chase ClarkSags (San)G212003-05-18USAYes200 Lbs6 ft6NoNoProspectNoNo32025-07-10FalseFalsePro & Farm620,000$0$0$No620,000$620,000$-------620,000$620,000$-------NoNo-------Lien
Dryden McKaySags (San)G261997-11-25USANo183 Lbs6 ft0NoNoN/AYesYes1FalseFalsePro & Farm560,000$0$0$No---------------------------Lien
Dyllan GillSags (San)D202004-06-07CANYes179 Lbs6 ft2NoNoDraftNoNo32025-07-10FalseFalsePro & Farm870,000$0$0$No870,000$870,000$-------870,000$870,000$-------NoNo-------Lien
Eetu LiukasSags (San)LW222002-09-25FINYes198 Lbs6 ft2NoNoTrade2025-11-19NoNo22024-06-25FalseFalsePro & Farm867,500$0$0$No867,500$--------867,500$--------No--------Lien
Gannon LaroqueSags (San)D212003-08-20CANYes200 Lbs6 ft2NoNoTrade2025-11-19NoNo22024-06-25FalseFalsePro & Farm836,667$0$0$No836,667$--------836,667$--------No--------Lien
Jack MatierSags (San)D212003-04-08CANYes196 Lbs6 ft5NoNoProspectNoNo22024-06-25FalseFalsePro & Farm801,667$0$0$No801,667$--------801,667$--------No--------Lien
Jack PeartSags (San)D212003-05-15USAYes194 Lbs6 ft0NoNoProspectNoNo32025-07-10FalseFalsePro & Farm925,000$0$0$No925,000$925,000$-------925,000$925,000$-------NoNo-------Lien
Jack ThompsonSags (San)D222002-03-19CANYes189 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm828,333$0$0$No---------------------------Lien
James StefanSags (San)RW212003-08-09USAYes174 Lbs6 ft0NoNoDraftNoNo32025-07-10FalseFalsePro & Farm870,000$0$0$No870,000$870,000$-------870,000$870,000$-------NoNo-------Lien
Jett WooSags (San)D242000-07-27CANYes205 Lbs6 ft0NoNoFree AgentNoYes12024-09-28FalseFalsePro & Farm700,000$0$0$No---------------------------Lien
Matthew Phillips (contrat à 1 volet)Sags (San)RW261998-04-06CANNo161 Lbs5 ft8NoNoFree AgentYesYes22025-08-28FalseFalsePro & Farm660,000$0$0$No660,000$--------660,000$--------No--------Lien
Michal TeplySags (San)LW232001-05-27CZEYes187 Lbs6 ft3NoNoFree AgentNoNo22024-09-11FalseFalsePro & Farm620,000$0$0$No620,000$--------620,000$--------No--------Lien
Oliver Kylington (contrat à 1 volet)Sags (San)D271997-05-19SWENo183 Lbs6 ft0YesNoFree AgentYesYes32025-09-28FalseFalsePro & Farm635,000$0$0$No635,000$635,000$-------635,000$635,000$-------NoNo-------Lien
Oscar PlandowskiSags (San)D212003-05-18CANYes200 Lbs6 ft0NoNoProspectNoNo32025-07-10FalseFalsePro & Farm620,000$0$0$No620,000$620,000$-------620,000$620,000$-------NoNo-------Lien
Philippe DaoustSags (San)C222001-11-05CANYes194 Lbs6 ft1NoNoTrade2024-01-20NoNo3FalseFalsePro & Farm600,000$0$0$No600,000$600,000$-------600,000$600,000$-------NoNo-------Lien
Rory KerinsSags (San)C222002-04-12CANYes185 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm846,667$0$0$No---------------------------Lien
Tucker RobertsonSags (San)C212003-06-22CANYes185 Lbs5 ft11NoNoProspectNoNo22024-06-25FalseFalsePro & Farm870,000$0$0$No870,000$--------870,000$--------No--------Lien
Ty TullioSags (San)RW222002-04-05CANYes185 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm833,333$0$0$No---------------------------Lien
Zayde WisdomSags (San)RW222002-05-20CANYes194 Lbs5 ft11NoNoN/ANoNo1FalseFalsePro & Farm797,500$0$0$No---------------------------Lien
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2522.40189 Lbs6 ft02.00770,367$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Michal TeplyBen MeyersMatthew Phillips40122
2Carter SavoieRory KerinsZayde Wisdom30122
3James StefanPhilippe DaoustTy Tullio20122
4Matthew PhillipsCameron BergJames Stefan10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Oliver KylingtonJack Thompson40122
2Jack MatierJack Peart30122
3Calle OdeliusAnttoni Honka20122
4Dyllan GillOliver Kylington10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Michal TeplyBen MeyersMatthew Phillips60122
2Carter SavoieRory KerinsZayde Wisdom40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Oliver KylingtonJack Thompson60122
2Jack MatierJack Peart40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Matthew PhillipsBen Meyers60122
2Rory KerinsZayde Wisdom40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Oliver KylingtonJack Thompson60122
2Jack MatierJack Peart40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Matthew Phillips60122Oliver KylingtonJack Thompson60122
2Ben Meyers40122Jack MatierJack Peart40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Matthew PhillipsBen Meyers60122
2Rory KerinsZayde Wisdom40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Oliver KylingtonJack Thompson60122
2Jack MatierJack Peart40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Michal TeplyBen MeyersMatthew PhillipsOliver KylingtonJack Thompson
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Michal TeplyBen MeyersMatthew PhillipsOliver KylingtonJack Thompson
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Ty Tullio, Philippe Daoust, Cameron BergTy Tullio, Philippe DaoustCameron Berg
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Calle Odelius, Anttoni Honka, Dyllan GillCalle OdeliusAnttoni Honka, Dyllan Gill
Tirs de pénalité
Matthew Phillips, Ben Meyers, Rory Kerins, Zayde Wisdom, Ty Tullio
Gardien
#1 : Aleksei Kolosov, #2 : Chase Clark


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
1Admirals1010000048-41010000048-40000000000000.00048120023595810241593432863336106010000%5420.00%028152653.42%31858354.55%28752654.56%614288695314697362
2Bears11000000422110000004220000000000021.0004711002359581025159343286332281117100.00%3233.33%028152653.42%31858354.55%28752654.56%614288695314697362
3Bruins10000010321100000103210000000000021.00034700235958102315934328633255411300.00%20100.00%028152653.42%31858354.55%28752654.56%614288695314697362
4Cabaret Lady Mary Ann11000000945110000009450000000000021.00091322002359581038159343286332686185240.00%30100.00%028152653.42%31858354.55%28752654.56%614288695314697362
5Caroline2200000014410110000008351100000061541.000141933002359581056159343286333857424250.00%110.00%028152653.42%31858354.55%28752654.56%614288695314697362
6Comets11000000532110000005320000000000021.00057120023595810271593432863327111017000%5340.00%028152653.42%31858354.55%28752654.56%614288695314697362
7Cougars1000010001-11000010001-10000000000010.5000000023595810815934328633311513100.00%10100.00%028152653.42%31858354.55%28752654.56%614288695314697362
8Firebirds21001000734000000000002100100073441.00071219002359581041159343286333146254125.00%3166.67%028152653.42%31858354.55%28752654.56%614288695314697362
9Heat11000000835000000000001100000083521.0008152300235958103315934328633341323174250.00%4250.00%028152653.42%31858354.55%28752654.56%614288695314697362
10Las Vegas21100000912-3110000006421010000038-520.5009172600235958104415934328633411212318225.00%6266.67%128152653.42%31858354.55%28752654.56%614288695314697362
11Manchots220000001477110000005321100000094541.000141933002359581034159343286336419153222100.00%5260.00%028152653.42%31858354.55%28752654.56%614288695314697362
12Marlies1000010056-1000000000001000010056-110.500581300235958103515934328633291011245240.00%3233.33%028152653.42%31858354.55%28752654.56%614288695314697362
13Minnesota20001001660000000000002000100166030.7506814002359581041159343286335917624700.00%30100.00%028152653.42%31858354.55%28752654.56%614288695314697362
14Monarchs2100010013942100010013940000000000030.75013203300235958105415934328633521831267228.57%30100.00%028152653.42%31858354.55%28752654.56%614288695314697362
15Monsters21100000770110000005321010000024-220.500711180023595810501593432863358188286116.67%40100.00%028152653.42%31858354.55%28752654.56%614288695314697362
16Oceanics1010000013-21010000013-20000000000000.00012300235958102015934328633247216500.00%10100.00%028152653.42%31858354.55%28752654.56%614288695314697362
17Phantoms1010000024-2000000000001010000024-200.000224002359581024159343286331810816200.00%4250.00%028152653.42%31858354.55%28752654.56%614288695314697362
18Roadrunners31100010181802100001012931010000069-340.667182644002359581091159343286331103322386350.00%6266.67%028152653.42%31858354.55%28752654.56%614288695314697362
19Senators1010000013-21010000013-20000000000000.0001121023595810101593432863310321711100.00%10100.00%028152653.42%31858354.55%28752654.56%614288695314697362
20Sound Tigers10001000541000000000001000100054121.00059140023595810401593432863332121119100.00%3166.67%028152653.42%31858354.55%28752654.56%614288695314697362
21Spiders2010100067-1100010005411010000013-220.50069150023595810451593432863356176364250.00%30100.00%028152653.42%31858354.55%28752654.56%614288695314697362
22Stars1010000012-1000000000001010000012-100.00012300235958101815934328633313416100.00%2150.00%028152653.42%31858354.55%28752654.56%614288695314697362
23Wolf Pack11000000422000000000001100000042221.0004610002359581030159343286333110219100.00%110.00%028152653.42%31858354.55%28752654.56%614288695314697362
Total33149043211461202617930122081612016560310165596440.667146225371102359581081115934328633857254282512782228.21%722663.89%128152653.42%31858354.55%28752654.56%614288695314697362
_Since Last GM Reset33149043211461202617930122081612016560310165596440.667146225371102359581081115934328633857254282512782228.21%722663.89%128152653.42%31858354.55%28752654.56%614288695314697362
_Vs Conference18660222080755114301120484357230110032320220.61180121201102359581045515934328633509162185281381231.58%401660.00%028152653.42%31858354.55%28752654.56%614288695314697362
_Vs Division522002201816242100120131031010010056-1101.0001826441023595810114159343286339327388315533.33%10280.00%028152653.42%31858354.55%28752654.56%614288695314697362

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
3344W114622537181185725428251210
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
331494321146120
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
179312208161
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
165631016559
Derniers 10 matchs
WLOTWOTL SOWSOL
540100
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
782228.21%722663.89%1
Tirs en 1e périodeTirs en 2e périodeTirs en 3e périodeTirs en 4e périodeButs en 1e périodeButs en 2e périodeButs en 3e périodeButs en 4e période
1593432863323595810
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
28152653.42%31858354.55%28752654.56%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
614288695314697362


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
3 - 2025-10-0920Las Vegas4Sags6WSommaire du match
5 - 2025-10-1136Admirals8Sags4LSommaire du match
8 - 2025-10-1455Caroline3Sags8WSommaire du match
11 - 2025-10-1775Sags6Roadrunners9LSommaire du match
12 - 2025-10-1888Manchots3Sags5WSommaire du match
15 - 2025-10-21100Sags5Sound Tigers4WXSommaire du match
17 - 2025-10-23116Sags4Wolf Pack2WSommaire du match
18 - 2025-10-24124Sags1Spiders3LSommaire du match
20 - 2025-10-26142Sags4Minnesota3WXSommaire du match
22 - 2025-10-28165Monarchs5Sags10WSommaire du match
24 - 2025-10-30176Spiders4Sags5WXSommaire du match
26 - 2025-11-01184Monsters3Sags5WSommaire du match
27 - 2025-11-02198Cougars1Sags0LXSommaire du match
30 - 2025-11-05217Sags3Firebirds2WXSommaire du match
32 - 2025-11-07230Oceanics3Sags1LSommaire du match
33 - 2025-11-08243Cabaret Lady Mary Ann4Sags9WSommaire du match
36 - 2025-11-11261Sags2Minnesota3LXXSommaire du match
38 - 2025-11-13276Sags8Heat3WSommaire du match
40 - 2025-11-15295Sags4Firebirds1WSommaire du match
43 - 2025-11-18314Roadrunners4Sags6WSommaire du match
45 - 2025-11-20330Monarchs4Sags3LXSommaire du match
47 - 2025-11-22342Senators3Sags1LSommaire du match
48 - 2025-11-23351Bruins2Sags3WXXSommaire du match
51 - 2025-11-26371Sags2Monsters4LSommaire du match
53 - 2025-11-28385Comets3Sags5WSommaire du match
54 - 2025-11-29400Sags3Las Vegas8LSommaire du match
56 - 2025-12-01410Roadrunners5Sags6WXXSommaire du match
58 - 2025-12-03425Bears2Sags4WSommaire du match
60 - 2025-12-05438Sags1Stars2LSommaire du match
62 - 2025-12-07455Sags6Caroline1WSommaire du match
64 - 2025-12-09469Sags2Phantoms4LSommaire du match
66 - 2025-12-11480Sags5Marlies6LXSommaire du match
68 - 2025-12-13497Sags9Manchots4WSommaire du match
71 - 2025-12-16527Heat-Sags-
73 - 2025-12-18543Stars-Sags-
75 - 2025-12-20561Firebirds-Sags-
78 - 2025-12-23586Sags-Las Vegas-
82 - 2025-12-27599Sags-Comets-
84 - 2025-12-29615Sags-Admirals-
86 - 2025-12-31625Minnesota-Sags-
89 - 2026-01-03649Thunder-Sags-
92 - 2026-01-06675Monsters-Sags-
93 - 2026-01-07681Sags-Monarchs-
96 - 2026-01-10700Stars-Sags-
97 - 2026-01-11715Las Vegas-Sags-
101 - 2026-01-15742Sags-Bears-
102 - 2026-01-16749Sags-Cougars-
105 - 2026-01-19773Sags-Cabaret Lady Mary Ann-
106 - 2026-01-20781Sags-Thunder-
109 - 2026-01-23808Wolf Pack-Sags-
113 - 2026-01-27838Sags-Comets-
115 - 2026-01-29854Sags-Oil Kings-
117 - 2026-01-31863Sags-Heat-
119 - 2026-02-02881Sags-Baby Hawks-
121 - 2026-02-04897Sags-Monsters-
143 - 2026-02-26928Heat-Sags-
145 - 2026-02-28936Oil Kings-Sags-
146 - 2026-03-01950Oceanics-Sags-
148 - 2026-03-03969Rocket-Sags-
151 - 2026-03-06989Chiefs-Sags-
152 - 2026-03-07999Sound Tigers-Sags-
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
155 - 2026-03-101013Sags-Crunch-
157 - 2026-03-121027Sags-Bruins-
159 - 2026-03-141048Sags-Rocket-
160 - 2026-03-151058Sags-Senators-
162 - 2026-03-171073Sags-Oil Kings-
164 - 2026-03-191092Crunch-Sags-
166 - 2026-03-211103Phantoms-Sags-
169 - 2026-03-241129Sags-Jayhawks-
171 - 2026-03-261143Sags-Chiefs-
173 - 2026-03-281158Sags-Monsters-
175 - 2026-03-301177Chiefs-Sags-
177 - 2026-04-011190Admirals-Sags-
178 - 2026-04-021202Marlies-Sags-
180 - 2026-04-041220Jayhawks-Sags-
182 - 2026-04-061231Baby Hawks-Sags-
184 - 2026-04-081246Oil Kings-Sags-
185 - 2026-04-091258Sags-Admirals-
187 - 2026-04-111275Comets-Sags-
189 - 2026-04-131287Sags-Jayhawks-
191 - 2026-04-151305Sags-Baby Hawks-
192 - 2026-04-161308Sags-Oceanics-



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité17501250
Prix des billets5020
Assistance18,62114,343
Assistance PCT62.59%67.50%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
-13 1939 - 64.64% 85,970$1,461,492$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursPlafond Salariale total des joueursSalaire des entraineurs
594,975$ 1,796,417$ 1,796,417$ 0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
9,308$ 594,975$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
2,063,283$ 125 9,308$ 1,163,500$




Sags 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

Sags 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

Sags 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

Sags 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

Sags 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