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Connexion

Monarchs
GP: 30 | W: 8 | L: 20 | OTL: 2 | P: 18
GF: 106 | GA: 170 | PP%: 16.85% | PK%: 58.51%
DG: Thomas Belair-Ferland | Morale : 50 | Moyenne d’équipe : 58
Prochains matchs #507 vs Heat

Centre de jeu
Monarchs
8-20-2, 18pts
5
FINAL
6 Roadrunners
14-16-3, 31pts
Team Stats
L5SéquenceL1
2-10-1Fiche domicile8-6-0
6-10-1Fiche domicile6-10-3
1-8-1Derniers 10 matchs5-3-2
3.53Buts par match 4.70
5.67Buts contre par match 5.52
16.85%Pourcentage en avantage numérique50.68%
58.51%Pourcentage en désavantage numérique58.56%
Monarchs
8-20-2, 18pts
2
FINAL
5 Firebirds
15-10-4, 34pts
Team Stats
L5SéquenceW2
2-10-1Fiche domicile4-8-3
6-10-1Fiche domicile11-2-1
1-8-1Derniers 10 matchs6-4-0
3.53Buts par match 4.03
5.67Buts contre par match 3.52
16.85%Pourcentage en avantage numérique37.08%
58.51%Pourcentage en désavantage numérique69.44%
Heat
14-16-2, 30pts
2025-12-13
Monarchs
8-20-2, 18pts
Statistiques d’équipe
W2SéquenceL5
7-6-1Fiche domicile2-10-1
7-10-1Fiche visiteur6-10-1
5-4-110 derniers matchs1-8-1
5.34Buts par match 3.53
5.63Buts contre par match 3.53
48.68%Pourcentage en avantage numérique16.85%
63.44%Pourcentage en désavantage numérique58.51%
Monarchs
8-20-2, 18pts
2025-12-15
Stars
23-6-3, 49pts
Statistiques d’équipe
L5SéquenceOTL1
2-10-1Fiche domicile11-3-1
6-10-1Fiche visiteur12-3-2
1-8-110 derniers matchs8-1-1
3.53Buts par match 4.97
5.67Buts contre par match 4.97
16.85%Pourcentage en avantage numérique66.67%
58.51%Pourcentage en désavantage numérique74.68%
Monarchs
8-20-2, 18pts
2025-12-17
Cabaret Lady Mary Ann
10-19-1, 21pts
Statistiques d’équipe
L5SéquenceW1
2-10-1Fiche domicile4-13-1
6-10-1Fiche visiteur6-6-0
1-8-110 derniers matchs3-7-0
3.53Buts par match 4.00
5.67Buts contre par match 4.00
16.85%Pourcentage en avantage numérique24.69%
58.51%Pourcentage en désavantage numérique64.41%
Meneurs d'équipe
Buts
Owen Sillinger
1
Passes
Jakub Lauko
1
Points
Jakub Lauko
1
Plus/Moins
Ilya Fedotov
1
Victoires
Hugo Alnefelt
0
Pourcentage d’arrêts
Hugo Alnefelt
0.821

Statistiques d’équipe
Buts pour
106
3.53 GFG
Tirs pour
719
23.97 Avg
Pourcentage en avantage numérique
16.9%
15 GF
Début de zone offensive
31.4%
Buts contre
170
5.67 GAA
Tirs contre
798
26.60 Avg
Pourcentage en désavantage numérique
58.5%%
39 GA
Début de la zone défensive
29.6%
Informations de l'équipe

Directeur généralThomas Belair-Ferland
DivisionAtlantique
ConférenceEst
Capitaine
Assistant #1
Assistant #2


Informations de l’aréna

Capacité3,000
Assistance1,938
Billets de saison300


Informations de la formation

Équipe Pro26
Équipe Mineure23
Limite contact 49 / 50
Espoirs15


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
1Oliver Wahlstrom (R)X100.006959727484827869476464676973630506802411,000,000$
2Chris TierneyX100.006244817377737766776359686684740506703021,425,000$
3Ivan Ivan (R)X100.00584177717374636653626260665150050630222845,000$
4Luke Toporowski (R)X100.00594467666165636343605758625250050590231560,000$
5Owen SillingerX100.00544568665768666245625654625550050590271825,000$
6Hunter Haight (R)X100.00554265626161606242586054615050050580201897,500$
7Justin Gill (R)XXX100.00554166596260585740545453575050050550213620,000$
8Topi Ronni (R)X100.00394543434538383943383843414545050420201825,000$
9Andreas EnglundX100.00758161738282826940646272688272050710283900,000$
10Tyler Kleven (R)X100.00745176728578727040646374695650050690221916,667$
11Nolan Allan (R)X100.00684177717576676640636070665250050660212825,000$
12Robert HäggX100.006044666667737064406057676278550506402921,000,000$
13Elias Petterssen (D) (R)X100.00634072697275626440616162655150050630203838,333$
14Brandon Scanlin (R)X100.00634468637067666340535962625450050610251925,000$
15Topias Vilen (R)X100.00634271656563616240635367605050050610212836,667$
16Ben Roger (R)X100.00434343434343434343434343434343050440213825,000$
Rayé
1Jakub LaukoXXX100.008269717580807670496665707059500506802421,300,000$
2Nick Henry (R)XHO6269796369373740473736573844440504802500$
3Ilya Fedotov (R)X100.00394543434538383943383843414545050420211925,000$
4Stiven Sardarian (R)X100.00333534343532323334323234343535050350211700,000$
5Jack Harvey (R)X100.00333735353731313335313135353737050350211620,000$
MOYENNE D’ÉQUIPE100.0058486461646259574454535857555105057
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
1Eric Comrie100.0084817976817978787882748275050730292920,000$
2Calle Clang (R)100.0069626466676669656767585150050610221878,333$
Rayé
1Zach SawchenkoHO726464626666686467685955500506002600$
2Hugo Alnefelt100.0068616065656570646563555146050590231850,833$
3Kyle Keyser (R)HO645856636058605458595153460505402500$
MOYENNE D’ÉQUIPE100.007165656668676965676859585305061
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
1Jakub LaukoMonarchs (LA )C/LW/RW1011000232040%01414.3200000000000084.62%1310001.4000000001
2Owen SillingerMonarchs (LA )C11010001121250.00%01414.2800000000000090.00%1001001.4000000100
3Ivan IvanMonarchs (LA )C1011100111110%01414.32000000000000100.00%122001.4000000010
4Oliver WahlstromMonarchs (LA )RW1000000012410%02121.470000000000000%001000000000000
5Stiven SardarianMonarchs (LA )RW1000020000000%11313.930000000000000%00000000000000
6Ilya FedotovMonarchs (LA )LW1000100103110%01212.320000000000000%00000000000000
7Justin GillMonarchs (LA )C/LW/RW1000-140201020%02222.100000000000000%20100000000000
Statistiques d’équipe totales ou en moyenne712316076117119.09%111216.1000000000000080.77%26314000.5300000111
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
1Hugo AlnefeltMonarchs (LA )10100.8215.00600052817000010000
Statistiques d’équipe totales ou en moyenne10100.8215.0060005281700010000


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
Andreas Englund (contrat à 1 volet)Monarchs (LA )D281996-01-21SWENo200 Lbs6 ft4NoNoFree AgentYesYes32024-09-11FalseFalsePro & Farm900,000$0$0$No900,000$900,000$-------900,000$900,000$-------NoNo-------Lien
Ben RogerMonarchs (LA )D212002-11-02CANYes201 Lbs5 ft4NoNoProspectNoNo32025-07-10FalseFalsePro & Farm825,000$0$0$No825,000$825,000$-------825,000$825,000$-------NoNo-------Lien
Brandon ScanlinMonarchs (LA )D251999-06-02CANYes214 Lbs6 ft4NoNoN/AYesYes1FalseFalsePro & Farm925,000$0$0$No---------------------------Lien
Calle ClangMonarchs (LA )G222002-05-20SWEYes194 Lbs6 ft2NoNoN/ANoNo1FalseFalsePro & Farm878,333$0$0$No---------------------------Lien
Chris Tierney (contrat à 1 volet)Monarchs (LA )C301994-07-01CANNo191 Lbs6 ft1NoNoN/AYesYes2FalseFalsePro & Farm1,425,000$505,000$329,689$No1,425,000$--------1,425,000$--------No--------Lien
Elias Petterssen (D)Monarchs (LA )D202004-02-16SWEYes185 Lbs6 ft2NoNoProspectNoNo32025-07-10FalseFalsePro & Farm838,333$0$0$No838,333$838,333$-------838,333$838,333$-------NoNo-------Lien
Eric Comrie (contrat à 1 volet)Monarchs (LA )G291995-07-06CANNo190 Lbs6 ft1NoNoTrade2025-08-18YesYes22024-09-06FalseFalsePro & Farm920,000$0$0$No920,000$--------920,000$--------No--------Lien
Hugo AlnefeltMonarchs (LA )G232001-06-04SWENo189 Lbs6 ft3NoNoN/ANoNo1FalseFalsePro & Farm850,833$0$0$No---------------------------Lien
Hunter HaightMonarchs (LA )C202004-04-04CANYes181 Lbs5 ft11NoNoN/ANoNo1FalseFalsePro & Farm897,500$0$0$No---------------------------Lien
Ilya FedotovMonarchs (LA )LW212003-03-19RUSYes182 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm925,000$0$0$No---------------------------Lien
Ivan IvanMonarchs (LA )C222002-08-20CZEYes190 Lbs6 ft0NoNoTrade2025-07-18NoNo22024-06-25FalseFalsePro & Farm845,000$0$0$No845,000$--------845,000$--------No--------Lien
Jack HarveyMonarchs (LA )C212003-03-30MNYes175 Lbs5 ft10NoNoN/ANoNo1FalseFalsePro & Farm620,000$0$0$No---------------------------Lien
Jakub Lauko (contrat à 1 volet)Monarchs (LA )C/LW/RW242000-03-28CZENo193 Lbs6 ft1NoNoFree Agent2025-07-18NoYes22025-08-28FalseFalsePro & Farm1,300,000$380,000$248,083$No1,300,000$--------1,300,000$--------No--------Lien
Justin GillMonarchs (LA )C/LW/RW212003-01-27CANYes190 Lbs6 ft1NoNoDraftNoNo32025-07-10FalseFalsePro & Farm620,000$0$0$No620,000$620,000$-------620,000$620,000$-------NoNo-------Lien
Kyle KeyserMonarchs (LA )G251999-03-08USAYes186 Lbs6 ft2NoNoTrade2025-03-06YesYes0FalseFalsePro & Farm0$0$No---------------------------Lien
Luke ToporowskiMonarchs (LA )LW232001-04-12USAYes183 Lbs5 ft11NoNoTrade2025-07-18NoNo1FalseFalsePro & Farm560,000$0$0$No---------------------------Lien
Nick Henry (contrat à 1 volet)Monarchs (LA )RW251999-07-04MANYes190 Lbs5 ft11NoNoFree AgentYesYes02024-09-11FalseFalsePro & Farm0$0$No---------------------------Lien
Nolan AllanMonarchs (LA )D212003-04-28CANYes195 Lbs6 ft2NoNoProspectNoNo22024-06-25FalseFalsePro & Farm825,000$0$0$No825,000$--------825,000$--------No--------Lien
Oliver WahlstromMonarchs (LA )RW242000-06-13USAYes205 Lbs6 ft2NoNoTrade2025-07-18NoYes12024-09-11FalseFalsePro & Farm1,000,000$0$0$No---------------------------Lien
Owen SillingerMonarchs (LA )C271997-09-23CANNo170 Lbs5 ft10NoNoN/AYesYes1FalseFalsePro & Farm825,000$0$0$No---------------------------Lien
Robert Hägg (contrat à 1 volet)Monarchs (LA )D291995-02-08SWENo205 Lbs6 ft2NoNoN/AYesYes2FalseFalsePro & Farm1,000,000$80,000$52,228$No1,000,000$--------1,000,000$--------No--------Lien
Stiven SardarianMonarchs (LA )RW212003-02-07RUSYes156 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm700,000$0$0$No---------------------------Lien
Topi RonniMonarchs (LA )C202004-05-05FINYes179 Lbs6 ft2NoNoN/ANoNo1FalseFalsePro & Farm825,000$0$0$No---------------------------Lien
Topias VilenMonarchs (LA )D212003-04-01FINYes194 Lbs6 ft1NoNoProspectNoNo22024-06-25FalseFalsePro & Farm836,667$0$0$No836,667$--------836,667$--------No--------Lien
Tyler KlevenMonarchs (LA )D222002-01-10USAYes221 Lbs6 ft5NoNoN/ANoNo1FalseFalsePro & Farm916,667$0$0$No---------------------------Lien
Zach SawchenkoMonarchs (LA )G261997-12-30CANNo185 Lbs6 ft1NoNoFree AgentYesYes02024-09-11FalseFalsePro & Farm0$0$No---------------------------Lien
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2623.50190 Lbs6 ft11.46779,167$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
140122
230122
320122
410122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
140122
230122
320122
410122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
160122
240122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
160122
240122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
160122
240122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
160122
240122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
16012260122
24012240122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
160122
240122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
160122
240122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
, , ,
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
, , ,
Tirs de pénalité
, , , ,
Gardien
#1 : , #2 :


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
1Admirals1010000046-2000000000001010000046-200.00048120029373732721623226563012129200.00%6350.00%019247840.17%18645041.33%24259240.88%725488588268492216
2Baby Hawks31200000718-1120200000315-121100000043120.33371320002937373602162322656903730378112.50%10550.00%019247840.17%18645041.33%24259240.88%725488588268492216
3Bears20200000616-1010100000110-91010000056-100.000611170029373735121623226567120155259444.44%10550.00%019247840.17%18645041.33%24259240.88%725488588268492216
4Bruins1010000034-11010000034-10000000000000.000369002937373292162322656125410200.00%220.00%019247840.17%18645041.33%24259240.88%725488588268492216
5Cabaret Lady Mary Ann1010000025-31010000025-30000000000000.000246002937373192162322656286614400.00%30100.00%119247840.17%18645041.33%24259240.88%725488588268492216
6Caroline11000000541110000005410000000000021.00059140029373731721623226561676146350.00%30100.00%019247840.17%18645041.33%24259240.88%725488588268492216
7Chiefs1010000067-1000000000001010000067-100.000612180029373732721623226563112412300.00%2150.00%019247840.17%18645041.33%24259240.88%725488588268492216
8Comets1000010034-11000010034-10000000000010.500369002937373312162322656301226400.00%110.00%019247840.17%18645041.33%24259240.88%725488588268492216
9Cougars10001000541100010005410000000000021.00051015002937373272162322656361021711100.00%10100.00%019247840.17%18645041.33%24259240.88%725488588268492216
10Firebirds1010000025-3000000000001010000025-300.0002460029373732721623226561864112150.00%20100.00%019247840.17%18645041.33%24259240.88%725488588268492216
11Jayhawks1010000024-2000000000001010000024-200.000246002937373192162322656184416400.00%20100.00%019247840.17%18645041.33%24259240.88%725488588268492216
12Las Vegas11000000431000000000001100000043121.0004812002937373282162322656128815300.00%4175.00%019247840.17%18645041.33%24259240.88%725488588268492216
13Manchots20200000614-81010000058-31010000016-500.00061218002937373572162322656511218209111.11%9455.56%019247840.17%18645041.33%24259240.88%725488588268492216
14Marlies1000010056-1000000000001000010056-110.500581300293737328216232265634121011200.00%5260.00%219247840.17%18645041.33%24259240.88%725488588268492216
15Minnesota11000000321000000000001100000032121.00035800293737392162322656204010000%000%019247840.17%18645041.33%24259240.88%725488588268492216
16Monsters1010000045-11010000045-10000000000000.00048120029373733021623226562668113133.33%4250.00%019247840.17%18645041.33%24259240.88%725488588268492216
17Oceanics2010100069-31010000037-41000100032120.50061117002937373472162322656641815221119.09%5180.00%019247840.17%18645041.33%24259240.88%725488588268492216
18Roadrunners1010000056-1000000000001010000056-100.00051015002937373242162322656329611100.00%3233.33%019247840.17%18645041.33%24259240.88%725488588268492216
19Rocket1010000047-3000000000001010000047-300.00046100029373732321623226561541015200.00%5340.00%019247840.17%18645041.33%24259240.88%725488588268492216
20Sags20101000913-40000000000020101000913-420.5009172600293737352216232265654208931300.00%7271.43%019247840.17%18645041.33%24259240.88%725488588268492216
21Senators2110000011921010000035-21100000084420.500112132002937373532162322656431222256233.33%6183.33%119247840.17%18645041.33%24259240.88%725488588268492216
22Spiders1010000037-41010000037-40000000000000.0003690029373732321623226563310626100.00%330.00%019247840.17%18645041.33%24259240.88%725488588268492216
23Stars10100000112-110000000000010100000112-1100.0001230029373731121623226563487217300.00%110.00%019247840.17%18645041.33%24259240.88%725488588268492216
Total3052003200106170-6413110011004078-3817410021006692-26180.3001062013070029373737192162322656798254493385891516.85%943958.51%419247840.17%18645041.33%24259240.88%725488588268492216
_Since Last GM Reset3052003200106170-6413110011004078-3817410021006692-26180.3001062013070029373737192162322656798254493385891516.85%943958.51%419247840.17%18645041.33%24259240.88%725488588268492216
_Vs Conference15111021005890-32606000001841-23915021004049-970.23358110168002937373391216232265642413033719046817.39%562555.36%319247840.17%18645041.33%24259240.88%725488588268492216
_Vs Division704021003035-5402000001318-5302021001717050.357305585002937373179216232265616849549217317.65%22863.64%419247840.17%18645041.33%24259240.88%725488588268492216

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
3018L510620130771979825449338500
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
305203200106170
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
1311011004078
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
1741021006692
Derniers 10 matchs
WLOTWOTL SOWSOL
180100
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
891516.85%943958.51%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
21623226562937373
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
19247840.17%18645041.33%24259240.88%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
725488588268492216


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
1 - 2025-10-073Monsters5Monarchs4LSommaire du match
2 - 2025-10-087Monarchs4Las Vegas3WSommaire du match
5 - 2025-10-1122Monarchs3Oceanics2WXSommaire du match
7 - 2025-10-1347Monarchs3Minnesota2WSommaire du match
10 - 2025-10-1671Manchots8Monarchs5LSommaire du match
12 - 2025-10-1886Caroline4Monarchs5WSommaire du match
15 - 2025-10-21104Monarchs6Chiefs7LSommaire du match
17 - 2025-10-23119Monarchs1Stars12LSommaire du match
19 - 2025-10-25137Monarchs2Jayhawks4LSommaire du match
20 - 2025-10-26144Monarchs4Baby Hawks3WSommaire du match
22 - 2025-10-28165Monarchs5Sags10LSommaire du match
24 - 2025-10-30177Cougars4Monarchs5WXSommaire du match
26 - 2025-11-01191Spiders7Monarchs3LSommaire du match
29 - 2025-11-04212Oceanics7Monarchs3LSommaire du match
31 - 2025-11-06226Cabaret Lady Mary Ann5Monarchs2LSommaire du match
34 - 2025-11-09245Monarchs1Manchots6LSommaire du match
36 - 2025-11-11257Monarchs4Rocket7LSommaire du match
38 - 2025-11-13269Monarchs5Marlies6LXSommaire du match
40 - 2025-11-15286Monarchs8Senators4WSommaire du match
42 - 2025-11-17304Monarchs5Bears6LSommaire du match
45 - 2025-11-20330Monarchs4Sags3WXSommaire du match
46 - 2025-11-21334Bruins4Monarchs3LSommaire du match
49 - 2025-11-24359Senators5Monarchs3LSommaire du match
53 - 2025-11-28384Monarchs4Admirals6LSommaire du match
54 - 2025-11-29401Comets4Monarchs3LXSommaire du match
57 - 2025-12-02420Bears10Monarchs1LSommaire du match
59 - 2025-12-04435Baby Hawks9Monarchs2LSommaire du match
61 - 2025-12-06449Baby Hawks6Monarchs1LSommaire du match
63 - 2025-12-08462Monarchs5Roadrunners6LSommaire du match
65 - 2025-12-10479Monarchs2Firebirds5LSommaire du match
68 - 2025-12-13507Heat-Monarchs-
70 - 2025-12-15518Monarchs-Stars-
72 - 2025-12-17529Monarchs-Cabaret Lady Mary Ann-
73 - 2025-12-18538Monarchs-Thunder-
77 - 2025-12-22574Monsters-Monarchs-
78 - 2025-12-23587Firebirds-Monarchs-
82 - 2025-12-27597Admirals-Monarchs-
84 - 2025-12-29611Monarchs-Monsters-
87 - 2026-01-01639Thunder-Monarchs-
89 - 2026-01-03655Minnesota-Monarchs-
91 - 2026-01-05666Minnesota-Monarchs-
93 - 2026-01-07681Sags-Monarchs-
95 - 2026-01-09695Monarchs-Oceanics-
96 - 2026-01-10709Monarchs-Oil Kings-
98 - 2026-01-12724Stars-Monarchs-
100 - 2026-01-14738Las Vegas-Monarchs-
102 - 2026-01-16753Admirals-Monarchs-
103 - 2026-01-17766Monarchs-Admirals-
106 - 2026-01-20787Wolf Pack-Monarchs-
110 - 2026-01-24816Monarchs-Chiefs-
112 - 2026-01-26828Monarchs-Monsters-
113 - 2026-01-27833Monarchs-Cougars-
115 - 2026-01-29844Monarchs-Crunch-
117 - 2026-01-31859Monarchs-Phantoms-
118 - 2026-02-01874Monarchs-Caroline-
121 - 2026-02-04902Firebirds-Monarchs-
122 - 2026-02-05909Monarchs-Las Vegas-
142 - 2026-02-25916Las Vegas-Monarchs-
143 - 2026-02-26929Oil Kings-Monarchs-
145 - 2026-02-28944Heat-Monarchs-
147 - 2026-03-02958Monsters-Monarchs-
150 - 2026-03-05982Sound Tigers-Monarchs-
152 - 2026-03-07997Rocket-Monarchs-
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
155 - 2026-03-101012Monarchs-Bruins-
158 - 2026-03-131041Monarchs-Sound Tigers-
159 - 2026-03-141050Monarchs-Spiders-
161 - 2026-03-161065Monarchs-Wolf Pack-
164 - 2026-03-191093Phantoms-Monarchs-
166 - 2026-03-211102Crunch-Monarchs-
167 - 2026-03-221118Monarchs-Roadrunners-
169 - 2026-03-241133Monarchs-Heat-
171 - 2026-03-261149Monarchs-Comets-
173 - 2026-03-281164Roadrunners-Monarchs-
177 - 2026-04-011189Chiefs-Monarchs-
178 - 2026-04-021204Jayhawks-Monarchs-
180 - 2026-04-041217Marlies-Monarchs-
182 - 2026-04-061232Jayhawks-Monarchs-
185 - 2026-04-091260Comets-Monarchs-
187 - 2026-04-111264Oil Kings-Monarchs-
189 - 2026-04-131291Monarchs-Firebirds-
190 - 2026-04-141300Monarchs-Comets-
192 - 2026-04-161310Monarchs-Heat-



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets5020
Assistance16,6148,583
Assistance PCT63.90%66.02%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
-10 1938 - 64.61% 91,030$1,183,392$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursPlafond Salariale total des joueursSalaire des entraineurs
529,794$ 1,471,333$ 1,471,333$ 0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
7,623$ 529,794$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
2,548,844$ 126 7,623$ 960,498$




Monarchs 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

Monarchs 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

Monarchs 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

Monarchs 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

Monarchs 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