Connexion

Monarchs
GP: 27 | W: 6 | L: 20 | OTL: 1 | P: 13
GF: 83 | GA: 125 | PP%: 10.14% | PK%: 79.17%
DG: Antoine Pelletier | Morale : 50 | Moyenne d’équipe : 54
Prochains matchs #406 vs Senators
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Centre de jeu
Jayhawks
16-6-1, 33pts
6
FINAL
4 Monarchs
6-20-1, 13pts
Team Stats
L1StreakL7
3-0-1Home Record3-10-1
13-6-0Away Record3-10-0
5-5-0Last 10 Games1-9-0
3.35Buts par match 3.07
2.39Buts contre par match 4.63
29.27%Pourcentage en avantage numérique10.14%
81.82%Pourcentage en désavantage numérique79.17%
Caroline
12-10-3, 27pts
5
FINAL
3 Monarchs
6-20-1, 13pts
Team Stats
W5StreakL7
3-5-1Home Record3-10-1
9-5-2Away Record3-10-0
7-3-0Last 10 Games1-9-0
3.24Buts par match 3.07
3.76Buts contre par match 4.63
19.48%Pourcentage en avantage numérique10.14%
83.58%Pourcentage en désavantage numérique79.17%
Monarchs
6-20-1, 13pts
2022-12-06
Senators
15-7-2, 32pts
Statistiques d’équipe
L7SéquenceW4
3-10-1Fiche domicile9-4-0
3-10-0Fiche visiteur6-3-2
1-9-010 derniers matchs9-0-1
3.07Buts par match 4.00
4.63Buts contre par match 4.00
10.14%Pourcentage en avantage numérique21.74%
79.17%Pourcentage en désavantage numérique77.91%
Monarchs
6-20-1, 13pts
2022-12-08
Marlies
16-10-0, 32pts
Statistiques d’équipe
L7SéquenceW1
3-10-1Fiche domicile7-6-0
3-10-0Fiche visiteur9-4-0
1-9-010 derniers matchs5-5-0
3.07Buts par match 3.73
4.63Buts contre par match 3.73
10.14%Pourcentage en avantage numérique23.44%
79.17%Pourcentage en désavantage numérique81.71%
Monarchs
6-20-1, 13pts
2022-12-10
Rocket
8-13-4, 20pts
Statistiques d’équipe
L7SéquenceL1
3-10-1Fiche domicile2-8-2
3-10-0Fiche visiteur6-5-2
1-9-010 derniers matchs5-4-1
3.07Buts par match 3.68
4.63Buts contre par match 3.68
10.14%Pourcentage en avantage numérique19.40%
79.17%Pourcentage en désavantage numérique74.68%
Meneurs d'équipe
Buts
Liam O'Brien
9
Passes
Mitchell Stephens
13
Points
Mitchell Stephens
20
Plus/Moins
Frederic Allard
6

Statistiques d’équipe
Buts pour
83
3.07 GFG
Tirs pour
936
34.67 Avg
Pourcentage en avantage numérique
10.1%
7 GF
Début de zone offensive
37.4%
Buts contre
125
4.63 GAA
Tirs contre
1245
46.11 Avg
Pourcentage en désavantage numérique
79.2%%
15 GA
Début de la zone défensive
42.4%
Informations de l'équipe

Directeur généralAntoine Pelletier
DivisionAtlantique
ConférenceEst
Capitaine
Assistant #1
Assistant #2


Informations de l’aréna

Capacité3,000
Assistance1,887
Billets de saison300


Informations de la formation

Équipe Pro20
Équipe Mineure19
Limite contact 39 / 50
Espoirs13


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 moyen
1Joseph BlandisiXX100.00686769796758576480596365605858050610271700,000$
2Mitchell StephensX100.00714390726959515579675577255353050600241800,000$
3Gage QuinneyXX100.00787292627253526278566267595151050590262715,000$
4Liam O'BrienXX100.00999934678055605845535764255252050580271600,000$
5Stefan MatteauX100.00707657647653525950585464515555050570271600,000$
6Graeme Clarke (R)XX100.00706483646460615873545761544444050560202850,833$
7Owen Sillinger (R)X100.00736591676541376075585763544444050560244825,000$
8Connor Zary (R)X100.00706580586554545873516161584444050550202925,000$
9Nick Henry (R)X100.00746985666947474950474660444444050520222783,935$
10Giovanni FioreXX100.007174885371524850493551645450500505102521,300,000$
11Dennis CholowskiX100.00654293777367626325544769255757050620232850,000$
12Frederic AllardX100.00726687616656594925404161395050050550262600,000$
13Andreas EnglundX100.00657152707150524725384055384444050530252900,000$
14Brandon Scanlin (R)X100.00858095618037364425333965374444050530224925,000$
Rayé
1Steven LorentzXXX100.00754495737858825845576267255758050610252728,333$
2Ben JohnsonXX100.00324343435929293243313143373230050360271660,000$
3Derrick PouliotX100.00734387777167646325644771256363050640271975,000$
MOYENNE D’ÉQUIPE100.0071647866705353555150516342504905056
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 moyen
1Hugo Alnefelt (R)100.0045405080454450524748304444050500204850,833$
2Zachary Sawchenko (R)100.0044415169434350514647304444050480232560,000$
Rayé
1Eamon McAdam100.0035433969343233323232313228050380271700,000$
MOYENNE D’ÉQUIPE100.004141477341404445424230403905045
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
1Mitchell StephensMonarchs (LA )C217132024015728620538.14%838518.3300011350000181059.66%47100011.0400000121
2Gage QuinneyMonarchs (LA )C/LW21811192140293274195510.81%538018.110005360000161172.73%2200001.0000000221
3Steven LorentzMonarchs (LA )C/LW/RW166814-1210018436326569.52%636422.7912310320002351042.17%43400000.7700000001
4Owen SillingerMonarchs (LA )C2131114-69524273314269.09%2832915.6900000000000060.61%3300000.8500010000
5Stefan MatteauMonarchs (LA )LW2149131553335517527.27%932315.41000131013290035.42%4800000.8000100100
6Liam O'BrienMonarchs (LA )C/LW219413-2415725055165316.36%130314.4401136000051045.78%36700000.8600001010
7Graeme ClarkeMonarchs (LA )C/RW216612-178048337624667.89%539718.920221450000000051.43%3500010.6000000100
8Derrick PouliotMonarchs (LA )D162911-13602823284177.14%2038123.811341531000137000.00%000000.5800000000
9Joseph BlandisiMonarchs (LA )C/LW75611-10012113182516.13%214420.621125230002110171.43%1400001.5200000101
10Brandon ScanlinMonarchs (LA )D2155104804017183927.78%2639919.02000437000040000.00%000000.5000000011
11Dennis CholowskiMonarchs (LA )D21279-1612028444313264.65%4950323.992132050000049000.00%000000.3600000000
12Frederic AllardMonarchs (LA )D2107761202810216140.00%2341019.54000339011245000.00%000000.3400000001
13Giovanni FioreMonarchs (LA )LW/RW21257-1000122610157.69%429113.8610112000000052.63%1900000.4800000000
14Connor ZaryMonarchs (LA )C212353551410258138.00%1914.34000480001150062.50%6400001.1000100001
15Andreas EnglundMonarchs (LA )D21134-5335675135147.69%2134516.4700039000019000.00%000000.2300100001
16Nick HenryMonarchs (LA )RW211231403524186215.56%336317.29000236000001048.57%3500000.1700000100
17Ben JohnsonMonarchs (LA )C/LW1000-200000000.00%066.130000000000000.00%000000.00%00000000
Statistiques d’équipe totales ou en moyenne31363109172-56171254614466651995159.47%211542017.3261016101407112113255250.58%154200020.6300311768
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


Astuces sur les filtres (anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
Nom du joueur Nom de l’équipePOS Âge Date de naissance Nouveau joueur Poids Taille Non-échange Disponible pour échange Ballotage forcé Contrat Type Salaire actuel Salaire moyen restantPlafond salarial 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 10Link
Andreas EnglundMonarchs (LA )D251996-01-21No189 Lbs6 ft3NoNoYes2Pro & Farm900,000$615,789$0$0$No900,000$Lien
Ben Johnson (contrat à 1 volet)Monarchs (LA )C/LW271994-06-07No188 Lbs5 ft11NoNoYes1Pro & Farm660,000$451,579$0$0$NoLien
Brandon ScanlinMonarchs (LA )D221999-06-02Yes214 Lbs6 ft4NoNoNo4Pro & Farm925,000$632,895$0$0$No925,000$925,000$925,000$
Connor ZaryMonarchs (LA )C202001-09-25Yes179 Lbs6 ft0NoNoNo2Pro & Farm925,000$632,895$0$0$No925,000$Lien
Dennis CholowskiMonarchs (LA )D231998-02-15No197 Lbs6 ft2NoNoNo2Pro & Farm850,000$581,579$0$0$No850,000$Lien
Derrick Pouliot (contrat à 1 volet)Monarchs (LA )D271994-01-16No196 Lbs6 ft0NoNoYes1Pro & Farm975,000$667,105$75,000$51,316$NoLien
Eamon McAdam (contrat à 1 volet)Monarchs (LA )G271994-09-24No188 Lbs6 ft2NoNoYes1Pro & Farm700,000$478,947$0$0$NoLien
Frederic Allard (contrat à 1 volet)Monarchs (LA )D261994-12-27No179 Lbs6 ft1NoNoYes2Pro & Farm600,000$410,526$0$0$No600,000$Lien
Gage Quinney (contrat à 1 volet)Monarchs (LA )C/LW261995-07-29No200 Lbs5 ft11NoNoYes2Pro & Farm715,000$489,211$0$0$No715,000$Lien
Giovanni FioreMonarchs (LA )LW/RW251996-08-13No194 Lbs6 ft1NoNoYes2Pro & Farm1,200,000$889,474$0$0$No1,100,000$Lien
Graeme ClarkeMonarchs (LA )C/RW202001-04-24Yes174 Lbs6 ft0NoNoNo2Pro & Farm850,833$582,149$0$0$No850,833$Lien
Hugo AlnefeltMonarchs (LA )G202001-06-04Yes201 Lbs6 ft3NoNoNo4Pro & Farm850,833$582,149$0$0$No850,833$850,833$850,833$Lien
Joseph Blandisi (contrat à 1 volet)Monarchs (LA )C/LW271994-07-18No184 Lbs6 ft0NoNoYes1Pro & Farm700,000$478,947$0$0$NoLien
Liam O'Brien (contrat à 1 volet)Monarchs (LA )C/LW271994-07-29No213 Lbs6 ft1NoNoYes1Pro & Farm600,000$410,526$0$0$NoLien
Mitchell StephensMonarchs (LA )C241997-02-05No190 Lbs5 ft11NoNoYes1Pro & Farm800,000$547,368$0$0$NoLien
Nick HenryMonarchs (LA )RW221999-07-04Yes190 Lbs5 ft11NoNoNo2Pro & Farm783,935$536,377$0$0$No783,935$Lien
Owen SillingerMonarchs (LA )C241997-09-23Yes183 Lbs5 ft10NoNoYes4Pro & Farm825,000$564,474$0$0$No825,000$825,000$825,000$
Stefan Matteau (contrat à 1 volet)Monarchs (LA )LW271994-02-23No208 Lbs6 ft2NoNoYes1Pro & Farm600,000$410,526$0$0$NoLien
Steven LorentzMonarchs (LA )C/LW/RW251996-04-13No206 Lbs6 ft4NoNoYes2Pro & Farm728,333$498,333$0$0$No728,333$Lien
Zachary SawchenkoMonarchs (LA )G231997-12-30Yes183 Lbs6 ft1NoNoNo2Pro & Farm560,000$383,158$0$0$No560,000$Lien
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2024.35193 Lbs6 ft11.95787,447$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Graeme Clarke40122
2Gage QuinneyMitchell StephensNick Henry30122
3Stefan MatteauLiam O'BrienGiovanni Fiore20122
4Owen Sillinger10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Dennis Cholowski40122
2Frederic AllardBrandon Scanlin30122
3Andreas EnglundOwen Sillinger20122
4Dennis Cholowski10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Graeme Clarke60122
2Gage QuinneyMitchell StephensNick Henry40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Dennis Cholowski60122
2Frederic AllardBrandon Scanlin40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
160122
2Mitchell StephensGage Quinney40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Dennis Cholowski60122
2Frederic AllardBrandon Scanlin40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
160122Dennis Cholowski60122
240122Frederic AllardBrandon Scanlin40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
160122
2Mitchell StephensGage Quinney40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Dennis Cholowski60122
2Frederic AllardBrandon Scanlin40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Graeme ClarkeDennis Cholowski
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Graeme ClarkeDennis Cholowski
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Connor Zary, Liam O'Brien, Stefan MatteauConnor Zary, Liam O'BrienStefan Matteau
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Andreas Englund, Frederic Allard, Brandon ScanlinAndreas EnglundFrederic Allard, Brandon Scanlin
Tirs de pénalité
, , Mitchell Stephens, Gage Quinney, Liam O'Brien
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
1Baby Hawks2100010010911000010045-11100000064230.7501018280029302318030331331468123144210220.00%6266.67%141288046.82%43199743.23%22247746.54%585395689196350169
2Bears1010000018-7000000000001010000018-700.000123002930231213033133146399221200.00%10100.00%041288046.82%43199743.23%22247746.54%585395689196350169
3Cabaret Lady Mary Ann1010000024-21010000024-20000000000000.000246002930231443033133146485818000.00%4250.00%041288046.82%43199743.23%22247746.54%585395689196350169
4Caroline1010000035-21010000035-20000000000000.00034710293023138303313314633913256116.67%30100.00%041288046.82%43199743.23%22247746.54%585395689196350169
5Chiefs11000000835000000000001100000083521.000813210029302313130331331462856143133.33%30100.00%041288046.82%43199743.23%22247746.54%585395689196350169
6Chill1010000045-1000000000001010000045-100.00047110029302313730331331469619217200.00%10100.00%041288046.82%43199743.23%22247746.54%585395689196350169
7Comets11000000404000000000001100000040421.00048120129302313430331331462781123100.00%30100.00%041288046.82%43199743.23%22247746.54%585395689196350169
8Cougars20200000517-121010000038-51010000029-700.0005101500293023153303313314613437838200.00%4250.00%041288046.82%43199743.23%22247746.54%585395689196350169
9Heat1010000024-2000000000001010000024-200.0002460029302312230331331464514819100.00%4175.00%041288046.82%43199743.23%22247746.54%585395689196350169
10Jayhawks1010000046-21010000046-20000000000000.00045900293023143303313314636112530400.00%4175.00%041288046.82%43199743.23%22247746.54%585395689196350169
11Las Vegas11000000743110000007430000000000021.00071421002930231893033133146741703211100.00%000.00%041288046.82%43199743.23%22247746.54%585395689196350169
12Manchots1010000038-5000000000001010000038-500.0003690029302313130331331468823425000.00%2150.00%041288046.82%43199743.23%22247746.54%585395689196350169
13Marlies11000000523110000005230000000000021.000510150029302313830331331462894204125.00%110.00%041288046.82%43199743.23%22247746.54%585395689196350169
14Minnesota2020000028-61010000002-21010000026-400.00024600293023174303313314678231049700.00%40100.00%041288046.82%43199743.23%22247746.54%585395689196350169
15Oceanics1010000045-11010000045-10000000000000.000461000293023125303313314639101219400.00%5340.00%041288046.82%43199743.23%22247746.54%585395689196350169
16Oil Kings1010000013-2000000000001010000013-200.0001230029302313130331331464291020200.00%5180.00%041288046.82%43199743.23%22247746.54%585395689196350169
17Seattle30300000513-820200000310-71010000023-100.0005813002930231893033133146150452467500.00%11190.91%041288046.82%43199743.23%22247746.54%585395689196350169
18Senators1010000013-21010000013-20000000000000.0001231029302313730331331463418431500.00%20100.00%041288046.82%43199743.23%22247746.54%585395689196350169
19Sharks1010000024-2000000000001010000024-200.0002460029302313030331331465019819300.00%40100.00%041288046.82%43199743.23%22247746.54%585395689196350169
20Stars1010000024-2000000000001010000024-200.0002460029302312930331331463312230300.00%10100.00%041288046.82%43199743.23%22247746.54%585395689196350169
21Thunder10001000431100010004310000000000021.00047110029302313430331331462044233133.33%10100.00%041288046.82%43199743.23%22247746.54%585395689196350169
22Wolf Pack1010000047-31010000047-30000000000000.0004711002930231263033133146428816100.00%30100.00%041288046.82%43199743.23%22247746.54%585395689196350169
Total275200110083125-4214210011004464-2013310000003961-22130.241831492322129302319363033133146124533718759869710.14%721579.17%141288046.82%43199743.23%22247746.54%585395689196350169
_Since Last GM Reset275200110083125-4214210011004464-2013310000003961-22130.241831492322129302319363033133146124533718759869710.14%721579.17%141288046.82%43199743.23%22247746.54%585395689196350169
_Vs Conference917010002845-17513010001820-2404000001025-1540.2222851791029302312793033133146436119481912428.33%20575.00%041288046.82%43199743.23%22247746.54%585395689196350169
_Vs Division613000001729-12511000001520-51020000029-720.1671733501029302312063033133146264732813014214.29%12558.33%041288046.82%43199743.23%22247746.54%585395689196350169

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
2713L783149232936124533718759821
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
27520110083125
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
1421011004464
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
1331000003961
Derniers 10 matchs
WLOTWOTL SOWSOL
190000
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
69710.14%721579.17%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
30331331462930231
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
41288046.82%43199743.23%22247746.54%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
585395689196350169


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
5 - 2022-10-114Las Vegas4Monarchs7BWSommaire du match
7 - 2022-10-1320Seattle6Monarchs1BLSommaire du match
9 - 2022-10-1534Monarchs2Minnesota6ALSommaire du match
11 - 2022-10-1744Monarchs2Cougars9ALSommaire du match
12 - 2022-10-1852Monarchs4Chill5ALSommaire du match
14 - 2022-10-2062Monarchs3Manchots8ALSommaire du match
16 - 2022-10-2280Monarchs1Bears8ALSommaire du match
19 - 2022-10-25106Thunder3Monarchs4BWXSommaire du match
21 - 2022-10-27120Oceanics5Monarchs4BLSommaire du match
23 - 2022-10-29130Marlies2Monarchs5BWSommaire du match
25 - 2022-10-31146Monarchs8Chiefs3AWSommaire du match
26 - 2022-11-01153Monarchs2Stars4ALSommaire du match
28 - 2022-11-03168Monarchs6Baby Hawks4AWSommaire du match
30 - 2022-11-05189Cabaret Lady Mary Ann4Monarchs2BLSommaire du match
33 - 2022-11-08206Minnesota2Monarchs0BLSommaire du match
35 - 2022-11-10220Baby Hawks5Monarchs4BLXSommaire du match
37 - 2022-11-12237Cougars8Monarchs3BLSommaire du match
39 - 2022-11-14245Monarchs2Heat4ALSommaire du match
41 - 2022-11-16259Monarchs1Oil Kings3ALSommaire du match
43 - 2022-11-18273Monarchs4Comets0AWSommaire du match
44 - 2022-11-19286Monarchs2Seattle3ALSommaire du match
47 - 2022-11-22302Wolf Pack7Monarchs4BLSommaire du match
50 - 2022-11-25331Monarchs2Sharks4ALSommaire du match
52 - 2022-11-27345Senators3Monarchs1BLSommaire du match
54 - 2022-11-29360Seattle4Monarchs2BLSommaire du match
56 - 2022-12-01375Jayhawks6Monarchs4BLSommaire du match
58 - 2022-12-03391Caroline5Monarchs3BLSommaire du match
61 - 2022-12-06406Monarchs-Senators-
63 - 2022-12-08420Monarchs-Marlies-
65 - 2022-12-10439Monarchs-Rocket-
66 - 2022-12-11443Monarchs-Monsters-
68 - 2022-12-13457Monarchs-Crunch-
70 - 2022-12-15472Monarchs-Bruins-
72 - 2022-12-17496Sharks-Monarchs-
75 - 2022-12-20516Admirals-Monarchs-
77 - 2022-12-22531Heat-Monarchs-
78 - 2022-12-23543Monarchs-Jayhawks-
82 - 2022-12-27557Las Vegas-Monarchs-
84 - 2022-12-29571Monarchs-Monsters-
86 - 2022-12-31580Phantoms-Monarchs-
89 - 2023-01-03608Stars-Monarchs-
91 - 2023-01-05621Bruins-Monarchs-
93 - 2023-01-07634Monarchs-Las Vegas-
95 - 2023-01-09647Oil Kings-Monarchs-
97 - 2023-01-11661Sharks-Monarchs-
100 - 2023-01-14690Spiders-Monarchs-
105 - 2023-01-19729Stars-Monarchs-
107 - 2023-01-21742Monarchs-Chill-
108 - 2023-01-22747Monarchs-Baby Hawks-
110 - 2023-01-24757Monarchs-Phantoms-
113 - 2023-01-27781Monarchs-Cabaret Lady Mary Ann-
114 - 2023-01-28791Monarchs-Thunder-
117 - 2023-01-31802Monarchs-Caroline-
128 - 2023-02-11845Manchots-Monarchs-
130 - 2023-02-13854Crunch-Monarchs-
134 - 2023-02-17882Monarchs-Admirals-
135 - 2023-02-18892Jayhawks-Monarchs-
138 - 2023-02-21913Monarchs-Minnesota-
140 - 2023-02-23923Monarchs-Spiders-
141 - 2023-02-24934Monarchs-Sound Tigers-
143 - 2023-02-26950Monarchs-Wolf Pack-
145 - 2023-02-28961Monarchs-Oceanics-
147 - 2023-03-02982Rocket-Monarchs-
149 - 2023-03-041000Chiefs-Monarchs-
151 - 2023-03-061011Bears-Monarchs-
154 - 2023-03-091033Monarchs-Monsters-
156 - 2023-03-111052Chill-Monarchs-
159 - 2023-03-141075Sound Tigers-Monarchs-
161 - 2023-03-161089Monsters-Monarchs-
163 - 2023-03-181104Comets-Monarchs-
165 - 2023-03-201119Heat-Monarchs-
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
170 - 2023-03-251153Oceanics-Monarchs-
171 - 2023-03-261169Chiefs-Monarchs-
173 - 2023-03-281183Monarchs-Heat-
175 - 2023-03-301197Monarchs-Oil Kings-
177 - 2023-04-011213Monarchs-Seattle-
178 - 2023-04-021224Monarchs-Comets-
180 - 2023-04-041237Oil Kings-Monarchs-
182 - 2023-04-061255Monarchs-Las Vegas-
184 - 2023-04-081272Monsters-Monarchs-
186 - 2023-04-101284Comets-Monarchs-
189 - 2023-04-131310Monarchs-Admirals-



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets5020
Assistance17,0589,359
Assistance PCT60.92%66.85%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
27 1887 - 62.90% 74,291$1,040,080$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursSalaire total moyen des joueursSalaire des entraineurs
335,191$ 1,019,893$ 1,029,893$ 0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
5,420$ 338,344$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
2,005,869$ 130 5,368$ 697,840$




Monarchs Leaders statistiques (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 (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