Monsters

GP: 49 | W: 36 | L: 11 | OTL: 2 | P: 74
GF: 160 | GA: 90 | PP%: 18.90% | PK%: 88.85%
DG: Patrick Auger | Morale : 80 | Moyenne d'Équipe : 58
Prochain matchs #757 vs Americans
La résolution de votre navigateur est trop petite pour cette page. Plusieurs informations sont cachées pour garder la page lisible.

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 SP
1Jake VirtanenX98.00865574807975837760677078557070180710
2Oscar LindbergX98.00845576788078827181637076557168185700
3Dylan Strome (R)X98.00635570827570707973707065557372182690
4Nick CousinsX98.00775573756966677668657378556564186680
5Oskar Lindblom (R)X100.00765571847072737650636565557070185670
6Chris KellyX100.00685578757768676450626061557575185630
7Nikita SoshnikovX100.00845578816762646550606162555252185620
8Daniel CatenacciX100.00595572626662695550555560557574185580
9Daniel AudetteX100.00585572676259695550555559557175185580
10Nick TarnaskyX100.00745558638179645550555555556465183580
11Mike BrownX100.00605566697569545550555555556970185580
12Zack StortiniX100.00645557618179745550555555556565185580
13Ryan HaggertyX100.00715569687569665550555555555050185570
14Colin White (R)X100.00565555555657576660555555557371178560
15Madison BoweyX98.00795579866974647825716075557878180710
16Brian LashoffX98.00555555605555785525555555556061183560
17Ville PokkaX100.00555557605757765725575757555353176560
Rayé
1Tim BozonX100.00745567627468645550555555555050119560
2Quinton HowdenX100.00565563656866695550555555555656120560
3Matt Buckles (R)X100.00825582557954545550555555555050120550
4Akim Aliu (R)X100.00565555555859605550555555557575120550
5Brandon AldersonX100.00565555555758595550555555557472120540
6Buddy RobinsonX100.00565555555758585550555555557472120540
7Cody Kunyk (R)X100.00565555555758585550555555557371120540
8Zach O'Brien (R)X100.00705568555461685550555555555050120540
9Emil Pettersson (R)X100.00555555555555555550555555557571120540
10Stephen MacAulay (R)X100.00565559626866545550555555555050120540
11Lauri KorpikoskiX100.00555555555555555550555555557473120540
12Joshua WinquistX100.00565555555556555550555555555050120520
13Olivier Archambault (R)X100.00555562555454545550555555555050120520
14Mike SisloX100.00565555555555555550555555555050120520
15Ryan SproulX100.00685566766779647425646268557070168660
16Mike WeberX100.00555555605555625525555555556970120550
17Kyle BurroughsX100.00565556605656635625565656555353120540
18Robin Norell (R)X100.00555555605555675525555555555353120540
19Jeremy Roy (R)X100.00565555605555565525555555556262120540
20Will O'NeillX100.00575555605555675525555555555353120540
21Josiah DidierX100.00555555605555595525555555555353120530
22John NegrinX100.00555555605555585525555555555353120530
23Brennan Menell (R)X100.00555555605555565525555555555555120530
MOYENNE D'ÉQUIPE99.7063556364646264594557575955636214858
Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Nom du Gardien CON SK DU EN SZ AG RB SC HS RT PH PS EX LD PO MO OV TA SP
1Jack Campbell100.0073778373707075706771556967179700
2Eamon McAdam100.0062707079656565656864556062185650
Rayé
1Jeff Malcolm100.0054645971585858585958555456119580
2Marcus Hogberg100.0055555555555555555555556565120550
3Filip Gustavsson (R)100.0055555555555555555555555060120540
MOYENNE D'ÉQUIPE100.006064646761616261616155606214560
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Randy Ladouceur42626738807060CAN564100,000$


Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Nom du Joueur Nom de l'ÉquipePOS GP 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
1Oscar LindbergMonsters (Clb)C49153146125159413715839879.49%16111522.77815235624521372205066.21%110400010.8201001422
2Ryan SproulMonsters (Clb)D38535402864054495923448.47%3688323.254913381610220133110.00%000000.9100000153
3Oskar LindblomMonsters (Clb)LW4917213822204162123257313.82%578916.1161319442531015473052.31%6500100.9600000022
4Nick CousinsMonsters (Clb)C482214361430067100130379616.92%1982417.1794133413701131396161.14%50700000.8700000642
5Madison BoweyMonsters (Clb)D451025352655966276175413.16%39110724.6171320542000001157310.00%000000.6300100212
6Dylan StromeMonsters (Clb)C451715321980261081293210913.18%673316.29257178220251205164.92%66700000.8700000264
7Nikita SoshnikovMonsters (Clb)RW4913173012360624384228015.48%781916.72481224242000001155.32%4700000.7300000223
8Jake VirtanenMonsters (Clb)RW2511132443955261100329611.00%859523.834372811701110963054.88%24600110.8101010241
9Daniel AudetteMonsters (Clb)LW4961622880272656235010.71%672514.8136912158000023061.11%3600000.6100000023
10Nick TarnaskyMonsters (Clb)LW4941014236410732133142312.12%167813.84235674000021051.72%2900000.4100011200
11Daniel CatenacciMonsters (Clb)RW4976138140323263233211.11%567813.8522412870000190136.36%4400010.3800000112
12Ville PokkaMonsters (Clb)D44279152754416215129.52%2790720.63123121630110137000.00%000000.2000010100
13Colin WhiteMonsters (Clb)C3517896081262816.67%63148.990111270000951059.02%6100000.5100000001
14Brian LashoffMonsters (Clb)D49268105154714184611.11%3686917.7512381080000117000.00%000000.1800010021
15Mike BrownMonsters (Clb)RW49358112019143092110.00%14529.24101240000000038.46%2600000.3500000000
16Chris KellyMonsters (Clb)C49235-317516343116336.45%63246.630001190001410055.33%19700000.3100001000
17Zack StortiniMonsters (Clb)C49112-4195249102610.00%32294.690002280000310054.47%12300000.1700001000
18Ryan HaggertyMonsters (Clb)RW491124120147961711.11%22204.5000017000070035.14%3700000.1800000000
19Tim BozonMonsters (Clb)LW5011140304110.00%0367.2400000000000025.00%400000.5500000000
20Brendan LeipsicColumbus Blue JacketsC/LW1011000032000.00%02020.0801117000030073.33%1500001.0000000000
Stats d'équipe Total ou en Moyenne82513923537416554945799810114233284812.17%2291232714.9454871413532164561132137532661.38%320800230.6102144233126
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
1Jack CampbellMonsters (Clb)4534920.9021.812656210808190510.0002450611
2Eamon McAdamMonsters (Clb)62200.9001.67287008800100.0000449000
Stats d'équipe Total ou en Moyenne51361120.9021.792944210888990610.00024949611


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 StatusType Salaire Actuel Cap Salariale Cap Salariale Restant Exclus du Cap Salarial Link
Akim AliuMonsters (Clb)RW271989-04-23Yes225 Lbs6 ft4NoNoNo3Avec RestrictionPro & Farm500,000$0$0$No
Brandon AldersonMonsters (Clb)RW241992-01-21No202 Lbs6 ft4NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Brennan MenellMonsters (Clb)D191997-05-24Yes183 Lbs5 ft11NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No
Brian LashoffMonsters (Clb)D261990-07-15No212 Lbs6 ft3NoNoNo4Avec RestrictionPro & Farm300,000$0$0$No
Buddy RobinsonMonsters (Clb)RW251991-09-30No235 Lbs6 ft5NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No
Chris KellyMonsters (Clb)C361980-11-11No198 Lbs6 ft0NoNoNo2Sans RestrictionPro & Farm300,000$0$0$No
Cody KunykMonsters (Clb)RW261990-05-20Yes195 Lbs5 ft11NoNoNo3Avec RestrictionPro & Farm500,000$0$0$No
Colin WhiteMonsters (Clb)C191997-01-30Yes190 Lbs6 ft1NoNoNo4Contrat d'EntréePro & Farm900,000$0$0$No
Daniel AudetteMonsters (Clb)LW201996-05-06No176 Lbs5 ft8NoNoNo3Contrat d'EntréePro & Farm500,000$0$0$No
Daniel CatenacciMonsters (Clb)RW231993-03-09No186 Lbs5 ft9NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No
Dylan StromeMonsters (Clb)C191997-03-07Yes185 Lbs6 ft3NoNoNo4Contrat d'EntréePro & Farm900,000$0$0$No
Eamon McAdamMonsters (Clb)G221994-09-23No199 Lbs6 ft3NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$No
Emil PetterssonMonsters (Clb)C221994-01-14Yes176 Lbs6 ft2NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No
Filip GustavssonMonsters (Clb)G181998-06-07Yes185 Lbs6 ft2NoNoNo4Contrat d'EntréePro & Farm500,000$0$0$No
Jack CampbellMonsters (Clb)G251992-01-09No195 Lbs6 ft3NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Jake VirtanenMonsters (Clb)RW201996-08-17No208 Lbs6 ft1NoNoNo1Contrat d'EntréePro & Farm900,000$0$0$No
Jeff MalcolmMonsters (Clb)G271989-04-13No179 Lbs6 ft2NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No
Jeremy RoyMonsters (Clb)D191997-05-14Yes187 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm500,000$0$0$No
John NegrinMonsters (Clb)D271989-03-25No194 Lbs6 ft2NoNoNo3Avec RestrictionPro & Farm500,000$0$0$No
Joshua WinquistMonsters (Clb)LW231993-09-06No185 Lbs5 ft11NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No
Josiah DidierMonsters (Clb)D231993-04-08No203 Lbs6 ft2NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No
Kyle BurroughsMonsters (Clb)D211995-07-12No198 Lbs6 ft0NoNoNo1Contrat d'EntréePro & Farm630,000$0$0$No
Lauri KorpikoskiMonsters (Clb)LW301986-07-28No205 Lbs6 ft1NoNoNo1Sans RestrictionPro & Farm976,882$0$0$No
Madison BoweyMonsters (Clb)D211995-04-22No194 Lbs6 ft1NoNoNo1Contrat d'EntréePro & Farm894,000$0$0$No
Marcus HogbergMonsters (Clb)G221994-11-25No209 Lbs6 ft5NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No
Matt BucklesMonsters (Clb)C211995-05-05Yes218 Lbs6 ft3NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No
Mike BrownMonsters (Clb)RW311985-06-23No205 Lbs5 ft11NoNoNo1Sans RestrictionPro & Farm300,000$0$0$No
Mike SisloMonsters (Clb)RW281988-01-19No195 Lbs5 ft11NoNoNo1Sans RestrictionPro & Farm300,000$0$0$No
Mike WeberMonsters (Clb)D291987-12-15No212 Lbs6 ft2NoNoNo1Sans RestrictionPro & Farm300,000$0$0$No
Nick CousinsMonsters (Clb)C231993-07-19No169 Lbs5 ft10NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No
Nick TarnaskyMonsters (Clb)LW321984-11-24No230 Lbs6 ft2NoNoNo4Sans RestrictionPro & Farm300,000$0$0$No
Nikita SoshnikovMonsters (Clb)RW231993-10-14No183 Lbs5 ft11NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No
Olivier ArchambaultMonsters (Clb)LW231993-02-15Yes176 Lbs5 ft11NoNoNo4Avec RestrictionPro & Farm500,000$0$0$No
Oscar LindbergMonsters (Clb)C251991-10-28No195 Lbs6 ft1NoNoNo3Avec RestrictionPro & Farm500,000$0$0$No
Oskar LindblomMonsters (Clb)LW201996-08-15Yes192 Lbs6 ft1NoNoNo4Contrat d'EntréePro & Farm500,000$0$0$No
Quinton HowdenMonsters (Clb)C241992-01-20No189 Lbs6 ft2NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No
Robin NorellMonsters (Clb)D211995-02-17Yes195 Lbs5 ft11NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$No
Ryan HaggertyMonsters (Clb)RW231993-03-03No201 Lbs6 ft0NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Ryan SproulMonsters (Clb)D241993-01-12No206 Lbs6 ft4NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No
Stephen MacAulayMonsters (Clb)C241992-04-20Yes182 Lbs6 ft2NoNoNo4Avec RestrictionPro & Farm500,000$0$0$No
Tim BozonMonsters (Clb)LW221994-03-24No196 Lbs6 ft1NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$No
Ville PokkaMonsters (Clb)D221994-06-02No214 Lbs6 ft0NoNoNo1Contrat d'EntréePro & Farm500,000$0$0$No
Will O'NeillMonsters (Clb)D281988-04-27No190 Lbs6 ft1NoNoNo1Sans RestrictionPro & Farm500,000$0$0$No
Zach O'BrienMonsters (Clb)RW241992-06-29Yes192 Lbs5 ft11NoNoNo4Avec RestrictionPro & Farm500,000$0$0$No
Zack StortiniMonsters (Clb)C311985-09-10No230 Lbs6 ft4NoNoNo3Sans RestrictionPro & Farm500,000$0$0$No
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
4524.04197 Lbs6 ft12.67437,797$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Oskar LindblomOscar LindbergJake Virtanen40122
2Nick TarnaskyDylan StromeNikita Soshnikov30122
3Daniel AudetteNick CousinsDaniel Catenacci20122
4Jake VirtanenChris KellyMike Brown10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Madison BoweyBrian Lashoff40122
2Ville PokkaNick Cousins30122
3Madison BoweyBrian Lashoff20122
4Ville PokkaOscar Lindberg10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Oskar LindblomOscar LindbergJake Virtanen60122
2Nick TarnaskyDylan StromeNikita Soshnikov40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Madison BoweyBrian Lashoff60122
2Ville PokkaNick Cousins40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Jake VirtanenOscar Lindberg60122
2Dylan StromeNick Cousins40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Madison BoweyBrian Lashoff60122
2Ville Pokka40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Jake Virtanen60122Madison BoweyBrian Lashoff60122
2Oscar Lindberg40122Ville Pokka40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Jake VirtanenOscar Lindberg60122
2Dylan StromeNick Cousins40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Madison BoweyBrian Lashoff60122
2Ville Pokka40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Oskar LindblomOscar LindbergJake VirtanenMadison BoweyBrian Lashoff
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Oskar LindblomOscar LindbergJake VirtanenMadison BoweyBrian Lashoff
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Zack Stortini, Ryan Haggerty, Colin WhiteZack Stortini, Ryan HaggertyColin White
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Madison Bowey, Brian Lashoff, Ville PokkaMadison BoweyBrian Lashoff, Ville Pokka
Tirs de Pénalité
Jake Virtanen, Oscar Lindberg, Dylan Strome, Nick Cousins, Oskar Lindblom
Gardien
#1 : Jack Campbell, #2 : Eamon McAdam


Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
LigueDomicileVisiteur
# VS Équipe GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff P PCT G A TP SO EG GP1 GP2 GP3 GP4 SHF SH1 SP2 SP3 SP4 SHA SHB Pim Hit PPA PPG PP% PKA PK GA PK% PK GF W OF FO T OF FO OF FO% W DF FO T DF FO DF FO% W NT FO T NT FO NT FO% PZ DF PZ OF PZ NT PC DF PC OF PC NT
1Admirals11000000404000000000001100000040421.00047110159544612542141244348161212325.00%30100.00%0954154161.91%778128460.59%41566862.13%13851015983336577309
2Americans4220000079-2211000005502110000024-240.500713200059544619242141244346523566439410.26%27677.78%0954154161.91%778128460.59%41566862.13%13851015983336577309
3Bears22000000606000000000002200000060641.000611170259544614242141244343517183414428.57%90100.00%0954154161.91%778128460.59%41566862.13%13851015983336577309
4Checkers21001000945000000000002100100094541.0009152400595446167421412443431141443500.00%7271.43%0954154161.91%778128460.59%41566862.13%13851015983336577309
5Comets3300000013492200000010461100000030361.0001321340159544617542141244346022476418422.22%17288.24%0954154161.91%778128460.59%41566862.13%13851015983336577309
6Crunch42100100121112010010057-22200000074350.62512193100595446111642141244347120467333515.15%23386.96%0954154161.91%778128460.59%41566862.13%13851015983336577309
7Devils421000011011-12010000147-32200000064250.6251018281059544619142141244348729406531619.35%20385.00%0954154161.91%778128460.59%41566862.13%13851015983336577309
8Griffins44000000215162200000091822000000124881.00021385901595446112342141244347120626926726.92%23291.30%2954154161.91%778128460.59%41566862.13%13851015983336577309
9IceHogs33000000817220000006061100000021161.000811190259544617342141244346418404711436.36%18194.44%0954154161.91%778128460.59%41566862.13%13851015983336577309
10Marlies66000000327254400000020416220000001239121.0003256880259544612294214124434973154125251040.00%250100.00%2954154161.91%778128460.59%41566862.13%13851015983336577309
11Penguins41300000813-52110000045-12020000048-420.250815230059544617142141244347224586426415.38%23482.61%0954154161.91%778128460.59%41566862.13%13851015983336577309
12Phantoms2110000037-4000000000002110000037-420.500358005954461424214124434511343301715.88%19478.95%0954154161.91%778128460.59%41566862.13%13851015983336577309
13Rocket440000001688220000008352200000085381.00016314700595446110942141244345818497734720.59%22195.45%1954154161.91%778128460.59%41566862.13%13851015983336577309
14Senators312000005502020000025-31100000030320.3335813015954461624214124434731457612229.09%23195.65%1954154161.91%778128460.59%41566862.13%13851015983336577309
Total49351101101160907024157001017745322520401000834538740.7551602794391105954461127842141244349002806158853286218.90%2693088.85%6954154161.91%778128460.59%41566862.13%13851015983336577309
16Wolves32100000651211000004401100000021140.66761117005954461614214124434571625571516.67%10190.00%0954154161.91%778128460.59%41566862.13%13851015983336577309
_Since Last GM Reset49351101101160907024157001017745322520401000834538740.7551602794391105954461127842141244349002806158853286218.90%2693088.85%6954154161.91%778128460.59%41566862.13%13851015983336577309
_Vs Conference1998001014447-3815001011524-911830000029236200.526447612013595446142442141244343891172623271432215.38%1171587.18%1954154161.91%778128460.59%41566862.13%13851015983336577309
_Vs Division25320000193454814010000149252411310000044202470.1409316525804595446173142141244344351263244691793519.55%1431390.91%6954154161.91%778128460.59%41566862.13%13851015983336577309

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
4974L11602794391278900280615885110
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
493511110116090
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
2415701017745
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
2520410008345
Derniers 10 Matchs
WLOTWOTL SOWSOL
531001
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
3286218.90%2693088.85%6
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
42141244345954461
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
954154161.91%778128460.59%41566862.13%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
13851015983336577309


Derniers Match Joués
Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
JourMatch Équipe Visiteuse Score Équipe Locale Score ST OT SO RI Lien
3 - 2018-09-072IceHogs0Monsters5WSommaire du Match
4 - 2018-09-0810IceHogs0Monsters1WSommaire du Match
6 - 2018-09-1027Monsters7Marlies2WSommaire du Match
9 - 2018-09-1332Penguins3Monsters1LSommaire du Match
11 - 2018-09-1547Penguins2Monsters3WSommaire du Match
17 - 2018-09-2172Monsters3Comets0WSommaire du Match
18 - 2018-09-2290Monsters3Devils2WSommaire du Match
22 - 2018-09-26102Monsters2Wolves1WSommaire du Match
24 - 2018-09-28112Monsters4Admirals0WSommaire du Match
25 - 2018-09-29120Monsters2IceHogs1WSommaire du Match
31 - 2018-10-05141Marlies2Monsters3WSommaire du Match
33 - 2018-10-07169Griffins0Monsters4WSommaire du Match
36 - 2018-10-10177Monsters2Americans1WSommaire du Match
38 - 2018-10-12182Rocket1Monsters4WSommaire du Match
39 - 2018-10-13192Rocket2Monsters4WSommaire du Match
45 - 2018-10-19223Monsters4Bears0WSommaire du Match
46 - 2018-10-20238Monsters2Bears0WSommaire du Match
52 - 2018-10-26266Marlies0Monsters7WSommaire du Match
54 - 2018-10-28293Monsters5Marlies1WSommaire du Match
57 - 2018-10-31301Marlies2Monsters4WSommaire du Match
59 - 2018-11-02308Crunch4Monsters3LXSommaire du Match
60 - 2018-11-03318Crunch3Monsters2LSommaire du Match
64 - 2018-11-07343Monsters3Phantoms2WSommaire du Match
66 - 2018-11-09354Monsters3Penguins4LSommaire du Match
67 - 2018-11-10369Monsters0Phantoms5LSommaire du Match
73 - 2018-11-16395Monsters0Americans3LSommaire du Match
74 - 2018-11-17404Americans2Monsters3WSommaire du Match
78 - 2018-11-21426Monsters5Griffins2WSommaire du Match
80 - 2018-11-23436Senators3Monsters1LSommaire du Match
81 - 2018-11-24449Senators2Monsters1LSommaire du Match
85 - 2018-11-28465Americans3Monsters2LSommaire du Match
87 - 2018-11-30477Griffins1Monsters5WSommaire du Match
88 - 2018-12-01492Monsters7Griffins2WSommaire du Match
95 - 2018-12-08531Comets2Monsters5WSommaire du Match
96 - 2018-12-09545Comets2Monsters5WSommaire du Match
99 - 2018-12-12553Monsters3Senators0WSommaire du Match
101 - 2018-12-14563Monsters3Crunch2WSommaire du Match
102 - 2018-12-15582Monsters3Devils2WSommaire du Match
108 - 2018-12-21610Monsters3Rocket2WSommaire du Match
109 - 2018-12-22616Monsters5Rocket3WSommaire du Match
113 - 2018-12-26647Marlies0Monsters6WSommaire du Match
114 - 2018-12-27653Wolves1Monsters2WSommaire du Match
116 - 2018-12-29675Wolves3Monsters2LSommaire du Match
122 - 2019-01-04688Monsters1Penguins4LSommaire du Match
123 - 2019-01-05702Monsters4Crunch2WSommaire du Match
126 - 2019-01-08713Monsters4Checkers3WXSommaire du Match
127 - 2019-01-09716Monsters5Checkers1WSommaire du Match
130 - 2019-01-12739Devils4Monsters3LXXSommaire du Match
131 - 2019-01-13749Devils3Monsters1LSommaire du Match
133 - 2019-01-15757Americans-Monsters-
136 - 2019-01-18769Monsters-Senators-
137 - 2019-01-19783Monsters-Senators-
138 - 2019-01-20799Monsters-Comets-
141 - 2019-01-23811Monsters-Americans-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
143 - 2019-01-25819Admirals-Monsters-
145 - 2019-01-27841Admirals-Monsters-
149 - 2019-01-31855Rocket-Monsters-
150 - 2019-02-01859Rocket-Monsters-
151 - 2019-02-02868Monsters-Marlies-
156 - 2019-02-07896Senators-Monsters-
157 - 2019-02-08898Senators-Monsters-
162 - 2019-02-13934Monsters-Admirals-
164 - 2019-02-15950Monsters-Wolves-
165 - 2019-02-16960Monsters-IceHogs-
171 - 2019-02-22988Monsters-Americans-
172 - 2019-02-23996Bears-Monsters-
173 - 2019-02-241009Bears-Monsters-
178 - 2019-03-011028Phantoms-Monsters-
179 - 2019-03-021041Phantoms-Monsters-
183 - 2019-03-061066Monsters-Rocket-
185 - 2019-03-081078Monsters-Rocket-
186 - 2019-03-091090Monsters-Senators-
188 - 2019-03-111104Americans-Monsters-
191 - 2019-03-141114Checkers-Monsters-
192 - 2019-03-151115Checkers-Monsters-
194 - 2019-03-171145Monsters-Marlies-



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité de l'Aréna20001000
Prix des Billets3515
Assistance00
Assistance PCT0.00%0.00%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacité de l'ArénaPopularité de l'Équipe
14 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
205,589$ 197,009$ 142,008$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 138,020$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 63 1,531$ 96,453$




LigueDomicileVisiteur
Année GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff P G A TP SO EG GP1 GP2 GP3 GP4 SHF SH1 SP2 SP3 SP4 SHA SHB Pim Hit PPA PPG PP% PKA PK GA PK% PK GF W OF FO T OF FO OF FO% W DF FO T DF FO DF FO% W NT FO T NT FO NT FO% PZ DF PZ OF PZ NT PC DF PC OF PC NT
1349351101101160907024157001017745322520401000834538741602794391105954461127842141244349002806158853286218.90%2693088.85%6954154161.91%778128460.59%41566862.13%13851015983336577309
Total Saison Régulière49351101101160907024157001017745322520401000834538741602794391105954461127842141244349002806158853286218.90%2693088.85%6954154161.91%778128460.59%41566862.13%13851015983336577309
Séries
1220164000004919301082000002191210820000028101832498313206121817246614315614621405121392396141149.93%1661093.98%338167356.61%36566654.80%16827960.22%509338516168266131
1220164000004919301082000002191210820000028101832498313206121817246614315614621405121392396141149.93%1661093.98%338167356.61%36566654.80%16827960.22%509338516168266131
Total Séries4032800000983860201640000042182420164000005620366498166264012243634493228631229242810242784792282289.93%3322093.98%6762134656.61%730133254.80%33655860.22%10186771032337533263