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Connexion

Fighting Pandas
GP: 54 | W: 30 | L: 17 | OTL: 7 | P: 67
GF: 231 | GA: 185 | PP%: 23.53% | PK%: 80.30%
DG: Hunter Jones | Morale : 55 | Moyenne d’équipe : 60
Prochains matchs #589 vs Bruins

Centre de jeu
Broncos
22-22-7, 51pts
1
FINAL
6 Fighting Pandas
30-17-7, 67pts
Team Stats
SOL1SéquenceSOW1
13-13-1Fiche domicile12-8-6
9-9-6Fiche domicile18-9-1
6-3-1Derniers 10 matchs7-3-0
3.35Buts par match 4.28
3.98Buts contre par match 3.43
21.36%Pourcentage en avantage numérique23.53%
63.74%Pourcentage en désavantage numérique80.30%
Fighting Pandas
30-17-7, 67pts
3
FINAL
2 Broncos
22-22-7, 51pts
Team Stats
SOW1SéquenceSOL1
12-8-6Fiche domicile13-13-1
18-9-1Fiche domicile9-9-6
7-3-0Derniers 10 matchs6-3-1
4.28Buts par match 3.35
3.43Buts contre par match 3.98
23.53%Pourcentage en avantage numérique21.36%
80.30%Pourcentage en désavantage numérique63.74%
Bruins
13-33-8, 34pts
Jour 116
Fighting Pandas
30-17-7, 67pts
Statistiques d’équipe
L2SéquenceSOW1
5-17-4Fiche domicile12-8-6
8-16-4Fiche visiteur18-9-1
1-7-210 derniers matchs7-3-0
3.30Buts par match 4.28
4.87Buts contre par match 4.28
25.69%Pourcentage en avantage numérique23.53%
74.48%Pourcentage en désavantage numérique80.30%
Fighting Pandas
30-17-7, 67pts
Jour 119
Butter Knives
24-23-4, 52pts
Statistiques d’équipe
SOW1SéquenceW1
12-8-6Fiche domicile14-10-3
18-9-1Fiche visiteur10-13-1
7-3-010 derniers matchs7-3-0
4.28Buts par match 4.61
3.43Buts contre par match 4.61
23.53%Pourcentage en avantage numérique20.59%
80.30%Pourcentage en désavantage numérique74.19%
Americiens
29-19-2, 60pts
Jour 120
Fighting Pandas
30-17-7, 67pts
Statistiques d’équipe
W2SéquenceSOW1
14-12-1Fiche domicile12-8-6
15-7-1Fiche visiteur18-9-1
6-4-010 derniers matchs7-3-0
4.86Buts par match 4.28
4.66Buts contre par match 4.28
22.52%Pourcentage en avantage numérique23.53%
72.36%Pourcentage en désavantage numérique80.30%
Meneurs d'équipe
Connor BrownButs
Connor Brown
32
Sean DurziPasses
Sean Durzi
45
Mackie SamoskevichPoints
Mackie Samoskevich
63
Alec MartinezPlus/Moins
Alec Martinez
26
Jakub DobesVictoires
Jakub Dobes
29
Cayden PrimeauPourcentage d’arrêts
Cayden Primeau
0.897

Statistiques d’équipe
Buts pour
231
4.28 GFG
Tirs pour
1705
31.57 Avg
Pourcentage en avantage numérique
23.5%
24 GF
Début de zone offensive
38.6%
Buts contre
185
3.43 GAA
Tirs contre
1695
31.39 Avg
Pourcentage en désavantage numérique
80.3%%
26 GA
Début de la zone défensive
39.3%
Informations de l'équipe

Directeur généralHunter Jones
EntraîneurPatrick Lalime
DivisionAtlantic
ConférenceEastern Conference
CapitaineJeff Skinner
Assistant #1Ryker Evans
Assistant #2Keegan Kolesar


Informations de l’aréna

Capacité3,000
Assistance0
Billets de saison300


Informations de la formation

Équipe Pro24
Équipe Mineure20
Limite contact 44 / 50
Espoirs28


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
1Keegan Kolesar (A)X100.00857189687772927550676567506768082670282500,000$
2Mackie Samoskevich (R)XX99.00765590766573837450707060545760082660232500,000$
3Connor BrownX100.00535494676574927150676976517374082660322500,000$
4Jeff Skinner (C)XX100.006556896971728373576572605178760816603312,000,000$
5Radek FaksaX100.00785979657772826679655572617575082640323500,000$
6Cole KoepkeX100.00826292667469836850636365506666078630271500,000$
7Beck MalenstynX99.00826183637567865861595168506768081610272700,000$
8Alexander HoltzX100.00655690677269706150625359535860076590242500,000$
9Samuel Helenius (R)X100.00846990657463676064585559505859078590233750,000$
10Ivan Ivan (R)XX100.00545590656864625955575757505859046570232500,000$
11Ben JonesX100.00747499606661555250534959506463060550264500,000$
12Alec MartinezX100.00635792657474677350656371568783080660382700,000$
13Ryker Evans (A)X100.00785979687175877350705571566064081660242500,000$
14Sean DurziX100.006166776770756274506763735666690846502711,000,000$
15Kaedan KorczakX100.00725696627466646950655170656165055630242500,000$
16Declan ChisholmX100.00635691656871806850605468556465080630262500,000$
Rayé
1Arshdeep BainsX100.00645089576560535250505054506261024530252500,000$
2Matej BlümelX100.00515089577350515050505050506061020510254500,000$
3Georgii MerkulovX100.00503983576150535050505050505860020510251500,000$
4Samuel BolducX100.00503595588261585050504949556163031540252500,000$
5Ryan JohnsonX100.00504740577050545050505050556062020520241500,000$
MOYENNE D’ÉQUIPE99.9067578664716671635360576253656606360
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
1Jakub Dobes (R)100.0073687079756864696765876164041670243750,000$
2Cayden Primeau100.0050656176615453555050596161064570264500,000$
Rayé
1Devon Levi100.0050646071505050505050505656045530241500,000$
MOYENNE D’ÉQUIPE100.005866647562575658565565596005059
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Patrick Lalime9090957070651CAN515500,000$


Astuces sur les filtres (anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Nom du joueur Nom de l’équipePOSGP G A P +/- PIM PIM5 HIT HTT SHT OSB OSM SHT% SB MP AMG PPG PPA PPP PPS PPM PKG PKA PKP PKS PKM GW GT FO% FOT GA TA EG HT P/20 PSG PSS FW FL FT S1 S2 S3
1Mackie SamoskevichFighting Pandas (OTT)C/RW5424396316215571011875312112.83%15114821.2625712820002683047.17%113200101.1022000471
2Connor BrownFighting Pandas (OTT)RW5432296114209912305114613.91%19102719.02371024800001480345.30%11700031.1924000702
3Keegan KolesarFighting Pandas (OTT)RW5428285621255116901694913216.57%13101018.7225713460000803049.79%24100001.1113001556
4Sean DurziFighting Pandas (OTT)D5484553262810825176266310.53%74124523.06178247700009831100.00%100000.8501101323
5Alec MartinezFighting Pandas (OTT)D5010425226220345681174512.35%60123124.633582475000184200%000000.8400000034
6Jeff SkinnerFighting Pandas (OTT)LW/RW54322052174050681956113116.41%11126623.45314258700031263151.61%9300020.8202000640
7Cole KoepkeFighting Pandas (OTT)LW54202848163958159143308813.99%6102719.0242617800000223148.28%8700000.9300010323
8Radek FaksaFighting Pandas (OTT)C541630462014010197118339513.56%894017.412351047000083262.29%100500000.9802000125
9Ryker EvansFighting Pandas (OTT)D545384316440148425225439.62%74130024.092461581000094000%000000.6600000026
10Beck MalenstynFighting Pandas (OTT)LW5413152824320856694317613.83%22100718.66000030000273250.93%10800000.5600000121
11Leo CarlssonOttawa SenatorsC/RW20916251000286174235912.16%647523.790338380000300152.85%61500001.0501000321
12Samuel HeleniusFighting Pandas (OTT)C541312257220698190276414.44%871313.2100000000013057.18%78700000.7000000003
13Alexander HoltzFighting Pandas (OTT)RW54617231610038419129706.59%998118.170004450001270044.23%10400000.4700000010
14Declan ChisholmFighting Pandas (OTT)D549142383401074449122218.37%7391917.02101514000028100%100000.5000000211
15Kaedan KorczakFighting Pandas (OTT)D322171982005725246198.33%3364620.22112944000065100%000000.5911000102
16Samuel BolducFighting Pandas (OTT)D290772401945170%1847016.2300002000029000%000000.3000000010
17Ivan IvanFighting Pandas (OTT)C/LW32156-200114191095.26%73019.4100000000020045.24%4200000.4000000000
18Ben JonesFighting Pandas (OTT)C29000-400010010%0642.22000020000100036.36%110000000000000
Statistiques d’équipe totales ou en moyenne8402284026302413212510829921697484119113.44%4561577718.782443671908120008855281153.48%434400150.80616112363438
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
1Jakub DobesFighting Pandas (OTT)54291750.8903.4330650117515860500.6005540210
2Cayden PrimeauFighting Pandas (OTT)20000.8973.16760043900000032000
Statistiques d’équipe totales ou en moyenne56291750.8903.42314101179162505055432210


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
Alec MartinezFighting Pandas (OTT)D381987-07-26USANo210 Lbs6 ft1NoNoTrade2025-03-05NoNo22024-08-21FalseFalsePro & Farm700,000$70,000$25,667$No700,000$--------700,000$--------No--------Lien / Lien NHL
Alexander HoltzFighting Pandas (OTT)RW242002-01-23SWENo198 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$18,333$No500,000$--------500,000$--------No--------Lien / Lien NHL
Arshdeep BainsFighting Pandas (OTT)LW252001-01-09CANNo184 Lbs6 ft0NoNoFree AgentNoNo22025-07-23FalseFalsePro & Farm500,000$50,000$18,333$No500,000$--------500,000$--------No--------Lien / Lien NHL
Beck MalenstynFighting Pandas (OTT)LW271998-02-04CANNo209 Lbs6 ft3NoNoAssign ManuallyNoNo22024-08-21FalseFalsePro & Farm700,000$70,000$25,667$No700,000$--------700,000$--------No--------Lien / Lien NHL
Ben JonesFighting Pandas (OTT)C261999-02-26CANNo187 Lbs6 ft0NoNoFree AgentNoNo42025-07-25FalseFalsePro & Farm500,000$50,000$18,333$No500,000$500,000$500,000$------500,000$500,000$500,000$------NoNoNo------Lien / Lien NHL
Cayden PrimeauFighting Pandas (OTT)G261999-08-11USANo205 Lbs6 ft3NoNoFree AgentNoNo42025-07-25FalseFalsePro & Farm500,000$50,000$18,333$No500,000$500,000$500,000$------500,000$500,000$500,000$------NoNoNo------Lien / Lien NHL
Cole KoepkeFighting Pandas (OTT)LW271998-05-17USANo207 Lbs6 ft1NoNoFree AgentNoNo12024-10-11FalseFalsePro & Farm500,000$50,000$18,333$No---------------------------Lien / Lien NHL
Connor BrownFighting Pandas (OTT)RW321994-01-14CANNo184 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$18,333$No500,000$--------500,000$--------No--------Lien / Lien NHL
Declan ChisholmFighting Pandas (OTT)D262000-01-12CANNo190 Lbs6 ft1NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$18,333$No500,000$--------500,000$--------No--------Lien / Lien NHL
Devon LeviFighting Pandas (OTT)G242001-12-27CANNo192 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm500,000$50,000$18,333$No---------------------------Lien / Lien NHL
Georgii MerkulovFighting Pandas (OTT)C252000-10-10RUSNo176 Lbs5 ft11NoNoFree AgentNoNo12024-09-09FalseFalsePro & Farm500,000$50,000$18,333$No---------------------------Lien / Lien NHL
Ivan IvanFighting Pandas (OTT)C/LW232002-08-20CZEYes190 Lbs6 ft0NoNoFree AgentNoNo22025-08-09FalseFalsePro & Farm500,000$50,000$18,333$No500,000$--------500,000$--------No--------Lien / Lien NHL
Jakub DobesFighting Pandas (OTT)G242001-05-27CZEYes215 Lbs6 ft4NoNoFree AgentNoNo32025-08-07FalseFalsePro & Farm750,000$75,000$27,500$No750,000$750,000$-------750,000$750,000$-------NoNo-------Lien / Lien NHL
Jeff SkinnerFighting Pandas (OTT)LW/RW331992-05-16CANNo200 Lbs5 ft11NoNoTrade2024-08-25NoNo1FalseFalsePro & Farm2,000,000$200,000$73,333$No---------------------------Lien / Lien NHL
Kaedan KorczakFighting Pandas (OTT)D242002-01-18CANNo202 Lbs6 ft3NoNoTrade2025-08-04NoNo2FalseFalsePro & Farm500,000$50,000$18,333$No500,000$--------500,000$--------No--------Lien / Lien NHL
Keegan KolesarFighting Pandas (OTT)RW281997-04-08CANNo216 Lbs6 ft2NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$18,333$No500,000$--------500,000$--------No--------Lien / Lien NHL
Mackie SamoskevichFighting Pandas (OTT)C/RW232002-11-15USAYes180 Lbs5 ft11NoNoTrade2025-08-04NoNo22024-08-10FalseFalsePro & Farm500,000$50,000$18,333$No500,000$--------500,000$--------No--------Lien / Lien NHL
Matej BlümelFighting Pandas (OTT)RW252000-05-31CZENo205 Lbs6 ft0NoNoFree AgentNoNo42025-07-25FalseFalsePro & Farm500,000$50,000$18,333$No500,000$500,000$500,000$------500,000$500,000$500,000$------NoNoNo------Lien
Radek FaksaFighting Pandas (OTT)C321994-01-09CZENo215 Lbs6 ft3NoNoN/ANoNo32024-08-19FalseFalsePro & Farm500,000$50,000$18,333$No500,000$500,000$-------500,000$500,000$-------NoNo-------Lien / Lien NHL
Ryan JohnsonFighting Pandas (OTT)D242001-07-24USANo195 Lbs6 ft1NoNoFree AgentNoNo12024-09-09FalseFalsePro & Farm500,000$50,000$18,333$No---------------------------Lien / Lien NHL
Ryker EvansFighting Pandas (OTT)D242001-12-13CANNo195 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$18,333$No500,000$--------500,000$--------No--------Lien / Lien NHL
Samuel BolducFighting Pandas (OTT)D252000-12-09CANNo224 Lbs6 ft4NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$18,333$No500,000$--------500,000$--------No--------Lien / Lien NHL
Samuel HeleniusFighting Pandas (OTT)C232002-11-26USAYes201 Lbs6 ft6NoNoFree AgentNoNo32025-08-10FalseFalsePro & Farm750,000$75,000$27,500$No750,000$750,000$-------750,000$750,000$-------NoNo-------Lien / Lien NHL
Sean DurziFighting Pandas (OTT)D271998-10-21CANNo196 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm1,000,000$100,000$36,667$No---------------------------Lien / Lien NHL
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2426.46199 Lbs6 ft12.13620,833$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Jeff SkinnerAlexander Holtz40122
2Cole KoepkeMackie SamoskevichConnor Brown30122
3Beck MalenstynRadek FaksaKeegan Kolesar20122
4Ivan IvanSamuel HeleniusMackie Samoskevich10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Alec MartinezRyker Evans40122
2Sean Durzi30122
3Declan ChisholmAlec Martinez20122
4Ryker EvansSean Durzi10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Jeff SkinnerAlexander Holtz60122
2Cole KoepkeMackie SamoskevichConnor Brown40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Alec MartinezRyker Evans60122
2Sean Durzi40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Jeff Skinner60122
2Mackie SamoskevichCole Koepke40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Alec MartinezRyker Evans60122
2Sean Durzi40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
160122Alec MartinezRyker Evans60122
2Mackie Samoskevich40122Sean Durzi40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Jeff Skinner60122
2Mackie SamoskevichCole Koepke40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Alec MartinezRyker Evans60122
2Sean Durzi40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Jeff SkinnerAlexander HoltzAlec MartinezRyker Evans
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Jeff SkinnerConnor BrownAlec MartinezRyker Evans
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Mackie Samoskevich, Radek Faksa, Cole KoepkeMackie Samoskevich, Radek FaksaMackie Samoskevich
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Sean Durzi, , Declan ChisholmSean DurziSean Durzi,
Tirs de pénalité
, Connor Brown, Keegan Kolesar, Jeff Skinner, Mackie Samoskevich
Gardien
#1 : Jakub Dobes, #2 : Cayden Primeau


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
1Aces1000010023-1000000000001000010023-110.50024600936471727578562550311434212150.00%2150.00%0923169954.33%911173152.63%54097155.61%143010331155359690362
2Admirals31200000111011010000034-12110000086220.333111930009364717835785625503170181449200.00%60100.00%0923169954.33%911173152.63%54097155.61%143010331155359690362
3Americiens21100000871110000005321010000034-120.50081523009364717705785625503174286504125.00%3166.67%0923169954.33%911173152.63%54097155.61%143010331155359690362
4Barons2010010069-31000010034-11010000035-210.250681400936471770578562550313513238300.00%10100.00%0923169954.33%911173152.63%54097155.61%143010331155359690362
5Barracuda210001009811000010067-11100000031230.75091726009364717545785625503147168498337.50%4175.00%0923169954.33%911173152.63%54097155.61%143010331155359690362
6Broncos632000102416811000000615522000101815380.6672440640093647171425785625503115542241174250.00%12375.00%0923169954.33%911173152.63%54097155.61%143010331155359690362
7Bruins320010001385220000008441000100054161.0001322350193647179657856255031921943638225.00%12191.67%0923169954.33%911173152.63%54097155.61%143010331155359690362
8Butter Knives532000002419532100000161152110000088060.600244165009364717196578562550311926030958112.50%15380.00%0923169954.33%911173152.63%54097155.61%143010331155359690362
9Canucks11000000431110000004310000000000021.0004711009364717335785625503127449300.00%2150.00%0923169954.33%911173152.63%54097155.61%143010331155359690362
10Firebirds2100010011742100010011740000000000030.7501120310093647177057856255031791918513133.33%9188.89%0923169954.33%911173152.63%54097155.61%143010331155359690362
11Griffins220000001156110000007251100000043141.0001121320093647175857856255031792313373133.33%4175.00%0923169954.33%911173152.63%54097155.61%143010331155359690362
12Ice Bats1100000010280000000000011000000102821.00010182800936471741578562550313057114250.00%110.00%0923169954.33%911173152.63%54097155.61%143010331155359690362
13Lions220000001376110000006331100000074341.0001323360093647178257856255031701819495120.00%7271.43%0923169954.33%911173152.63%54097155.61%143010331155359690362
14Lynx330000001477110000007342200000074361.000142539009364717955785625503173191052400.00%3166.67%0923169954.33%911173152.63%54097155.61%143010331155359690362
15Marlies403000101220-830300000413-91000001087120.2501222340093647171085785625503111132287410330.00%12375.00%0923169954.33%911173152.63%54097155.61%143010331155359690362
16Nordiks3110000118171201000011214-21100000063330.500183149009364717131578562550311454119778450.00%60100.00%0923169954.33%911173152.63%54097155.61%143010331155359690362
17Quacken1010000013-2000000000001010000013-200.0001230093647172257856255031181216400.00%000%0923169954.33%911173152.63%54097155.61%143010331155359690362
18Roadrunners623000011317-43110000178-13120000069-350.4171322350093647171725785625503116742421211100.00%20575.00%0923169954.33%911173152.63%54097155.61%143010331155359690362
19Tomahawks1000010034-11000010034-10000000000010.500347009364717325785625503153118222150.00%40100.00%0923169954.33%911173152.63%54097155.61%143010331155359690362
20Wombats2200000013580000000000022000000135841.00013233600936471749578562550318118842400.00%30100.00%0923169954.33%911173152.63%54097155.61%143010331155359690362
21Wranglers2110000011831010000036-31100000082620.5001119300093647177457856255031832812462150.00%6183.33%0923169954.33%911173152.63%54097155.61%143010331155359690362
Total54271701522231185462612800402111971428159011201208832670.620231403634019364717170557856255031169546032110891022423.53%1322680.30%0923169954.33%911173152.63%54097155.61%143010331155359690362
_Since Last GM Reset54271701522231185462612800402111971428159011201208832670.620231403634019364717170557856255031169546032110891022423.53%1322680.30%0923169954.33%911173152.63%54097155.61%143010331155359690362
_Vs Conference3618130112114311627179600101675413199701020766214440.6111432493920193647171081578562550311094297223714581017.24%951881.05%0923169954.33%911173152.63%54097155.61%143010331155359690362
_Vs Division179601010716110106400000403467320101031274220.647711251960193647175655785625503154215811733434720.59%45980.00%0923169954.33%911173152.63%54097155.61%143010331155359690362

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
5467SOW123140363417051695460321108901
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
5427171522231185
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
26128040211197
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
28159112012088
Derniers 10 matchs
WLOTWOTL SOWSOL
630010
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
1022423.53%1322680.30%0
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
578562550319364717
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
923169954.33%911173152.63%54097155.61%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
143010331155359690362


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
27Marlies6Fighting Pandas1LSommaire du match
623Fighting Pandas3Americiens4LSommaire du match
834Americiens3Fighting Pandas5WSommaire du match
938Fighting Pandas2Broncos1WSommaire du match
1254Bruins4Fighting Pandas5WSommaire du match
1361Fighting Pandas3Broncos6LSommaire du match
1674Butter Knives2Fighting Pandas7WSommaire du match
1884Fighting Pandas5Bruins4WXSommaire du match
2098Lynx3Fighting Pandas7WSommaire du match
21105Fighting Pandas0Roadrunners3LSommaire du match
25119Roadrunners1Fighting Pandas3WSommaire du match
26125Fighting Pandas2Lynx1WSommaire du match
29138Fighting Pandas3Broncos5LSommaire du match
31147Marlies3Fighting Pandas0LSommaire du match
32154Fighting Pandas8Marlies7WXXSommaire du match
35169Firebirds2Fighting Pandas7WSommaire du match
39184Tomahawks4Fighting Pandas3LXSommaire du match
41193Fighting Pandas3Barracuda1WSommaire du match
43201Fighting Pandas3Butter Knives5LSommaire du match
45212Nordiks7Fighting Pandas6LXXSommaire du match
48226Fighting Pandas7Broncos1WSommaire du match
49234Nordiks7Fighting Pandas6LSommaire du match
51249Barracuda7Fighting Pandas6LXSommaire du match
53260Fighting Pandas7Lions4WSommaire du match
55271Fighting Pandas8Wranglers2WSommaire du match
56278Admirals4Fighting Pandas3LSommaire du match
59293Firebirds5Fighting Pandas4LXSommaire du match
61305Fighting Pandas2Roadrunners4LSommaire du match
63315Wranglers6Fighting Pandas3LSommaire du match
66325Fighting Pandas4Griffins3WSommaire du match
68337Lions3Fighting Pandas6WSommaire du match
70348Fighting Pandas2Aces3LXSommaire du match
72358Fighting Pandas7Wombats1WSommaire du match
74365Marlies4Fighting Pandas3LSommaire du match
76380Barons4Fighting Pandas3LXSommaire du match
78388Fighting Pandas10Ice Bats2WSommaire du match
80401Griffins2Fighting Pandas7WSommaire du match
82412Fighting Pandas4Roadrunners2WSommaire du match
84424Canucks3Fighting Pandas4WSommaire du match
85431Fighting Pandas1Quacken3LSommaire du match
88442Fighting Pandas5Butter Knives3WSommaire du match
90451Butter Knives3Fighting Pandas4WSommaire du match
92461Fighting Pandas3Barons5LSommaire du match
95471Roadrunners3Fighting Pandas2LXXSommaire du match
97482Fighting Pandas6Nordiks3WSommaire du match
99492Fighting Pandas6Admirals3WSommaire du match
100501Bruins0Fighting Pandas3WSommaire du match
102512Fighting Pandas5Lynx3WSommaire du match
104523Butter Knives6Fighting Pandas5LSommaire du match
107535Fighting Pandas2Admirals3LSommaire du match
108544Roadrunners4Fighting Pandas2LSommaire du match
110555Fighting Pandas6Wombats4WSommaire du match
112567Broncos1Fighting Pandas6WSommaire du match
114577Fighting Pandas3Broncos2WXXSommaire du match
116589Bruins-Fighting Pandas-
119598Fighting Pandas-Butter Knives-
120608Americiens-Fighting Pandas-
122621Fighting Pandas-Lynx-
124632Lynx-Fighting Pandas-
126643Fighting Pandas-Americiens-
128652Marlies-Fighting Pandas-
130657Fighting Pandas-Canucks-
132673Quacken-Fighting Pandas-
133677Fighting Pandas-Tomahawks-
135686Fighting Pandas-Americiens-
136692Fighting Pandas-Barons-
138703Americiens-Fighting Pandas-
141719Fighting Pandas-Firebirds-
143725Wombats-Fighting Pandas-
146743Aces-Fighting Pandas-
147748Fighting Pandas-Bruins-
150765Admirals-Fighting Pandas-
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
154785Aces-Fighting Pandas-
158804Lynx-Fighting Pandas-
159809Fighting Pandas-Marlies-
162825Wombats-Fighting Pandas-
164834Fighting Pandas-Bruins-
167846Fighting Pandas-Marlies-
168854Fighting Pandas-Firebirds-
169858Ice Bats-Fighting Pandas-
173875Broncos-Fighting Pandas-
177892Ice Bats-Fighting Pandas-



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets4020
Assistance00
Assistance PCT0.00%0.00%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
15 0 - 0.00% 0$0$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursPlafond Salariale total des joueursSalaire des entraineurs
1,277,018$ 1,490,000$ 1,490,000$ 500,000$0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
1,490,000$ 960,368$ 24 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
0$ 66 11,056$ 729,696$




Fighting Pandas 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

Fighting Pandas 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

Fighting Pandas 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

Fighting Pandas 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

Fighting Pandas 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