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Fighting Pandas
GP: 35 | W: 17 | L: 12 | OTL: 6 | P: 40
GF: 147 | GA: 130 | PP%: 25.71% | PK%: 81.19%
DG: Hunter Jones | Morale : 46 | Moyenne d’équipe : 60
Prochains matchs #388 vs Ice Bats

Centre de jeu
Marlies
21-12-3, 45pts
4
FINAL
3 Fighting Pandas
17-12-6, 40pts
Team Stats
W2SéquenceOTL1
9-6-2Fiche domicile7-6-5
12-6-1Fiche domicile10-6-1
7-3-0Derniers 10 matchs3-4-3
4.31Buts par match 4.20
3.47Buts contre par match 3.71
26.80%Pourcentage en avantage numérique25.71%
78.02%Pourcentage en désavantage numérique81.19%
Barons
18-15-3, 39pts
4
FINAL
3 Fighting Pandas
17-12-6, 40pts
Team Stats
W1SéquenceOTL1
10-6-2Fiche domicile7-6-5
8-9-1Fiche domicile10-6-1
7-3-0Derniers 10 matchs3-4-3
3.50Buts par match 4.20
3.56Buts contre par match 3.71
20.55%Pourcentage en avantage numérique25.71%
91.18%Pourcentage en désavantage numérique81.19%
Fighting Pandas
17-12-6, 40pts
Jour 78
Ice Bats
12-16-7, 31pts
Statistiques d’équipe
OTL1SéquenceOTW1
7-6-5Fiche domicile5-10-2
10-6-1Fiche visiteur7-6-5
3-4-310 derniers matchs3-6-1
4.20Buts par match 3.91
3.71Buts contre par match 3.91
25.71%Pourcentage en avantage numérique18.67%
81.19%Pourcentage en désavantage numérique80.00%
Griffins
20-16-0, 40pts
Jour 80
Fighting Pandas
17-12-6, 40pts
Statistiques d’équipe
W3SéquenceOTL1
10-8-0Fiche domicile7-6-5
10-8-0Fiche visiteur10-6-1
7-3-010 derniers matchs3-4-3
5.75Buts par match 4.20
5.06Buts contre par match 4.20
20.00%Pourcentage en avantage numérique25.71%
72.62%Pourcentage en désavantage numérique81.19%
Fighting Pandas
17-12-6, 40pts
Jour 82
Roadrunners
26-8-1, 53pts
Statistiques d’équipe
OTL1SéquenceW4
7-6-5Fiche domicile16-2-0
10-6-1Fiche visiteur10-6-1
3-4-310 derniers matchs8-2-0
4.20Buts par match 3.83
3.71Buts contre par match 3.83
25.71%Pourcentage en avantage numérique30.12%
81.19%Pourcentage en désavantage numérique86.08%
Meneurs d'équipe
Connor BrownButs
Connor Brown
25
Alec MartinezPasses
Alec Martinez
28
Connor BrownPoints
Connor Brown
45
Keegan KolesarPlus/Moins
Keegan Kolesar
11
Jakub DobesVictoires
Jakub Dobes
16
Cayden PrimeauPourcentage d’arrêts
Cayden Primeau
0.897

Statistiques d’équipe
Buts pour
147
4.20 GFG
Tirs pour
1135
32.43 Avg
Pourcentage en avantage numérique
25.7%
18 GF
Début de zone offensive
39.0%
Buts contre
130
3.71 GAA
Tirs contre
1119
31.97 Avg
Pourcentage en désavantage numérique
81.2%%
19 GA
Début de la zone défensive
39.2%
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 Pro25
Équipe Mineure20
Limite contact 45 / 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
1Leo CarlssonXX100.00595690737378887661707266675258022670202500,000$
2Connor BrownX100.00535494676574927050676876517273067660312500,000$
3Keegan Kolesar (A)X100.00857188687772927550676567506667068660282500,000$
4Mackie Samoskevich (R)XX100.00765590756473837450697060545659066650232500,000$
5Jeff Skinner (C)XX100.006556896971728372576570605177750586503312,000,000$
6Radek FaksaX100.00785979657772826579645472617474064640313500,000$
7Cole KoepkeX100.00816292667469836750616365506565065630271500,000$
8Beck MalenstynX100.00826183637567865861595167506667065610272700,000$
9Alexander HoltzX100.00655690667169706150625359535759058590232500,000$
10Samuel Helenius (R)X100.00846989657463676064585560505758062590233750,000$
11Ivan Ivan (R)XX100.00545589646764626055575857505859031570232500,000$
12Ben JonesX100.00747499606661555250534959506463055550264500,000$
13Alec MartinezX100.00645792657474677150656170568682057660382700,000$
14Ryker Evans (A)X100.00775979677075877250695470565963062660242500,000$
15Sean DurziX100.006166766770756272506660725665680666402711,000,000$
16Declan ChisholmX100.00635691656871806750605367556364062630252500,000$
Rayé
1Arshdeep BainsX100.00645089576560535250505054506261042530242500,000$
2Matej BlümelX100.00515089577350515050505050506061020510254500,000$
3Georgii MerkulovX100.00503983576150535050505050505860020510251500,000$
4Kaedan KorczakX100.00725696627466646950655170656165050630232500,000$
5Samuel BolducX100.00503595588261585050504949556163050540252500,000$
6Ryan JohnsonX100.00504740577050545050505050556062020520241500,000$
MOYENNE D’ÉQUIPE100.0066578664716771635460576254646505160
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)100.0072687079746863686665876063019670243750,000$
2Cayden Primeau100.0050656176615453555050596161045570264500,000$
Rayé
1Devon Levi100.0050646071505050505050505656054530231500,000$
MOYENNE D’ÉQUIPE100.005766647562575558555565596003959
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
1Connor BrownFighting Pandas (OTT)RW352520458203691773710114.12%1368319.5236920540001480348.98%9800021.3222000501
2Mackie SamoskevichFighting Pandas (OTT)C/RW35152742101752760112286913.39%868319.522578550001522048.08%75700101.2322000341
3Keegan KolesarFighting Pandas (OTT)RW35231639112559672126369018.25%976021.7224612450000803049.33%22300001.0302001524
4Alec MartinezFighting Pandas (OTT)D315283311120183250112310.00%2873223.622571651000165000%000000.9000000013
5Radek FaksaFighting Pandas (OTT)C358233110120806274216910.81%462517.86134744000021263.33%73900000.9901000122
6Sean DurziFighting Pandas (OTT)D35326291021563284819366.25%4076121.751451753000074100%000000.7601001222
7Cole KoepkeFighting Pandas (OTT)LW351514297140473892186516.30%566118.91314954000092146.15%5200000.8800000112
8Jeff SkinnerFighting Pandas (OTT)LW/RW35141428-1203147119437411.76%782923.6911212600002960050.72%6900000.6802000330
9Ryker EvansFighting Pandas (OTT)D35224267340111292915276.90%5183023.721231159000075000%000000.6300000013
10Beck MalenstynFighting Pandas (OTT)LW3511102111140554981276813.58%1358816.80000010000233142.86%4900000.7100000021
11Kaedan KorczakFighting Pandas (OTT)D322171982005725246198.33%3364620.22112944000065100%000000.5911000102
12Samuel HeleniusFighting Pandas (OTT)C35108186140446574164513.51%749214.0700000000012056.37%55700000.7300000002
13Declan ChisholmFighting Pandas (OTT)D357916028090313591520.00%5157516.4310146000018100%000000.5600000210
14Alexander HoltzFighting Pandas (OTT)RW3541115210012345821506.90%659717.070000170001270047.95%7300000.5000000010
15Samuel BolducFighting Pandas (OTT)D290772401945170%1847016.2300002000029000%000000.3000000010
16Leo CarlssonFighting Pandas (OTT)C/RW6235-3006282051510.00%214123.630003150000100057.74%16800000.7100000001
17Ivan IvanFighting Pandas (OTT)C/LW13022-300075320%3725.5900000000000038.24%3400000.5500000000
18Ben JonesFighting Pandas (OTT)C29000-400010010%0642.22000020000100036.36%110000000000000
Statistiques d’équipe totales ou en moyenne5601462594059222915759681112931677612.93%2981021618.24183250128568000669216754.17%283000120.79511002232124
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)35161240.8803.79191600121101004000350110
2Cayden PrimeauFighting Pandas (OTT)20000.8973.16760043900000013000
Statistiques d’équipe totales ou en moyenne37161240.8813.76199300125104904003513110


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$40,056$No700,000$--------700,000$--------No--------Lien / Lien NHL
Alexander HoltzFighting Pandas (OTT)RW232002-01-23SWENo198 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Arshdeep BainsFighting Pandas (OTT)LW242001-01-09CANNo184 Lbs6 ft0NoNoFree AgentNoNo22025-07-23FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Beck MalenstynFighting Pandas (OTT)LW271998-02-04CANNo209 Lbs6 ft3NoNoAssign ManuallyNoNo22024-08-21FalseFalsePro & Farm700,000$70,000$40,056$No700,000$--------700,000$--------No--------Lien / Lien NHL
Ben JonesFighting Pandas (OTT)C261999-02-26CANNo187 Lbs6 ft0NoNoFree AgentNoNo42025-07-25FalseFalsePro & Farm500,000$50,000$28,611$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$28,611$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$28,611$No---------------------------Lien / Lien NHL
Connor BrownFighting Pandas (OTT)RW311994-01-14CANNo184 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Declan ChisholmFighting Pandas (OTT)D252000-01-12CANNo190 Lbs6 ft1NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Devon LeviFighting Pandas (OTT)G232001-12-27CANNo192 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm500,000$50,000$28,611$No---------------------------Lien / Lien NHL
Georgii MerkulovFighting Pandas (OTT)C252000-10-10RUSNo176 Lbs5 ft11NoNoFree AgentNoNo12024-09-09FalseFalsePro & Farm500,000$50,000$28,611$No---------------------------Lien / Lien NHL
Ivan IvanFighting Pandas (OTT)C/LW232002-08-20CZEYes190 Lbs6 ft0NoNoFree AgentNoNo22025-08-09FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Jakub DobesFighting Pandas (OTT)G242001-05-27CZEYes215 Lbs6 ft4NoNoFree AgentNoNo32025-08-07FalseFalsePro & Farm750,000$75,000$42,917$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$114,444$No---------------------------Lien / Lien NHL
Kaedan KorczakFighting Pandas (OTT)D232002-01-18CANNo202 Lbs6 ft3NoNoTrade2025-08-04NoNo2FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Keegan KolesarFighting Pandas (OTT)RW281997-04-08CANNo216 Lbs6 ft2NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Leo CarlssonFighting Pandas (OTT)C/RW202004-12-26SWENo203 Lbs6 ft3NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$28,611$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$28,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Matej BlümelFighting Pandas (OTT)RW252000-05-31CZENo205 Lbs6 ft0NoNoFree AgentNoNo42025-07-25FalseFalsePro & Farm500,000$50,000$28,611$No500,000$500,000$500,000$------500,000$500,000$500,000$------NoNoNo------Lien
Radek FaksaFighting Pandas (OTT)C311994-01-09CZENo215 Lbs6 ft3NoNoN/ANoNo32024-08-19FalseFalsePro & Farm500,000$50,000$28,611$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$28,611$No---------------------------Lien / Lien NHL
Ryker EvansFighting Pandas (OTT)D242001-12-13CANNo195 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Samuel BolducFighting Pandas (OTT)D252000-12-09CANNo224 Lbs6 ft4NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$28,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Samuel HeleniusFighting Pandas (OTT)C232002-11-26USAYes201 Lbs6 ft6NoNoFree AgentNoNo32025-08-10FalseFalsePro & Farm750,000$75,000$42,917$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$57,222$No---------------------------Lien / Lien NHL
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2525.92199 Lbs6 ft12.12616,000$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Jeff SkinnerLeo CarlssonAlexander 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 SkinnerLeo CarlssonAlexander 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
1Leo CarlssonJeff 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
1Leo Carlsson60122Alec MartinezRyker Evans60122
2Mackie Samoskevich40122Sean Durzi40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Leo CarlssonJeff Skinner60122
2Mackie SamoskevichCole Koepke40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Alec MartinezRyker Evans60122
2Sean Durzi40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Jeff SkinnerLeo CarlssonAlexander HoltzAlec MartinezRyker Evans
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Jeff SkinnerLeo CarlssonConnor 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
Leo Carlsson, 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
1Aces1000010023-1000000000001000010023-110.50024600574048627376377371221434212150.00%2150.00%0616111955.05%609112654.09%34762455.61%923663756235445233
2Admirals1010000034-11010000034-10000000000000.00036900574048622376377371222561017000%50100.00%0616111955.05%609112654.09%34762455.61%923663756235445233
3Americiens21100000871110000005321010000034-120.50081523005740486703763773712274286504125.00%3166.67%0616111955.05%609112654.09%34762455.61%923663756235445233
4Barons1000010034-11000010034-10000000000010.5003470057404864337637737122184218200.00%10100.00%0616111955.05%609112654.09%34762455.61%923663756235445233
5Barracuda210001009811000010067-11100000031230.75091726005740486543763773712247168498337.50%4175.00%0616111955.05%609112654.09%34762455.61%923663756235445233
6Broncos422000001513200000000000422000001513240.50015264100574048695376377371221132720833266.67%10280.00%0616111955.05%609112654.09%34762455.61%923663756235445233
7Bruins210010001082110000005411000100054141.0001016260057404867537637737122641120457228.57%9188.89%0616111955.05%609112654.09%34762455.61%923663756235445233
8Butter Knives211000001073110000007251010000035-220.5001017270057404868037637737122671822436116.67%11281.82%0616111955.05%609112654.09%34762455.61%923663756235445233
9Firebirds2100010011742100010011740000000000030.7501120310057404867037637737122791918513133.33%9188.89%0616111955.05%609112654.09%34762455.61%923663756235445233
10Griffins11000000431000000000001100000043121.00048120057404863137637737122388720100.00%20100.00%0616111955.05%609112654.09%34762455.61%923663756235445233
11Lions220000001376110000006331100000074341.0001323360057404868237637737122701819495120.00%7271.43%0616111955.05%609112654.09%34762455.61%923663756235445233
12Lynx22000000945110000007341100000021141.0009162500574048661376377371225412438200.00%10100.00%0616111955.05%609112654.09%34762455.61%923663756235445233
13Marlies403000101220-830300000413-91000001087120.2501222340057404861083763773712211132287410330.00%12375.00%0616111955.05%609112654.09%34762455.61%923663756235445233
14Nordiks201000011214-2201000011214-20000000000010.25012213300574048688376377371221013415454125.00%40100.00%0616111955.05%609112654.09%34762455.61%923663756235445233
15Roadrunners3120000058-3110000003122020000027-520.3335914005740486923763773712276172473500.00%11463.64%0616111955.05%609112654.09%34762455.61%923663756235445233
16Tomahawks1000010034-11000010034-10000000000010.500347005740486323763773712253118222150.00%40100.00%0616111955.05%609112654.09%34762455.61%923663756235445233
17Wombats11000000716000000000001100000071621.000713200057404863137637737122327218400.00%000%0616111955.05%609112654.09%34762455.61%923663756235445233
18Wranglers2110000011831010000036-31100000082620.5001119300057404867437637737122832812462150.00%6183.33%0616111955.05%609112654.09%34762455.61%923663756235445233
Total351512015111471301718760040178753178601110695514400.5711472604070057404861135376377371221119299229762701825.71%1011981.19%0616111955.05%609112654.09%34762455.61%923663756235445233
_Since Last GM Reset351512015111471301718760040178753178601110695514400.5711472604070057404861135376377371221119299229762701825.71%1011981.19%0616111955.05%609112654.09%34762455.61%923663756235445233
_Vs Conference231010011109079111164001004537812460101045423250.5439016025000574048670437637737122695177154492441022.73%711480.28%0616111955.05%609112654.09%34762455.61%923663756235445233
_Vs Division1255010104946374300000282535120101021210140.5834986135005740486394376377371223701018025029724.14%36780.56%0616111955.05%609112654.09%34762455.61%923663756235445233

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
3540OTL11472604071135111929922976200
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
3515121511147130
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
187604017875
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
178611106955
Derniers 10 matchs
WLOTWOTL SOWSOL
340300
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
701825.71%1011981.19%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
376377371225740486
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
616111955.05%609112654.09%34762455.61%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
923663756235445233


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 Pandas-Ice Bats-
80401Griffins-Fighting Pandas-
82412Fighting Pandas-Roadrunners-
84424Canucks-Fighting Pandas-
85431Fighting Pandas-Quacken-
88442Fighting Pandas-Butter Knives-
90451Butter Knives-Fighting Pandas-
92461Fighting Pandas-Barons-
95471Roadrunners-Fighting Pandas-
97482Fighting Pandas-Nordiks-
99492Fighting Pandas-Admirals-
100501Bruins-Fighting Pandas-
102512Fighting Pandas-Lynx-
104523Butter Knives-Fighting Pandas-
107535Fighting Pandas-Admirals-
108544Roadrunners-Fighting Pandas-
110555Fighting Pandas-Wombats-
112567Broncos-Fighting Pandas-
114577Fighting Pandas-Broncos-
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-
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
23 0 - 0.00% 0$0$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursPlafond Salariale total des joueursSalaire des entraineurs
854,913$ 1,540,000$ 1,540,000$ 500,000$0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
1,540,000$ 641,020$ 25 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
0$ 103 11,333$ 1,167,299$




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