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Fighting Pandas
GP: 27 | W: 14 | L: 9 | OTL: 4 | P: 32
GF: 117 | GA: 102 | PP%: 31.37% | PK%: 83.13%
DG: Hunter Jones | Morale : 50 | Moyenne d’équipe : 60
Prochains matchs #305 vs Roadrunners

Centre de jeu
Admirals
5-20-1, 11pts
4
FINAL
3 Fighting Pandas
14-9-4, 32pts
Team Stats
L1SéquenceOTL1
2-11-1Fiche domicile6-4-4
3-9-0Fiche domicile8-5-0
3-7-0Derniers 10 matchs4-3-3
3.50Buts par match 4.33
5.81Buts contre par match 3.78
17.46%Pourcentage en avantage numérique31.37%
69.81%Pourcentage en désavantage numérique83.13%
Firebirds
16-11-0, 32pts
5
FINAL
4 Fighting Pandas
14-9-4, 32pts
Team Stats
OTW1SéquenceOTL1
9-4-0Fiche domicile6-4-4
7-7-0Fiche domicile8-5-0
7-3-0Derniers 10 matchs4-3-3
4.85Buts par match 4.33
4.37Buts contre par match 3.78
19.70%Pourcentage en avantage numérique31.37%
80.00%Pourcentage en désavantage numérique83.13%
Fighting Pandas
14-9-4, 32pts
Jour 61
Roadrunners
20-7-1, 41pts
Statistiques d’équipe
OTL1SéquenceL1
6-4-4Fiche domicile11-2-0
8-5-0Fiche visiteur9-5-1
4-3-310 derniers matchs7-2-1
4.33Buts par match 3.89
3.78Buts contre par match 3.89
31.37%Pourcentage en avantage numérique29.41%
83.13%Pourcentage en désavantage numérique86.15%
Wranglers
16-9-2, 34pts
Jour 63
Fighting Pandas
14-9-4, 32pts
Statistiques d’équipe
OTW1SéquenceOTL1
9-5-0Fiche domicile6-4-4
7-4-2Fiche visiteur8-5-0
5-5-010 derniers matchs4-3-3
4.89Buts par match 4.33
4.67Buts contre par match 4.33
12.24%Pourcentage en avantage numérique31.37%
66.67%Pourcentage en désavantage numérique83.13%
Fighting Pandas
14-9-4, 32pts
Jour 66
Griffins
14-13-0, 28pts
Statistiques d’équipe
OTL1SéquenceW1
6-4-4Fiche domicile7-6-0
8-5-0Fiche visiteur7-7-0
4-3-310 derniers matchs6-4-0
4.33Buts par match 5.63
3.78Buts contre par match 5.63
31.37%Pourcentage en avantage numérique18.75%
83.13%Pourcentage en désavantage numérique68.75%
Meneurs d'équipe
Connor BrownButs
Connor Brown
22
Sean DurziPasses
Sean Durzi
21
Connor BrownPoints
Connor Brown
39
Keegan KolesarPlus/Moins
Keegan Kolesar
9
Jakub DobesVictoires
Jakub Dobes
13
Cayden PrimeauPourcentage d’arrêts
Cayden Primeau
0.897

Statistiques d’équipe
Buts pour
117
4.33 GFG
Tirs pour
867
32.11 Avg
Pourcentage en avantage numérique
31.4%
16 GF
Début de zone offensive
38.6%
Buts contre
102
3.78 GAA
Tirs contre
887
32.85 Avg
Pourcentage en désavantage numérique
83.1%%
14 GA
Début de la zone défensive
39.6%
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 Mineure19
Limite contact 43 / 50
Espoirs30


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
1Connor BrownX100.00535494676574927050676876517273067660312500,000$
2Keegan Kolesar (A)X99.00857188687772927450676467506667066660282500,000$
3Mackie Samoskevich (R)XX100.00765590756473837350686960545659066650232500,000$
4Jeff Skinner (C)XX99.006556896971728373576570605177750616503312,000,000$
5Radek FaksaX100.00785978657772826579645472617474063640313500,000$
6Cole KoepkeX100.00816292667469836750616366506565064630271500,000$
7Beck MalenstynX100.00826183637567865861595167506667064610272700,000$
8Alexander HoltzX100.00655689667169706150625359535759059590232500,000$
9Samuel Helenius (R)X100.00846989657463676063585560505758062590233750,000$
10Ben JonesX100.00747499606661555250534959506463056550264500,000$
11Arshdeep BainsX100.00645089576560535250505054506261048530242500,000$
12Ryker Evans (A)X100.00775979677075877250685470565963063660232500,000$
13Sean DurziX100.006166766770756271506660725665680656402711,000,000$
14Kaedan KorczakX100.00725696627466646850655070656165062630232500,000$
15Declan ChisholmX100.00635691656871806750605267556364062630252500,000$
16Samuel BolducX100.00503594588261585050504949556163059540242500,000$
Rayé
1Ivan Ivan (R)XX100.00555589646764626055575858505858032570232500,000$
2Matej BlümelX100.00515089577350515050505050506061023510254500,000$
3Georgii MerkulovX100.00503983576150535050505050505860023510251500,000$
4Alec MartinezX100.00645792657474677050636170568682057660382700,000$
5Ryan JohnsonX100.00504740577050545050505050556062023520241500,000$
MOYENNE D’ÉQUIPE99.9067578664716671635360566253646505560
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)98.0072687079746864696765876063022670243750,000$
2Devon Levi100.0050646071505050505050505656056530231500,000$
Rayé
1Cayden Primeau100.0050656176615453555050596161045570264500,000$
MOYENNE D’ÉQUIPE99.335766647562575658565565596004159
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)RW27221739420154144317715.28%1253819.9425713390001420348.84%8600021.4522000501
2Keegan KolesarFighting Pandas (OTT)RW2718143291808262102347117.65%762723.2524612400000722050.50%20000001.0202000513
3Mackie SamoskevichFighting Pandas (OTT)C/RW271219316100153977214915.58%550818.832357410001412047.39%59500101.2222000240
4Cole KoepkeFighting Pandas (OTT)LW271313264120332869135018.84%450918.88314839000021146.34%4100001.0200000112
5Radek FaksaFighting Pandas (OTT)C2752025510071465514539.09%249418.33134639000001164.37%59500001.0101000022
6Alec MartinezFighting Pandas (OTT)D2542024812012233751810.81%2457523.042461239000158000%000000.8300000002
7Jeff SkinnerFighting Pandas (OTT)LW/RW27121224620203791296113.19%764323.841127400002780052.63%5700000.7502000230
8Sean DurziFighting Pandas (OTT)D2722123519547233412295.88%2756620.991451436000062100%000000.8101001122
9Ryker EvansFighting Pandas (OTT)D2721820728091202313248.70%3963423.511231042000059000%000000.6300000003
10Kaedan KorczakFighting Pandas (OTT)D271141551403920174145.88%3254120.06011536000056100%000000.5511000101
11Beck MalenstynFighting Pandas (OTT)LW27114153120383872255615.28%1044616.55000010000193143.18%4400000.6700000021
12Samuel HeleniusFighting Pandas (OTT)C2787153100395760143813.33%641215.2800000000002055.60%47300000.7300000002
13Declan ChisholmFighting Pandas (OTT)D275914122072253281115.63%4144216.3810145000013000%000000.6300000210
14Alexander HoltzFighting Pandas (OTT)RW27110114605264419352.27%544216.38000010001250044.26%6100000.5000000000
15Samuel BolducFighting Pandas (OTT)D270775401745170%1844016.3000002000027000%000000.3200000010
16Ben JonesFighting Pandas (OTT)C27000-200000010%0471.7700002000090030.00%100000000000000
17Ivan IvanFighting Pandas (OTT)C/LW7000-300041120%1253.6800000000000038.71%310000000000000
Statistiques d’équipe totales ou en moyenne43711620532170181558250686324459613.44%240789918.0816284498407000656913653.85%219300120.81511001191719
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)2713920.8803.891435009377804000270110
2Cayden PrimeauFighting Pandas (OTT)20000.8973.1676004390000007000
Statistiques d’équipe totales ou en moyenne2913920.8813.85151100978170400277110


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$47,056$No700,000$--------700,000$--------No--------Lien / Lien NHL
Alexander HoltzFighting Pandas (OTT)RW232002-01-23SWENo198 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$33,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Arshdeep BainsFighting Pandas (OTT)LW242001-01-09CANNo184 Lbs6 ft0NoNoFree AgentNoNo22025-07-23FalseFalsePro & Farm500,000$50,000$33,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Beck MalenstynFighting Pandas (OTT)LW271998-02-04CANNo209 Lbs6 ft3NoNoAssign ManuallyNoNo22024-08-21FalseFalsePro & Farm700,000$70,000$47,056$No700,000$--------700,000$--------No--------Lien / Lien NHL
Ben JonesFighting Pandas (OTT)C261999-02-26CANNo187 Lbs6 ft0NoNoFree AgentNoNo42025-07-25FalseFalsePro & Farm500,000$50,000$33,611$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$33,611$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$33,611$No---------------------------Lien / Lien NHL
Connor BrownFighting Pandas (OTT)RW311994-01-14CANNo184 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$33,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Declan ChisholmFighting Pandas (OTT)D252000-01-12CANNo190 Lbs6 ft1NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$33,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Devon LeviFighting Pandas (OTT)G232001-12-27CANNo192 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm500,000$50,000$33,611$No---------------------------Lien / Lien NHL
Georgii MerkulovFighting Pandas (OTT)C252000-10-10RUSNo176 Lbs5 ft11NoNoFree AgentNoNo12024-09-09FalseFalsePro & Farm500,000$50,000$33,611$No---------------------------Lien / Lien NHL
Ivan IvanFighting Pandas (OTT)C/LW232002-08-20CZEYes190 Lbs6 ft0NoNoFree AgentNoNo22025-08-09FalseFalsePro & Farm500,000$50,000$33,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Jakub DobesFighting Pandas (OTT)G242001-05-27CZEYes215 Lbs6 ft4NoNoFree AgentNoNo32025-08-07FalseFalsePro & Farm750,000$75,000$50,417$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$134,444$No---------------------------Lien / Lien NHL
Kaedan KorczakFighting Pandas (OTT)D232002-01-18CANNo202 Lbs6 ft3NoNoTrade2025-08-04NoNo2FalseFalsePro & Farm500,000$50,000$33,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Keegan KolesarFighting Pandas (OTT)RW281997-04-08CANNo216 Lbs6 ft2NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$33,611$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$33,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Matej BlümelFighting Pandas (OTT)RW252000-05-31CZENo205 Lbs6 ft0NoNoFree AgentNoNo42025-07-25FalseFalsePro & Farm500,000$50,000$33,611$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$33,611$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$33,611$No---------------------------Lien / Lien NHL
Ryker EvansFighting Pandas (OTT)D232001-12-13CANNo195 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$33,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Samuel BolducFighting Pandas (OTT)D242000-12-09CANNo224 Lbs6 ft4NoNoN/ANoNo2FalseFalsePro & Farm500,000$50,000$33,611$No500,000$--------500,000$--------No--------Lien / Lien NHL
Samuel HeleniusFighting Pandas (OTT)C232002-11-26USAYes201 Lbs6 ft6NoNoFree AgentNoNo32025-08-10FalseFalsePro & Farm750,000$75,000$50,417$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$67,222$No---------------------------Lien / Lien NHL
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2426.08199 Lbs6 ft12.13620,833$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Jeff SkinnerMackie SamoskevichKeegan Kolesar40122
2Cole KoepkeRadek FaksaConnor Brown30122
3Beck MalenstynSamuel HeleniusAlexander Holtz20122
4Keegan KolesarJeff Skinner10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Ryker Evans40122
2Sean DurziKaedan Korczak30122
3Declan ChisholmSamuel Bolduc20122
4Ryker Evans10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Jeff SkinnerMackie SamoskevichKeegan Kolesar60122
2Cole KoepkeRadek FaksaConnor Brown40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Ryker Evans60122
2Sean DurziKaedan Korczak40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Keegan KolesarJeff Skinner60122
2Connor BrownMackie Samoskevich40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Ryker Evans60122
2Sean DurziKaedan Korczak40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Keegan Kolesar60122Ryker Evans60122
2Jeff Skinner40122Sean DurziKaedan Korczak40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Keegan KolesarJeff Skinner60122
2Connor BrownMackie Samoskevich40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Ryker Evans60122
2Sean DurziKaedan Korczak40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Jeff SkinnerMackie SamoskevichKeegan KolesarRyker Evans
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Jeff SkinnerMackie SamoskevichKeegan KolesarRyker Evans
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Ben Jones, Beck Malenstyn, Alexander HoltzBen Jones, Beck MalenstynAlexander Holtz
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Declan Chisholm, Samuel Bolduc, Sean DurziDeclan ChisholmSamuel Bolduc, Sean Durzi
Tirs de pénalité
Keegan Kolesar, Jeff Skinner, Connor Brown, Mackie Samoskevich, Radek Faksa
Gardien
#1 : Jakub Dobes, #2 :


Astuces sur les filtres (anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
TotalDomicileVisiteur
# VS Équipe GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff P PCT G A TP SO EG GP1 GP2 GP3 GP4 SHF SH1 SP2 SP3 SP4 SHA SHB Pim Hit PPA PPG PP% PKA PK GA PK% PK GF W OF FO T OF FO OF FO% W DF FO T DF FO DF FO% W NT FO T NT FO NT FO% PZ DF PZ OF PZ NT PC DF PC OF PC NT
1Admirals1010000034-11010000034-10000000000000.00036900423142622265288303222561017000%50100.00%048286155.98%46888353.00%26948455.58%709506588182346180
2Americiens21100000871110000005321010000034-120.50081523004231426702652883032274286504125.00%3166.67%048286155.98%46888353.00%26948455.58%709506588182346180
3Barracuda210001009811000010067-11100000031230.75091726004231426542652883032247168498337.50%4175.00%048286155.98%46888353.00%26948455.58%709506588182346180
4Broncos422000001513200000000000422000001513240.50015264100423142695265288303221132720833266.67%10280.00%048286155.98%46888353.00%26948455.58%709506588182346180
5Bruins210010001082110000005411000100054141.0001016260042314267526528830322641120457228.57%9188.89%048286155.98%46888353.00%26948455.58%709506588182346180
6Butter Knives211000001073110000007251010000035-220.5001017270042314268026528830322671822436116.67%11281.82%048286155.98%46888353.00%26948455.58%709506588182346180
7Firebirds2100010011742100010011740000000000030.7501120310042314267026528830322791918513133.33%9188.89%048286155.98%46888353.00%26948455.58%709506588182346180
8Lions11000000743000000000001100000074321.0007132000423142637265288303224088273133.33%4175.00%048286155.98%46888353.00%26948455.58%709506588182346180
9Lynx22000000945110000007341100000021141.0009162500423142661265288303225412438200.00%10100.00%048286155.98%46888353.00%26948455.58%709506588182346180
10Marlies30200010916-72020000019-81000001087120.333916250042314268626528830322802426546233.33%11372.73%048286155.98%46888353.00%26948455.58%709506588182346180
11Nordiks201000011214-2201000011214-20000000000010.25012213300423142688265288303221013415454125.00%40100.00%048286155.98%46888353.00%26948455.58%709506588182346180
12Roadrunners2110000034-1110000003121010000003-320.500369004231426612652883032254101441200.00%7271.43%048286155.98%46888353.00%26948455.58%709506588182346180
13Tomahawks1000010034-11000010034-10000000000010.500347004231426322652883032253118222150.00%40100.00%048286155.98%46888353.00%26948455.58%709506588182346180
14Wranglers11000000826000000000001100000082621.000813210042314263626528830322361621911100.00%10100.00%048286155.98%46888353.00%26948455.58%709506588182346180
Total27129013111171021514640030163585136501010544410320.59311720632300423142686726528830322887240181584511631.37%831483.13%048286155.98%46888353.00%26948455.58%709506588182346180
_Since Last GM Reset27129013111171021514640030163585136501010544410320.59311720632300423142686726528830322887240181584511631.37%831483.13%048286155.98%46888353.00%26948455.58%709506588182346180
_Vs Conference20980111078708106300100423391035010103637-1230.575781382160042314266202652883032261015514042233927.27%661281.82%048286155.98%46888353.00%26948455.58%709506588182346180
_Vs Division1154010104642464200000252145120101021210140.636468012600423142637226528830322339937823025624.00%35780.00%048286155.98%46888353.00%26948455.58%709506588182346180

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
2732OTL111720632386788724018158400
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
271291311117102
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
146403016358
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
136510105444
Derniers 10 matchs
WLOTWOTL SOWSOL
430201
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
511631.37%831483.13%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
265288303224231426
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
48286155.98%46888353.00%26948455.58%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
709506588182346180


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 Pandas-Roadrunners-
63315Wranglers-Fighting Pandas-
66325Fighting Pandas-Griffins-
68337Lions-Fighting Pandas-
70348Fighting Pandas-Aces-
72358Fighting Pandas-Wombats-
74365Marlies-Fighting Pandas-
76380Barons-Fighting Pandas-
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
27 0 - 0.00% 0$0$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursPlafond Salariale total des joueursSalaire des entraineurs
652,304$ 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$ 488,402$ 24 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
0$ 121 11,056$ 1,337,776$




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