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Thunderbirds
GP: 58 | W: 29 | L: 25 | OTL: 4 | P: 62
GF: 241 | GA: 236 | PP%: 24.32% | PK%: 78.18%
DG: Francois Prevost | Morale : 39 | Moyenne d’équipe : 67
Prochains matchs #758 vs Griffins

Centre de jeu
Thunderbirds
29-25-4, 62pts
3
1 Senators
37-17-4, 78pts
Team Stats
L1SéquenceW1
15-12-2Fiche domicile20-9-0
14-13-2Fiche domicile17-8-4
3-5-2Derniers 10 matchs5-4-1
4.16Buts par match 4.84
4.07Buts contre par match 4.00
24.32%Pourcentage en avantage numérique26.69%
78.18%Pourcentage en désavantage numérique76.21%
Bears
33-20-5, 71pts
6
3 Thunderbirds
29-25-4, 62pts
Team Stats
W1SéquenceL1
17-8-4Fiche domicile15-12-2
16-12-1Fiche domicile14-13-2
6-3-1Derniers 10 matchs3-5-2
4.14Buts par match 4.16
3.59Buts contre par match 4.07
18.72%Pourcentage en avantage numérique24.32%
78.95%Pourcentage en désavantage numérique78.18%
Thunderbirds
29-25-4, 62pts
Jour 121
Griffins
26-26-4, 56pts
Statistiques d’équipe
L1SéquenceW1
15-12-2Fiche domicile14-13-2
14-13-2Fiche visiteur12-13-2
3-5-210 derniers matchs3-6-1
4.16Buts par match 3.75
4.07Buts contre par match 3.75
24.32%Pourcentage en avantage numérique16.08%
78.18%Pourcentage en désavantage numérique79.62%
Penguins
27-29-4, 58pts
Jour 122
Thunderbirds
29-25-4, 62pts
Statistiques d’équipe
L1SéquenceL1
11-16-2Fiche domicile15-12-2
16-13-2Fiche visiteur14-13-2
5-4-110 derniers matchs3-5-2
3.97Buts par match 4.16
4.03Buts contre par match 4.16
20.00%Pourcentage en avantage numérique24.32%
77.83%Pourcentage en désavantage numérique78.18%
Thunderbirds
29-25-4, 62pts
Jour 125
Rockets
24-28-5, 53pts
Statistiques d’équipe
L1SéquenceW2
15-12-2Fiche domicile11-14-4
14-13-2Fiche visiteur13-14-1
3-5-210 derniers matchs5-5-0
4.16Buts par match 4.07
4.07Buts contre par match 4.07
24.32%Pourcentage en avantage numérique22.40%
78.18%Pourcentage en désavantage numérique78.03%
Meneurs d'équipe
Buts
Robert Thomas
40
Passes
Robert Thomas
49
Points
Robert Thomas
89
Plus/Moins
Sami Niku
27
Victoires
Emil Larmi
22
Pourcentage d’arrêts
Emil Larmi
0.877

Statistiques d’équipe
Buts pour
241
4.16 GFG
Tirs pour
1819
31.36 Avg
Pourcentage en avantage numérique
24.3%
45 GF
Début de zone offensive
35.1%
Buts contre
236
4.07 GAA
Tirs contre
1883
32.47 Avg
Pourcentage en désavantage numérique
78.2%%
48 GA
Début de la zone défensive
37.6%
Informations de l'équipe

Directeur généralFrancois Prevost
EntraîneurTeemu Selanne
DivisionJohn-Ahearne
ConférenceRobert-Lebel
CapitaineAnthony Richard
Assistant #1Dylan Sikura
Assistant #2Sami Niku


Informations de l’aréna

Capacité3,000
Assistance2,809
Billets de saison300


Informations de la formation

Équipe Pro26
Équipe Mineure20
Limite contact 46 / 250
Espoirs0


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
1Robert Thomas (R)X100.006735817371788080869080616556496355740213975,000$
2Anthony Richard (R) (C)X100.007233827676836377787870708263574443730241900,000$
3Dylan Sikura (R) (A)X100.007141817877767177737775727755513930730241950,000$
4Emil Bemstrom (R)X100.007225828266736583677475617747455453710212900,000$
5Noah Gregor (R)X100.007033837871727272717179717347485153710222750,000$
6Artturi LehkonenX100.007133788276737073726972657353513753700251750,000$
7Rasmus Kupari (R)X100.006434798060676080726671587843446844680203650,000$
8Paul Cotter (R)X100.007633796376697273727278567048465853680213650,000$
9Trey Fix-Wolansky (R)X100.004840707961726683626768647845456253670213650,000$
10Alex Newhook (R)X100.006448787068646471677467607444416831660191500,000$
11Jakob Pelletier (R)X100.005038807146716264828363425641416553630191500,000$
12Nicholas Robertson (R)X100.005229727453675177716962547841416943630191500,000$
13Henri Jokiharju (R)X100.007830917572787369528061845662465652750211975,000$
14Matt RoyX100.006649837375756477617669776264563952720251900,000$
15Sami Niku (R) (A)X100.006137748163698078487767684660523953690241800,000$
16Spencer Stastney (R)X100.007135806964646470437152775448445535680203800,000$
17Calen Addison (R)X100.005833807858546677397757664643435941650203650,000$
18Louis Crevier (R)X100.006628836563586848336739744243427053630192500,000$
Rayé
1Luke Evangelista (R)X100.006142776263666766686165525940407320610182500,000$
2Brendan Brisson (R)X100.005336726746596071816563414741436219600192500,000$
3Sean Farrell (R)X100.005536735744646563667365405040408020580182500,000$
4Adam Ginning (R)X100.006746656979755152346649605042425120630202500,000$
5Alex Vlasic (R)X100.007025885960675545256649704141417121620191500,000$
MOYENNE D’ÉQUIPE100.00643679726569667162736563624846584167
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
1Emil Larmi98.00736877646583816963787051475168710241700,000$
2Arturs Silovs100.00795260747351516469537441416368620191500,000$
Rayé
1Joel Blomqvist (R)100.00625575474569705545735840407520570182500,000$
MOYENNE D’ÉQUIPE99.3371587162616867635968674443635263
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Teemu Selanne77697390877445Fin433650,000$


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
1Robert ThomasThunderbirds (Stl)C58404989-22601051162107212319.05%32115719.96131831361680002555354.45%14953016121.5400000743
2Emil BemstromThunderbirds (Stl)RW583943821424072641895612820.63%2092315.9313173038159000002139.58%485315041.7811000842
3Anthony RichardThunderbirds (Stl)C582035551030099129146499913.70%27109718.93336812431481871052.41%10152717001.0003000237
4Trey Fix-WolanskyThunderbirds (Stl)RW58202545102406352144479913.89%1585814.79437161241015824240.00%353312101.0511000203
5Dylan SikuraThunderbirds (Stl)RW48192039-1226071591844610710.33%3594219.630000410181561349.33%754116000.8300000221
6Artturi LehkonenThunderbirds (Stl)LW58161531123209660114266014.04%2186714.96000090111751241.86%43239000.7100000221
7Noah GregorThunderbirds (Stl)C58161430-2607759117288513.68%1865311.270110141126890149.83%2952711100.9200000110
8Rasmus KupariThunderbirds (Stl)C551612286140514993395117.20%958410.64000010000201055.29%2931911000.9602000010
9Alex NewhookThunderbirds (Stl)LW57131427-252206756103397512.62%1474313.0416741110000102039.53%431917000.7300000112
10Paul CotterThunderbirds (Stl)LW581215275260884647164525.53%1076613.224376158000015160.71%281217000.7000000121
11Matt RoyThunderbirds (Stl)D5432326636076896528474.62%76127223.5712341040114113100%02544000.4100000110
12Calen AddisonThunderbirds (Stl)D5661723-1118068826133159.84%78116820.87461015160000047100%0938000.3900000200
13Nicholas RobertsonThunderbirds (Stl)RW58121123-7220332763224019.05%135068.73011018000004057.89%19158100.9103000021
14Sami NikuThunderbirds (Stl)D57121222722072866136351.64%62116220.3912361250112160000%01629000.3801000111
15Spencer StastneyThunderbirds (Stl)D4811920-918049723715262.70%5991819.140113870112124000%0229000.4400000001
16Henri JokiharjuThunderbirds (Stl)D3301616-14057713720230%5686526.230112880110117000%01627000.3700000200
17Alex VlasicThunderbirds (Stl)D3221113-66049503815185.26%4177024.092248840000109000%0118000.3400000010
18Louis CrevierThunderbirds (Stl)D5801111131402855258150%3969912.06000111000034000%0120000.3100000000
19Jakob PelletierThunderbirds (Stl)LW584711-714020105615287.14%55028.6600000000001038.89%18102000.4400000000
20Luke EvangelistaThunderbirds (Stl)RW1005536015664110%419419.48000010110230053.33%1509000.5100000000
21Adam GinningThunderbirds (Stl)D24022180172413680%2136315.14000110000036000%005000.1100000000
22Brendan BrissonThunderbirds (Stl)C3000100011110%0248.2600000000000061.11%180000001000000
Statistiques d’équipe totales ou en moyenne1057240385625263980127312631810621113913.26%6551704516.13466611214815696814381445291352.73%3440379370460.73212000322523
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
1Emil LarmiThunderbirds (Stl)47221640.8773.882675211731405661810.66764513112
2Arturs SilovsThunderbirds (Stl)187900.8764.328190059476273110.80051345001
Statistiques d’équipe totales ou en moyenne65292540.8773.98349521232188193492115858113


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 Salaire restantPlafond 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
Adam GinningThunderbirds (Stl)D202000-01-01SWEYes196 Lbs6 ft3NoNoTrade2025-12-11NoNo22025-09-30FalseFalsePro & Farm500,000$144,970$0$0$No500,000$--------500,000$--------No--------Lien
Alex NewhookThunderbirds (Stl)LW192001-01-01CANYes198 Lbs5 ft11NoNoTrade2026-02-25NoNo12024-07-10FalseFalsePro & Farm500,000$144,970$0$0$No---------------------------Lien
Alex VlasicThunderbirds (Stl)D192001-01-01USAYes216 Lbs6 ft6NoNoAssign ManuallyNoNo12024-07-10FalseFalsePro & Farm500,000$144,970$0$0$No---------------------------Lien
Anthony RichardThunderbirds (Stl)C241996-01-01CANYes163 Lbs5 ft10NoNoFree AgentNoNo12025-08-28FalseFalsePro & Farm900,000$260,947$0$0$No---------------------------Lien
Artturi LehkonenThunderbirds (Stl)LW251995-01-01FINNo185 Lbs5 ft11NoNoFree AgentNoNo12024-08-20FalseFalsePro & Farm750,000$217,456$0$0$No---------------------------Lien
Arturs SilovsThunderbirds (Stl)G192001-01-01LVANo203 Lbs6 ft4NoNoAssign ManuallyNoNo12024-07-10FalseFalsePro & Farm500,000$144,970$0$0$No---------------------------Lien
Brendan BrissonThunderbirds (Stl)C192001-01-01USAYes179 Lbs5 ft11NoNoAssign ManuallyNoNo22025-07-16FalseFalsePro & Farm500,000$144,970$0$0$No500,000$--------500,000$--------No--------
Calen AddisonThunderbirds (Stl)D202000-01-01CANYes173 Lbs5 ft11NoNoFree AgentNoNo32025-08-28FalseFalsePro & Farm650,000$188,462$0$0$No750,000$850,000$-------750,000$850,000$-------NoNo-------Lien
Dylan SikuraThunderbirds (Stl)RW241996-01-01CANYes166 Lbs5 ft11NoNoN/ANoNo1FalseFalsePro & Farm950,000$275,444$0$0$No---------------------------Lien
Emil BemstromThunderbirds (Stl)RW211999-01-01SWEYes193 Lbs6 ft0NoNoFree AgentNoNo22024-08-20FalseFalsePro & Farm900,000$260,947$0$0$No975,000$--------800,000$--------No--------Lien
Emil LarmiThunderbirds (Stl)G241996-01-01FINNo185 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm700,000$202,959$0$0$No---------------------------Lien
Henri JokiharjuThunderbirds (Stl)D211999-01-01FINYes200 Lbs6 ft0NoNoFree AgentNoNo12024-08-20FalseFalsePro & Farm975,000$282,692$0$0$No---------------------------Lien
Jakob PelletierThunderbirds (Stl)LW192001-01-01CANYes170 Lbs5 ft9NoNoTrade2025-02-03NoNo12024-07-10FalseFalsePro & Farm500,000$144,970$0$0$No---------------------------Lien
Joel BlomqvistThunderbirds (Stl)G182002-01-01FINYes178 Lbs6 ft1NoNoAssign ManuallyNoNo22025-07-16FalseFalsePro & Farm500,000$144,970$0$0$No500,000$--------500,000$--------No--------
Louis CrevierThunderbirds (Stl)D192001-01-01CANYes228 Lbs6 ft8NoNoAssign ManuallyNoNo22025-07-16FalseFalsePro & Farm500,000$144,970$0$0$No500,000$--------500,000$--------No--------
Luke EvangelistaThunderbirds (Stl)RW182002-01-01CANYes175 Lbs5 ft11NoNoAssign ManuallyNoNo22025-07-16FalseFalsePro & Farm500,000$144,970$0$0$No500,000$--------500,000$--------No--------
Matt RoyThunderbirds (Stl)D251995-01-01USANo200 Lbs6 ft1NoNoFree AgentNoNo12024-08-20FalseFalsePro & Farm900,000$260,947$0$0$No---------------------------Lien
Nicholas RobertsonThunderbirds (Stl)RW192001-01-01USAYes179 Lbs5 ft9NoNoAssign ManuallyNoNo12024-07-10FalseFalsePro & Farm500,000$144,970$0$0$No---------------------------Lien
Noah GregorThunderbirds (Stl)C221998-01-01CANYes185 Lbs6 ft0NoNoFree Agent2025-04-15NoNo22025-08-28FalseFalsePro & Farm750,000$217,456$0$0$No850,000$--------850,000$--------No--------Lien
Paul CotterThunderbirds (Stl)LW211999-01-01USAYes212 Lbs6 ft2NoNoFree AgentNoNo32025-08-28FalseFalsePro & Farm650,000$188,462$0$0$No750,000$850,000$-------750,000$850,000$-------NoNo-------Lien
Rasmus KupariThunderbirds (Stl)C202000-01-01FINYes200 Lbs6 ft2NoNoFree AgentNoNo32025-08-28FalseFalsePro & Farm650,000$188,462$0$0$No750,000$850,000$-------750,000$850,000$-------NoNo-------Lien
Robert ThomasThunderbirds (Stl)C211999-01-01CANYes218 Lbs6 ft0NoNoTrade2025-11-20NoNo32024-08-21FalseFalsePro & Farm975,000$282,692$0$0$No2,500,000$3,750,000$-------925,000$925,000$-------NoNo-------Lien
Sami NikuThunderbirds (Stl)D241996-01-01FINYes176 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm800,000$231,953$0$0$No---------------------------Lien
Sean FarrellThunderbirds (Stl)LW182002-01-01USAYes175 Lbs5 ft9NoNoAssign ManuallyNoNo22025-07-16FalseFalsePro & Farm500,000$144,970$0$0$No500,000$--------500,000$--------No--------
Spencer StastneyThunderbirds (Stl)D202000-01-01USAYes184 Lbs6 ft0NoNoTrade2026-02-25NoNo32025-08-28FalseFalsePro & Farm800,000$231,953$0$0$No800,000$800,000$-------800,000$800,000$-------NoNo-------Lien
Trey Fix-WolanskyThunderbirds (Stl)RW211999-01-01CANYes186 Lbs5 ft7NoNoFree AgentNoNo32025-08-28FalseFalsePro & Farm650,000$188,462$0$0$No750,000$850,000$-------750,000$850,000$-------NoNo-------Lien
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2620.77189 Lbs6 ft01.77673,077$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Alex NewhookRobert ThomasDylan Sikura40122
2Artturi LehkonenAnthony RichardEmil Bemstrom30122
3Paul CotterRasmus KupariTrey Fix-Wolansky20122
4Jakob PelletierNoah GregorNicholas Robertson10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Matt RoyHenri Jokiharju40122
2Spencer StastneyCalen Addison30122
3Louis CrevierSami Niku20122
4Sami NikuCalen Addison10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Alex NewhookRobert ThomasEmil Bemstrom60122
2Paul CotterAnthony RichardTrey Fix-Wolansky40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Calen AddisonHenri Jokiharju60122
2Matt RoySami Niku40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Anthony RichardDylan Sikura60122
2Noah GregorTrey Fix-Wolansky40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Henri JokiharjuMatt Roy60122
2Spencer StastneyLouis Crevier40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Anthony Richard60122Louis CrevierHenri Jokiharju60122
2Noah Gregor40122Matt RoySpencer Stastney40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Robert ThomasAlex Newhook60122
2Anthony RichardArtturi Lehkonen40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Henri JokiharjuSpencer Stastney60122
2Matt RoySami Niku40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Alex NewhookRobert ThomasDylan SikuraSami NikuCalen Addison
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Artturi LehkonenNoah GregorTrey Fix-WolanskyHenri JokiharjuSpencer Stastney
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Trey Fix-Wolansky, Dylan Sikura, Jakob PelletierNicholas Robertson, Artturi LehkonenDylan Sikura
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Spencer Stastney, Calen Addison, Sami NikuCalen AddisonSpencer Stastney, Louis Crevier
Tirs de pénalité
Anthony Richard, Nicholas Robertson, Rasmus Kupari, Trey Fix-Wolansky, Emil Bemstrom
Gardien
#1 : Arturs Silovs, #2 : Emil Larmi
Lignes d’attaque personnalisées en prolongation
Noah Gregor, Trey Fix-Wolansky, Robert Thomas, Anthony Richard, Paul Cotter, Emil Bemstrom, Artturi Lehkonen, Alex Newhook, Nicholas Robertson, Jakob Pelletier
Lignes de défense personnalisées en prolongation
Calen Addison, Henri Jokiharju, Louis Crevier, Spencer Stastney, Matt Roy


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
1Admirals514000001826-8312000001214-220200000612-620.20018325010766895318065058258117175552611414321.43%13469.23%0636121152.52%704129654.32%49094351.96%125972112175311022510
2Americans431000001916322000000105521100000911-260.7501929480076689531216505825811713139189114321.43%9277.78%1636121152.52%704129654.32%49094351.96%125972112175311022510
3Barracuda220000001410400000000000220000001410441.0001424380076689536365058258117662224515240.00%12375.00%0636121152.52%704129654.32%49094351.96%125972112175311022510
4Bears642000002322131200000815-733000000157880.66723335600766895319165058258117199574813210330.00%24291.67%1636121152.52%704129654.32%49094351.96%125972112175311022510
5Canucks21100000972211000009720000000000020.500913220076689536765058258117521783913323.08%40100.00%0636121152.52%704129654.32%49094351.96%125972112175311022510
6Checkers22000000936110000005141100000042241.0009132200766895355650582581175818104510110.00%5180.00%0636121152.52%704129654.32%49094351.96%125972112175311022510
7Comets41300000921-1220200000415-112110000056-120.2509132200766895311665058258117136463275400.00%16381.25%1636121152.52%704129654.32%49094351.96%125972112175311022510
8Condors42200000171611010000025-3321000001511440.50017314800766895312665058258117150472810114535.71%14285.71%0636121152.52%704129654.32%49094351.96%125972112175311022510
9Crunch21100000660110000003211010000034-120.500610160076689536365058258117671810376116.67%5180.00%0636121152.52%704129654.32%49094351.96%125972112175311022510
10Eagles1100000010461100000010460000000000021.0001016260076689533665058258117331012213266.67%6266.67%0636121152.52%704129654.32%49094351.96%125972112175311022510
11Firebirds21000010743110000005321000001021141.000712190076689534865058258117542816519222.22%8187.50%0636121152.52%704129654.32%49094351.96%125972112175311022510
12Griffins1000000145-11000000145-10000000000010.50047110076689533265058258117288425300.00%2150.00%0636121152.52%704129654.32%49094351.96%125972112175311022510
13Gulls421001002116511000000651311001001511450.62521335400766895313265058258117116532410321523.81%12375.00%0636121152.52%704129654.32%49094351.96%125972112175311022510
14Icehogs11000000541110000005410000000000021.00051015007668953266505825811740141420300.00%7271.43%0636121152.52%704129654.32%49094351.96%125972112175311022510
15Islander320000011468210000019541100000051450.83314213501766895393650582581177842166011436.36%8187.50%0636121152.52%704129654.32%49094351.96%125972112175311022510
16Little Stars11000000972110000009720000000000021.000913220076689533065058258117381212234250.00%6183.33%0636121152.52%704129654.32%49094351.96%125972112175311022510
17Marlies11000000752110000007520000000000021.0007111800766895334650582581173116122033100.00%60100.00%2636121152.52%704129654.32%49094351.96%125972112175311022510
18Moose302010001214-220101000910-11010000034-120.33312203200766895385650582581171073332685120.00%16662.50%0636121152.52%704129654.32%49094351.96%125972112175311022510
19Penguins1010000024-2000000000001010000024-200.000224107668953286505825811728710267114.29%50100.00%0636121152.52%704129654.32%49094351.96%125972112175311022510
20Punishers1010000056-11010000056-10000000000000.0005813007668953376505825811730812254125.00%6350.00%0636121152.52%704129654.32%49094351.96%125972112175311022510
21Roadrunners40300100920-1120200000410-620100100510-510.12591322007668953135650582581171305140891119.09%20575.00%1636121152.52%704129654.32%49094351.96%125972112175311022510
22Senators21100000770000000000002110000077020.500712190076689536165058258117682320444125.00%11463.64%0636121152.52%704129654.32%49094351.96%125972112175311022510
23Wolfpack1010000023-1000000000001010000023-100.0002460076689532965058258117369618200.00%3166.67%0636121152.52%704129654.32%49094351.96%125972112175311022510
24Wranglers1010000034-1000000000001010000034-100.000347007668953316505825811732114215120.00%20100.00%0636121152.52%704129654.32%49094351.96%125972112175311022510
Total58272501212241236529141201002126128-2291313002101151087620.534241384625217668953181965058258117188364443812991854524.32%2204878.18%6636121152.52%704129654.32%49094351.96%125972112175311022510
_Since Last GM Reset58272501212241236529141201002126128-2291313002101151087620.534241384625217668953181965058258117188364443812991854524.32%2204878.18%6636121152.52%704129654.32%49094351.96%125972112175311022510
_Vs Conference36161701101147151-420810010018288-616870010065632360.50014723538211766895311526505825811711983982748061072725.23%1383276.81%3636121152.52%704129654.32%49094351.96%125972112175311022510
_Vs Division1578000005864-6725000002234-128530000036306140.467589615410766895349765058258117524159102347381128.95%51884.31%1636121152.52%704129654.32%49094351.96%125972112175311022510

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
5862L124138462518191883644438129921
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
5827251212241236
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
2914121002126128
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
2913130210115108
Derniers 10 matchs
WLOTWOTL SOWSOL
350101
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
1854524.32%2204878.18%6
Tirs en 1e périodeTirs en 2e périodeTirs en 3e périodeTirs en 4e périodeButs en 1e périodeButs en 2e périodeButs en 3e périodeButs en 4e période
650582581177668953
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
636121152.52%704129654.32%49094351.96%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
125972112175311022510


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
212Condors5Thunderbirds2LSommaire du match
531Bears6Thunderbirds1LSommaire du match
639Thunderbirds4Condors2WSommaire du match
857Moose5Thunderbirds6WXSommaire du match
1065Thunderbirds3Gulls4LXSommaire du match
1170Thunderbirds3Admirals4LSommaire du match
1387Thunderbirds4Bears2WSommaire du match
1598Bears3Thunderbirds4WSommaire du match
18113Admirals5Thunderbirds3LSommaire du match
20125Thunderbirds4Senators6LSommaire du match
22137Islander0Thunderbirds5WSommaire du match
24150Thunderbirds7Bears2WSommaire du match
26163Thunderbirds7Barracuda5WSommaire du match
28175Americans3Thunderbirds5WSommaire du match
30185Thunderbirds2Comets4LSommaire du match
32198Thunderbirds4Americans8LSommaire du match
34204Canucks2Thunderbirds7WSommaire du match
36219Thunderbirds4Bears3WSommaire du match
38231Marlies5Thunderbirds7WSommaire du match
40244Thunderbirds2Wolfpack3LSommaire du match
42252Gulls5Thunderbirds6WSommaire du match
44268Thunderbirds3Wranglers4LSommaire du match
45277Moose5Thunderbirds3LSommaire du match
48291Thunderbirds2Penguins4LSommaire du match
50306Firebirds3Thunderbirds5WSommaire du match
52320Thunderbirds5Condors2WSommaire du match
54328Thunderbirds5Islander1WSommaire du match
55335Icehogs4Thunderbirds5WSommaire du match
58355Griffins5Thunderbirds4LXXSommaire du match
61377Admirals4Thunderbirds6WSommaire du match
63389Thunderbirds5Americans3WSommaire du match
65404Roadrunners6Thunderbirds2LSommaire du match
67417Thunderbirds3Crunch4LSommaire du match
69431Crunch2Thunderbirds3WSommaire du match
71445Thunderbirds2Firebirds1WXXSommaire du match
72454Admirals5Thunderbirds3LSommaire du match
77480Punishers6Thunderbirds5LR1Sommaire du match
79497Checkers1Thunderbirds5WSommaire du match
81506Thunderbirds4Checkers2WSommaire du match
83518Thunderbirds7Barracuda5WSommaire du match
85530Little Stars7Thunderbirds9WSommaire du match
87547Thunderbirds6Condors7LSommaire du match
89557Roadrunners4Thunderbirds2LSommaire du match
91575Canucks5Thunderbirds2LSommaire du match
93583Thunderbirds1Roadrunners5LSommaire du match
95599Thunderbirds3Comets2WSommaire du match
97608Comets10Thunderbirds1LSommaire du match
99619Thunderbirds10Gulls2WSommaire du match
101633Eagles4Thunderbirds10WSommaire du match
103649Thunderbirds2Gulls5LSommaire du match
104660Islander5Thunderbirds4LXXSommaire du match
107674Thunderbirds4Roadrunners5LXSommaire du match
108685Americans2Thunderbirds5WSommaire du match
111699Thunderbirds3Admirals8LSommaire du match
113711Comets5Thunderbirds3LSommaire du match
115727Thunderbirds3Moose4LSommaire du match
117737Thunderbirds3Senators1WSommaire du match
118745Bears6Thunderbirds3LSommaire du match
121758Thunderbirds-Griffins-
122768Penguins-Thunderbirds-
125786Thunderbirds-Rockets-
126796Wranglers-Thunderbirds-
130814Thunderbirds-Eagles-
131820Thunderbirds-Admirals-
132828Wolfpack-Thunderbirds-
135849Eagles-Thunderbirds-
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
138867Icehogs-Thunderbirds-
139877Thunderbirds-Canucks-
142894Bears-Thunderbirds-
144905Thunderbirds-Marlies-
147918Rockets-Thunderbirds-
149935Thunderbirds-Little Stars-
151946Senators-Thunderbirds-
154962Thunderbirds-Little Stars-
155975Condors-Thunderbirds-
156977Thunderbirds-Icehogs-
160995Barracuda-Thunderbirds-
1631014Thunderbirds-Punishers-
1651025Condors-Thunderbirds-
1661034Thunderbirds-Punishers-



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets7040
Assistance54,64026,815
Assistance PCT94.21%92.47%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
11 2809 - 93.63% 177,320$5,142,273$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursPlafond Salariale total des joueursSalaire des entraineurs
2,000,767$ 2,627,500$ 2,627,500$ 650,000$0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
15,547$ 1,534,945$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
1,950,517$ 49 19,393$ 950,257$




Thunderbirds 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

Thunderbirds 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

Thunderbirds 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

Thunderbirds 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

Thunderbirds 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