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Wranglers
GP: 80 | W: 39 | L: 30 | OTL: 11 | P: 89
GF: 312 | GA: 320 | PP%: 20.74% | PK%: 78.15%
DG: Steve Landry | Morale : 39 | Moyenne d’équipe : 64
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Centre de jeu
Reign
42-28-10, 94pts
9
FINAL
3 Wranglers
39-30-11, 89pts
Team Stats
W4SéquenceW1
22-15-3Fiche domicile19-16-5
20-13-7Fiche domicile20-14-6
9-1-0Derniers 10 matchs5-3-2
4.16Buts par match 3.90
3.78Buts contre par match 4.00
22.79%Pourcentage en avantage numérique20.74%
79.02%Pourcentage en désavantage numérique78.15%
Wranglers
39-30-11, 89pts
3
FINAL
2 Comets
36-36-8, 80pts
Team Stats
W1SéquenceL1
19-16-5Fiche domicile20-16-4
20-14-6Fiche domicile16-20-4
5-3-2Derniers 10 matchs6-3-1
3.90Buts par match 4.06
4.00Buts contre par match 4.15
20.74%Pourcentage en avantage numérique18.86%
78.15%Pourcentage en désavantage numérique80.86%
Meneurs d'équipe
Buts
Evgeny Svechnikov
44
Passes
Rasmus Andersson
68
Points
Evgeny Svechnikov
106
Plus/Moins
Taylor Raddysh
14
Victoires
Michael Hutchinson
34
Pourcentage d’arrêts
Michael Hutchinson
0.886

Statistiques d’équipe
Buts pour
312
3.90 GFG
Tirs pour
2630
32.88 Avg
Pourcentage en avantage numérique
20.7%
62 GF
Début de zone offensive
36.0%
Buts contre
320
4.00 GAA
Tirs contre
2607
32.59 Avg
Pourcentage en désavantage numérique
78.2%%
71 GA
Début de la zone défensive
37.3%
Informations de l'équipe

Directeur généralSteve Landry
EntraîneurCraig MacTavish
DivisionFritz-Kraatz
ConférenceRobert-Lebel
Capitaine
Assistant #1
Assistant #2


Informations de l’aréna

Capacité3,000
Assistance2,763
Billets de saison300


Informations de la formation

Équipe Pro32
Équipe Mineure22
Limite contact 54 / 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
1Evgeny Svechnikov (R)X100.007232887287787775787190577160505356740221975,000$
2Peter Cehlarik (R)X100.006944807368747374757672617252484849700232850,000$
3Barclay GoodrowX100.007929756973707171676971726654523549690252900,000$
4Nico Sturm (R)X100.006629877278787765727559697051504559690233750,000$
5Ryan SpoonerX100.006431767473727768767966636555513359680261900,000$
6Jesper Bratt (R)X100.006546747370757171747270617347446459680203750,000$
7Eric Karlsson (R)X100.007833747078707065656773595650503859660242750,000$
8Ryan Donato (R)X100.007438756480657166746775496744444759660223800,000$
9Taylor Raddysh (R)X100.005946806470706070696267586743435962640201500,000$
10Vladislav Kamenev (R)X100.007131735172666869605975577344444856630223700,000$
11Tage Thompson (R)X100.005932846965786556657254625946455950620213700,000$
12Vitali Kravtsov (R)X100.006943726262587063616065536442416749610192500,000$
13Ivan Chekhovich (R)X100.006325754876555961586077376541416321590191500,000$
14Radim Zohorna (R)X100.005739746170736260555669445747454821590223600,000$
15Skyler Brind'Amour (R)X100.004433765154695647545554555441416221530191500,000$
16Rasmus Andersson (R)X100.0075378381727376834681687458634854457402211,500,000$
17Frank CorradoX100.007140757277747671596961716062533655700252850,000$
18Dominik Masin (R)X100.006831877071697257356346795353464821670222650,000$
19John GilmourX100.007034776863766975517257665848503021670251800,000$
20Trevor Carrick (R)X100.006238758166667576427555674449503221670243700,000$
21Erik Brannstrom (R)X98.255529727455537485418362724046427448660191500,000$
22Petteri LindbohmX100.006039756467677071466871695749543436660253750,000$
23Brandon Hickey (R)X100.006627846364646455366947785348453859650221600,000$
24Christian Jaros (R)X100.006729776562616265367150725445464356650222650,000$
25Darren Raddysh (R)X100.006325926560696348456737663950464821620221500,000$
Rayé
1Demetrios Koumontzis (R)X100.004128605549545055676258304740405820520182500,000$
2Filip Hallander (R)X100.004529675847494356445948394440406820500182500,000$
3Tim Berni (R)X100.005827574051495557185750343140406020480182500,000$
MOYENNE D’ÉQUIPE99.94643477656767676656686260584846504264
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
1Michael Hutchinson100.00707975757474747678717371662460740282995,000$
2Vitek Vanecek100.00637067606173775459766246464722650221600,000$
Rayé
1Collin Delia100.00725765726958515863556742425420600243700,000$
2Joel Hofer (R)100.00665263746950435861537240406941570182500,000$
MOYENNE D’ÉQUIPE100.0068656870686461626564695049493664
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Craig MacTavish78756975766752CAN562800,000$


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
1Evgeny SvechnikovWranglers (Cal)RW784462106-82201148135011418412.57%37152519.5512213356252000089352.44%1647621021.3948000892
2Nico SturmWranglers (Cal)C80365086-123201241891665110521.69%42168621.08917262120900071519049.24%23601521021.0201000631
3Rasmus AnderssonWranglers (Cal)D72156883-15320102126249911066.02%135189026.2581927602560115256110%07159000.8800000435
4Peter CehlarikWranglers (Cal)LW803642789740137972778017013.00%57160520.07336126100062371251.49%4354929100.9714000424
5Jesper BrattWranglers (Cal)LW80324476-11500137832325613413.79%24142517.82101525382561017992050.59%855515001.0749000237
6Ryan SpoonerWranglers (Cal)C8026366253401061241685911715.48%24128016.006511202121012534055.66%9813312000.9701000123
7Erik BrannstromWranglers (Cal)D80124860-16606610017261606.98%81153719.226152134266000010020%02950000.7800000023
8Barclay GoodrowWranglers (Cal)RW801821390520148123145429012.41%41144618.091671115100032692148.33%603322000.5400000132
9Eric KarlssonWranglers (Cal)LW801719361026012481147509011.56%1898412.3100031511241073236.36%223217000.7300000313
10Ryan DonatoWranglers (Cal)C80151833536010490128407811.72%18112414.06145819311211051049.18%5513112000.5900000120
11Frank CorradoWranglers (Cal)D7742327126351041128832424.55%92151119.63224918500009000100.00%12345000.3600000001
12Petteri LindbohmWranglers (Cal)D62621273555811207328428.22%104152524.601126122000118401100.00%12950000.3500010132
13Taylor RaddyshWranglers (Cal)RW80141226141754645101356713.86%2384110.51314551000000250.00%181113000.6200100023
14Vladislav KamenevWranglers (Cal)C7811617-5240654074214114.86%146348.1300002000041045.45%231104000.5400000110
15Vitali KravtsovWranglers (Cal)LW8010717-4200572777153212.99%176548.18000000000161046.15%1392000.5201000010
16Tage ThompsonWranglers (Cal)RW7851217-1112032535819408.62%186878.82000217000000041.18%171517000.4900000100
17Brandon HickeyWranglers (Cal)D80213151114053944722244.26%62127215.910000220111194000%01149000.2400000000
18Christian JarosWranglers (Cal)D8021012233572833416225.88%66112114.020000120001197010%0939000.2100010002
19Dominik MasinWranglers (Cal)D32189-7803837261173.85%4772522.660003780000118000%0616000.2500000012
20John GilmourWranglers (Cal)D41120402271114.29%15814.530000400015000%102000.6900000000
21Trevor CarrickWranglers (Cal)D4011-100057540%36416.000000400008000%123000.3100000000
22Chris BrownFlamesC1000100203010%01818.6000004000010033.33%91000000000000
23Filip HallanderWranglers (Cal)C1000000000000%000.700000000000000%00000000000000
24Skyler Brind'AmourWranglers (Cal)C3000000001000%082.690000000006000%50000000000000
25Ivan ChekhovichWranglers (Cal)LW3000000000000%031.260000000001000%00000000000000
26Radim ZohornaWranglers (Cal)LW3000020000000%020.730000000000000%00000000000000
Statistiques d’équipe totales ou en moyenne1456307522829-367620171417122630849145711.67%9242363616.23621091712882382448392128341550.47%4955550498140.70924120333840
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
1Michael HutchinsonWranglers (Cal)68342660.8863.653763412292006975640.87516666214
2Vitek VanecekWranglers (Cal)164340.8734.217992056440238110.44491313000
3Joel HoferWranglers (Cal)91110.8335.24298002615683000.3333161000
Statistiques d’équipe totales ou en moyenne933930110.8803.844861613112602129675288080214


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 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 Non Activé Plafond salarial restant Exclus du plafond salarial Salaire annuel 2Salaire annuel 3Salaire annuel 4Salaire annuel 5Salaire annuel 6Salaire annuel 7Salaire annuel 8Salaire annuel 9Salaire annuel 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
Barclay GoodrowWranglers (Cal)RW251993-01-01No215 Lbs6 ft2NoNoN/ANoNo2FalseFalsePro & Farm900,000$4,478$0$0$No900,000$--------No--------
Brandon HickeyWranglers (Cal)D221996-01-01Yes201 Lbs6 ft2NoNoN/ANoNo1FalseFalsePro & Farm600,000$2,985$0$0$No------------------
Christian JarosWranglers (Cal)D221996-01-01Yes222 Lbs6 ft3NoNoN/ANoNo2FalseFalsePro & Farm650,000$3,234$0$0$No700,000$--------No--------
Collin DeliaWranglers (Cal)G241994-01-01No207 Lbs6 ft2NoNoN/ANoNo3FalseFalsePro & Farm700,000$3,483$0$0$No700,000$700,000$-------NoNo-------
Darren RaddyshWranglers (Cal)D221996-01-01Yes200 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm500,000$2,488$0$0$No------------------
Demetrios KoumontzisWranglers (Cal)LW182000-01-01Yes190 Lbs5 ft11NoNoN/ANoNo2FalseFalsePro & Farm500,000$2,488$0$0$No500,000$--------No--------Lien
Dominik MasinWranglers (Cal)D221996-01-01Yes196 Lbs6 ft2NoNoN/ANoNo2FalseFalsePro & Farm650,000$3,234$0$0$No700,000$--------No--------
Eric KarlssonWranglers (Cal)LW241994-01-01Yes161 Lbs5 ft11NoNoN/ANoNo2FalseFalsePro & Farm750,000$3,731$0$0$No850,000$--------No--------
Erik BrannstromWranglers (Cal)D191999-01-01Yes185 Lbs5 ft10NoNoN/ANoNo1FalseFalsePro & Farm500,000$2,488$0$0$No------------------
Evgeny SvechnikovWranglers (Cal)RW221996-01-01Yes208 Lbs6 ft3NoNoN/ANoNo1FalseFalsePro & Farm975,000$4,851$0$0$No------------------
Filip HallanderWranglers (Cal)C182000-01-01Yes190 Lbs6 ft1NoNoN/ANoNo2FalseFalsePro & Farm500,000$2,488$0$0$No500,000$--------No--------Lien
Frank CorradoWranglers (Cal)D251993-01-01No195 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm850,000$4,229$0$0$No900,000$--------No--------
Ivan ChekhovichWranglers (Cal)LW191999-01-01Yes187 Lbs5 ft10NoNoN/ANoNo1FalseFalsePro & Farm500,000$2,488$0$0$No------------------
Jesper BrattWranglers (Cal)LW201998-01-01Yes185 Lbs5 ft10NoNoN/ANoNo3FalseFalsePro & Farm750,000$3,731$0$0$No900,000$950,000$-------NoNo-------
Joel HoferWranglers (Cal)G182000-01-01Yes179 Lbs6 ft5NoNoN/ANoNo2FalseFalsePro & Farm500,000$2,488$0$0$No500,000$--------No--------Lien
John GilmourWranglers (Cal)D251993-01-01No185 Lbs6 ft0NoNoN/ANoNo1FalseFalsePro & Farm800,000$3,980$0$0$No------------------
Michael HutchinsonWranglers (Cal)G281990-01-01No202 Lbs6 ft3NoNoN/ANoNo2FalseFalsePro & Farm995,000$4,950$0$0$No995,000$--------No--------
Nico SturmWranglers (Cal)C231995-01-01Yes207 Lbs6 ft3NoNoN/ANoNo3FalseFalsePro & Farm750,000$3,731$0$0$No900,000$950,000$-------NoNo-------
Peter CehlarikWranglers (Cal)LW231995-01-01Yes185 Lbs6 ft2NoNoN/ANoNo2FalseFalsePro & Farm850,000$4,229$0$0$No900,000$--------No--------
Petteri LindbohmWranglers (Cal)D251993-01-01No209 Lbs6 ft3NoNoN/ANoNo3FalseFalsePro & Farm750,000$3,731$0$0$No800,000$850,000$-------NoNo-------
Radim ZohornaWranglers (Cal)LW221996-01-01Yes229 Lbs6 ft6NoNoN/ANoNo3FalseFalsePro & Farm600,000$2,985$0$0$No650,000$700,000$-------NoNo-------
Rasmus AnderssonWranglers (Cal)D221996-01-01Yes214 Lbs6 ft1NoNoN/ANoNo1FalseFalsePro & Farm1,500,000$7,463$0$0$No------------------
Ryan DonatoWranglers (Cal)C221996-01-01Yes193 Lbs6 ft0NoNoN/ANoNo3FalseFalsePro & Farm800,000$3,980$0$0$No850,000$900,000$-------NoNo-------
Ryan SpoonerWranglers (Cal)C261992-01-01No181 Lbs5 ft11NoNoN/ANoNo1FalseFalsePro & Farm900,000$4,478$0$0$No------------------
Skyler Brind'AmourWranglers (Cal)C191999-01-01Yes185 Lbs6 ft2NoNoN/ANoNo1FalseFalsePro & Farm500,000$2,488$0$0$No------------------
Tage ThompsonWranglers (Cal)RW211997-01-01Yes218 Lbs6 ft7NoNoN/ANoNo3FalseFalsePro & Farm700,000$3,483$0$0$No800,000$900,000$-------NoNo-------
Taylor RaddyshWranglers (Cal)RW201998-01-01Yes198 Lbs6 ft3NoNoN/ANoNo1FalseFalsePro & Farm500,000$2,488$0$0$No------------------
Tim BerniWranglers (Cal)D182000-01-01Yes181 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm500,000$2,488$0$0$No500,000$--------No--------Lien
Trevor CarrickWranglers (Cal)D241994-01-01Yes171 Lbs6 ft1NoNoN/ANoNo3FalseFalsePro & Farm700,000$3,483$0$0$No750,000$825,000$-------NoNo-------
Vitali KravtsovWranglers (Cal)LW191999-01-01Yes186 Lbs6 ft3NoNoN/ANoNo2FalseFalsePro & Farm500,000$2,488$0$0$No500,000$--------No--------Lien
Vitek VanecekWranglers (Cal)G221996-01-01No190 Lbs5 ft11NoNoN/ANoNo1FalseFalsePro & Farm600,000$2,985$0$0$No------------------
Vladislav KamenevWranglers (Cal)C221996-01-01Yes194 Lbs6 ft2NoNoN/ANoNo3FalseFalsePro & Farm700,000$3,483$0$0$No800,000$825,000$-------NoNo-------
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
3221.91195 Lbs6 ft11.94702,188$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Peter CehlarikNico SturmEvgeny Svechnikov40122
2Jesper BrattRyan SpoonerBarclay Goodrow30122
3Eric KarlssonRyan DonatoTaylor Raddysh20122
4Vitali KravtsovVladislav KamenevTage Thompson10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Rasmus AnderssonFrank Corrado40122
2Dominik MasinTrevor Carrick30122
3John GilmourErik Brannstrom20122
4Petteri LindbohmChristian Jaros10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Peter CehlarikNico SturmEvgeny Svechnikov60122
2Jesper BrattRyan SpoonerBarclay Goodrow40122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Rasmus AnderssonFrank Corrado60122
2Dominik MasinTrevor Carrick40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Evgeny SvechnikovPeter Cehlarik60122
2Barclay GoodrowNico Sturm40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Rasmus AnderssonFrank Corrado60122
2Dominik MasinTrevor Carrick40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Evgeny Svechnikov60122Rasmus AnderssonFrank Corrado60122
2Peter Cehlarik40122Dominik MasinTrevor Carrick40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Evgeny SvechnikovPeter Cehlarik60122
2Barclay GoodrowNico Sturm40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Rasmus AnderssonFrank Corrado60122
2Dominik MasinTrevor Carrick40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Peter CehlarikNico SturmEvgeny SvechnikovRasmus AnderssonFrank Corrado
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Peter CehlarikNico SturmEvgeny SvechnikovRasmus AnderssonFrank Corrado
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Ivan Chekhovich, Radim Zohorna, Skyler Brind'AmourIvan Chekhovich, Radim ZohornaSkyler Brind'Amour
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Brandon Hickey, Darren Raddysh, John GilmourBrandon HickeyDarren Raddysh, John Gilmour
Tirs de pénalité
Evgeny Svechnikov, Peter Cehlarik, Barclay Goodrow, Nico Sturm, Jesper Bratt
Gardien
#1 : Michael Hutchinson, #2 : Vitek Vanecek
Lignes d’attaque personnalisées en prolongation
Evgeny Svechnikov, Peter Cehlarik, Barclay Goodrow, Nico Sturm, Jesper Bratt, Ryan Spooner, Ryan Spooner, Eric Karlsson, Ryan Donato, Taylor Raddysh, Vladislav Kamenev
Lignes de défense personnalisées en prolongation
Rasmus Andersson, Frank Corrado, Dominik Masin, Trevor Carrick, John Gilmour


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
1Admirals31200000912-31010000012-121100000810-220.3339152400991128916918799597704610534166310330.00%8362.50%0890176750.37%935183251.04%676130751.72%168993117247611452719
2Americans3200001015692200000012481000001032161.0001524390099112891610187995977046883718689111.11%90100.00%0890176750.37%935183251.04%676130751.72%168993117247611452719
3Barracuda32001000161062100100014951100000021161.000162844009911289169487995977046972722609222.22%11281.82%0890176750.37%935183251.04%676130751.72%168993117247611452719
4Bears20200000510-51010000037-41010000023-100.000561100991128916608799597704663192447400.00%12283.33%0890176750.37%935183251.04%676130751.72%168993117247611452719
5Canucks20100001911-21000000167-11010000034-110.25091625009911289167687995977046693410411119.09%5180.00%0890176750.37%935183251.04%676130751.72%168993117247611452719
6Checkers210000101385100000106511100000073441.000132134009911289167887995977046762816365480.00%8275.00%0890176750.37%935183251.04%676130751.72%168993117247611452719
7Comets311000011116-51000000145-121100000711-430.5001120310099112891689879959770461035416804125.00%8450.00%0890176750.37%935183251.04%676130751.72%168993117247611452719
8Condors300001201312110000010321200001101010050.83313193200991128916114879959770469841147510220.00%7442.86%0890176750.37%935183251.04%676130751.72%168993117247611452719
9Crunch220000001165110000007341100000043141.000111728009911289165987995977046642022379111.11%10280.00%0890176750.37%935183251.04%676130751.72%168993117247611452719
10Eagles210001001174110000008351000010034-130.750111930009911289166787995977046802016518225.00%8187.50%0890176750.37%935183251.04%676130751.72%168993117247611452719
11Griffins312000001013-321100000910-11010000013-220.333101626009911289161028799597704610430245415213.33%12375.00%0890176750.37%935183251.04%676130751.72%168993117247611452719
12Icehogs320001001394110000005322100010086250.833132538009911289161138799597704610831225714535.71%11372.73%0890176750.37%935183251.04%676130751.72%168993117247611452719
13Islander30101100911-22010010069-31000100032130.500917260099112891689879959770461033526766116.67%130100.00%0890176750.37%935183251.04%676130751.72%168993117247611452719
14Little Stars30200100914-51000010067-12020000037-410.1679142320991128916938799597704610941186212325.00%9455.56%1890176750.37%935183251.04%676130751.72%168993117247611452719
15Marlies6210110126251311001001213-1310010011412280.6672645711099112891620887995977046179597513829620.69%31583.87%0890176750.37%935183251.04%676130751.72%168993117247611452719
16Moose320010001174220000007431000100043161.00011193000991128916978799597704679262863500.00%14285.71%1890176750.37%935183251.04%676130751.72%168993117247611452719
17Penguins4220000014113211000008532110000066040.500142337009911289161328799597704612441267921628.57%13376.92%0890176750.37%935183251.04%676130751.72%168993117247611452719
18Phantoms6320010025241312000001316-332000100128470.58325436810991128916203879959770461817482137291137.93%41978.05%1890176750.37%935183251.04%676130751.72%168993117247611452719
19Punishers2020000059-41010000024-21010000035-200.000571200991128916668799597704669251650800.00%8187.50%0890176750.37%935183251.04%676130751.72%168993117247611452719
20Reign1026010103350-17503010101325-12523000002025-580.40033558800991128916308879959770463131051011814349.30%421564.29%0890176750.37%935183251.04%676130751.72%168993117247611452719
21Rocket3100200015123100010007612100100086261.00015233800991128916101879959770461033828719111.11%14285.71%0890176750.37%935183251.04%676130751.72%168993117247611452719
22Senators3110000113121211000007521000000167-130.50013253800991128916104879959770469828185713430.77%90100.00%0890176750.37%935183251.04%676130751.72%168993117247611452719
23Silver Knights2020000069-31010000035-21010000034-100.00061016109911289166287995977046702214436116.67%7185.71%1890176750.37%935183251.04%676130751.72%168993117247611452719
24Thunderbirds2110000045-11010000025-31100000020220.500471101991128916588799597704657311644300.00%8187.50%0890176750.37%935183251.04%676130751.72%168993117247611452719
25Wolfpack20200000611-51010000025-31010000046-200.0006814009911289166587995977046672414447114.29%7185.71%0890176750.37%935183251.04%676130751.72%168993117247611452719
Total80273007754312320-840131603332166169-340141404422146151-5890.55631252283451991128916263087995977046260792468217142996220.74%3257178.15%4890176750.37%935183251.04%676130751.72%168993117247611452719
_Since Last GM Reset80273007754312320-840131603332166169-340141404422146151-5890.55631252283451991128916263087995977046260792468217142996220.74%3257178.15%4890176750.37%935183251.04%676130751.72%168993117247611452719
_Vs Conference491914064421991927251090312010399424950332296933640.65319933753620991128916163687995977046155353044810241954121.03%2094877.03%2890176750.37%935183251.04%676130751.72%168993117247611452719
_Vs Division2279022118499-151126011103854-1611530110146451230.5238414322720991128916719879959770466732382584561012120.79%1142974.56%1890176750.37%935183251.04%676130751.72%168993117247611452719

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
8089W131252283426302607924682171451
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
8027307754312320
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
4013163332166169
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
4014144422146151
Derniers 10 matchs
WLOTWOTL SOWSOL
431101
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
2996220.74%3257178.15%4
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
87995977046991128916
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
890176750.37%935183251.04%676130751.72%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
168993117247611452719


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
27Reign1Wranglers2WXXR1Sommaire du match
526Wranglers3Reign6LSommaire du match
636Wranglers6Phantoms2WSommaire du match
844Wranglers4Reign6LR1Sommaire du match
952Reign2Wranglers3WXSommaire du match
1371Reign5Wranglers3LR1Sommaire du match
1581Wranglers8Marlies7WXSommaire du match
1795Marlies4Wranglers3LXSommaire du match
19107Wranglers3Admirals8LSommaire du match
22119Wranglers1Little Stars3LSommaire du match
24127Phantoms8Wranglers5LSommaire du match
27145Wranglers3Phantoms4LXSommaire du match
28153Wranglers5Reign3WR1Sommaire du match
29156Senators4Wranglers2LSommaire du match
33176Wranglers4Condors5LXSommaire du match
34182Marlies4Wranglers2LSommaire du match
37200Wranglers4Marlies2WSommaire du match
39207Griffins7Wranglers4LSommaire du match
44232Thunderbirds5Wranglers2LSommaire du match
47248Crunch3Wranglers7WSommaire du match
50258Wranglers3Phantoms2WSommaire du match
52265Wranglers4Moose3WXSommaire du match
54279Penguins3Wranglers2LSommaire du match
58297Wranglers2Little Stars4LSommaire du match
60308Wranglers2Bears3LSommaire du match
61311Canucks7Wranglers6LXXSommaire du match
64332Wranglers3Silver Knights4LSommaire du match
65337Checkers5Wranglers6WXXSommaire du match
68353Wranglers6Condors5WXXSommaire du match
69362Islander3Wranglers2LXSommaire du match
72376Wranglers2Marlies3LXXSommaire du match
74387Eagles3Wranglers8WSommaire du match
76399Wranglers3Punishers5LSommaire du match
78406Wranglers4Wolfpack6LSommaire du match
79414Barracuda4Wranglers8WSommaire du match
82429Wranglers1Reign7LR1Sommaire du match
84441Marlies5Wranglers7WSommaire du match
86449Wranglers3Islander2WXSommaire du match
89467Admirals2Wranglers1LSommaire du match
94489Punishers4Wranglers2LSommaire du match
96505Wranglers7Checkers3WSommaire du match
98516Comets5Wranglers4LXXSommaire du match
100526Wranglers3Americans2WXXSommaire du match
103541Rocket6Wranglers7WXSommaire du match
105555Wranglers5Admirals2WSommaire du match
107566Wranglers2Thunderbirds0WSommaire du match
109571Silver Knights5Wranglers3LSommaire du match
114596Americans1Wranglers5WSommaire du match
117616Moose2Wranglers4WSommaire du match
121634Wranglers4Crunch3WSommaire du match
122642Islander6Wranglers4LSommaire du match
126665Condors2Wranglers3WXXSommaire du match
129684Wranglers4Penguins2WSommaire du match
131693Senators1Wranglers5WSommaire du match
134710Wranglers2Barracuda1WSommaire du match
135719Icehogs3Wranglers5WSommaire du match
140740Little Stars7Wranglers6LXSommaire du match
141750Wranglers3Canucks4LSommaire du match
143766Phantoms6Wranglers4LSommaire du match
145779Wranglers5Icehogs2WSommaire du match
147789Bears7Wranglers3LSommaire du match
150805Wranglers3Icehogs4LXSommaire du match
153816Phantoms2Wranglers4WSommaire du match
156830Wranglers1Griffins3LSommaire du match
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
159845Griffins3Wranglers5WSommaire du match
162859Wranglers7Reign3WR1Sommaire du match
164868Wranglers2Penguins4LSommaire du match
165874Barracuda5Wranglers6WXSommaire du match
169896Wolfpack5Wranglers2LSommaire du match
173918Moose2Wranglers3WSommaire du match
175927Wranglers4Rocket3WXSommaire du match
177937Wranglers3Eagles4LXSommaire du match
179948Penguins2Wranglers6WSommaire du match
181960Wranglers4Rocket3WSommaire du match
183973Reign8Wranglers2LR1Sommaire du match
184976Wranglers6Senators7LXXSommaire du match
188998Americans3Wranglers7WSommaire du match
1911011Wranglers4Comets9LSommaire du match
1961024Reign9Wranglers3LR1Sommaire du match
1991036Wranglers3Comets2WSommaire du match



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets7040
Assistance73,56636,969
Assistance PCT91.96%92.42%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
0 2763 - 92.11% 173,995$6,959,798$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursSalaire total moyen des joueursSalaire des entraineurs
3,023,608$ 2,247,000$ 2,074,500$ 0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
11,179$ 2,222,763$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
0$ 1 15,159$ 15,159$




Wranglers 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

Wranglers 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

Wranglers 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

Wranglers 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

Wranglers 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