Connexion

Gulls
GP: 49 | W: 23 | L: 22 | OTL: 4 | P: 50
GF: 149 | GA: 171 | PP%: 29.10% | PK%: 76.19%
DG: Tommy Rhéaume | Morale : 46 | Moyenne d’équipe : 63
Prochains matchs #797 vs Monsters
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Centre de jeu
Gulls
23-22-4, 50pts
3
FINAL
5 Silver Knights
26-17-4, 56pts
Team Stats
W1SéquenceW1
16-6-3Fiche domicile12-12-1
7-16-1Fiche domicile14-5-3
7-3-0Derniers 10 matchs6-3-1
3.04Buts par match 3.45
3.49Buts contre par match 3.21
29.10%Pourcentage en avantage numérique23.38%
76.19%Pourcentage en désavantage numérique72.61%
Eagles
29-18-3, 61pts
3
FINAL
4 Gulls
23-22-4, 50pts
Team Stats
L1SéquenceW1
13-10-1Fiche domicile16-6-3
16-8-2Fiche domicile7-16-1
6-3-1Derniers 10 matchs7-3-0
3.46Buts par match 3.04
3.06Buts contre par match 3.49
19.88%Pourcentage en avantage numérique29.10%
79.45%Pourcentage en désavantage numérique76.19%
Gulls
23-22-4, 50pts
2023-12-11
Monsters
32-19-0, 64pts
Statistiques d’équipe
W1SéquenceW1
16-6-3Fiche domicile16-8-0
7-16-1Fiche visiteur16-11-0
7-3-010 derniers matchs3-7-0
3.04Buts par match 3.57
3.49Buts contre par match 3.57
29.10%Pourcentage en avantage numérique17.42%
76.19%Pourcentage en désavantage numérique74.31%
Roadrunners
19-24-5, 43pts
2023-12-13
Gulls
23-22-4, 50pts
Statistiques d’équipe
L5SéquenceW1
11-12-2Fiche domicile16-6-3
8-12-3Fiche visiteur7-16-1
1-8-110 derniers matchs7-3-0
3.19Buts par match 3.04
3.27Buts contre par match 3.04
26.95%Pourcentage en avantage numérique29.10%
79.04%Pourcentage en désavantage numérique76.19%
Gulls
23-22-4, 50pts
2023-12-15
Eagles
29-18-3, 61pts
Statistiques d’équipe
W1SéquenceL1
16-6-3Fiche domicile13-10-1
7-16-1Fiche visiteur16-8-2
7-3-010 derniers matchs6-3-1
3.04Buts par match 3.46
3.49Buts contre par match 3.46
29.10%Pourcentage en avantage numérique19.88%
76.19%Pourcentage en désavantage numérique79.45%
Meneurs d'équipe
Buts
Eric Cornel
18
Passes
Markus Niemelainen
23
Points
Adam Ruzicka
33
Plus/Moins
Zach Gallant
7
Victoires
Linus Soderstrom
19
Pourcentage d’arrêts
Zane McIntyre
0.914

Statistiques d’équipe
Buts pour
149
3.04 GFG
Tirs pour
1411
28.80 Avg
Pourcentage en avantage numérique
29.1%
39 GF
Début de zone offensive
39.7%
Buts contre
171
3.49 GAA
Tirs contre
1476
30.12 Avg
Pourcentage en désavantage numérique
76.2%%
35 GA
Début de la zone défensive
40.6%
Informations de l'équipe

Directeur généralTommy Rhéaume
EntraîneurKirk Muller
DivisionDivision Pacifique
ConférenceConference 2
Capitaine
Assistant #1Eric Cornel
Assistant #2Arttu Ruotsalainen


Informations de l’aréna

Capacité3,000
Assistance2,458
Billets de saison300


Informations de la formation

Équipe Pro27
Équipe Mineure19
Limite contact 46 / 55
Espoirs27


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
1Adam Ruzicka (R)X100.006645917273727972727572707050456051660243900,000$
2Eric Cornel (A)X100.0058459272707274727467717560544957586402731,200,000$
3JC LiponX100.0073688171707370706069706968464556586403031,200,000$
4Nikolai ProkhorkinX100.0076526968736976706467686660676251466403031,200,000$
5Vitaly AbramovX100.006445847267717271677167726648466058630252750,000$
6Daniel Muzito-BagendaX100.006245847273747465686164666057486448620273900,000$
7Damien BrunnerX100.0057459769677369666566666667585325586203721,000,000$
8Marc-Olivier RoyX100.006143837271666263746260615858525558610293800,000$
9Conner BleackleyX100.006445787269697465656467675855485758610273800,000$
10Zach GallantX100.007653597273666766696262706047466842610241750,000$
11Logan NelsonX100.006043887171667061676059655753495319590301700,000$
12Shane EisermanX100.006845797271605059626059655347455856580283500,000$
13Andreas BorgmanX100.0077547071717176674571707562625658606602811,300,000$
14Conor Timmins (R)X99.706745897271747374457768716250456036650253900,000$
15Jyrki JokipakkaX100.0060439469747276714570657262614850586503221,250,000$
16Noah JuulsenX100.006145907472737469456469726062486659650261750,000$
17Markus NiemelainenX100.008145897272717269456563735648466038640252900,000$
18Wiley ShermanX100.007345787174736867456363725755505859640281900,000$
Rayé
1Zack MitchellX100.005440926770636566636365654558555120600302800,000$
2Evan AllenX100.006043887170686662686158615851465319590283500,000$
3Eric GelinasX100.0060449370746969704568667063625447366403221,250,000$
4Ludwig BystromX100.006245917368696567456761685946455918620292900,000$
5Alex CoulombeX100.006041806771686761456160654555555920600283700,000$
MOYENNE D’ÉQUIPE99.96654684717170706758666569595449564563
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
1Linus Soderstrom99.00838875808078847878787648596635650271750,000$
2Tyler Beskorowany100.007678768378778280818279455042566403331,200,000$
Rayé
1Zane McIntyre98.007772717976778379788178475053276303131,000,000$
2Edward Pasquale100.00757573817975797979797445515028620332900,000$
MOYENNE D’ÉQUIPE99.2578787481787782797980774653533764
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Kirk Muller82708177999932CAN591100,000$


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
1Adam RuzickaGulls (Ana)C47141933-141402379120288111.67%1083917.862681710000051092050.13%115700100.7913000140
2Jyrki JokipakkaGulls (Ana)D49102030-11120323767223414.93%5694419.2877143694000261210%000000.6300000210
3Vitaly AbramovGulls (Ana)LW4913173023205444115226711.30%1171214.54281017600001190155.56%4500000.8400000015
4Eric CornelGulls (Ana)C4918112921002783120379615.00%877115.752358491014642248.38%92400010.7502000204
5Markus NiemelainenGulls (Ana)D3852328-5260683843122811.63%5680121.0938111849000190000%000000.7000000131
6Damien BrunnerGulls (Ana)RW4962127-14015256617449.09%672714.8436913108000000144.64%5600000.7400000030
7Andreas BorgmanGulls (Ana)D3561925-85010603141143114.63%2667319.255492077000127110%000000.7400100302
8JC LiponGulls (Ana)RW49111324-133355454102318410.78%879516.23437191070112712147.56%8200000.6022100321
9Conor TimminsGulls (Ana)D3912122-1410025294517272.22%4578220.061892481000192100%000000.5600000012
10Noah JuulsenGulls (Ana)D4861622-1818046349126516.59%5192919.363473597000454200%000000.4700000000
11Arttu RuotsalainenGulls (Ana)C3171421-78037627912478.86%752817.05191010660002693051.25%67900000.7901000112
12Conner BleackleyGulls (Ana)RW4911819-440243284214813.10%860812.4200000000020239.02%4100000.6200000012
13Nikolai ProkhorkinGulls (Ana)LW439716-16275653476207511.84%1065815.3122413810001461262.32%6900000.4901001111
14Wiley ShermanGulls (Ana)D4851015-2335944143162211.63%63101921.232025290000103100%000000.2900001000
15Eric GelinasGulls (Ana)D3231114110013282511212.00%5165620.5001105000056100%000000.4300000000
16Daniel Muzito-BagendaGulls (Ana)LW494913-120022517620485.26%366513.59000050001171052.22%36000000.3900000011
17Zach GallantGulls (Ana)C2965117321042284683013.04%236112.4500000000040050.38%39700000.6100002130
18Shane EisermanGulls (Ana)LW48347-68031274911336.12%360212.5600000000050050.00%4200000.2300000010
19Marc-Olivier RoyGulls (Ana)RW49055-6803417529350%563813.0300002000060065.79%3800000.1600000000
20Logan NelsonGulls (Ana)C6101-42011232733.33%27412.4400000000001058.75%8000000.2700000000
21Evan AllenGulls (Ana)C10011-220575430%1898.9000000000000048.84%8600000.2200000000
22Ludwig BystromGulls (Ana)D50002401133020%810120.320000000006000%00000000000000
Statistiques d’équipe totales ou en moyenne851139254393-12934735783796135135090510.29%4401398216.433769106235102211225910201150.44%405600110.5639204152321
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
1Linus SoderstromGulls (Ana)42191930.8803.5023154113511230200.75012407200
2Tyler BeskorowanyGulls (Ana)143310.8993.30582203231800000840001
3Zane McIntyreGulls (Ana)11000.9143.0060003350000011000
Statistiques d’équipe totales ou en moyenne57232240.8853.452957611701476020124948201


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 Type Salaire actuel Plafond 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
Adam RuzickaGulls (Ana)C241999-05-11Yes215 Lbs6 ft4NoNoN/ANoNo3Pro & Farm900,000$0$0$No900,000$900,000$-------NoNo-------Lien
Alex CoulombeGulls (Ana)D281995-04-09No202 Lbs6 ft3NoNoN/ANoNo3Pro & Farm700,000$0$0$No700,000$700,000$-------NoNo-------Lien
Andreas BorgmanGulls (Ana)D281995-06-18No199 Lbs6 ft0NoNoN/ANoNo1Pro & Farm1,300,000$0$0$No------------------Lien
Conner BleackleyGulls (Ana)RW271996-02-07No192 Lbs6 ft0NoNoN/ANoNo3Pro & Farm800,000$0$0$No800,000$800,000$-------NoNo-------Lien
Conor TimminsGulls (Ana)D251998-09-18Yes202 Lbs6 ft2NoNoN/ANoNo3Pro & Farm900,000$0$0$No900,000$900,000$-------NoNo-------Lien
Damien BrunnerGulls (Ana)RW371986-03-09No185 Lbs5 ft10NoNoN/ANoNo2Pro & Farm1,000,000$0$0$No1,000,000$--------No--------Lien
Daniel Muzito-BagendaGulls (Ana)LW271996-06-16No198 Lbs6 ft1NoNoN/ANoNo3Pro & Farm900,000$0$0$No900,000$900,000$-------NoNo-------Lien
Edward PasqualeGulls (Ana)G331990-11-20No218 Lbs6 ft3NoNoN/ANoNo2Pro & Farm900,000$0$0$No900,000$--------No--------Lien
Eric CornelGulls (Ana)C271996-04-11No198 Lbs6 ft2NoNoN/ANoNo3Pro & Farm1,200,000$0$0$No1,200,000$1,200,000$-------NoNo-------Lien
Eric GelinasGulls (Ana)D321991-05-08No215 Lbs6 ft2NoNoN/ANoNo2Pro & Farm1,250,000$0$0$No1,250,000$--------No--------Lien
Evan AllenGulls (Ana)C281995-02-03No196 Lbs5 ft11NoNoN/ANoNo3Pro & Farm500,000$0$0$No500,000$500,000$-------NoNo-------Lien
JC LiponGulls (Ana)RW301993-07-10No189 Lbs6 ft0NoNoN/ANoNo3Pro & Farm1,200,000$0$0$No1,200,000$1,200,000$-------NoNo-------Lien
Jyrki JokipakkaGulls (Ana)D321991-08-20No207 Lbs6 ft3NoNoN/ANoNo2Pro & Farm1,250,000$0$0$No1,250,000$--------No--------Lien
Linus SoderstromGulls (Ana)G271996-08-23No192 Lbs6 ft4NoNoN/ANoNo1Pro & Farm750,000$0$0$No------------------Lien
Logan NelsonGulls (Ana)C301993-09-09No209 Lbs6 ft1NoNoN/ANoNo1Pro & Farm700,000$0$0$No------------------Lien
Ludwig BystromGulls (Ana)D291994-07-20No174 Lbs6 ft0NoNoN/ANoNo2Pro & Farm900,000$0$0$No900,000$--------No--------Lien
Marc-Olivier RoyGulls (Ana)RW291994-11-05No182 Lbs6 ft1NoNoN/ANoNo3Pro & Farm800,000$0$0$No800,000$800,000$-------NoNo-------Lien
Markus NiemelainenGulls (Ana)D251998-06-08No190 Lbs6 ft6NoNoN/ANoNo2Pro & Farm900,000$0$0$No900,000$--------No--------Lien
Nikolai ProkhorkinGulls (Ana)LW301993-09-17No195 Lbs6 ft1NoNoN/ANoNo3Pro & Farm1,200,000$0$0$No1,200,000$1,200,000$-------NoNo-------Lien
Noah JuulsenGulls (Ana)D261997-04-02No194 Lbs6 ft2NoNoN/ANoNo1Pro & Farm750,000$0$0$No------------------Lien
Shane EisermanGulls (Ana)LW281995-10-10No200 Lbs6 ft2NoNoN/ANoNo3Pro & Farm500,000$0$0$No500,000$500,000$-------NoNo-------Lien
Tyler BeskorowanyGulls (Ana)G331990-04-28No210 Lbs6 ft5NoNoN/ANoNo3Pro & Farm1,200,000$0$0$No1,200,000$1,200,000$-------NoNo-------Lien
Vitaly AbramovGulls (Ana)LW251998-05-08No187 Lbs5 ft10NoNoN/ANoNo2Pro & Farm750,000$0$0$No750,000$--------No--------Lien
Wiley ShermanGulls (Ana)D281995-05-24No200 Lbs6 ft6NoNoN/ANoNo1Pro & Farm900,000$0$0$No------------------Lien
Zach GallantGulls (Ana)C241999-03-06No192 Lbs6 ft2NoNoN/ANoNo1Pro & Farm750,000$0$0$No------------------Lien
Zack MitchellGulls (Ana)RW301993-01-07No193 Lbs6 ft0NoNoN/ANoNo2Pro & Farm800,000$0$0$No800,000$--------No--------Lien
Zane McIntyreGulls (Ana)G311992-08-20No206 Lbs6 ft2NoNoN/ANoNo3Pro & Farm1,000,000$0$0$No1,000,000$1,000,000$-------NoNo-------Lien
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2728.63198 Lbs6 ft22.26914,815$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Nikolai ProkhorkinAdam RuzickaJC Lipon25122
2Vitaly AbramovEric CornelDamien Brunner25122
3Daniel Muzito-BagendaZach GallantMarc-Olivier Roy25122
4Shane EisermanLogan NelsonConner Bleackley25122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Andreas BorgmanConor Timmins25122
2Jyrki JokipakkaNoah Juulsen25122
3Markus NiemelainenWiley Sherman25122
4Markus NiemelainenWiley Sherman25122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Nikolai ProkhorkinAdam RuzickaJC Lipon50122
2Vitaly AbramovEric CornelDamien Brunner50122
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Andreas BorgmanConor Timmins50122
2Jyrki JokipakkaNoah Juulsen50122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Adam RuzickaNikolai Prokhorkin50122
2Eric CornelVitaly Abramov50122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Markus NiemelainenWiley Sherman50122
2Jyrki JokipakkaNoah Juulsen50122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Adam Ruzicka50122Andreas BorgmanConor Timmins50122
2Eric Cornel50122Jyrki JokipakkaNoah Juulsen50122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Adam RuzickaNikolai Prokhorkin50122
2Eric CornelVitaly Abramov50122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Andreas BorgmanConor Timmins50122
2Jyrki JokipakkaNoah Juulsen50122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Nikolai ProkhorkinAdam RuzickaJC LiponAndreas BorgmanConor Timmins
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Nikolai ProkhorkinAdam RuzickaJC LiponAndreas BorgmanConor Timmins
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Vitaly Abramov, Damien Brunner, Daniel Muzito-BagendaVitaly Abramov, Damien BrunnerVitaly Abramov
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Noah Juulsen, Markus Niemelainen, Wiley ShermanNoah JuulsenNoah Juulsen, Markus Niemelainen
Tirs de pénalité
Adam Ruzicka, JC Lipon, Nikolai Prokhorkin, Eric Cornel, Vitaly Abramov
Gardien
#1 : Linus Soderstrom, #2 : Tyler Beskorowany, #3 : 0


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
1Admirals3110001013112100000104312110000098140.667132134005343514109508427468201193334526233.33%16475.00%0823163750.27%855167651.01%40281449.39%11457771165367650326
2Americans2020000029-7000000000002020000029-700.000246005343514465084274682065202133300.00%20100.00%0823163750.27%855167651.01%40281449.39%11457771165367650326
3Barracuda21100000770211000007700000000000020.500712190053435147650842746820471611264125.00%30100.00%0823163750.27%855167651.01%40281449.39%11457771165367650326
4Canucks211000004311010000012-11100000031220.50048120053435144450842746820451110347228.57%50100.00%0823163750.27%855167651.01%40281449.39%11457771165367650326
5Condors11000000431110000004310000000000021.000471100534351436508427468202950153266.67%000%0823163750.27%855167651.01%40281449.39%11457771165367650326
6Crunch1000010023-11000010023-10000000000010.5002460053435143150842746820447619100.00%3166.67%0823163750.27%855167651.01%40281449.39%11457771165367650326
7Eagles2110000067-12110000067-10000000000020.500612180053435144650842746820801516488112.50%8362.50%0823163750.27%855167651.01%40281449.39%11457771165367650326
8Firebirds1010000023-11010000023-10000000000000.0002350053435141950842746820377417100.00%2150.00%0823163750.27%855167651.01%40281449.39%11457771165367650326
9Griffins41300000818-102110000067-120200000211-920.25081422005343514985084274682013240305713430.77%13469.23%0823163750.27%855167651.01%40281449.39%11457771165367650326
10Icehogs32100000981220000006421010000034-140.667916250053435149650842746820762614559111.11%5180.00%0823163750.27%855167651.01%40281449.39%11457771165367650326
11Iowa Wild2010001079-2100000105411010000025-320.500712190053435145150842746820512212326233.33%5180.00%0823163750.27%855167651.01%40281449.39%11457771165367650326
12Islanders1010000025-3000000000001010000025-300.00024600534351416508427468203781719200.00%6266.67%0823163750.27%855167651.01%40281449.39%11457771165367650326
13Marlies11000000734000000000001100000073421.000713200053435142750842746820411412134375.00%6266.67%0823163750.27%855167651.01%40281449.39%11457771165367650326
14Monsters32100000990220000007341010000026-440.667915240153435148950842746820903032477342.86%10190.00%1823163750.27%855167651.01%40281449.39%11457771165367650326
15Moose1010000037-4000000000001010000037-400.00036900534351425508427468203258193266.67%3166.67%0823163750.27%855167651.01%40281449.39%11457771165367650326
16Phantoms11000000633110000006330000000000021.0006101600534351430508427468202698162150.00%4175.00%0823163750.27%855167651.01%40281449.39%11457771165367650326
17Reign2010000179-2000000000002010000179-210.250714210053435147650842746820532118242150.00%9277.78%0823163750.27%855167651.01%40281449.39%11457771165367650326
18Roadrunners42200000814-62110000047-32110000047-340.50081422005343514985084274682011537225516318.75%11372.73%0823163750.27%855167651.01%40281449.39%11457771165367650326
19SenatorsF11000000321000000000001100000032121.000369005343514185084274682025124242150.00%2150.00%0823163750.27%855167651.01%40281449.39%11457771165367650326
20Silver Knights422000001815311000000422312000001413140.50018335100534351499508427468201355128659333.33%13376.92%0823163750.27%855167651.01%40281449.39%11457771165367650326
21StarsF1010000015-4000000000001010000015-400.0001230053435142850842746820336617300.00%30100.00%0823163750.27%855167651.01%40281449.39%11457771165367650326
22Wolf Pack1000010023-11000010023-10000000000010.50024600534351423508427468201750153133.33%000%0823163750.27%855167651.01%40281449.39%11457771165367650326
23Wolves21000100761210001007610000000000030.750712190053435148350842746820471610347228.57%50100.00%0823163750.27%855167651.01%40281449.39%11457771165367650326
24Wranglers431000001293220000007432110000055060.7501222340053435141475084274682010033386313430.77%13469.23%0823163750.27%855167651.01%40281449.39%11457771165367650326
Total49212200321149171-22251460032080719247160000169100-31500.51014926841701534351414115084274682014764493617991343929.10%1473576.19%1823163750.27%855167651.01%40281449.39%11457771165367650326
_Since Last GM Reset49212200321149171-22251460032080719247160000169100-31500.51014926841701534351414115084274682014764493617991343929.10%1473576.19%1823163750.27%855167651.01%40281449.39%11457771165367650326
_Vs Conference37171700021113127-1419125000206153818512000015274-22390.52711320231501534351410935084274682011053462715901062927.36%1142677.19%1823163750.27%855167651.01%40281449.39%11457771165367650326

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
4950W11492684171411147644936179901
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
4921220321149171
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
2514603208071
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
24716000169100
Derniers 10 matchs
WLOTWOTL SOWSOL
730000
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
1343929.10%1473576.19%1
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
508427468205343514
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
823163750.27%855167651.01%40281449.39%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
11457771165367650326


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
1 - 2023-08-254Roadrunners5Gulls0BLSommaire du match
5 - 2023-08-2932Gulls3Icehogs4ALSommaire du match
7 - 2023-08-3148Icehogs1Gulls2BWSommaire du match
8 - 2023-09-0158Gulls4Silver Knights5ALSommaire du match
10 - 2023-09-0376Gulls2Americans5ALSommaire du match
12 - 2023-09-0590Silver Knights2Gulls4BWSommaire du match
15 - 2023-09-08114Wranglers1Gulls2BWSommaire du match
17 - 2023-09-10127Gulls3Reign4ALSommaire du match
19 - 2023-09-12143Wolves2Gulls4BWSommaire du match
21 - 2023-09-14159Gulls3Wranglers1AWSommaire du match
23 - 2023-09-16169Canucks2Gulls1BLSommaire du match
25 - 2023-09-18189Gulls5Admirals3AWSommaire du match
27 - 2023-09-20201Monsters0Gulls2BWSommaire du match
30 - 2023-09-23225Gulls4Reign5ALXXSommaire du match
32 - 2023-09-25235Griffins4Gulls1BLSommaire du match
35 - 2023-09-28259Iowa Wild4Gulls5BWXXSommaire du match
38 - 2023-10-01281Gulls1Griffins5ALSommaire du match
40 - 2023-10-03293Admirals3Gulls4BWXXSommaire du match
42 - 2023-10-05307Gulls3Moose7ALSommaire du match
44 - 2023-10-07323Wranglers3Gulls5BWSommaire du match
46 - 2023-10-09343Gulls1StarsF5ALSommaire du match
48 - 2023-10-11355Gulls3SenatorsF2AWSommaire du match
50 - 2023-10-13369Firebirds3Gulls2BLSommaire du match
53 - 2023-10-16389Crunch3Gulls2BLXSommaire du match
56 - 2023-10-19413Gulls2Iowa Wild5ALSommaire du match
57 - 2023-10-20426Icehogs3Gulls4BWSommaire du match
60 - 2023-10-23447Gulls4Admirals5ALSommaire du match
61 - 2023-10-24456Griffins3Gulls5BWSommaire du match
64 - 2023-10-27471Gulls1Roadrunners5ALSommaire du match
66 - 2023-10-29483Roadrunners2Gulls4BWSommaire du match
68 - 2023-10-31496Gulls7Marlies3AWSommaire du match
70 - 2023-11-02514Gulls2Wranglers4ALSommaire du match
72 - 2023-11-04525Wolves4Gulls3BLXSommaire du match
75 - 2023-11-07544Gulls2Monsters6ALSommaire du match
77 - 2023-11-09558Barracuda4Gulls3BLSommaire du match
80 - 2023-11-12584Wolf Pack3Gulls2BLXSommaire du match
83 - 2023-11-15608Eagles4Gulls2BLSommaire du match
85 - 2023-11-17619Gulls0Americans4ALSommaire du match
87 - 2023-11-19635Gulls3Canucks1AWSommaire du match
89 - 2023-11-21648Phantoms3Gulls6BWSommaire du match
91 - 2023-11-23663Gulls2Islanders5ALSommaire du match
93 - 2023-11-25679Gulls3Roadrunners2AWSommaire du match
94 - 2023-11-26687Barracuda3Gulls4BWSommaire du match
97 - 2023-11-29710Condors3Gulls4BWSommaire du match
99 - 2023-12-01729Gulls1Griffins6ALSommaire du match
100 - 2023-12-02738Gulls7Silver Knights3AWSommaire du match
102 - 2023-12-04752Monsters3Gulls5BWSommaire du match
104 - 2023-12-06769Gulls3Silver Knights5ALSommaire du match
106 - 2023-12-08783Eagles3Gulls4BWSommaire du match
109 - 2023-12-11797Gulls-Monsters-
111 - 2023-12-13814Roadrunners-Gulls-
113 - 2023-12-15831Gulls-Eagles-
114 - 2023-12-16844Thunderbirds-Gulls-
118 - 2023-12-20868Gulls-Reign-
119 - 2023-12-21878Bears-Gulls-
123 - 2023-12-25904Checkers-Gulls-
126 - 2023-12-28928Gulls-Barracuda-
127 - 2023-12-29935Admirals-Gulls-
130 - 2024-01-01959Gulls-Barracuda-
132 - 2024-01-03969Rocket-Gulls-
134 - 2024-01-05983Gulls-Condors-
136 - 2024-01-07998Bears-Gulls-
137 - 2024-01-081006Gulls-Iowa Wild-
140 - 2024-01-111031Canucks-Gulls-
142 - 2024-01-131042Gulls-Condors-
143 - 2024-01-141054Gulls-PenguinsF-
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
146 - 2024-01-171072Silver Knights-Gulls-
147 - 2024-01-181080Gulls-Eagles-
150 - 2024-01-211100Condors-Gulls-
152 - 2024-01-231116Gulls-Icehogs-
154 - 2024-01-251125Gulls-StarsF-
156 - 2024-01-271137Thunderbirds-Gulls-
159 - 2024-01-301162Iowa Wild-Gulls-
162 - 2024-02-021186Gulls-Thunderbirds-
164 - 2024-02-041199Gulls-Comets-
165 - 2024-02-051203Gulls-Canucks-
166 - 2024-02-061211Reign-Gulls-
167 - 2024-02-071223Gulls-BruinsF-
170 - 2024-02-101244Reign-Gulls-
175 - 2024-02-151269StarsF-Gulls-
178 - 2024-02-181288StarsF-Gulls-
179 - 2024-02-191295Gulls-Thunderbirds-



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets3515
Assistance37,84523,612
Assistance PCT75.69%94.45%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
16 2458 - 81.94% 97,368$2,434,198$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursSalaire total moyen des joueursSalaire des entraineurs
1,665,192$ 2,620,000$ 2,620,000$ 0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
0$ 1,606,409$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
1,557,887$ 75 14,945$ 1,120,875$




Gulls 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

Gulls 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

Gulls 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

Gulls 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

Gulls 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