Connexion

Phantoms
GP: 82 | W: 43 | L: 28 | OTL: 11 | P: 97
GF: 319 | GA: 275 | PP%: 24.47% | PK%: 81.49%
DG: Richard Duguay | Morale : 50 | Moyenne d’équipe : 56
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Centre de jeu
Chill
29-49-4, 62pts
2
FINAL
4 Phantoms
43-28-11, 97pts
Team Stats
SOL1StreakW3
16-23-2Home Record18-16-7
13-26-2Away Record25-12-4
1-8-1Last 10 Games6-4-0
2.98Goals Per Game3.89
3.62Goals Against Per Game3.35
16.67%Power Play Percentage24.47%
75.38%Penalty Kill Percentage81.49%
Phantoms
43-28-11, 97pts
7
FINAL
2 Crunch
31-46-5, 67pts
Team Stats
W3StreakL1
18-16-7Home Record19-21-1
25-12-4Away Record12-25-4
6-4-0Last 10 Games2-7-1
3.89Goals Per Game3.13
3.35Goals Against Per Game3.89
24.47%Power Play Percentage20.26%
81.49%Penalty Kill Percentage73.62%
Meneurs d'équipe
Buts
Connor Bunnaman
41
Passes
Connor Bunnaman
48
Points
Connor Bunnaman
89
Plus/Moins
Connor Bunnaman
16
Victoires
Beck Warm
36
Pourcentage d’arrêts
Arvid Holm
0.928

Statistiques d’équipe
Buts pour
319
3.89 GFG
Tirs pour
3442
41.98 Avg
Pourcentage en avantage numérique
24.5%
69 GF
Début de zone offensive
41.4%
Buts contre
275
3.35 GAA
Tirs contre
3109
37.91 Avg
Pourcentage en désavantage numérique
81.5%
52 GA
Début de la zone défensive
40.3%
Information d’équipe

Directeur généralRichard Duguay
DivisionNord-Est
ConférenceEst
CapitaineChris Bigras
Assistant #1
Assistant #2Gavin Bayreuther


Informations de l’aréna

Capacité3,000
Assistance2,874
Billets de saison300


Information formation

Équipe Pro23
Équipe Mineure23
Limite contact 46 / 50
Espoirs10


Historique d'équipe

Saison actuelle43-28-11 (97PTS)
Historique43-28-5 (0.566%)
Apparitions séries éliminatoires
Historique séries éliminatoires (W-L)-


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 moyen
1Tyler BensonX100.0072707771707071675068636460444405062X0221792,500$
2Morgan Barron (R)XXX100.00808372728357556780626968664444050620213925,000$
3Jonah GadjovichX100.00757674667656556250477464704444050590212783,333$
4Morgan GeekieXX100.00714390627355746666635856254748050580221763,333$
5Austin PoganskiX100.00767186657569545748565463254444050570243750,000$
6Antti SuomelaXX100.00736786746755555670604663444949050570262700,000$
7Kurtis GabrielX100.00999930638048575625505571254747050560273560,000$
8Tyler KelleherX100.00413485735469845954525445586464050560253525,000$
9Skyler McKenzieX100.00655490665462655450465757544444050540221741,666$
10Logan Hutsko (R)X100.00514475696248515055474347455454050500214867,000$
11Jordy Stallard (R)X100.00323737375831313237323237343230050350231525,000$
12Juuso Valimaki (R)X100.00674385757867566125524870254848050610212925,000$
13Matt BartkowskiX100.00777289657260635225464068386465050600322655,000$
14Chris Bigras (C)X100.00767188637155565325464266405757050580255560,000$
15Mac HollowellX100.00776896676266635528504565394444050580224799,766$
16Gavin Bayreuther (A)X100.00784478637267646025395158534646050570262650,000$
17Sebastian AhoDX100.00675984636567515224523957254646050550241750,000$
Rayé
MOYENNE D’ÉQUIPE100.0069617866695959564351516043484805056
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 SP
1Beck Warm (R)100.0060627866626853636462304444050600
2Hunter Jones (R)100.0051688581465750565352304444050560
Rayé
1Arvid Holm (R)100.0054706679565659575253535858050590
2Hunter Miska100.0058507065606053625857304444050560
3Felix Sandstrom (R)100.0052536675535350575252304444050540
4Landon Bow100.0050517390475150554949304444050540
MOYENNE D’ÉQUIPE100.005459737654585358555434464605057
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire


Astuces sur les filtres (anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
# Nom du joueur Nom de l’équipePOSGP G A P +/- PIM PIM5 HIT HTT SHT OSB OSM SHT% SB MP AMG PPG PPA PPP PPS PPM PKG PKA PKP PKS PKM GW GT FO% FOT GA TA EG HT P/20 PSG PSS FW FL FT S1 S2 S3
1Connor BunnamanPhiladelphieC/LW8141488916195902313459824811.88%35157819.491915348221600051355062.43%138400001.1327010456
2Radim ZohornaPhiladelphieC/LW/RW7739468513120642333729123110.48%19172622.42511165819912362145050.96%259200110.98310000665
3Dominic ToninatoPhiladelphieC/LW81353974104915103177395992418.86%18140417.3471118641710001491258.80%128400001.0514201144
4Juuso ValimakiPhantoms (Phi)D7614567025195111103140449810.00%131180723.7851520672001343201510.00%000100.7700010513
5Jonah GadjovichPhantoms (Phi)LW82402262555151509632110226112.46%13144717.666915421530001565253.13%12800000.8644003416
6Tyler BensonPhantoms (Phi)LW46213859178048110236501778.90%21104222.6748123911821371383347.83%11500001.1306000452
7Mac HollowellPhantoms (Phi)D8274754250014483130371015.38%127179221.8601414542050004214000.00%100000.6000000004
8Tyler KelleherPhantoms (Phi)RW78163551-36403198249501936.43%14126116.1751318561750001441147.62%16800000.8117000011
9Chris BigrasPhantoms (Phi)D7810374797551835092426110.87%119164821.1441418392090001181200.00%000000.5700010022
10Ryan ReavesPhiladelphieRW75123547546026999184391306.52%19145619.4158133420900041243240.11%18700000.65210000313
11Austin PoganskiPhantoms (Phi)RW78103040-216010296212781394.72%14117715.090226370000150041.13%14100000.6800000012
12Morgan GeekiePhantoms (Phi)C/RW82142539-219546108189681447.41%10120514.700228550111392158.04%33600000.6500001211
13Matt BartkowskiPhantoms (Phi)D57132437-2034012874111348611.71%126133023.35538521220000119000.00%000000.5600000230
14Antti SuomelaPhantoms (Phi)C/LW781124351012044160176441216.25%25110914.230002120002741048.93%60700000.6300000101
15Kurtis GabrielPhantoms (Phi)RW71121325-157511062103177711.65%155077.15000150001191041.67%3600000.9900100021
16Gavin BayreutherPhantoms (Phi)D82101323206202434284304911.90%97147618.0022423920002106110.00%200000.3100000211
17Sebastian AhoDPhantoms (Phi)D70419237480914132133112.50%72110915.86112528000049000.00%100000.4100000000
18Gustav OlofssonPhiladelphieD192111311804825347125.88%3046224.321121347011159000.00%000000.5600000111
19Morgan BarronPhantoms (Phi)C/LW/RW11549-44024266618477.58%523421.3013412270002270059.76%16400000.7702000000
20Skyler McKenziePhantoms (Phi)LW28145-1401111328203.13%92298.2100000000000036.36%1100000.4400000000
21Trey Fix-WolanskyPhiladelphieRW1011100028150.00%02323.4300013000010050.00%600000.8500000000
22Logan HutskoPhantoms (Phi)RW11000000201010.00%0312.8400001000000027.27%1100000.0000000000
Statistiques d’équipe totales ou en moyenne1344317571888756115520421927351297024739.03%9192406317.907013220265822934812421874351354.24%717400210.741350335353543
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
1Beck WarmPhantoms (Phi)59361930.9113.2434856118821060010.833305920522
2Arvid HolmPhantoms (Phi)197640.9282.88108220527180020.50041749241
3Felix SandstromPhantoms (Phi)40130.9043.4122900131350000.714737000
Statistiques d’équipe totales ou en moyenne824326100.9143.1647988125329590030.780417976763


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 Nouveau joueur Poids Taille Non-échange Disponible pour échange Ballotage forcé Contrat Type Salaire actuel Salaire restantPlafond salarial 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 10Link
Antti SuomelaPhantoms (Phi)C/LW261994-03-16No180 Lbs6 ft0NoNoNo2Pro & Farm700,000$70,000$0$No700,000$Lien
Arvid HolmPhantoms (Phi)G211998-11-03Yes214 Lbs6 ft4NoNoNo3Pro & Farm845,833$84,583$0$No845,833$845,833$Lien
Austin PoganskiPhantoms (Phi)RW241996-02-16No201 Lbs6 ft2NoNoNo3Pro & Farm750,000$75,000$0$No750,000$750,000$Lien
Beck WarmPhantoms (Phi)G211999-04-21Yes181 Lbs6 ft0YesNoNo4Pro & Farm650,000$65,000$0$No650,000$650,000$650,000$Lien
Chris BigrasPhantoms (Phi)D251995-02-21No191 Lbs6 ft1NoNoNo5Pro & Farm560,000$56,000$0$No560,000$560,000$560,000$560,000$Lien
Felix SandstromPhantoms (Phi)G231997-01-12Yes191 Lbs6 ft2NoNoNo5Pro & Farm600,000$60,000$0$No600,000$600,000$600,000$600,000$Lien
Gavin BayreutherPhantoms (Phi)D261994-05-12No195 Lbs6 ft1NoNoNo2Pro & Farm650,000$65,000$0$No650,000$Lien
Hunter JonesPhantoms (Phi)G202000-09-21Yes194 Lbs6 ft4NoNoNo3Pro & Farm825,833$82,583$0$No825,833$825,833$Lien
Hunter MiskaPhantoms (Phi)G251995-07-06No175 Lbs6 ft1NoNoNo1Pro & Farm925,000$92,500$0$NoLien
Jonah GadjovichPhantoms (Phi)LW211998-10-12No209 Lbs6 ft2NoNoNo2Pro & Farm783,333$78,333$0$No783,333$Lien
Jordy StallardPhantoms (Phi)C231997-09-18Yes185 Lbs6 ft1NoNoNo1Pro & Farm525,000$52,500$0$NoLien
Juuso ValimakiPhantoms (Phi)D211998-10-06Yes212 Lbs6 ft2NoNoNo2Pro & Farm925,000$92,500$0$No925,000$Lien
Kurtis GabrielPhantoms (Phi)RW271993-04-20No200 Lbs6 ft4YesNoNo3Pro & Farm560,000$56,000$0$No560,000$560,000$Lien
Landon BowPhantoms (Phi)G251995-08-23No220 Lbs6 ft5NoNoNo2Pro & Farm700,000$70,000$0$No700,000$Lien
Logan HutskoPhantoms (Phi)RW211999-02-11Yes172 Lbs5 ft11NoNoNo4Pro & Farm867,000$86,700$0$No867,000$867,000$867,000$Lien
Mac HollowellPhantoms (Phi)D221998-09-26No170 Lbs5 ft10NoNoNo4Pro & Farm799,766$79,977$0$No799,766$799,766$799,766$Lien
Matt BartkowskiPhantoms (Phi)D321988-06-04No196 Lbs6 ft1NoNoNo2Pro & Farm600,000$65,500$0$No575,000$Lien
Morgan BarronPhantoms (Phi)C/LW/RW211998-12-02Yes220 Lbs6 ft4NoNoNo3Pro & Farm925,000$925,000$0$No925,000$925,000$Lien
Morgan GeekiePhantoms (Phi)C/RW221998-07-20No192 Lbs6 ft3NoNoNo1Pro & Farm763,333$76,333$0$NoLien
Sebastian AhoDPhantoms (Phi)D241996-02-16No184 Lbs5 ft10NoNoNo1Pro & Farm750,000$75,000$0$NoLien
Skyler McKenziePhantoms (Phi)LW221998-01-20No154 Lbs5 ft8NoNoNo1Pro & Farm741,666$74,167$0$NoLien
Tyler BensonPhantoms (Phi)LW221998-03-15No192 Lbs6 ft0NoYesNo1Pro & Farm792,500$79,250$0$NoLien
Tyler KelleherPhantoms (Phi)RW251995-01-02No161 Lbs5 ft6NoNoNo3Pro & Farm525,000$52,500$0$No525,000$525,000$Lien
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2323.43191 Lbs6 ft12.52728,881$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Tyler Benson40050
2Jonah GadjovichAustin Poganski30050
3Antti SuomelaKurtis Gabriel20122
4Tyler BensonMorgan GeekieTyler Kelleher10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Gavin Bayreuther40122
2Mac HollowellMatt Bartkowski30122
3Chris Bigras20122
4Matt Bartkowski10122
Attaque en svantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Tyler Benson60122
2Jonah Gadjovich40122
Défense en svantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Matt Bartkowski60122
2Chris Bigras40122
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
160122
2Antti SuomelaTyler Benson40122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Matt Bartkowski60122
2Chris BigrasMac Hollowell40122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
160122Matt Bartkowski60122
240122Chris BigrasMac Hollowell40122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Morgan Geekie60122
2Antti Suomela40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Matt Bartkowski60122
2Mac Hollowell40122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Tyler BensonMac Hollowell
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Kurtis GabrielMatt Bartkowski
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Antti Suomela, Austin Poganski, , Jonah GadjovichKurtis Gabriel
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Chris Bigras, Gavin Bayreuther, Mac HollowellGavin Bayreuther, Chris Bigras
Tirs de pénalité
, Jonah Gadjovich, , Tyler Benson,
Gardien
#1 : Beck Warm, #2 :


Astuces sur les filtres (anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
TotalDomicileVisiteur
# VS Équipe GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff GP W L T OTW OTL SOW SOL GF GA Diff P PCT G A TP SO EG GP1 GP2 GP3 GP4 SHF SH1 SP2 SP3 SP4 SHA SHB Pim Hit PPA PPG PP% PKA PK GA PK% PK GF W OF FO T OF FO OF FO% W DF FO T DF FO DF FO% W NT FO T NT FO NT FO% PZ DF PZ OF PZ NT PC DF PC OF PC NT
1Admirals210001004311000010001-11100000042230.75047110011711481129811291164110491822316686116.67%8187.50%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
2Baby Hawks210000019811000000134-11100000064230.750917260011711481128311291164110491872516416233.33%80100.00%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
3Bears40300001814-62020000037-42010000157-210.125815230011711481121401129116411049115256379918211.11%16381.25%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
4Bruins312000001013-32110000089-11010000024-220.333101929001171148112116112911641104911194422819222.22%110100.00%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
5Cabaret Lady Mary Ann33000000191181100000074322000000127561.0001932510011711481121431129116411049112328248311545.45%12558.33%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
6Caroline430000102011922000000103721000010108281.0002035550011711481121951129116411049116143368417317.65%18572.22%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
7Chiefs20200000710-31010000035-21010000045-100.0007142110117114811278112911641104914516144010330.00%7357.14%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
8Chill22000000844110000004221100000042241.000815230011711481127811291164110491601822514250.00%100100.00%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
9Comets2020000068-21010000034-11010000034-100.000610160011711481128511291164110491812317499222.22%6266.67%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
10Cougars30300000610-41010000034-12020000036-300.000612180011711481121121129116411049111732188213215.38%8187.50%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
11Crunch33000000177102200000010551100000072561.000173148001171148112141112911641104911193710809333.33%50100.00%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
12Heat2010001035-21010000014-31000001021120.5003360011711481126111291164110491743112534125.00%50100.00%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
13Jayhawks211000001082110000006331010000045-120.50010162600117114811210511291164110491852825459444.44%9455.56%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
14Las Vegas220000001147110000007251100000042241.000112031001171148112971129116411049173172355200.00%5180.00%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
15Manchots42100001171522010000169-322000000116550.62517284500117114811216611291164110491140442011911327.27%8187.50%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
16Marlies3120000079-22020000049-51100000030320.3337111811117114811293112911641104911242320641000.00%10190.00%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
17Minnesota22000000853110000005321100000032141.0008152300117114811297112911641104917519852500.00%40100.00%11679318852.67%1622310052.32%773140455.06%2001138119156061073545
18Monarchs200000119901000000134-11000001065130.7509162500117114811292112911641104917523354013323.08%8362.50%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
19Monsters413000001117-6211000008802020000039-620.2501121320011711481121581129116411049116437349014321.43%17570.59%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
20Monsters2010010058-31010000024-21000010034-110.25058130011711481128111291164110491572414498112.50%7271.43%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
21Oceanics20000011880100000105411000000134-130.75081321001171148112701129116411049185216615120.00%3166.67%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
22Oil Kings211000007701010000034-11100000043120.500713200011711481128411291164110491672412489444.44%6183.33%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
23Rocket311001001412220100100911-21100000051430.500142539001171148112991129116411049111830285815320.00%13376.92%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
24Senators321000001082110000003212110000076140.6671019290011711481121051129116411049112328206812433.33%10190.00%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
25Sharks20100100611-51010000026-41000010045-110.25061117001171148112701129116411049189212352500.00%9277.78%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
26Sound Tigers42000020191362000002010822200000095481.0001927460011711481121831129116411049113339538514321.43%18383.33%11679318852.67%1622310052.32%773140455.06%2001138119156061073545
27Spiders412001001818020100100912-32110000096330.37518345210117114811216611291164110491143375010415746.67%12283.33%11679318852.67%1622310052.32%773140455.06%2001138119156061073545
28Stars220000001028110000003121100000071641.00010152500117114811273112911641104917218164722100.00%80100.00%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
29Thunder330000002161511000000633220000001531261.000213859001171148112180112911641104911143610885120.00%5180.00%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
30Wolf Pack4110011011110200001106602110000055050.62511182910117114811219311291164110491152563012712216.67%15193.33%01679318852.67%1622310052.32%773140455.06%2001138119156061073545
Total82362800675319275444114160044315215114122120023216712443970.591319558877411171148112344211291164110491310990167120632826924.47%2815281.49%31679318852.67%1622310052.32%773140455.06%2001138119156061073545
_Since Last GM Reset82362800675319275444114160044315215114122120023216712443970.591319558877411171148112344211291164110491310990167120632826924.47%2815281.49%31679318852.67%1622310052.32%773140455.06%2001138119156061073545
_Vs Conference4617160045416715982359003427790-132312700112906921520.565167292459311171148112190811291164110491175550639811971533422.22%1602584.38%21679318852.67%1622310052.32%773140455.06%2001138119156061073545
_Vs Division2856002301049951412002305253-114440000052466180.32110417828220117114811212011129116411049110453122607081012322.77%1042080.77%21679318852.67%1622310052.32%773140455.06%2001138119156061073545

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
8297W331955887734423109901671206341
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
8236280675319275
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
4114160443152151
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
4122120232167124
Derniers 10 matchs
WLOTWOTL SOWSOL
640000
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
2826924.47%2815281.49%3
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
112911641104911171148112
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
1679318852.67%1622310052.32%773140455.06%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
2001138119156061073545


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
3 - 2021-10-1413Baby Hawks4Phantoms3LXXSommaire du match
8 - 2021-10-1945Spiders7Phantoms5LSommaire du match
11 - 2021-10-2273Phantoms3Comets4LSommaire du match
14 - 2021-10-2588Phantoms2Heat1WXXSommaire du match
15 - 2021-10-2695Phantoms4Oil Kings3WSommaire du match
18 - 2021-10-29118Stars1Phantoms3WSommaire du match
20 - 2021-10-31130Las Vegas2Phantoms7WSommaire du match
23 - 2021-11-03151Phantoms6Baby Hawks4WSommaire du match
25 - 2021-11-05165Monsters4Phantoms3LSommaire du match
26 - 2021-11-06174Phantoms5Sound Tigers2WSommaire du match
28 - 2021-11-08182Phantoms6Manchots3WSommaire du match
31 - 2021-11-11196Phantoms6Spiders1WSommaire du match
32 - 2021-11-12210Marlies4Phantoms1LSommaire du match
35 - 2021-11-15226Caroline2Phantoms4WSommaire du match
37 - 2021-11-17240Rocket5Phantoms4LXSommaire du match
39 - 2021-11-19254Phantoms3Marlies0WSommaire du match
40 - 2021-11-20268Phantoms2Bruins4LSommaire du match
43 - 2021-11-23283Bears4Phantoms1LSommaire du match
45 - 2021-11-25298Phantoms2Senators3LSommaire du match
46 - 2021-11-26307Sound Tigers4Phantoms5WXXSommaire du match
49 - 2021-11-29319Phantoms6Cabaret Lady Mary Ann3WSommaire du match
51 - 2021-12-01336Phantoms5Caroline4WSommaire du match
53 - 2021-12-03349Heat4Phantoms1LSommaire du match
55 - 2021-12-05367Comets4Phantoms3LSommaire du match
57 - 2021-12-07384Phantoms2Monsters5LSommaire du match
59 - 2021-12-09397Cougars4Phantoms3LSommaire du match
60 - 2021-12-10404Phantoms5Rocket1WSommaire du match
63 - 2021-12-13428Marlies5Phantoms3LSommaire du match
65 - 2021-12-15442Jayhawks3Phantoms6WSommaire du match
67 - 2021-12-17452Senators2Phantoms3WSommaire du match
71 - 2021-12-21486Phantoms3Monsters4LXSommaire du match
74 - 2021-12-24507Phantoms3Minnesota2WSommaire du match
75 - 2021-12-25515Phantoms3Oceanics4LXXSommaire du match
77 - 2021-12-27528Admirals1Phantoms0LXSommaire du match
79 - 2021-12-29540Crunch3Phantoms7WSommaire du match
81 - 2021-12-31558Phantoms5Senators3WSommaire du match
83 - 2022-01-02574Wolf Pack3Phantoms4WXXSommaire du match
88 - 2022-01-07601Phantoms4Sharks5LXSommaire du match
89 - 2022-01-08610Phantoms4Admirals2WSommaire du match
91 - 2022-01-10622Phantoms6Monarchs5WXXSommaire du match
93 - 2022-01-12638Phantoms4Las Vegas2WSommaire du match
95 - 2022-01-14650Phantoms4Jayhawks5LSommaire du match
98 - 2022-01-17668Phantoms5Caroline4WXXSommaire du match
99 - 2022-01-18676Bears3Phantoms2LSommaire du match
102 - 2022-01-21695Thunder3Phantoms6WSommaire du match
104 - 2022-01-23711Bruins3Phantoms5WSommaire du match
106 - 2022-01-25726Phantoms4Chiefs5LSommaire du match
107 - 2022-01-26731Rocket6Phantoms5LSommaire du match
109 - 2022-01-28750Monarchs4Phantoms3LXXSommaire du match
112 - 2022-01-31763Manchots4Phantoms3LXXSommaire du match
122 - 2022-02-10786Phantoms5Manchots3WSommaire du match
123 - 2022-02-11799Monsters4Phantoms2LSommaire du match
125 - 2022-02-13811Phantoms2Cougars3LSommaire du match
128 - 2022-02-16832Spiders5Phantoms4LXSommaire du match
130 - 2022-02-18850Phantoms3Bears4LSommaire du match
132 - 2022-02-20861Cabaret Lady Mary Ann4Phantoms7WSommaire du match
133 - 2022-02-21869Phantoms4Sound Tigers3WSommaire du match
135 - 2022-02-23883Phantoms6Cabaret Lady Mary Ann4WSommaire du match
137 - 2022-02-25897Phantoms8Thunder1WSommaire du match
140 - 2022-02-28920Monsters4Phantoms5WSommaire du match
142 - 2022-03-02936Phantoms1Monsters4LSommaire du match
144 - 2022-03-04949Oceanics4Phantoms5WXXSommaire du match
147 - 2022-03-07972Sharks6Phantoms2LSommaire du match
150 - 2022-03-10994Wolf Pack3Phantoms2LXSommaire du match
152 - 2022-03-121011Phantoms1Wolf Pack3LSommaire du match
155 - 2022-03-151029Phantoms2Bears3LXXSommaire du match
156 - 2022-03-161037Caroline1Phantoms6WSommaire du match
158 - 2022-03-181057Crunch2Phantoms3WSommaire du match
161 - 2022-03-211073Bruins6Phantoms3LSommaire du match
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
163 - 2022-03-231085Phantoms7Thunder2WSommaire du match
165 - 2022-03-251098Minnesota3Phantoms5WSommaire du match
166 - 2022-03-261109Oil Kings4Phantoms3LSommaire du match
168 - 2022-03-281125Chiefs5Phantoms3LSommaire du match
171 - 2022-03-311149Phantoms7Stars1WSommaire du match
172 - 2022-04-011159Phantoms4Chill2WSommaire du match
175 - 2022-04-041179Sound Tigers4Phantoms5WXXSommaire du match
177 - 2022-04-061196Phantoms1Cougars3LSommaire du match
179 - 2022-04-081206Phantoms3Spiders5LSommaire du match
180 - 2022-04-091216Manchots5Phantoms3LSommaire du match
183 - 2022-04-121239Phantoms4Wolf Pack2WSommaire du match
184 - 2022-04-131246Chill2Phantoms4WSommaire du match
186 - 2022-04-151261Phantoms7Crunch2WSommaire du match



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets3515
Assistance78,37339,456
Assistance PCT95.58%96.23%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
0 2874 - 95.80% 81,339$3,334,895$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursSalaire total moyen des joueursSalaire des entraineurs
2,144,211$ 2,508,926$ 2,514,426$ 0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
13,446$ 2,145,032$ 23 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
0$ 0 13,417$ 0$




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