Please rotate your device to landscape mode for a better experience.
Connexion

San Diego
GP: 75 | W: 43 | L: 20 | OTL: 12 | P: 98
GF: 257 | GA: 208 | PP%: 22.70% | PK%: 81.99%
DG: Michael Chouinard | Morale : 50 | Moyenne d’équipe : N/A
Prochains matchs #888 vs Coachella Valley

Centre de jeu
San Diego
43-20-12, 98pts
2
1 Rockford
39-25-6, 84pts
Team Stats
L1SéquenceL1
18-9-8Fiche domicile22-10-3
25-11-4Fiche domicile17-15-3
6-1-3Derniers 10 matchs7-2-1
3.43Buts par match 2.83
2.77Buts contre par match 2.71
22.70%Pourcentage en avantage numérique17.86%
81.99%Pourcentage en désavantage numérique81.69%
Eagles
43-20-6, 92pts
4
2 San Diego
43-20-12, 98pts
Team Stats
W4SéquenceL1
22-11-2Fiche domicile18-9-8
21-9-4Fiche domicile25-11-4
6-3-1Derniers 10 matchs6-1-3
3.52Buts par match 3.43
2.68Buts contre par match 2.77
19.48%Pourcentage en avantage numérique22.70%
81.43%Pourcentage en désavantage numérique81.99%
San Diego
43-20-12, 98pts
Jour 164
Coachella Valley
40-21-10, 90pts
Statistiques d’équipe
L1SéquenceW2
18-9-8Fiche domicile22-10-3
25-11-4Fiche visiteur18-11-7
6-1-310 derniers matchs7-2-1
3.43Buts par match 3.20
2.77Buts contre par match 3.20
22.70%Pourcentage en avantage numérique14.90%
81.99%Pourcentage en désavantage numérique81.78%
Bakersfield
28-37-7, 63pts
Jour 166
San Diego
43-20-12, 98pts
Statistiques d’équipe
L3SéquenceL1
12-19-4Fiche domicile18-9-8
16-18-3Fiche visiteur25-11-4
3-5-210 derniers matchs6-1-3
2.90Buts par match 3.43
3.31Buts contre par match 3.43
17.41%Pourcentage en avantage numérique22.70%
83.19%Pourcentage en désavantage numérique81.99%
Rochester
32-29-7, 71pts
Jour 171
San Diego
43-20-12, 98pts
Statistiques d’équipe
W1SéquenceL1
18-15-3Fiche domicile18-9-8
14-14-4Fiche visiteur25-11-4
4-6-010 derniers matchs6-1-3
3.12Buts par match 3.43
3.10Buts contre par match 3.43
14.62%Pourcentage en avantage numérique22.70%
80.34%Pourcentage en désavantage numérique81.99%
Meneurs d'équipe
Buts
Karson Kulhman
28
Sam LaffertyPasses
Sam Lafferty
53
Sam LaffertyPoints
Sam Lafferty
73
Sam LaffertyPlus/Moins
Sam Lafferty
17
Casey DeSmithVictoires
Casey DeSmith
23
Casey DeSmithPourcentage d’arrêts
Casey DeSmith
0.926

Statistiques d’équipe
Buts pour
257
3.43 GFG
Tirs pour
2861
38.15 Avg
Pourcentage en avantage numérique
22.7%
74 GF
Début de zone offensive
43.9%
Buts contre
208
2.77 GAA
Tirs contre
2433
32.44 Avg
Pourcentage en désavantage numérique
82.0%%
47 GA
Début de la zone défensive
39.2%
Informations de l'équipe

Directeur généralMichael Chouinard
EntraîneurJared Bednar
DivisionPacifique
ConférenceConference ouest
CapitaineAdam Henrique
Assistant #1Mikko Lehtonen
Assistant #2Joe Colborne


Informations de l’aréna

Capacité3,000
Assistance2,198
Billets de saison300


Informations de la formation

Équipe Pro24
Équipe Mineure19
Limite contact 43 / 50
Espoirs58


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
1Karson KulhmanXX100.00683093787976997271687272766059435000282900,000$
2Jakob SilfverbergXX100.00653393717380867563737369827360315000332550,000$
3Adam Henrique (C)XX100.00663688637585886989677163847981305000342951,000$
4Joe Colborne (A)XXX100.00673781638476886599676758796968375000342956,000$
5Chris TierneyXX100.006825997584879969976668668158464850003031,100,000$
6Sam LaffertyXXX99.00833474778180867291737073756156485000291950,000$
7Jansen HarkinsXX100.00672991828374916873676963775351535000273875,000$
8Michael BuntingXX100.00813185798084906568636863736052585000282825,000$
9Vladislav NamestnikovXXX100.006430988470879970796868627956515150003111,160,000$
10Peyton Krebs (R)XXX100.00865583818079997285737365704343725000232900,000$
11Gage Goncalves (R)XX100.00773594787978997070697165704343685000232500,000$
12Nikolai Kovalenko (R)XX100.00813092797879917170727165704646685000242500,000$
13Matt DumbaX100.007437759693909971536560808162545750002931,760,000$
14Mikko Lehtonen (A)X100.00712778788280836650565089766558425000304990,000$
15Caleb JonesX100.00762977798491995753575588795554605000271925,000$
16Nick Blankenburg (R)X100.00743093827584935250535292705353685000261500,000$
17Jacob Bernard-Docker (R)X100.00714090698177894650464789704646675000242800,000$
18Tobias Bjornfot (R)X100.00693096788378994150404187704343585000232900,000$
Rayé
1Steven LorentzXX100.00662991769875906590656564775644525000282956,000$
2Joakim NygardX100.00652191757673826270636461756761355000312825,000$
3Brendan SmithX100.00713271638674915450514588757764315000352725,000$
MOYENNE D’ÉQUIPE99.9572328776818092647063637276585451500
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
1Calvin Pickard100.00689088787787888787857575755350003231,000,000$
2Casey DeSmith100.0068838574748788878881736969525000322961,000$
Rayé
1Joonas Korpisalo100.00687286767485858585797463625150003023,300,000$
MOYENNE D’ÉQUIPE100.006882867675868786878274696952500
Nom de l’entraîneur PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Jared Bednar82879692898981CAN5222,510,000$


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
1Sam LaffertySan Diego (ANA)C/LW/RW75205373177010170151251781777.97%20149819.98520255426000052013456.13%213800000.9701020772
2Matt DumbaSan Diego (ANA)D752146677741092891966212810.71%76162221.63131629102279000150300%000000.8302200254
3Nikolai KovalenkoSan Diego (ANA)LW/RW7521406163008975273721977.69%10147119.625192460271000034148.33%12000000.8300000402
4Karson KulhmanSan Diego (ANA)LW/RW75282856128032842077115913.53%13133517.81391225132314162041245.00%10000100.8400000534
5Mikko LehtonenSan Diego (ANA)D75163955135209490161381189.94%142176423.5391120802660111214300%000000.6200000234
6Caleb JonesSan Diego (ANA)D751438521754077671375510110.22%131177623.699918712800222214220%000000.5900000144
7Peyton KrebsSan Diego (ANA)C/LW/RW7023285118630140114245711669.39%4133719.117111855249000002053.80%157800000.7600122260
8Chris TierneySan Diego (ANA)C/LW75113041512039107131331098.40%995412.7311213291830000103059.34%127900000.8617000042
9Brayden SchennAnaheimC/LW/RW601723401741011456212611508.02%12101216.8710717652360002665049.40%25100010.7913110413
10Jakob SilfverbergSan Diego (ANA)LW/RW45132437121751737151391038.61%478217.39461033156000041049.49%9900010.9501001411
11Gage GoncalvesSan Diego (ANA)C/LW7415163181804350171481278.77%6133318.02167191080001502144.30%15800000.4600000101
12Nick BlankenburgSan Diego (ANA)D631020307160506012741697.87%71130720.764812682330003176300%000000.4600000012
13Vladislav NamestnikovSan Diego (ANA)C/LW/RW755202512602258122271074.10%990112.0201152100092191050.00%9400000.5513000102
14Adam HenriqueSan Diego (ANA)C/LW751672381355027124259312.90%790412.07000224000043153.33%9000000.5107001111
15Jansen HarkinsSan Diego (ANA)LW/RW7581018-28022249227598.70%34846.4600001000001050.00%3800000.7427000102
16Jacob Bernard-DockerSan Diego (ANA)D75311143731572437022454.29%112134317.921012110221185110%000000.2100012010
17Tobias BjornfotSan Diego (ANA)D7431114526046515417325.56%65100113.5300001000020200%000000.2800000110
18Joe ColborneSan Diego (ANA)C/LW/RW756713117528636623489.09%477410.320001311241250060.65%99100000.3403100021
19Brendan SmithSan Diego (ANA)D81232609311239.09%1913917.43101615000012000%000000.4300000000
20Michael BuntingSan Diego (ANA)LW/RW75213-5602517428284.76%12323.1000000000000057.69%2600000.2600000001
21Cal FooteAnaheimD41231401177414.29%87919.8810128000012000%000000.7500000001
22Steven LorentzSan Diego (ANA)C/LW5000000228150%2377.5800000000000050.00%40000000000000
23Joakim NygardSan Diego (ANA)LW2000000203100%0147.48000000000000100.00%10000000000000
Statistiques d’équipe totales ou en moyenne13802544567101316709012361269286182920288.88%7282211216.027413520967927454711451778401255.79%696700120.64534566364037
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
1Casey DeSmithSan Diego (ANA)3523750.9262.322097828111000100.71473438443
2Calvin PickardSan Diego (ANA)41201370.9092.9624562312113320010.800254135511
Statistiques d’équipe totales ou en moyenne764320120.9172.6645541052022432011327573954


Astuces sur les filtres (anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
Nom du joueur Nom de l’équipePOS Âge Date de naissance Pays Recrue Poids Taille Non-échange Disponible pour échange Acquis ParDate de la Dernière TransactionBallotage forcé Waiver Possible Contrat Date du Signature du ContratForcer UFA Rappel d'urgence Type Salaire actuel Salaire restantPlafond salarial Plafond salarial restant Exclus du plafond salarial Salaire année 2Salaire année 3Salaire année 4Salaire année 5Salaire année 6Salaire année 7Salaire année 8Salaire année 9Salaire année 10Plafond salarial année 2Plafond salarial année 3Plafond salarial année 4Plafond salarial année 5Plafond salarial année 6Plafond salarial année 7Plafond salarial année 8Plafond salarial année 9Plafond salarial année 10Non-échange année 2Non-échange année 3Non-échange année 4Non-échange année 5Non-échange année 6Non-échange année 7Non-échange année 8Non-échange année 9Non-échange année 10Lien
Adam HenriqueSan Diego (ANA)C/LW341990-02-06CANNo199 Lbs6 ft0NoNoFree AgentNoNo22025-09-23FalseFalsePro & Farm951,000$160,938$0$0$No951,000$--------951,000$--------No--------Lien / Lien NHL
Brendan SmithSan Diego (ANA)D351989-02-08CANNo216 Lbs6 ft2NoNoN/ANoNo22025-09-11FalseFalsePro & Farm725,000$122,692$0$0$No725,000$--------725,000$--------No--------Lien / Lien NHL
Caleb JonesSan Diego (ANA)D271997-06-06USANo194 Lbs6 ft1NoNoN/ANoNo12024-09-15FalseFalsePro & Farm925,000$156,538$0$0$No---------------------------
Calvin PickardSan Diego (ANA)G321992-04-15CANNo205 Lbs6 ft1NoNoFree AgentNoNo32025-09-21FalseFalsePro & Farm1,000,000$169,231$0$0$No1,000,000$1,000,000$-------1,000,000$1,000,000$-------NoNo-------Lien / Lien NHL
Casey DeSmithSan Diego (ANA)G321991-08-13USANo186 Lbs6 ft0NoNoFree AgentNoNo22024-09-24FalseFalsePro & Farm961,000$162,631$0$0$No961,000$--------961,000$--------No--------Lien / Lien NHL
Chris TierneySan Diego (ANA)C/LW301994-07-01CANNo201 Lbs6 ft1NoNoFree Agent2024-09-14NoNo32025-09-21FalseFalsePro & Farm1,100,000$186,154$0$0$No1,100,000$1,100,000$-------1,100,000$1,100,000$-------NoNo-------Lien / Lien NHL
Gage GoncalvesSan Diego (ANA)C/LW232001-01-16CANYes181 Lbs6 ft0NoNoProspectNoNo22025-10-16FalseFalsePro & Farm500,000$84,615$0$0$No500,000$--------500,000$--------No--------
Jacob Bernard-DockerSan Diego (ANA)D242000-06-30CANYes177 Lbs5 ft9NoNoProspectNoNo22025-10-17FalseFalsePro & Farm800,000$135,385$0$0$No800,000$--------800,000$--------No--------
Jakob SilfverbergSan Diego (ANA)LW/RW331990-10-13SWENo210 Lbs6 ft1NoNoFree AgentNoNo22024-11-01FalseFalsePro & Farm550,000$93,077$0$0$No550,000$--------550,000$--------No--------Lien / Lien NHL
Jansen HarkinsSan Diego (ANA)LW/RW271997-05-23USANo182 Lbs6 ft1NoNoTrade2024-11-07NoNo32025-09-09FalseFalsePro & Farm875,000$148,077$0$0$No875,000$875,000$-------875,000$875,000$-------NoNo-------Lien
Joakim NygardSan Diego (ANA)LW311993-01-08SWENo180 Lbs6 ft0NoNoN/ANoNo2FalseFalsePro & Farm825,000$139,615$0$0$No825,000$--------825,000$--------No--------Lien
Joe ColborneSan Diego (ANA)C/LW/RW341990-01-30CANNo218 Lbs6 ft5NoNoFree AgentNoNo22025-09-30FalseFalsePro & Farm956,000$161,785$0$0$No956,000$--------956,000$--------No--------Lien
Joonas Korpisalo (contrat à 1 volet)San Diego (ANA)G301994-04-28FINNo189 Lbs6 ft3NoNoTrade2026-04-12YesYes2FalseFalsePro & Farm3,300,000$627,000$33,000$6,270$No3,300,000$--------3,300,000$--------No--------Lien / Lien NHL
Karson KulhmanSan Diego (ANA)LW/RW281995-10-26USANo184 Lbs5 ft11NoNoN/ANoNo22024-09-15FalseFalsePro & Farm900,000$152,308$0$0$No900,000$--------900,000$--------No--------
Matt DumbaSan Diego (ANA)D291994-07-25CANNo199 Lbs6 ft0NoNoFree AgentNoNo32025-09-30FalseFalsePro & Farm1,760,000$297,846$0$0$No1,760,000$1,760,000$-------1,760,000$1,760,000$-------NoNo-------Lien
Michael BuntingSan Diego (ANA)LW/RW281995-09-17CANNo197 Lbs5 ft11NoNoTrade2025-01-04NoNo22025-09-09FalseFalsePro & Farm825,000$139,615$0$0$No825,000$--------825,000$--------No--------Lien / Lien NHL
Mikko LehtonenSan Diego (ANA)D301994-01-16FINNo196 Lbs6 ft0NoNoN/ANoNo42025-09-09FalseFalsePro & Farm990,000$167,538$0$0$No990,000$990,000$990,000$------990,000$990,000$990,000$------NoNoNo------Lien
Nick BlankenburgSan Diego (ANA)D261998-05-12USAYes177 Lbs5 ft9NoNoProspectNoNo12025-10-17FalseFalsePro & Farm500,000$84,615$0$0$No---------------------------
Nikolai KovalenkoSan Diego (ANA)LW/RW241999-10-17USAYes180 Lbs5 ft10NoNoProspectNoNo22025-10-16FalseFalsePro & Farm500,000$84,615$0$0$No500,000$--------500,000$--------No--------
Peyton KrebsSan Diego (ANA)C/LW/RW232001-01-26NAYes187 Lbs6 ft0NoNoProspectNoNo22025-10-16FalseFalsePro & Farm900,000$152,308$0$0$No900,000$--------900,000$--------No--------
Sam LaffertySan Diego (ANA)C/LW/RW291995-03-06USANo198 Lbs6 ft1NoNoN/ANoNo12024-09-15FalseFalsePro & Farm950,000$160,769$0$0$No---------------------------Lien / Lien NHL
Steven LorentzSan Diego (ANA)C/LW281996-04-13CANNo208 Lbs6 ft4NoNoFree Agent2025-08-19NoNo22025-09-30FalseFalsePro & Farm956,000$161,785$0$0$No956,000$--------956,000$--------No--------Lien / Lien NHL
Tobias BjornfotSan Diego (ANA)D232001-04-06SWEYes200 Lbs6 ft0NoNoProspectNoNo22025-10-17FalseFalsePro & Farm900,000$152,308$0$0$No900,000$--------900,000$--------No--------
Vladislav NamestnikovSan Diego (ANA)C/LW/RW311992-11-22RUSNo188 Lbs5 ft11NoNoN/ANoNo1FalseFalsePro & Farm1,160,000$196,308$0$0$No---------------------------Lien / Lien NHL
Nombre de joueursÂge moyenPoids moyenTaille moyenneContrat moyenSalaire moyen 1e année
2428.79194 Lbs6 ft02.08992,042$



Attaque à 5 contre 5
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Nikolai KovalenkoPeyton KrebsGage Goncalves35014
2Jakob SilfverbergSam LaffertyKarson Kulhman30014
3Adam HenriqueJoe Colborne25122
4Jansen HarkinsChris TierneyVladislav Namestnikov10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% tempsPHYDFOF
1Matt DumbaCaleb Jones35023
2Mikko LehtonenNick Blankenburg30023
3Tobias BjornfotJacob Bernard-Docker25023
4Mikko LehtonenJacob Bernard-Docker10122
Attaque en avantage numérique
Ligne #Ailier gaucheCentreAilier droit% tempsPHYDFOF
1Gage GoncalvesSam LaffertyNikolai Kovalenko50014
2Jakob SilfverbergPeyton Krebs50014
Défense en avantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Mikko LehtonenNick Blankenburg50014
2Caleb JonesMatt Dumba50014
Attaque à 4 en désavantage numérique
Ligne #CentreAilier% tempsPHYDFOF
1Joe ColborneKarson Kulhman50122
2Sam LaffertyVladislav Namestnikov50122
Défense à 4 en désavantage numérique
Ligne #DéfenseDéfense% tempsPHYDFOF
1Nick BlankenburgMikko Lehtonen50122
2Caleb JonesJacob Bernard-Docker50122
3 joueurs en désavantage numérique
Ligne #Ailier% tempsPHYDFOFDéfenseDéfense% tempsPHYDFOF
1Sam Lafferty50122Caleb JonesJacob Bernard-Docker50122
2Joe Colborne50122Tobias BjornfotNick Blankenburg50122
Attaque à 4 contre 4
Ligne #CentreAilier% tempsPHYDFOF
1Jakob SilfverbergMichael Bunting50122
2Joe ColborneGage Goncalves50122
Défense à 4 contre 4
Ligne #DéfenseDéfense% tempsPHYDFOF
1Tobias BjornfotMikko Lehtonen50122
2Caleb JonesJacob Bernard-Docker50122
Attaque dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Nikolai KovalenkoPeyton KrebsJakob SilfverbergMikko LehtonenMatt Dumba
Défense dernière minute
Ailier gaucheCentreAilier droitDéfenseDéfense
Adam HenriqueSam LaffertyKarson KulhmanMikko LehtonenNick Blankenburg
Attaquants supplémentaires
Normal Avantage numériqueDésavantage numérique
Nikolai Kovalenko, Gage Goncalves, Peyton KrebsVladislav Namestnikov, Adam HenriqueGage Goncalves
Défenseurs supplémentaires
Normal Avantage numériqueDésavantage numérique
Mikko Lehtonen, Jacob Bernard-Docker, Matt DumbaNick BlankenburgMikko Lehtonen, Caleb Jones
Tirs de pénalité
Adam Henrique, Chris Tierney, Jansen Harkins, Joe Colborne, Vladislav Namestnikov
Gardien
#1 : Casey DeSmith, #2 : Calvin Pickard
Lignes d’attaque personnalisées en prolongation
Peyton Krebs, Nikolai Kovalenko, Sam Lafferty, Gage Goncalves, Chris Tierney, Jakob Silfverberg, , Karson Kulhman, Joe Colborne, Michael Bunting
Lignes de défense personnalisées en prolongation
Mikko Lehtonen, Matt Dumba, Nick Blankenburg, Tobias Bjornfot, Jacob Bernard-Docker


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
1Abbotsford54000010241311210000101174330000001367101.00024436700104658462079269529457214844418522731.82%17382.35%11707302056.52%1511269656.05%670116257.66%190813251675563978501
2Bakersfield302000101112-11010000023-12010001099020.3331117280010465846869269529457210334243813430.77%10370.00%01707302056.52%1511269656.05%670116257.66%190813251675563978501
3Bridgeport210001005501000010034-11100000021130.7505914001046584689926952945725614103211218.18%30100.00%01707302056.52%1511269656.05%670116257.66%190813251675563978501
4Charlotte320001001266210001007431100000052350.83312183001104658461249269529457210430143612216.67%6266.67%01707302056.52%1511269656.05%670116257.66%190813251675563978501
5Cleveland22000000936110000005231100000041341.00091726001046584678926952945726425163113215.38%80100.00%01707302056.52%1511269656.05%670116257.66%190813251675563978501
6Coachella Valley31100100710-3210001006511010000015-430.500712190010465846979269529457211420325212216.67%16287.50%01707302056.52%1511269656.05%670116257.66%190813251675563978501
7Eagles513010001213-130201000710-32110000053240.40012223411104658461569269529457217453509019421.05%19384.21%01707302056.52%1511269656.05%670116257.66%190813251675563978501
8Grand Rapids22000000826110000002021100000062441.00081422011046584610992695294572551443416425.00%20100.00%01707302056.52%1511269656.05%670116257.66%190813251675563978501
9Hartford2110000067-11010000024-21100000043120.5006121800104658467792695294572851910375120.00%5180.00%01707302056.52%1511269656.05%670116257.66%190813251675563978501
10Hershey32000001853210000016421100000021150.8338152300104658461159269529457210519304611436.36%14192.86%01707302056.52%1511269656.05%670116257.66%190813251675563978501
11Iowa4300000116115210000017702200000094570.87516304600104658461789269529457212844376820315.00%15193.33%11707302056.52%1511269656.05%670116257.66%190813251675563978501
12Laval2110000067-11010000035-21100000032120.50061117001046584684926952945726622183113323.08%8275.00%01707302056.52%1511269656.05%670116257.66%190813251675563978501
13Lehigh Valley2020000048-41010000025-31010000023-100.0004812001046584674926952945727023233710330.00%8362.50%01707302056.52%1511269656.05%670116257.66%190813251675563978501
14Manitoba3210000010731010000024-22200000083540.667101828001046584611892695294572792483811218.18%4250.00%01707302056.52%1511269656.05%670116257.66%190813251675563978501
15Ontario41200100914-5210001007702020000027-530.3759172600104658461409269529457214236717315213.33%16475.00%01707302056.52%1511269656.05%670116257.66%190813251675563978501
16Providence22000000743110000005411100000020241.0007132001104658466892695294572801918437114.29%8187.50%01707302056.52%1511269656.05%670116257.66%190813251675563978501
17Rochester3120000089-1000000000003120000089-120.333813210010465846109926952945728925256219526.32%9277.78%01707302056.52%1511269656.05%670116257.66%190813251675563978501
18Rockford622001011415-12010000168-24210010087160.50014243800104658462089269529457218669659428621.43%23578.26%01707302056.52%1511269656.05%670116257.66%190813251675563978501
19San Jose4300010015114220000007342100010088070.87515284300104658461689269529457212629366119526.32%16381.25%01707302056.52%1511269656.05%670116257.66%190813251675563978501
20Springfield4300010015105210001007612200000084470.8751526410010465846150926952945721294932581715.88%15286.67%01707302056.52%1511269656.05%670116257.66%190813251675563978501
21Syracuse20000011880100000105411000000134-130.750811190010465846879269529457267241043400.00%50100.00%11707302056.52%1511269656.05%670116257.66%190813251675563978501
22Texas311000011316-300000000000311000011316-330.50013233600104658461039269529457211037354611327.27%15660.00%01707302056.52%1511269656.05%670116257.66%190813251675563978501
23Utica 220000001921711000000111101100000081741.000193453001046584612192695294572391129315360.00%70100.00%11707302056.52%1511269656.05%670116257.66%190813251675563978501
24Wilkes-Barre/Scranton4220000011101321000009721010000023-140.50011213201104658461159269529457211444427013538.46%12191.67%01707302056.52%1511269656.05%670116257.66%190813251675563978501
Total75392001735257208493515901523122104184024110021213510431980.6532574567131510465846286192695294572243372868012363267422.70%2614781.99%41707302056.52%1511269656.05%670116257.66%190813251675563978501
_Since Last GM Reset75392001735257208493515901523122104184024110021213510431980.6532574567131510465846286192695294572243372868012363267422.70%2614781.99%41707302056.52%1511269656.05%670116257.66%190813251675563978501
_Vs Conference4421120152314613214197501312626022514700211847212560.636146260406111046584616119269529457214394394317031873920.86%1663479.52%21707302056.52%1511269656.05%670116257.66%190813251675563978501
_Vs Division1995003206660695100210332581044001103335-2250.65866117183001046584669892695294572633163204309812024.69%751580.00%11707302056.52%1511269656.05%670116257.66%190813251675563978501

Total pour les joueurs
Matchs jouésPointsSéquenceButsPassesPointsTirs pourTirs contreTirs bloquésMinutes de pénalitésMises en échecButs en filet désertBlanchissages
7598L125745671328612433728680123615
Tous les matchs
GPWLOTWOTL SOWSOLGFGA
7539201735257208
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
351591523122104
Matchs extérieurs
GPWLOTWOTL SOWSOLGFGA
4024110212135104
Derniers 10 matchs
WLOTWOTL SOWSOL
610201
Tentatives en avantage numériqueButs en avantage numérique% en avantage numériqueTentatives en désavantage numériqueButs contre en désavantage numérique% en désavantage numériqueButs pour en désavantage numérique
3267422.70%2614781.99%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
9269529457210465846
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
1707302056.52%1511269656.05%670116257.66%
Temps avec la rondelle
En zone offensiveContrôle en zone offensiveEn zone défensiveContrôle en zone défensiveEn zone neutreContrôle en zone neutre
190813251675563978501


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
16Eagles2San Diego3WXSommaire du match
419San Diego3Rochester4LSommaire du match
936San Diego4Abbotsford3WSommaire du match
1143Wilkes-Barre/Scranton0San Diego2WSommaire du match
1356Eagles4San Diego2LSommaire du match
1569San Diego3Texas6LSommaire du match
1671San Diego2Rockford3LXSommaire du match
2090Coachella Valley1San Diego3WSommaire du match
23106Iowa2San Diego3WSommaire du match
25115San Diego3Manitoba1WSommaire du match
26122San Diego4Rockford2WSommaire du match
29134San Diego6Texas5WSommaire du match
31145Ontario3San Diego4WSommaire du match
34163Manitoba4San Diego2LSommaire du match
36175San Diego2Rochester3LSommaire du match
38186San Diego2Eagles3LSommaire du match
40195Charlotte0San Diego4WSommaire du match
42206San Diego2Hershey1WSommaire du match
43218Charlotte4San Diego3LXSommaire du match
48236San Diego1Coachella Valley5LSommaire du match
50244Hershey3San Diego2LXXSommaire du match
52259San Diego2Providence0WSommaire du match
55271Bridgeport4San Diego3LXSommaire du match
57282San Diego6Abbotsford2WSommaire du match
59293Syracuse4San Diego5WXXSommaire du match
62310San Diego3Laval2WSommaire du match
63316Cleveland2San Diego5WSommaire du match
66327San Diego2Lehigh Valley3LSommaire du match
68336San Diego3Eagles0WSommaire du match
70347Providence4San Diego5WSommaire du match
72361San Diego7San Jose6WSommaire du match
74371San Diego0Rockford1LSommaire du match
75376Abbotsford3San Diego6WSommaire du match
77395Wilkes-Barre/Scranton4San Diego3LSommaire du match
79408San Diego4Springfield2WSommaire du match
82422Lehigh Valley5San Diego2LSommaire du match
83432San Diego2Ontario3LSommaire du match
86446San Jose2San Diego4WSommaire du match
87458San Diego6Grand Rapids2WSommaire du match
90471Grand Rapids0San Diego2WSommaire du match
92481San Diego2Wilkes-Barre/Scranton3LSommaire du match
94489San Diego3Rochester2WSommaire du match
96499San Jose1San Diego3WSommaire du match
98509San Diego2Bridgeport1WSommaire du match
100519San Diego4Hartford3WSommaire du match
101525Laval5San Diego3LSommaire du match
104545San Diego5Charlotte2WSommaire du match
105550Wilkes-Barre/Scranton3San Diego4WSommaire du match
107563San Diego0Ontario4LSommaire du match
109575Abbotsford4San Diego5WXXSommaire du match
111591San Diego5Iowa2WSommaire du match
113601Springfield2San Diego4WSommaire du match
114609San Diego3Syracuse4LXXSommaire du match
118625Coachella Valley4San Diego3LXSommaire du match
121644San Diego4Texas5LXXSommaire du match
122649Hartford4San Diego2LSommaire du match
126669San Diego5Manitoba2WSommaire du match
127675Rockford5San Diego4LSommaire du match
130691San Diego3Abbotsford1WSommaire du match
132699San Diego4Iowa2WSommaire du match
133706Ontario4San Diego3LXSommaire du match
135722San Diego5Bakersfield4WXXSommaire du match
137730Rockford3San Diego2LXXSommaire du match
139747San Diego4Bakersfield5LSommaire du match
141758Bakersfield3San Diego2LSommaire du match
143773Iowa5San Diego4LXXSommaire du match
146784San Diego1San Jose2LXSommaire du match
149799Springfield4San Diego3LXSommaire du match
151809San Diego4Cleveland1WSommaire du match
153824Utica 1San Diego11WSommaire du match
154832San Diego4Springfield2WSommaire du match
156841San Diego8Utica 1WSommaire du match
Date limite d’échanges --- Les échanges ne peuvent plus se faire après la simulation de cette journée!
158850Hershey1San Diego4WSommaire du match
160865San Diego2Rockford1WSommaire du match
162875Eagles4San Diego2LSommaire du match
164888San Diego-Coachella Valley-
166900Bakersfield-San Diego-
171924Rochester-San Diego-
177950Texas-San Diego-
182973Rochester-San Diego-
188997Texas-San Diego-
1931020Manitoba-San Diego-



Capacité de l’aréna - Tendance du prix des billets - %
Niveau 1Niveau 2
Capacité20001000
Prix des billets5030
Assistance51,78825,137
Assistance PCT73.98%71.82%

Revenu
Matchs à domicile restantsAssistance moyenne - %Revenu moyen par matchRevenu annuel à ce jourCapacitéPopularité de l’équipe
6 2198 - 73.26% 128,964$4,513,735$3000100

Dépenses
Dépenses annuelles à ce jourSalaire total des joueursPlafond Salariale total des joueursSalaire des entraineurs
3,779,858$ 2,050,900$ 2,050,900$ 2,510,000$0$
Plafond salarial par jourPlafond salarial à ce jourJoueurs Inclus dans le plafond salarialJoueurs exclut du plafond Salarial
10,517$ 1,694,600$ 0 0

Estimation
Revenus de la saison estimésJours restants de la saisonDépenses par jourDépenses de la saison estimées
773,783$ 33 23,389$ 771,837$




San Diego 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

San Diego 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

San Diego 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

San Diego 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

San Diego 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