Admirals

GP: 27 | W: 20 | L: 4 | OTL: 3 | P: 43
GF: 92 | GA: 57 | PP%: 16.43% | PK%: 83.62%
DG: Stéphane Fournier | Morale : 67 | Moyenne d'Équipe : 60
Prochain matchs #381 vs Griffins
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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 SP
1Sonny MilanoXX98.00735583867883768350667770557070152710
2Peter Holland (A)X100.00685569747568706775626363556464174630
3Vladislav KamenevX99.00795560767869736360616260555050174620
4Jordan SzwarzX99.00625567687669696660626060555050174610
5Marek HrivikX100.00615564717972626150616260555050174600
6Chase Balisy (A)X100.00635565666461726060596060557172175600
7Mike HalmoX100.00605559647568715550555556555050174570
8Greg ChaseX100.00755566617264565550555555556650174560
9Stefan FournierX100.00555556607873645550555555555050151560
10Jaedon Descheneau (R)X100.00655566626662545550555555555050166550
11Sam Anas (R)X100.00605559625859735550555556555050174550
12Travis SanheimX100.00735583906578767825676388557475161740
13Gustav OlofssonX98.00725582738172686825676073555353174660
14John GilmourX100.00755579626381657225626473555353122640
15Dean KukanX100.00665570667971657225626066555353168630
16Philip Larsen (R)X100.00555555605555755525555555556262174560
17Ben Thomas (R)X100.00555555605555725525555555555353159550
18Emil Johansson (R)X100.00555555605555715525555555555555158550
Rayé
1Danny KristoX100.00565555555657575550555555557170123540
2Linden VeyX100.00555555555555555550555555557168127540
3Jansen Harkins (R)X100.00565555555557565550555555555050123530
4Sam KurkerX100.00565555555555555550555555555050123520
5Nick SeelerX96.63725571627776587225626071555353145630
6Andrey PedanX100.00555555605555635525555555555353123540
7Dysin MayoX100.00555556605656605625565656555353123540
8Jacob Middleton (R)X100.00555555605555665525555555555353123540
9Dominik Masin (R)X100.00555555605555715525555555555353127540
10Gus YoungX100.00555555605555595525555555555353143540
MOYENNE D'ÉQUIPE99.6462556364656465614058586055575615258
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
1Ryan Miller100.0085828787858485848383559696130830
2Marek Langhamer100.0057796963696968696564556263174650
Rayé
1Mantas Armalis (R)100.0055717080616162616561555860151620
MOYENNE D'ÉQUIPE100.006677757772717271716955727315270
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Chris Taylor84888185735878CAN465100,000$


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'ÉquipePOS GP 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
1Travis SanheimAdmirals (Nas)D196182412260293649153612.24%1745223.8247113583000294300.00%000001.0600000301
2Vladislav KamenevAdmirals (Nas)LW2412112315300462966195418.18%140516.9124611790111293175.86%2900001.1300000331
3Lukas SedlakNashville PredatorsC/LW1613102317240323064172720.31%334421.5134718643146773171.02%28300011.3411000312
4Peter HollandAdmirals (Nas)C271292113260245555175321.82%540715.103369480111552268.41%44000001.0312000320
5Dean KukanAdmirals (Nas)D27020201066104223326210.00%2249018.180442071011083000.00%000000.8101002011
6Gustav OlofssonAdmirals (Nas)D247121912240372838192018.42%1757323.88639281070111117020.00%000000.6600000033
7Jordan SzwarzAdmirals (Nas)RW27510151518021215115339.80%339514.64022598000000166.67%1500000.7600000020
8Sonny MilanoAdmirals (Nas)LW/RW9581371602821539309.43%223225.8602215540005460157.85%12100001.1201000210
9Emil JohanssonAdmirals (Nas)D19369163002291021230.00%1331416.53224462000020100.00%000000.5700000001
10Stefan FournierAdmirals (Nas)RW14639211510172652323.08%318313.0800001000000287.50%1600000.9800010012
11Jaedon DescheneauAdmirals (Nas)RW2426898010101751611.76%330812.84101347000000066.67%1500000.5200000001
12Ben ThomasAdmirals (Nas)D16167716019591411.11%1024815.54011217101119000.00%000000.5600000000
13Chandler StephensonNashville PredatorsC/LW73472608261941215.79%113519.380227340000190067.54%11400001.0300000101
14Chase BalisyAdmirals (Nas)C273473135112024112012.50%31987.35000000001251065.36%17900000.7100001000
15Philip LarsenAdmirals (Nas)D271561422030883912.50%1836413.50022232112189100.00%000000.3300000020
16Greg ChaseAdmirals (Nas)C27145240158551320.00%11124.1700014000071058.82%10200000.8901000010
17Marek HrivikAdmirals (Nas)LW273255160142127113011.11%22509.2800001000081057.14%1400000.4000000012
18Nick SeelerAdmirals (Nas)D71345607782412.50%814721.12000429000134100.00%000000.5400000100
19John GilmourAdmirals (Nas)D803331209822460.00%1216921.140001433000039000.00%000000.3501000000
20Mike HalmoAdmirals (Nas)LW2711212058199125.26%31465.4400001000030118.18%1100000.2700000010
21Sam AnasAdmirals (Nas)C2710142083100810.00%01174.370000100000120048.00%2500000.1700000000
22Gus YoungAdmirals (Nas)D11101426022240425.00%718216.5910115000026100.00%000000.1100000000
Stats d'équipe Total ou en Moyenne441871452321784042044939561617944714.12%154618114.02223658179889561120810181166.28%136400010.7527013161915
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
1Marek LanghamerAdmirals (Nas)1510410.8902.3191001353190000.83361515111
2Ryan MillerAdmirals (Nas)1210020.9051.7373003212210100.7504120111
Stats d'équipe Total ou en Moyenne2720430.8962.05164104565400100.800102715222


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 StatusType Salaire Actuel Cap Salariale Cap Salariale Restant Exclus du Cap Salarial Salaire Année 2 Salaire Année 3 Salaire Année 4 Salaire Année 5 Salaire Année 6 Salaire Année 7 Salaire Année 8 Salaire Année 9 Salaire Année 10 Link
Andrey PedanAdmirals (Nas)D231993-07-02No214 Lbs6 ft5NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Ben ThomasAdmirals (Nas)D201996-05-28Yes190 Lbs6 ft1NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$No300,000$300,000$
Chase BalisyAdmirals (Nas)C241992-02-02No170 Lbs5 ft11NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Danny KristoAdmirals (Nas)LW261990-06-18No195 Lbs6 ft0NoNoNo3Avec RestrictionPro & Farm500,000$0$0$No500,000$500,000$
Dean KukanAdmirals (Nas)D231993-07-08No209 Lbs6 ft2NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No300,000$
Dominik MasinAdmirals (Nas)D201996-02-01Yes189 Lbs6 ft2NoNoNo3Contrat d'EntréePro & Farm500,000$0$0$No500,000$500,000$
Dysin MayoAdmirals (Nas)D201996-08-17No194 Lbs6 ft2NoNoNo3Contrat d'EntréePro & Farm500,000$0$0$No500,000$500,000$
Emil JohanssonAdmirals (Nas)D201996-05-06Yes190 Lbs5 ft11NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No300,000$300,000$300,000$
Greg ChaseAdmirals (Nas)C221995-01-01No189 Lbs6 ft0NoNoNo1Contrat d'EntréePro & Farm894,000$0$0$No
Gus YoungAdmirals (Nas)D251991-07-10No190 Lbs6 ft2NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Gustav OlofssonAdmirals (Nas)D221994-12-01No191 Lbs6 ft4NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No300,000$300,000$300,000$
Jacob MiddletonAdmirals (Nas)D211996-01-01Yes200 Lbs6 ft3NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$No300,000$300,000$
Jaedon DescheneauAdmirals (Nas)RW261990-03-04Yes186 Lbs5 ft9NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No300,000$300,000$
Jansen HarkinsAdmirals (Nas)C191997-05-23Yes194 Lbs6 ft2NoNoNo4Contrat d'EntréePro & Farm500,000$0$0$No500,000$500,000$500,000$
John GilmourAdmirals (Nas)D231993-05-16No180 Lbs5 ft11NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No300,000$300,000$
Jordan SzwarzAdmirals (Nas)RW251991-05-14No200 Lbs5 ft11NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Linden VeyAdmirals (Nas)RW251991-07-17No189 Lbs6 ft0NoNoNo3Avec RestrictionPro & Farm500,000$0$0$No500,000$500,000$
Mantas ArmalisAdmirals (Nas)G241992-09-05Yes194 Lbs6 ft4NoNoNo4Avec RestrictionPro & Farm300,000$0$0$No300,000$300,000$300,000$
Marek HrivikAdmirals (Nas)LW251991-08-28No200 Lbs6 ft2NoNoNo2Avec RestrictionPro & Farm300,000$0$0$No300,000$
Marek LanghamerAdmirals (Nas)G221994-07-21No184 Lbs6 ft2NoNoNo2Contrat d'EntréePro & Farm300,000$0$0$No300,000$
Mike HalmoAdmirals (Nas)LW251991-05-14No209 Lbs5 ft10NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Nick SeelerAdmirals (Nas)D231993-06-02No200 Lbs6 ft2NoNoNo3Avec RestrictionPro & Farm300,000$0$0$No300,000$300,000$
Peter HollandAdmirals (Nas)C261991-01-13No194 Lbs6 ft2NoNoNo1Avec RestrictionPro & Farm500,000$0$0$No
Philip LarsenAdmirals (Nas)D271989-12-07Yes182 Lbs6 ft0NoNoNo1Avec RestrictionPro & Farm450,000$0$0$No
Ryan MillerAdmirals (Nas)G361980-07-16No168 Lbs6 ft2NoNoNo3Sans RestrictionPro & Farm8,500,000$0$0$No8,500,000$8,500,000$
Sam AnasAdmirals (Nas)C231993-05-31Yes160 Lbs5 ft8NoNoNo4Avec RestrictionPro & Farm300,000$0$0$No300,000$300,000$300,000$
Sam KurkerAdmirals (Nas)RW221994-04-08No203 Lbs6 ft2NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$No300,000$300,000$300,000$
Sonny MilanoAdmirals (Nas)LW/RW201996-05-12No183 Lbs6 ft0NoNoNo1Contrat d'EntréePro & Farm900,000$0$0$No
Stefan FournierAdmirals (Nas)RW241992-04-29No205 Lbs6 ft3NoNoNo1Avec RestrictionPro & Farm300,000$0$0$No
Travis SanheimAdmirals (Nas)D201996-03-29No181 Lbs6 ft3NoNoNo3Contrat d'EntréePro & Farm900,000$0$0$No900,000$900,000$
Vladislav KamenevAdmirals (Nas)LW201996-08-12No203 Lbs6 ft2NoNoNo1Contrat d'EntréePro & Farm925,000$0$0$No
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
3123.26191 Lbs6 ft12.39686,097$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Sonny Milano40122
2Vladislav KamenevJordan Szwarz30122
3Marek HrivikPeter HollandJaedon Descheneau20122
4Mike HalmoChase BalisySonny Milano10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Travis SanheimGustav Olofsson40122
2John Gilmour30122
3Dean KukanPhilip Larsen20122
4Travis SanheimGustav Olofsson10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Sonny Milano60122
2Vladislav KamenevJordan Szwarz40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Travis SanheimGustav Olofsson60122
2John Gilmour40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Sonny Milano60122
240122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Travis SanheimGustav Olofsson60122
2John Gilmour40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Sonny Milano60122Travis SanheimGustav Olofsson60122
240122John Gilmour40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Sonny Milano60122
240122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Travis SanheimGustav Olofsson60122
2John Gilmour40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Sonny MilanoTravis SanheimGustav Olofsson
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Sonny MilanoTravis SanheimGustav Olofsson
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Greg Chase, Sam Anas, Peter HollandGreg Chase, Sam AnasPeter Holland
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Dean Kukan, Philip Larsen, John GilmourDean KukanPhilip Larsen, John Gilmour
Tirs de Pénalité
Sonny Milano, , , , Peter Holland
Gardien
#1 : Ryan Miller, #2 : Marek Langhamer


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
LigueDomicileVisiteur
# 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
1Bears11000000321110000003210000000000021.000358003127305232262372402113710218112.50%5260.00%050685559.18%42475955.86%23338460.68%751545578179312169
2Griffins22000000514000000000002200000051441.00058130131273055622623724021331226266116.67%12191.67%050685559.18%42475955.86%23338460.68%751545578179312169
3Gulls1010000014-3000000000001010000014-300.000123003127305172262372402117619189111.11%7271.43%050685559.18%42475955.86%23338460.68%751545578179312169
4IceHogs431000001714332100000131121100000043160.750173451003127305100226237240217829797420420.00%30776.67%150685559.18%42475955.86%23338460.68%751545578179312169
5Monsters1010000004-41010000004-40000000000000.0000000031273058226237240212582418300.00%12375.00%050685559.18%42475955.86%23338460.68%751545578179312169
6Moose2200000012482200000012480000000000041.0001221330031273058522623724021321045457457.14%15380.00%150685559.18%42475955.86%23338460.68%751545578179312169
7Rampage3100110011741000010001-121001000116550.83311193000312730587226237240216814326022522.73%16193.75%150685559.18%42475955.86%23338460.68%751545578179312169
8Reign22000000817110000005051100000031241.000812200131273058022623724021341120461119.09%90100.00%050685559.18%42475955.86%23338460.68%751545578179312169
9Stars4200100114591100000030331001001115670.8751428420231273058922623724021682378812015.00%33487.88%250685559.18%42475955.86%23338460.68%751545578179312169
Total2716402221925735128300100402713158102121523022430.79692162254043127305714226237240215401754295221402316.43%1772983.62%550685559.18%42475955.86%23338460.68%751545578179312169
11Wild320001001073110000004222100010065150.83310172700312730583226237240217323386224312.50%16287.50%050685559.18%42475955.86%23338460.68%751545578179312169
12Wolves4110002011831010000003-331000020115660.75011162700312730586226237240219932587110220.00%22481.82%050685559.18%42475955.86%23338460.68%751545578179312169
_Since Last GM Reset2716402221925735128300100402713158102121523022430.79692162254043127305714226237240215401754295221402316.43%1772983.62%550685559.18%42475955.86%23338460.68%751545578179312169
_Vs Conference20112022216842267420010020173137002121482523330.82568122190033127305501226237240214191333113741021615.69%1291985.27%450685559.18%42475955.86%23338460.68%751545578179312169
_Vs Division7720112020137222001005325500102015105211.50020305001312730518322623724021150499713530413.33%38684.21%050685559.18%42475955.86%23338460.68%751545578179312169

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
2743OTW29216225471454017542952204
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
2716422219257
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
128301004027
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
158121215230
Derniers 10 Matchs
WLOTWOTL SOWSOL
702001
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
1402316.43%1772983.62%5
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
226237240213127305
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
50685559.18%42475955.86%23338460.68%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
751545578179312169


Derniers Match 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
4 - 2018-09-0819Admirals6Stars0WSommaire du Match
7 - 2018-09-1128Admirals7Rampage3WSommaire du Match
11 - 2018-09-1548Bears2Admirals3WSommaire du Match
12 - 2018-09-1661Admirals6Wolves2WSommaire du Match
15 - 2018-09-1968Admirals3Reign1WSommaire du Match
17 - 2018-09-2180Admirals1Gulls4LSommaire du Match
19 - 2018-09-2398Admirals2Wolves1WXXSommaire du Match
21 - 2018-09-25101Rampage1Admirals0LXSommaire du Match
24 - 2018-09-28112Monsters4Admirals0LSommaire du Match
25 - 2018-09-29122Moose2Admirals7WSommaire du Match
28 - 2018-10-02136Moose2Admirals5WSommaire du Match
31 - 2018-10-05149IceHogs3Admirals5WSommaire du Match
32 - 2018-10-06159Admirals3Griffins0WSommaire du Match
36 - 2018-10-10174Wolves3Admirals0LSommaire du Match
38 - 2018-10-12188IceHogs5Admirals2LSommaire du Match
40 - 2018-10-14207Admirals3Wolves2WXXSommaire du Match
43 - 2018-10-17221Admirals3Wild4LXSommaire du Match
45 - 2018-10-19232Admirals3Wild1WSommaire du Match
46 - 2018-10-20242Reign0Admirals5WSommaire du Match
50 - 2018-10-24261Wild2Admirals4WSommaire du Match
52 - 2018-10-26267Admirals2Griffins1WSommaire du Match
53 - 2018-10-27280IceHogs3Admirals6WSommaire du Match
59 - 2018-11-02314Stars0Admirals3WSommaire du Match
60 - 2018-11-03326Admirals4IceHogs3WSommaire du Match
64 - 2018-11-07345Admirals2Stars3LXXSommaire du Match
66 - 2018-11-09357Admirals3Stars2WXSommaire du Match
67 - 2018-11-10371Admirals4Rampage3WXSommaire du Match
70 - 2018-11-13381Griffins-Admirals-
73 - 2018-11-16398Stars-Admirals-
74 - 2018-11-17406Admirals-Griffins-
78 - 2018-11-21431Admirals-IceHogs-
81 - 2018-11-24452Wolves-Admirals-
85 - 2018-11-28469Wolves-Admirals-
88 - 2018-12-01493Admirals-Wild-
92 - 2018-12-05516Admirals-IceHogs-
95 - 2018-12-08539Admirals-Penguins-
96 - 2018-12-09549Admirals-Bears-
99 - 2018-12-12555Griffins-Admirals-
102 - 2018-12-15584Gulls-Admirals-
104 - 2018-12-17592Admirals-Moose-
106 - 2018-12-19600Admirals-Moose-
109 - 2018-12-22622Admirals-Griffins-
110 - 2018-12-23635Admirals-Wolves-
112 - 2018-12-25644Rampage-Admirals-
115 - 2018-12-28662Wild-Admirals-
116 - 2018-12-29668Moose-Admirals-
122 - 2019-01-04690Moose-Admirals-
123 - 2019-01-05706IceHogs-Admirals-
127 - 2019-01-09721Stars-Admirals-
129 - 2019-01-11728Penguins-Admirals-
130 - 2019-01-12734Wolves-Admirals-
133 - 2019-01-15758Rampage-Admirals-
138 - 2019-01-20795Admirals-Moose-
139 - 2019-01-21806Admirals-Moose-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
143 - 2019-01-25819Admirals-Monsters-
145 - 2019-01-27841Admirals-Monsters-
148 - 2019-01-30853Admirals-Stars-
150 - 2019-02-01864Admirals-Rampage-
152 - 2019-02-03885Admirals-Rampage-
154 - 2019-02-05889Stars-Admirals-
157 - 2019-02-08906Wolves-Admirals-
158 - 2019-02-09917Griffins-Admirals-
159 - 2019-02-10928Admirals-Wolves-
162 - 2019-02-13934Monsters-Admirals-
165 - 2019-02-16961Griffins-Admirals-
171 - 2019-02-22989IceHogs-Admirals-
172 - 2019-02-23998Admirals-IceHogs-
176 - 2019-02-271023Admirals-IceHogs-
179 - 2019-03-021049Admirals-Wolves-
180 - 2019-03-031055Admirals-Griffins-
183 - 2019-03-061067Rampage-Admirals-
185 - 2019-03-081079Wolves-Admirals-
186 - 2019-03-091091Wild-Admirals-
189 - 2019-03-121106IceHogs-Admirals-
193 - 2019-03-161131Griffins-Admirals-
194 - 2019-03-171149Admirals-IceHogs-



Capacité de l'Aréna - Tendance du Prix des Billets - %
Niveau 1Niveau 2
Capacité de l'Aréna20001000
Prix des Billets3515
Assistance00
Assistance PCT0.00%0.00%

Revenus
Matchs à domicile RestantsAssistance Moyenne - %Revenus Moyen par MatchRevenus Annuels à ce JourCapacité de l'ArénaPopularité de l'Équipe
26 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
93,230$ 212,690$ 120,380$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 58,677$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 127 1,612$ 204,724$




LigueDomicileVisiteur
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
1327164022219257351283001004027131581021215230224392162254043127305714226237240215401754295221402316.43%1772983.62%550685559.18%42475955.86%23338460.68%751545578179312169
Total Saison Régulière27164022219257351283001004027131581021215230224392162254043127305714226237240215401754295221402316.43%1772983.62%550685559.18%42475955.86%23338460.68%751545578179312169
Séries
121064000003818205320000020515532000001813512387010804161381254878579321056130191501020.00%57787.72%317327363.37%15230050.67%7814155.32%2491772357111758
121064000003818205320000020515532000001813512387010804161381254878579321056130191501020.00%57787.72%317327363.37%15230050.67%7814155.32%2491772357111758
Total Séries2012800000763640106400000401030106400000362610247614021608322616250817417015864201122603821002020.00%1141487.72%634654663.37%30460050.67%15628255.32%498354470142234117