Devils

GP: 49 | W: 31 | L: 14 | OTL: 4 | P: 66
GF: 118 | GA: 76 | PP%: 15.92% | PK%: 90.15%
DG: Thomas Perron-Bibeau | Morale : 68 | Moyenne d'Équipe : 63
Prochain matchs #689 vs Americans
La résolution de votre navigateur est trop petite pour cette page. Plusieurs informations sont cachées pour garder la page lisible.

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
1Nate ThompsonX100.008042876179739159786258775985752580660
2Christian FischerX100.008144876683738465526467546665627781640
3Robert Thomas (R)XX100.005337896972738368727463576660628475640
4Josh LeivoXX100.005749826479768963546266596371686080630
5Tyler Benson (R)X100.005838866472939163796855595561636480630
6Klim KostinX100.007143725986939057615654635960628080620
7German Rubtsov (R)X100.005935956476746963705862596461638280610
8Martin Necas (R)XX100.005736906378807060716259585560628781610
9Alexandre FortinX100.005935946071786659565960585563645480600
10Remi ElieX100.008136885981736256535857595867647380600
11Michael McLeod (R)X100.007144826078826859726454585261638580600
12Gabriel GagneX100.006836925587908556575452585365636224590
13Dmitry KulikovX100.007848766278837361306856745277696879670
14Rasmus AnderssonX100.006443866780819265306658675365637471660
15Nathan BeaulieuX100.007261736680826164307157635273677179650
16Filip HronekX100.006842836571847264307362635863627280640
17Dylan CoghlanX100.006236915877939057305954564961635735620
18Libor Hajek (R)X100.006037885881886958305456624661637681620
Rayé
1Sebastian Repo (R)X100.007138855986928958535657595865636220610
2Dmitry Sokolov (R)X100.005335955866918757525658535761636871590
3Jakob Stukel (R)X100.006138845871706756535961586268635820580
4Sheldon RempalX100.005336925863796957585956605467645220580
5Ty Dellandrea (R)X100.006336905975727358695657596059628020580
6Hunter ShinkarukX100.005436905667908554505253555669657820570
7Reece WillcoxX100.007136905687928955305651584569656047630
8Roland McKeownX100.006239835676939156305752544565637120610
9Niklas HanssonX100.005737885673928956305552544667646120600
MOYENNE D'ÉQUIPE100.00654087617783795951615760556664685962
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
1Connor Ingram100.00797169807877797877797863676776740
2Tom McCollum100.00747573847372747372747378844380730
Rayé
1Richard Bachman100.00766866677574767574767580864020720
2Ville Husso100.00687371846766686766686767717220670
MOYENNE D'ÉQUIPE100.0074727079737274737274737277564972
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Randy Carlyle77797176938751CAN6243,000,000$


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
1Nate ThompsonDevils (New)C49111930203809214111740779.40%24108922.241672318800022292163.02%147100000.5519000433
2Christian FischerDevils (New)RW491117281161511437104427610.58%799920.392682518300021822145.61%11400000.5628001134
3Josh LeivoDevils (New)LW/RW491216281853252733103297611.65%997419.9047111918910161432052.54%5900200.5725113144
4Tyler BensonDevils (New)LW4971926-312020429324727.53%484617.2751419271970111292060.94%6400000.6123000301
5Filip HronekDevils (New)D49520251432051466822337.35%54108322.115712531860000194200.00%000000.4611000102
6Robert ThomasDevils (New)C/RW4412132531402156111425010.81%287920.0047112917700021442253.71%52500000.5718000301
7Dmitry KulikovDevils (New)D495192488010116566930417.25%51116023.694610511930001196200.00%000000.4100011042
8Rasmus AnderssonDevils (New)D4341923715536455519457.27%38102623.88369371710112195200.00%000000.4500001122
9Klim KostinDevils (New)RW49614205821093366317479.52%170414.39461017128000003155.00%4000000.5722101031
10Nathan BeaulieuDevils (New)D49712191911935894754234112.96%39105521.544711391820003183000.00%000000.3600204221
11Martin NecasDevils (New)C/RW4997165175124367224813.43%664113.10123769000042261.97%37600000.5000001213
12Libor HajekDevils (New)D4941014439532292781414.81%3276715.66112212000169100.00%000000.3600001101
13German RubtsovDevils (New)C49103134100115589156311.24%262012.661016380000261056.08%55100000.4202000032
14Alexandre FortinDevils (New)LW49461010808315715337.02%34198.5600000000000052.63%1900000.4800000001
15Remi ElieDevils (New)LW49268134054184414364.55%14819.83000030000371044.44%3600000.3301000010
16Michael McLeodDevils (New)C49314224028403110199.68%33597.33000230003371059.88%32400000.2200000021
17Reece WillcoxDevils (New)D290228475421010490.00%2047916.52000329000023000.00%000000.0800100100
18Gabriel GagneDevils (New)RW6011320112020.00%0386.4200000000000050.00%400000.5200000000
19Dylan CoghlanDevils (New)D22011-72603189090.00%1034015.4900017000016000.00%000000.0600000001
20Dmitry SokolovDevils (New)RW43000040212328120.00%13538.21000020000100036.67%3000000.0000000000
Stats d'équipe Total ou en Moyenne87311220531713271710588078612053848039.29%3071432116.413975114341196512323172625758.90%361300200.4411395213202830
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
1Connor IngramDevils (New)49311440.9291.4629596117210200000.79539490943
2Tom McCollumDevils (New)10000.8002.502400150000.0000049000
Stats d'équipe Total ou en Moyenne50311440.9291.4729836117310250000.795394949943


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 Cap Salariale Cap Salariale Restant Exclus du Cap Salarial Salaire Année 2Salaire Année 3Salaire Année 4Salaire Année 5Salaire Année 6Salaire Année 7Salaire Année 8Salaire Année 9Salaire Année 10Link
Alexandre FortinDevils (New)LW221997-02-25No184 Lbs6 ft0NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Christian FischerDevils (New)RW221997-04-15No214 Lbs6 ft2NoNoNo2Pro & Farm900,000$0$0$No900,000$Lien
Connor IngramDevils (New)G221997-03-31No196 Lbs6 ft2NoNoNo3Pro & Farm500,000$0$0$No500,000$500,000$Lien
Dmitry KulikovDevils (New)D281990-10-29No204 Lbs6 ft1NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Dmitry SokolovDevils (New)RW211998-04-14Yes221 Lbs5 ft1NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$500,000$Lien
Dylan CoghlanDevils (New)D211998-02-19No190 Lbs6 ft2NoNoNo1Pro & Farm0$0$NoLien
Filip HronekDevils (New)D211997-11-02No170 Lbs6 ft0NoNoNo3Pro & Farm900,000$0$0$No900,000$900,000$Lien
Gabriel GagneDevils (New)RW221996-11-11No186 Lbs6 ft5NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
German RubtsovDevils (New)C211998-06-27Yes178 Lbs6 ft2NoNoNo4Pro & Farm900,000$0$0$No900,000$900,000$900,000$Lien
Hunter ShinkarukDevils (New)LW241994-10-13No181 Lbs5 ft10NoNoNo1Pro & Farm900,000$0$0$NoLien
Jakob StukelDevils (New)LW221997-03-06Yes182 Lbs6 ft0NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Josh LeivoDevils (New)LW/RW261993-05-26No192 Lbs6 ft2NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$500,000$Lien
Klim KostinDevils (New)RW201999-05-05No212 Lbs6 ft3NoNoNo4Pro & Farm900,000$0$0$No900,000$900,000$900,000$Lien
Libor HajekDevils (New)D211998-02-04Yes204 Lbs6 ft2NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$500,000$Lien
Martin NecasDevils (New)C/RW201999-01-15Yes189 Lbs6 ft2NoNoNo4Pro & Farm900,000$0$0$No900,000$900,000$900,000$Lien
Michael McLeodDevils (New)C211998-02-03Yes188 Lbs6 ft2NoNoNo4Pro & Farm900,000$0$0$No900,000$900,000$900,000$Lien
Nate ThompsonDevils (New)C341984-10-05No207 Lbs6 ft1NoNoNo3Pro & Farm500,000$0$0$No500,000$500,000$Lien
Nathan BeaulieuDevils (New)D261992-12-05No200 Lbs6 ft2NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$500,000$Lien
Niklas HanssonDevils (New)D241995-01-08No180 Lbs6 ft1NoNoNo3Pro & Farm300,000$0$0$No300,000$300,000$Lien
Rasmus AnderssonDevils (New)D221996-10-27No214 Lbs6 ft1NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Reece WillcoxDevils (New)D251994-03-20No205 Lbs6 ft4NoNoNo2Pro & Farm300,000$0$0$No300,000$Lien
Remi ElieDevils (New)LW241995-04-16No215 Lbs6 ft1NoNoNo1Pro & Farm300,000$0$0$NoLien
Richard BachmanDevils (New)G311987-07-25No183 Lbs5 ft10NoNoNo2Pro & Farm750,000$0$0$No750,000$Lien
Robert ThomasDevils (New)C/RW201999-07-02Yes188 Lbs6 ft0NoNoNo4Pro & Farm900,000$0$0$No900,000$900,000$900,000$Lien
Roland McKeownDevils (New)D231996-01-20No195 Lbs6 ft1NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Sebastian RepoDevils (New)RW231996-06-23Yes211 Lbs6 ft3NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Sheldon RempalDevils (New)RW231995-08-07No165 Lbs5 ft10NoNoNo4Pro & Farm300,000$0$0$No300,000$300,000$300,000$Lien
Tom McCollumDevils (New)G291989-12-07No220 Lbs6 ft2NoNoNo3Pro & Farm750,000$0$0$No750,000$750,000$Lien
Ty DellandreaDevils (New)C182000-07-21Yes190 Lbs6 ft1NoNoNo1Pro & Farm0$0$NoLien
Tyler BensonDevils (New)LW211998-03-15Yes190 Lbs6 ft0NoNoNo4Pro & Farm500,000$0$0$No500,000$500,000$500,000$Lien
Ville HussoDevils (New)G241995-02-06No205 Lbs6 ft3NoNoNo2Pro & Farm500,000$0$0$No500,000$Lien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
3123.26195 Lbs6 ft12.90541,935$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Josh LeivoNate ThompsonChristian Fischer40122
2Tyler BensonRobert ThomasKlim Kostin30122
3Remi ElieGerman RubtsovMartin Necas20122
4Alexandre FortinMichael McLeodGabriel Gagne10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Dmitry KulikovRasmus Andersson40122
2Nathan BeaulieuFilip Hronek30122
3Dylan CoghlanLibor Hajek20122
4Dmitry KulikovRasmus Andersson10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Josh LeivoNate ThompsonChristian Fischer60122
2Tyler BensonRobert ThomasKlim Kostin40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Dmitry KulikovRasmus Andersson60122
2Nathan BeaulieuFilip Hronek40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Nate ThompsonChristian Fischer60122
2Robert ThomasJosh Leivo40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Dmitry KulikovRasmus Andersson60122
2Nathan BeaulieuFilip Hronek40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Nate Thompson60122Dmitry KulikovRasmus Andersson60122
2Christian Fischer40122Nathan BeaulieuFilip Hronek40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Nate ThompsonChristian Fischer60122
2Robert ThomasJosh Leivo40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Dmitry KulikovRasmus Andersson60122
2Nathan BeaulieuFilip Hronek40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Josh LeivoNate ThompsonChristian FischerDmitry KulikovRasmus Andersson
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Josh LeivoNate ThompsonChristian FischerDmitry KulikovRasmus Andersson
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
German Rubtsov, Martin Necas, Remi ElieGerman Rubtsov, Martin NecasRemi Elie
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Dylan Coghlan, Libor Hajek, Nathan BeaulieuDylan CoghlanLibor Hajek, Nathan Beaulieu
Tirs de Pénalité
Nate Thompson, Christian Fischer, Robert Thomas, Josh Leivo, Tyler Benson
Gardien
#1 : Connor Ingram, #2 : Tom McCollum


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
1Americans42000110633210000104222100010021170.875691501423535119338238941162761840641800.00%20195.00%0916154059.48%812137958.88%40064562.02%13439631051340592310
2Bears211000005411010000012-11100000042220.500591400423535116338238941162531120337228.57%80100.00%0916154059.48%812137958.88%40064562.02%13439631051340592310
3Bruins11000000101110000001010000000000021.0001230142353511203823894116220111014100.00%50100.00%0916154059.48%812137958.88%40064562.02%13439631051340592310
4Checkers22000000918220000009180000000000041.000915240142353511843823894116233943465240.00%70100.00%0916154059.48%812137958.88%40064562.02%13439631051340592310
5Comets742000011394320000018354220000056-190.643132437024235351114038238941162126457710741614.63%30390.00%0916154059.48%812137958.88%40064562.02%13439631051340592310
6Crunch934000112025-5522000101112-141200001913-490.5002036560042353511221382389411622276116716460813.33%541375.93%1916154059.48%812137958.88%40064562.02%13439631051340592310
7Marlies55000000254212200000011293300000014212101.0002543680242353511189382389411628741729521733.33%31293.55%0916154059.48%812137958.88%40064562.02%13439631051340592310
8Monsters22000000615220000006150000000000041.0006121801423535113638238941162451218371119.09%90100.00%0916154059.48%812137958.88%40064562.02%13439631051340592310
9Penguins21100000422211000004220000000000020.500471101423535114838238941162458303712216.67%12191.67%0916154059.48%812137958.88%40064562.02%13439631051340592310
10Phantoms4110001156-12010000115-42100001041350.62558130142353511733823894116283231078123313.04%24291.67%0916154059.48%812137958.88%40064562.02%13439631051340592310
11Rocket21100000642000000000002110000064220.50061117004235351146382389411623810182713215.38%80100.00%0916154059.48%812137958.88%40064562.02%13439631051340592310
12Senators42100010844110000004133110001043160.75081422004235351188382389411628128597513323.08%26292.31%0916154059.48%812137958.88%40064562.02%13439631051340592310
13Sound Tigers2020000026-41010000014-31010000012-100.0002460042353511363823894116243122850400.00%13284.62%0916154059.48%812137958.88%40064562.02%13439631051340592310
14Thunderbirds10000010431000000000001000001043121.000459004235351124382389411622812121911100.00%60100.00%0916154059.48%812137958.88%40064562.02%13439631051340592310
Total49251400163118764226147000326539262311700131533716660.6731182053230104235351112053823894116210253077238802453915.92%2642690.15%1916154059.48%812137958.88%40064562.02%13439631051340592310
16Wolf Pack20100010440201000104400000000000020.500461000423535114438238941162406223115213.33%110100.00%0916154059.48%812137958.88%40064562.02%13439631051340592310
_Since Last GM Reset49251400163118764226147000326539262311700131533716660.6731182053230104235351112053823894116210253077238802453915.92%2642690.15%1916154059.48%812137958.88%40064562.02%13439631051340592310
_Vs Conference28111100042555231777000213331211440002122211320.57155981530442353511629382389411626371724615221462114.38%1622087.65%1916154059.48%812137958.88%40064562.02%13439631051340592310
_Vs Division263400021704327112300011311714151100010392613110.212701201900442353511681382389411625571813784581272116.54%1501888.00%1916154059.48%812137958.88%40064562.02%13439631051340592310

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
4966W111820532312051025307723880010
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
492514016311876
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
2614700326539
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
2311701315337
Derniers 10 Matchs
WLOTWOTL SOWSOL
540010
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
2453915.92%2642690.15%1
Tirs en 1e PériodeTirs en 2e PériodeTirs en 3e PériodeTirs en 4e PériodeButs en 1e PériodeButs en 2e PériodeButs en 3e PériodeButs en 4e Période
3823894116242353511
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
916154059.48%812137958.88%40064562.02%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
13439631051340592310


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 - 2019-09-0517Marlies0Devils6WSommaire du Match
8 - 2019-09-0929Wolf Pack1Devils2WXXSommaire du Match
10 - 2019-09-1137Devils4Rocket1WSommaire du Match
11 - 2019-09-1244Devils2Rocket3LSommaire du Match
15 - 2019-09-1664Devils0Senators1LSommaire du Match
17 - 2019-09-1875Crunch1Devils3WSommaire du Match
18 - 2019-09-1990Monsters0Devils4WSommaire du Match
22 - 2019-09-23105Devils2Senators1WSommaire du Match
24 - 2019-09-25110Checkers1Devils5WSommaire du Match
25 - 2019-09-26124Phantoms4Devils1LSommaire du Match
31 - 2019-10-02146Phantoms1Devils0LXXSommaire du Match
32 - 2019-10-03162Devils2Phantoms0WSommaire du Match
36 - 2019-10-07173Devils1Sound Tigers2LSommaire du Match
38 - 2019-10-09186Checkers0Devils4WSommaire du Match
39 - 2019-10-10199Comets0Devils3WSommaire du Match
43 - 2019-10-14217Devils4Crunch2WSommaire du Match
46 - 2019-10-17240Devils7Marlies1WSommaire du Match
47 - 2019-10-18250Devils5Marlies0WSommaire du Match
50 - 2019-10-21260Crunch3Devils2LSommaire du Match
52 - 2019-10-23265Devils2Crunch3LXXSommaire du Match
53 - 2019-10-24284Americans1Devils2WXXSommaire du Match
57 - 2019-10-28304Comets1Devils4WSommaire du Match
60 - 2019-10-31327Americans1Devils2WSommaire du Match
61 - 2019-11-01335Devils4Bears2WSommaire du Match
66 - 2019-11-06350Devils2Crunch4LSommaire du Match
67 - 2019-11-07370Comets2Devils1LXXSommaire du Match
68 - 2019-11-08377Devils4Thunderbirds3WXXSommaire du Match
71 - 2019-11-11386Devils0Americans1LXSommaire du Match
73 - 2019-11-13391Devils1Comets0WSommaire du Match
74 - 2019-11-14410Senators1Devils4WSommaire du Match
78 - 2019-11-18425Devils2Marlies1WSommaire du Match
80 - 2019-11-20438Bruins0Devils1WSommaire du Match
81 - 2019-11-21458Devils1Comets2LSommaire du Match
85 - 2019-11-25468Crunch2Devils3WXXSommaire du Match
86 - 2019-11-26472Devils1Comets3LSommaire du Match
88 - 2019-11-28491Devils2Senators1WXXSommaire du Match
90 - 2019-11-30508Penguins2Devils1LSommaire du Match
94 - 2019-12-04521Marlies2Devils5WSommaire du Match
95 - 2019-12-05538Sound Tigers4Devils1LSommaire du Match
99 - 2019-12-09554Crunch5Devils0LSommaire du Match
101 - 2019-12-11567Devils2Phantoms1WXXSommaire du Match
102 - 2019-12-12582Monsters1Devils2WSommaire du Match
106 - 2019-12-16598Bears2Devils1LSommaire du Match
108 - 2019-12-18606Devils2Comets1WSommaire du Match
109 - 2019-12-19626Crunch1Devils3WSommaire du Match
111 - 2019-12-21636Devils1Crunch4LSommaire du Match
113 - 2019-12-23650Devils2Americans0WSommaire du Match
115 - 2019-12-25659Wolf Pack3Devils2LSommaire du Match
116 - 2019-12-26679Penguins0Devils3WSommaire du Match
122 - 2020-01-01689Americans-Devils-
123 - 2020-01-02705Senators-Devils-
130 - 2020-01-09739Devils-Monsters-
131 - 2020-01-10749Devils-Monsters-
136 - 2020-01-15775Rocket-Devils-
137 - 2020-01-16789Rocket-Devils-
138 - 2020-01-17797Devils-Americans-
143 - 2020-01-22823Senators-Devils-
144 - 2020-01-23835Comets-Devils-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
150 - 2020-01-29858Devils-Checkers-
151 - 2020-01-30869Devils-Checkers-
157 - 2020-02-05905Thunderbirds-Devils-
158 - 2020-02-06916Americans-Devils-
159 - 2020-02-07926Devils-Americans-
162 - 2020-02-10936Devils-Wolf Pack-
165 - 2020-02-13958Devils-Crunch-
171 - 2020-02-19987Devils-Thunderbirds-
172 - 2020-02-201000Devils-Bruins-
176 - 2020-02-241022Devils-Rocket-
178 - 2020-02-261031Thunderbirds-Devils-
179 - 2020-02-271048Americans-Devils-
185 - 2020-03-041076Devils-Penguins-
186 - 2020-03-051093Marlies-Devils-
187 - 2020-03-061098Devils-Wolf Pack-
192 - 2020-03-111121Devils-Americans-
193 - 2020-03-121136Rocket-Devils-
194 - 2020-03-131144Devils-Penguins-



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
12 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
1,942,400$ 168,000$ 18,890$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 102,184$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 75 16,330$ 1,224,750$




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
1449251400163118764226147000326539262311700131533716661182053230104235351112053823894116210253077238802453915.92%2642690.15%1916154059.48%812137958.88%40064562.02%13439631051340592310
Total Saison Régulière49251400163118764226147000326539262311700131533716661182053230104235351112053823894116210253077238802453915.92%2642690.15%1916154059.48%812137958.88%40064562.02%13439631051340592310