Americans

GP: 25 | W: 16 | L: 7 | OTL: 2 | P: 34
GF: 54 | GA: 36 | PP%: 14.52% | PK%: 92.92%
DG: Frederic Goldstyn | Morale : 61 | Moyenne d'Équipe : 62
Prochain matchs #395 vs Monsters
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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
1Drew StaffordXX100.007438886483727063586162596384744856630
2Greg CareyX100.005838866371949562656161586277695667630
3Brooks MacekXX100.005135936557928964706362576373675864630
4John QuennevilleXX100.006238856376766462716063626065647867610
5Sheldon DriesX100.006640796366726762686159655669655167610
6Erik CondraX100.005637876369847261535960565782764167610
7Sam Lafferty (R)XX100.006142756074939159706254575367646168610
8Cameron DarcyX100.005838855672939155565453575069656067590
9Eric CornelX100.006437895579959654575452565365636267590
10Kailer Yamamoto (R)XX100.005636906359767162565761585761638267590
11Justin KloosXX100.005236915964766958635957565571665167580
12Conner BleackleyX100.005938865573918654575452545365636267580
13Mikey EyssimontXX100.005538845570928954565353555465636267580
14Niklas KronwallX100.007340806573859264307658695288783363690
15Casey NelsonX100.006443896274795361306658674973673764630
16Robbie RussoX100.005937885973939058306252564771666067620
17Calle RosenX100.005637886475876863306258575069655167620
18Blake SiebenalerX100.006636915579777153305251544565636267590
19Justin HollX100.005635935886735356305751524873676167580
Rayé
1Steven WhitneyX100.005337895561918754565253545175685725570
2Deven SideroffX100.005237895667777154635751525463626325560
MOYENNE D'ÉQUIPE100.00603887607184785952595658547167576260
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
1Antti Niemi100.00777270827675777675777686902467750
2Charlie Lindgren100.00766664747574767574767571754767720
Rayé
1Marek Mazanec100.00727472847170727170727175814525710
MOYENNE D'ÉQUIPE100.0075716980747375747375747782395373
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Lindy Ruff75707166898357CAN5955,000,000$


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'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
1John QuennevilleAmericans (Buf)C/LW2571118516030608916437.87%850320.16347231050003573058.97%48500000.7103000223
2Brooks MacekAmericans (Buf)C/RW25711185160146461206511.48%564725.89145910600021170156.67%78000000.5614000304
3Greg CareyAmericans (Buf)LW2511617560313278173314.10%656522.626392910700001243150.75%6700000.6004000431
4Erik CondraAmericans (Buf)RW2579161140122864152610.94%343917.5923516105000042036.36%3300000.7312000031
5Calle RosenAmericans (Buf)D2559147140251741123012.20%2255322.1523529102000085210.00%000000.5100000011
6Niklas KronwallAmericans (Buf)D2511213-343560374311242.33%3059023.6114536108011181000.00%000000.4400000101
7Robbie RussoAmericans (Buf)D250101072004122198280.00%2656422.5804412103000191000.00%000000.3500000001
8Drew StaffordAmericans (Buf)LW/RW216287260502744112613.64%439018.621018901015490051.72%5800000.4100000111
9Sam LaffertyAmericans (Buf)C/LW25358323537163614408.33%643217.312248105000023060.00%2000000.3700001020
10Casey NelsonAmericans (Buf)D25077-1291523283711240.00%1959923.9704419107011199000.00%000000.2300120000
11Sheldon DriesAmericans (Buf)C25134-218035465518401.82%535914.37000000000331049.02%30600000.2201000002
12Justin HollAmericans (Buf)D2503328018810250.00%1235314.1400012000020000.00%000000.1700000000
13Eric CornelAmericans (Buf)RW25213-140157103620.00%11245.0000000000000057.14%700000.4800000000
14Kailer YamamotoAmericans (Buf)C/RW25213-14014177218402.78%032513.0100019000000054.55%1100000.1800000002
15Cameron DarcyAmericans (Buf)C250111206129320.00%31124.4900000000030057.80%10900000.1800000000
16Blake SiebenalerAmericans (Buf)D25011320028610190.00%1636214.4800004000029000.00%000000.0600000000
17Justin KloosAmericans (Buf)C/RW25000100000000.00%090.3600001000000033.33%300000.0000000000
18Conner BleackleyAmericans (Buf)C25000-100104160.00%1281.1300005000050062.50%1600000.0000000000
19Mikey EyssimontAmericans (Buf)C/LW25000-3801416267150.00%231812.7600000000000055.56%900000.0000000000
Stats d'équipe Total ou en Moyenne471529214435271254544437081884627.34%169728115.4618314919110661231380514355.41%190400000.40214121111217
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
1Antti NiemiAmericans (Buf)2516720.9371.30152028335200010.87516250640
Stats d'équipe Total ou en Moyenne2516720.9371.30152028335200010.87516250640


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 Link
Antti NiemiAmericans (Buf)G351983-08-29No209 Lbs6 ft2NoNoNo1Sans RestrictionPro & Farm2,000,000$0$0$NoLien
Blake SiebenalerAmericans (Buf)D231996-02-27No208 Lbs6 ft1NoNoNo2Avec RestrictionPro & Farm300,000$0$0$NoLien
Brooks MacekAmericans (Buf)C/RW271992-05-15No180 Lbs5 ft1NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Calle RosenAmericans (Buf)D251994-02-02No186 Lbs6 ft1NoNoNo3Avec RestrictionPro & Farm300,000$0$0$NoLien
Cameron DarcyAmericans (Buf)C251994-03-02No190 Lbs6 ft0NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Casey NelsonAmericans (Buf)D261992-07-18No185 Lbs6 ft1NoNoNo2Avec RestrictionPro & Farm300,000$0$0$NoLien
Charlie LindgrenAmericans (Buf)G251993-12-18No182 Lbs6 ft1NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Conner BleackleyAmericans (Buf)C231996-02-07No192 Lbs6 ft0NoNoNo2Avec RestrictionPro & Farm500,000$0$0$NoLien
Deven SideroffAmericans (Buf)RW221997-04-14No171 Lbs5 ft11NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$NoLien
Drew StaffordAmericans (Buf)LW/RW331985-10-30No215 Lbs6 ft2NoNoNo1Sans RestrictionPro & Farm1,000,000$0$0$NoLien
Eric CornelAmericans (Buf)RW231996-04-11No195 Lbs6 ft2NoNoNo2Avec RestrictionPro & Farm500,000$0$0$NoLien
Erik CondraAmericans (Buf)RW321986-08-06No185 Lbs5 ft11NoNoNo2Sans RestrictionPro & Farm350,000$0$0$NoLien
Greg CareyAmericans (Buf)LW291990-04-05No204 Lbs5 ft10NoNoNo2Sans RestrictionPro & Farm300,000$0$0$NoLien
John QuennevilleAmericans (Buf)C/LW231996-04-16No195 Lbs6 ft1NoNoNo2Avec RestrictionPro & Farm500,000$0$0$NoLien
Justin HollAmericans (Buf)D271992-01-30No205 Lbs6 ft4NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Justin KloosAmericans (Buf)C/RW251993-11-30No175 Lbs5 ft9NoNoNo3Avec RestrictionPro & Farm300,000$0$0$NoLien
Kailer YamamotoAmericans (Buf)C/RW201998-09-29Yes153 Lbs5 ft8NoNoNo4Contrat d'EntréePro & Farm900,000$0$0$NoLien
Marek MazanecAmericans (Buf)G271991-07-18No187 Lbs6 ft4NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Mikey EyssimontAmericans (Buf)C/LW221996-09-09No180 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$NoLien
Niklas KronwallAmericans (Buf)D381981-01-12No194 Lbs6 ft0NoNoNo1Sans RestrictionPro & Farm2,496,000$0$0$NoLien
Robbie RussoAmericans (Buf)D261993-02-15No191 Lbs6 ft0NoNoNo3Avec RestrictionPro & Farm300,000$0$0$NoLien
Sam LaffertyAmericans (Buf)C/LW241995-03-06Yes184 Lbs6 ft1NoNoNo4Avec RestrictionPro & Farm300,000$0$0$NoLien
Sheldon DriesAmericans (Buf)C251994-04-23No185 Lbs5 ft9NoNoNo4Avec RestrictionPro & Farm300,000$0$0$NoLien
Steven WhitneyAmericans (Buf)RW281991-02-18No168 Lbs5 ft7NoNoNo1Sans RestrictionPro & Farm300,000$0$0$NoLien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2426.38188 Lbs6 ft02.13543,583$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Greg CareyBrooks MacekDrew Stafford40122
2Sam LaffertyJohn QuennevilleErik Condra30122
3Mikey EyssimontSheldon DriesKailer Yamamoto20122
4Brooks MacekCameron DarcyEric Cornel10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Niklas KronwallCasey Nelson40122
2Robbie RussoCalle Rosen30122
3Blake SiebenalerJustin Holl20122
4Niklas KronwallCasey Nelson10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Greg CareyBrooks MacekDrew Stafford60122
2Sam LaffertyJohn QuennevilleErik Condra40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Niklas KronwallCasey Nelson60122
2Robbie RussoCalle Rosen40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Brooks MacekGreg Carey60122
2Drew StaffordJohn Quenneville40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Niklas KronwallCasey Nelson60122
2Robbie RussoCalle Rosen40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Brooks Macek60122Niklas KronwallCasey Nelson60122
2Greg Carey40122Robbie RussoCalle Rosen40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Brooks MacekGreg Carey60122
2Drew StaffordJohn Quenneville40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Niklas KronwallCasey Nelson60122
2Robbie RussoCalle Rosen40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Greg CareyBrooks MacekDrew StaffordNiklas KronwallCasey Nelson
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Greg CareyBrooks MacekDrew StaffordNiklas KronwallCasey Nelson
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Conner Bleackley, Justin Kloos, Sheldon DriesConner Bleackley, Justin KloosSheldon Dries
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
Blake Siebenaler, Justin Holl, Robbie RussoBlake SiebenalerJustin Holl, Robbie Russo
Tirs de Pénalité
Brooks Macek, Greg Carey, Drew Stafford, John Quenneville, Sheldon Dries
Gardien
#1 : Antti Niemi, #2 : Charlie Lindgren


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
1Bears2110000013-2110000001011010000003-320.500123012113173662492152382235810351200.00%40100.00%049286556.88%39270955.29%17131753.94%678487541178302155
2Bruins21100000330110000001011010000023-120.5003690121131734524921523822611627388225.00%10190.00%049286556.88%39270955.29%17131753.94%678487541178302155
3Checkers22000000927220000009270000000000041.000916250121131731012492152382233812296350.00%50100.00%049286556.88%39270955.29%17131753.94%678487541178302155
4Comets421000101284311000108711100000041360.750122133002113173124249215238225819327125312.00%14285.71%049286556.88%39270955.29%17131753.94%678487541178302155
5Crunch31100010862210000106331010000023-140.6678111900211317385249215238227426385920525.00%16475.00%049286556.88%39270955.29%17131753.94%678487541178302155
6Devils3010100134-1100010001012010000124-230.5003580121131735524921523822683130591417.14%150100.00%049286556.88%39270955.29%17131753.94%678487541178302155
7Marlies11000000413110000004130000000000021.000461000211317346249215238222666293133.33%30100.00%049286556.88%39270955.29%17131753.94%678487541178302155
8Monsters1010000024-21010000024-20000000000000.000246102113173252492152382231112316300.00%9188.89%149286556.88%39270955.29%17131753.94%678487541178302155
9Penguins11000000202110000002020000000000021.00024601211317330249215238221431315600.00%30100.00%049286556.88%39270955.29%17131753.94%678487541178302155
10Rocket11000000312110000003120000000000021.0003470021131732224921523822251228133133.33%120100.00%049286556.88%39270955.29%17131753.94%678487541178302155
11Senators11000000202000000000001100000020221.000246012113173212492152382262613500.00%30100.00%049286556.88%39270955.29%17131753.94%678487541178302155
12Sound Tigers21100000110000000000002110000011020.5001230121131734024921523822441221421100.00%90100.00%049286556.88%39270955.29%17131753.94%678487541178302155
13Thunderbirds11000000202000000000001100000020221.000246012113173232492152382222912176233.33%60100.00%049286556.88%39270955.29%17131753.94%678487541178302155
Total25137010225436181492010203718191145000021718-1340.6805493147182113173709249215238225211692734541241814.52%113892.92%149286556.88%39270955.29%17131753.94%678487541178302155
15Wolf Pack1000000123-1000000000001000000123-110.50024600211317326249215238222461518200.00%40100.00%049286556.88%39270955.29%17131753.94%678487541178302155
_Since Last GM Reset25137010225436181492010203718191145000021718-1340.6805493147182113173709249215238225211692734541241814.52%113892.92%149286556.88%39270955.29%17131753.94%678487541178302155
_Vs Conference971000103012187510001024111322000000615160.889305181022113173316249215238221645490159431023.26%40295.00%049286556.88%39270955.29%17131753.94%678487541178302155

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
2534OTW1549314770952116927345418
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
2513710225436
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
149210203718
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
114500021718
Derniers 10 Matchs
WLOTWOTL SOWSOL
241012
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
1241814.52%113892.92%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
249215238222113173
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
49286556.88%39270955.29%17131753.94%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
678487541178302155


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
3 - 2019-09-043Checkers0Americans6WSommaire du Match
4 - 2019-09-0515Checkers2Americans3WSommaire du Match
8 - 2019-09-0930Crunch1Americans3WSommaire du Match
11 - 2019-09-1243Americans1Sound Tigers0WSommaire du Match
12 - 2019-09-1358Americans0Sound Tigers1LSommaire du Match
15 - 2019-09-1663Americans4Comets1WSommaire du Match
17 - 2019-09-1874Marlies1Americans4WSommaire du Match
18 - 2019-09-1986Americans2Senators0WSommaire du Match
24 - 2019-09-25111Comets2Americans3WSommaire du Match
25 - 2019-09-26125Rocket1Americans3WSommaire du Match
31 - 2019-10-02145Bears0Americans1WSommaire du Match
32 - 2019-10-03156Americans0Bears3LSommaire du Match
36 - 2019-10-07177Monsters4Americans2LSommaire du Match
38 - 2019-10-09187Crunch2Americans3WXXSommaire du Match
45 - 2019-10-16229Americans2Thunderbirds0WSommaire du Match
46 - 2019-10-17245Americans2Bruins3LSommaire du Match
47 - 2019-10-18249Americans2Wolf Pack3LXXSommaire du Match
52 - 2019-10-23270Comets2Americans3WXXSommaire du Match
53 - 2019-10-24284Americans1Devils2LXXSommaire du Match
59 - 2019-10-30312Penguins0Americans2WSommaire du Match
60 - 2019-10-31327Americans1Devils2LSommaire du Match
64 - 2019-11-04344Comets3Americans2LSommaire du Match
66 - 2019-11-06353Bruins0Americans1WSommaire du Match
67 - 2019-11-07366Americans2Crunch3LSommaire du Match
71 - 2019-11-11386Devils0Americans1WXSommaire du Match
73 - 2019-11-13395Monsters-Americans-
74 - 2019-11-14404Americans-Monsters-
78 - 2019-11-18430Senators-Americans-
80 - 2019-11-20437Wolf Pack-Americans-
81 - 2019-11-21453Wolf Pack-Americans-
85 - 2019-11-25465Americans-Monsters-
87 - 2019-11-27481Marlies-Americans-
88 - 2019-11-28490Americans-Comets-
92 - 2019-12-02512Sound Tigers-Americans-
94 - 2019-12-04524Americans-Rocket-
95 - 2019-12-05532Americans-Rocket-
101 - 2019-12-11568Marlies-Americans-
102 - 2019-12-12574Americans-Marlies-
108 - 2019-12-18608Americans-Penguins-
109 - 2019-12-19625Americans-Phantoms-
113 - 2019-12-23650Devils-Americans-
115 - 2019-12-25661Comets-Americans-
116 - 2019-12-26673Americans-Crunch-
122 - 2020-01-01689Americans-Devils-
123 - 2020-01-02701Americans-Wolf Pack-
127 - 2020-01-06719Crunch-Americans-
129 - 2020-01-08725Thunderbirds-Americans-
130 - 2020-01-09740Americans-Comets-
133 - 2020-01-12757Americans-Monsters-
136 - 2020-01-15774Crunch-Americans-
137 - 2020-01-16782Americans-Crunch-
138 - 2020-01-17797Devils-Americans-
141 - 2020-01-20811Monsters-Americans-
143 - 2020-01-22818Americans-Comets-
144 - 2020-01-23834Senators-Americans-
148 - 2020-01-27852Americans-Comets-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
151 - 2020-01-30871Americans-Crunch-
152 - 2020-01-31884Crunch-Americans-
157 - 2020-02-05904Sound Tigers-Americans-
158 - 2020-02-06916Americans-Devils-
159 - 2020-02-07926Devils-Americans-
162 - 2020-02-10938Comets-Americans-
164 - 2020-02-12946Americans-Checkers-
165 - 2020-02-13955Americans-Checkers-
171 - 2020-02-19988Monsters-Americans-
172 - 2020-02-20994Americans-Marlies-
173 - 2020-02-211011Americans-Marlies-
176 - 2020-02-241021Phantoms-Americans-
178 - 2020-02-261032Rocket-Americans-
179 - 2020-02-271048Americans-Devils-
185 - 2020-03-041077Crunch-Americans-
186 - 2020-03-051089Americans-Crunch-
188 - 2020-03-071104Americans-Monsters-
192 - 2020-03-111121Devils-Americans-
193 - 2020-03-121130Americans-Crunch-
194 - 2020-03-131142Americans-Senators-



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
24 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
1,903,334$ 130,460$ 135,750$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 47,618$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 122 26,446$ 3,226,412$




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
1425137010225436181492010203718191145000021718-1345493147182113173709249215238225211692734541241814.52%113892.92%149286556.88%39270955.29%17131753.94%678487541178302155
Total Saison Régulière25137010225436181492010203718191145000021718-1345493147182113173709249215238225211692734541241814.52%113892.92%149286556.88%39270955.29%17131753.94%678487541178302155