Checkers

GP: 5 | W: 0 | L: 5 | OTL: 0 | P: 0
GF: 2 | GA: 27 | PP%: 0.00% | PK%: 75.00%
DG: Sebastien Chando | Morale : 47 | Moyenne d'Équipe : 60
Prochain matchs #84 vs Bears
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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
1Alex BelzileX100.005640796170949560686156585475685747620
2Jordan Kyrou (R)X100.005236926369776662726461596562627647610
3Martin Kaut (R)X100.005937875776928956585554565560628347590
4Anders BjorkX100.005435925872776256536057595665635551580
5Kevin RoyX100.005836915763736056635854595371666351570
6Blake SpeersX100.005837875569857954625352555463626351570
7Tyler Steenbergen (R)X100.005136925659928955585553525461636447570
8Tyler Vesel (R)X100.005235955558928854585551535269656047570
9Skyler McKenzie (R)X100.005237875659878155565453525561636447560
10Nic HagueX100.007837885795949556305654624761636447650
11Anton LindholmX100.007835905971766958306052714669655451640
12Henri Jokiharju (R)X100.006938866573837864307356625361638247640
13Adam ClendeningX100.005539816274786661306254664773676551620
14Julius BergmanX100.006536915678857955305452575167646851610
15Stefan Elliott (R)X100.005635946075797059306154575375686247610
16Dylan BlujusX100.006539815582857954305350564869657251600
17Kale Clague (R)X100.005438865970908458306053564861637747600
18Lucas JohansenX100.006037885677868055305651544563627351590
19James De HaasX100.005634805576767255305652564564655351580
Rayé
1Anthony LouisX100.005336905758949556615753525567646145580
2David WarsofskyX100.005239816063908459306352534777695145600
3Logan PyettX100.006339745771747056306058535466675241590
MOYENNE D'ÉQUIPE100.00593787587185795744585457526665654860
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
1Calvin Pickard100.00767775797574767574767573776347730
2Jake Paterson100.00626462736160626160626169737151620
Rayé
MOYENNE D'ÉQUIPE100.0069716976686769686769687175674968
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Ian Laperriere73676461686380CAN4551,000,000$


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
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


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
Adam ClendeningCheckers (Car)D261992-10-26No196 Lbs6 ft0NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Alex BelzileCheckers (Car)C271991-08-31No188 Lbs5 ft11NoNoNo4Avec RestrictionPro & Farm300,000$0$0$NoLien
Anders BjorkCheckers (Car)LW221996-08-05No190 Lbs6 ft0NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$NoLien
Anthony LouisCheckers (Car)LW241995-02-10No151 Lbs5 ft7NoNoNo2Avec RestrictionPro & Farm300,000$0$0$NoLien
Anton LindholmCheckers (Car)D241994-11-29No191 Lbs5 ft11NoNoNo2Avec RestrictionPro & Farm300,000$0$0$NoLien
Blake SpeersCheckers (Car)C221997-01-02No185 Lbs5 ft11NoNoNo3Contrat d'EntréePro & Farm500,000$0$0$NoLien
Calvin PickardCheckers (Car)G271992-04-15No207 Lbs6 ft1NoNoNo4Avec RestrictionPro & Farm750,000$0$0$NoLien
David WarsofskyCheckers (Car)D291990-05-30No170 Lbs5 ft9NoNoNo3Sans RestrictionPro & Farm500,000$0$0$NoLien
Dylan BlujusCheckers (Car)D251994-01-22No191 Lbs6 ft3NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Henri JokiharjuCheckers (Car)D201999-06-17Yes193 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm900,000$0$0$NoLien
Jake PatersonCheckers (Car)G251994-05-03No176 Lbs6 ft1NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
James De HaasCheckers (Car)D251994-05-03No210 Lbs6 ft4NoNoNo3Avec RestrictionPro & Farm300,000$0$0$NoLien
Jordan KyrouCheckers (Car)C211998-05-05Yes175 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm500,000$0$0$NoLien
Julius BergmanCheckers (Car)D231995-11-02No205 Lbs6 ft1NoNoNo1Avec RestrictionPro & Farm500,000$0$0$NoLien
Kale ClagueCheckers (Car)D211998-06-05Yes177 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm500,000$0$0$NoLien
Kevin RoyCheckers (Car)LW261993-05-20No170 Lbs5 ft9NoNoNo2Avec RestrictionPro & Farm500,000$0$0$NoLien
Logan PyettCheckers (Car)D311988-05-26No199 Lbs5 ft10NoNoNo1Sans RestrictionPro & Farm300,000$0$0$NoLien
Lucas JohansenCheckers (Car)D211997-11-16No182 Lbs6 ft2NoNoNo3Contrat d'EntréePro & Farm500,000$0$0$NoLien
Martin KautCheckers (Car)RW191999-10-02Yes180 Lbs6 ft2NoNoNo4Contrat d'EntréePro & Farm900,000$0$0$NoLien
Nic HagueCheckers (Car)D201998-12-05No215 Lbs6 ft6NoNoNo4Contrat d'EntréePro & Farm900,000$0$0$NoLien
Skyler McKenzieCheckers (Car)LW211998-01-20Yes154 Lbs5 ft8NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$NoLien
Stefan ElliottCheckers (Car)D281991-01-30Yes190 Lbs6 ft1NoNoNo1Sans RestrictionPro & Farm300,000$0$0$NoLien
Tyler SteenbergenCheckers (Car)C211998-01-07Yes188 Lbs5 ft1NoNoNo4Contrat d'EntréePro & Farm500,000$0$0$NoLien
Tyler VeselCheckers (Car)C251994-04-14Yes182 Lbs5 ft1NoNoNo4Avec RestrictionPro & Farm300,000$0$0$NoLien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
2423.88186 Lbs5 ft112.79460,417$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
140122
230122
320122
410122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
140122
230122
320122
410122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
160122
240122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
160122
240122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
160122
240122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
160122
240122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
16012260122
24012240122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
160122
240122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
160122
240122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
, , ,
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
, , ,
Tirs de Pénalité
, , , ,
Gardien
#1 : , #2 :


Astuces sur les Filtres (Anglais seulement)
PriorityTypeDescription
1| or  OR Logical "or" (Vertical bar). Filter the column for content that matches text from either side of the bar
2 &&  or  AND Logical "and". Filter the column for content that matches text from either side of the operator.
3/\d/Add any regex to the query to use in the query ("mig" flags can be included /\w/mig)
4< <= >= >Find alphabetical or numerical values less than or greater than or equal to the filtered query
5! or !=Not operator, or not exactly match. Filter the column with content that do not match the query. Include an equal (=), single (') or double quote (") to exactly not match a filter.
6" or =To exactly match the search query, add a quote, apostrophe or equal sign to the beginning and/or end of the query
7 -  or  to Find a range of values. Make sure there is a space before and after the dash (or the word "to")
8?Wildcard for a single, non-space character.
8*Wildcard for zero or more non-space characters.
9~Perform a fuzzy search (matches sequential characters) by adding a tilde to the beginning of the query
10textAny text entered in the filter will match text found within the column
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
1Americans2020000029-7000000000002020000029-700.000246001100333216250101241435500.00%6350.00%0139214.13%3318917.46%86811.76%5231201354615
2Bears1010000007-71010000007-70000000000000.0000000011001632162505612822200.00%4175.00%0139214.13%3318917.46%86811.76%5231201354615
3Comets1010000008-8000000000001010000008-800.000000001100932162504615812300.00%40100.00%0139214.13%3318917.46%86811.76%5231201354615
4Crunch1010000003-3000000000001010000003-300.00000000110015321625058251218200.00%6183.33%0139214.13%3318917.46%86811.76%5231201354615
Total50500000227-251010000007-740400000220-1800.0002460011007332162502617642871200.00%20575.00%0139214.13%3318917.46%86811.76%5231201354615
_Since Last GM Reset50500000227-251010000007-740400000220-1800.0002460011007332162502617642871200.00%20575.00%0139214.13%3318917.46%86811.76%5231201354615
_Vs Conference30300000217-150000000000030300000217-1500.000246001100423216250147392247800.00%10370.00%0139214.13%3318917.46%86811.76%5231201354615

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
50L52467326176428700
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
5050000227
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
101000007
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
4040000220
Derniers 10 Matchs
WLOTWOTL SOWSOL
050000
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
1200.00%20575.00%0
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
32162501100
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
139214.13%3318917.46%86811.76%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
5231201354615


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 - 2019-09-043Checkers0Americans6LSommaire du Match
4 - 2019-09-0515Checkers2Americans3LSommaire du Match
10 - 2019-09-1133Checkers0Comets8LSommaire du Match
11 - 2019-09-1246Checkers0Crunch3LSommaire du Match
17 - 2019-09-1870Bears7Checkers0LSommaire du Match
18 - 2019-09-1984Bears-Checkers-
22 - 2019-09-23104Checkers-Comets-
24 - 2019-09-25110Checkers-Devils-
25 - 2019-09-26119Checkers-Crunch-
31 - 2019-10-02139Bruins-Checkers-
32 - 2019-10-03154Bruins-Checkers-
38 - 2019-10-09186Checkers-Devils-
39 - 2019-10-10198Checkers-Phantoms-
40 - 2019-10-11205Checkers-Sound Tigers-
43 - 2019-10-14219Checkers-Phantoms-
45 - 2019-10-16228Checkers-Penguins-
46 - 2019-10-17243Checkers-Penguins-
49 - 2019-10-20253Senators-Checkers-
50 - 2019-10-21258Senators-Checkers-
53 - 2019-10-24278Rocket-Checkers-
54 - 2019-10-25290Rocket-Checkers-
59 - 2019-10-30311Checkers-Bruins-
60 - 2019-10-31321Checkers-Wolf Pack-
61 - 2019-11-01332Checkers-Bruins-
66 - 2019-11-06355Checkers-Wolf Pack-
67 - 2019-11-07368Checkers-Thunderbirds-
72 - 2019-11-12390Wolf Pack-Checkers-
74 - 2019-11-14402Wolf Pack-Checkers-
75 - 2019-11-15415Comets-Checkers-
77 - 2019-11-17423Comets-Checkers-
80 - 2019-11-20435Sound Tigers-Checkers-
81 - 2019-11-21454Sound Tigers-Checkers-
87 - 2019-11-27482Checkers-Rocket-
88 - 2019-11-28486Checkers-Rocket-
90 - 2019-11-30504Checkers-Marlies-
94 - 2019-12-04519Checkers-Senators-
95 - 2019-12-05534Checkers-Senators-
96 - 2019-12-06548Checkers-Marlies-
101 - 2019-12-11561Penguins-Checkers-
102 - 2019-12-12575Penguins-Checkers-
105 - 2019-12-15594Sound Tigers-Checkers-
106 - 2019-12-16595Sound Tigers-Checkers-
109 - 2019-12-19618Marlies-Checkers-
110 - 2019-12-20631Marlies-Checkers-
115 - 2019-12-25658Checkers-Bruins-
116 - 2019-12-26670Checkers-Wolf Pack-
122 - 2020-01-01684Bruins-Checkers-
123 - 2020-01-02699Bruins-Checkers-
126 - 2020-01-05713Monsters-Checkers-
127 - 2020-01-06716Monsters-Checkers-
130 - 2020-01-09737Checkers-Bears-
131 - 2020-01-10755Checkers-Bears-
136 - 2020-01-15772Checkers-Thunderbirds-
137 - 2020-01-16786Checkers-Bruins-
138 - 2020-01-17794Checkers-Sound Tigers-
143 - 2020-01-22816Phantoms-Checkers-
144 - 2020-01-23829Phantoms-Checkers-
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 - 2020-02-05902Checkers-Phantoms-
158 - 2020-02-06913Checkers-Phantoms-
164 - 2020-02-12946Americans-Checkers-
165 - 2020-02-13955Americans-Checkers-
168 - 2020-02-16973Crunch-Checkers-
169 - 2020-02-17978Crunch-Checkers-
172 - 2020-02-20995Wolf Pack-Checkers-
173 - 2020-02-211008Wolf Pack-Checkers-
176 - 2020-02-241018Checkers-Sound Tigers-
178 - 2020-02-261033Checkers-Wolf Pack-
179 - 2020-02-271039Checkers-Sound Tigers-
182 - 2020-03-011059Phantoms-Checkers-
183 - 2020-03-021063Phantoms-Checkers-
186 - 2020-03-051086Thunderbirds-Checkers-
187 - 2020-03-061097Thunderbirds-Checkers-
191 - 2020-03-101114Checkers-Monsters-
192 - 2020-03-111115Checkers-Monsters-



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
37 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
96,902$ 110,500$ 22,970$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 9,282$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 177 5,724$ 1,013,148$




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
1450500000227-251010000007-740400000220-1802460011007332162502617642871200.00%20575.00%0139214.13%3318917.46%86811.76%5231201354615
Total Saison Régulière50500000227-251010000007-740400000220-1802460011007332162502617642871200.00%20575.00%0139214.13%3318917.46%86811.76%5231201354615