Bears

GP: 7 | W: 6 | L: 1 | OTL: 0 | P: 12
GF: 25 | GA: 6 | PP%: 16.67% | PK%: 93.10%
DG: Mathieu Girard | Morale : 56 | Moyenne d'Équipe : 62
Prochain matchs #84 vs Checkers
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
1Jason DickinsonXX100.008243806580748064766761736267667757660
2Paul ThompsonX100.006343726277949361645960586179815452630
3Kyle CliffordX100.008578656383678562546164566375706361630
4Brett SutterX100.006138856074939059645858575980725357620
5Christoffer Ehn (R)XX100.007037886380737262706158675665646652620
6Anton BlidhX100.007338865675867355535654655267645557600
7Cooper Marody (R)X100.005336906171766960696257595665635652600
8Timothy Gettinger (R)X100.007836925896777257545655615361635652600
9Gage QuinneyX100.005436925962939058645756545967646352590
10Dennis YanX100.006239825977908557545355565263626357590
11Reid DukeX100.005938845673857956585553545065636257580
12Jayden HalbgewachsX100.005336915860928957535854525663626352580
13Kyle WoodX100.008638855898939156306052634665636252660
14Sami NikuX100.005637896473777163306858614865635657620
15Michael KaplaX100.006136925674939054305852534669656057610
16Trevor MurphyX100.005140785866928957305853564867646157600
Rayé
1Rich CluneX100.006338845673666454535152535774714843560
MOYENNE D'ÉQUIPE100.00654084607684815853595659546866605461
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
1Vitek Vanecek100.00777876747675777675777665695357730
Rayé
MOYENNE D'ÉQUIPE100.0077787674767577767577766569535773
Nom du Coach PH DF OF PD EX LD PO CNT Âge Contrat Salaire
Jay Leach73676657605786USA394100,000$


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
1Trevor MurphyBears (Was)D74610660196152926.67%1515321.97213918000025100.00%000001.3000000200
2Sami NikuBears (Was)D745981002016147928.57%1415522.23011521000018100.00%000001.1600000101
3Kyle CliffordBears (Was)LW76396161021926102523.08%114921.382023170001232053.45%5800001.2001101111
4Jason DickinsonBears (Was)C/LW735874025105913395.08%616423.49011615000071061.60%12500000.9701000010
5Brett SutterBears (Was)C744864091639142310.26%414220.33022423000000155.00%14000001.1200000110
6Anton BlidhBears (Was)LW7336610015153612198.33%416523.60011516000160025.00%1200000.7311000011
7Michael KaplaBears (Was)D703321401764230.00%911316.270000000008000.00%000000.5300000001
8Reid DukeBears (Was)C7022200093040.00%0456.5300000000000051.72%5800000.8700000000
9Dennis YanBears (Was)LW7000000000100.00%020.350000000000000.00%000000.0000000000
Stats d'équipe Total ou en Moyenne63243155436410126871966113112.24%53109317.354610321130002905155.47%39300001.0113101544
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
1Vitek VanecekBears (Was)76100.9680.854240361860101.000470131
Stats d'équipe Total ou en Moyenne76100.9680.854240361860101.000470131


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
Anton BlidhBears (Was)LW241995-03-14No201 Lbs6 ft0NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Brett SutterBears (Was)C321987-06-02No200 Lbs6 ft0NoNoNo2Sans RestrictionPro & Farm300,000$0$0$NoLien
Christoffer EhnBears (Was)C/LW231996-04-05Yes181 Lbs6 ft3NoNoNo4Avec RestrictionPro & Farm500,000$0$0$NoLien
Cooper MarodyBears (Was)C221996-12-20Yes184 Lbs6 ft0NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$NoLien
Dennis YanBears (Was)LW221997-04-14No197 Lbs6 ft1NoNoNo3Contrat d'EntréePro & Farm500,000$0$0$NoLien
Gage QuinneyBears (Was)C231995-07-29No200 Lbs5 ft1NoNoNo4Avec RestrictionPro & Farm300,000$0$0$NoLien
Jason DickinsonBears (Was)C/LW241995-07-04No200 Lbs6 ft2NoNoNo1Avec RestrictionPro & Farm900,000$0$0$NoLien
Jayden HalbgewachsBears (Was)LW221997-03-22No160 Lbs5 ft8NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$NoLien
Kyle CliffordBears (Was)LW281991-01-13No211 Lbs6 ft2NoNoNo1Sans RestrictionPro & Farm300,000$0$0$NoLien
Kyle WoodBears (Was)D231996-05-04No235 Lbs6 ft7NoNoNo2Avec RestrictionPro & Farm500,000$0$0$NoLien
Michael KaplaBears (Was)D241994-09-19No200 Lbs6 ft0NoNoNo3Avec RestrictionPro & Farm300,000$0$0$NoLien
Paul ThompsonBears (Was)RW301988-11-30No200 Lbs6 ft1NoNoNo4Sans RestrictionPro & Farm500,000$0$0$NoLien
Reid DukeBears (Was)C231996-01-28No191 Lbs6 ft0NoNoNo3Avec RestrictionPro & Farm300,000$0$0$NoLien
Rich CluneBears (Was)LW321987-04-25No207 Lbs5 ft10NoNoNo3Sans RestrictionPro & Farm500,000$0$0$NoLien
Sami NikuBears (Was)D221996-10-10No176 Lbs6 ft1NoNoNo3Contrat d'EntréePro & Farm300,000$0$0$NoLien
Timothy GettingerBears (Was)LW211998-04-14Yes220 Lbs6 ft6NoNoNo4Contrat d'EntréePro & Farm300,000$0$0$NoLien
Trevor MurphyBears (Was)D231995-07-17No180 Lbs5 ft10NoNoNo1Avec RestrictionPro & Farm300,000$0$0$NoLien
Vitek VanecekBears (Was)G231996-01-09No181 Lbs6 ft1NoNoNo2Avec RestrictionPro & Farm500,000$0$0$NoLien
Joueurs TotalÂge MoyenPoids MoyenTaille MoyenneContrat MoyenSalaire Moyen 1e Année
1824.50196 Lbs6 ft02.72400,000$



Attaque à 5 contre 5
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Kyle CliffordJason Dickinson40122
2Brett SutterAnton Blidh30122
3Anton BlidhReid DukeKyle Clifford20122
4Jason Dickinson10122
Défense à 5 contre 5
Ligne #DéfenseDéfense% TempsPHYDFOF
1Sami Niku40122
2Trevor Murphy30122
3Michael Kapla20122
4Sami Niku10122
Attaque en Avantage Numérique
Ligne #Ailier GaucheCentreAilier Droit% TempsPHYDFOF
1Kyle CliffordJason Dickinson60122
2Brett SutterAnton Blidh40122
Défense en Avantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Sami Niku60122
2Trevor Murphy40122
Attaque à 4 en Désavantage Numérique
Ligne #CentreAilier% TempsPHYDFOF
1Kyle Clifford60122
2Jason DickinsonAnton Blidh40122
Défense à 4 en Désavantage Numérique
Ligne #DéfenseDéfense% TempsPHYDFOF
1Sami Niku60122
2Trevor Murphy40122
3 joueurs en Désavantage Numérique
Ligne #Ailier% TempsPHYDFOFDéfenseDéfense% TempsPHYDFOF
1Kyle Clifford60122Sami Niku60122
240122Trevor Murphy40122
Attaque à 4 contre 4
Ligne #CentreAilier% TempsPHYDFOF
1Kyle Clifford60122
2Jason DickinsonAnton Blidh40122
Défense à 4 contre 4
Ligne #DéfenseDéfense% TempsPHYDFOF
1Sami Niku60122
2Trevor Murphy40122
Attaque Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Kyle CliffordJason DickinsonSami Niku
Défense Dernière Minute
Ailier GaucheCentreAilier DroitDéfenseDéfense
Kyle CliffordJason DickinsonSami Niku
Attaquants Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
, Dennis Yan, , Dennis Yan
Défenseurs Supplémentaires
Normal Avantage NumériqueDésavantage Numérique
, Michael Kapla, Trevor MurphyMichael Kapla, Trevor Murphy
Tirs de Pénalité
Kyle Clifford, , Jason Dickinson, Anton Blidh,
Gardien
#1 : Vitek Vanecek, #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
1Admirals11000000321000000000001100000032121.00035800996116686183539151920100.00%60100.00%010019052.63%11724048.75%478952.81%181134164477838
2Checkers11000000707000000000001100000070721.000712190199615668618351674234125.00%20100.00%010019052.63%11724048.75%478952.81%181134164477838
3Crunch11000000211110000002110000000000021.0002350099612168618352652265120.00%110.00%010019052.63%11724048.75%478952.81%181134164477838
4Griffins11000000404000000000001100000040421.0004610019961286861835251317183133.33%60100.00%010019052.63%11724048.75%478952.81%181134164477838
5IceHogs11000000606000000000001100000060621.000611170199615268618351934136116.67%20100.00%010019052.63%11724048.75%478952.81%181134164477838
6Penguins1010000012-1000000000001010000012-100.0001120099611768618353491218400.00%60100.00%010019052.63%11724048.75%478952.81%181134164477838
7Sound Tigers10000010211100000102110000000000021.0002130099612368618352781219100.00%6183.33%010019052.63%11724048.75%478952.81%181134164477838
Total7510001025619210000104225410000021417120.8572539640399612136861835186607013724416.67%29293.10%010019052.63%11724048.75%478952.81%181134164477838
_Since Last GM Reset7510001025619210000104225410000021417120.8572539640399612136861835186607013724416.67%29293.10%010019052.63%11724048.75%478952.81%181134164477838
_Vs Conference31100010541210000104221010000012-140.66755100099616168618358722266310110.00%13284.62%010019052.63%11724048.75%478952.81%181134164477838
_Vs Division311000001037110000002112010000082620.333101424019961966861835772428609111.11%14192.86%010019052.63%11724048.75%478952.81%181134164477838

Total Pour les Joueurs
Matchs JouésPointsSéquenceButsPassesPointsTirs PourTirs ContreTirs BloquésMinutes de PénalitéMises en ÉchecButs en Filet DésertBlanchissage
712W1253964213186607013703
Tous les Matchs
GPWLOTWOTL SOWSOLGFGA
7510010256
Matchs locaux
GPWLOTWOTL SOWSOLGFGA
210001042
Matchs Éxtérieurs
GPWLOTWOTL SOWSOLGFGA
5410000214
Derniers 10 Matchs
WLOTWOTL SOWSOL
510010
Tentatives en Avantage NumériqueButs en Avantage Numérique% en Avantage NumériqueTentatives en Désavantage NumériqueButs Contre en Désavantage Numérique% en Désavantage NumériqueButs Pour en Désavantage Numérique
24416.67%29293.10%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
68618359961
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
10019052.63%11724048.75%478952.81%
Temps Avec la Rondelle
En Zone OffensiveContrôle en Zone OffensiveEn Zone DéfensiveContrôle en Zone DéfensiveEn Zone NeutreContrôle en Zone Neutre
181134164477838


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 - 2019-09-0511Crunch1Bears2WSommaire du Match
5 - 2019-09-0626Sound Tigers1Bears2WXXSommaire du Match
10 - 2019-09-1134Bears4Griffins0WSommaire du Match
11 - 2019-09-1248Bears3Admirals2WSommaire du Match
12 - 2019-09-1362Bears6IceHogs0WSommaire du Match
15 - 2019-09-1666Bears1Penguins2LSommaire du Match
17 - 2019-09-1870Bears7Checkers0WSommaire du Match
18 - 2019-09-1984Bears-Checkers-
24 - 2019-09-25109Bears-Penguins-
25 - 2019-09-26118Penguins-Bears-
26 - 2019-09-27134Phantoms-Bears-
31 - 2019-10-02145Bears-Americans-
32 - 2019-10-03156Americans-Bears-
33 - 2019-10-04168Wolf Pack-Bears-
39 - 2019-10-10195Thunderbirds-Bears-
40 - 2019-10-11209Bears-Crunch-
45 - 2019-10-16223Monsters-Bears-
46 - 2019-10-17238Monsters-Bears-
52 - 2019-10-23268Bears-Phantoms-
53 - 2019-10-24282Bears-Penguins-
57 - 2019-10-28300Penguins-Bears-
60 - 2019-10-31323Thunderbirds-Bears-
61 - 2019-11-01335Devils-Bears-
66 - 2019-11-06352Bears-Phantoms-
67 - 2019-11-07364Wolf Pack-Bears-
68 - 2019-11-08379Penguins-Bears-
74 - 2019-11-14408Bears-Bruins-
75 - 2019-11-15417Bears-Thunderbirds-
78 - 2019-11-18427Bears-Phantoms-
80 - 2019-11-20434Penguins-Bears-
81 - 2019-11-21456Phantoms-Bears-
87 - 2019-11-27479Bears-Penguins-
88 - 2019-11-28488Bruins-Bears-
89 - 2019-11-29501Bruins-Bears-
95 - 2019-12-05536Griffins-Bears-
96 - 2019-12-06549Admirals-Bears-
101 - 2019-12-11566Bears-Thunderbirds-
102 - 2019-12-12580Bears-Phantoms-
103 - 2019-12-13590Phantoms-Bears-
106 - 2019-12-16598Bears-Devils-
109 - 2019-12-19624Bears-Penguins-
110 - 2019-12-20632Bears-Sound Tigers-
113 - 2019-12-23649Bears-Phantoms-
116 - 2019-12-26672Rocket-Bears-
123 - 2020-01-02700Phantoms-Bears-
124 - 2020-01-03711Senators-Bears-
127 - 2020-01-06718Bears-Thunderbirds-
130 - 2020-01-09737Checkers-Bears-
131 - 2020-01-10755Checkers-Bears-
134 - 2020-01-13762Penguins-Bears-
137 - 2020-01-16781Phantoms-Bears-
138 - 2020-01-17801Bears-Phantoms-
143 - 2020-01-22822Bears-Penguins-
144 - 2020-01-23833IceHogs-Bears-
145 - 2020-01-24840Bears-Sound Tigers-
148 - 2020-01-27851Bears-Wolf Pack-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
150 - 2020-01-29857Bears-Comets-
151 - 2020-01-30870Sound Tigers-Bears-
157 - 2020-02-05901Bears-Bruins-
158 - 2020-02-06912Bears-Bruins-
159 - 2020-02-07923Bears-Wolf Pack-
162 - 2020-02-10935Thunderbirds-Bears-
164 - 2020-02-12945Marlies-Bears-
165 - 2020-02-13956Sound Tigers-Bears-
168 - 2020-02-16972Penguins-Bears-
172 - 2020-02-20996Bears-Monsters-
173 - 2020-02-211009Bears-Monsters-
179 - 2020-02-271040Bruins-Bears-
180 - 2020-02-281054Bears-Sound Tigers-
184 - 2020-03-031072Bears-Marlies-
185 - 2020-03-041073Bears-Senators-
186 - 2020-03-051083Bears-Rocket-
189 - 2020-03-081105Phantoms-Bears-
192 - 2020-03-111122Bears-Wolf Pack-
193 - 2020-03-121129Comets-Bears-
194 - 2020-03-131146Wolf Pack-Bears-



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
36 0 - 0.00% 0$0$3000100

Dépenses
Dépenses Annuelles à Ce JourSalaire Total des JoueursSalaire Total Moyen des JoueursSalaire des Coachs
15,079$ 72,000$ 24,940$ 0$
Cap Salarial Par JourCap salarial à ce jourJoueurs Inclut dans la Cap SalarialeJoueurs Exclut dans la Cap Salariale
0$ 6,307$ 0 0

Éstimation
Revenus de la Saison ÉstimésJours Restants de la SaisonDépenses Par JourDépenses de la Saison Éstimées
0$ 177 887$ 156,999$




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
147510001025619210000104225410000021417122539640399612136861835186607013724416.67%29293.10%010019052.63%11724048.75%478952.81%181134164477838
Total Saison Régulière7510001025619210000104225410000021417122539640399612136861835186607013724416.67%29293.10%010019052.63%11724048.75%478952.81%181134164477838