LAAHS - Milwaukee Admirals

Jets
Formation
Points
Informations Joueurs
Lignes
Statistiques d'Équipe
Calendrier
Finance

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
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
10	text	Any 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	Âge	Contrat	Salaire
1	Frederick Gaudreau	X				100.00	74	65	95	64	65	65	66	64	80	60	62	68	59	58	58	65	35	61		0	27	1	700,000$
2	Evan Weinger			X		100.00	78	70	98	66	70	64	66	60	50	56	60	65	57	44	44	63	49	59	X	0	23	1	700,000$
3	Rem Pitlick (R)	X	X			100.00	74	70	82	65	70	66	68	60	75	53	62	63	59	44	44	62	43	58		0	23	1	874,125$
4	Axel Jonsson-Fjallby		X			100.00	76	68	94	66	68	58	59	55	50	48	58	63	55	44	44	60	40	56		0	23	2	860,000$
5	Reese Johnson (R)			X		100.00	70	71	67	63	71	73	80	48	50	44	46	58	44	44	44	53	39	53		0	22	1	880,834$
6	Luke Gazdic		X			100.00	86	84	90	58	84	44	45	45	50	38	44	69	42	60	60	53	24	52		0	31	1	700,000$
7	Drake Rymsha	X				100.00	70	68	75	53	68	54	57	47	59	44	44	58	42	44	44	51	47	50		0	22	2	733,333$
8	Jordan Gross				X	97.00	75	68	91	66	68	72	77	56	25	46	52	63	49	44	44	60	49	59		0	25	1	925,000$
9	Chris Bigras				X	100.00	75	71	83	63	71	60	63	54	25	49	41	65	39	57	57	55	50	58		0	25	1	700,000$
10	Logan Day				X	100.00	74	75	70	68	75	68	73	50	25	40	46	61	44	44	44	55	41	58		0	26	1	750,000$
11	Hubert Labrie				X	100.00	69	66	77	67	66	70	77	47	25	38	39	59	37	49	49	52	47	56		0	29	1	700,000$
12	Thomas Hickey				X	100.00	72	68	80	55	68	51	52	50	25	40	39	66	37	68	70	51	38	56		0	32	2	2,500,000$
13	Tobias Geisser				X	100.00	84	77	99	52	77	46	48	41	25	28	39	63	37	44	44	49	42	52	X	0	22	2	803,333$
Rayé
1	Troy Terry	X		X		96.00	57	40	95	77	65	68	84	74	36	68	59	54	53	54	54	64	42	62		0	23	3	1,450,000$
2	Brett Ritchie			X		97.00	98	70	78	77	81	56	64	62	36	62	58	55	25	65	66	63	53	61		0	27	1	1,000,000$
3	Tanner MacMaster	X				100.00	70	68	75	65	68	70	73	60	75	58	58	61	55	44	44	61	20	58		0	25	0	0$

	MOYENNE D'ÉQUIPE					99.38	75	69	84	64	71	62	66	55	44	48	50	62	46	50	51	57	41	57

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
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
10	text	Any 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	SP
1	Anton Forsberg	98.00	57	61	77	78	58	59	52	60	55	54	30	48	48	45	45	58	0
2	Mat Robson (R)	100.00	52	58	73	78	51	55	50	57	53	52	30	44	44	53	45	55	0
Rayé

	MOYENNE D'ÉQUIPE	99.00	55	60	75	78	55	57	51	59	54	53	30	46	46	49	45	57

Nom du Coach	PH	DF	OF	PD	EX	LD	PO	CNT	Âge	Contrat	Salaire
Ben Simon	64	66	65	71	64	59	84	USA	41	1	60,000$

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
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
10	text	Any text entered in the filter will match text found within the column

#	Nom du Joueur	Nom de l'Équipe	POS	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
1	Troy Terry	Milwaukee Admirals (WIN)	C/RW	40	29	35	64	-25	6	0	50	95	146	50	106	19.86%	37	1006	25.15	12	7	19	21	65	0	0	0	2	82	2	1	39.23%	961	34	21	0	1	1.27	1	4	0	0	0	7	4	1
2	Brett Ritchie	Milwaukee Admirals (WIN)	RW	40	15	37	52	-28	92	40	68	66	109	38	56	13.76%	38	893	22.34	2	15	17	10	62	0	1	1	3	64	2	1	35.71%	168	32	26	0	0	1.16	1	4	3	1	4	1	1	1
3	Evan Weinger	Milwaukee Admirals (WIN)	RW	41	17	16	33	-19	10	10	51	78	124	22	75	13.71%	33	843	20.57	4	1	5	12	50	0	0	0	2	28	1	1	41.18%	34	25	14	0	0	0.78	1	1	1	0	1	1	2	1
4	Rem Pitlick	Milwaukee Admirals (WIN)	C/LW	41	17	14	31	-23	90	40	78	56	79	35	50	21.52%	22	880	21.48	6	1	7	9	68	1	0	1	3	35	3	0	61.90%	63	23	21	0	1	0.70	0	1	1	3	4	2	2	0
5	Frederick Gaudreau	Milwaukee Admirals (WIN)	C	31	14	17	31	-12	19	15	50	60	100	37	40	14.00%	22	640	20.65	1	6	7	8	43	1	0	1	2	24	0	0	57.11%	380	14	12	0	0	0.97	2	3	1	1	1	0	2	2
6	Axel Jonsson-Fjallby	Milwaukee Admirals (WIN)	LW	41	10	17	27	-17	31	25	56	64	55	13	40	18.18%	25	780	19.04	4	1	5	6	53	1	1	2	2	31	0	0	33.33%	30	18	15	0	0	0.69	0	0	1	3	1	0	0	0
7	Logan Day	Milwaukee Admirals (WIN)	D	41	3	23	26	-5	114	60	76	81	57	19	25	5.26%	42	1040	25.37	1	6	7	4	69	0	1	1	3	71	1	0	0.00%	2	13	41	0	0	0.50	0	0	2	5	5	1	0	1
8	Jordan Gross	Milwaukee Admirals (WIN)	D	41	3	20	23	-18	52	40	60	91	70	20	34	4.29%	65	1099	26.82	1	4	5	4	76	0	2	2	5	87	0	0	50.00%	2	12	40	0	0	0.42	0	0	2	2	4	1	0	1
9	Reese Johnson	Milwaukee Admirals (WIN)	RW	41	11	9	20	-15	36	10	65	59	52	12	29	21.15%	19	665	16.23	1	1	2	1	9	0	0	0	0	1	0	0	27.27%	44	15	14	0	0	0.60	0	0	0	2	0	0	1	1
10	Drake Rymsha	Milwaukee Admirals (WIN)	C	41	10	8	18	-21	18	10	53	76	35	11	20	28.57%	20	675	16.47	0	2	2	0	14	0	1	1	1	18	0	0	45.36%	302	9	10	1	0	0.53	0	0	0	0	2	0	0	0
11	Luke Gazdic	Milwaukee Admirals (WIN)	LW	16	6	5	11	1	0	0	15	22	20	1	12	30.00%	6	238	14.90	0	0	0	0	0	1	0	1	2	9	1	0	40.00%	10	3	7	0	0	0.92	0	0	0	0	0	0	0	0
12	Hubert Labrie	Milwaukee Admirals (WIN)	D	41	0	10	10	-23	18	10	37	45	22	14	13	0.00%	35	716	17.48	0	4	4	1	16	0	0	0	0	15	0	0	16.67%	6	5	24	0	0	0.28	0	0	0	0	2	0	0	0
13	Tobias Geisser	Milwaukee Admirals (WIN)	D	41	1	7	8	-23	5	5	29	44	15	8	6	6.67%	31	637	15.54	0	0	0	0	1	0	1	1	0	35	0	0	14.29%	7	0	16	0	0	0.25	0	0	0	1	0	0	0	0
14	Chris Bigras	Milwaukee Admirals (WIN)	D	41	1	6	7	-27	49	25	49	71	34	11	21	2.94%	43	871	21.24	0	3	3	2	51	0	0	0	0	62	0	0	20.00%	5	6	31	0	0	0.16	0	0	2	1	2	0	0	0
15	Thomas Hickey	Milwaukee Admirals (WIN)	D	39	1	6	7	-28	72	30	32	45	25	13	10	4.00%	55	731	18.75	1	2	3	4	39	0	0	0	1	42	0	1	33.33%	6	6	20	0	0	0.19	0	0	4	1	1	0	1	0
16	Janne Kuokkanen	Jets	C/LW/RW	2	2	1	3	2	0	0	2	4	12	2	8	16.67%	1	48	24.43	1	0	1	2	2	0	0	0	0	5	0	0	52.11%	71	5	0	0	0	1.23	0	0	0	0	0	0	0	1
Stats d'équipe Total ou en Moyenne				578	140	231	371	-281	612	320	771	957	955	306	545	14.66%	494	11768	20.36	34	53	87	84	625	4	7	11	26	617	10	4	43.66%	2091	220	312	1	2	0.63	5	13	17	20	27	13	13	9

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
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
10	text	Any text entered in the filter will match text found within the column

#	Nom du Gardien	Nom de l'Équipe	GP	W	L	OTL	PCT	GAA	MP	PIM	GA	SA	SAR	A	PS %	PSA	ST	BG	S1	S3
1	Anton Forsberg	Milwaukee Admirals (WIN)	37	13	14	6	0.858	4.94	2004	2	165	1165	632	3	0.667	12	36	5	1	1
2	Mat Robson	Milwaukee Admirals (WIN)	14	0	8	0	0.845	5.81	485	2	47	304	176	1	0.000	0	5	35	0	0
Stats d'équipe Total ou en Moyenne			51	13	22	6	0.856	5.11	2490	4	212	1469	808	4	0.667	12	41	40	1	1

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
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
10	text	Any text entered in the filter will match text found within the column

Nom du Joueur	Nom de l'Équipe	POS	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 2	Salaire Année 3	Link
Anton Forsberg	Milwaukee Admirals (WIN)	G	28	1992-11-26	No	192 Lbs	6 ft3	No	No	No	1	Pro & Farm	700,000$	0$	0$	No			Lien
Axel Jonsson-Fjallby	Milwaukee Admirals (WIN)	LW	23	1998-02-10	No	185 Lbs	6 ft0	No	No	No	2	Pro & Farm	860,000$	0$	0$	No	860,000$		Lien
Brett Ritchie	Milwaukee Admirals (WIN)	RW	27	1993-07-01	No	217 Lbs	6 ft3	No	No	No	1	Pro & Farm	1,000,000$	0$	0$	No			Lien
Chris Bigras	Milwaukee Admirals (WIN)	D	25	1995-02-22	No	190 Lbs	6 ft1	No	No	No	1	Pro & Farm	700,000$	0$	0$	No			Lien
Drake Rymsha	Milwaukee Admirals (WIN)	C	22	1998-08-05	No	187 Lbs	6 ft0	No	No	No	2	Pro & Farm	733,333$	0$	0$	No	733,333$		Lien
Evan Weinger	Milwaukee Admirals (WIN)	RW	23	1997-04-18	No	194 Lbs	6 ft0	No	Yes	No	1	Pro & Farm	700,000$	0$	0$	No			Lien
Frederick Gaudreau	Milwaukee Admirals (WIN)	C	27	1993-05-01	No	179 Lbs	6 ft0	No	No	No	1	Pro & Farm	700,000$	0$	0$	No			Lien
Hubert Labrie	Milwaukee Admirals (WIN)	D	29	1991-07-12	No	180 Lbs	5 ft11	Yes	No	No	1	Pro & Farm	700,000$	0$	0$	No			Lien
Jordan Gross	Milwaukee Admirals (WIN)	D	25	1995-05-09	No	190 Lbs	5 ft10	No	No	No	1	Pro & Farm	925,000$	0$	0$	No			Lien
Logan Day	Milwaukee Admirals (WIN)	D	26	1994-09-19	No	209 Lbs	6 ft1	No	No	No	1	Pro & Farm	750,000$	0$	0$	No			Lien
Luke Gazdic	Milwaukee Admirals (WIN)	LW	31	1989-07-24	No	225 Lbs	6 ft4	Yes	No	No	1	Pro & Farm	700,000$	0$	0$	No			Lien
Mat Robson	Milwaukee Admirals (WIN)	G	24	1996-03-26	Yes	190 Lbs	6 ft3	No	No	No	2	Pro & Farm	925,000$	0$	0$	No	925,000$		Lien
Reese Johnson	Milwaukee Admirals (WIN)	RW	22	1998-07-10	Yes	192 Lbs	6 ft1	No	No	No	1	Pro & Farm	880,834$	0$	0$	No			Lien
Rem Pitlick	Milwaukee Admirals (WIN)	C/LW	23	1997-04-01	Yes	196 Lbs	5 ft11	No	No	No	1	Pro & Farm	874,125$	0$	0$	No
Tanner MacMaster	Milwaukee Admirals (WIN)	C	25	1996-01-08	No	185 Lbs	6 ft0	No	No	No	0	Pro & Farm		0$	0$	No			Lien
Thomas Hickey	Milwaukee Admirals (WIN)	D	32	1989-02-08	No	188 Lbs	6 ft0	No	No	No	2	Pro & Farm	2,500,000$	0$	0$	No	2,500,000$		Lien
Tobias Geisser	Milwaukee Admirals (WIN)	D	22	1999-02-13	No	201 Lbs	6 ft4	No	Yes	No	2	Pro & Farm	803,333$	0$	0$	No	803,333$		Lien
Troy Terry	Milwaukee Admirals (WIN)	C/RW	23	1997-09-10	No	174 Lbs	6 ft1	No	No	No	3	Pro & Farm	1,450,000$	0$	0$	No	1,450,000$	1,450,000$	Lien

Joueurs Total	Âge Moyen	Poids Moyen	Taille Moyenne	Contrat Moyen	Salaire Moyen 1e Année
18	25.39	193 Lbs	6 ft1	1.33	883,424$

Attaque à 5 contre 5
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Rem Pitlick			40	1	2	2
2	Axel Jonsson-Fjallby	Frederick Gaudreau	Evan Weinger	30	1	2	2
3	Luke Gazdic	Drake Rymsha	Reese Johnson	20	1	2	2
4			Frederick Gaudreau	10	1	2	2

Défense à 5 contre 5
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jordan Gross	Logan Day	40	1	2	2
2	Chris Bigras	Thomas Hickey	30	1	2	2
3	Hubert Labrie	Tobias Geisser	20	1	2	2
4	Jordan Gross	Logan Day	10	1	2	2

Attaque en Avantage Numérique
Ligne #	Ailier Gauche	Centre	Ailier Droit	% Temps	PHY	DF	OF
1	Rem Pitlick			60	1	2	2
2	Axel Jonsson-Fjallby	Frederick Gaudreau	Evan Weinger	40	1	2	2

Défense en Avantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jordan Gross	Logan Day	60	1	2	2
2	Chris Bigras	Thomas Hickey	40	1	2	2

Attaque à 4 en Désavantage Numérique
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1			60	1	2	2
2	Frederick Gaudreau	Evan Weinger	40	1	2	2

Défense à 4 en Désavantage Numérique
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jordan Gross	Logan Day	60	1	2	2
2	Chris Bigras	Thomas Hickey	40	1	2	2

3 joueurs en Désavantage Numérique
Ligne #	Ailier	% Temps	PHY	DF	OF	Défense	Défense	% Temps	PHY	DF	OF
1		60	1	2	2	Jordan Gross	Logan Day	60	1	2	2
2		40	1	2	2	Chris Bigras	Thomas Hickey	40	1	2	2

Attaque à 4 contre 4
Ligne #	Centre	Ailier	% Temps	PHY	DF	OF
1			60	1	2	2
2	Frederick Gaudreau	Evan Weinger	40	1	2	2

Défense à 4 contre 4
Ligne #	Défense	Défense	% Temps	PHY	DF	OF
1	Jordan Gross	Logan Day	60	1	2	2
2	Chris Bigras	Thomas Hickey	40	1	2	2

Attaque Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Rem Pitlick			Jordan Gross	Logan Day

Défense Dernière Minute
Ailier Gauche	Centre	Ailier Droit	Défense	Défense
Rem Pitlick			Jordan Gross	Logan Day

Attaquants Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Reese Johnson, Luke Gazdic, Drake Rymsha	Reese Johnson, Luke Gazdic	Drake Rymsha

Défenseurs Supplémentaires
Normal	Avantage Numérique	Désavantage Numérique
Hubert Labrie, Tobias Geisser, Chris Bigras	Hubert Labrie	Tobias Geisser, Chris Bigras

Tirs de Pénalité
, , Frederick Gaudreau, Evan Weinger, Rem Pitlick

Gardien
#1 : Anton Forsberg, #2 : Mat Robson

Lignes d'Attaque Perso. en Prol.
, , , , , , , , , ,

Lignes de Défense Perso. en Prol.
, , , ,

Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
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
10	text	Any text entered in the filter will match text found within the column

	Ligue												Domicile											Visiteur
#	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
1	Binghampton Senators	5	0	3	0	0	1	0	1	13	23	-10	2	0	1	0	0	0	0	1	5	9	-4	3	0	2	0	0	1	0	0	8	14	-6	2	0.200	13	23	36	0	0	31	48	63	7	93	240	355	363	25	170	56	42	103	11	3	27.27%	11	5	54.55%	0	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
2	Boisbriand Armada	3	1	1	0	0	0	1	0	16	15	1	2	1	0	0	0	0	1	0	12	9	3	1	0	1	0	0	0	0	0	4	6	-2	4	0.667	16	25	41	0	0	31	48	63	7	110	240	355	363	25	103	39	53	56	10	4	40.00%	9	4	55.56%	0	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
3	Brampton Battalion	1	0	1	0	0	0	0	0	2	7	-5	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	2	7	-5	0	0.000	2	2	4	0	0	31	48	63	7	27	240	355	363	25	42	11	21	11	4	1	25.00%	3	1	66.67%	0	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
4	Drummondville Voltigeurs	1	0	0	0	1	0	0	0	7	6	1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	7	6	1	2	1.000	7	13	20	0	0	31	48	63	7	55	240	355	363	25	30	16	17	31	4	2	50.00%	1	0	100.00%	0	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
5	Hartford Wolf Pack	1	1	0	0	0	0	0	0	5	3	2	1	1	0	0	0	0	0	0	5	3	2	0	0	0	0	0	0	0	0	0	0	0	2	1.000	5	8	13	0	0	31	48	63	7	21	240	355	363	25	33	10	7	26	3	0	0.00%	1	0	100.00%	0	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
6	Lake Erie Monsters	2	1	1	0	0	0	0	0	7	7	0	1	0	1	0	0	0	0	0	2	4	-2	1	1	0	0	0	0	0	0	5	3	2	2	0.500	7	13	20	0	0	31	48	63	7	53	240	355	363	25	91	36	32	37	3	2	66.67%	6	1	83.33%	0	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
7	Las Vegas Wranglers	1	0	1	0	0	0	0	0	4	8	-4	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	4	8	-4	0	0.000	4	7	11	0	0	31	48	63	7	28	240	355	363	25	57	19	43	14	1	0	0.00%	4	3	25.00%	1	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
8	Laval Rockets	1	0	0	0	0	1	0	0	4	5	-1	1	0	0	0	0	1	0	0	4	5	-1	0	0	0	0	0	0	0	0	0	0	0	1	0.500	4	7	11	0	0	31	48	63	7	28	240	355	363	25	46	13	7	16	1	1	100.00%	1	0	100.00%	0	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
9	London Knights	2	1	1	0	0	0	0	0	7	11	-4	1	0	1	0	0	0	0	0	3	8	-5	1	1	0	0	0	0	0	0	4	3	1	2	0.500	7	13	20	0	0	31	48	63	7	29	240	355	363	25	75	20	40	29	4	0	0.00%	5	1	80.00%	0	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
10	Lowell Devils	2	1	1	0	0	0	0	0	7	8	-1	1	1	0	0	0	0	0	0	4	2	2	1	0	1	0	0	0	0	0	3	6	-3	2	0.500	7	12	19	0	0	31	48	63	7	52	240	355	363	25	94	34	10	34	3	1	33.33%	5	1	80.00%	1	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
11	Manchester Monarchs	4	1	2	0	0	1	0	0	14	21	-7	3	1	1	0	0	1	0	0	11	14	-3	1	0	1	0	0	0	0	0	3	7	-4	3	0.375	14	23	37	0	0	31	48	63	7	90	240	355	363	25	129	49	53	79	13	3	23.08%	14	5	64.29%	0	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
12	Manitoba Moose	1	0	1	0	0	0	0	0	4	6	-2	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	4	6	-2	0	0.000	4	5	9	0	0	31	48	63	7	18	240	355	363	25	25	9	23	25	3	0	0.00%	4	1	75.00%	0	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
13	Peoria Riverman	2	0	1	0	1	0	0	0	5	7	-2	2	0	1	0	1	0	0	0	5	7	-2	0	0	0	0	0	0	0	0	0	0	0	2	0.500	5	9	14	0	0	31	48	63	7	43	240	355	363	25	64	25	19	29	4	2	50.00%	7	1	85.71%	0	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
14	Portland Pirates	1	0	1	0	0	0	0	0	2	6	-4	1	0	1	0	0	0	0	0	2	6	-4	0	0	0	0	0	0	0	0	0	0	0	0	0.000	2	4	6	0	0	31	48	63	7	15	240	355	363	25	29	17	11	19	2	2	100.00%	3	2	33.33%	0	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
15	Rochester Americans	1	1	0	0	0	0	0	0	12	9	3	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	12	9	3	2	1.000	12	19	31	0	0	31	48	63	7	36	240	355	363	25	44	21	21	22	3	3	100.00%	3	2	33.33%	2	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
16	Seattle Thunderbirds	1	0	1	0	0	0	0	0	2	9	-7	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	2	9	-7	0	0.000	2	2	4	0	0	31	48	63	7	19	240	355	363	25	53	14	42	17	1	0	0.00%	1	0	100.00%	0	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
17	Sherbrooke Phoenix	2	0	2	0	0	0	0	0	4	15	-11	1	0	1	0	0	0	0	0	2	6	-4	1	0	1	0	0	0	0	0	2	9	-7	0	0.000	4	5	9	0	0	31	48	63	7	41	240	355	363	25	101	24	42	42	2	0	0.00%	6	1	83.33%	0	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
18	Texas Stars	2	0	1	0	0	0	0	1	7	10	-3	0	0	0	0	0	0	0	0	0	0	0	2	0	1	0	0	0	0	1	7	10	-3	1	0.250	7	11	18	0	0	31	48	63	7	39	240	355	363	25	63	25	15	40	6	2	33.33%	5	1	80.00%	0	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
19	Toronto Marlies	6	2	2	0	0	1	1	0	13	17	-4	3	1	2	0	0	0	0	0	5	10	-5	3	1	0	0	0	1	1	0	8	7	1	7	0.583	13	19	32	0	0	31	48	63	7	112	240	355	363	25	111	30	35	120	9	5	55.56%	10	2	80.00%	0	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
20	Victoriaville Tigres	2	0	2	0	0	0	0	0	11	21	-10	1	0	1	0	0	0	0	0	4	9	-5	1	0	1	0	0	0	0	0	7	12	-5	0	0.000	11	14	25	0	0	31	48	63	7	61	240	355	363	25	109	31	85	47	8	3	37.50%	15	12	20.00%	0	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
	Total	41	9	22	0	2	4	2	2	146	214	-68	20	5	10	0	1	2	1	1	64	92	-28	21	4	12	0	1	2	1	1	82	122	-40	32	0.390	146	234	380	0	0	31	48	63	7	970	240	355	363	25	1469	499	618	797	95	34	35.79%	114	43	62.28%	4	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
	_Since Last GM Reset	41	9	22	0	2	4	2	2	146	214	-68	20	5	10	0	1	2	1	1	64	92	-28	21	4	12	0	1	2	1	1	82	122	-40	32	0.390	146	234	380	0	0	31	48	63	7	970	240	355	363	25	1469	499	618	797	95	34	35.79%	114	43	62.28%	4	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460
	_Vs Conference	32	8	17	0	1	3	2	1	117	168	-51	15	4	8	0	0	1	1	1	48	71	-23	17	4	9	0	1	2	1	0	69	97	-28	26	0.406	117	188	305	0	0	31	48	63	7	787	240	355	363	25	1156	386	494	625	75	27	36.00%	92	38	58.70%	4	271	617	43.92%	374	860	43.49%	280	678	41.30%	715	299	882	415	892	460

			Total Pour les Joueurs
Matchs Joués	Points	Séquence	Buts	Passes	Points	Tirs Pour	Tirs Contre	Tirs Bloqués	Minutes de Pénalité	Mises en Échec	Buts en Filet Désert	Blanchissage
41	32	OTW1	146	234	380	970	1469	499	618	797	0	0

Tous les Matchs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	9	22	2	4	2	2	146	214

Matchs locaux
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
20	5	10	1	2	1	1	64	92

Matchs Éxtérieurs
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
21	4	12	1	2	1	1	82	122

Derniers 10 Matchs
W	L	OTW	OTL	SOW	SOL
1	6	1	2	0	0

Tentatives en Avantage Numérique	Buts en Avantage Numérique	% en Avantage Numérique	Tentatives en Désavantage Numérique	Buts Contre en Désavantage Numérique	% en Désavantage Numérique	Buts Pour en Désavantage Numérique
95	34	35.79%	114	43	62.28%	4

Tirs en 1e Période	Tirs en 2e Période	Tirs en 3e Période	Tirs en 4e Période	Buts en 1e Période	Buts en 2e Période	Buts en 3e Période	Buts en 4e Période
240	355	363	25	31	48	63	7

Mises en Jeu
Gagnées en Zone Offensive	Total en Zone Offensive	% Gagnées en Zone Offensive	Gagnées en Zone Défensive	Total en Zone Défensive	% Gagnées en Zone Défensive	Gagnées en Zone Neutre	Total en Zone Neutre	% Gagnées en Zone Neutre
271	617	43.92%	374	860	43.49%	280	678	41.30%

Temps Avec la Rondelle
En Zone Offensive	Contrôle en Zone Offensive	En Zone Défensive	Contrôle en Zone Défensive	En Zone Neutre	Contrôle en Zone Neutre
715	299	882	415	892	460

Derniers Match Joués Afficher ou Masquer des Colonnes

Astuces sur les Filtres (Anglais seulement)

Priority	Type	Description
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
10	text	Any text entered in the filter will match text found within the column

Jour	Match	Équipe Visiteuse	Score	Équipe Locale	Score	ST	OT	SO	Lien
1 - 2020-11-10	8	Milwaukee Admirals	4	Boisbriand Armada	6	L			Sommaire du Match
3 - 2020-11-12	23	Lowell Devils	2	Milwaukee Admirals	4	W			Sommaire du Match
4 - 2020-11-13	40	Boisbriand Armada	4	Milwaukee Admirals	6	W			Sommaire du Match
6 - 2020-11-15	55	Milwaukee Admirals	3	Binghampton Senators	4	L	X		Sommaire du Match
9 - 2020-11-18	74	Toronto Marlies	1	Milwaukee Admirals	4	W			Sommaire du Match
11 - 2020-11-20	95	Manchester Monarchs	6	Milwaukee Admirals	2	L			Sommaire du Match
12 - 2020-11-21	102	Milwaukee Admirals	3	Lowell Devils	6	L			Sommaire du Match
14 - 2020-11-23	127	Peoria Riverman	5	Milwaukee Admirals	2	L			Sommaire du Match
15 - 2020-11-24	135	Milwaukee Admirals	3	Binghampton Senators	5	L			Sommaire du Match
17 - 2020-11-26	158	Sherbrooke Phoenix	6	Milwaukee Admirals	2	L			Sommaire du Match
19 - 2020-11-28	174	Milwaukee Admirals	4	Texas Stars	5	L	X	X	Sommaire du Match
20 - 2020-11-29	178	Milwaukee Admirals	3	Toronto Marlies	4	L	X		Sommaire du Match
22 - 2020-12-01	196	Milwaukee Admirals	3	Toronto Marlies	2	W			Sommaire du Match
23 - 2020-12-02	210	Toronto Marlies	6	Milwaukee Admirals	1	L			Sommaire du Match
25 - 2020-12-04	228	Binghampton Senators	5	Milwaukee Admirals	4	L	X	X	Sommaire du Match
26 - 2020-12-05	236	Milwaukee Admirals	2	Sherbrooke Phoenix	9	L			Sommaire du Match
29 - 2020-12-08	263	Lake Erie Monsters	4	Milwaukee Admirals	2	L			Sommaire du Match
31 - 2020-12-10	278	Milwaukee Admirals	3	Texas Stars	5	L			Sommaire du Match
33 - 2020-12-12	294	Manchester Monarchs	1	Milwaukee Admirals	3	W			Sommaire du Match
34 - 2020-12-13	308	Milwaukee Admirals	2	Seattle Thunderbirds	9	L			Sommaire du Match
36 - 2020-12-15	321	Milwaukee Admirals	5	Lake Erie Monsters	3	W			Sommaire du Match
38 - 2020-12-17	336	Victoriaville Tigres	9	Milwaukee Admirals	4	L			Sommaire du Match
40 - 2020-12-19	355	Boisbriand Armada	5	Milwaukee Admirals	6	W	X	X	Sommaire du Match
42 - 2020-12-21	375	Peoria Riverman	2	Milwaukee Admirals	3	W	X		Sommaire du Match
43 - 2020-12-22	385	Milwaukee Admirals	2	Binghampton Senators	5	L			Sommaire du Match
45 - 2020-12-24	402	Milwaukee Admirals	2	Brampton Battalion	7	L			Sommaire du Match
46 - 2020-12-25	415	Portland Pirates	6	Milwaukee Admirals	2	L			Sommaire du Match
49 - 2020-12-28	435	Milwaukee Admirals	2	Toronto Marlies	1	W	X	X	Sommaire du Match
50 - 2020-12-29	443	Milwaukee Admirals	12	Rochester Americans	9	W			Sommaire du Match
52 - 2020-12-31	459	Hartford Wolf Pack	3	Milwaukee Admirals	5	W			Sommaire du Match
54 - 2021-01-02	476	Binghampton Senators	4	Milwaukee Admirals	1	L			Sommaire du Match
56 - 2021-01-04	499	Milwaukee Admirals	4	Manitoba Moose	6	L			Sommaire du Match
58 - 2021-01-06	511	Milwaukee Admirals	4	Las Vegas Wranglers	8	L			Sommaire du Match
59 - 2021-01-07	520	Toronto Marlies	3	Milwaukee Admirals	0	L			Sommaire du Match
60 - 2021-01-08	538	Laval Rockets	5	Milwaukee Admirals	4	L	X		Sommaire du Match
62 - 2021-01-10	553	Milwaukee Admirals	4	London Knights	3	W			Sommaire du Match
64 - 2021-01-12	571	London Knights	8	Milwaukee Admirals	3	L			Sommaire du Match
66 - 2021-01-14	587	Milwaukee Admirals	3	Manchester Monarchs	7	L			Sommaire du Match
67 - 2021-01-15	600	Manchester Monarchs	7	Milwaukee Admirals	6	L	X		Sommaire du Match
69 - 2021-01-17	622	Milwaukee Admirals	7	Victoriaville Tigres	12	L			Sommaire du Match
70 - 2021-01-18	624	Milwaukee Admirals	7	Drummondville Voltigeurs	6	W	X		Sommaire du Match
72 - 2021-01-20	643	Calgary Hitman	-	Milwaukee Admirals	-
74 - 2021-01-22	663	Milwaukee Admirals	-	Chicago Wolves	-
76 - 2021-01-24	676	Brampton Battalion	-	Milwaukee Admirals	-
78 - 2021-01-26	696	Milwaukee Admirals	-	Lake Erie Monsters	-
79 - 2021-01-27	705	Boisbriand Armada	-	Milwaukee Admirals	-
80 - 2021-01-28	720	Milwaukee Admirals	-	Rochester Americans	-
82 - 2021-01-30	735	Lowell Devils	-	Milwaukee Admirals	-
83 - 2021-01-31	752	Milwaukee Admirals	-	Philadelphia Phantoms	-
85 - 2021-02-02	770	Sherbrooke Phoenix	-	Milwaukee Admirals	-
87 - 2021-02-04	782	Milwaukee Admirals	-	Lowell Devils	-
88 - 2021-02-05	798	Lowell Devils	-	Milwaukee Admirals	-
91 - 2021-02-08	821	Drummondville Voltigeurs	-	Milwaukee Admirals	-
93 - 2021-02-10	839	Milwaukee Admirals	-	Calgary Hitman	-
94 - 2021-02-11	850	London Knights	-	Milwaukee Admirals	-
96 - 2021-02-13	866	Milwaukee Admirals	-	Lowell Devils	-
97 - 2021-02-14	878	Milwaukee Admirals	-	Quebec Rempart	-
99 - 2021-02-16	890	Rimouski Oceanic	-	Milwaukee Admirals	-
102 - 2021-02-19	912	Milwaukee Admirals	-	Worcester Sharks	-
103 - 2021-02-20	922	Brampton Battalion	-	Milwaukee Admirals	-
105 - 2021-02-22	942	Rochester Americans	-	Milwaukee Admirals	-
106 - 2021-02-23	953	Milwaukee Admirals	-	Boisbriand Armada	-
109 - 2021-02-26	973	Las Vegas Wranglers	-	Milwaukee Admirals	-
110 - 2021-02-27	989	Milwaukee Admirals	-	Grand Rapids Griffins	-
112 - 2021-03-01	1004	Calgary Hitman	-	Milwaukee Admirals	-
114 - 2021-03-03	1020	Milwaukee Admirals	-	Sherbrooke Phoenix	-
115 - 2021-03-04	1030	Lowell Devils	-	Milwaukee Admirals	-
116 - 2021-03-05	1045	Milwaukee Admirals	-	Boisbriand Armada	-
118 - 2021-03-07	1064	Bridgeport Sound Tigers	-	Milwaukee Admirals	-
120 - 2021-03-09	1083	Milwaukee Admirals	-	Sherbrooke Phoenix	-
121 - 2021-03-10	1097	Victoriaville Tigres	-	Milwaukee Admirals	-
123 - 2021-03-12	1115	Milwaukee Admirals	-	Calgary Hitman	-
125 - 2021-03-14	1126	Peoria Riverman	-	Milwaukee Admirals	-
128 - 2021-03-17	1151	Toronto Marlies	-	Milwaukee Admirals	-
129 - 2021-03-18	1166	Milwaukee Admirals	-	Drummondville Voltigeurs	-
131 - 2021-03-20	1182	Hershey Bears	-	Milwaukee Admirals	-
134 - 2021-03-23	1205	Milwaukee Admirals	-	Drummondville Voltigeurs	-
Date Limite d'Échange --- Les échange ne peuvent plus se faire après la simulation de cette journée!
136 - 2021-03-25	1215	Chicoutimi Sagueneens	-	Milwaukee Admirals	-
137 - 2021-03-26	1217	Milwaukee Admirals	-	Brampton Battalion	-
139 - 2021-03-28	1241	Binghampton Senators	-	Milwaukee Admirals	-
141 - 2021-03-30	1245	Milwaukee Admirals	-	Brampton Battalion	-
143 - 2021-04-01	1265	Milwaukee Admirals	-	Las Vegas Wranglers	-

Capacité de l'Aréna - Tendance du Prix des Billets - %
	Niveau 1	Niveau 2
Capacité	2000	1000
Prix des Billets	24	10
Assistance	39,669	19,712
Assistance PCT	99.17%	98.56%

Revenus
Matchs à domicile Restants	Assistance Moyenne - %	Revenus Moyen par Match	Revenus Annuels à ce Jour	Capacité	Popularité de l'Équipe
21	2969 - 98.97%	85,614$	1,712,272$	3000	100

Dépenses
Dépenses Annuelles à Ce Jour	Salaire Total des Joueurs	Salaire Total Moyen des Joueurs	Salaire des Coachs
781,747$	1,590,161$	1,542,749$	0$

Cap Salarial Par Jour	Cap salarial à ce jour	Joueurs Inclut dans la Cap Salariale	Joueurs Exclut dans la Cap Salariale
0$	781,747$	0	0

Éstimation
Revenus de la Saison Éstimés	Jours Restants de la Saison	Dépenses Par Jour	Dépenses de la Saison Éstimées
1,797,886$	75	11,380$	853,500$

Afficher ou Masquer des Colonnes

	Ligue											Domicile											Visiteur
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

	Milwaukee Admirals
	GP: 41 \| W: 13 \| L: 22 \| OTL: 6 \| P: 32 GF: 146 \| GA: 214 \| PP%: 35.79% \| PK%: 62.28% DG: Sebastien Doyon \| Morale : 35 \| Moyenne d'Équipe : 57 Prochain matchs #643 vs Calgary Hitman