Game Center
	Bears 50-25-7, 107pts	3	FINAL	4	Bruins 38-35-9, 85pts

Team Stats
L2	Streak	L1
30-11-0	Home Record	19-18-4
20-14-7	Away Record	19-17-5
6-4-0	Last 10 Games	6-3-1
3.61	Goals Per Game	3.33
2.94	Goals Against Per Game	3.46
20.00%	Power Play Percentage	22.53%
84.51%	Penalty Kill Percentage	78.21%

Spiders
59-20-3, 121pts

2

FINAL

1

Bears
50-25-7, 107pts

Team Stats
W5	Streak	L2
33-6-2	Home Record	30-11-0
26-14-1	Away Record	20-14-7
9-0-1	Last 10 Games	6-4-0
3.84	Goals Per Game	3.61
2.49	Goals Against Per Game	2.94
26.10%	Power Play Percentage	20.00%
81.28%	Penalty Kill Percentage	84.51%

Team Leaders
	Goals Joe Veleno 44	Assists Philip Tomasino 63

	Points Philip Tomasino 106	Plus/Minus Joe Veleno 33

	Wins Alex Lyon 48	Save Percentage Colton Point 0.947

Team Stats
Goals For 296 3.61 GFG	Shots For 2999 36.57 Avg	Power Play Percentage 20.0% 50 GF	Offensive Zone Start 40.1%
Goals Against 241 2.94 GAA	Shots Against 2880 35.12 Avg	Penalty Kill Percentage 84.5%% 46 GA	Defensive Zone Start 40.7%

Team Info
	General Manager	JF Langlais
	Division	Est
	Conference	Est
	Captain
	Assistant #1
	Assistant #2
Arena Info
	Capacity	3,000
	Attendance	2,178
	Season Tickets	300
Roster Info
	Pro Team	23
	Farm Team	20
	Contract Limit	43 / 50
	Prospects	14

Show or Hide Column

Filter Tips

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

#	Player Name	C	L	R	D	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	SP	Age	Contract	Salary Average

1	Philip Tomasino (R)	X				100.00	67	42	95	78	65	59	78	70	38	73	66	62	25	54	54	0	50	64	0	20	3	894,167$
2	Joe Veleno	X	X			100.00	84	45	89	82	75	65	64	62	72	60	62	69	25	52	52	0	50	63	0	21	3	894,167$
3	Boris Katchouk		X			100.00	88	57	81	72	77	56	71	61	36	57	57	74	25	50	50	0	50	61	0	23	2	750,000$
4	John Hayden		X	X		100.00	84	99	59	78	85	58	68	57	31	54	56	70	63	64	64	0	50	61	0	26	6	800,000$
5	Byron Froese	X		X		100.00	75	74	76	62	74	59	58	64	80	56	65	68	62	59	59	0	50	60	0	30	2	850,000$
6	Michael Dal Colle		X	X		100.00	81	76	92	66	76	60	61	61	50	57	59	70	56	57	57	0	50	60	0	25	2	750,000$
7	Tim Schaller	X	X			100.00	80	76	88	63	76	64	67	56	70	50	54	70	51	62	63	0	50	59	0	30	4	1,000,000$
8	Antoine Morand	X				100.00	72	66	87	67	66	62	66	50	63	47	47	60	45	44	44	0	50	54	0	22	2	778,334$
9	Sam Miletic	X	X			100.00	79	71	96	65	71	54	55	51	64	50	47	63	45	44	44	0	50	54	0	24	3	894,167$
10	Otto Kivenmaki (R)	X				100.00	45	40	81	69	60	55	64	51	55	47	44	45	48	50	50	0	50	50	0	21	3	560,000$
11	Sean Day				X	100.00	82	80	86	66	80	71	76	55	25	51	45	66	43	44	44	0	50	61	0	23	2	750,000$
12	Alex Vlasic (R)				X	100.00	75	44	95	75	77	64	43	55	25	46	49	73	25	45	45	0	50	60	0	20	4	916,667$
13	Mikko Lehtonen				X	100.00	73	44	89	67	71	63	61	58	25	53	44	67	24	46	46	0	50	59	0	27	3	925,000$
14	Dakota Mermis				X	100.00	68	71	61	67	71	58	60	54	25	49	41	61	39	55	55	0	50	57	0	27	2	655,000$
15	Jack Rathbone				X	100.00	67	41	92	71	63	64	60	58	25	39	47	65	25	45	45	0	50	57	0	22	2	925,000$
16	Kaedan Korczak (R)				X	100.00	76	74	82	73	74	56	58	49	25	43	40	61	38	44	44	0	50	57	0	20	2	795,000$
17	Layton Ahac (R)				X	100.00	76	70	91	61	70	47	48	47	25	39	39	61	37	44	44	0	50	53	0	20	2	897,500$
18	Kim Nousiainen (R)				X	100.00	42	35	99	67	60	58	75	44	25	42	38	42	40	58	58	0	50	51	0	20	4	859,167$
Scratches
1	Ethan Phillips (R)			X		100.00	39	30	99	68	54	53	68	41	46	38	36	37	38	54	54	0	50	46	0	20	4	650,000$

	TEAM AVERAGE					100.00	71	60	86	69	71	59	63	55	42	50	49	62	40	51	51	0	50	57

#	Player Name	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	Age	Contract	Salary Average

Show or Hide Column

Filter Tips

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

#	Goalie Name	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	SP	Age	Contract	Salary Average

1	Alex Lyon	100.00	59	44	55	75	61	61	68	69	68	68	95	46	46	0	50	61	0	28	4	800,000$
2	Colton Point (R)	100.00	45	46	58	95	45	45	45	50	45	45	45	44	44	0	50	51	0	23	2	750,000$
Scratches
1	Kaden Fulcher (R)	100.00	44	42	53	80	45	44	45	49	45	45	45	44	44	0	50	49	0	23	2	800,000$
2	Daniil Chechelev (R)	100.00	44	40	50	76	45	44	45	49	45	45	45	44	44	0	50	48	0	20	4	650,000$

	TEAM AVERAGE	100.00	48	43	54	82	49	49	51	54	51	51	58	45	45	0	50	52

#	Goalie Name	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	TA	SP	Age	Contract	Salary Average

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary

Show or Hide Column

Filter Tips

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

#	Player Name	Team Name	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	Philip Tomasino	Bears (Was)	C	82	43	63	106	30	6	0	29	217	298	77	257	14.43%	21	1892	23.07	8	13	21	51	207	0	2	2	8	273	9	3	36.22%	2681	0	0	0	0	1.12	5	8	0	0	0	6	10	6
2	Joe Veleno	Bears (Was)	C/LW	82	44	52	96	33	22	0	128	168	377	97	257	11.67%	33	1906	23.26	7	9	16	59	211	2	1	3	11	281	8	4	61.36%	132	0	0	1	1	1.01	3	8	0	0	0	6	4	6
3	Boris Katchouk	Bears (Was)	LW	82	30	47	77	24	72	0	209	185	319	102	236	9.40%	21	1662	20.27	5	10	15	41	188	0	0	0	4	171	2	1	40.88%	137	0	0	0	0	0.93	2	6	0	0	0	10	6	3
4	John Hayden	Bears (Was)	LW/RW	82	29	45	74	24	106	30	271	134	260	65	166	11.15%	23	1590	19.40	3	12	15	52	208	0	0	0	9	139	7	2	37.23%	274	0	0	1	1	0.93	3	8	0	3	2	6	4	3
5	Michael Dal Colle	Bears (Was)	LW/RW	79	20	47	67	23	48	10	77	119	282	64	210	7.09%	13	1417	17.94	0	16	16	48	186	0	0	0	0	2	2	0	45.76%	118	0	0	0	0	0.95	0	1	1	0	1	1	5	4
6	Byron Froese	Bears (Was)	C/RW	82	31	33	64	21	20	0	81	197	318	86	213	9.75%	16	1524	18.59	9	7	16	60	192	0	0	0	2	41	4	3	60.47%	1910	0	0	0	1	0.84	0	1	0	0	0	6	2	8
7	Alex Vlasic	Bears (Was)	D	82	13	42	55	22	36	0	96	94	159	42	96	8.18%	152	1754	21.39	5	14	19	73	216	0	2	2	6	252	3	1	0.00%	0	0	0	0	0	0.63	0	0	0	0	0	2	2	1
8	Tim Schaller	Bears (Was)	C/LW	82	29	22	51	1	72	10	132	172	283	59	200	10.25%	26	1315	16.04	0	0	0	5	23	0	0	0	0	23	2	0	54.95%	1403	0	0	0	1	0.78	0	0	0	1	1	3	3	2
9	Mikko Lehtonen	Bears (Was)	D	82	11	30	41	9	36	0	110	79	110	28	81	10.00%	122	1609	19.62	9	4	13	52	193	0	0	0	2	222	2	2	0.00%	0	0	0	0	0	0.51	0	0	0	0	0	0	0	4
10	Sean Day	Bears (Was)	D	82	6	33	39	12	96	10	253	48	84	29	71	7.14%	123	1900	23.17	2	7	9	30	217	0	1	1	2	229	0	0	0.00%	0	0	0	0	0	0.41	0	0	1	0	1	0	2	3
11	Sam Miletic	Bears (Was)	C/LW	82	14	22	36	5	25	5	83	86	177	72	123	7.91%	24	1358	16.57	0	1	1	0	1	1	0	1	2	105	3	0	56.29%	151	0	0	0	0	0.53	0	0	1	0	0	1	2	1
12	Antoine Morand	Bears (Was)	C	82	9	23	32	1	20	0	74	88	116	35	110	7.76%	13	1125	13.72	1	0	1	1	2	0	0	0	0	4	1	0	56.25%	288	0	0	0	0	0.57	0	0	0	0	0	0	2	2
13	Jack Rathbone	Bears (Was)	D	82	0	27	27	22	26	0	62	65	81	20	49	0.00%	73	1121	13.68	0	0	0	7	18	0	0	0	0	37	0	0	0.00%	0	0	0	0	0	0.48	0	0	0	0	0	0	2	0
14	Kaedan Korczak	Bears (Was)	D	82	4	13	17	8	64	0	183	41	66	20	37	6.06%	97	1595	19.46	1	3	4	25	191	0	0	0	1	206	2	0	0.00%	0	0	0	0	0	0.21	0	0	0	0	0	0	1	3
15	Layton Ahac	Bears (Was)	D	82	6	11	17	28	36	0	131	26	31	10	20	19.35%	67	938	11.45	0	0	0	0	4	0	0	0	0	59	2	0	100.00%	1	0	0	0	0	0.36	0	0	0	0	0	0	2	1
16	Dakota Mermis	Bears (Was)	D	65	4	8	12	3	20	0	79	16	20	7	13	20.00%	25	520	8.01	0	0	0	0	17	0	0	0	0	14	0	1	50.00%	2	0	0	0	0	0.46	0	0	0	0	0	0	0	0
17	Kim Nousiainen	Bears (Was)	D	82	0	1	1	1	0	0	1	9	4	1	4	0.00%	17	521	6.35	0	0	0	0	0	0	0	0	2	24	0	0	0.00%	0	0	0	0	0	0.04	0	0	0	0	0	0	0	0
18	Otto Kivenmaki	Bears (Was)	C	82	0	0	0	-11	0	0	0	6	9	4	5	0.00%	1	158	1.93	0	0	0	2	31	0	0	0	0	0	0	0	30.30%	66	0	0	0	0	0.00%	0	0	0	0	0	0	0	0
19	Ethan Phillips	Bears (Was)	RW	20	0	0	0	2	0	0	0	4	5	3	2	0.00%	1	319	15.97	0	0	0	0	7	0	0	0	0	0	0	0	22.22%	27	0	0	0	0	0.00%	0	0	0	0	0	0	0	0
Team Total or Average				1476	293	519	812	258	705	65	1999	1754	2999	821	2150	9.77%	868	24231	16.42	50	96	146	506	2122	3	6	9	49	2089	47	17	48.19%	7190	0	0	2	4	0.67	13	32	3	4	5	41	47	47

#	Player Name	Team Name	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

Show or Hide Column

Filter Tips

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

#	Goalie Name	Team Name	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA	ST	BG	S1	S2	S3

1	Alex Lyon	Bears (Was)	82	48	24	6	0.917	2.86	4716	4	3	225	2709	0	4	2	0.517	29	82	0	7	5	8
2	Colton Point	Bears (Was)	7	2	1	1	0.947	2.18	248	0	0	9	169	0	0	0	0.667	3	0	82	0	0	0
Team Total or Average			89	50	25	7	0.919	2.83	4965	4	3	234	2878	0	4	2		32	82	82	7	5	8

#	Goalie Name	Team Name	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA	ST	BG	S1	S2	S3

Show or Hide Column

Filter Tips

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

Player Name	Team Name	POS	Age	Birthday	Rookie	Weight	Height	No Trade	Available For Trade	Force Waivers	Contract	Type	Current Salary	Salary Cap	Salary Cap Remaining	Exclude from Salary Cap	Salary Year 2	Salary Year 3	Salary Year 4	Salary Year 5	Salary Year 6	Link

Alex Lyon (1 Way Contract)	Bears (Was)	G	28	1992-12-08	No	201 Lbs	6 ft1	No	No	Yes	4	Pro & Farm	800,000$	0$	0$	No	800,000$	800,000$	800,000$			Link
Alex Vlasic	Bears (Was)	D	20	2001-06-05	Yes	198 Lbs	6 ft6	No	No	No	4	Pro & Farm	916,667$	0$	0$	No	916,667$	916,667$	916,667$			Link
Antoine Morand	Bears (Was)	C	22	1999-02-18	No	184 Lbs	5 ft11	No	No	No	2	Pro & Farm	778,334$	0$	0$	No	778,334$					Link
Boris Katchouk	Bears (Was)	LW	23	1998-06-17	No	206 Lbs	6 ft2	No	No	No	2	Pro & Farm	750,000$	0$	0$	No	750,000$					Link
Byron Froese (1 Way Contract)	Bears (Was)	C/RW	30	1991-03-12	No	202 Lbs	6 ft1	No	No	Yes	2	Pro & Farm	850,000$	0$	0$	No	850,000$					Link
Colton Point	Bears (Was)	G	23	1998-03-04	Yes	230 Lbs	6 ft5	Yes	No	No	2	Pro & Farm	750,000$	0$	0$	No	750,000$					Link
Dakota Mermis (1 Way Contract)	Bears (Was)	D	27	1994-01-05	No	196 Lbs	6 ft0	No	No	Yes	2	Pro & Farm	655,000$	0$	0$	No	655,000$					Link
Daniil Chechelev	Bears (Was)	G	20	2001-02-23	Yes	187 Lbs	6 ft3	No	No	No	4	Pro & Farm	650,000$	0$	0$	No	650,000$	650,000$	650,000$			Link
Ethan Phillips	Bears (Was)	RW	20	2001-05-07	Yes	154 Lbs	5 ft9	No	No	No	4	Pro & Farm	650,000$	0$	0$	No	650,000$	650,000$	650,000$			Link
Jack Rathbone	Bears (Was)	D	22	1999-05-20	No	177 Lbs	5 ft10	No	No	No	2	Pro & Farm	925,000$	0$	0$	No	925,000$					Link
Joe Veleno	Bears (Was)	C/LW	21	2000-01-13	No	206 Lbs	6 ft1	No	No	No	3	Pro & Farm	894,167$	0$	0$	No	894,167$	894,167$				Link
John Hayden (1 Way Contract)	Bears (Was)	LW/RW	26	1995-02-14	No	223 Lbs	6 ft3	No	No	Yes	6	Pro & Farm	800,000$	0$	0$	No	800,000$	800,000$	800,000$	800,000$	800,000$	Link
Kaden Fulcher	Bears (Was)	G	23	1998-09-23	Yes	201 Lbs	6 ft3	No	No	No	2	Pro & Farm	800,000$	0$	0$	No	800,000$					Link
Kaedan Korczak	Bears (Was)	D	20	2001-01-29	Yes	192 Lbs	6 ft4	No	No	No	2	Pro & Farm	795,000$	0$	0$	No	795,000$					Link
Kim Nousiainen	Bears (Was)	D	20	2000-11-14	Yes	170 Lbs	5 ft9	No	No	No	4	Pro & Farm	859,167$	0$	0$	No	859,167$	859,167$	859,167$			Link
Layton Ahac	Bears (Was)	D	20	2001-02-22	Yes	187 Lbs	6 ft2	No	No	No	2	Pro & Farm	897,500$	0$	0$	No	897,500$					Link
Michael Dal Colle	Bears (Was)	LW/RW	25	1996-06-20	No	200 Lbs	6 ft3	No	No	Yes	2	Pro & Farm	750,000$	0$	0$	No	750,000$					Link
Mikko Lehtonen (1 Way Contract)	Bears (Was)	D	27	1994-01-16	No	196 Lbs	6 ft0	No	No	Yes	3	Pro & Farm	925,000$	25,000$	0$	No	925,000$	925,000$				Link
Otto Kivenmaki	Bears (Was)	C	21	2000-03-24	Yes	172 Lbs	5 ft9	No	No	No	3	Pro & Farm	560,000$	0$	0$	No	560,000$	560,000$				Link
Philip Tomasino	Bears (Was)	C	20	2001-07-28	Yes	179 Lbs	6 ft0	No	No	No	3	Pro & Farm	894,167$	0$	0$	No	894,167$	894,167$				Link
Sam Miletic	Bears (Was)	C/LW	24	1997-05-04	No	196 Lbs	6 ft0	No	No	Yes	3	Pro & Farm	894,167$	0$	0$	No	894,167$	894,167$				Link
Sean Day	Bears (Was)	D	23	1998-01-09	No	218 Lbs	6 ft3	No	No	No	2	Pro & Farm	750,000$	0$	0$	No	750,000$					Link
Tim Schaller (1 Way Contract)	Bears (Was)	C/LW	30	1990-11-16	No	205 Lbs	6 ft2	No	No	Yes	4	Pro & Farm	1,000,000$	100,000$	0$	No	1,000,000$	1,000,000$	1,000,000$			Link

Player Name	Team Name	POS	Age	Birthday	Rookie	Weight	Height	No Trade	Available For Trade	Force Waivers	Contract	Type	Current Salary	Salary Ave Remaining	Salary Cap	Salary Cap Remaining	Exclude from Salary Cap	Salary Year 2	Salary Year 3	Salary Year 4	Salary Year 5	Salary Year 6	Salary Year 7	Salary Year 8	Salary Year 9	Salary Year 10	Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
23	23.26	195 Lbs	6 ft1	2.91	806,268$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Joe Veleno	Philip Tomasino	John Hayden	40	1	2	2
2	Boris Katchouk	Byron Froese	Michael Dal Colle	30	1	2	2
3	Sam Miletic	Tim Schaller	Antoine Morand	20	1	2	2
4	Philip Tomasino	Antoine Morand	Joe Veleno	10	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Sean Day	Alex Vlasic	40	1	2	2
2	Mikko Lehtonen	Kaedan Korczak	30	1	2	2
3	Dakota Mermis	Jack Rathbone	20	1	2	2
4	Layton Ahac	Kim Nousiainen	10	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1	Joe Veleno	Philip Tomasino	John Hayden	60	1	2	2
2	Boris Katchouk	Byron Froese	Michael Dal Colle	40	1	2	2

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Sean Day	Alex Vlasic	60	1	2	2
2	Mikko Lehtonen	Kaedan Korczak	40	1	2	2

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Philip Tomasino	Joe Veleno	60	1	2	2
2	John Hayden	Boris Katchouk	40	1	2	2

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Sean Day	Alex Vlasic	60	1	2	2
2	Mikko Lehtonen	Kaedan Korczak	40	1	2	2

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1	Philip Tomasino	60	1	2	2	Sean Day	Alex Vlasic	60	1	2	2
2	Joe Veleno	40	1	2	2	Mikko Lehtonen	Kaedan Korczak	40	1	2	2

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Philip Tomasino	Joe Veleno	60	1	2	2
2	John Hayden	Boris Katchouk	40	1	2	2

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1	Sean Day	Alex Vlasic	60	1	2	2
2	Mikko Lehtonen	Kaedan Korczak	40	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
Joe Veleno	Philip Tomasino	John Hayden	Sean Day	Alex Vlasic

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
Joe Veleno	Philip Tomasino	John Hayden	Sean Day	Alex Vlasic

Extra Forwards
Normal	PowerPlay	Penalty Kill
Otto Kivenmaki, Tim Schaller, Sam Miletic	Otto Kivenmaki, Tim Schaller	Sam Miletic

Extra Defensemen
Normal	PowerPlay	Penalty Kill
Dakota Mermis, Jack Rathbone, Layton Ahac	Dakota Mermis	Jack Rathbone, Layton Ahac

Penalty Shots
Philip Tomasino, Joe Veleno, John Hayden, Boris Katchouk, Michael Dal Colle

Goalie
#1 : Alex Lyon, #2 : Colton Point

Custom OT Lines Forwards
, , , , , , , , , ,

Custom OT Lines Defensemen
, , , ,

Show or Hide Column

Filter Tips

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

	Overall											Home										Visitor
#	VS Team	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	Admirals	2	2	0	0	0	0	0	0	9	4	5	1	1	0	0	0	0	0	0	4	3	1	1	1	0	0	0	0	0	0	5	1	4	4	1.000	9	15	24	0	0	113	102	76	15	101	984	1021	957	69	83	19	33	60	3	0	0.00%	8	0	100.00%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
2	Baby Hawks	2	0	2	0	0	0	0	0	1	6	-5	1	0	1	0	0	0	0	0	0	2	-2	1	0	1	0	0	0	0	0	1	4	-3	0	0.000	1	2	3	0	0	113	102	76	15	49	984	1021	957	69	62	31	22	31	5	0	0.00%	10	2	80.00%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
3	Bruins	3	2	1	0	0	0	0	0	13	8	5	1	1	0	0	0	0	0	0	4	3	1	2	1	1	0	0	0	0	0	9	5	4	4	0.667	13	24	37	0	0	113	102	76	15	107	984	1021	957	69	104	39	22	75	14	6	42.86%	10	2	80.00%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
4	Cabaret Lady Mary Ann	3	3	0	0	0	0	0	0	20	4	16	2	2	0	0	0	0	0	0	14	2	12	1	1	0	0	0	0	0	0	6	2	4	6	1.000	20	36	56	0	0	113	102	76	15	173	984	1021	957	69	113	33	21	88	4	2	50.00%	8	1	87.50%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
5	Caroline	3	2	0	0	0	1	0	0	10	5	5	1	1	0	0	0	0	0	0	5	2	3	2	1	0	0	0	1	0	0	5	3	2	5	0.833	10	15	25	0	1	113	102	76	15	105	984	1021	957	69	74	17	16	69	9	1	11.11%	8	1	87.50%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
6	Chiefs	2	1	1	0	0	0	0	0	9	10	-1	1	0	1	0	0	0	0	0	4	7	-3	1	1	0	0	0	0	0	0	5	3	2	2	0.500	9	15	24	0	0	113	102	76	15	98	984	1021	957	69	82	20	18	43	6	3	50.00%	9	2	77.78%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
7	Chill	2	2	0	0	0	0	0	0	11	7	4	1	1	0	0	0	0	0	0	5	4	1	1	1	0	0	0	0	0	0	6	3	3	4	1.000	11	19	30	0	0	113	102	76	15	87	984	1021	957	69	78	17	14	42	5	2	40.00%	7	2	71.43%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
8	Comets	2	2	0	0	0	0	0	0	7	4	3	1	1	0	0	0	0	0	0	3	2	1	1	1	0	0	0	0	0	0	4	2	2	4	1.000	7	12	19	0	0	113	102	76	15	62	984	1021	957	69	71	17	21	66	1	0	0.00%	8	1	87.50%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
9	Cougars	3	3	0	0	0	0	0	0	11	6	5	2	2	0	0	0	0	0	0	8	4	4	1	1	0	0	0	0	0	0	3	2	1	6	1.000	11	20	31	0	0	113	102	76	15	97	984	1021	957	69	116	38	30	64	8	2	25.00%	14	1	92.86%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
10	Crunch	3	2	1	0	0	0	0	0	16	9	7	2	1	1	0	0	0	0	0	9	6	3	1	1	0	0	0	0	0	0	7	3	4	4	0.667	16	30	46	0	0	113	102	76	15	146	984	1021	957	69	111	29	31	80	13	4	30.77%	11	1	90.91%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
11	Heat	2	2	0	0	0	0	0	0	9	5	4	1	1	0	0	0	0	0	0	4	3	1	1	1	0	0	0	0	0	0	5	2	3	4	1.000	9	17	26	0	0	113	102	76	15	68	984	1021	957	69	64	24	14	50	5	1	20.00%	6	1	83.33%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
12	Jayhawks	2	0	2	0	0	0	0	0	4	6	-2	1	0	1	0	0	0	0	0	2	3	-1	1	0	1	0	0	0	0	0	2	3	-1	0	0.000	4	7	11	0	0	113	102	76	15	66	984	1021	957	69	65	15	20	35	9	1	11.11%	10	3	70.00%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
13	Las Vegas	2	1	0	0	1	0	0	0	12	7	5	1	1	0	0	0	0	0	0	8	4	4	1	0	0	0	1	0	0	0	4	3	1	4	1.000	12	23	35	0	0	113	102	76	15	81	984	1021	957	69	81	23	10	56	9	4	44.44%	5	0	100.00%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
14	Manchots	3	2	0	0	0	0	1	0	11	8	3	2	1	0	0	0	0	1	0	9	7	2	1	1	0	0	0	0	0	0	2	1	1	6	1.000	11	17	28	0	0	113	102	76	15	89	984	1021	957	69	117	40	32	65	7	0	0.00%	16	3	81.25%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
15	Marlies	3	1	1	0	0	0	0	1	12	12	0	1	1	0	0	0	0	0	0	4	2	2	2	0	1	0	0	0	0	1	8	10	-2	3	0.500	12	20	32	0	0	113	102	76	15	103	984	1021	957	69	118	33	47	74	9	2	22.22%	16	2	87.50%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
16	Minnesota	2	1	1	0	0	0	0	0	6	6	0	1	1	0	0	0	0	0	0	4	1	3	1	0	1	0	0	0	0	0	2	5	-3	2	0.500	6	11	17	0	0	113	102	76	15	77	984	1021	957	69	53	21	21	49	6	2	33.33%	3	0	100.00%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
17	Monarchs	2	1	0	0	0	0	0	1	11	5	6	1	1	0	0	0	0	0	0	8	1	7	1	0	0	0	0	0	0	1	3	4	-1	3	0.750	11	20	31	0	0	113	102	76	15	92	984	1021	957	69	60	14	4	58	5	2	40.00%	2	0	100.00%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
18	Monsters	4	1	2	0	0	0	0	1	10	16	-6	2	0	2	0	0	0	0	0	2	9	-7	2	1	0	0	0	0	0	1	8	7	1	3	0.375	10	18	28	0	0	113	102	76	15	136	984	1021	957	69	155	53	38	106	14	1	7.14%	18	1	94.44%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
19	Monsters	2	0	1	0	0	0	0	1	5	8	-3	1	0	1	0	0	0	0	0	3	5	-2	1	0	0	0	0	0	0	1	2	3	-1	1	0.250	5	10	15	0	0	113	102	76	15	75	984	1021	957	69	93	12	14	39	5	1	20.00%	7	1	85.71%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
20	Oceanics	2	1	0	0	1	0	0	0	7	5	2	1	1	0	0	0	0	0	0	3	2	1	1	0	0	0	1	0	0	0	4	3	1	4	1.000	7	13	20	0	0	113	102	76	15	77	984	1021	957	69	55	24	10	45	10	1	10.00%	5	0	100.00%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
21	Oil Kings	2	1	1	0	0	0	0	0	6	3	3	1	1	0	0	0	0	0	0	4	0	4	1	0	1	0	0	0	0	0	2	3	-1	2	0.500	6	9	15	0	1	113	102	76	15	80	984	1021	957	69	51	22	12	35	9	0	0.00%	6	0	100.00%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
22	Phantoms	4	2	1	0	0	1	0	0	14	11	3	2	1	1	0	0	0	0	0	5	3	2	2	1	0	0	0	1	0	0	9	8	1	5	0.625	14	23	37	0	1	113	102	76	15	128	984	1021	957	69	121	36	31	98	19	4	21.05%	12	3	75.00%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
23	Rocket	3	2	0	0	0	0	1	0	12	9	3	2	1	0	0	0	0	1	0	8	6	2	1	1	0	0	0	0	0	0	4	3	1	6	1.000	12	16	28	0	0	113	102	76	15	95	984	1021	957	69	102	25	25	71	8	2	25.00%	8	3	62.50%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
24	Seattle	2	1	1	0	0	0	0	0	7	9	-2	1	0	1	0	0	0	0	0	2	6	-4	1	1	0	0	0	0	0	0	5	3	2	2	0.500	7	12	19	0	0	113	102	76	15	58	984	1021	957	69	66	19	18	43	4	0	0.00%	9	4	55.56%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
25	Senators	3	2	1	0	0	0	0	0	8	7	1	1	1	0	0	0	0	0	0	5	4	1	2	1	1	0	0	0	0	0	3	3	0	4	0.667	8	16	24	0	0	113	102	76	15	88	984	1021	957	69	96	29	14	52	15	0	0.00%	6	2	66.67%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
26	Sharks	2	0	2	0	0	0	0	0	4	11	-7	1	0	1	0	0	0	0	0	1	5	-4	1	0	1	0	0	0	0	0	3	6	-3	0	0.000	4	8	12	0	0	113	102	76	15	69	984	1021	957	69	90	33	30	45	5	0	0.00%	14	3	78.57%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
27	Sound Tigers	4	1	1	0	0	0	1	1	14	14	0	2	1	0	0	0	0	1	0	9	6	3	2	0	1	0	0	0	0	1	5	8	-3	5	0.625	14	24	38	1	0	113	102	76	15	132	984	1021	957	69	148	44	20	111	9	1	11.11%	9	2	77.78%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
28	Spiders	4	1	3	0	0	0	0	0	6	10	-4	2	1	1	0	0	0	0	0	3	3	0	2	0	2	0	0	0	0	0	3	7	-4	2	0.250	6	11	17	1	0	113	102	76	15	124	984	1021	957	69	131	35	55	102	8	2	25.00%	19	3	84.21%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
29	Stars	2	1	1	0	0	0	0	0	4	7	-3	1	1	0	0	0	0	0	0	4	2	2	1	0	1	0	0	0	0	0	0	5	-5	2	0.500	4	8	12	0	0	113	102	76	15	79	984	1021	957	69	57	27	18	39	6	0	0.00%	9	0	100.00%	3	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
30	Thunder	3	2	1	0	0	0	0	0	12	10	2	1	1	0	0	0	0	0	0	4	2	2	2	1	1	0	0	0	0	0	8	8	0	4	0.667	12	22	34	0	0	113	102	76	15	107	984	1021	957	69	97	34	16	69	8	2	25.00%	8	1	87.50%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
31	Wolf Pack	4	3	1	0	0	0	0	0	15	9	6	2	2	0	0	0	0	0	0	9	3	6	2	1	1	0	0	0	0	0	6	6	0	6	0.750	15	26	41	0	0	113	102	76	15	150	984	1021	957	69	166	45	32	139	12	4	33.33%	16	1	93.75%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
	Total	82	45	25	0	2	2	3	5	296	241	55	41	27	11	0	0	0	3	0	157	112	45	41	18	14	0	2	2	0	5	139	129	10	107	0.652	296	519	815	2	3	113	102	76	15	2999	984	1021	957	69	2880	868	709	1999	250	50	20.00%	297	46	84.51%	3	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
	_Since Last GM Reset	82	45	25	0	2	2	3	5	296	241	55	41	27	11	0	0	0	3	0	157	112	45	41	18	14	0	2	2	0	5	139	129	10	107	0.652	296	519	815	2	3	113	102	76	15	2999	984	1021	957	69	2880	868	709	1999	250	50	20.00%	297	46	84.51%	3	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540
	_Vs Conference	45	23	14	0	1	1	2	4	157	137	20	21	14	5	0	0	0	2	0	75	57	18	24	9	9	0	1	1	0	4	82	80	2	57	0.633	157	276	433	2	1	113	102	76	15	1590	984	1021	957	69	1619	495	398	1141	143	27	18.88%	166	25	84.94%	0	1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%	1991	1365	1889	616	1088	540

	Overall											Home										Visitor
#	VS Team	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

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
82	107	L2	296	519	815	2999	2880	868	709	1999	2	3

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
82	45	25	2	2	3	5	296	241

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	27	11	0	0	3	0	157	112

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
41	18	14	2	2	0	5	139	129

Last 10 Games
W	L	OTW	OTL	SOW	SOL
6	4	0	0	0	0

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
250	50	20.00%	297	46	84.51%	3

Shots 1 Period	Shots 2 Period	Shots 3 Period	Shots 4+ Period	Goals 1 Period	Goals 2 Period	Goals 3 Period	Goals 4+ Period
984	1021	957	69	113	102	76	15

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
1429	2886	49.51%	1390	2928	47.47%	646	1376	46.95%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
1991	1365	1889	616	1088	540

Last Played Games Show or Hide Column

Filter Tips

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

Day	Game	Visitor Team	Score	Home Team	Score	ST	OT	SO	Link

6 - 2022-10-12	5	Bruins	3	Bears	4	BW			BoxScore
7 - 2022-10-13	15	Bears	3	Marlies	4	AL			BoxScore
9 - 2022-10-15	29	Rocket	3	Bears	4	BW	X	X	BoxScore
11 - 2022-10-17	43	Comets	2	Bears	3	BW			BoxScore
14 - 2022-10-20	65	Bears	2	Senators	1	AW			BoxScore
16 - 2022-10-22	80	Monarchs	1	Bears	8	BW			BoxScore
18 - 2022-10-24	93	Bears	1	Spiders	3	AL			BoxScore
21 - 2022-10-27	117	Bears	0	Stars	5	AL			BoxScore
23 - 2022-10-29	136	Bears	6	Chill	3	AW			BoxScore
25 - 2022-10-31	145	Bears	3	Caroline	0	AW			BoxScore
26 - 2022-11-01	150	Las Vegas	4	Bears	8	BW			BoxScore
28 - 2022-11-03	164	Bears	3	Cougars	2	AW			BoxScore
30 - 2022-11-05	179	Jayhawks	3	Bears	2	BL			BoxScore
32 - 2022-11-07	195	Oil Kings	0	Bears	4	BW			BoxScore
34 - 2022-11-09	208	Manchots	3	Bears	4	BW			BoxScore
36 - 2022-11-11	222	Thunder	2	Bears	4	BW			BoxScore
38 - 2022-11-13	242	Bears	6	Thunder	4	AW			BoxScore
40 - 2022-11-15	253	Bears	6	Cabaret Lady Mary Ann	2	AW			BoxScore
42 - 2022-11-17	269	Bears	5	Chiefs	3	AW			BoxScore
44 - 2022-11-19	279	Monsters	5	Bears	3	BL			BoxScore
48 - 2022-11-23	312	Phantoms	0	Bears	4	BW			BoxScore
50 - 2022-11-25	322	Heat	3	Bears	4	BW			BoxScore
51 - 2022-11-26	336	Bears	2	Spiders	4	AL			BoxScore
54 - 2022-11-29	359	Bears	4	Comets	2	AW			BoxScore
56 - 2022-12-01	374	Bears	5	Seattle	3	AW			BoxScore
58 - 2022-12-03	390	Bears	5	Heat	2	AW			BoxScore
60 - 2022-12-05	400	Bears	2	Oil Kings	3	AL			BoxScore
62 - 2022-12-07	413	Bears	5	Phantoms	6	AL	X		BoxScore
64 - 2022-12-09	427	Seattle	6	Bears	2	BL			BoxScore
66 - 2022-12-11	446	Bears	4	Oceanics	3	AW	X		BoxScore
68 - 2022-12-13	463	Bears	1	Baby Hawks	4	AL			BoxScore
70 - 2022-12-15	471	Stars	2	Bears	4	BW			BoxScore
72 - 2022-12-17	491	Marlies	2	Bears	4	BW			BoxScore
74 - 2022-12-19	503	Cougars	3	Bears	6	BW			BoxScore
77 - 2022-12-22	528	Bears	1	Senators	2	AL			BoxScore
78 - 2022-12-23	537	Oceanics	2	Bears	3	BW			BoxScore
82 - 2022-12-27	549	Bears	4	Wolf Pack	3	AW			BoxScore
84 - 2022-12-29	566	Senators	4	Bears	5	BW			BoxScore
86 - 2022-12-31	581	Rocket	3	Bears	4	BW			BoxScore
89 - 2023-01-03	599	Crunch	4	Bears	3	BL			BoxScore
91 - 2023-01-05	617	Bears	6	Monsters	4	AW			BoxScore
92 - 2023-01-06	622	Chill	4	Bears	5	BW			BoxScore
94 - 2023-01-08	639	Monsters	4	Bears	1	BL			BoxScore
97 - 2023-01-11	659	Bears	4	Phantoms	2	AW			BoxScore
100 - 2023-01-14	681	Phantoms	3	Bears	1	BL			BoxScore
102 - 2023-01-16	702	Bears	4	Sound Tigers	5	AL	X	X	BoxScore
103 - 2023-01-17	707	Minnesota	1	Bears	4	BW			BoxScore
105 - 2023-01-19	726	Bears	2	Jayhawks	3	AL			BoxScore
107 - 2023-01-21	745	Bears	4	Las Vegas	3	AW	X		BoxScore
110 - 2023-01-24	764	Bears	2	Monsters	3	AL	X	X	BoxScore
112 - 2023-01-26	773	Manchots	4	Bears	5	BW	X	X	BoxScore
115 - 2023-01-29	800	Bears	5	Marlies	6	AL	X	X	BoxScore
117 - 2023-01-31	804	Bears	2	Monsters	3	AL	X	X	BoxScore
128 - 2023-02-11	838	Bears	6	Bruins	1	AW			BoxScore
129 - 2023-02-12	847	Sharks	5	Bears	1	BL			BoxScore
131 - 2023-02-14	855	Caroline	2	Bears	5	BW			BoxScore
133 - 2023-02-16	870	Cabaret Lady Mary Ann	1	Bears	6	BW			BoxScore
135 - 2023-02-18	889	Bears	2	Caroline	3	AL	X		BoxScore
138 - 2023-02-21	909	Cougars	1	Bears	2	BW			BoxScore
140 - 2023-02-23	920	Admirals	3	Bears	4	BW			BoxScore
142 - 2023-02-25	937	Wolf Pack	1	Bears	3	BW			BoxScore
143 - 2023-02-26	947	Bears	7	Crunch	3	AW			BoxScore
146 - 2023-03-01	973	Bears	5	Admirals	1	AW			BoxScore
Trade Deadline --- Trades can’t be done after this day is simulated!
149 - 2023-03-04	992	Bears	3	Sharks	6	AL			BoxScore
151 - 2023-03-06	1011	Bears	3	Monarchs	4	AL	X	X	BoxScore
154 - 2023-03-09	1029	Spiders	1	Bears	2	BW			BoxScore
156 - 2023-03-11	1048	Bears	1	Sound Tigers	3	AL			BoxScore
159 - 2023-03-14	1067	Bears	2	Wolf Pack	3	AL			BoxScore
160 - 2023-03-15	1076	Crunch	2	Bears	6	BW			BoxScore
162 - 2023-03-17	1093	Chiefs	7	Bears	4	BL			BoxScore
164 - 2023-03-19	1109	Bears	2	Minnesota	5	AL			BoxScore
166 - 2023-03-21	1121	Monsters	5	Bears	1	BL			BoxScore
168 - 2023-03-23	1135	Baby Hawks	2	Bears	0	BL			BoxScore
170 - 2023-03-25	1162	Bears	2	Manchots	1	AW			BoxScore
174 - 2023-03-29	1187	Sound Tigers	3	Bears	5	BW			BoxScore
175 - 2023-03-30	1194	Bears	2	Thunder	4	AL			BoxScore
178 - 2023-04-02	1217	Wolf Pack	2	Bears	6	BW			BoxScore
182 - 2023-04-06	1247	Bears	4	Rocket	3	AW			BoxScore
184 - 2023-04-08	1262	Cabaret Lady Mary Ann	1	Bears	8	BW			BoxScore
186 - 2023-04-10	1278	Sound Tigers	3	Bears	4	BW	X	X	BoxScore
187 - 2023-04-11	1290	Bears	3	Bruins	4	AL			BoxScore
189 - 2023-04-13	1301	Spiders	2	Bears	1	BL			BoxScore

Day	Game	Visitor Team	Score	Home Team	Score	ST	OT	SO	RI	Link

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Capacity	2000	1000
Ticket Price	40	20
Attendance	62,626	26,681
Attendance PCT	76.37%	65.08%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Capacity	Team Popularity
0	2178 - 72.61%	74,114$	3,038,660$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
1,358,002$	1,351,418$	1,351,418$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
7,113$	1,358,002$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	0	7,113$	0$

Bears Stat Leaders (Regular Season)

Show or Hide Column

#	Player Name	GP	G	A	P	+/-	PIM	HIT	HTT	SHT	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PKG	PKA	PKP	PKS	GW	GT	FO%	HT	P/20	PSG	PSS

Bears Goalies Stat Leaders (Regular Season)

Show or Hide Column

#	Goalie Name	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA

Bears Career Team Stats

Show or Hide Column

	Overall											Home											Visitor
Year	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

Bears Stat Leaders (Play-Off)

Show or Hide Column

#	Player Name	GP	G	A	P	+/-	PIM	HIT	HTT	SHT	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PKG	PKA	PKP	PKS	GW	GT	FO%	HT	P/20	PSG	PSS

Bears Goalies Stat Leaders (Play-Off)

Show or Hide Column

#	Goalie Name	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA

	Bears
	GP: 82 \| W: 50 \| L: 25 \| OTL: 7 \| P: 107 GF: 296 \| GA: 241 \| PP%: 20.00% \| PK%: 84.51% GM : JF Langlais \| Morale : 50 \| Team Overall : 56