Association Québécoise de Hockey Simulé

Association Québécoise de Hockey Simulé - Bandits

Home
Main
Pro League
Farm League
League
- Coaches
- Transactions
- Waivers
- Team/GM
- Trade History
- Prospects
- RSS Feed
- Unassigned
  - Players
  - Goalies
- Available For Trade
  - Players
  - Goalies
- Free Agents
  - Players
  - Goalies
History
Team's Link
- Pro Team
  - Conférence Canadienne
  - Conférence Américaine
- Farm Team
  - Conference 1
    - Bandits
    - CoolFm
    - Farmers
    - Goons
    - Grizzlies
    - Hunters
    - Husky
    - Igloos
    - Marlies
    - Marmots
    - Rockets
    - Smirnoff Ice
    - TigersCats
    - Warriors
    - Xpress
  - Conference 2
    - Barracudas
    - Bayou
    - Chiwawa
    - Outlaws
    - Predateurs
    - Raptors
    - Saguenéens
    - Scorpions
    - Snowbirds
    - Spartans
    - Supreme
    - Thugs
    - Twins
    - Vandals
    - Vipers
    - Wolves
Search
Legacy Pages
Help
- Latest STHS Client
- STHS Client Manual

Bandits

GP: 49 | W: 28 | L: 19 | OTL: 2 | P: 58
GF: 207 | GA: 165 | PP%: 33.97% | PK%: 75.33%
GM : Guillaume Blais-Lortie | Morale : 90 | Team Overall : 61
Next Games #756 vs Rockets

Your browser screen resolution is too small for this page. Some information are hidden to keep the page readable.

Ottawa Lumberjacks
Roster
Scoring
Players Info
Lines
Team Stats
Schedule
Finance
Career Team Stat

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
1	Rasmus Asplund (R)	X				99.00	54	45	92	82	58	71	97	59	30	59	58	71	68	84	75	0	90	64	0
2	Oskar Lindblom		X			100.00	70	45	87	82	63	69	95	58	30	55	62	67	68	85	76	0	90	64	0
3	Morgan Frost (R)	X				100.00	64	45	88	84	49	70	66	57	78	56	57	63	68	82	73	0	90	63	0
4	Jay Beagle	X				100.00	80	48	73	74	77	69	40	47	87	42	52	75	50	96	87	0	90	63	0
5	Aliaksei Protas (R)	X				100.00	59	45	95	76	90	59	40	56	75	55	57	60	67	81	70	0	90	62	0
6	Nils Hoglander (R)			X		100.00	61	45	84	83	52	65	72	57	30	51	64	56	67	81	71	0	90	61	0
7	Josh Archibald		X			100.00	77	48	85	79	50	50	40	47	30	44	50	73	50	89	76	0	90	57	0
8	Joey Anderson (R)			X		100.00	50	45	95	75	69	50	40	45	30	40	50	55	50	83	70	0	90	53	0
9	Riley Stillman				X	100.00	99	57	76	81	61	63	63	55	30	56	55	74	67	84	73	0	90	67	0
10	Chris Wideman				X	100.00	61	48	66	88	47	62	77	64	30	69	58	55	69	92	81	0	90	66	0
11	Jordan Spence (R)				X	100.00	76	45	93	86	47	82	40	60	30	61	60	65	68	81	75	0	90	65	0
12	Dylan Samberg (R)				X	100.00	70	45	90	82	62	62	40	55	30	61	50	73	67	83	72	0	90	64	0
13	Josh Mahura (R)				X	100.00	84	45	86	82	54	59	46	54	30	49	60	61	66	83	71	0	90	62	0
14	Troy Stecher				X	100.00	72	48	83	79	49	69	40	50	30	46	54	65	50	88	79	0	90	61	0
Scratches
1	Jake DeBrusk			X		100.00	64	45	91	86	66	75	93	67	30	58	77	70	82	85	77	0	90	68	0
2	Kailer Yamamoto		X			100.00	78	45	82	89	40	84	98	66	30	61	70	69	71	83	77	0	90	67	0
3	Jack Studnicka (R)	X				100.00	65	45	90	80	63	63	40	51	79	52	50	56	65	83	73	0	90	59	0
4	Jeffrey Viel (R)		X			100.00	99	69	25	77	72	50	41	51	30	45	57	67	65	85	72	0	90	59	0
5	Ryan MacInnis (R)	X				100.00	50	45	95	76	74	50	40	45	30	40	50	60	50	86	73	0	90	54	0
6	Samuel Fagemo (R)		X			100.00	50	45	95	76	66	69	40	45	30	40	50	57	50	82	73	0	90	54	0
7	Robert Bortuzzo				X	100.00	88	51	82	74	77	57	88	49	30	46	52	76	50	93	81	0	90	68	0
8	Kevin Connauton				X	100.00	89	45	92	76	68	55	47	49	30	44	53	70	50	92	80	0	90	64	0
9	Matt Irwin				X	100.00	87	45	87	80	65	53	40	54	30	52	56	64	66	94	82	0	90	64	0
10	Nikita Okhotyuk (R)				X	100.00	64	45	92	77	61	63	40	50	30	44	56	62	65	81	70	0	90	60	0
11	Declan Chisholm (R)				X	100.00	50	45	95	78	56	54	40	45	30	40	50	58	50	82	70	0	90	57	0
12	Brinson Pasichnuk (R)				X	100.00	50	45	95	75	66	50	40	45	30	40	50	55	50	83	71	0	90	56	0
13	Joey Keane (R)				X	100.00	50	45	92	77	55	57	40	45	30	40	50	55	50	82	70	0	90	56	0
14	Ben Gleason (R)				X	100.00	50	45	95	78	53	50	40	45	30	40	50	55	50	80	69	0	90	55	0

	TEAM AVERAGE					99.96	68	47	86	80	61	62	54	53	37	50	56	64	60	85	75	0	90	61

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
Scratches
1	Harri Sateri	100.00	70	50	61	88	70	70	70	70	70	70	70	92	80	0	90	63	0

	TEAM AVERAGE	100.00	70	50	61	88	70	70	70	70	70	70	70	92	80	0	90	63

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Paul Maurice	87	80	84	84	90	75	60	CAN	57	5	3,500,000$

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	HT	P/20	PSG	PSS	FW	FL	FT	S1	S2	S3
1	Rasmus Asplund	Bandits (OTT)	C	49	50	34	84	14	4	0	35	52	349	103	197	14.33%	18	951	19.42	15	9	24	69	146	0	1	1	2	28	8	1	42.77%	809	82	25	4	1.77	2	2	0	0	0	17	4	4
2	Nils Hoglander	Bandits (OTT)	RW	49	28	20	48	2	16	10	31	20	269	71	159	10.41%	11	747	15.26	13	3	16	47	78	0	0	0	0	0	3	0	46.81%	47	73	11	1	1.28	0	0	0	1	1	6	2	2
3	Oskar Lindblom	Bandits (OTT)	LW	28	19	12	31	-6	13	5	27	39	102	29	71	18.63%	14	473	16.89	6	8	14	12	39	1	0	1	3	18	1	1	37.50%	40	40	12	0	1.31	0	1	0	0	1	0	5	3
4	Chris Wideman	Bandits (OTT)	D	49	3	21	24	20	42	0	35	31	136	52	56	2.21%	29	821	16.77	2	5	7	16	52	0	0	0	5	50	1	0	0.00%	0	21	27	0	0.58	0	1	0	0	0	0	2	4
5	Morgan Frost	Bandits (OTT)	C	28	9	13	22	3	7	5	15	31	80	25	52	11.25%	8	350	12.51	1	2	3	3	8	0	0	0	0	0	1	0	62.33%	369	30	13	0	1.26	0	0	0	0	1	2	1	0
6	Jake DeBrusk	Bandits (OTT)	RW	14	7	15	22	2	9	5	21	24	104	26	56	6.73%	11	325	23.26	1	4	5	14	33	0	0	0	1	33	0	1	44.32%	88	29	11	0	1.35	0	0	1	0	0	0	3	0
7	Riley Stillman	Bandits (OTT)	D	28	4	11	15	-6	24	10	42	30	58	18	26	6.90%	31	630	22.52	4	5	9	9	82	0	0	0	3	65	0	0	0.00%	1	11	31	0	0.48	0	0	1	0	1	0	1	2
8	Aliaksei Protas	Bandits (OTT)	C	28	9	5	14	7	5	5	10	17	65	21	48	13.85%	4	324	11.58	0	0	0	0	1	0	0	0	0	0	1	0	54.74%	95	15	10	1	0.86	0	0	0	0	1	1	2	3
9	Jordan Spence	Bandits (OTT)	D	41	6	8	14	16	24	10	27	31	84	27	35	7.14%	30	695	16.95	2	1	3	10	46	0	0	0	4	53	1	0	0.00%	0	13	43	0	0.40	1	1	1	1	0	2	0	2
10	Kailer Yamamoto	Bandits (OTT)	LW	7	7	5	12	-2	4	0	10	6	31	9	17	22.58%	2	164	23.53	1	1	2	3	15	1	1	2	2	13	0	0	41.67%	12	11	5	0	1.46	0	0	0	0	0	2	1	0
11	Jay Beagle	Bandits (OTT)	C	20	1	7	8	-4	21	5	37	33	16	9	9	6.25%	6	313	15.70	0	2	2	1	25	0	0	0	0	4	1	0	60.48%	415	1	11	0	0.51	0	0	1	0	0	0	1	0
12	Dylan Samberg	Bandits (OTT)	D	49	0	7	7	3	12	10	11	14	17	9	5	0.00%	18	395	8.07	0	0	0	0	2	0	0	0	0	1	0	0	0.00%	0	5	34	0	0.35	0	0	1	0	1	0	2	1
13	Josh Archibald	Bandits (OTT)	LW	16	3	4	7	-3	7	5	19	16	23	4	7	13.04%	5	183	11.47	0	0	0	0	0	0	0	0	0	0	0	0	33.33%	12	4	3	0	0.76	0	0	1	0	0	0	0	1
14	Kevin Connauton	Bandits (OTT)	D	33	1	5	6	-1	2	0	19	11	11	5	8	9.09%	17	413	12.54	0	0	0	0	0	1	0	1	1	10	0	0	0.00%	0	2	14	0	0.29	0	0	0	0	0	0	2	2
15	Matt Irwin	Bandits (OTT)	D	33	1	4	5	-3	28	10	24	12	22	8	7	4.55%	22	403	12.22	0	0	0	0	1	0	0	0	0	0	0	0	0.00%	0	4	21	0	0.25	0	0	1	0	1	0	1	1
16	Robert Bortuzzo	Bandits (OTT)	D	9	1	2	3	-5	15	5	14	9	13	5	5	7.69%	13	211	23.45	1	0	1	4	24	0	1	1	0	17	0	0	0.00%	1	4	14	0	0.28	0	0	0	0	1	0	0	0
17	Joey Anderson	Bandits (OTT)	RW	16	1	2	3	-3	0	0	4	13	14	2	9	7.14%	3	180	11.26	0	0	0	0	0	0	0	0	0	0	0	0	44.44%	9	4	5	0	0.33	0	0	0	0	0	0	0	0
18	Jeffrey Viel	Bandits (OTT)	LW	12	2	0	2	4	27	15	9	13	8	2	4	25.00%	5	148	12.39	0	0	0	0	0	0	0	0	0	3	0	0	70.00%	10	6	5	0	0.27	0	0	1	2	0	0	0	1
19	Troy Stecher	Bandits (OTT)	D	16	0	2	2	5	4	0	4	2	1	0	1	0.00%	2	107	6.71	0	0	0	0	0	0	0	0	0	4	0	0	0.00%	0	0	5	0	0.37	0	0	0	0	0	0	0	0
20	Josh Mahura	Bandits (OTT)	D	49	0	2	2	1	6	0	24	10	11	3	4	0.00%	7	381	7.79	0	0	0	0	0	0	0	0	0	15	0	0	0.00%	0	4	14	0	0.10	0	0	0	0	0	0	0	1
Team Total or Average				574	152	179	331	44	270	100	418	414	1414	428	776	10.75%	256	8223	14.33	46	40	86	188	560	3	3	6	21	323	17	3	51.10%	1908	359	314	6	0.81	3	5	8	4	8	30	27	27

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	Harri Sateri	Bandits (OTT)	6	2	4	0	0.882	4.17	360	0	0	25	212	105	1	0	0.000	0	6	0	0	0	0
Team Total or Average			6	2	4	0	0.882	4.17	360	0	0	25	212	105	1	0	0.000	0	6	0	0	0	0

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	Status	Type	Current Salary	Salary Cap	Salary Cap Remaining	Exclude from Salary Cap
Aliaksei Protas	Bandits (OTT)	C	21	2000-01-06	Yes	225 Lbs	6 ft6	No	No	No	4	RFA	Pro & Farm	950,000$	0$	0$	No
Ben Gleason	Bandits (OTT)	D	23	1997-10-12	Yes	180 Lbs	6 ft1	No	No	No	1	RFA	Pro & Farm	800,000$	0$	0$	No
Brinson Pasichnuk	Bandits (OTT)	D	24	1996-11-24	Yes	205 Lbs	6 ft0	No	No	No	5	RFA	Pro & Farm	750,000$	0$	0$	No
Chris Wideman	Bandits (OTT)	D	31	1989-10-12	No	180 Lbs	5 ft10	No	No	No	5	UFA	Pro & Farm	950,000$	0$	0$	No
Declan Chisholm	Bandits (OTT)	D	22	1999-01-12	Yes	185 Lbs	6 ft1	No	No	No	4	RFA	Pro & Farm	800,000$	0$	0$	No
Dylan Samberg	Bandits (OTT)	D	23	1998-01-24	Yes	190 Lbs	6 ft3	No	No	No	4	RFA	Pro & Farm	950,000$	0$	0$	No
Harri Sateri	Bandits (OTT)	G	32	1988-10-12	No	205 Lbs	6 ft1	No	No	No	1	UFA	Pro & Farm	700,000$	0$	0$	No
Jack Studnicka	Bandits (OTT)	C	23	1998-02-18	Yes	186 Lbs	6 ft2	No	No	No	1	RFA	Pro & Farm	1,000,000$	0$	0$	No
Jake DeBrusk	Bandits (OTT)	RW	25	1995-10-17	No	200 Lbs	6 ft0	No	No	No	3	RFA	Pro & Farm	2,750,000$	0$	0$	No
Jay Beagle	Bandits (OTT)	C	36	1984-10-16	No	210 Lbs	6 ft3	No	No	No	2	UFA	Pro & Farm	1,250,000$	0$	0$	No
Jeffrey Viel	Bandits (OTT)	LW	25	1996-01-28	Yes	205 Lbs	6 ft2	No	No	No	5	RFA	Pro & Farm	800,000$	0$	0$	No
Joey Anderson	Bandits (OTT)	RW	24	1997-06-19	Yes	207 Lbs	6 ft0	No	No	No	1	RFA	Pro & Farm	800,000$	0$	0$	No
Joey Keane	Bandits (OTT)	D	23	1998-07-02	Yes	187 Lbs	6 ft0	No	No	No	3	RFA	Pro & Farm	800,000$	0$	0$	No
Jordan Spence	Bandits (OTT)	D	21	2000-02-24	Yes	180 Lbs	5 ft10	No	No	No	4	RFA	Pro & Farm	1,000,000$	0$	0$	No
Josh Archibald	Bandits (OTT)	LW	29	1991-10-06	No	176 Lbs	5 ft10	No	No	No	5	UFA	Pro & Farm	1,000,000$	0$	0$	No
Josh Mahura	Bandits (OTT)	D	24	1997-05-05	Yes	185 Lbs	6 ft0	No	No	No	1	RFA	Pro & Farm	800,000$	0$	0$	No
Kailer Yamamoto	Bandits (OTT)	LW	24	1997-09-29	No	153 Lbs	5 ft8	No	No	No	1	RFA	Pro & Farm	1,400,000$	0$	0$	No
Kevin Connauton	Bandits (OTT)	D	32	1989-02-23	No	205 Lbs	6 ft2	No	No	No	2	UFA	Pro & Farm	800,000$	0$	0$	No
Matt Irwin	Bandits (OTT)	D	34	1986-11-29	No	200 Lbs	6 ft2	No	No	No	3	UFA	Pro & Farm	800,000$	0$	0$	No
Morgan Frost	Bandits (OTT)	C	23	1998-05-14	Yes	170 Lbs	5 ft11	No	No	No	1	RFA	Pro & Farm	1,250,000$	0$	0$	No
Nikita Okhotyuk	Bandits (OTT)	D	21	1999-12-04	Yes	195 Lbs	6 ft1	No	No	No	4	RFA	Pro & Farm	800,000$	0$	0$	No
Nils Hoglander	Bandits (OTT)	RW	21	1999-12-20	Yes	185 Lbs	5 ft9	No	No	No	5	RFA	Pro & Farm	1,000,000$	0$	0$	No
Oskar Lindblom	Bandits (OTT)	LW	26	1995-08-15	No	191 Lbs	6 ft1	No	No	No	2	RFA	Pro & Farm	1,500,000$	0$	0$	No
Rasmus Asplund	Bandits (OTT)	C	24	1996-12-03	Yes	189 Lbs	5 ft11	No	No	No	3	RFA	Pro & Farm	1,250,000$	0$	0$	No
Riley Stillman	Bandits (OTT)	D	24	1997-03-09	No	196 Lbs	6 ft1	No	No	No	1	RFA	Pro & Farm	950,000$	0$	0$	No
Robert Bortuzzo	Bandits (OTT)	D	33	1988-03-18	No	216 Lbs	6 ft4	No	No	No	1	UFA	Pro & Farm	1,150,000$	0$	0$	No
Ryan MacInnis	Bandits (OTT)	C	26	1995-02-14	Yes	201 Lbs	6 ft4	No	No	No	3	RFA	Pro & Farm	800,000$	0$	0$	No
Samuel Fagemo	Bandits (OTT)	LW	22	1999-03-14	Yes	200 Lbs	6 ft0	No	No	No	4	RFA	Pro & Farm	800,000$	0$	0$	No
Troy Stecher	Bandits (OTT)	D	28	1993-04-07	No	184 Lbs	5 ft10	No	No	No	3	UFA	Pro & Farm	800,000$	0$	0$	No

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
29	25.66	193 Lbs	6 ft1	2.83	1,013,793$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1		Rasmus Asplund		35	1	2	2
2	Oskar Lindblom	Jay Beagle	Nils Hoglander	30	1	2	2
3	Josh Archibald	Morgan Frost	Joey Anderson	25	1	2	2
4		Aliaksei Protas		10	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1		Riley Stillman	35	1	2	2
2	Chris Wideman	Jordan Spence	30	1	2	2
3	Dylan Samberg	Josh Mahura	25	1	2	2
4	Troy Stecher		10	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1		Rasmus Asplund		55	1	2	2
2	Oskar Lindblom	Jay Beagle	Nils Hoglander	45	1	2	2

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1		Riley Stillman	55	1	2	2
2	Chris Wideman	Jordan Spence	45	1	2	2

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1			55	1	2	2
2	Oskar Lindblom	Rasmus Asplund	45	1	2	2

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1		Riley Stillman	55	1	2	2
2	Chris Wideman	Jordan Spence	45	1	2	2

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1		55	1	2	2		Riley Stillman	55	1	2	2
2		45	1	2	2	Chris Wideman	Jordan Spence	45	1	2	2

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1			55	1	2	2
2	Oskar Lindblom	Rasmus Asplund	45	1	2	2

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1		Riley Stillman	55	1	2	2
2	Chris Wideman	Jordan Spence	45	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
	Rasmus Asplund			Riley Stillman

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
	Rasmus Asplund			Riley Stillman

Extra Forwards
Normal	PowerPlay	Penalty Kill
Morgan Frost, Aliaksei Protas, Josh Archibald	Morgan Frost, Aliaksei Protas	Josh Archibald

Extra Defensemen
Normal	PowerPlay	Penalty Kill
Dylan Samberg, Josh Mahura, Troy Stecher	Dylan Samberg	Josh Mahura, Troy Stecher

Penalty Shots
, , Oskar Lindblom, Rasmus Asplund, Jay Beagle

Goalie
#1 : , #2 :

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	Barracudas	1	0	1	0	0	0	0	0	4	5	-1	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	4	5	-1	0	0.000	4	7	11	0	0	39	85	79	5	28	499	654	672	14	17	5	24	11	1	1	100.00%	7	2	71.43%	1	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
2	Bayou	1	1	0	0	0	0	0	0	7	2	5	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	7	2	5	2	1.000	7	13	20	0	0	39	85	79	5	43	499	654	672	14	29	9	12	14	6	1	16.67%	1	0	100.00%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
3	Chiwawa	2	1	1	0	0	0	0	0	6	5	1	1	1	0	0	0	0	0	0	5	2	3	1	0	1	0	0	0	0	0	1	3	-2	2	0.500	6	9	15	0	0	39	85	79	5	62	499	654	672	14	67	23	12	32	4	2	50.00%	6	1	83.33%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
4	CoolFm	2	0	2	0	0	0	0	0	6	11	-5	2	0	2	0	0	0	0	0	6	11	-5	0	0	0	0	0	0	0	0	0	0	0	0	0.000	6	10	16	0	0	39	85	79	5	84	499	654	672	14	58	19	18	25	4	1	25.00%	9	5	44.44%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
5	Farmers	4	2	1	0	1	0	0	0	18	11	7	3	2	1	0	0	0	0	0	15	9	6	1	0	0	0	1	0	0	0	3	2	1	6	0.750	18	27	45	0	0	39	85	79	5	157	499	654	672	14	118	40	62	66	21	10	47.62%	11	1	90.91%	1	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
6	Goons	3	1	1	0	1	0	0	0	13	12	1	2	1	0	0	1	0	0	0	11	7	4	1	0	1	0	0	0	0	0	2	5	-3	4	0.667	13	22	35	0	0	39	85	79	5	110	499	654	672	14	94	27	12	56	6	2	33.33%	6	2	66.67%	1	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
7	Hunters	4	3	1	0	0	0	0	0	19	11	8	2	1	1	0	0	0	0	0	6	7	-1	2	2	0	0	0	0	0	0	13	4	9	6	0.750	19	31	50	0	0	39	85	79	5	155	499	654	672	14	100	31	29	56	12	4	33.33%	12	1	91.67%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
8	Husky	3	0	2	0	0	0	1	0	9	12	-3	2	0	1	0	0	0	1	0	5	6	-1	1	0	1	0	0	0	0	0	4	6	-2	2	0.333	9	14	23	0	0	39	85	79	5	131	499	654	672	14	77	22	25	45	11	4	36.36%	10	2	80.00%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
9	Igloos	1	1	0	0	0	0	0	0	4	3	1	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	4	3	1	2	1.000	4	8	12	0	0	39	85	79	5	37	499	654	672	14	23	11	8	18	1	0	0.00%	4	0	100.00%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
10	Marlies	2	1	1	0	0	0	0	0	7	4	3	1	1	0	0	0	0	0	0	6	1	5	1	0	1	0	0	0	0	0	1	3	-2	2	0.500	7	12	19	0	0	39	85	79	5	75	499	654	672	14	54	18	10	30	3	1	33.33%	5	1	80.00%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
11	Marmots	1	1	0	0	0	0	0	0	6	5	1	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	6	5	1	2	1.000	6	11	17	0	0	39	85	79	5	44	499	654	672	14	19	6	6	15	4	1	25.00%	3	1	66.67%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
12	Outlaws	2	2	0	0	0	0	0	0	11	3	8	1	1	0	0	0	0	0	0	7	1	6	1	1	0	0	0	0	0	0	4	2	2	4	1.000	11	19	30	0	0	39	85	79	5	58	499	654	672	14	48	18	17	28	10	5	50.00%	6	2	66.67%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
13	Raptors	1	1	0	0	0	0	0	0	5	3	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	5	3	2	2	1.000	5	10	15	0	0	39	85	79	5	30	499	654	672	14	29	1	13	15	6	0	0.00%	4	1	75.00%	1	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
14	Saguenéens	1	1	0	0	0	0	0	0	4	2	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	4	2	2	2	1.000	4	8	12	0	0	39	85	79	5	43	499	654	672	14	31	12	2	18	4	1	25.00%	1	1	0.00%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
15	Scorpions	3	0	2	0	0	1	0	0	10	14	-4	1	0	1	0	0	0	0	0	1	2	-1	2	0	1	0	0	1	0	0	9	12	-3	1	0.167	10	17	27	0	0	39	85	79	5	109	499	654	672	14	98	24	26	60	8	3	37.50%	13	6	53.85%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
16	Snowbirds	1	0	0	0	0	1	0	0	4	5	-1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	4	5	-1	1	0.500	4	8	12	0	0	39	85	79	5	32	499	654	672	14	22	6	9	13	4	1	25.00%	2	0	100.00%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
17	Spartans	2	1	1	0	0	0	0	0	10	8	2	1	1	0	0	0	0	0	0	7	2	5	1	0	1	0	0	0	0	0	3	6	-3	2	0.500	10	17	27	0	0	39	85	79	5	69	499	654	672	14	53	16	54	32	9	3	33.33%	7	4	42.86%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
18	Supreme	2	2	0	0	0	0	0	0	10	2	8	1	1	0	0	0	0	0	0	4	2	2	1	1	0	0	0	0	0	0	6	0	6	4	1.000	10	18	28	0	1	39	85	79	5	95	499	654	672	14	40	19	19	27	5	3	60.00%	7	1	85.71%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
19	TigersCats	1	0	1	0	0	0	0	0	1	3	-2	1	0	1	0	0	0	0	0	1	3	-2	0	0	0	0	0	0	0	0	0	0	0	0	0.000	1	2	3	0	0	39	85	79	5	23	499	654	672	14	23	5	8	11	2	0	0.00%	4	1	75.00%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
	Total	49	24	19	0	2	2	2	0	207	165	42	24	12	9	0	1	0	2	0	102	73	29	25	12	10	0	1	2	0	0	105	92	13	58	0.592	207	357	564	0	2	39	85	79	5	1832	499	654	672	14	1333	402	500	799	156	53	33.97%	150	37	75.33%	5	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
21	Twins	1	1	0	0	0	0	0	0	6	3	3	1	1	0	0	0	0	0	0	6	3	3	0	0	0	0	0	0	0	0	0	0	0	2	1.000	6	10	16	0	0	39	85	79	5	37	499	654	672	14	18	3	0	18	4	2	50.00%	0	0	0.00%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
22	Vandals	2	0	1	0	0	0	1	0	8	9	-1	1	0	0	0	0	0	1	0	5	4	1	1	0	1	0	0	0	0	0	3	5	-2	2	0.500	8	12	20	0	0	39	85	79	5	66	499	654	672	14	70	21	6	38	4	1	25.00%	3	0	100.00%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
23	Vipers	1	1	0	0	0	0	0	0	5	3	2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	5	3	2	2	1.000	5	10	15	0	0	39	85	79	5	30	499	654	672	14	30	7	17	16	0	0	0.00%	6	1	83.33%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
24	Warriors	2	1	1	0	0	0	0	0	8	8	0	1	0	1	0	0	0	0	0	2	5	-3	1	1	0	0	0	0	0	0	6	3	3	2	0.500	8	15	23	0	0	39	85	79	5	75	499	654	672	14	60	18	8	41	2	0	0.00%	4	0	100.00%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
25	Wolves	2	1	1	0	0	0	0	0	4	3	1	1	0	1	0	0	0	0	0	1	3	-2	1	1	0	0	0	0	0	0	3	0	3	2	0.500	4	8	12	0	1	39	85	79	5	57	499	654	672	14	32	12	19	48	9	2	22.22%	7	1	85.71%	0	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
26	Xpress	4	2	2	0	0	0	0	0	22	18	4	2	2	0	0	0	0	0	0	14	5	9	2	0	2	0	0	0	0	0	8	13	-5	4	0.500	22	39	61	0	0	39	85	79	5	182	499	654	672	14	123	29	84	66	16	5	31.25%	12	3	75.00%	1	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
	_Since Last GM Reset	49	24	19	0	2	2	2	0	207	165	42	24	12	9	0	1	0	2	0	102	73	29	25	12	10	0	1	2	0	0	105	92	13	58	0.592	207	357	564	0	2	39	85	79	5	1832	499	654	672	14	1333	402	500	799	156	53	33.97%	150	37	75.33%	5	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
	_Vs Conference	27	12	12	0	2	0	1	0	113	98	15	16	7	7	0	1	0	1	0	66	54	12	11	5	5	0	1	0	0	0	47	44	3	30	0.556	113	191	304	0	0	39	85	79	5	1073	499	654	672	14	749	226	270	429	82	28	34.15%	80	17	78.75%	3	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
	_Vs Division	17	7	8	0	1	0	1	0	73	67	6	10	4	4	0	1	0	1	0	42	36	6	7	3	4	0	0	0	0	0	31	31	0	18	0.529	73	124	197	0	0	39	85	79	5	699	499	654	672	14	475	139	176	266	50	16	32.00%	53	13	75.47%	2	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
49	58	W1	207	357	564	1832	1333	402	500	799	0	2

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
49	24	19	2	2	2	0	207	165

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
24	12	9	1	0	2	0	102	73

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
25	12	10	1	2	0	0	105	92

Last 10 Games
W	L	OTW	OTL	SOW	SOL
5	4	0	0	1	0

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
156	53	33.97%	150	37	75.33%	5

Shots 1 Period	Shots 2 Period	Shots 3 Period	Shots 4+ Period	Goals 1 Period	Goals 2 Period	Goals 3 Period	Goals 4+ Period
499	654	672	14	39	85	79	5

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
589	1110	53.06%	465	901	51.61%	423	819	51.65%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
1232	842	992	366	729	372

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
1 - 2023-04-05	15	Bandits	1	Chiwawa	3	L			BoxScore
2 - 2023-04-06	20	Goons	4	Bandits	5	W	X		BoxScore
3 - 2023-04-07	37	Farmers	2	Bandits	6	W			BoxScore
4 - 2023-04-08	48	Bandits	2	Goons	5	L			BoxScore
6 - 2023-04-10	68	CoolFm	5	Bandits	4	L			BoxScore
7 - 2023-04-11	81	Bandits	4	Husky	6	L			BoxScore
9 - 2023-04-13	103	Goons	3	Bandits	6	W			BoxScore
10 - 2023-04-14	112	Bandits	6	Hunters	1	W			BoxScore
12 - 2023-04-16	130	Bandits	4	Igloos	3	W			BoxScore
13 - 2023-04-17	144	Farmers	2	Bandits	5	W			BoxScore
14 - 2023-04-18	160	Bandits	7	Hunters	3	W			BoxScore
15 - 2023-04-19	167	Bandits	6	Supreme	0	W			BoxScore
16 - 2023-04-20	182	Xpress	1	Bandits	6	W			BoxScore
17 - 2023-04-21	197	Bandits	6	Xpress	7	L			BoxScore
19 - 2023-04-23	215	Husky	3	Bandits	1	L			BoxScore
20 - 2023-04-24	234	Wolves	3	Bandits	1	L			BoxScore
21 - 2023-04-25	248	Bandits	2	Scorpions	4	L			BoxScore
23 - 2023-04-27	266	Husky	3	Bandits	4	W	X	X	BoxScore
24 - 2023-04-28	278	Bandits	3	Wolves	0	W			BoxScore
26 - 2023-04-30	296	TigersCats	3	Bandits	1	L			BoxScore
27 - 2023-05-01	308	Bandits	5	Raptors	3	W			BoxScore
28 - 2023-05-02	328	Twins	3	Bandits	6	W			BoxScore
29 - 2023-05-03	340	Bandits	5	Vipers	3	W			BoxScore
30 - 2023-05-04	352	Bandits	4	Outlaws	2	W			BoxScore
31 - 2023-05-05	367	Spartans	2	Bandits	7	W			BoxScore
32 - 2023-05-06	386	Bandits	4	Snowbirds	5	L	X		BoxScore
33 - 2023-05-07	397	Supreme	2	Bandits	4	W			BoxScore
34 - 2023-05-08	411	Bandits	6	Warriors	3	W			BoxScore
36 - 2023-05-10	425	Bandits	6	Marmots	5	W			BoxScore
37 - 2023-05-11	440	Scorpions	2	Bandits	1	L			BoxScore
38 - 2023-05-12	460	Hunters	5	Bandits	2	L			BoxScore
39 - 2023-05-13	474	Bandits	4	Saguenéens	2	W			BoxScore
40 - 2023-05-14	491	Hunters	2	Bandits	4	W			BoxScore
42 - 2023-05-16	510	Bandits	3	Spartans	6	L			BoxScore
43 - 2023-05-17	522	Warriors	5	Bandits	2	L			BoxScore
44 - 2023-05-18	535	Bandits	3	Vandals	5	L			BoxScore
46 - 2023-05-20	551	Bandits	3	Farmers	2	W	X		BoxScore
47 - 2023-05-21	563	CoolFm	6	Bandits	2	L			BoxScore
48 - 2023-05-22	582	Bandits	7	Scorpions	8	L	X		BoxScore
49 - 2023-05-23	588	Marlies	1	Bandits	6	W			BoxScore
51 - 2023-05-25	613	Outlaws	1	Bandits	7	W			BoxScore
52 - 2023-05-26	629	Bandits	4	Barracudas	5	L			BoxScore
53 - 2023-05-27	644	Farmers	5	Bandits	4	L			BoxScore
54 - 2023-05-28	657	Bandits	2	Xpress	6	L			BoxScore
56 - 2023-05-30	671	Bandits	7	Bayou	2	W			BoxScore
57 - 2023-05-31	682	Xpress	4	Bandits	8	W			BoxScore
58 - 2023-06-01	703	Vandals	4	Bandits	5	W	X	X	BoxScore
60 - 2023-06-03	720	Bandits	1	Marlies	3	L			BoxScore
61 - 2023-06-04	738	Chiwawa	2	Bandits	5	W			BoxScore
63 - 2023-06-06	756	Bandits	-	Rockets	-
64 - 2023-06-07	768	Bayou	-	Bandits	-
66 - 2023-06-09	795	Raptors	-	Bandits	-
67 - 2023-06-10	805	Bandits	-	Hunters	-
68 - 2023-06-11	817	Bandits	-	Husky	-
69 - 2023-06-12	829	Snowbirds	-	Bandits	-
71 - 2023-06-14	854	CoolFm	-	Bandits	-
72 - 2023-06-15	864	Bandits	-	CoolFm	-
73 - 2023-06-16	877	Bandits	-	Predateurs	-
74 - 2023-06-17	892	Igloos	-	Bandits	-
75 - 2023-06-18	907	Bandits	-	TigersCats	-
76 - 2023-06-19	920	Rockets	-	Bandits	-
78 - 2023-06-21	946	Bandits	-	CoolFm	-
79 - 2023-06-22	955	Marmots	-	Bandits	-
80 - 2023-06-23	973	Bandits	-	TigersCats	-
82 - 2023-06-25	986	Vipers	-	Bandits	-
83 - 2023-06-26	1007	Saguenéens	-	Bandits	-
85 - 2023-06-28	1027	Bandits	-	Grizzlies	-
86 - 2023-06-29	1036	Bandits	-	Supreme	-
87 - 2023-06-30	1050	Smirnoff Ice	-	Bandits	-
88 - 2023-07-01	1070	Igloos	-	Bandits	-
89 - 2023-07-02	1082	Bandits	-	Twins	-
Trade Deadline --- Trades can’t be done after this day is simulated!
90 - 2023-07-03	1100	Thugs	-	Bandits	-
92 - 2023-07-05	1111	Bandits	-	Igloos	-
93 - 2023-07-06	1123	Bandits	-	Thugs	-
95 - 2023-07-08	1144	Goons	-	Bandits	-
96 - 2023-07-09	1153	Bandits	-	Smirnoff Ice	-
98 - 2023-07-11	1169	Goons	-	Bandits	-
101 - 2023-07-14	1199	Grizzlies	-	Bandits	-
104 - 2023-07-17	1220	Barracudas	-	Bandits	-
105 - 2023-07-18	1228	Bandits	-	Goons	-
108 - 2023-07-21	1250	Predateurs	-	Bandits	-
109 - 2023-07-22	1264	Bandits	-	Goons	-

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Arena Capacity	2000	1000
Ticket Price	35	15
Attendance	47,996	23,979
Attendance PCT	99.99%	99.91%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Arena Capacity	Team Popularity
17	2999 - 99.97%	101,977$	2,447,454$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
3,473,253$	2,940,000$	2,735,000$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
0$	1,546,197$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
1,733,613$	50	58,018$	2,900,900$

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
Regular Season
32	82	35	27	0	5	11	2	2	262	261	1	41	21	11	0	2	5	1	1	131	129	2	41	14	16	0	3	6	1	1	131	132	-1	70	262	424	686	0	3	49	91	115	9	2045	520	689	810	40	2104	686	520	1281	210	56	26.67%	216	46	78.70%	11	790	1399	56.47%	763	1439	53.02%	628	1177	53.36%	1680	1005	1895	685	1379	688
34	82	49	24	0	3	3	2	1	338	256	82	41	25	10	0	2	2	1	1	179	132	47	41	24	14	0	1	1	1	0	159	124	35	98	338	564	902	0	2	72	119	142	6	2674	713	973	973	22	2238	684	557	1428	240	69	28.75%	241	56	76.76%	6	924	1684	54.87%	808	1591	50.79%	716	1320	54.24%	1788	1114	1839	671	1323	660
41	82	43	22	0	6	3	3	5	340	276	64	41	18	12	0	3	2	2	4	169	151	18	41	25	10	0	3	1	1	1	171	125	46	112	340	575	915	2	5	60	135	136	14	2589	616	948	1007	51	2328	797	611	1365	214	65	30.37%	225	51	77.33%	4	888	1665	53.33%	881	1593	55.30%	648	1302	49.77%	1725	1038	1862	683	1383	693
42	76	29	39	0	3	3	1	1	279	296	-17	38	17	20	0	1	0	0	0	139	136	3	38	12	19	0	2	3	1	1	140	160	-20	70	279	487	766	3	0	45	102	128	4	2418	563	851	994	16	2073	617	691	1114	220	62	28.18%	256	62	75.78%	5	805	1571	51.24%	700	1316	53.19%	643	1219	52.75%	1833	1215	1549	601	1194	593
45	49	24	19	0	2	2	2	0	207	165	42	24	12	9	0	1	0	2	0	102	73	29	25	12	10	0	1	2	0	0	105	92	13	58	207	357	564	0	2	39	85	79	5	1832	499	654	672	14	1333	402	500	799	156	53	33.97%	150	37	75.33%	5	589	1110	53.06%	465	901	51.61%	423	819	51.65%	1232	842	992	366	729	372
Total Regular Season	371	180	131	0	19	22	10	9	1426	1254	172	185	93	62	0	9	9	6	6	720	621	99	186	87	69	0	10	13	4	3	706	633	73	408	1426	2407	3833	5	12	265	532	600	38	11558	2911	4115	4456	143	10076	3186	2879	5987	1040	305	29.33%	1088	252	76.84%	31	3996	7429	53.79%	3617	6840	52.88%	3058	5837	52.39%	8259	5217	8139	3007	6010	3008

Back to the Top