LHSDB - Ligue de hockey simulee des debiles barbus

Toronto Leafs
Roster
Scoring
Informations
Lines
Stats
Schedule
Finance
Records
Web Lines Editor

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	Jayson Megna	0	X	X	X		98.00	63	37	92	65	72	86	85	64	74	66	61	63	66	75	80	23	50	65	0
2	Josiah Slavin	0		X			98.00	72	39	83	60	81	73	86	59	61	58	57	63	55	67	72	42	50	62	0
3	Clark Bishop	0	X	X			98.00	68	39	75	62	77	76	84	57	66	59	58	65	56	69	71	47	50	62	0
4	Brett Seney	0	X	X			100.00	71	37	72	64	62	80	84	62	67	63	57	61	62	69	73	46	50	62	0
5	Stefan Matteau	0		X			98.00	73	51	67	58	82	79	64	57	67	60	56	59	57	71	76	62	50	61	0
6	Henrik Rybinski	0			X		98.00	63	38	88	59	72	71	81	58	64	61	56	57	59	64	66	55	50	60	0
7	Ty Glover	0	X				98.00	74	40	84	58	83	71	73	57	60	55	56	59	57	65	67	42	50	60	0
8	Chase DeLeo	0	X	X	X		100.00	57	35	93	61	66	80	75	59	65	62	58	56	61	70	72	49	50	60	0
Scratches
1	Mitchell Vande Sompel	0				X	92.30	61	36	95	56	72	62	70	55	30	54	52	53	45	68	70	53	50	58	0

	TEAM AVERAGE					97.78	67	39	83	60	74	75	78	59	62	60	57	60	58	69	72	47	50	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
1	Jack Campbell	97.00	74	81	76	83	73	72	74	73	72	74	73	75	87	52	50	74	0
2	Malcolm Subban	100.00	72	82	77	83	71	70	72	71	70	72	71	72	86	50	50	72	0
Scratches

	TEAM AVERAGE	98.50	73	82	77	83	72	71	73	72	71	73	72	74	87	51	50	73

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Greg Cronin	83	79	78	79	91	85	54	USA	61	7	1,000,000$

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	PKS	PKM	GW	GT	FO%	FOT	HT	P/20	PSG	PSS	FT	S1	S2	S3
1	Michael Carcone	Toronto Leafs	C/LW/RW	14	9	10	19	10	13	5	16	19	56	16	26	16.07%	3	298	21.30	2	6	8	20	54	2	49	2	0	54.66%	247	1	1.27	1	2	1	1	1	1
2	Jayson Megna	Marlies (Tor)	C/LW/RW	5	5	7	12	5	8	0	9	22	33	9	25	15.15%	3	130	26.17	2	1	3	8	15	0	12	1	0	66.46%	164	0	1.83	0	0	0	1	1	0
3	Chase DeLeo	Marlies (Tor)	C/LW/RW	5	7	3	10	4	0	0	4	8	29	6	19	24.14%	2	98	19.73	1	2	3	3	17	0	3	2	0	33.33%	12	1	2.03	0	0	0	1	1	2
4	Ty Glover	Marlies (Tor)	C	5	4	2	6	4	0	0	4	12	28	9	22	14.29%	3	94	18.89	0	0	0	2	4	0	3	2	0	55.17%	87	0	1.27	0	0	0	1	1	0
5	Henrik Rybinski	Marlies (Tor)	RW	5	2	3	5	2	6	0	7	7	10	5	9	20.00%	4	107	21.52	2	1	3	4	14	0	0	0	0	50.00%	6	0	0.93	0	0	0	0	0	0
6	Dominic Toninato	Toronto Leafs	C/LW	2	1	4	5	3	0	0	8	2	14	4	10	7.14%	0	48	24.33	0	0	0	1	5	0	3	0	0	80.00%	5	0	2.05	0	0	0	1	0	0
7	Josiah Slavin	Marlies (Tor)	LW	5	2	3	5	2	2	0	6	10	21	9	12	9.52%	3	104	20.85	0	0	0	0	3	0	2	0	0	59.46%	37	0	0.96	0	0	0	1	0	0
8	Clark Bishop	Marlies (Tor)	C/LW	5	1	3	4	2	2	0	7	9	24	10	16	4.17%	3	99	20.00	0	1	1	5	15	0	5	0	1	50.00%	4	0	0.80	0	0	0	0	0	0
9	Stefan Matteau	Marlies (Tor)	LW	5	1	3	4	4	14	0	8	14	29	5	12	3.45%	3	104	20.85	0	0	0	1	2	0	2	0	0	62.50%	8	0	0.77	0	0	0	0	0	1
10	Brett Seney	Marlies (Tor)	C/LW	2	1	3	4	2	2	0	6	4	12	5	6	8.33%	0	38	19.44	1	1	2	2	5	0	1	0	0	74.29%	35	0	2.06	0	0	0	0	1	0
11	Mark Kastelic	Toronto Leafs	C/RW	4	1	0	1	1	21	5	9	8	6	0	5	16.67%	1	56	14.06	0	0	0	1	13	0	7	0	1	57.75%	71	0	0.36	0	0	1	1	0	0
12	Mitchell Vande Sompel	Marlies (Tor)	D	3	0	0	0	1	2	0	5	2	4	1	2	0.00%	1	37	12.36	0	0	0	0	5	0	2	0	0	0.00%	0	0	0.00	0	0	0	0	0	0
Team Total or Average				60	34	41	75	40	70	10	89	117	266	79	164	12.78%	26	1219	20.32	8	12	20	47	158	2	95	7	2	59.02%	676	2	1.23	1	2	2	7	5	4

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	Jack Campbell	Marlies (Tor)	5	5	0	0	0.933	1.60	300	0	1	8	119	0	0	0	0.000	0	5	0	0	0	1
Team Total or Average			5	5	0	0	0.933	1.60	300	0	1	8	119	0	0	0	0.000	0	5	0	0	0	1

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	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	Link
Brett Seney	C/LW	29	1996-02-28	No	156 Lbs	5 ft9	No	No	No	1	UFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Chase DeLeo	C/LW/RW	30	1995-10-25	No	185 Lbs	5 ft9	No	No	No	2	UFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Clark Bishop	C/LW	29	1996-03-29	No	197 Lbs	6 ft1	No	No	No	1	UFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Henrik Rybinski	RW	24	2001-06-26	No	172 Lbs	6 ft1	No	No	No	1	RFA	Pro & Farm	300,000$	0$	0$	No	Link / NHL Link
Jack Campbell	G	34	1992-01-09	No	200 Lbs	6 ft3	No	No	No	4	UFA	Pro & Farm	750,000$	0$	0$	No	Link / NHL Link
Jayson Megna	C/LW/RW	35	1990-02-01	No	190 Lbs	6 ft0	No	No	No	1	UFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Josiah Slavin	LW	27	1998-12-31	No	189 Lbs	6 ft3	No	No	No	3	UFA	Pro & Farm	300,000$	0$	0$	No	Link / NHL Link
Malcolm Subban	G	32	1993-12-21	No	215 Lbs	6 ft2	No	No	No	2	UFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Mitchell Vande Sompel (Out of Payroll)	D	28	1997-02-11	No	198 Lbs	5 ft11	No	No	No	3	UFA	Pro & Farm	500,000$	0$	0$	Yes	Link / NHL Link
Stefan Matteau	LW	31	1994-02-23	No	208 Lbs	6 ft2	No	No	No	2	UFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Ty Glover	C	25	2000-10-01	No	200 Lbs	6 ft3	No	No	No	2	RFA	Pro & Farm	300,000$	0$	0$	No	Link / NHL Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
11	29.45	192 Lbs	6 ft1	2.00	468,182$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1		Jayson Megna	Chase DeLeo	40	1	2	2
2	Clark Bishop		Henrik Rybinski	30	1	2	2
3	Josiah Slavin	Ty Glover	Stefan Matteau	20	1	2	2
4	Stefan Matteau	Jayson Megna		10	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			40	1	2	2
2			30	1	2	2
3			20	1	2	2
4			10	1	2	2

Power Play Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1		Jayson Megna	Chase DeLeo	60	1	2	2
2	Clark Bishop		Henrik Rybinski	40	1	2	2

Power Play Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			60	1	2	2
2			40	1	2	2

Penalty Kill 4 Players Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Jayson Megna		60	1	2	2
2		Clark Bishop	40	1	2	2

Penalty Kill 4 Players Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			60	1	2	2
2			40	1	2	2

Penalty Kill 3 Players
Line #	Wing	Time %	PHY	DF	OF	Defense	Defense	Time %	PHY	DF	OF
1	Jayson Megna	60	1	2	2			60	1	2	2
2		40	1	2	2			40	1	2	2

4 vs 4 Forward
Line #	Center	Wing	Time %	PHY	DF	OF
1	Jayson Megna		60	1	2	2
2		Clark Bishop	40	1	2	2

4 vs 4 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			60	1	2	2
2			40	1	2	2

Last Minutes Offensive
Left Wing	Center	Right Wing	Defense	Defense
	Jayson Megna	Chase DeLeo

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense
	Jayson Megna	Chase DeLeo

Extra Forwards
Normal	PowerPlay	Penalty Kill
Josiah Slavin, Ty Glover, Stefan Matteau	Josiah Slavin, Ty Glover	Stefan Matteau

Extra Defensemen
Normal	PowerPlay	Penalty Kill
, ,		,

Penalty Shots
Jayson Megna, , , Clark Bishop, Josiah Slavin

Goalie
#1 : Jack Campbell, #2 : Malcolm Subban

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

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
33	22	W6	87	159	246	1022	1457	429	278	637	0	1

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
33	9	21	1	2	0	0	87	155

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
18	7	10	1	0	0	0	56	85

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
15	2	11	0	2	0	0	31	70

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
140	13	9.29%	120	31	74.17%	1

Shots 1 Period	Shots 2 Period	Shots 3 Period	Shots 4+ Period	Goals 1 Period	Goals 2 Period	Goals 3 Period	Goals 4+ Period
362	314	343	3	30	30	26	1

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
362	887	40.81%	412	1206	34.16%	175	515	33.98%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
583	409	1045	236	354	145

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	Link
9 - 2025-08-23	9	Gulls	5	Marlies	6	W		BoxScore
10 - 2025-08-24	26	Gulls	2	Marlies	5	W		BoxScore
15 - 2025-08-29	37	Marlies	1	Americans	5	L		BoxScore
16 - 2025-08-30	43	Americans	7	Marlies	0	L		BoxScore
17 - 2025-08-31	62	Comets	6	Marlies	1	L		BoxScore
22 - 2025-09-05	73	Senators	7	Marlies	2	L		BoxScore
23 - 2025-09-06	88	Marlies	0	Senators	6	L		BoxScore
30 - 2025-09-13	125	Marlies	3	Monsters	7	L		BoxScore
37 - 2025-09-20	156	Marlies	4	Checkers	5	L	X	BoxScore
38 - 2025-09-21	173	Marlies	4	Checkers	5	L		BoxScore
41 - 2025-09-24	184	Marlies	3	Monsters	6	L		BoxScore
44 - 2025-09-27	201	Senators	7	Marlies	2	L		BoxScore
45 - 2025-09-28	217	Americans	6	Marlies	1	L		BoxScore
48 - 2025-10-01	223	Bears	7	Marlies	1	L		BoxScore
51 - 2025-10-04	242	Barracuda	6	Marlies	0	L		BoxScore
52 - 2025-10-05	256	Barracuda	7	Marlies	2	L		BoxScore
57 - 2025-10-10	269	Marlies	3	Monsters	4	L	X	BoxScore
64 - 2025-10-17	312	Marlies	0	Rocket	5	L		BoxScore
65 - 2025-10-18	318	Marlies	1	Rocket	3	L		BoxScore
68 - 2025-10-21	341	Phantoms	4	Marlies	3	L		BoxScore
72 - 2025-10-25	361	Condors	2	Marlies	5	W		BoxScore
73 - 2025-10-26	376	Condors	5	Marlies	6	W	X	BoxScore
76 - 2025-10-29	390	Marlies	0	Penguins	7	L		BoxScore
78 - 2025-10-31	401	Marlies	1	Phantoms	5	L		BoxScore
79 - 2025-11-01	414	Marlies	2	Bears	7	L		BoxScore
84 - 2025-11-06	428	Senators	5	Marlies	1	L		BoxScore
86 - 2025-11-08	444	Marlies	0	Senators	2	L		BoxScore
90 - 2025-11-12	466	Crunch	4	Marlies	6	W		BoxScore
93 - 2025-11-15	481	Monsters	1	Marlies	8	W		BoxScore
94 - 2025-11-16	495	Monsters	2	Marlies	4	W		BoxScore
97 - 2025-11-19	502	Penguins	2	Marlies	3	W		BoxScore
99 - 2025-11-21	510	Marlies	5	Crunch	0	W		BoxScore
100 - 2025-11-22	527	Marlies	4	Comets	3	W		BoxScore
106 - 2025-11-28	563	Marlies	-	Moose	-
108 - 2025-11-30	579	Marlies	-	Moose	-
111 - 2025-12-03	598	Marlies	-	Condors	-
114 - 2025-12-06	613	Marlies	-	Barracuda	-
115 - 2025-12-07	632	Marlies	-	Barracuda	-
118 - 2025-12-10	644	Marlies	-	Gulls	-
120 - 2025-12-12	658	Marlies	-	Gulls	-
121 - 2025-12-13	674	Marlies	-	Condors	-
127 - 2025-12-19	678	Rocket	-	Marlies	-
128 - 2025-12-20	691	Rocket	-	Marlies	-
132 - 2025-12-24	712	Crunch	-	Marlies	-
134 - 2025-12-26	722	Marlies	-	Senators	-
135 - 2025-12-27	730	Senators	-	Marlies	-
137 - 2025-12-29	752	Rocket	-	Marlies	-
141 - 2026-01-02	769	Marlies	-	Crunch	-
142 - 2026-01-03	780	Marlies	-	Americans	-
143 - 2026-01-04	796	Moose	-	Marlies	-
146 - 2026-01-07	805	Rocket	-	Marlies	-
149 - 2026-01-10	825	Moose	-	Marlies	-
153 - 2026-01-14	852	Monsters	-	Marlies	-
Trade Deadline --- Trades can’t be done after this day is simulated!
155 - 2026-01-16	863	Marlies	-	Bruins	-
156 - 2026-01-17	875	Marlies	-	Islanders	-
160 - 2026-01-21	893	Bruins	-	Marlies	-
163 - 2026-01-24	912	Checkers	-	Marlies	-
164 - 2026-01-25	927	Checkers	-	Marlies	-
167 - 2026-01-28	938	Marlies	-	Rocket	-
169 - 2026-01-30	946	Marlies	-	Senators	-
170 - 2026-01-31	958	Senators	-	Marlies	-
176 - 2026-02-06	994	Marlies	-	Rocket	-
177 - 2026-02-07	1007	Marlies	-	Senators	-
178 - 2026-02-08	1020	Islanders	-	Marlies	-
181 - 2026-02-11	1029	Comets	-	Marlies	-
183 - 2026-02-13	1039	Marlies	-	Crunch	-
184 - 2026-02-14	1051	Crunch	-	Marlies	-
191 - 2026-02-21	1091	Marlies	-	Monsters	-
192 - 2026-02-22	1108	Monsters	-	Marlies	-
195 - 2026-02-25	1114	Marlies	-	Comets	-
197 - 2026-02-27	1130	Marlies	-	Americans	-
198 - 2026-02-28	1138	Americans	-	Marlies	-

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Arena Capacity	2000	1000
Ticket Price	35	15
Attendance	33,593	17,027
Attendance PCT	93.31%	94.59%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Arena Capacity	Team Popularity
18	2812 - 93.74%	69,968$	1,259,421$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
533,824$	46,500$	46,500$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
0$	33,824$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
1,259,421$	100	5,232$	523,200$

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
12	82	9	68	0	2	2	0	1	170	454	-284	41	6	32	0	0	2	0	1	91	224	-133	41	3	36	0	2	0	0	0	79	230	-151	25	170	331	501	1	0	0	66	56	46	2056	0	706	670	665	3666	1018	775	1475	392	47	11.99%	321	102	68.22%	2	686	1998	34.33%	901	2849	31.63%	399	1355	29.45%	1241	824	2800	620	894	352
13	76	4	68	0	1	3	0	0	118	417	-299	38	1	36	0	0	1	0	0	60	210	-150	38	3	32	0	1	2	0	0	58	207	-149	13	118	230	348	0	0	0	46	35	36	1763	0	580	601	579	3257	875	646	1275	323	30	9.29%	261	71	72.80%	1	527	1803	29.23%	702	2546	27.57%	326	1211	26.92%	1050	685	2692	580	827	303
14	76	4	69	0	0	2	1	0	123	440	-317	38	2	33	0	0	2	1	0	60	216	-156	38	2	36	0	0	0	0	0	63	224	-161	12	123	234	357	0	0	0	47	41	34	1643	0	544	519	576	3644	1020	713	1260	277	24	8.66%	303	74	75.58%	4	415	1718	24.16%	676	2732	24.74%	310	1205	25.73%	991	636	2786	561	791	289
14	76	4	69	0	0	2	1	0	123	440	-317	38	2	33	0	0	2	1	0	60	216	-156	38	2	36	0	0	0	0	0	63	224	-161	12	123	234	357	0	0	0	47	41	34	1643	0	544	519	576	3644	1020	713	1260	277	24	8.66%	303	74	75.58%	4	415	1718	24.16%	676	2732	24.74%	310	1205	25.73%	991	636	2786	561	791	289
14	76	4	69	0	0	2	1	0	123	440	-317	38	2	33	0	0	2	1	0	60	216	-156	38	2	36	0	0	0	0	0	63	224	-161	12	123	234	357	0	0	0	47	41	34	1643	0	544	519	576	3644	1020	713	1260	277	24	8.66%	303	74	75.58%	4	415	1718	24.16%	676	2732	24.74%	310	1205	25.73%	991	636	2786	561	791	289
15	82	32	31	0	6	6	2	5	200	180	20	41	16	14	0	4	3	2	2	110	93	17	41	16	17	0	2	3	0	3	90	87	3	91	200	344	544	3	10	0	79	61	52	2102	0	679	704	683	2258	656	1189	1618	386	62	16.06%	465	57	87.74%	2	1152	2444	47.14%	1147	2662	43.09%	553	1151	48.05%	2005	1416	2016	580	959	478
15	82	32	31	0	6	6	2	5	200	180	20	41	16	14	0	4	3	2	2	110	93	17	41	16	17	0	2	3	0	3	90	87	3	91	200	344	544	3	10	0	79	61	52	2102	0	679	704	683	2258	656	1189	1618	386	62	16.06%	465	57	87.74%	2	1152	2444	47.14%	1147	2662	43.09%	553	1151	48.05%	2005	1416	2016	580	959	478
16	82	21	48	0	2	4	4	3	164	253	-89	41	16	18	0	1	3	1	2	100	121	-21	41	5	30	0	1	1	3	1	64	132	-68	61	164	262	426	2	4	0	65	53	40	1858	0	601	621	614	2791	792	942	1665	362	64	17.68%	374	69	81.55%	0	659	2101	31.37%	889	2953	30.10%	357	1137	31.40%	1663	1155	2399	579	903	416
17	72	7	62	0	2	0	0	1	141	423	-282	36	5	29	0	1	0	0	1	75	210	-135	36	2	33	0	1	0	0	0	66	213	-147	19	141	273	414	1	0	0	57	45	37	1880	0	682	558	630	3287	919	588	1326	255	34	13.33%	256	73	71.48%	1	574	1753	32.74%	743	2503	29.68%	360	1169	30.80%	1108	742	2454	518	768	307
18	72	9	62	0	1	0	0	0	161	425	-264	36	7	29	0	0	0	0	0	83	198	-115	36	2	33	0	1	0	0	0	78	227	-149	20	161	311	472	1	0	0	62	48	50	1985	0	637	662	685	3555	1000	562	1371	286	35	12.24%	238	68	71.43%	2	612	1806	33.89%	765	2607	29.34%	337	1168	28.85%	1115	757	2442	520	762	304
19	70	7	59	0	0	3	0	1	152	404	-252	35	6	27	0	0	1	0	1	86	193	-107	35	1	32	0	0	2	0	0	66	211	-145	18	152	296	448	0	0	0	52	61	39	1813	0	562	640	606	3403	959	707	1265	255	39	15.29%	298	105	64.77%	0	577	1698	33.98%	823	2568	32.05%	412	1158	35.58%	1024	673	2440	506	745	293
20	33	9	21	0	1	2	0	0	87	155	-68	18	7	10	0	1	0	0	0	56	85	-29	15	2	11	0	0	2	0	0	31	70	-39	22	87	159	246	0	1	30	30	26	1	1022	362	314	343	3	1457	429	278	637	140	13	9.29%	120	31	74.17%	1	362	887	40.81%	412	1206	34.16%	175	515	33.98%	583	409	1045	236	354	145
Total Regular Season	879	142	657	0	21	32	11	16	1762	4211	-2449	441	86	308	0	11	19	8	9	951	2075	-1124	438	56	349	0	10	13	3	7	811	2136	-1325	396	1762	3252	5014	11	25	30	677	569	455	21510	362	7072	7060	6876	36864	10364	9015	16030	3616	458	12.67%	3707	855	76.94%	23	7546	22088	34.16%	9557	30752	31.08%	4402	13630	32.30%	14775	9993	28666	6406	9549	3950