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	Michael Carcone	0	X	X	X		100.00	74	39	86	73	66	80	85	69	59	68	67	63	66	71	70	35	50	67	0
2	Jayson Megna	0	X	X	X		100.00	63	38	88	65	72	84	85	62	67	64	63	66	61	76	81	21	50	65	0
3	Taylor Raddysh	0			X		100.00	67	36	94	69	81	80	88	64	55	67	61	62	63	70	69	58	50	65	0
4	Brett Chorske	0	X				100.00	90	41	94	59	99	73	83	60	58	55	61	67	56	65	67	42	50	64	0
5	Dominic Toninato	0	X	X			100.00	74	42	69	63	81	86	83	62	65	61	59	64	60	72	74	36	50	64	0
6	Mackenzie MacEachern	0		X			100.00	72	41	70	62	79	81	72	61	59	67	63	61	63	72	74	39	50	64	0
7	Josiah Slavin	0		X			100.00	72	39	88	59	84	95	86	58	61	57	56	63	55	68	73	41	50	63	0
8	Clark Bishop	0	X	X			100.00	68	39	79	61	77	78	87	58	64	55	62	60	59	70	72	46	50	62	0
9	Brett Seney	0	X	X			100.00	59	38	71	64	62	82	83	63	70	67	59	56	62	70	75	39	50	62	0
10	Henrik Rybinski	0			X		100.00	63	38	88	60	72	73	82	58	65	62	57	59	61	65	67	55	50	61	0
11	Haydn Fleury	0				X	100.00	72	38	95	69	88	85	74	65	30	68	55	71	50	72	70	69	50	69	0
12	Ashton Sautner	0				X	100.00	67	40	80	55	76	65	85	52	30	54	53	56	45	72	74	27	50	60	0
13	Mitchell Vande Sompel	0				X	100.00	63	37	89	57	72	62	68	56	30	55	53	58	46	69	71	52	50	59	0
Scratches

	TEAM AVERAGE					100.00	70	39	84	63	78	79	82	61	55	62	59	62	57	70	72	43	50	63

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	Michal Postava	100.00	79	80	76	77	78	77	79	78	77	79	78	64	71	43	50	75	0
2	Owen Flores	100.00	72	71	68	76	71	70	72	71	70	72	71	62	67	44	50	69	0
Scratches

	TEAM AVERAGE	100.00	76	76	72	77	75	74	76	75	74	76	75	63	69	44	50	72

Coaches Name	PH	DF	OF	PD	EX	LD	PO	CNT	Age	Contract	Salary
Greg Cronin	75	74	70	74	94	88	52	USA	62	6	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	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

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
Ashton Sautner	D	32	1994-05-27	No	195 Lbs	6 ft1	No	No	No	1	UFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Brett Chorske	C	25	2001-05-24	No	216 Lbs	6 ft7	No	No	No	2	RFA	Pro & Farm	300,000$	0$	0$	No	Link
Brett Seney	C/LW	30	1996-02-28	No	156 Lbs	5 ft9	No	No	No	1	UFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Clark Bishop	C/LW	30	1996-03-29	No	197 Lbs	6 ft1	No	No	No	4	UFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Dominic Toninato	C/LW	32	1994-03-09	No	201 Lbs	6 ft2	No	No	No	1	UFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Haydn Fleury	D	30	1996-07-08	No	207 Lbs	6 ft4	No	No	No	1	UFA	Pro & Farm	1,000,000$	0$	0$	No	Link / NHL Link
Henrik Rybinski	RW	25	2001-06-26	No	172 Lbs	6 ft1	No	No	No	2	RFA	Pro & Farm	300,000$	0$	0$	No	Link / NHL Link
Jayson Megna	C/LW/RW	36	1990-02-01	No	190 Lbs	6 ft0	No	No	No	1	UFA	Pro & Farm	300,000$	0$	0$	No	Link / NHL Link
Josiah Slavin	LW	27	1998-12-31	No	205 Lbs	6 ft3	No	No	No	2	UFA	Pro & Farm	300,000$	0$	0$	No	Link / NHL Link
Mackenzie MacEachern	LW	32	1994-03-09	No	193 Lbs	6 ft2	No	No	No	2	UFA	Pro & Farm	300,000$	0$	0$	No	Link / NHL Link
Michael Carcone	C/LW/RW	30	1996-05-19	No	182 Lbs	5 ft9	No	No	No	1	UFA	Pro & Farm	1,000,000$	0$	0$	No	Link / NHL Link
Michal Postava	G	24	2002-02-28	No	200 Lbs	6 ft1	No	No	No	2	RFA	Pro & Farm	750,000$	0$	0$	No	Link
Mitchell Vande Sompel	D	29	1997-02-11	No	198 Lbs	5 ft11	No	No	No	2	UFA	Pro & Farm	500,000$	0$	0$	No	Link / NHL Link
Owen Flores	G	22	2004-05-09	No	176 Lbs	6 ft2	No	No	No	2	ELC	Pro & Farm	300,000$	0$	0$	No	Link
Taylor Raddysh	RW	28	1998-02-18	No	201 Lbs	6 ft2	No	No	No	1	UFA	Pro & Farm	990,000$	0$	0$	No	Link / NHL Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
15	28.80	193 Lbs	6 ft1	1.67	536,000$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1				40	1	2	2
2				30	1	2	2
3				20	1	2	2
4				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				60	1	2	2
2				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			60	1	2	2
2			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		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			60	1	2	2
2			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

Last Minutes Defensive
Left Wing	Center	Right Wing	Defense	Defense

Extra Forwards
Normal	PowerPlay	Penalty Kill
, ,	,

Extra Defensemen
Normal	PowerPlay	Penalty Kill
, ,		,

Penalty Shots
, , , ,

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

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
0	0	N/A	0	0	0	0	0	0	0	0	0	0

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
0	0	0	0	0	0	0	0	0

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
0	0	0	0	0	0	0	0	0

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
0	0	0	0	0	0	0	0	0

Last 10 Games
W	L	OTW	OTL	SOW	SOL
0	0	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
0	0	0.00%	0	0	0.00%	0

Shots 1 Period	Shots 2 Period	Shots 3 Period	Shots 4+ Period	Goals 1 Period	Goals 2 Period	Goals 3 Period	Goals 4+ Period
0	0	0	0	0	0	0	0

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
0	0	0.00%	0	0	0.00%	0	0	0.00%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
0	0	0	0	0	0

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
9 - 2026-09-03	9	Gulls	-	Marlies	-
10 - 2026-09-04	26	Gulls	-	Marlies	-
15 - 2026-09-09	37	Marlies	-	Americans	-
16 - 2026-09-10	43	Americans	-	Marlies	-
17 - 2026-09-11	62	Comets	-	Marlies	-
22 - 2026-09-16	73	Senators	-	Marlies	-
23 - 2026-09-17	88	Marlies	-	Senators	-
30 - 2026-09-24	125	Marlies	-	Monsters	-
37 - 2026-10-01	156	Marlies	-	Checkers	-
38 - 2026-10-02	173	Marlies	-	Checkers	-
41 - 2026-10-05	184	Marlies	-	Monsters	-
44 - 2026-10-08	201	Senators	-	Marlies	-
45 - 2026-10-09	217	Americans	-	Marlies	-
48 - 2026-10-12	223	Bears	-	Marlies	-
51 - 2026-10-15	242	Barracuda	-	Marlies	-
52 - 2026-10-16	256	Barracuda	-	Marlies	-
57 - 2026-10-21	269	Marlies	-	Monsters	-
64 - 2026-10-28	312	Marlies	-	Rocket	-
65 - 2026-10-29	318	Marlies	-	Rocket	-
68 - 2026-11-01	341	Phantoms	-	Marlies	-
72 - 2026-11-05	361	Condors	-	Marlies	-
73 - 2026-11-06	376	Condors	-	Marlies	-
76 - 2026-11-09	390	Marlies	-	Penguins	-
78 - 2026-11-11	401	Marlies	-	Phantoms	-
79 - 2026-11-12	414	Marlies	-	Bears	-
84 - 2026-11-17	428	Senators	-	Marlies	-
86 - 2026-11-19	444	Marlies	-	Senators	-
90 - 2026-11-23	466	Crunch	-	Marlies	-
93 - 2026-11-26	481	Monsters	-	Marlies	-
94 - 2026-11-27	495	Monsters	-	Marlies	-
97 - 2026-11-30	502	Penguins	-	Marlies	-
99 - 2026-12-02	510	Marlies	-	Crunch	-
100 - 2026-12-03	527	Marlies	-	Comets	-
106 - 2026-12-09	563	Marlies	-	Moose	-
108 - 2026-12-11	579	Marlies	-	Moose	-
111 - 2026-12-14	598	Marlies	-	Condors	-
114 - 2026-12-17	613	Marlies	-	Barracuda	-
115 - 2026-12-18	632	Marlies	-	Barracuda	-
118 - 2026-12-21	644	Marlies	-	Gulls	-
120 - 2026-12-23	658	Marlies	-	Gulls	-
121 - 2026-12-24	674	Marlies	-	Condors	-
127 - 2026-12-30	678	Rocket	-	Marlies	-
128 - 2026-12-31	691	Rocket	-	Marlies	-
132 - 2027-01-04	712	Crunch	-	Marlies	-
134 - 2027-01-06	722	Marlies	-	Senators	-
135 - 2027-01-07	730	Senators	-	Marlies	-
137 - 2027-01-09	752	Rocket	-	Marlies	-
141 - 2027-01-13	769	Marlies	-	Crunch	-
142 - 2027-01-14	780	Marlies	-	Americans	-
143 - 2027-01-15	796	Moose	-	Marlies	-
146 - 2027-01-18	805	Rocket	-	Marlies	-
149 - 2027-01-21	825	Moose	-	Marlies	-
153 - 2027-01-25	852	Monsters	-	Marlies	-
155 - 2027-01-27	863	Marlies	-	Bruins	-
156 - 2027-01-28	875	Marlies	-	Islanders	-
160 - 2027-02-01	893	Bruins	-	Marlies	-
163 - 2027-02-04	912	Checkers	-	Marlies	-
164 - 2027-02-05	927	Checkers	-	Marlies	-
167 - 2027-02-08	938	Marlies	-	Rocket	-
169 - 2027-02-10	946	Marlies	-	Senators	-
170 - 2027-02-11	958	Senators	-	Marlies	-
176 - 2027-02-17	994	Marlies	-	Rocket	-
177 - 2027-02-18	1007	Marlies	-	Senators	-
178 - 2027-02-19	1020	Islanders	-	Marlies	-
181 - 2027-02-22	1029	Comets	-	Marlies	-
183 - 2027-02-24	1039	Marlies	-	Crunch	-
Trade Deadline --- Trades can’t be done after this day is simulated!
184 - 2027-02-25	1051	Crunch	-	Marlies	-
191 - 2027-03-04	1091	Marlies	-	Monsters	-
192 - 2027-03-05	1108	Monsters	-	Marlies	-
195 - 2027-03-08	1114	Marlies	-	Comets	-
197 - 2027-03-10	1130	Marlies	-	Americans	-
198 - 2027-03-11	1138	Americans	-	Marlies	-

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Arena Capacity	2000	1000
Ticket Price	35	15
Attendance	0.00%	0.00%
Attendance PCT	0.00%	0.00%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Arena Capacity	Team Popularity
36	0 - 0.00%	0$	0$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Average Salaries	Coaches Salaries
38,003$	80,400$	80,400$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
0$	2,828$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
0$	192	5,429$	1,042,368$

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	72	16	48	0	2	3	2	1	198	316	-118	36	9	23	0	2	1	1	0	108	170	-62	36	7	25	0	0	2	1	1	90	146	-56	44	198	348	546	1	2	0	76	62	56	2140	0	747	696	689	2912	851	660	1320	298	37	12.42%	280	64	77.14%	1	898	1983	45.28%	959	2430	39.47%	472	1141	41.37%	1367	967	2189	507	785	337
Total Regular Season	918	149	684	0	22	33	13	17	1873	4372	-2499	459	88	321	0	12	20	9	9	1003	2160	-1157	459	61	363	0	10	13	4	8	870	2212	-1342	418	1873	3441	5314	12	26	0	723	605	510	22628	0	7505	7413	7562	38319	10786	9397	16713	3774	482	12.77%	3867	888	77.04%	23	8082	23184	34.86%	10104	31976	31.60%	4699	14256	32.96%	15558	10550	29810	6677	9980	4142