Ultimate Hockey League - Today's Games Farm

Roster

Player	#	POS	CON	CK	FG	DI	SK	ST	EN	DU	PH	FO	PA	SC	DF	PS	EX	LD	PO	MO	OV	AGE	CONTRACT
Vitali Kravtsov (R)	0	LW/RW	100.00	66	69	90	67	69	58	83	63	73	60	63	66	60	48	48	68	75	68	25	475,000$/1yrs
Connor Dewar	0	C/LW	100.00	80	70	95	71	68	55	58	60	68	64	63	75	55	62	59	66	75	66	26	800,000$/2yrs
Tomas Nosek	0	C/LW	100.00	74	40	90	69	75	49	71	58	80	63	58	72	54	69	59	66	75	66	32	450,000$/1yrs
Joakim Kemell (R)	0	RW	100.00	65	66	85	63	66	56	84	59	55	55	60	49	57	38	38	64	75	64	21	875,000$/1yrs
Ryan Greene (R)	0	C	100.00	66	71	87	64	70	54	83	55	74	55	52	52	49	35	35	63	75	63	21	725,000$/1yrs
David Edstrom (R)	0	C	100.00	66	70	89	64	69	54	78	56	49	47	58	65	55	34	34	63	75	63	20	875,000$/2yrs
Marcus Davidsson (R)	0	C/LW/RW	100.00	66	70	89	64	70	54	80	56	45	47	59	57	55	44	44	63	75	63	26	475,000$/1yrs
Oscar Fisker Molgaard (R)	0	C	100.00	65	63	93	63	63	56	78	58	57	56	57	58	54	34	34	63	75	63	20	725,000$/2yrs
Hugh McGing (R)	0	C/LW	100.00	64	63	79	63	63	56	78	56	34	49	58	54	55	43	43	62	75	62	26	775,000$/2yrs
David Gustafsson (R)	0	C	100.00	73	66	93	68	74	47	43	58	78	62	60	77	54	58	58	62	75	62	25	835,000$/2yrs
Landon Slaggert (R)	0	C/LW	100.00	60	63	76	70	67	60	40	60	54	62	60	67	55	57	57	61	75	61	23	912,500$/1yrs
Linus Karlsson (R)	0	C/RW	100.00	66	52	78	68	67	58	28	62	55	61	65	66	55	60	58	60	75	60	25	775,000$/1yrs
Kyle Capobianco (R)	0	D	100.00	65	71	70	67	71	57	84	53	25	47	43	71	41	46	46	65	75	65	27	775,000$/2yrs
Shai Buium (R)	0	D	100.00	65	78	82	65	79	57	85	50	25	43	40	61	39	35	35	62	75	62	22	925,000$/3yrs
Jack St. Ivany (R)	0	D	100.00	59	61	73	68	78	64	23	57	41	60	55	72	55	61	58	59	75	59	25	450,000$/1yrs
Cade Webber (R)	0	D	100.00	65	78	79	64	78	54	81	45	25	34	40	62	38	33	33	59	75	59	24	875,000$/1yrs
Sean Behrens (R)	0	D	100.00	65	65	69	65	64	55	63	51	25	44	41	65	39	33	33	59	75	59	22	905,833$/3yrs
Adam Ginning (R)	0	D	100.00	65	76	69	63	76	53	80	47	25	38	40	59	38	43	43	59	75	59	25	787,500$/2yrs
Scratches
Cameron Berg (R)	0	C/LW	100.00	66	69	86	63	68	55	75	56	43	48	58	45	55	35	35	61	75	61	23	525,000$/3yrs
Nikita Nesterenko (R)	0	C/LW	100.00	60	52	76	73	73	62	24	62	55	62	63	67	60	59	57	60	75	60	23	874,125$/1yrs
Victor Stjernborg (R)	0	C/LW	100.00	66	70	92	63	70	52	78	50	43	46	48	62	46	38	38	60	75	60	22	500,000$/2yrs
Jani Nyman (R)	0	RW	100.00	58	57	72	61	78	66	14	64	55	61	67	66	55	55	54	59	75	59	20	725,000$/1yrs
Jordan Dumais (R)	0	RW	100.00	65	62	91	64	62	54	54	55	30	48	58	53	55	36	36	58	75	58	21	625,000$/1yrs
Lucas Edmonds (R)	0	LW/RW	100.00	65	66	89	63	66	52	62	50	32	45	50	51	47	36	36	57	75	57	24	861,667$/1yrs
Ilya Fedotov (R)	0	LW	100.00	64	65	81	63	65	54	55	54	34	47	57	45	54	37	37	57	75	57	22	475,000$/2yrs
Jesse Nurmi (R)	0	LW	100.00	65	64	93	63	64	50	68	49	27	45	47	50	45	33	33	57	75	57	20	550,000$/2yrs
David Spacek (R)	0	D	100.00	65	68	84	66	68	56	83	51	25	45	41	50	39	37	37	59	75	59	22	550,000$/1yrs
Nikita Okhotyuk (R)	0	D	100.00	65	67	82	64	67	52	72	46	25	36	40	62	38	42	42	58	75	58	24	475,000$/1yrs
Matias Rajaniemi (R)	0	D	100.00	65	77	83	64	77	53	80	44	25	33	40	53	38	35	35	57	75	57	22	475,000$/1yrs
Mason Langenbrunner (R)	0	D	100.00	65	72	80	64	72	52	75	46	25	36	41	55	39	33	33	57	75	57	22	450,000$/1yrs
Emil Pieniniemi (R)	0	D	100.00	65	67	86	64	67	54	75	49	25	42	41	51	39	34	34	57	75	57	20	625,000$/2yrs

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	#	CON	SK	DU	EN	SZ	AG	RB	SC	HS	RT	PH	PS	EX	LD	PO	MO	OV	AGE	CONTRACT
Carl Lindbom	0	98.50	67	54	64	59	67	74	67	67	67	67	60	35	35	61	75	61	22	840,000$/2yrs
Dylan Garand	0	100.00	66	55	66	58	66	73	66	66	66	66	59	43	43	60	75	60	23	828,333$/1yrs
Scratches
Ben Gaudreau	0	100.00	46	55	69	61	46	53	46	46	46	46	39	40	40	47	75	47	22	450,000$/1yrs
Louis Domingue	0	100.00	42	51	62	67	42	49	42	42	42	42	35	60	60	45	75	45	33	450,000$/1yrs

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

General Manager

Player Stats

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	SHT	OSB	OSM	SHT%	SB	MP	PPP	PKM	GW	FO%	FOT	P/20
1	Tomas Nosek	Blades (ROC)	C/LW	2	2	3	5	4	2	0	2	5	2	4	40.00%	0	18.77	0	3	1	57.45%	47	2.66
2	Linus Karlsson	Blades (ROC)	C/RW	2	2	2	4	4	2	0	1	9	2	6	22.22%	1	17.90	0	1	0	100.00%	1	2.23
3	Vitali Kravtsov	Blades (ROC)	LW/RW	2	0	3	3	2	0	0	2	6	5	2	0.00%	0	23.46	0	6	0	40.91%	22	1.28
4	Kyle Capobianco	Blades (ROC)	D	2	1	1	2	1	4	0	4	3	0	1	33.33%	2	21.91	0	2	0	0.00%	0	0.91
5	Joakim Kemell	Blades (ROC)	RW	2	2	0	2	1	0	0	1	4	1	4	50.00%	0	16.13	0	3	1	0.00%	2	1.24
6	Adam Ginning	Blades (ROC)	D	2	0	2	2	3	0	0	3	2	0	0	0.00%	2	21.38	0	4	0	0.00%	0	0.94
7	Marcus Davidsson	Blades (ROC)	C/LW/RW	2	1	1	2	4	0	0	3	6	0	5	16.67%	0	17.38	0	0	0	0.00%	1	1.15
8	Oscar Fisker Molgaard	Blades (ROC)	C	2	2	0	2	0	0	0	2	4	3	7	50.00%	0	18.16	0	0	0	63.16%	19	1.10
9	Connor Dewar	Blades (ROC)	C/LW	2	0	1	1	1	2	0	5	10	7	5	0.00%	0	18.66	0	5	0	69.23%	39	0.54
10	Jack St. Ivany	Blades (ROC)	D	2	0	1	1	1	0	0	7	4	0	0	0.00%	0	16.63	0	4	0	0.00%	0	0.60
11	Landon Slaggert	Blades (ROC)	C/LW	2	0	1	1	1	2	0	2	3	2	1	0.00%	0	6.80	0	1	0	100.00%	1	1.47
12	Sean Behrens	Blades (ROC)	D	2	0	1	1	3	2	0	6	1	1	0	0.00%	5	21.34	0	4	0	0.00%	0	0.47
13	Shai Buium	Blades (ROC)	D	2	0	1	1	1	2	0	3	1	1	2	0.00%	4	21.72	0	4	0	0.00%	0	0.46
14	Ryan Greene	Blades (ROC)	C	2	0	1	1	-1	4	0	3	1	1	2	0.00%	0	14.88	0	3	0	66.67%	27	0.67
15	Hugh McGing	Blades (ROC)	C/LW	2	0	0	0	-1	2	0	2	2	2	1	0.00%	0	15.28	0	2	0	66.67%	3	0.00
16	Cade Webber	Blades (ROC)	D	2	0	0	0	1	0	0	3	2	0	1	0.00%	0	16.00	0	2	0	0.00%	0	0.00
17	David Edstrom	Blades (ROC)	C	2	0	0	0	0	0	0	0	0	0	0	0.00%	0	3.11	0	0	0	60.00%	5	0.00
18	David Gustafsson	Blades (ROC)	C	2	0	0	0	0	0	0	0	1	0	1	0.00%	0	1.73	0	0	0	0.00%	0	0.00
Team Total or Average			36	10	18	28	25	22	0	49	64	27	42	15.63%	14	16.18	0	46	50	2	59.88%	167	0.96

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	Carl Lindbom	Blades (ROC)	2	2	0	0	0.871	4.00	120	0	0	8	62	0	0	0	0.000	0	2	0	0	0	0
Team Total or Average		2	2	0	0	0.871	4.00	120	0	0	8	62	0	0	0	0.000	0	2	0	0	0	0

Salary

Player Name	POS	Age	Cap Hit	2017-18	2018-19	2019-20
Adam Ginning	D	25	787,500$	787,500$	787,500$	RFA
Ben Gaudreau	G	22	450,000$	450,000$
Cade Webber	D	24	875,000$	875,000$	RFA
Cameron Berg	C/LW	23	525,000$	525,000$	525,000$	525,000$
Carl Lindbom	G	22	840,000$	840,000$	840,000$
Connor Dewar	C/LW	26	800,000$	800,000$	800,000$	RFA
David Edstrom	C	20	875,000$	875,000$	875,000$
David Gustafsson	C	25	835,000$	835,000$	835,000$	RFA
David Spacek	D	22	550,000$	550,000$
Dylan Garand	G	23	828,333$	828,333$
Emil Pieniniemi	D	20	625,000$	625,000$	625,000$
Hugh McGing	C/LW	26	775,000$	775,000$	775,000$	RFA
Ilya Fedotov	LW	22	475,000$	475,000$	475,000$
Jack St. Ivany	D	25	450,000$	450,000$	RFA
Jani Nyman	RW	20	725,000$	725,000$
Jesse Nurmi	LW	20	550,000$	550,000$	550,000$
Joakim Kemell	RW	21	875,000$	875,000$
Jordan Dumais	RW	21	625,000$	625,000$
Kyle Capobianco	D	27	775,000$	775,000$	775,000$	RFA
Landon Slaggert	C/LW	23	912,500$	912,500$
Linus Karlsson	C/RW	25	775,000$	775,000$	RFA
Louis Domingue	G	33	450,000$	450,000$	UFA
Lucas Edmonds	LW/RW	24	861,667$	861,667$	RFA
Marcus Davidsson	C/LW/RW	26	475,000$	475,000$	RFA
Mason Langenbrunner	D	22	450,000$	450,000$
Matias Rajaniemi	D	22	475,000$	475,000$
Nikita Nesterenko	C/LW	23	874,125$	874,125$	RFA
Nikita Okhotyuk	D	24	475,000$	475,000$	RFA
Oscar Fisker Molgaard	C	20	725,000$	725,000$	725,000$
Ryan Greene	C	21	725,000$	725,000$
Sean Behrens	D	22	905,833$	905,833$	905,833$	905,833$
Shai Buium	D	22	925,000$	925,000$	925,000$	925,000$
Tomas Nosek	C/LW	32	450,000$	450,000$	UFA
Victor Stjernborg	C/LW	22	500,000$	500,000$	500,000$
Vitali Kravtsov	LW/RW	25	475,000$	475,000$	RFA

Lines

Forward Lines
# - Tomas Nosek	# - Ryan Greene	# - Vitali Kravtsov
# - Connor Dewar	# - David Edstrom	# - Joakim Kemell
# - Landon Slaggert	# - Oscar Fisker Molgaard	# - Marcus Davidsson
# - Hugh McGing	# - David Gustafsson	# - Linus Karlsson

Defensive Pairings
# - Kyle Capobianco	# - Shai Buium
# - Jack St. Ivany	# - Cade Webber
# - Sean Behrens	# - Adam Ginning

1st Power Play Unit
# - Connor Dewar		# - Tomas Nosek		# - Vitali Kravtsov
# - Kyle Capobianco			# - Shai Buium

2nd Power Play Unit
# - Tomas Nosek		# - Ryan Greene		# - Joakim Kemell
# - Jack St. Ivany			# - Cade Webber

Goalies
# - Carl Lindbom		# - Dylan Garand

Team Stats

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

#	VS Team	GP	W	GF	GA	Diff	P	PCT	G	A	TP	GP1	GP2	GP3	SHF	SH1	SP2	SP3	SHA	SHB	Pim	Hit	PPA	PP%	PKA	PK GA	PK%	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	GF%	SH%	SV%	PDO	PDOBRK
1	Ninjas	1	1	5.00	4.00	1.00	2	1.000	5.0	9.0	14.0	3	3	4	37.0	21	22	21	34.0	12.0	10.00	29.0	4.00	0.00%	4.00	1.00	75.00%	28	53	52.83%	46	75	61.33%	26	39	66.67%	44.00	29.00	47.00	15.00	28.00	14.00	62.5%	13.5%	88.2%	101.7	FUN
2	Huskies	1	1	5.00	4.00	1.00	2	1.000	5.0	9.0	14.0	3	3	4	27.0	21	22	21	28.0	2.0	12.00	20.0	1.00	0.00%	5.00	2.00	60.00%	28	53	52.83%	46	75	61.33%	26	39	66.67%	44.00	29.00	47.00	15.00	28.00	14.00	71.4%	18.5%	85.7%	104.2	FUN
	_Vs Division	1	1	5.00	4.00	1.00	2	1.000	5.0	9.0	14.0	3	3	4	37.0	21	22	21	34.0	12.0	10.00	29.0	4.00	0.00%	4.00	1.00	75.00%	28	53	52.83%	46	75	61.33%	26	39	66.67%	44.00	29.00	47.00	15.00	28.00	14.00	62.5%	13.5%	88.2%	101.7	FUN
	_Vs Conference	2	2	5.00	4.00	1.00	4	1.000	5.0	9.0	14.0	3	3	4	32.0	21	22	21	31.0	7.0	11.00	24.5	2.50	0.00%	4.50	1.50	66.67%	28	53	52.83%	46	75	61.33%	26	39	66.67%	22.00	14.50	23.50	7.50	14.00	7.00	66.7%	15.6%	87.1%	102.7	FUN
	_Since Last GM Reset	2	2	5.00	4.00	1.00	4	1.000	5.0	9.0	14.0	3	3	4	32.0	21	22	21	31.0	7.0	11.00	24.5	2.50	0.00%	4.50	1.50	66.67%	28	53	52.83%	46	75	61.33%	26	39	66.67%	22.00	14.50	23.50	7.50	14.00	7.00	66.7%	15.6%	87.1%	102.7	FUN
	Total	2	2	5.00	4.00	1.00	4	1.000	5.0	9.0	14.0	3	3	4	32.0	21	22	21	31.0	7.0	11.00	24.5	2.50	0.00%	4.50	1.50	66.67%	28	53	52.83%	46	75	61.33%	26	39	66.67%	22.00	14.50	23.50	7.50	14.00	7.00	66.7%	15.6%	87.1%	102.7	FUN

	Puck Time
Offensive Zone	22
Neutral Zone	14
Defensive Zone	23

	Puck Time
Offensive Zone Start	53
Neutral Zone Start	39
Defensive Zone Start	75

	Puck Time
With Puck	29
Without Puck	30

	Faceoffs
Faceoffs Won	100
Faceoffs Lost	67

	Team Average Shots after	League Average Shots after
1st Period	10.5	9.57
2nd Period	21.5	20.31
3rd Period	32.0	30.68
Overtime	32.0	31.4

Goals in	Team Average Goals after	League Average Goals after
1st Period	1.5	0.64
2nd Period	3.0	1.65
3rd Period	5.0	2.67
Overtime	5.0	2.83


Even Strenght Goal	10
PP Goal	0
PK Goal	0
Empty Net Goal	0

	Home	Away
Win	2	0
Lost	0	0
Overtime Lost	0	0


PP Attempt	5
PP Goal	0
PK Attempt	9
PK Goal Against	3

	Home
Shots For	32.0
Shots Against	31.0
Goals For	5.0
Goals Against	4.0
Hits	24.5
Shots Blocked	7.0
Pim	11.0

Schedule

Date	Matchup			Result	Detail
1	NIN	@	BLA	NIN4,BLA5	RECAP
2	CAN	@	BLA	CAN4,BLA5	RECAP
4	BLA	@	BUL
6	WPK	@	BLA
7	BLA	@	ASS
9	BLA	@	NIN
10	THI	@	BLA
12	BLA	@	NIN
13	BLA	@	KAN
15	BKS	@	BLA
17	SEN	@	BLA
18	SPA	@	BLA
21	BLA	@	RAV
24	BLA	@	BKS
26	BLA	@	DEV
29	ASS	@	BLA
31	BLA	@	IND
34	OHK	@	BLA
35	BLA	@	IND
37	BUL	@	BLA
39	ASS	@	BLA
42	DEV	@	BLA
46	IND	@	BLA
49	KAN	@	BLA
53	BLA	@	VIK
57	BLA	@	SAI
59	BLA	@	VEN
63	BLA	@	PIM
68	BLA	@	JWO
70	RAV	@	BLA
72	VEN	@	BLA
76	BLA	@	CHE
78	SEN	@	BLA
80	VIK	@	BLA
82	SQU	@	BLA
84	GRG	@	BLA
86	BLA	@	PIM
89	BLA	@	BKS
93	BLA	@	BAR
95	BLA	@	RAV
98	BLA	@	KAN
101	BLA	@	GRG
104	EAG	@	BLA
106	BLA	@	CAN
108	BLA	@	ASS
111	BLA	@	JWO
115	KAN	@	BLA
117	BLA	@	THI
120	BLA	@	ZOL
122	BUL	@	BLA
127	BLA	@	DEV
130	SAI	@	BLA
133	BLA	@	WPK
134	BLA	@	OHK
136	BLA	@	SQU
138	BLA	@	EAG
141	IND	@	BLA
142	BAR	@	BLA
145	KAN	@	BLA
146	BLA	@	BUL
148	BUL	@	BLA
149	BLA	@	SEN
151	BLA	@	SHO
Trade Deadline --- Trades can�t be done after this day is simulated!
154	PIM	@	BLA
156	JWO	@	BLA
158	BLA	@	CAN
161	BLA	@	SHA
163	BLA	@	PIM
167	BLA	@	SEN
169	CAN	@	BLA
171	BAR	@	BLA
173	BLA	@	IND
174	ASS	@	BLA
177	BLA	@	SPA
181	CHE	@	BLA
183	SHA	@	BLA
184	NIN	@	BLA
188	ZOL	@	BLA
189	SEN	@	BLA
191	KAN	@	BLA
192	NIN	@	BLA
194	SHO	@	BLA