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
Mackie Samoskevich (R)	0	C/RW	100.00	77	40	91	79	66	58	87	66	51	65	70	68	54	56	58	74	75	74	22	925,000$/1yrs
Zachary L'Heureux (R)	0	C/LW	100.00	80	72	83	71	71	55	75	61	52	64	63	73	54	56	58	69	75	69	22	863,334$/2yrs
Joey Muldowney (R)	0	RW	100.00	64	61	88	63	61	56	80	60	45	56	60	59	57	33	33	64	75	64	21	450,000$/1yrs
Jagger Firkus (R)	0	RW	100.00	64	61	90	63	61	56	85	56	30	49	58	57	55	40	40	63	75	63	21	875,000$/1yrs
Jan Mysak (R)	0	C/LW	100.00	65	66	91	63	66	55	76	59	52	54	60	51	57	38	38	63	75	63	23	828,333$/1yrs
Sam Harris (R)	0	LW	100.00	64	66	78	64	66	55	86	56	39	48	58	55	55	33	33	63	75	63	21	475,000$/2yrs
Nathan Legare (R)	0	LW/RW	100.00	65	72	69	63	73	53	73	54	52	47	57	54	53	41	41	61	75	61	24	450,000$/3yrs
Michal Teply (R)	11	LW/RW	100.00	66	70	91	63	70	52	85	50	41	45	48	52	45	38	38	60	75	60	24	450,000$/3yrs
Jordan Gustafson (R)	0	C	100.00	64	67	71	64	67	54	54	55	47	48	57	57	53	37	37	58	75	58	21	625,000$/1yrs
Beckett Hendrickson (R)	0	C/LW	100.00	65	67	88	62	67	51	75	48	32	44	47	48	45	34	34	58	75	58	20	550,000$/2yrs
Shane Bowers (R)	0	C/LW/RW	100.00	66	69	88	62	68	52	74	49	36	45	47	49	45	41	41	58	75	58	25	450,000$/3yrs
Emil Jarventie (R)	0	LW	100.00	65	63	90	62	62	49	62	47	25	42	46	52	44	33	33	55	75	55	20	500,000$/2yrs
David Reinbacher (R)	0	D	100.00	65	69	72	65	69	57	57	54	25	45	49	77	47	33	33	62	75	62	20	875,000$/2yrs
Topias Vilen (R)	0	D	100.00	65	70	85	64	70	56	76	50	25	44	40	61	38	37	37	60	75	60	22	836,667$/2yrs
Ryan Mast (R)	0	D	100.00	65	79	78	63	79	52	66	46	25	36	40	68	38	37	37	59	75	59	22	830,556$/2yrs
Corson Ceulemans (R)	0	D	100.00	65	73	76	64	74	51	67	47	25	36	41	62	39	33	33	57	75	57	22	925,000$/2yrs
Simon Lundmark (R)	0	D	100.00	65	70	84	65	70	53	84	46	25	36	41	49	39	37	37	57	75	57	24	450,000$/2yrs
Eemil Viro (R)	0	D	100.00	65	67	80	64	67	53	71	44	25	32	39	52	37	39	39	55	75	55	23	828,333$/1yrs
Scratches
Kasperi Kapanen	0	LW/RW	100.00	69	40	88	74	72	55	81	60	65	63	63	69	54	70	59	69	75	69	28	1,600,000$/1yrs
Cam Atkinson	0	LW/RW	100.00	67	66	90	66	62	47	47	60	52	63	63	63	58	78	65	61	75	61	36	3,000,000$/1yrs
Kasper Halttunen (R)	0	LW/RW	100.00	67	77	78	64	77	54	72	55	61	47	58	53	55	36	36	61	75	61	20	725,000$/2yrs
Koehn Ziemmer (R)	0	C/RW	100.00	66	73	82	64	74	55	69	55	52	46	59	49	55	38	38	61	75	61	20	625,000$/2yrs
Bryce McConnell-Barker (R)	0	C	100.00	66	70	87	63	70	53	81	49	45	44	48	56	45	37	37	60	75	60	21	625,000$/1yrs
Eetu Liukas (R)	0	LW	100.00	66	75	81	62	75	54	73	49	50	44	48	46	46	36	36	58	75	58	22	867,500$/2yrs
Danny Zhilkin (R)	0	C	100.00	66	69	91	62	70	51	72	48	47	45	47	45	45	36	36	57	75	57	21	625,000$/1yrs
Lassi Thomson (R)	0	D	100.00	65	68	82	68	68	58	85	55	25	46	50	60	47	39	39	63	75	63	24	450,000$/2yrs
Luke Coughlin (R)	0	D	100.00	65	65	82	64	64	53	75	46	25	36	41	61	39	35	35	58	75	58	20	475,000$/2yrs
Kalle Kangas (R)	0	D	100.00	65	77	83	63	78	51	76	45	25	34	40	54	37	32	32	57	75	57	20	450,000$/2yrs
Aleksi Heimosalmi (R)	0	D	100.00	65	66	83	65	66	55	77	47	25	36	41	47	39	34	34	56	75	56	22	450,000$/2yrs
Kim Nousiainen (R)	0	D	100.00	65	64	66	64	64	53	74	46	25	36	40	45	38	37	37	55	75	55	24	450,000$/3yrs

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
Justus Annunen	0	100.00	73	70	76	88	89	84	75	62	83	66	56	57	55	73	75	73	25	837,500$/2yrs
Aleksei Kolosov	0	100.00	63	55	64	69	63	73	67	62	65	65	59	58	55	60	75	60	23	500,000$/4yrs
Scratches
Hugo Ollas	0	100.00	45	47	63	77	45	52	45	45	45	45	38	33	33	47	75	47	23	450,000$/2yrs
Cameron Whitehead	0	100.00	41	55	65	66	41	48	41	41	41	41	34	35	35	45	75	45	22	500,000$/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	PPG	PPA	PPP	PKM	GW	FO%	FOT	P/20
1	Corson Ceulemans	Pimps (AVG)	D	1	1	1	2	1	0	0	0	2	0	0	50.00%	0	16.28	0	0	0	0	0	0.00%	0	2.46
2	Michal Teply	Pimps (AVG)	LW/RW	1	0	2	2	1	0	0	0	3	1	3	0.00%	0	15.13	0	0	0	1	0	0.00%	1	2.64
3	Nathan Legare	Pimps (AVG)	LW/RW	1	1	0	1	1	0	0	1	4	1	3	25.00%	1	14.20	0	0	0	1	1	0.00%	0	1.41
4	Beckett Hendrickson	Pimps (AVG)	C/LW	1	0	1	1	1	0	0	0	3	0	1	0.00%	0	14.13	0	0	0	0	0	28.57%	14	1.42
5	David Reinbacher	Pimps (AVG)	D	1	1	0	1	0	0	0	0	3	1	1	33.33%	0	22.95	1	0	1	3	0	0.00%	0	0.87
6	Sam Harris	Pimps (AVG)	LW	1	0	1	1	0	0	0	4	2	0	0	0.00%	0	17.40	0	1	1	0	0	100.00%	1	1.15
7	Eemil Viro	Pimps (AVG)	D	1	0	0	0	1	2	0	1	0	0	0	0.00%	1	17.13	0	0	0	1	0	0.00%	0	0.00
8	Mackie Samoskevich	Pimps (AVG)	C/RW	1	0	0	0	0	0	0	4	3	2	2	0.00%	0	23.88	0	0	0	3	0	46.43%	28	0.00
9	Ryan Mast	Pimps (AVG)	D	1	0	0	0	0	2	0	0	1	0	3	0.00%	1	19.30	0	0	0	1	0	0.00%	0	0.00
10	Topias Vilen	Pimps (AVG)	D	1	0	0	0	0	0	0	3	0	0	0	0.00%	1	22.95	0	0	0	3	0	0.00%	0	0.00
11	Zachary L'Heureux	Pimps (AVG)	C/LW	1	0	0	0	0	2	0	6	5	0	2	0.00%	0	17.00	0	0	0	2	0	100.00%	1	0.00
12	Jagger Firkus	Pimps (AVG)	RW	1	0	0	0	0	0	0	1	4	0	1	0.00%	0	19.12	0	0	0	1	0	0.00%	0	0.00
13	Joey Muldowney	Pimps (AVG)	RW	1	0	0	0	0	0	0	2	3	0	0	0.00%	0	16.42	0	0	0	1	0	100.00%	2	0.00
14	Jordan Gustafson	Pimps (AVG)	C	1	0	0	0	0	0	0	1	1	0	0	0.00%	0	8.88	0	0	0	0	0	20.00%	10	0.00
15	Emil Jarventie	Pimps (AVG)	LW	1	0	0	0	0	0	0	0	1	0	0	0.00%	0	2.42	0	0	0	0	0	0.00%	1	0.00
16	Jan Mysak	Pimps (AVG)	C/LW	1	0	0	0	0	0	0	2	2	2	1	0.00%	0	18.48	0	0	0	0	0	38.46%	26	0.00
17	Simon Lundmark	Pimps (AVG)	D	1	0	0	0	0	0	0	2	0	0	0	0.00%	4	20.18	0	0	0	2	0	0.00%	0	0.00
18	Shane Bowers	Pimps (AVG)	C/LW/RW	1	0	0	0	0	0	0	0	0	0	1	0.00%	0	8.13	0	0	0	0	0	0.00%	0	0.00
Team Total or Average			18	3	5	8	5	6	0	27	37	7	18	8.11%	8	16.33	1	1	2	26	23	1	39.29%	84	0.54

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	Justus Annunen	Pimps (AVG)	1	1	0	0	0.969	1.00	60	0	0	1	32	0	1	0	0.000	0	1	0	1	0	0
Team Total or Average		1	1	0	0	0.969	1.00	60	0	0	1	32	0	1	0	0.000	0	1	0	1	0	0

Salary

Player Name	POS	Age	Cap Hit	2017-18	2018-19	2019-20	2020-21
Aleksei Kolosov	G	23	500,000$	500,000$	500,000$	500,000$	500,000$
Aleksi Heimosalmi	D	22	450,000$	450,000$	450,000$
Beckett Hendrickson	C/LW	20	550,000$	550,000$	550,000$
Bryce McConnell-Barker	C	21	625,000$	625,000$
Cam Atkinson	LW/RW	36	3,000,000$	3,000,000$	UFA
Cameron Whitehead	G	22	500,000$	500,000$
Corson Ceulemans	D	22	925,000$	925,000$	925,000$
Danny Zhilkin	C	21	625,000$	625,000$
David Reinbacher	D	20	875,000$	875,000$	875,000$
Eemil Viro	D	23	828,333$	828,333$
Eetu Liukas	LW	22	867,500$	867,500$	867,500$
Emil Jarventie	LW	20	500,000$	500,000$	500,000$
Hugo Ollas	G	23	450,000$	450,000$	450,000$
Jagger Firkus	RW	21	875,000$	875,000$
Jan Mysak	C/LW	23	828,333$	828,333$
Joey Muldowney	RW	21	450,000$	450,000$
Jordan Gustafson	C	21	625,000$	625,000$
Justus Annunen	G	25	837,500$	837,500$	837,500$	RFA
Kalle Kangas	D	20	450,000$	450,000$	450,000$
Kasper Halttunen	LW/RW	20	725,000$	725,000$	725,000$
Kasperi Kapanen	LW/RW	28	1,600,000$	1,600,000$	RFA
Kim Nousiainen	D	24	450,000$	450,000$	450,000$	450,000$	RFA
Koehn Ziemmer	C/RW	20	625,000$	625,000$	625,000$
Lassi Thomson	D	24	450,000$	450,000$	450,000$	RFA
Luke Coughlin	D	20	475,000$	475,000$	475,000$
Mackie Samoskevich	C/RW	22	925,000$	925,000$
Michal Teply	LW/RW	24	450,000$	450,000$	450,000$	450,000$	RFA
Nathan Legare	LW/RW	24	450,000$	450,000$	450,000$	450,000$	RFA
Ryan Mast	D	22	830,556$	830,556$	830,556$
Sam Harris	LW	21	475,000$	475,000$	475,000$
Shane Bowers	C/LW/RW	25	450,000$	450,000$	450,000$	450,000$	RFA
Simon Lundmark	D	24	450,000$	450,000$	450,000$	RFA
Topias Vilen	D	22	836,667$	836,667$	836,667$
Zachary L'Heureux	C/LW	22	863,334$	863,334$	863,334$

Lines

Forward Lines
# - Zachary L'Heureux	# - Mackie Samoskevich	# - Joey Muldowney
# - Sam Harris	# - Jan Mysak	# - Jagger Firkus
# - Nathan Legare	# - Beckett Hendrickson	# - Michal Teply
# - Shane Bowers	# - Jordan Gustafson	# - Mackie Samoskevich

Defensive Pairings
# - David Reinbacher	# - Topias Vilen
# - Ryan Mast	# - Simon Lundmark
# - Corson Ceulemans	# - Eemil Viro

1st Power Play Unit
# - Zachary L'Heureux		# - Mackie Samoskevich		# - Joey Muldowney
# - David Reinbacher			# - Topias Vilen

2nd Power Play Unit
# - Sam Harris		# - Jan Mysak		# - Jagger Firkus
# - Ryan Mast			# - Simon Lundmark

Goalies
# - Justus Annunen		# - Aleksei Kolosov

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	GP3	SHF	SH1	SP2	SP3	SHA	SHB	Pim	Hit	PPA	PPG	PP%	PKA	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	Junior Wolves	1	1	3.00	1.00	2.00	2	1.000	3.0	5.0	8.0	2	1	37.0	19	8	10	32.0	8.0	6.00	27.0	3.00	1.00	33.33%	3.00	100.00%	12	33	36.36%	12	33	36.36%	9	18	50.00%	20.00	12.00	24.00	8.00	14.00	7.00	66.7%	8.1%	96.9%	105.0	DULL
	_Vs Division	1	1	3.00	1.00	2.00	2	1.000	3.0	5.0	8.0	2	1	37.0	19	8	10	32.0	8.0	6.00	27.0	3.00	1.00	33.33%	3.00	100.00%	12	33	36.36%	12	33	36.36%	9	18	50.00%	20.00	12.00	24.00	8.00	14.00	7.00	66.7%	8.1%	96.9%	105.0	DULL
	_Vs Conference	1	1	3.00	1.00	2.00	2	1.000	3.0	5.0	8.0	2	1	37.0	19	8	10	32.0	8.0	6.00	27.0	3.00	1.00	33.33%	3.00	100.00%	12	33	36.36%	12	33	36.36%	9	18	50.00%	20.00	12.00	24.00	8.00	14.00	7.00	66.7%	8.1%	96.9%	105.0	DULL
	_Since Last GM Reset	1	1	3.00	1.00	2.00	2	1.000	3.0	5.0	8.0	2	1	37.0	19	8	10	32.0	8.0	6.00	27.0	3.00	1.00	33.33%	3.00	100.00%	12	33	36.36%	12	33	36.36%	9	18	50.00%	20.00	12.00	24.00	8.00	14.00	7.00	66.7%	8.1%	96.9%	105.0	DULL
	Total	1	1	3.00	1.00	2.00	2	1.000	3.0	5.0	8.0	2	1	37.0	19	8	10	32.0	8.0	6.00	27.0	3.00	1.00	33.33%	3.00	100.00%	12	33	36.36%	12	33	36.36%	9	18	50.00%	20.00	12.00	24.00	8.00	14.00	7.00	66.7%	8.1%	96.9%	105.0	DULL

	Puck Time
Offensive Zone	20
Neutral Zone	14
Defensive Zone	24

	Puck Time
Offensive Zone Start	33
Neutral Zone Start	18
Defensive Zone Start	33

	Puck Time
With Puck	28
Without Puck	31

	Faceoffs
Faceoffs Won	33
Faceoffs Lost	51

	Team Average Shots after	League Average Shots after
1st Period	19.0	9.57
2nd Period	27.0	20.31
3rd Period	37.0	30.68
Overtime	37.0	31.4

Goals in	Team Average Goals after	League Average Goals after
1st Period	2.0	0.64
2nd Period	2.0	1.65
3rd Period	3.0	2.67
Overtime	3.0	2.83


Even Strenght Goal	2
PP Goal	1
PK Goal	0
Empty Net Goal	0

	Home	Away
Win	0	1
Lost	0	0
Overtime Lost	0	0


PP Attempt	3
PP Goal	1
PK Attempt	3
PK Goal Against	0

	Home
Shots For	37.0
Shots Against	32.0
Goals For	3.0
Goals Against	1.0
Hits	27.0
Shots Blocked	8.0
Pim	6.0

Schedule

Date	Matchup			Result	Detail
1	PIM	@	JWO	PIM3,JWO1	RECAP
4	BAR	@	PIM
6	SAI	@	PIM
9	PIM	@	CHE
12	BKS	@	PIM
13	WPK	@	PIM
15	PIM	@	SPA
16	PIM	@	CAN
18	PIM	@	ZOL
22	PIM	@	BUL
25	PIM	@	KAN
27	OHK	@	PIM
29	PIM	@	DEV
31	PIM	@	BKS
33	PIM	@	BAR
37	CHE	@	PIM
39	THI	@	PIM
43	JWO	@	PIM
46	SHA	@	PIM
48	BUL	@	PIM
50	DEV	@	PIM
53	SPA	@	PIM
57	PIM	@	VEN
58	PIM	@	OHK
60	PIM	@	VIK
63	BLA	@	PIM
65	JWO	@	PIM
67	CAN	@	PIM
69	PIM	@	NIN
70	PIM	@	CAN
74	PIM	@	BAR
78	PIM	@	BKS
80	PIM	@	RAV
84	RAV	@	PIM
86	BLA	@	PIM
89	CAN	@	PIM
90	PIM	@	SHA
93	KAN	@	PIM
96	ZOL	@	PIM
97	ASS	@	PIM
101	EAG	@	PIM
103	BKS	@	PIM
105	RAV	@	PIM
106	DEV	@	PIM
109	PIM	@	BAR
111	PIM	@	DEV
114	PIM	@	RAV
117	PIM	@	CAN
120	PIM	@	IND
121	PIM	@	NIN
124	PIM	@	THI
127	IND	@	PIM
128	NIN	@	PIM
130	PIM	@	SEN
132	VEN	@	PIM
133	VIK	@	PIM
135	PIM	@	SEN
136	SHO	@	PIM
138	PIM	@	BUL
139	PIM	@	THI
142	PIM	@	SHO
145	SQU	@	PIM
146	BAR	@	PIM
148	BKS	@	PIM
Trade Deadline --- Trades can�t be done after this day is simulated!
152	JWO	@	PIM
154	PIM	@	BLA
157	IND	@	PIM
159	PIM	@	ASS
163	BLA	@	PIM
164	RAV	@	PIM
166	GRG	@	PIM
169	PIM	@	WPK
172	PIM	@	SQU
174	PIM	@	EAG
176	PIM	@	SAI
180	RAV	@	PIM
182	PIM	@	GRG
184	PIM	@	ASS
186	PIM	@	KAN
189	PIM	@	JWO
192	SEN	@	PIM
194	DEV	@	PIM