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$/1yrs
Kasperi Kapanen	0	LW/RW	100.00	69	40	88	74	72	55	81	60	65	63	63	69	54	70	59	69	75	69	28	1,600,000$/1yrs
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$/1yrs
Jan Mysak (R)	0	C/LW	100.00	65	66	91	63	66	55	80	59	52	54	60	51	57	38	38	63	75	63	23	828,333$/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$/2yrs
Nathan Legare (R)	0	LW/RW	100.00	65	72	69	63	73	53	85	54	52	47	57	54	53	41	41	61	75	61	24	450,000$/3yrs
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
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$/1yrs
Michal Teply (R)	11	LW/RW	100.00	66	70	91	63	70	52	85	50	41	45	48	52	45	38	38	60	75	60	24	450,000$/3yrs
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$/2yrs
Lassi Thomson (R)	0	D	100.00	65	68	82	68	68	58	85	55	25	46	50	60	47	39	39	63	75	63	24	450,000$/2yrs
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$/2yrs
Luke Coughlin (R)	0	D	100.00	65	65	82	64	64	53	75	46	25	36	41	61	39	35	35	58	75	58	20	475,000$/2yrs
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$/2yrs
Scratches
Koehn Ziemmer (R)	0	C/RW	100.00	66	73	82	64	74	55	80	55	52	46	59	49	55	38	38	61	75	61	20	625,000$/2yrs
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$/2yrs
Jordan Gustafson (R)	0	C	100.00	64	67	71	64	67	54	54	55	47	48	57	57	53	37	37	58	75	58	21	625,000$/1yrs
Shane Bowers (R)	0	C/LW/RW	100.00	66	69	88	62	68	52	81	49	36	45	47	49	45	41	41	58	75	58	25	450,000$/3yrs
Danny Zhilkin (R)	0	C	100.00	66	69	91	62	70	51	72	48	47	45	47	45	45	36	36	57	75	57	21	625,000$/1yrs
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$/2yrs
David Reinbacher (R)	0	D	95.72	65	69	72	65	69	57	57	54	25	45	49	77	47	33	33	62	75	62	20	875,000$/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$/2yrs
Kalle Kangas (R)	0	D	100.00	65	77	83	63	78	51	76	45	25	34	40	54	37	32	32	57	75	57	20	450,000$/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$/2yrs
Eemil Viro (R)	0	D	100.00	65	67	80	64	67	53	73	44	25	32	39	52	37	39	39	55	75	55	23	828,333$/1yrs
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	99.00	73	70	76	88	89	84	75	62	83	66	56	57	55	73	75	73	25	837,500$/2yrs
Aleksei Kolosov	0	100.00	63	67	64	69	63	73	67	62	65	65	59	58	55	60	75	60	23	500,000$/4yrs
Scratches
Hugo Ollas	0	100.00	45	71	63	77	45	52	45	45	45	45	38	33	33	47	75	47	23	450,000$/2yrs
Cameron Whitehead	0	100.00	41	76	65	66	41	48	41	41	41	41	34	35	35	45	75	45	22	500,000$/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	PKG	PKA	PKP	PKM	GW	GT	FO%	FOT	EG	HT	P/20	PSG	PSS
1	Mackie Samoskevich	Pimps (AVG)	C/RW	73	44	62	106	27	24	0	142	359	113	259	12.26%	30	22.06	6	6	12	2	0	2	200	5	5	44.53%	2113	0	2	1.32	0	7
2	Zachary L'Heureux	Pimps (AVG)	C/LW	59	30	45	75	26	43	5	138	258	67	159	11.63%	25	17.93	0	10	10	1	1	2	110	5	1	47.83%	807	0	1	1.42	1	5
3	Kasperi Kapanen	Pimps (AVG)	LW/RW	60	32	34	66	25	18	0	86	256	64	173	12.50%	25	19.12	3	3	6	0	0	0	114	3	2	59.87%	157	0	0	1.15	3	6
4	Jagger Firkus	Pimps (AVG)	RW	72	29	35	64	20	18	0	60	203	59	144	14.29%	13	18.05	6	6	12	1	0	1	50	6	0	29.41%	85	0	1	0.98	0	1
5	Sam Harris	Pimps (AVG)	LW	73	23	27	50	25	32	0	105	198	51	107	11.62%	24	18.63	1	7	8	0	0	0	84	4	2	34.65%	101	0	0	0.74	0	2
6	Jan Mysak	Pimps (AVG)	C/LW	73	19	29	48	12	10	0	76	165	44	93	11.52%	10	15.67	4	5	9	0	0	0	6	2	1	46.18%	1191	0	0	0.84	1	1
7	Joey Muldowney	Pimps (AVG)	RW	63	23	24	47	19	4	0	52	152	36	75	15.13%	11	15.44	1	3	4	0	1	1	64	6	0	36.67%	390	0	3	0.97	0	1
8	Cam Atkinson	Pimps (AVG)	LW/RW	58	22	23	45	-1	12	0	50	145	39	102	15.17%	14	14.56	0	1	1	0	0	0	34	0	2	46.59%	88	0	1	1.07	0	1
9	Lassi Thomson	Pimps (AVG)	D	63	3	34	37	15	26	0	105	73	34	53	4.11%	88	23.19	0	5	5	0	2	2	146	0	0	0.00%	0	1	0	0.51	0	0
10	David Reinbacher	Pimps (AVG)	D	40	7	30	37	3	36	0	85	78	22	60	8.97%	80	22.35	5	3	8	0	0	0	84	0	0	0.00%	0	0	0	0.83	0	0
11	Ryan Mast	Pimps (AVG)	D	49	12	21	33	13	32	0	105	54	19	31	22.22%	67	20.81	5	0	5	0	1	1	105	1	1	0.00%	0	0	0	0.65	0	0
12	Nathan Legare	Pimps (AVG)	LW/RW	73	14	16	30	9	39	5	130	126	29	90	11.11%	34	16.76	1	0	1	0	0	0	63	4	1	53.33%	60	0	0	0.49	0	0
13	Kasper Halttunen	Pimps (AVG)	LW/RW	59	9	19	28	14	19	5	61	120	28	65	7.50%	4	12.35	0	0	0	0	0	0	2	1	0	51.13%	399	0	0	0.77	0	0
14	Corson Ceulemans	Pimps (AVG)	D	30	3	19	22	13	26	0	55	27	8	21	11.11%	46	17.18	0	0	0	1	1	2	24	0	1	0.00%	0	0	0	0.85	0	0
15	Simon Lundmark	Pimps (AVG)	D	65	1	19	20	35	26	0	89	28	8	22	3.57%	64	18.92	0	4	4	0	0	0	102	0	0	0.00%	0	0	0	0.33	0	0
16	Bryce McConnell-Barker	Pimps (AVG)	C	40	4	10	14	-7	12	0	20	58	14	32	6.90%	8	10.42	0	0	0	0	0	0	16	0	0	41.33%	467	0	0	0.67	0	0
17	Jordan Gustafson	Pimps (AVG)	C	34	6	8	14	-4	8	0	25	43	9	24	13.95%	4	8.16	0	0	0	0	0	0	20	1	1	44.80%	346	0	0	1.01	0	0
18	Luke Coughlin	Pimps (AVG)	D	39	3	10	13	12	24	0	61	38	13	24	7.89%	49	20.68	1	1	2	0	0	0	79	1	0	0.00%	0	0	0	0.32	0	0
19	Michal Teply	Pimps (AVG)	LW/RW	52	4	7	11	-14	8	0	33	50	18	51	8.00%	8	11.29	0	0	0	0	0	0	13	0	0	39.02%	41	0	0	0.37	0	0
20	Koehn Ziemmer	Pimps (AVG)	C/RW	12	4	5	9	5	6	0	9	9	6	9	44.44%	5	11.53	0	0	0	0	0	0	0	0	2	50.51%	99	0	0	1.30	0	0
21	Aleksi Heimosalmi	Pimps (AVG)	D	30	0	7	7	5	16	0	58	11	3	4	0.00%	22	16.68	0	1	1	0	0	0	26	0	0	0.00%	0	0	0	0.28	0	0
22	Beckett Hendrickson	Pimps (AVG)	C/LW	39	2	4	6	-5	4	0	9	31	5	16	6.45%	4	7.21	0	0	0	0	0	0	5	1	0	35.56%	239	0	0	0.43	0	0
23	Eemil Viro	Pimps (AVG)	D	34	1	4	5	8	30	0	72	19	5	14	5.26%	39	18.95	0	0	0	0	0	0	72	0	0	0.00%	0	0	0	0.16	0	0
24	Shane Bowers	Pimps (AVG)	C/LW/RW	38	3	2	5	-7	4	0	9	25	6	20	12.00%	4	7.66	0	0	0	0	0	0	0	0	1	45.16%	62	0	1	0.34	0	0
25	Kalle Kangas	Pimps (AVG)	D	6	0	1	1	3	2	0	6	6	3	1	0.00%	6	16.74	0	0	0	0	0	0	5	0	0	0.00%	0	0	0	0.20	0	0
26	Eetu Liukas	Pimps (AVG)	LW	3	0	0	0	0	0	0	2	3	0	2	0.00%	0	7.99	0	0	0	0	0	0	2	0	0	50.00%	2	0	0	0.00	0	0
27	Danny Zhilkin	Pimps (AVG)	C	1	0	0	0	-1	0	0	0	0	1	0	0.00%	0	14.03	0	0	0	0	0	0	0	0	0	33.33%	12	0	0	0.00	0	0
28	Emil Jarventie	Pimps (AVG)	LW	33	0	0	0	1	2	0	5	3	0	3	0.00%	1	2.88	0	0	0	0	0	0	1	0	0	34.00%	50	0	0	0.00	0	0
Team Total or Average			1271	298	495	793	251	481	15	1648	2538	704	1654	11.74%	685	16.27	33	55	88	1654	5	6	11	1439	40	20	44.72%	6709	1	9	0.77	5	24

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)	48	33	9	4	0.908	3.15	2781	4	0	146	1588	0	5	1	0.870	23	46	17	5	0	1
2	Aleksei Kolosov	Pimps (AVG)	25	9	10	2	0.885	4.43	1314	2	0	97	842	0	2	0	0.667	3	21	52	0	2	0
3	Hugo Ollas	Pimps (AVG)	7	2	3	1	0.854	5.09	342	0	0	29	198	0	0	1	0.000	0	6	4	0	0	0
Team Total or Average		80	44	22	7	0.896	3.68	4437	6	0	272	2628	0	7	2	0.846	26	73	73	5	2	1

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
Zachary L'Heureux	C/LW	22	863,334$	863,334$	863,334$

Lines

Forward Lines
# - Kasperi Kapanen	# - Mackie Samoskevich	# - Jan Mysak
# - Sam Harris	# - Zachary L'Heureux	# - Jagger Firkus
# - Cam Atkinson	# - Joey Muldowney	# - Kasper Halttunen
# - Nathan Legare	# - Bryce McConnell-Barker	# - Michal Teply

Defensive Pairings
# - Lassi Thomson
# - Ryan Mast	# - Luke Coughlin
# - Simon Lundmark	# - Nathan Legare

1st Power Play Unit
# - Kasperi Kapanen		# - Mackie Samoskevich		# - Cam Atkinson
# - Lassi Thomson

2nd Power Play Unit
# - Sam Harris		# - Zachary L'Heureux		# - Jagger Firkus
# - Ryan Mast			# - Luke Coughlin

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	L	OTW	OTL	SOW	SOL	GF	GA	Diff	P	PCT	G	A	TP	SO	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	GF%	SH%	SV%	PDO	PDOBRK
1	HK	2	1	1	0	0	0	0	4.00	4.00	0.00	2	0.500	4.0	6.5	10.5	0	123	92	82	11	33.0	831	890	809	70	36.0	10.5	4.00	23.0	4.50	0.50	11.11%	1.00	0.50	50.00%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	811.00	538.50	907.00	289.00	502.50	242.00	50.0%	12.1%	88.9%	101.0	FUN
2	Ninjas	3	1	1	0	1	0	0	5.00	4.67	0.33	3	0.500	5.0	8.3	13.3	0	123	92	82	11	38.0	831	890	809	70	33.3	12.3	11.33	23.7	1.67	0.33	20.00%	5.67	1.33	76.47%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	540.67	359.00	604.67	192.67	335.00	161.33	58.3%	13.2%	86.0%	99.2	FUN
3	Junior Wolves	4	2	1	0	0	0	1	3.25	2.75	0.50	5	0.625	3.3	5.0	8.3	0	123	92	82	11	32.0	831	890	809	70	34.5	9.5	6.00	21.0	1.50	0.50	33.33%	2.75	0.75	72.73%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	405.50	269.25	453.50	144.50	251.25	121.00	57.9%	10.2%	92.0%	102.2	LUCKY
4	Bulldogs	3	3	0	0	0	0	0	4.67	2.33	2.33	6	1.000	4.7	7.7	12.3	0	123	92	82	11	33.7	831	890	809	70	33.3	8.0	5.33	20.0	3.00	0.67	22.22%	2.67	0.00	100.00%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	540.67	359.00	604.67	192.67	335.00	161.33	63.2%	13.9%	93.0%	106.9	LUCKY
5	Shockers	2	0	1	0	0	1	0	4.00	5.50	-1.50	2	0.500	4.0	6.0	10.0	0	123	92	82	11	31.0	831	890	809	70	45.0	11.5	6.00	27.5	1.50	0.00	0.00%	3.00	0.50	83.33%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	811.00	538.50	907.00	289.00	502.50	242.00	44.4%	12.9%	87.8%	100.7	FUN
6	Gargoyles	1	0	1	0	0	0	0	2.00	5.00	-3.00	0	0.000	2.0	4.0	6.0	0	123	92	82	11	47.0	831	890	809	70	22.0	4.0	2.00	26.0	6.00	0.00	0.00%	1.00	1.00	0.00%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	1,622.00	1,077.00	1,814.00	578.00	1,005.00	484.00	33.3%	4.3%	77.3%	81.5	Unlucky
7	Sailors	1	0	0	0	0	0	1	5.00	6.00	-1.00	1	0.500	5.0	9.0	14.0	0	123	92	82	11	28.0	831	890	809	70	40.0	11.0	0.00	24.0	2.00	2.00	100.00%	0.00	0.00	0.00%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	1,622.00	1,077.00	1,814.00	578.00	1,005.00	484.00	33.3%	17.9%	85.0%	102.9	FUN
8	Devils	4	2	0	0	2	0	0	4.00	3.50	0.50	6	0.750	4.0	7.0	11.0	0	123	92	82	11	36.8	831	890	809	70	32.5	8.8	5.00	21.3	3.50	0.25	7.14%	2.50	0.25	90.00%	1	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	405.50	269.25	453.50	144.50	251.25	121.00	53.6%	10.9%	89.2%	100.1	FUN
9	Eagles	1	0	1	0	0	0	0	2.00	7.00	-5.00	0	0.000	2.0	3.0	5.0	0	123	92	82	11	40.0	831	890	809	70	53.0	14.0	2.00	24.0	3.00	0.00	0.00%	1.00	0.00	100.00%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	1,622.00	1,077.00	1,814.00	578.00	1,005.00	484.00	22.2%	5.0%	86.8%	91.8	Unlucky
10	Ravens	5	3	1	1	0	0	0	5.20	3.60	1.60	8	0.800	5.2	8.0	13.2	0	123	92	82	11	35.8	831	890	809	70	37.2	7.0	6.80	23.4	2.60	0.00	0.00%	3.00	0.60	80.00%	1	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	324.40	215.40	362.80	115.60	201.00	96.80	63.4%	14.5%	90.3%	104.8	FUN
11	Venom	2	0	1	0	0	1	0	4.50	4.50	0.00	2	0.500	4.5	6.5	11.0	0	123	92	82	11	40.0	831	890	809	70	41.0	13.5	11.00	23.5	3.50	0.50	14.29%	5.00	1.50	70.00%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	811.00	538.50	907.00	289.00	502.50	242.00	57.1%	11.3%	89.0%	100.3	FUN
12	Indians	3	1	0	1	1	0	0	3.67	3.00	0.67	5	0.833	3.7	6.0	9.7	0	123	92	82	11	41.0	831	890	809	70	41.3	13.0	4.67	26.0	1.33	0.00	0.00%	2.33	0.00	100.00%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	540.67	359.00	604.67	192.67	335.00	161.33	55.0%	8.9%	92.7%	101.7	Unlucky
13	Squirrels	2	1	0	1	0	0	0	5.00	3.00	2.00	4	1.000	5.0	9.0	14.0	0	123	92	82	11	31.5	831	890	809	70	41.5	14.5	8.00	22.0	2.00	1.00	50.00%	3.50	0.00	100.00%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	811.00	538.50	907.00	289.00	502.50	242.00	57.1%	15.9%	92.8%	108.6	LUCKY
14	Blades	4	0	1	0	0	2	1	5.25	5.25	0.00	5	0.625	5.3	7.5	12.8	0	123	92	82	11	39.3	831	890	809	70	44.3	12.3	5.00	27.0	2.50	0.75	30.00%	2.00	0.75	62.50%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	405.50	269.25	453.50	144.50	251.25	121.00	50.0%	13.4%	88.1%	101.5	FUN
15	Sharknado	2	2	0	0	0	0	0	6.00	4.50	1.50	4	1.000	6.0	11.0	17.0	0	123	92	82	11	39.0	831	890	809	70	37.0	8.5	9.50	26.0	3.00	0.50	16.67%	3.50	1.00	71.43%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	811.00	538.50	907.00	289.00	502.50	242.00	61.1%	15.4%	87.8%	103.2	FUN
16	Barking Spiders	5	2	2	1	0	0	0	4.00	3.20	0.80	6	0.600	4.0	7.2	11.2	0	123	92	82	11	37.6	831	890	809	70	27.0	9.2	5.60	21.6	2.80	0.20	7.14%	2.40	0.80	66.67%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	324.40	215.40	362.80	115.60	201.00	96.80	61.3%	10.6%	88.1%	98.8	FUN
17	Space Invaders	2	1	1	0	0	0	0	4.50	5.50	-1.00	2	0.500	4.5	8.0	12.5	0	123	92	82	11	27.5	831	890	809	70	43.0	11.5	9.00	31.0	2.50	1.50	60.00%	4.00	1.50	62.50%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	811.00	538.50	907.00	289.00	502.50	242.00	42.9%	16.4%	87.2%	103.6	FUN
18	Checkers	2	2	0	0	0	0	0	6.00	2.00	4.00	4	1.000	6.0	10.0	16.0	0	123	92	82	11	36.5	831	890	809	70	27.0	13.5	10.00	23.5	4.50	1.50	33.33%	5.00	1.50	70.00%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	811.00	538.50	907.00	289.00	502.50	242.00	90.0%	16.4%	92.6%	109.0	LUCKY
19	Vikings	2	1	1	0	0	0	0	2.00	2.50	-0.50	2	0.500	2.0	3.0	5.0	0	123	92	82	11	34.5	831	890	809	70	35.5	14.0	10.00	23.5	1.50	0.00	0.00%	5.00	1.00	80.00%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	811.00	538.50	907.00	289.00	502.50	242.00	57.1%	5.8%	93.0%	98.8	DULL
20	Kangaroos	2	0	2	0	0	0	0	2.00	5.00	-3.00	0	0.000	2.0	3.5	5.5	1	123	92	82	11	27.0	831	890	809	70	32.5	12.0	5.00	19.5	2.00	0.00	0.00%	2.50	0.50	80.00%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	811.00	538.50	907.00	289.00	502.50	242.00	30.8%	7.4%	84.6%	92.0	Unlucky
21	Thistles	3	3	0	0	0	0	0	5.67	3.00	2.67	6	1.000	5.7	10.0	15.7	0	123	92	82	11	38.3	831	890	809	70	37.3	11.0	8.00	26.3	2.67	0.67	25.00%	3.33	0.67	80.00%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	540.67	359.00	604.67	192.67	335.00	161.33	68.2%	14.8%	92.0%	106.7	LUCKY
22	Barracudas	5	4	1	0	0	0	0	4.20	3.00	1.20	8	0.800	4.2	6.8	11.0	0	123	92	82	11	33.0	831	890	809	70	33.2	7.4	8.00	22.4	3.60	0.40	11.11%	3.80	0.80	78.95%	1	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	324.40	215.40	362.80	115.60	201.00	96.80	63.3%	12.7%	91.0%	103.7	FUN
23	Senators	2	2	0	0	0	0	0	4.50	2.00	2.50	4	1.000	4.5	9.0	13.5	0	123	92	82	11	32.0	831	890	809	70	36.5	9.5	11.00	25.0	3.00	1.00	33.33%	4.50	0.50	88.89%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	811.00	538.50	907.00	289.00	502.50	242.00	70.0%	14.1%	94.5%	108.6	LUCKY
24	Wolf Pack	2	0	2	0	0	0	0	1.50	4.00	-2.50	0	0.000	1.5	3.0	4.5	1	123	92	82	11	31.5	831	890	809	70	37.5	8.5	11.00	17.5	1.50	0.00	0.00%	5.50	1.50	72.73%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	811.00	538.50	907.00	289.00	502.50	242.00	37.5%	4.8%	89.3%	94.1	Unlucky
25	Assassins	2	0	2	0	0	0	0	1.50	5.50	-4.00	0	0.000	1.5	2.5	4.0	0	123	92	82	11	39.5	831	890	809	70	39.5	10.0	4.00	28.0	4.00	0.00	0.00%	2.00	0.50	75.00%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	811.00	538.50	907.00	289.00	502.50	242.00	23.1%	3.8%	86.1%	89.9	Unlucky
26	Zolos	2	0	2	0	0	0	0	3.00	5.50	-2.50	0	0.000	3.0	5.0	8.0	0	123	92	82	11	28.5	831	890	809	70	39.0	12.5	8.00	26.5	3.50	0.50	14.29%	4.00	1.50	62.50%	0	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	811.00	538.50	907.00	289.00	502.50	242.00	38.5%	10.5%	85.9%	96.4	FUN
27	Huskies	5	3	0	2	0	0	0	5.40	3.60	1.80	10	1.000	5.4	9.4	14.8	0	123	92	82	11	36.2	831	890	809	70	33.0	9.4	10.40	23.8	4.00	0.80	20.00%	4.20	1.80	57.14%	2	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	324.40	215.40	362.80	115.60	201.00	96.80	71.9%	14.9%	89.1%	104.0	FUN
	_Vs Division	28	16	5	4	2	0	1	4.39	3.29	1.11	43	0.768	4.4	7.3	11.7	0	123	92	82	11	35.3	831	890	809	70	32.9	8.5	7.07	22.3	3.04	0.36	11.76%	3.14	0.86	72.73%	5	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	57.93	38.46	64.79	20.64	35.89	17.29	62.4%	12.4%	90.0%	102.4	FUN
	_Vs Conference	47	23	11	5	4	2	2	4.26	3.57	0.68	66	0.702	4.3	7.0	11.3	1	123	92	82	11	35.7	831	890	809	70	34.9	9.6	6.85	23.1	2.79	0.38	13.74%	3.11	0.72	76.71%	5	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	34.51	22.91	38.60	12.30	21.38	10.30	57.6%	11.9%	89.7%	101.6	FUN
	_Since Last GM Reset	73	34	22	6	4	4	3	4.21	3.79	0.41	95	0.651	4.2	7.0	11.2	2	123	92	82	11	35.3	831	890	809	70	36.0	10.3	7.16	23.7	2.82	0.47	16.50%	3.25	0.79	75.53%	5	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	22.22	14.75	24.85	7.92	13.77	6.63	55.5%	11.9%	89.5%	101.4	FUN
	Total	73	34	22	6	4	4	3	4.21	3.79	0.41	95	0.651	4.2	7.0	11.2	2	123	92	82	11	35.3	831	890	809	70	36.0	10.3	7.16	23.7	2.82	0.47	16.50%	3.25	0.79	75.53%	5	1130	2500	45.20%	1259	2785	45.21%	611	1327	46.04%	22.22	14.75	24.85	7.92	13.77	6.63	55.5%	11.9%	89.5%	101.4	FUN

	Puck Time
Offensive Zone	22
Neutral Zone	13
Defensive Zone	24

	Puck Time
Offensive Zone Start	2500
Neutral Zone Start	1327
Defensive Zone Start	2785

	Puck Time
With Puck	29
Without Puck	31

	Faceoffs
Faceoffs Won	3000
Faceoffs Lost	3612

	Team Average Shots after	League Average Shots after
1st Period	11.4	9.57
2nd Period	23.6	20.31
3rd Period	34.7	30.68
Overtime	35.6	31.4

Goals in	Team Average Goals after	League Average Goals after
1st Period	1.7	0.64
2nd Period	2.9	1.65
3rd Period	4.1	2.67
Overtime	4.2	2.83


Even Strenght Goal	266
PP Goal	34
PK Goal	5
Empty Net Goal	2

	Home	Away
Win	20	24
Lost	12	10
Overtime Lost	6	1


PP Attempt	206
PP Goal	34
PK Attempt	237
PK Goal Against	58

	Home
Shots For	35.3
Shots Against	36.0
Goals For	4.2
Goals Against	3.8
Hits	23.7
Shots Blocked	10.3
Pim	7.2

Schedule

Date	Matchup			Result	Detail
Sun, Dec 28	PIM	@	JWO	PIM3,JWO1	RECAP
Wed, Dec 31	BAR	@	PIM	BAR2,PIM4	RECAP
Fri, Jan 2	SAI	@	PIM	SAI6,PIM5 (SO)	RECAP
Sun, Jan 4 10:02	PIM	@	CHE	PIM7,CHE2	RECAP
Tue, Jan 6	BKS	@	PIM	BKS4,PIM3	RECAP
Wed, Jan 7	WPK	@	PIM	WPK4,PIM1	RECAP
Fri, Jan 9	PIM	@	SPA	PIM3,SPA8	RECAP
Sat, Jan 10 10:02	PIM	@	CAN	PIM7,CAN6	RECAP
Sun, Jan 11	PIM	@	ZOL	PIM2,ZOL5	RECAP
Fri, Jan 16	PIM	@	BUL	PIM4,BUL2	RECAP
Sun, Jan 18 10:02	PIM	@	KAN	PIM1,KAN5	RECAP
Tue, Jan 20 12:28	OHK	@	PIM	OHK4,PIM3	RECAP
Wed, Jan 21	PIM	@	DEV	PIM3,DEV4 (OT)	RECAP
Tue, Jan 27	PIM	@	BKS	PIM5,BKS1	RECAP
Thu, Jan 29	PIM	@	BAR	PIM5,BAR3	RECAP
Sun, Feb 1	CHE	@	PIM	CHE2,PIM5	RECAP
Tue, Feb 3	THI	@	PIM	THI2,PIM7	RECAP
Fri, Feb 6	JWO	@	PIM	JWO3,PIM2 (SO)	RECAP
Wed, Feb 11 08:23	SHA	@	PIM	SHA4,PIM6	RECAP
Thu, Feb 12	BUL	@	PIM	BUL1,PIM4	RECAP
Sat, Feb 14 10:02	DEV	@	PIM	DEV2,PIM5	RECAP
Mon, Feb 16	SPA	@	PIM	SPA3,PIM6	RECAP
Fri, Feb 20	PIM	@	VEN	PIM4,VEN5	RECAP
Sat, Feb 21	PIM	@	OHK	PIM5,OHK4	RECAP
Mon, Feb 23	PIM	@	VIK	PIM3,VIK1	RECAP
Thu, Feb 26	BLA	@	PIM	BLA5,PIM6 (SO)	RECAP
Sat, Feb 28 10:02	JWO	@	PIM	JWO2,PIM7	RECAP
Sun, Mar 1 10:02	CAN	@	PIM	CAN4,PIM5 (OT)	RECAP
Mon, Mar 2	PIM	@	NIN	PIM7,NIN4	RECAP
Tue, Mar 3	PIM	@	CAN	PIM2,CAN1 (OT)	RECAP
Sat, Mar 7 10:02	PIM	@	BAR	PIM5,BAR3	RECAP
Mon, Mar 9	PIM	@	BKS	PIM4,BKS2	RECAP
Wed, Mar 11	PIM	@	RAV	PIM3,RAV9	RECAP
Sat, Mar 14 22:02	RAV	@	PIM	RAV1,PIM4	RECAP
Sun, Mar 15 22:02	BLA	@	PIM	BLA6,PIM5 (SO)	RECAP
Wed, Mar 18	CAN	@	PIM	CAN3,PIM8	RECAP
Thu, Mar 19	PIM	@	SHA	PIM6,SHA5	RECAP
Sat, Mar 21 22:02	KAN	@	PIM	KAN5,PIM3	RECAP
Mon, Mar 23	ZOL	@	PIM	ZOL6,PIM4	RECAP
Tue, Mar 24	ASS	@	PIM	ASS6,PIM2	RECAP
Sat, Mar 28 10:02	EAG	@	PIM	EAG7,PIM2	RECAP
Sun, Mar 29 09:02	BKS	@	PIM	BKS5,PIM3	RECAP
Mon, Mar 30	RAV	@	PIM	RAV5,PIM6 (OT)	RECAP
Tue, Mar 31	DEV	@	PIM	DEV5,PIM4 (OT)	RECAP
Fri, Apr 3	PIM	@	BAR	PIM2,BAR5	RECAP
Sat, Apr 4 21:02	PIM	@	DEV	PIM4,DEV3	RECAP
Tue, Apr 7 05:11	PIM	@	RAV	PIM5,RAV1	RECAP
Thu, Apr 9 21:02	PIM	@	CAN	PIM5,CAN4	RECAP
Sat, Apr 11 21:02	PIM	@	IND	PIM4,IND2	RECAP
Sun, Apr 12 09:02	PIM	@	NIN	PIM4,NIN5	RECAP
Wed, Apr 15 04:23	PIM	@	THI	PIM5,THI3	RECAP
Fri, Apr 17	IND	@	PIM	IND4,PIM3 (OT)	RECAP
Sat, Apr 18 09:02	NIN	@	PIM	NIN5,PIM4 (OT)	RECAP
Sun, Apr 19 09:02	PIM	@	SEN	PIM5,SEN2	RECAP
Mon, Apr 20	VEN	@	PIM	VEN4,PIM5 (SO)	RECAP
Tue, Apr 21	VIK	@	PIM	VIK4,PIM1	RECAP
Thu, Apr 23	PIM	@	SEN	PIM4,SEN2	RECAP
Fri, Apr 24	SHO	@	PIM	SHO5,PIM1	RECAP
Sat, Apr 25 21:02	PIM	@	BUL	PIM6,BUL4	RECAP
Sun, Apr 26 09:02	PIM	@	THI	PIM5,THI4	RECAP
Tue, Apr 28	PIM	@	SHO	PIM7,SHO6 (SO)	RECAP
Fri, May 1	SQU	@	PIM	SQU3,PIM4 (OT)	RECAP
Sat, May 2 09:02	BAR	@	PIM	BAR2,PIM5	RECAP
Sun, May 3 09:03	BKS	@	PIM	BKS4,PIM5 (OT)	RECAP
Wed, May 6	JWO	@	PIM	JWO5,PIM1	RECAP
Fri, May 8	PIM	@	BLA	PIM3,BLA4	RECAP
Sun, May 10 09:02	IND	@	PIM	IND3,PIM4 (OT)	RECAP
Mon, May 11	PIM	@	ASS	PIM1,ASS5	RECAP
Fri, May 15	BLA	@	PIM	BLA6,PIM7 (SO)	RECAP
Sat, May 16	RAV	@	PIM	RAV2,PIM8	RECAP
Trade Deadline --- Trades can�t be done after this day is simulated!
Mon, May 18	GRG	@	PIM	GRG5,PIM2	RECAP
Thu, May 21	PIM	@	WPK	PIM2,WPK4	RECAP
Sun, May 24 09:02	PIM	@	SQU	PIM6,SQU3	RECAP
Sun, May 24 PM	PIM	@	EAG
Tue, May 26	PIM	@	SAI
Sat, May 30 AM	RAV	@	PIM
Sun, May 31 AM	PIM	@	GRG
Mon, Jun 1	PIM	@	ASS
Wed, Jun 3	PIM	@	KAN
Sat, Jun 6 AM	PIM	@	JWO
Sun, Jun 7 PM	SEN	@	PIM
Tue, Jun 9	DEV	@	PIM

Finance

Salary Cap
Players Total Salaries	Retained Salary	Total Cap Hit	Estimated Cap Space
2,098,055$	0$	0$	75,000,000$

Arena	About us
	Name
	City	Bellefeueille
	Capacity	3000
	Season Ticket Holders	10%

Arena Capacity - Ticket Price Attendance - %

Arena Capacity	2000	1000
Ticket Price	35$	15$	$	$	$
Attendance	61669	31121
Attendance PCT	81.14%	81.90%	0.00%	0.00%	0.00%

Income

Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Arena Capacity	Team Popularity
3	2442 - 81.39%	69,085$	2,625,230$	3000	100

Expenses
Players Total Salaries	Players Total Average Salaries	Coaches Salaries	Special Salary Cap Value
2,098,055$	2,098,055$	0$	0$

Year To Date Expenses	Salary Cap Per Days	Salary Cap To Date	Luxury Taxe Total
1,878,817$	10,815$	1,878,817$	0$

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
207,255$	21	10,815$	227,115$

Team Total Estimate
Estimated Season Expenses	Estimated Season Salary Cap	Current Bank Account	Projected Bank Account
227,115$	0$	0$	0$

Sponsors
TV Rights	Primary Sponsor	Secondary Sponsor	Secondary Sponsor

Depth Chart

Rookie

Injured

Cold Streak

Hot Streak

Left Wing

Center

Right Wing

Defense #1

Defense #2

Goalie

Transactions

Injuries

No Injury or Suspension.

Game Center

Pimps44-22-7, 95pts

24Final

Wolf Pack52-15-4, 108pts

Pimps44-22-7, 95pts

63Final

Squirrels29-31-11, 69pts

Pimps44-22-7, 95pts

Sun, May 31

Squirrels29-31-11, 69pts

Barracudas37-35-2, 76pts

Sun, Dec 14

Pimps44-22-7, 95pts

Sailors34-28-9, 77pts

Tue, Dec 16

Pimps44-22-7, 95pts

TRANSACTIONS

Team Info


	Head Coach
	Division	Howe
	City	Bellefeueille
	Stadium Capacity	3,000

Pimps Affiliation


	General Manager	Mika Laakso
	Head Coach
	Stadium
	Capacity	18,000

Team Leaders

GOALS Mackie Samoskevich Pimps	44 GOALS
POINTS Mackie Samoskevich Pimps	106 POINTS
WINS Justus Annunen Pimps	33 WINS
Expanded Player Leaderboard

Team Stats

Goals For 307 4.21 GFG	Goals Against 277 3.79 GAA
Power Play Percentage 16.5% 34 GF	Penalty Kill Percentage 75.5% 58 GA
Expanded Team Stats

Team Captain - Alternate Captains

Captain

Alternate Captain