Utah Hockey Club
Home
Roster
Scoring
Players Info
Lines
Team Stats
Schedule
Finance
Web Lines Editor

Game Center
	Henderson Silver Knights 20-1-2, 42pts	4	FINAL	0	Tucson Roadrunners 5-18-3, 13pts

Team Stats
W2	Streak	SOL1
11-1-1	Home Record	2-9-1
9-0-1	Home Record	3-9-2
8-0-2	Last 10 Games	1-6-3
3.39	Goals Per Game	1.77
0.39	Goals Against Per Game	4.23
14.29%	Power Play Percentage	7.50%
96.00%	Penalty Kill Percentage	75.51%

Tucson Roadrunners
5-18-3, 13pts

FINAL

Hartford Wolf Pack
3-22-0, 6pts

Team Stats
SOL1	Streak	SOW1
2-9-1	Home Record	3-10-0
3-9-2	Home Record	0-12-0
1-6-3	Last 10 Games	2-8-0
1.77	Goals Per Game	1.84
4.23	Goals Against Per Game	4.28
7.50%	Power Play Percentage	2.94%
75.51%	Penalty Kill Percentage	78.13%

San Jose Barracuda
16-7-1, 33pts

Day 61

Tucson Roadrunners
5-18-3, 13pts

Team Stats
W2	Streak	SOL1
8-4-1	Home Record	2-9-1
8-3-0	Away Record	3-9-2
9-1-0	Last 10 Games	1-6-3
2.71	Goals Per Game	1.77
1.25	Goals Against Per Game	1.77
16.13%	Power Play Percentage	7.50%
93.94%	Penalty Kill Percentage	75.51%

Tucson Roadrunners
5-18-3, 13pts

Day 64

Providence Bruins
16-9-1, 33pts

Team Stats
SOL1	Streak	W3
2-9-1	Home Record	5-6-1
3-9-2	Away Record	11-3-0
1-6-3	Last 10 Games	7-2-1
1.77	Goals Per Game	3.50
4.23	Goals Against Per Game	3.50
7.50%	Power Play Percentage	6.90%
75.51%	Penalty Kill Percentage	82.98%

Utica Comets
22-3-0, 44pts

Day 65

Tucson Roadrunners
5-18-3, 13pts

Team Stats
W3	Streak	SOL1
12-1-0	Home Record	2-9-1
10-2-0	Away Record	3-9-2
9-1-0	Last 10 Games	1-6-3
3.92	Goals Per Game	1.77
0.80	Goals Against Per Game	1.77
17.31%	Power Play Percentage	7.50%
93.33%	Penalty Kill Percentage	75.51%

Team Leaders
	Goals Brayden Burke 9	Assists Brayden Burke 6

	Points Brayden Burke 15	Plus/Minus Brayden Burke -8

Team Stats
Goals For 46 1.77 GFG	Shots For 674 25.92 Avg	Power Play Percentage 7.5% 3 GF	Offensive Zone Start 33.8%
Goals Against 110 4.23 GAA	Shots Against 1136 43.69 Avg	Penalty Kill Percentage 75.5%% 12 GA	Defensive Zone Start 43.1%

Team Info
	General Manager	Alexandre Champoux
	Division	PACIFIC
	Conference	Conference 2
	Captain
	Assistant #1
	Assistant #2
Arena Info
	Capacity	3,000
	Attendance	2,916
	Season Tickets	300
Roster Info
	Pro Team	21
	Farm Team	18
	Contract Limit	39 / 250
	Prospects	37

Show or Hide Columns

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	Age	Contract	Salary
1	Phil Kessel	X		X		100.00	40	35	40	78	35	80	90	40	20	40	40	40	30	90	76	11	50	0	0	36	2	1,500,000$
2	Brendan Leipsic		X			100.00	40	35	40	40	35	80	90	40	20	40	40	40	30	25	25	11	50	0	0	30	1	700,000$
3	Zac Dalpe	X	X	X		100.00	71	39	82	66	80	86	84	63	65	61	64	60	66	75	79	38	39	0	0	34	1	800,000$
4	Samuel Henley	X	X			100.00	40	35	40	40	35	80	90	40	20	40	40	40	30	25	25	11	50	0	0	31	1	1,999,999$
5	Zach Nastasiuk			X		100.00	40	35	40	40	35	80	90	40	20	40	40	40	30	25	25	11	50	0	0	29	2	750,000$
6	Adam Helewka		X			100.00	40	35	40	40	35	80	90	40	20	40	40	40	30	25	25	11	50	0	0	29	2	750,000$
7	Otto Somppi	X		X		100.00	40	35	40	40	35	80	90	40	20	40	40	40	30	25	25	11	50	0	0	26	2	750,000$
8	Brayden Burke (R)	X		X		100.00	40	35	40	61	35	80	90	40	20	40	40	40	30	66	68	11	50	0	0	27	1	900,000$
9	Semyon Der-Arguchintsev	X				100.00	40	35	40	65	35	80	90	40	20	40	40	40	30	63	65	11	50	0	0	24	1	750,000$
10	Dmitri Voronkov (R)	X	X			100.00	83	45	78	68	97	82	74	69	75	65	67	63	68	64	65	58	47	0	0	24	1	925,000$
11	Matt Coronato (R)			X		100.00	58	36	90	71	70	86	74	68	70	69	63	64	71	62	66	85	47	0	0	21	1	925,000$
12	Derrick Pouliot				X	100.00	70	39	71	66	78	83	81	65	30	67	58	62	52	71	72	69	47	0	0	30	4	775,000$
13	Adam Clendening				X	100.00	40	35	40	59	35	80	90	40	20	40	40	40	30	71	73	11	50	0	0	31	1	700,000$
14	Zachary Leslie				X	100.00	40	35	40	40	35	80	90	40	20	40	40	40	30	25	25	11	50	0	0	30	1	700,000$
15	Nick Seeler				X	100.00	76	67	73	63	83	86	88	61	30	70	56	84	49	72	71	45	31	0	0	31	5	800,000$
16	Louie Belpedio				X	100.00	66	41	69	64	72	80	82	63	30	66	65	67	53	68	69	53	50	0	0	28	3	750,000$
17	Jack St. Ivany (R)				X	100.00	73	39	84	62	83	77	85	58	30	60	55	66	49	65	67	58	50	0	0	25	1	845,000$
Scratches
1	Jean-Sebastien Dea	X				100.00	40	35	40	63	35	80	90	40	20	40	40	40	30	69	71	11	50	0	0	30	1	700,000$

	TEAM AVERAGE					100.00	52	38	55	57	53	81	87	49	31	50	48	50	41	55	55	29	48	0

Show or Hide Columns

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	Age	Contract	Salary
1	Jesper Wallstedt (R)	100.00	62	99	82	86	63	63	63	63	63	53	76	62	70	82	47	0	0	21	1	925,000$
2	Drew Commesso (R)	100.00	64	95	73	77	65	60	60	60	60	50	76	62	69	74	47	0	0	22	1	925,000$
Scratches
1	Kenneth Appleby	100.00	56	95	70	93	57	60	60	60	60	50	67	69	83	36	50	0	0	29	1	750,000$

	TEAM AVERAGE	100.00	61	96	75	85	62	61	61	61	61	51	73	64	74	64	48	0

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

Show or Hide Columns

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
1	Brayden Burke	Tucson Roadrunners (UHC)	C/RW	26	9	6	15	-8	18	10	84	45	91	28	81	9.89%	29	411	15.83	0	0	0	1	2	0	0	0	0	0	1	0	76.92%	13	0	0	0	0	0.73	1	3	0	2	0	0	2	2
Team Total or Average				26	9	6	15	-8	18	10	84	45	91	28	81	9.89%	29	411	15.83	0	0	0	1	2	0	0	0	0	0	1	0	76.92%	13	0	0	0	0	0.73	1	3	0	2	0	0	2	2

Show or Hide Columns

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 Columns

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	Age	Birthday	Country	Rookie	Weight	Height	No Trade	Available For Trade	Acquired By	Last Trade Date	Force Waivers	Waiver Possible	Contract	Contract Signature Date	Force UFA	Emergency Recall	Type	Current Salary	Salary Remaining	Salary Cap	Salary Cap Remaining	Exclude from Salary Cap	Salary Year 2	Salary Year 3	Salary Year 4	Salary Year 5	Salary Year 6	Salary Year 7	Salary Year 8	Salary Year 9	Salary Year 10	Salary Cap Year 2	Salary Cap Year 3	Salary Cap Year 4	Salary Cap Year 5	Salary Cap Year 6	Salary Cap Year 7	Salary Cap Year 8	Salary Cap Year 9	Salary Cap Year 10	No Trade Year 2	No Trade Year 3	No Trade Year 4	No Trade Year 5	No Trade Year 6	No Trade Year 7	No Trade Year 8	No Trade Year 9	No Trade Year 10	Link
Adam Clendening	Tucson Roadrunners (UHC)	D	31	1992-10-26	USA	No	194 Lbs	6 ft0	No	No	N/A		No	No	1		False	False	Pro & Farm	700,000$	487,113$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Adam Helewka	Tucson Roadrunners (UHC)	LW	29	1995-07-21	CAN	No	200 Lbs	6 ft1	No	No	N/A		No	No	2		False	False	Pro & Farm	750,000$	521,907$	0$	0$	No	750,000$	-	-	-	-	-	-	-	-	750,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Brayden Burke	Tucson Roadrunners (UHC)	C/RW	27	1997-01-01	CAN	Yes	165 Lbs	5 ft11	No	No	N/A		No	No	1		False	False	Pro & Farm	900,000$	626,289$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Brendan Leipsic	Tucson Roadrunners (UHC)	LW	30	1994-05-19	CAN	No	180 Lbs	5 ft11	No	No	N/A		No	No	1		False	False	Pro & Farm	700,000$	487,113$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Derrick Pouliot	Tucson Roadrunners (UHC)	D	30	1994-01-16	CAN	No	204 Lbs	6 ft1	No	No	Trade	2024-02-02	No	No	4	2024-05-19	False	False	Pro & Farm	775,000$	539,304$	0$	0$	No	775,000$	775,000$	775,000$	-	-	-	-	-	-	775,000$	775,000$	775,000$	-	-	-	-	-	-	No	No	No	-	-	-	-	-	-	Link
Dmitri Voronkov	Tucson Roadrunners (UHC)	C/LW	24	2000-09-10	RUS	Yes	240 Lbs	6 ft5	No	No	Assign Manually		No	No	1	2024-08-20	False	False	Pro & Farm	925,000$	643,686$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Drew Commesso	Tucson Roadrunners (UHC)	G	22	2002-07-19	USA	Yes	180 Lbs	6 ft2	No	No	Assign Manually		No	No	1	2024-08-20	False	False	Pro & Farm	925,000$	643,686$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Jack St. Ivany	Tucson Roadrunners (UHC)	D	25	1999-07-22	USA	Yes	198 Lbs	6 ft3	No	No	Trade	2024-06-01	No	No	1		False	False	Pro & Farm	845,000$	588,015$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Jean-Sebastien Dea	Tucson Roadrunners (UHC)	C	30	1994-02-08	CAN	No	174 Lbs	5 ft11	No	No	N/A		No	No	1		False	False	Pro & Farm	700,000$	487,113$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Jesper Wallstedt	Tucson Roadrunners (UHC)	G	21	2002-11-14	SWE	Yes	214 Lbs	6 ft3	No	No	Assign Manually		No	No	1	2024-08-20	False	False	Pro & Farm	925,000$	643,686$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Kenneth Appleby	Tucson Roadrunners (UHC)	G	29	1995-04-10	CAN	No	216 Lbs	6 ft5	No	No	Trade	2024-05-31	No	No	1		False	False	Pro & Farm	750,000$	521,907$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Louie Belpedio	Tucson Roadrunners (UHC)	D	28	1996-05-14	USA	No	197 Lbs	5 ft11	No	No	N/A		No	No	3		False	False	Pro & Farm	750,000$	521,907$	0$	0$	No	750,000$	750,000$	-	-	-	-	-	-	-	750,000$	750,000$	-	-	-	-	-	-	-	No	No	-	-	-	-	-	-	-	Link
Matt Coronato	Tucson Roadrunners (UHC)	RW	21	2002-11-14	USA	Yes	183 Lbs	5 ft10	No	No	Assign Manually		No	No	1	2024-08-20	False	False	Pro & Farm	925,000$	643,686$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Nick Seeler	Tucson Roadrunners (UHC)	D	31	1993-06-03	USA	No	197 Lbs	6 ft3	No	No	N/A		No	No	5	2024-05-19	False	False	Pro & Farm	800,000$	556,701$	0$	0$	No	800,000$	800,000$	800,000$	800,000$	-	-	-	-	-	800,000$	800,000$	800,000$	800,000$	-	-	-	-	-	No	No	No	No	-	-	-	-	-	Link
Otto Somppi	Tucson Roadrunners (UHC)	C/RW	26	1998-01-12	FIN	No	192 Lbs	6 ft2	No	No	N/A		No	No	2		False	False	Pro & Farm	750,000$	521,907$	0$	0$	No	750,000$	-	-	-	-	-	-	-	-	750,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Phil Kessel	Tucson Roadrunners (UHC)	C/RW	36	1988-07-27	USA	No	208 Lbs	5 ft11	No	No	Trade	2024-02-02	No	No	2		False	False	Pro & Farm	1,500,000$	1,043,814$	0$	0$	No	1,500,000$	-	-	-	-	-	-	-	-	1,500,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Samuel Henley	Tucson Roadrunners (UHC)	C/LW	31	1993-07-25	CAN	No	209 Lbs	6 ft4	No	No	N/A		No	No	1		False	False	Pro & Farm	1,999,999$	1,391,752$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Semyon Der-Arguchintsev	Tucson Roadrunners (UHC)	C	24	2000-09-15	RUS	No	173 Lbs	5 ft10	No	No	N/A		No	No	1		False	False	Pro & Farm	750,000$	521,907$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Zac Dalpe	Tucson Roadrunners (UHC)	C/LW/RW	34	1989-11-01	CAN	No	197 Lbs	6 ft2	No	No	N/A		No	No	1		False	False	Pro & Farm	800,000$	556,701$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link
Zach Nastasiuk	Tucson Roadrunners (UHC)	RW	29	1995-03-30	CAN	No	202 Lbs	6 ft2	No	No	N/A		No	No	2		False	False	Pro & Farm	750,000$	521,907$	0$	0$	No	750,000$	-	-	-	-	-	-	-	-	750,000$	-	-	-	-	-	-	-	-	No	-	-	-	-	-	-	-	-	Link
Zachary Leslie	Tucson Roadrunners (UHC)	D	30	1994-01-31	CAN	No	175 Lbs	6 ft0	No	No	N/A		No	No	1		False	False	Pro & Farm	700,000$	487,113$	0$	0$	No	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	Link

Total Players	Average Age	Average Weight	Average Height	Average Contract	Average Year 1 Salary
21	28.00	195 Lbs	6 ft1	1.62	886,667$

5 vs 5 Forward
Line #	Left Wing	Center	Right Wing	Time %	PHY	DF	OF
1				38	1	2	2
2			Brayden Burke	30	1	2	2
3				22	1	2	2
4				10	1	2	2

5 vs 5 Defense
Line #	Defense	Defense	Time %	PHY	DF	OF
1			38	1	2	2
2			30	1	2	2
3			22	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			Brayden Burke	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 Columns

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

	Overall												Home											Visitor
#	VS Team	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	PCT	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
1	Abbotsford Canucks	1	0	1	0	0	0	0	0	0	6	-6	1	0	1	0	0	0	0	0	0	6	-6	0	0	0	0	0	0	0	0	0	0	0	0	0.000	0	0	0	0	0	18	17	9	3	12	214	225	220	30	47	12	8	18	2	0	0.00%	4	2	50.00%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
2	Belleville Senators	1	0	1	0	0	0	0	0	2	5	-3	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	2	5	-3	0	0.000	2	3	5	0	0	18	17	9	3	23	214	225	220	30	54	17	6	25	1	0	0.00%	3	0	100.00%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
3	Bridgeport Islanders	1	0	1	0	0	0	0	0	2	4	-2	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	2	4	-2	0	0.000	2	4	6	0	0	18	17	9	3	32	214	225	220	30	44	9	2	25	1	0	0.00%	1	1	0.00%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
4	Calgary Wranglers	1	0	0	0	0	0	1	0	4	3	1	1	0	0	0	0	0	1	0	4	3	1	0	0	0	0	0	0	0	0	0	0	0	2	1.000	4	5	9	0	0	18	17	9	3	41	214	225	220	30	43	10	4	33	3	1	33.33%	2	0	100.00%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
5	Charlotte Checkers	1	0	0	0	0	1	0	0	1	2	-1	1	0	0	0	0	1	0	0	1	2	-1	0	0	0	0	0	0	0	0	0	0	0	1	0.500	1	2	3	0	0	18	17	9	3	28	214	225	220	30	50	17	4	39	2	0	0.00%	2	0	100.00%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
6	Chicago Wolves	1	0	1	0	0	0	0	0	2	4	-2	1	0	1	0	0	0	0	0	2	4	-2	0	0	0	0	0	0	0	0	0	0	0	0	0.000	2	4	6	0	0	18	17	9	3	19	214	225	220	30	38	10	2	20	0	0	0%	1	1	0.00%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
7	Cleveland Monsters	1	1	0	0	0	0	0	0	3	2	1	1	1	0	0	0	0	0	0	3	2	1	0	0	0	0	0	0	0	0	0	0	0	2	1.000	3	6	9	0	0	18	17	9	3	27	214	225	220	30	56	11	6	16	2	0	0.00%	3	0	100.00%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
8	Grand Rapids Griffins	2	0	2	0	0	0	0	0	6	9	-3	1	0	1	0	0	0	0	0	3	5	-2	1	0	1	0	0	0	0	0	3	4	-1	0	0.000	6	11	17	0	0	18	17	9	3	53	214	225	220	30	92	24	6	33	2	0	0.00%	4	1	75.00%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
9	Hartford Wolf Pack	1	0	0	0	0	0	0	1	4	5	-1	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	4	5	-1	1	0.500	4	8	12	0	0	18	17	9	3	63	214	225	220	30	43	12	2	31	0	0	0%	1	1	0.00%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
10	Henderson Silver Knights	2	0	2	0	0	0	0	0	1	9	-8	1	0	1	0	0	0	0	0	0	4	-4	1	0	1	0	0	0	0	0	1	5	-4	0	0.000	1	2	3	0	0	18	17	9	3	18	214	225	220	30	75	31	14	26	3	0	0.00%	7	1	85.71%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
11	Ontario Reign	4	0	4	0	0	0	0	0	1	20	-19	2	0	2	0	0	0	0	0	1	9	-8	2	0	2	0	0	0	0	0	0	11	-11	0	0.000	1	2	3	1	0	18	17	9	3	85	214	225	220	30	195	70	21	102	6	0	0.00%	7	1	85.71%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
12	Providence Bruins	1	0	1	0	0	0	0	0	3	6	-3	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	3	6	-3	0	0.000	3	5	8	0	0	18	17	9	3	28	214	225	220	30	58	13	5	18	3	1	33.33%	0	0	0%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
13	San Diego Gulls	4	2	1	0	0	0	1	0	14	12	2	0	0	0	0	0	0	0	0	0	0	0	4	2	1	0	0	0	1	0	14	12	2	6	0.750	14	25	39	0	0	18	17	9	3	173	214	225	220	30	166	60	8	122	7	0	0.00%	3	1	66.67%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
14	San Jose Barracuda	1	0	1	0	0	0	0	0	0	5	-5	1	0	1	0	0	0	0	0	0	5	-5	0	0	0	0	0	0	0	0	0	0	0	0	0.000	0	0	0	0	0	18	17	9	3	18	214	225	220	30	36	5	15	22	2	0	0.00%	5	1	80.00%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
15	Springfield Thunderbirds	2	0	1	0	0	1	0	0	2	4	-2	1	0	1	0	0	0	0	0	1	2	-1	1	0	0	0	0	1	0	0	1	2	-1	1	0.250	2	4	6	0	0	18	17	9	3	36	214	225	220	30	64	21	4	37	1	0	0.00%	2	0	100.00%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
16	Texas Stars	2	0	2	0	0	0	0	0	1	14	-13	1	0	1	0	0	0	0	0	1	5	-4	1	0	1	0	0	0	0	0	0	9	-9	0	0.000	1	2	3	0	0	18	17	9	3	18	214	225	220	30	75	23	8	25	5	1	20.00%	4	2	50.00%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
	Total	26	3	18	0	0	2	2	1	46	110	-64	12	1	9	0	0	1	1	0	16	47	-31	14	2	9	0	0	1	1	1	30	63	-33	13	0.250	46	83	129	1	0	18	17	9	3	674	214	225	220	30	1136	345	115	592	40	3	7.50%	49	12	75.51%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
	_Since Last GM Reset	26	3	18	0	0	2	2	1	46	110	-64	12	1	9	0	0	1	1	0	16	47	-31	14	2	9	0	0	1	1	1	30	63	-33	13	0.250	46	83	129	1	0	18	17	9	3	674	214	225	220	30	1136	345	115	592	40	3	7.50%	49	12	75.51%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119
	_Vs Conference	20	3	14	0	0	1	2	0	32	84	-52	10	1	8	0	0	0	1	0	13	41	-28	10	2	6	0	0	1	1	0	19	43	-24	11	0.275	32	57	89	1	0	18	17	9	3	481	214	225	220	30	849	267	94	434	33	2	6.06%	41	9	78.05%	0	246	569	43.23%	285	725	39.31%	166	388	42.78%	443	310	856	174	279	119

			Total For Players
Games Played	Points	Streak	Goals	Assists	Points	Shots For	Shots Against	Shots Blocked	Penalty Minutes	Hits	Empty Net Goals	Shutouts
26	13	SOL1	46	83	129	674	1136	345	115	592	1	0

All Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
26	3	18	0	2	2	1	46	110

Home Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
12	1	9	0	1	1	0	16	47

Visitor Games
GP	W	L	OTW	OTL	SOW	SOL	GF	GA
14	2	9	0	1	1	1	30	63

Last 10 Games
W	L	OTW	OTL	SOW	SOL
1	6	0	2	0	1

Power Play Attemps	Power Play Goals	Power Play %	Penalty Kill Attemps	Penalty Kill Goals Against	Penalty Kill %	Penalty Kill Goals For
40	3	7.50%	49	12	75.51%	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
214	225	220	30	18	17	9	3

Face Offs
Won Offensive Zone	Total Offensive	Won Offensive %	Won Defensif Zone	Total Defensive	Won Defensive %	Won Neutral Zone	Total Neutral	Won Neutral %
246	569	43.23%	285	725	39.31%	166	388	42.78%

Puck Time
In Offensive Zone	Control In Offensive Zone	In Defensive Zone	Control In Defensive Zone	In Neutral Zone	Control In Neutral Zone
443	310	856	174	279	119

Last Played Games Show or Hide Columns

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	SO	Link
2	6	Grand Rapids Griffins	5	Tucson Roadrunners	3	L			BoxScore
7	29	Tucson Roadrunners	3	San Diego Gulls	2	W	X	X	BoxScore
8	38	Tucson Roadrunners	3	Providence Bruins	6	L			BoxScore
11	57	Ontario Reign	6	Tucson Roadrunners	0	L			BoxScore
13	77	Calgary Wranglers	3	Tucson Roadrunners	4	W	X	X	BoxScore
15	85	Tucson Roadrunners	2	Bridgeport Islanders	4	L			BoxScore
17	104	Tucson Roadrunners	1	Henderson Silver Knights	5	L			BoxScore
19	115	Cleveland Monsters	2	Tucson Roadrunners	3	W			BoxScore
21	133	Tucson Roadrunners	0	Ontario Reign	6	L			BoxScore
23	146	Ontario Reign	3	Tucson Roadrunners	1	L			BoxScore
26	169	San Jose Barracuda	5	Tucson Roadrunners	0	L			BoxScore
29	188	Tucson Roadrunners	4	San Diego Gulls	5	L			BoxScore
30	205	Tucson Roadrunners	0	Texas Stars	9	L			BoxScore
32	217	Texas Stars	5	Tucson Roadrunners	1	L			BoxScore
35	233	Chicago Wolves	4	Tucson Roadrunners	2	L			BoxScore
37	251	Tucson Roadrunners	5	San Diego Gulls	4	W			BoxScore
39	265	Tucson Roadrunners	0	Ontario Reign	5	L			BoxScore
41	279	Charlotte Checkers	2	Tucson Roadrunners	1	L	X		BoxScore
43	293	Tucson Roadrunners	1	Springfield Thunderbirds	2	L	X		BoxScore
45	308	Tucson Roadrunners	2	Belleville Senators	5	L			BoxScore
46	316	Abbotsford Canucks	6	Tucson Roadrunners	0	L			BoxScore
48	338	Springfield Thunderbirds	2	Tucson Roadrunners	1	L			BoxScore
51	357	Tucson Roadrunners	3	Grand Rapids Griffins	4	L			BoxScore
54	373	Tucson Roadrunners	2	San Diego Gulls	1	W			BoxScore
55	382	Henderson Silver Knights	4	Tucson Roadrunners	0	L			BoxScore
59	396	Tucson Roadrunners	4	Hartford Wolf Pack	5	L	X	X	BoxScore
61	410	San Jose Barracuda	-	Tucson Roadrunners	-
64	434	Tucson Roadrunners	-	Providence Bruins	-
65	442	Utica Comets	-	Tucson Roadrunners	-
68	465	Calgary Wranglers	-	Tucson Roadrunners	-
70	473	Tucson Roadrunners	-	Colorado Eagles	-
73	494	Tucson Roadrunners	-	Bridgeport Islanders	-
75	506	Hershey Bears	-	Tucson Roadrunners	-
77	520	Tucson Roadrunners	-	Calgary Wranglers	-
79	529	Tucson Roadrunners	-	Texas Stars	-
80	542	Cleveland Monsters	-	Tucson Roadrunners	-
88	565	Tucson Roadrunners	-	Bakersfield Condors	-
89	572	Ontario Reign	-	Tucson Roadrunners	-
91	599	Tucson Roadrunners	-	Toronto Marlies	-
93	609	Milwaukee Admirals	-	Tucson Roadrunners	-
97	634	Colorado Eagles	-	Tucson Roadrunners	-
99	653	Tucson Roadrunners	-	San Jose Barracuda	-
100	663	Coachella Valley Firebirds	-	Tucson Roadrunners	-
102	682	Tucson Roadrunners	-	Milwaukee Admirals	-
104	697	Rochester Americans	-	Tucson Roadrunners	-
107	717	Tucson Roadrunners	-	Wilkes-Barre/Scranton Penguins	-
108	728	LeHigh Valley Phantoms	-	Tucson Roadrunners	-
112	755	Syracuse Crunch	-	Tucson Roadrunners	-
113	766	Tucson Roadrunners	-	LeHigh Valley Phantoms	-
116	786	LeHigh Valley Phantoms	-	Tucson Roadrunners	-
118	802	Tucson Roadrunners	-	Henderson Silver Knights	-
121	818	San Diego Gulls	-	Tucson Roadrunners	-
125	835	Tucson Roadrunners	-	Manitoba Moose	-
127	854	Hershey Bears	-	Tucson Roadrunners	-
129	869	Tucson Roadrunners	-	Rockford IceHogs	-
142	879	Tucson Roadrunners	-	Laval Rocket	-
144	892	Bakersfield Condors	-	Tucson Roadrunners	-
148	918	San Diego Gulls	-	Tucson Roadrunners	-
150	938	Tucson Roadrunners	-	Coachella Valley Firebirds	-
151	948	Tucson Roadrunners	-	Rochester Americans	-
153	954	Grand Rapids Griffins	-	Tucson Roadrunners	-
157	978	Tucson Roadrunners	-	San Jose Barracuda	-
Trade Deadline --- Trades can’t be done after this day is simulated!
158	987	Springfield Thunderbirds	-	Tucson Roadrunners	-
163	1009	Tucson Roadrunners	-	Wilkes-Barre/Scranton Penguins	-
166	1016	Bakersfield Condors	-	Tucson Roadrunners	-
168	1029	Tucson Roadrunners	-	Iowa Wild	-
171	1042	Tucson Roadrunners	-	Abbotsford Canucks	-
172	1054	Iowa Wild	-	Tucson Roadrunners	-
174	1065	Tucson Roadrunners	-	Cleveland Monsters	-
177	1084	Grand Rapids Griffins	-	Tucson Roadrunners	-
184	1116	Rockford IceHogs	-	Tucson Roadrunners	-
192	1143	Texas Stars	-	Tucson Roadrunners	-

Arena Capacity - Ticket Price Attendance - %
	Level 1	Level 2
Capacity	2000	1000
Ticket Price	30	15
Attendance	23,547	11,444
Attendance PCT	98.11%	95.37%

Income
Home Games Left	Average Attendance - %	Average Income per Game	Year to Date Revenue	Capacity	Team Popularity
24	2916 - 97.20%	86,344$	1,036,123$	3000	100

Expenses
Year To Date Expenses	Players Total Salaries	Players Total Salaries Cap	Coaches Salaries
548,725$	1,862,000$	1,532,500$	0$

Salary Cap Per Days	Salary Cap To Date	Players In Salary Cap	Players Out of Salary Cap
9,598$	548,725$	0	0

Estimate
Estimated Season Revenue	Remaining Season Days	Expenses Per Days	Estimated Season Expenses
2,072,246$	135	9,598$	1,295,730$

Tucson Roadrunners Players Stat Leaders (Regular Season)

Show or Hide Columns

#	Player Name	GP	G	A	P	+/-	PIM	HIT	HTT	SHT	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PKG	PKA	PKP	PKS	GW	GT	FO%	HT	P/20	PSG	PSS

Tucson Roadrunners Goalies Stat Leaders (Regular Season)

Show or Hide Columns

#	Goalie Name	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA

Tucson Roadrunners Career Team Stats

Show or Hide Columns

	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

Tucson Roadrunners Players Stat Leaders (Play-Off)

Show or Hide Columns

#	Player Name	GP	G	A	P	+/-	PIM	HIT	HTT	SHT	SHT%	SB	MP	AMG	PPG	PPA	PPP	PPS	PKG	PKA	PKP	PKS	GW	GT	FO%	HT	P/20	PSG	PSS

Tucson Roadrunners Goalies Stat Leaders (Play-Off)

Show or Hide Columns

#	Goalie Name	GP	W	L	OTL	PCT	GAA	MP	PIM	SO	GA	SA	SAR	A	EG	PS %	PSA

	Tucson Roadrunners
	GP: 26 \| W: 5 \| L: 18 \| OTL: 3 \| P: 13 GF: 46 \| GA: 110 \| PP%: 7.50% \| PK%: 75.51% GM : Alexandre Champoux \| Morale : 50 \| Team Overall : N/A Next Games #410 vs San Jose Barracuda