Blog

Finally, the start of the season we’ve all been waiting for. Throw out all the fast times from September, but apply all the lessons learned through the summer and fall. Regional week is here and with it comes the many projections and previews I’ll write.

Just as last year and throughout the season, I’m projecting these using race simulations. Through a season’s worth of speed ratings, one can determine a runner’s average rating and the variability of those ratings. Both those factors are then weighed towards more recent meets, which are then simulated thousands of times using a Monte Carlo analysis (also described by TullyRunners here). Over those thousands of race simulations, one can determine a team’s average place and average score, a runner’s average place, odds on winning, and odds on qualifying.

These are useful in cross country scenarios, where you have consistent runners and those who are a bit more up and down. For example:

Tanner Time	Tanner Rating	Tanner Avg.	Tanner StDev.
15:30	210
15:45	205
16:00	200
16:15	195
16:30	190	200	7.91
MIXCSR Time	MIXCSR Rating	MIXCSR Avg.	MIXCSR StDev.
16:09	197
16:12	196
16:15	195
16:18	194
16:21	193	195	1.58

Jacob at his best might be faster than I in four of the five races during the season. I’d almost never be All-State in Division 1, but he might contend for a top-5 spot or place 60^th.

If we did 20 simulations, this is how it might turn out:

Race	Tanner Avg.	Tanner StDev	Tanner Rating	MIXCSR Avg.	MIXCSR StDev	MIXCSR Rating
1	200	7.9	202.1	195	1.6	195.6
2	200	7.9	196.2	195	1.6	194.1
3	200	7.9	190.0	195	1.6	199.2
4	200	7.9	193.4	195	1.6	192.5
5	200	7.9	206.8	195	1.6	195.7
6	200	7.9	201.8	195	1.6	196.1
7	200	7.9	171.6	195	1.6	194.9
8	200	7.9	189.2	195	1.6	195.9
9	200	7.9	205.5	195	1.6	197.0
10	200	7.9	203.8	195	1.6	192.6
11	200	7.9	192.3	195	1.6	196.5
12	200	7.9	196.9	195	1.6	196.4
13	200	7.9	198.5	195	1.6	195.6
14	200	7.9	203.2	195	1.6	194.1
15	200	7.9	193.9	195	1.6	192.6
16	200	7.9	199.3	195	1.6	193.2
17	200	7.9	208.7	195	1.6	194.2
18	200	7.9	213.7	195	1.6	194.6
19	200	7.9	212.6	195	1.6	197.1
20	200	7.9	206.3	195	1.6	193.9

I’d win four times, but I’d never have the ability to win a title like he had in Race 18. He’s usually All-State, but may cost his team a title through his effort in Race 7.

The simulations give the possibilities of those scenarios and all the various scoring scripts. A sixth girl that can pop off on any certain day, a reliable boy who always runs 17-flat, you name it. Hopefully, they better elucidate our contenders, qualifiers, and how these races play out over the weekend.

But always remember, races aren’t run on paper or on a computer. These projections are meant to better our sport, give more attention where little is given. Although I take pride in their accuracy, this endeavor would be quite boring if it were always perfect. I look forward to these being proven wrong and I expect to hear about it as well.

SUMMER PREVIEW
MID-SEASON

GIRLS

Ranked Teams

#4 East Grand Rapids
#17 Middleville Thornapple-Kellogg
#21 Forest Hills Eastern
#24 Grand Rapids South Christian

Projected Individual Qualifier Cutoff

15^th place.

Projected Place	Team	Avg. Score	Avg. Place	Win %	Top 3 %
1	East Grand Rapids	42	1.0	95+	95+
2	Middleville Thornapple-Kellogg	92	2.2		95+
3	Forest Hills Eastern	106	3.3		63
4	Grand Rapids South Christian	108	3.5		41
5	GR West Michigan Aviation	136	4.9
6	Richland Gull Lake	195	6.6
7	Marshall	207	7.6
8	Hopkins	214	8.0
9	Battle Creek Harper Creek	218	8.2
10	Belding	239	9.6
11	Portland	272	11.0
12	Hastings	339	12.0
13	Wayland Union	397	13.2
14	Lake Odessa Lakewood	412	13.8

Projected Place	Grade	Name	Team	Avg. Place
1	12	Drew Muller	East Grand Rapids	1.0
2	11	Sadey Seyferth	East Grand Rapids	2.0
3	12	Addison Washler	Forest Hills Eastern	4.7
4	11	Ava Crews	Middleville Thornapple-Kellogg	5.1
5	12	Emma DeVries	GR West Michigan Aviation	5.3
6	12	Camille DeCola	Marshall	5.7
7	9	Caroline Randall	Hastings	6.4
8	9	Adeline Armstrong	East Grand Rapids	7.5
9	12	Ellia Agar	Grand Rapids South Christian	7.8
10	10	Jillian Clason	Forest Hills Eastern	11.5
11	9	Lane Isom	Richland Gull Lake	12.2
12	10	Madelyn Baar	East Grand Rapids	12.5
13	9	Ainsley Sullivan	Forest Hills Eastern	14.5
14	12	Jaeden Harshberger	Hopkins	16.1
15	9	Meghan-Jane Skidmore	Middleville Thornapple-Kellogg	19.3
16	12	Emma Monroe	Battle Creek Harper Creek	19.4
17	9	Ellie Harmon	Middleville Thornapple-Kellogg	19.7
18	10	Rory Workman	East Grand Rapids	20.5
19	11	Desiree Duque-Rico	GR West Michigan Aviation	20.9
20	11	Avery Wezensky	Portland	21.0
21	11	Rachel Berkenpas	Grand Rapids South Christian	23.2
22	10	Catherine Schellhammer	East Grand Rapids	23.6
23	10	Madison Kietzman	Middleville Thornapple-Kellogg	24.6
24	9	Peyton Hardy	Middleville Thornapple-Kellogg	25.5
25	10	Chloe Rinzema	Grand Rapids South Christian	25.5
26	11	Kambria Moored	Hopkins	26.8
27	11	Mackenzie Zahm	Belding	27.3
28	12	Riley VanderLaan	Grand Rapids South Christian	28.1
29	10	Leah McPartlin	Marshall	28.6
30	11	Quincy Stiffler	East Grand Rapids	30.0

BOYS

Ranked Teams

#1 Forest Hills Eastern
#4 East Grand Rapids
#8 Marshall

Projected Individual Qualifier Cutoff

You throw three top-8 teams in a region and you get a potential 23^rd place qualifier.

Projected Place	Team	Avg. Score	Avg. Place	Win %	Top 3 %
1	Forest Hills Eastern	39	1.0	95+	95+
2	East Grand Rapids	51	2.0		95+
3	Marshall	84	3.0		95+
4	Battle Creek Harper Creek	158	4.1
5	Hastings	191	5.5
6	Grand Rapids South Christian	194	5.7
7	Wayland Union	221	8.0
8	Richland Gull Lake	223	8.1
9	GR West Michigan Aviation	226	8.4
10	Ionia	235	9.2
11	Middleville Thornapple-Kellogg	302	11.3
12	Belding	326	12.3
13	Portland	330	12.6
14	Hopkins	356	13.9
15	Lake Odessa Lakewood	421	15.2
16	Battle Creek Pennfield	437	15.9

Projected Place	Grade	Name	Team	Avg. Place
1	12	Alex Thole	East Grand Rapids	1.4
2	11	Jack Bidwell	Marshall	1.9
3	11	Henry Dixon	Forest Hills Eastern	3.7
4	12	Brendan Hoving	Forest Hills Eastern	4.9
5	10	Jonah Workman	East Grand Rapids	5.8
6	12	Liam Hinman	Forest Hills Eastern	6.0
7	12	Ryan Brinker	East Grand Rapids	6.4
8	11	Dominic Lowrie	Battle Creek Harper Creek	6.9
9	12	Cooper Jacobsen	Forest Hills Eastern	9.3
10	12	Connor Griffith	Marshall	9.5
11	12	Jacob Montgomery	Ionia	11.6
12	12	Lucas Van Meter	Middleville Thornapple-Kellogg	14.2
13	11	Reece Davis	Marshall	15.1
14	12	Tate Miller	Forest Hills Eastern	15.9
15	10	Jack Edison	East Grand Rapids	16.1
16	12	William O’Dell	Battle Creek Harper Creek	16.5
17	10	Gavin Markucki	Richland Gull Lake	17.6
18	9	Abraham McHugh	Marshall	18.7
19	11	Dylan Pallett	Wayland Union	18.7
20	12	Davis Christy	East Grand Rapids	21.9
21	12	Luke Ueland	Forest Hills Eastern	22.0
22	11	Tyler Endres	Forest Hills Eastern	22.7
23	12	Riley Shults	Hastings	22.9
24	11	Brandon Simmons	Hastings	23.1
25	10	Jeryn Frisbie	Belding	24.9
26	12	Isaac Dyer	Wayland Union	25.4
27	10	Thad VanderLaan	Grand Rapids South Christian	27.2
28	10	Jack Estelle	Hopkins	28.9
29	10	Cooper Gardner	Battle Creek Harper Creek	28.9
30	10	Finn Tauren	Richland Gull Lake	29.1

Finally, the start of the season we’ve all been waiting for. Throw out all the fast times from September, but apply all the lessons learned through the summer and fall. Regional week is here and with it comes the many projections and previews I’ll write.

Just as last year and throughout the season, I’m projecting these using race simulations. Through a season’s worth of speed ratings, one can determine a runner’s average rating and the variability of those ratings. Both those factors are then weighed towards more recent meets, which are then simulated thousands of times using a Monte Carlo analysis (also described by TullyRunners here). Over those thousands of race simulations, one can determine a team’s average place and average score, a runner’s average place, odds on winning, and odds on qualifying.

These are useful in cross country scenarios, where you have consistent runners and those who are a bit more up and down. For example:

Tanner Time	Tanner Rating	Tanner Avg.	Tanner StDev.
15:30	210
15:45	205
16:00	200
16:15	195
16:30	190	200	7.91
MIXCSR Time	MIXCSR Rating	MIXCSR Avg.	MIXCSR StDev.
16:09	197
16:12	196
16:15	195
16:18	194
16:21	193	195	1.58

Jacob at his best might be faster than I in four of the five races during the season. I’d almost never be All-State in Division 1, but he might contend for a top-5 spot or place 60^th.

If we did 20 simulations, this is how it might turn out:

Race	Tanner Avg.	Tanner StDev	Tanner Rating	MIXCSR Avg.	MIXCSR StDev	MIXCSR Rating
1	200	7.9	202.1	195	1.6	195.6
2	200	7.9	196.2	195	1.6	194.1
3	200	7.9	190.0	195	1.6	199.2
4	200	7.9	193.4	195	1.6	192.5
5	200	7.9	206.8	195	1.6	195.7
6	200	7.9	201.8	195	1.6	196.1
7	200	7.9	171.6	195	1.6	194.9
8	200	7.9	189.2	195	1.6	195.9
9	200	7.9	205.5	195	1.6	197.0
10	200	7.9	203.8	195	1.6	192.6
11	200	7.9	192.3	195	1.6	196.5
12	200	7.9	196.9	195	1.6	196.4
13	200	7.9	198.5	195	1.6	195.6
14	200	7.9	203.2	195	1.6	194.1
15	200	7.9	193.9	195	1.6	192.6
16	200	7.9	199.3	195	1.6	193.2
17	200	7.9	208.7	195	1.6	194.2
18	200	7.9	213.7	195	1.6	194.6
19	200	7.9	212.6	195	1.6	197.1
20	200	7.9	206.3	195	1.6	193.9

I’d win four times, but I’d never have the ability to win a title like he had in Race 18. He’s usually All-State, but may cost his team a title through his effort in Race 7.

The simulations give the possibilities of those scenarios and all the various scoring scripts. A sixth girl that can pop off on any certain day, a reliable boy who always runs 17-flat, you name it. Hopefully, they better elucidate our contenders, qualifiers, and how these races play out over the weekend.

But always remember, races aren’t run on paper or on a computer. These projections are meant to better our sport, give more attention where little is given. Although I take pride in their accuracy, this endeavor would be quite boring if it were always perfect. I look forward to these being proven wrong and I expect to hear about it as well.

SUMMER PREVIEW
MID-SEASON

GIRLS

Ranked Teams

#9 Buckley
#12 Grand Traverse Academy
#13 Mason County Eastern
#26 Leland

Projected Individual Qualifier Cutoff

18th place.

Projected Place	Team	Avg. Score	Avg. Place	Win %	Top 3 %
1	Buckley	43	1.3	71	95+
2	Grand Traverse Academy	52	1.8	29	95+
3	Mason County Eastern	70	2.9		95+
4	Leland	105	4.1
5	Frankfort	120	5.1
6	Lake Leelanau St. Mary	130	5.8
7	Pentwater	171	7.0

Projected Place	Grade	Name	Team	Avg. Place
1	12	Aiden Harrand	Buckley	1.0
2	11	Ella Knudsen	Leland	2.2
3	9	Addy Zeller	Bear Lake-Onekama	3.9
4	10	Kinsey Peer	Buckley	7.5
5	9	Addison Patrzik	Grand Traverse Academy	7.6
6	10	Lauren Niedzielski	Mason County Eastern	8.6
7	10	Brooklynn Frazee	Buckley	8.8
8	12	Paige Bell	Grand Traverse Academy	10.1
9	10	Shenoah Collier	Grand Traverse Academy	10.7
10	11	Lucy Shoup	Mason County Eastern	10.8
11	9	Mikayla Kulawiak	Buckley	11.3
12	10	Addison Chownyk	Frankfort	12.4
13	9	Natalie Burpee	Leland	12.5
14	8	Claire Couturier	Lake Leelanau St. Mary	13.4
15	9	Bailey Stephen	Grand Traverse Academy	15.3
16	10	Jennifer Kmiecik	Bear Lake-Onekama	15.9
17	9	Brailyn Johnson	Mason County Eastern	16.7
18	12	Abby Kissling	Brethren	20.3
19	9	Addison Malburg	Mason County Eastern	21.9
20	10	Natalie Brown	Bear Lake-Onekama	22.8
21	9	Kaylee Swanson	Buckley	23.0
22	12	Olivia Wing	Mason County Eastern	23.1
23	9	Ellen Schwaiger	Leland	23.1
24	10	Sofia Alaimo	Frankfort	23.5
25	9	Madison Smith	Grand Traverse Academy	24.8
26	12	Ava Butera	Grand Traverse Academy	24.8
27	9	Bailey Case	Pentwater	25.4
28	9	Tess Hoedel	Grand Traverse Academy	25.7
29	12	Kendra Couturier	Lake Leelanau St. Mary	26.9
30	11	Emily Alaimo	Maple City Glen Lake	28.2

BOYS

Ranked Teams

#5 Mason County Eastern
#11 Maple City Glen Lake
#19 Frankfort
#27 Pentwater

Projected Individual Qualifier Cutoff

17th place.

Projected Place	Team	Avg. Score	Avg. Place	Win %	Top 3 %
1	Mason County Eastern	60	1.0	95+	95+
2	Maple City Glen Lake	86	2.1		95+
3	Frankfort	103	2.9		95+
4	Pentwater	129	4.4
5	Leland	136	5.0
6	Bear Lake-Onekama	149	6.0
7	Lake Leelanau St. Mary	170	7.7
8	Mesick	171	7.8
9	Grand Traverse Academy	178	8.2
10	Walkerville	250	10.0

Projected Place	Grade	Name	Team	Avg. Place
1	11	Mason Sinke	Bear Lake-Onekama	1.8
2	12	Abe VanDuinen	Pentwater	2.6
3	9	Kyle Redman	Mesick	3.3
4	11	Colebrook Sutherland	Maple City Glen Lake	4.2
5	11	Alex Tyndall	Mason County Eastern	4.6
6	9	Ty Redman	Mesick	5.4
7	12	Kaden Forward	Bear Lake-Onekama	6.4
8	12	Mitchel Daniels	Pentwater	8.7
9	9	Sebastian Dunaway	Frankfort	9.8
10	11	Carroll Robotham	Frankfort	10.6
11	12	Luke Niedzielski	Mason County Eastern	11.5
12	9	Abraham Feeney	Maple City Glen Lake	12.9
13	11	Henry Malburg	Mason County Eastern	14.0
14	9	Milo Shoup	Mason County Eastern	15.0
15	12	Peter Hybza	Mason County Eastern	15.5
16	10	Keith Crompton	Frankfort	15.7
17	12	Agustin Creamer	Leland	16.0
18	10	Liam McCaw	Maple City Glen Lake	17.0
19	11	Greyson Hoeflinger	Mason County Eastern	19.6
20	11	Parker Rubin	Grand Traverse Academy	20.1
21	10	Cody Couturier	Lake Leelanau St. Mary	21.5
22	10	Matthew Bentley	Buckley	22.4
23	9	Evan Keen	Leland	24.4
24	10	Oliver Mitchell	Lake Leelanau St. Mary	24.9
25	9	Joel Martin	Maple City Glen Lake	27.2
26	9	Aron Mascorro	Walkerville	30.0
27	11	Ron Hasenbank	Mason County Eastern	30.2
28	10	Wyatt Roberts	Pentwater	30.2
29	10	Easton Nowak	Bear Lake-Onekama	31.5
30	10	Jacob Plamondon	Maple City Glen Lake	32.2

Finally, the start of the season we’ve all been waiting for. Throw out all the fast times from September, but apply all the lessons learned through the summer and fall. Regional week is here and with it comes the many projections and previews I’ll write.

Just as last year and throughout the season, I’m projecting these using race simulations. Through a season’s worth of speed ratings, one can determine a runner’s average rating and the variability of those ratings. Both those factors are then weighed towards more recent meets, which are then simulated thousands of times using a Monte Carlo analysis (also described by TullyRunners here). Over those thousands of race simulations, one can determine a team’s average place and average score, a runner’s average place, odds on winning, and odds on qualifying.

These are useful in cross country scenarios, where you have consistent runners and those who are a bit more up and down. For example:

Tanner Time	Tanner Rating	Tanner Avg.	Tanner StDev.
15:30	210
15:45	205
16:00	200
16:15	195
16:30	190	200	7.91
MIXCSR Time	MIXCSR Rating	MIXCSR Avg.	MIXCSR StDev.
16:09	197
16:12	196
16:15	195
16:18	194
16:21	193	195	1.58

Jacob at his best might be faster than I in four of the five races during the season. I’d almost never be All-State in Division 1, but he might contend for a top-5 spot or place 60^th.

If we did 20 simulations, this is how it might turn out:

Race	Tanner Avg.	Tanner StDev	Tanner Rating	MIXCSR Avg.	MIXCSR StDev	MIXCSR Rating
1	200	7.9	202.1	195	1.6	195.6
2	200	7.9	196.2	195	1.6	194.1
3	200	7.9	190.0	195	1.6	199.2
4	200	7.9	193.4	195	1.6	192.5
5	200	7.9	206.8	195	1.6	195.7
6	200	7.9	201.8	195	1.6	196.1
7	200	7.9	171.6	195	1.6	194.9
8	200	7.9	189.2	195	1.6	195.9
9	200	7.9	205.5	195	1.6	197.0
10	200	7.9	203.8	195	1.6	192.6
11	200	7.9	192.3	195	1.6	196.5
12	200	7.9	196.9	195	1.6	196.4
13	200	7.9	198.5	195	1.6	195.6
14	200	7.9	203.2	195	1.6	194.1
15	200	7.9	193.9	195	1.6	192.6
16	200	7.9	199.3	195	1.6	193.2
17	200	7.9	208.7	195	1.6	194.2
18	200	7.9	213.7	195	1.6	194.6
19	200	7.9	212.6	195	1.6	197.1
20	200	7.9	206.3	195	1.6	193.9

I’d win four times, but I’d never have the ability to win a title like he had in Race 18. He’s usually All-State, but may cost his team a title through his effort in Race 7.

The simulations give the possibilities of those scenarios and all the various scoring scripts. A sixth girl that can pop off on any certain day, a reliable boy who always runs 17-flat, you name it. Hopefully, they better elucidate our contenders, qualifiers, and how these races play out over the weekend.

But always remember, races aren’t run on paper or on a computer. These projections are meant to better our sport, give more attention where little is given. Although I take pride in their accuracy, this endeavor would be quite boring if it were always perfect. I look forward to these being proven wrong and I expect to hear about it as well.

SUMMER PREVIEW
MID-SEASON

GIRLS

Ranked Teams

#8 Kingston
#20 Unionville-Sebewaing
#23 Ubly

Projected Individual Qualifier Cutoff

Tons of USA, Kingston, and Ubly girls occupying the 11th-20th spots moves the expected cutoff to 19th.

Projected Place	Team	Avg. Score	Avg. Place	Win %	Top 3 %
1	Kingston	32	1.0	95+	95+
2	Unionville-Sebewaing	68	2.1		95+
3	Ubly	80	2.9		95+
4	Dryden	107	4.7
5	Brown City	107	4.9
6	Harbor Beach	111	5.4
7	Deckerville	166	7.0

Projected Place	Grade	Name	Team	Avg. Place
1	10	Lilah Kiley	Kingston	1.3
2	12	Alberta Reinbold	Unionville-Sebewaing	3.4
3	10	Katie Sweeney	Ubly	3.6
4	12	Lily Lemanski	Marlette	3.9
5	12	Gracy Walker	Kingston	6.0
6	11	Reece Wruble	Harbor Beach	6.2
7	11	Kyra Beemer	Brown City	7.8
8	12	Zoe Van Rijn	Kingston	8.3
9	11	Julia Rogers	New Life Christian Academy	10.0
10	10	Cara Prusakiewicz	Dryden	10.4
11	11	Meeghan Flikkie	Kingston	10.8
12	10	Molly Walker	Kingston	10.9
13	11	Sarah Nimtz	Unionville-Sebewaing	12.0
14	11	Cambree Torma	Unionville-Sebewaing	13.6
15	12	Audrey Napolitano	Dryden	14.4
16	12	Erica Klee	Ubly	14.7
17	10	Hailey McGuire	Kingston	16.6
18	11	Megan Peter	Unionville-Sebewaing	19.4
19	11	Bridget Anderson	Harbor Beach	20.4
20	12	Aran Harris	Ubly	20.8
21	11	Emma Ramisch	Harbor Beach	22.5
22	12	Lily Finnigan	Dryden	22.9
23	10	HarLee Leasher	Brown City	23.2
24	11	Skylar Vincent	Brown City	24.9
25	9	Nicole Klee	Ubly	25.3
26	12	Emily Greyerbiehl	Ubly	27.9
27	10	Mya Garza	Deckerville	28.7
28	11	Danielle Hunt	Unionville-Sebewaing	28.7
29	12	Johanna Kubacki	Deckerville	29.3
30	10	Maleah Roth	Ubly	30.0

BOYS

Ranked Teams

#10 Harbor Beach

Projected Individual Qualifier Cutoff

15th place.

Projected Place	Team	Avg. Score	Avg. Place	Win %	Top 3 %
1	Harbor Beach	53	1.0	95+	95+
2	Dryden	97	2.5		93
3	Mayville	99	2.8		84
4	Marlette	111	3.7		23
5	Unionville-Sebewaing	143	5.7
6	Ubly	146	6.0
7	New Life Christian Academy	158	7.1
8	Kingston	163	7.2
9	Deckerville	194	8.9
10	Brown City	241	10.0

Projected Place	Grade	Name	Team	Avg. Place
1	10	Brody Karg	Harbor Beach	1.2
2	12	Aiden Fitchett	Dryden	2.5
3	10	Carson Burgess	Brown City	3.5
4	12	Utah Gusa	Ubly	4.4
5	11	Matthew Pasiak	Harbor Beach	4.4
6	11	Turlough Bennett	Marlette	6.8
7	12	Gavin Helgeson	Mayville	7.6
8	11	Zack Becker	Unionville-Sebewaing	8.6
9	11	Samuel Shatto	Harbor Beach	9.4
10	10	Michael Walsh	Ubly	10.4
11	11	Isaac Bignall	Unionville-Sebewaing	11.3
12	9	Thijs Van Rijn	Kingston	12.6
13	11	Noah Hall	Dryden	13.5
14	11	Joel Enos	Mayville	13.5
15	12	Ben Gura	New Life Christian Academy	16.3
16	10	Nino Perna	Marlette	17.1
17	10	Logan Romain	Dryden	18.0
18	9	Owen Wruble	Harbor Beach	18.3
19	9	Brennan Robinson	Kingston	18.8
20	11	Isaac Roggenbuck	Harbor Beach	21.2
21	11	Colin Beck	Deckerville	22.1
22	12	Sam Keller	New Life Christian Academy	22.4
23	12	Christopher Gonzales	Mayville	24.3
24	12	Jacob Bulgrien	Harbor Beach	26.4
25	11	Caden Meyer	Marlette	26.7
26	10	Kole Franzel	Mayville	27.9
27	12	Connor Wolff	Dryden	28.0
28	10	James Nelson	New Life Christian Academy	28.3
29	10	Luke Thomas	Marlette	28.4
30	9	Donovan Glasgow	Capac	28.5