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UTR — Universal Trench

Universal Trench at Brook Bank

Brook Bank

Saturday, 12 July 2025 · CPSA Results →

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Event Rating

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Entries

86.0%

Field Average

98.0%

Highest Score

67.3%

Lowest Score

Top 3 Overall

🥈

Matthew Panter

195/200

97.50%

🥇

David Lloyd

196/200

98.00%

🥉

David Kirk

191/200

95.50%

Event Difficulty

86.0% This Event

vs. Brook Bank historical average (Universal Trench)

↑ +4.2% easier than usual

Ground avg: 81.8%

Class Winners

A 23 entered

196/200 — 98.00%

David Lloyd

2: Matthew Panter — 97.50%

B 21 entered

183/200 — 91.50%

Oliver Austin

2: John Reilly — 90.50%

C 17 entered

183/200 — 91.50%

Nigel Watson

2=: James Ward — 90.00%

D 5 entered

160/200 — 80.00%

Michele Rowe(tied) Rhys Dixon(tied)

3: Jacquelyn Lewis — 79.00%

Field Shape

86.0%

Field avg

Universal Trench at Brook Bank had a 30.7 point scoring spread across 66 entries.

Spread 30.7 pts

Front 98.0%

Largest class A

A compact read of how the field performed: average, spread and where most shooters sat.

Score Distribution

90–100%

19 28.8%

80–89%

37 56.1%

70–79%

9 13.6%

60–69%

1 1.5%

50–59%

0 0.0%

<50%

0 0.0%

Field Achievement

% of competitors reaching each score threshold

90%+

28.8%

19 / 66

80%+

84.8%

56 / 66

70%+

98.5%

65 / 66

60%+

100.0%

66 / 66

50%+

100.0%

66 / 66

Unlock the full event analysis

Register free for historical ground trends, class averages and spread, score charts, class composition and a detailed classification breakdown.

Historical Trends at Brook Bank

Universal Trench performance across events held at this ground.

Last 12 months

Overall Average Trend

Classification Trend Comparison

Classification Deep Dive

4 active classifications

—

Member insight

Typical —

Best —

Lowest —

Middle 50% score range

vs Ground Avg (12m)—

—

Member insight

Typical —

Best —

Lowest —

Middle 50% score range

vs Ground Avg (12m)—

—

Member insight

Typical —

Best —

Lowest —

Middle 50% score range

vs Ground Avg (12m)—

—

Member insight

Typical —

Best —

Lowest —

Middle 50% score range

vs Ground Avg (12m)—

Event Performance Analysis

Registered insight

Average by Class

Class averages make it easier to see where the event played hardest across the field.

Field Spread by Class

82.0-98.0%

71.0-91.5%

74.5-91.5%

67.3-80.0%

Boxes show the middle 50% of each class. Orange line is the median.

Top 10 Score Lollipop

A quick visual read of how far the front of the field separated itself.

Score Histogram

High gun: 98.0%. The high-gun band is outlined.

Class Breakdown

Rotate your device to see High/Low scores and the class winner

Class	Count	Average	High	Low	Winner
A	23	91.0%	98.0%	82.0%	David Lloyd
B	21	85.7%	91.5%	71.0%	Oliver Austin
C	17	82.8%	91.5%	74.5%	Nigel Watson
D	5	75.4%	80.0%	67.3%	Michele Rowe

Class Composition

23 34.8%

21 31.8%

17 25.8%

5 7.6%

Largest class: A — 23 competitors (34.8%)

Full Results — 66 entries

🔒 The Class Rank and vs Class Avg columns are hidden — register free to unlock both insights.

Class A

Score 196/200

vs Class Avg +0.0%

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Class A

Score 195/200

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Class A

Score 191/200

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Class A

Score 190/200

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Class A

Score 190/200

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Class A

Score 189/200

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Class A

Score 188/200

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Class A

Score 186/200

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Class A

Score 185/200

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Class A

Score 184/200

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Class A

Score 184/200

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Class A

Score 183/200

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Class A

Score 183/200

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Class C

Score 183/200

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Class B

Score 183/200

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Class A

Score 182/200

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Class B

Score 181/200

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Class C

Score 180/200

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Class C

Score 180/200

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Class B

Score 178/200

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Class A

Score 178/200

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Class B

Score 178/200

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Class A

Score 178/200

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Class B

Score 178/200

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Class B

Score 177/200

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Class B

Score 177/200

vs Class Avg +0.0%

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Class C

Score 176/200

vs Class Avg +0.0%

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Class B

Score 176/200

vs Class Avg +0.0%

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Class A

Score 175/200

vs Class Avg +0.0%

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Class B

Score 175/200

vs Class Avg +0.0%

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Class A

Score 175/200

vs Class Avg +0.0%

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Class B

Score 174/200

vs Class Avg +0.0%

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Class B

Score 174/200

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Class A

Score 173/200

vs Class Avg +0.0%

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Class A

Score 173/200

vs Class Avg +0.0%

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Class B

Score 172/200

vs Class Avg +0.0%

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Class B

Score 172/200

vs Class Avg +0.0%

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Class B

Score 172/200

vs Class Avg +0.0%

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Class A

Score 172/200

vs Class Avg +0.0%

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Class B

Score 171/200

vs Class Avg +0.0%

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Class A

Score 170/200

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Class C

Score 170/200

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Class C

Score 170/200

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Class B

Score 167/200

vs Class Avg +0.0%

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Class B

Score 167/200

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Class B

Score 166/200

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Class C

Score 165/200

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Class C

Score 165/200

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Class A

Score 164/200

vs Class Avg +0.0%

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Class C

Score 163/200

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Class C

Score 163/200

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Class C

Score 162/200

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Class C

Score 161/200

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Class D

Score 160/200

vs Class Avg +0.0%

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Class D

Score 160/200

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Class C

Score 159/200

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Class C

Score 159/200

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Class C

Score 159/200

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Class D

Score 158/200

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Class C

Score 151/200

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Class B

Score 151/200

vs Class Avg +0.0%

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Class B

Score 149/175

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Class C

Score 149/200

vs Class Avg +0.0%

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Class B

Score 142/200

vs Class Avg +0.0%

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Class D

Score 141/200

vs Class Avg +0.0%

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Class D

Score 101/150

vs Class Avg +0.0%

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#	Name	Class	Score	Score %	Class Rank 🔒	vs Class Avg
1	David Lloyd	A	196/200	98.00%	🔒 1	🔒 +0.0%
2	Matthew Panter	A	195/200	97.50%	🔒 1	🔒 +0.0%
3	David Kirk	A	191/200	95.50%	🔒 1	🔒 +0.0%
4=	Daniel Hudson	A	190/200	95.00%	🔒 1	🔒 +0.0%
4=	Ian Moore	A	190/200	95.00%	🔒 1	🔒 +0.0%
6	Lewis Kirk	A	189/200	94.50%	🔒 1	🔒 +0.0%
7	Wesley Hann	A	188/200	94.00%	🔒 1	🔒 +0.0%
8	David Hardy	A	186/200	93.00%	🔒 1	🔒 +0.0%
9	Kevin Borley	A	185/200	92.50%	🔒 1	🔒 +0.0%
10=	Carl Burrow	A	184/200	92.00%	🔒 1	🔒 +0.0%
10=	Richard Fletton	A	184/200	92.00%	🔒 1	🔒 +0.0%
12=	George Seaborne	A	183/200	91.50%	🔒 1	🔒 +0.0%
12=	Michael Wensley	A	183/200	91.50%	🔒 1	🔒 +0.0%
12=	Nigel Watson	C	183/200	91.50%	🔒 1	🔒 +0.0%
12=	Oliver Austin	B	183/200	91.50%	🔒 1	🔒 +0.0%
16	Bayram Baysal	A	182/200	91.00%	🔒 1	🔒 +0.0%
17	John Reilly	B	181/200	90.50%	🔒 1	🔒 +0.0%
18=	James Ward	C	180/200	90.00%	🔒 1	🔒 +0.0%
18=	Phil Rowley	C	180/200	90.00%	🔒 1	🔒 +0.0%
20=	Andrew Dunne	B	178/200	89.00%	🔒 1	🔒 +0.0%
20=	Chris Tate	A	178/200	89.00%	🔒 1	🔒 +0.0%
20=	David O'Donnell	B	178/200	89.00%	🔒 1	🔒 +0.0%
20=	Matthew Mills	A	178/200	89.00%	🔒 1	🔒 +0.0%
20=	Ted Philipson	B	178/200	89.00%	🔒 1	🔒 +0.0%
25=	Matthew Rowland	B	177/200	88.50%	🔒 1	🔒 +0.0%
25=	Nicholas Harris	B	177/200	88.50%	🔒 1	🔒 +0.0%
27=	Nigel Caves	C	176/200	88.00%	🔒 1	🔒 +0.0%
27=	Robert Dalzell	B	176/200	88.00%	🔒 1	🔒 +0.0%
29=	Kath Bright	A	175/200	87.50%	🔒 1	🔒 +0.0%
29=	Leanne Palmer	B	175/200	87.50%	🔒 1	🔒 +0.0%
29=	Paul Atkinson	A	175/200	87.50%	🔒 1	🔒 +0.0%
32=	Andrew Dearman	B	174/200	87.00%	🔒 1	🔒 +0.0%
32=	Katie Cowell	B	174/200	87.00%	🔒 1	🔒 +0.0%
34=	Howard Batt	A	173/200	86.50%	🔒 1	🔒 +0.0%
34=	Ray Sutton	A	173/200	86.50%	🔒 1	🔒 +0.0%
36=	Alan Haynes	B	172/200	86.00%	🔒 1	🔒 +0.0%
36=	Darrin Donaldson	B	172/200	86.00%	🔒 1	🔒 +0.0%
36=	Philip Sanders	B	172/200	86.00%	🔒 1	🔒 +0.0%
36=	Tim Smith	A	172/200	86.00%	🔒 1	🔒 +0.0%
40	Christopher McReynolds	B	171/200	85.50%	🔒 1	🔒 +0.0%
41=	David Wright	A	170/200	85.00%	🔒 1	🔒 +0.0%
41=	Mark Baker	C	170/200	85.00%	🔒 1	🔒 +0.0%
41=	Michael Barker	C	170/200	85.00%	🔒 1	🔒 +0.0%
44=	Emyr Davies	B	167/200	83.50%	🔒 1	🔒 +0.0%
44=	Stefan Roberts	B	167/200	83.50%	🔒 1	🔒 +0.0%
46	Brett Winfield	B	166/200	83.00%	🔒 1	🔒 +0.0%
47=	Graham Carter	C	165/200	82.50%	🔒 1	🔒 +0.0%
47=	Karl Weaver	C	165/200	82.50%	🔒 1	🔒 +0.0%
49	Phillip Dando	A	164/200	82.00%	🔒 1	🔒 +0.0%
50=	John Slatter	C	163/200	81.50%	🔒 1	🔒 +0.0%
50=	Stephen Parker	C	163/200	81.50%	🔒 1	🔒 +0.0%
52	Mark Saunders	C	162/200	81.00%	🔒 1	🔒 +0.0%
53	Howard Beaumont	C	161/200	80.50%	🔒 1	🔒 +0.0%
54=	Michele Rowe	D	160/200	80.00%	🔒 1	🔒 +0.0%
54=	Rhys Dixon	D	160/200	80.00%	🔒 1	🔒 +0.0%
56=	John Stephenson	C	159/200	79.50%	🔒 1	🔒 +0.0%
56=	Paul Insley	C	159/200	79.50%	🔒 1	🔒 +0.0%
56=	Sue Bate	C	159/200	79.50%	🔒 1	🔒 +0.0%
59	Jacquelyn Lewis	D	158/200	79.00%	🔒 1	🔒 +0.0%
60=	Anne Phillips	C	151/200	75.50%	🔒 1	🔒 +0.0%
60=	Julian Fairburn	B	151/200	75.50%	🔒 1	🔒 +0.0%
62=	Neil Leeming	B	149/175	85.14%	🔒 1	🔒 +0.0%
62=	Robert Gallagher	C	149/200	74.50%	🔒 1	🔒 +0.0%
64	Paul Titterington	B	142/200	71.00%	🔒 1	🔒 +0.0%
65	Michael Feather	D	141/200	70.50%	🔒 1	🔒 +0.0%
66	Amanda Gallagher	D	101/150	67.33%	🔒 1	🔒 +0.0%

Other UTR Events at Brook Bank

⚙ Performance Stats DRAG TO MOVE

0.030s ● FAST

DB 40% · Py 40% · Render 20%

12 queries · avg 1.0ms · max 1.6ms

Render Breakdown

DB Logic (Pre-Render) 0.0122s

Python Logic (Pre-Render) 0.0119s

Full Page Render 0.0060s

QUERY TABLES ▾

⚠ event_review 4× 0.003s

shoot_event 2× 0.003s

shoot_score 2× 0.003s

site_configuration 2× 0.002s

ground 1× 0.001s

admin_menu_item 1× 0.001s

⚠ = queried 3+ times (cache candidate)

SQL QUERIES (12) — click to expand

#1 [00:18:44.749] 1.6ms

shoot_event

SELECT shoot_event.id AS shoot_event_id, shoot_event.cpsa_fixture_id AS shoot_event_cpsa_fixture_id, shoot_event.name AS shoot_event_name, shoot_event.date AS shoot_event_date, shoot_event.discipline AS shoot_event_discipline, shoot_event.ground AS shoot_event_ground, shoot_event.url AS shoot_event_…

#2 [00:18:44.752] 1.6ms

shoot_score

SELECT shoot_score.id AS shoot_score_id, shoot_score.targets AS shoot_score_targets, shoot_score.hits AS shoot_score_hits, shoot_score.points AS shoot_score_points, shoot_score.percentage AS shoot_score_percentage, shoot_score.shooter_id AS shoot_score_shooter_id, shoot_score.event_id AS shoot_score…

#3 [00:18:44.756] 1.4ms

shoot_score

SELECT avg(shoot_score.percentage) AS avg_1 
FROM shoot_score JOIN shoot_event ON shoot_score.event_id = shoot_event.id 
WHERE shoot_event.ground = %(ground_1)s AND shoot_event.discipline = %(discipline_1)s AND shoot_event.is_scraped = true AND shoot_event.admin_hidden != true AND shoot_event.id != …

#4 [00:18:44.758] 1.0ms

shoot_event

SELECT shoot_event.id AS shoot_event_id, shoot_event.cpsa_fixture_id AS shoot_event_cpsa_fixture_id, shoot_event.name AS shoot_event_name, shoot_event.date AS shoot_event_date, shoot_event.discipline AS shoot_event_discipline, shoot_event.ground AS shoot_event_ground, shoot_event.url AS shoot_event_…

#5 [00:18:44.759] 0.9ms

event_review

SELECT avg(event_review.rating) AS avg_1, count(event_review.id) AS count_1 
FROM event_review 
WHERE event_review.event_id = %(event_id_1)s

#6 [00:18:44.761] 0.8ms

event_review

SELECT count(*) AS count_1 
FROM (SELECT event_review.id AS event_review_id, event_review.event_id AS event_review_event_id, event_review.rating AS event_review_rating, event_review.review_text AS event_review_review_text, event_review.reviewer_name AS event_review_reviewer_name, event_review.review…

#7 [00:18:44.762] 0.8ms

event_review

SELECT event_review.id AS event_review_id, event_review.event_id AS event_review_event_id, event_review.rating AS event_review_rating, event_review.review_text AS event_review_review_text, event_review.reviewer_name AS event_review_reviewer_name, event_review.reviewer_hash AS event_review_reviewer_h…

#8 [00:18:44.763] 0.8ms

event_review

SELECT event_review.id AS event_review_id, event_review.event_id AS event_review_event_id, event_review.rating AS event_review_rating, event_review.review_text AS event_review_review_text, event_review.reviewer_name AS event_review_reviewer_name, event_review.reviewer_hash AS event_review_reviewer_h…

#9 [00:18:44.764] 0.8ms

ground

SELECT ground.id AS ground_id, ground.name AS ground_name, ground.url AS ground_url, ground.postcode AS ground_postcode, ground.what3words AS ground_what3words, ground.email AS ground_email, ground.website AS ground_website, ground.num_events_cpsa AS ground_num_events_cpsa, ground.num_events_cpsa_la…

#10 [00:18:44.766] 0.8ms

site_configuration

SELECT site_configuration.id AS site_configuration_id, site_configuration.key AS site_configuration_key, site_configuration.value AS site_configuration_value 
FROM site_configuration 
WHERE site_configuration.key LIKE %(key_1)s OR site_configuration.key LIKE %(key_2)s

#11 [00:18:44.767] 0.8ms

site_configuration

SELECT site_configuration.id AS site_configuration_id, site_configuration.key AS site_configuration_key, site_configuration.value AS site_configuration_value 
FROM site_configuration 
WHERE site_configuration.key IN (%(key_1_1)s, %(key_1_2)s, %(key_1_3)s, %(key_1_4)s)

#12 [00:18:44.769] 0.8ms

admin_menu_item

SELECT DISTINCT admin_menu_item.active_tab AS admin_menu_item_active_tab 
FROM admin_menu_item

LIVE REQUESTS (0) — click to expand

Universal Trench at Brook Bank

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