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DTL — Down The Line

Down The Line at Brook Bank

Brook Bank

Saturday, 02 May 2026 · CPSA Results →

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Event Rating

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Entries

92.8%

Field Average

97.7%

Highest Score

78.3%

Lowest Score

Top 3 Overall

🥈 =2

Malcolm Brown

Martin Taylor

292/300

97.33%

🥇

Peter Bugler

293/300

97.67%

Event Difficulty

92.8% This Event

vs. Brook Bank historical average (Down The Line)

↑ +1.5% easier than usual

Ground avg: 91.3%

Class Winners

A 6 entered

293/300 — 97.67%

Peter Bugler

2=: Malcolm Brown — 97.33%

B 2 entered

287/300 — 95.67%

Geoff Eltham

2: Christopher Holmes — 91.00%

C 3 entered

272/300 — 90.67%

Ian Stones

2: Andrew King — 88.67%

Field Shape

92.8%

Field avg

Down The Line at Brook Bank had a 19.4 point scoring spread across 11 entries.

Spread 19.4 pts

Front 97.7%

Largest class A

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

Score Distribution

90–100%

9 81.8%

80–89%

1 9.1%

70–79%

1 9.1%

60–69%

0 0.0%

50–59%

0 0.0%

<50%

0 0.0%

Field Achievement

% of competitors reaching each score threshold

90%+

81.8%

9 / 11

80%+

90.9%

10 / 11

70%+

100.0%

11 / 11

60%+

100.0%

11 / 11

50%+

100.0%

11 / 11

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

Historical Trends at Brook Bank

Down The Line performance across events held at this ground.

Last 12 months

Overall Average Trend

Classification Trend Comparison

Classification Deep Dive

3 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)—

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

92.0-97.7%

91.0-95.7%

78.3-90.7%

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: 97.7%. 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	6	96.0%	97.7%	92.0%	Peter Bugler
B	2	93.3%	95.7%	91.0%	Geoff Eltham
C	3	85.9%	90.7%	78.3%	Ian Stones

Class Composition

6 54.5%

2 18.2%

3 27.3%

Largest class: A — 6 competitors (54.5%)

Full Results — 11 entries

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

Class A

Score 293/300

vs Class Avg +0.0%

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Class A

Score 292/300

vs Class Avg +0.0%

Profile Open →

Class A

Score 292/300

vs Class Avg +0.0%

Profile Open →

Class A

Score 290/300

vs Class Avg +0.0%

Profile Open →

Class B

Score 287/300

vs Class Avg +0.0%

Profile Open →

Class A

Score 285/300

vs Class Avg +0.0%

Profile Open →

Class A

Score 276/300

vs Class Avg +0.0%

Profile Open →

Class B

Score 273/300

vs Class Avg +0.0%

Profile Open →

Class C

Score 272/300

vs Class Avg +0.0%

Profile Open →

Class C

Score 266/300

vs Class Avg +0.0%

Profile Open →

Class C

Score 235/300

vs Class Avg +0.0%

Profile Open →

#	Name	Class	Score	Score %	Class Rank 🔒	vs Class Avg
1	Peter Bugler	A	293/300	97.67%	🔒 1	🔒 +0.0%
2=	Malcolm Brown	A	292/300	97.33%	🔒 1	🔒 +0.0%
2=	Martin Taylor	A	292/300	97.33%	🔒 1	🔒 +0.0%
4	Neil Bygrave	A	290/300	96.67%	🔒 1	🔒 +0.0%
5	Geoff Eltham	B	287/300	95.67%	🔒 1	🔒 +0.0%
6	Brian Prettejohn	A	285/300	95.00%	🔒 1	🔒 +0.0%
7	Liam Richings	A	276/300	92.00%	🔒 1	🔒 +0.0%
8	Christopher Holmes	B	273/300	91.00%	🔒 1	🔒 +0.0%
9	Ian Stones	C	272/300	90.67%	🔒 1	🔒 +0.0%
10	Andrew King	C	266/300	88.67%	🔒 1	🔒 +0.0%
11	Brian Richings	C	235/300	78.33%	🔒 1	🔒 +0.0%

Other DTL Events at Brook Bank

⚙ Performance Stats DRAG TO MOVE

0.027s ● FAST

DB 44% · Py 41% · Render 15%

12 queries · avg 1.0ms · max 1.6ms

Render Breakdown

DB Logic (Pre-Render) 0.0120s

Python Logic (Pre-Render) 0.0112s

Full Page Render 0.0040s

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 [00:18:20.375] 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 [00:18:20.378] 1.1ms

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 [00:18:20.380] 1.5ms

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 [00:18:20.382] 1.1ms

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 [00:18:20.384] 0.8ms

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 [00:18:20.386] 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 [00:18:20.387] 0.9ms

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 [00:18:20.389] 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 [00:18:20.390] 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 [00:18:20.391] 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 [00:18:20.393] 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 [00:18:20.394] 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

Down The Line at Brook Bank

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Historical Trends at Brook Bank

Classification Deep Dive

Event Performance Analysis

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