Kirk-Train — CS2 Stats

GB76561198133191262[U:1:172925534]Steam profile ↗✓ No bans

171Tracked matches32%Win rate2026Tracked since

183Hours in CS6Hrs last 2 wks

CSDB Rating1.2 LearningAll-Rounder

Ladder ranks via Leetify

Performance scores

Aim3

Positioning23

Utility22

0–100 skill scores via Leetify.

Recent form

STEADY38–52–10Last 10038%Win rateLWWLLWLLLW

Last 10 vs previous 10: 0pp win rate · −0.02 avg rating · +4.7pp headshot accuracy · +23ms reaction

Win rate 0pp across the last 10 against the 10 before — within the normal variation of a 10-match window (±20pp), so no real shift yet.

Last 5 · 10 · 20 matches

Last 5

2–3 · 40% win rate
Avg rating -0.12
Avg headshot accuracy 8%
Avg reaction 813ms

Last 10

4–6 · 40% win rate
Avg rating -0.11
Avg headshot accuracy 11%
Avg reaction 819ms

Last 20

8–12 · 40% win rate
Avg rating -0.10
Avg headshot accuracy 9%
Avg reaction 808ms

Newest first, from the last 100 tracked matches. Each block is its own sample — one result moves a 5-match win rate by 20 points.

Player DNA

Primary style: All-Rounder — No style dimension stands clear of the others in this profile.

Aim0.3

Utility2.2

Positioning2.3

Opening Duels0.0

Limited utility dependence

Style profile from tracked-match aggregates — how this player plays, not how good they are. Classification rules are deterministic and documented in code.

What this cannot see yet: which weapons you use — so CSDB cannot identify an AWPer, and no style here implies a rifle or a sniper. It also cannot see how often you take opening duels, only how often you win them, nor where you hold, so roles that depend on those (entry, lurk, anchor) are deliberately absent rather than guessed. All of it needs round-by-round demo data, which is the next thing being built.

Your pro match

Plays most like jL 64% playstyle similarity

Most alike: utility contribution, positioning profile.

Where you differ: lower opening-duel success; lower aim profile.

Similarity of playstyle shape across shared dimensions — it says how you play, not that you play at their level. Full comparison →

Strengths & areas to improve

Areas to improve

Reaction time. 791ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 58% of shots are taken properly stopped — moving-shot inaccuracy is quietly taxing every duel.

Generated by fixed rules over this profile's own numbers — no model, no guessing; silent when the sample is too small to support a claim.

CSDB Rating breakdown

Aim0.3

Positioning2.3

Utility2.2

Mechanics1.9

Opening Duels0.0

Win Impact0.0

Composite 1.2/10 (Learning), a weighted mean of the bars with a small opposition adjustment (×1.00 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.09−0.01

first ⅓ avg -0.09 → last ⅓ avg -0.09

Reaction time784ms−50ms

first ⅓ avg 833ms → last ⅓ avg 784ms

Headshot accuracy9.6%+0.8%

first ⅓ avg 8.8% → last ⅓ avg 9.6%

Rolling 5-match average across the last 100 tracked matches, oldest to newest. The delta compares the first third of the window with the last.

Personal bests

0.04Best match rating · 13–2 · cache, 26 Aug →

30%Best headshot accuracy · 13–5 · inferno, 27 Sept →

523msFastest reaction time · 9–13 · cache, 11 Aug →

13–0Biggest win · vertigo, 28 Jul →

Across the last 100 tracked matches.

Highlights

5Longest win streak

3–5In matches decided by ≤2 rounds

2Overtime games

Map breakdown

overpassBest map · 60% over 5infernoWeakest map · 25% over 12

Map	Grade	Played	Record	Win rate	Avg rating
inferno	D	12	3–9	25%	-0.09
anubis	C	12	5–7	42%	-0.09
cache	B	11	5–6	45%	-0.08
ancient	C	10	4–6	40%	-0.08
vertigo	C	9	4–5	44%	-0.07
train	D	8	2–6	25%	-0.09
office	C	8	3–5	38%	-0.08
nuke	D	7	2–5	29%	-0.10
overpass	A	5	3–2	60%	-0.11
italy	—	4	1–3	25%	-0.09
dust2	—	4	2–2	50%	-0.06
mirage	—	4	1–3	25%	-0.08
boulder	—	3	2–1	67%	-0.06
shelter	—	2	1–1	50%	-0.12
fachwerk	—	1	0–1	0%	-0.10

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (25% over 12). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

1,651Lifetime kills

0.29K/D

304Matches

34.5%Match win rate

32.9%Headshot %

0.0%Shot accuracy

143MVPs

100Hours (in match)

335Bombs planted

22Bombs defused

Most-used weapons

M4A4 / M4A1-S419

UMP-45292

Galil AR236

AK-47230

Glock-18117

P2000 / USP-S93

P9056

G3SG146

Five-SeveN30

XM101429

Lifetime map wins

288inferno

224train

189office

166nuke

136vertigo

102italy

88dust2

16lake

Lifetime totals via Steam — visible because this profile's game details are public. Spans CS:GO and CS2.

Skill profile

Aggregate performance across tracked matches — stats via Leetify. Percentile context against other CSDB-tracked players arrives as our own benchmark data accumulates.

9.1%Headshot accuracy

24.1%Accuracy (enemy spotted)

24.3%Spray accuracy

58.5%Counter-strafing

16.2°Preaim

791msReaction time

15.5%T opening success

23.3%CT opening success

0.47Enemies flashed / flash

4.9%Flash assists

0.01HE damage / grenade

4.74Flashes / match

Recommended for you

Chosen by comparing your tracked metrics against the thresholds we flag — the measurement behind each one is shown, so you can disagree with it.

Advanced Mechanics
Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.
Preaim 16.1733° — above the 12° mark we flag
Aim Training →
Best CS2 Crosshair
Your shots are landing on bodies more often than heads — usually a crosshair-height and placement habit rather than raw aim.
Headshot accuracy 9.1241% — below the 15% mark we flag
Aim Training →
Best CS2 Settings
Slow first shots are as often a setup problem as a reflex one — framerate, sensitivity and crosshair visibility all move this number.
Reaction time 791.3721ms — above the 700ms mark we flag
Aim Training →

Spend your practice time on Inferno

Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.

25% win rate across 12 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
train	9–13	-0.14	10%	11 Oct →
italy	13–10	-0.12	0%	3 Oct →
train	13–10	-0.11	0%	3 Oct →
ancient	1–13	-0.14	14%	28 Sept →
overpass	2–13	-0.10	14%	27 Sept →
inferno	13–5	-0.06	30%	27 Sept →
vertigo	7–13	-0.06	14%	23 Sept →
vertigo	8–13	-0.13	8%	23 Sept →
inferno	5–13	-0.16	10%	22 Sept →
ancient	13–7	-0.08	13%	20 Sept →
nuke	10–13	-0.10	4%	20 Sept →
anubis	13–3	-0.07	0%	20 Sept →
ancient	7–13	-0.10	9%	17 Sept →
dust2	8–13	-0.08	9%	17 Sept →
office	6–13	-0.11	11%	17 Sept →
anubis	13–11	-0.08	6%	16 Sept →
anubis	7–13	-0.08	8%	16 Sept →
ancient	16–14	-0.11	11%	13 Sept →
cache	13–4	-0.11	0%	13 Sept →
ancient	11–13	-0.07	9%	11 Sept →
nuke	12–12	-0.11	4%	9 Sept →
office	13–6	-0.06	11%	9 Sept →
inferno	4–13	-0.09	0%	8 Sept →
ancient	9–13	-0.05	18%	8 Sept →
boulder	1–13	-0.07	19%	6 Sept →
mirage	4–13	-0.11	10%	6 Sept →
office	7–13	-0.10	13%	6 Sept →
mirage	12–12	-0.07	6%	2 Sept →
office	11–13	-0.09	10%	2 Sept →
vertigo	0–13	-0.06	11%	29 Aug →
anubis	13–10	-0.06	5%	29 Aug →
nuke	3–13	-0.12	23%	29 Aug →
anubis	13–7	-0.08	8%	26 Aug →
inferno	8–13	-0.07	3%	26 Aug →
cache	13–2	0.04	6%	26 Aug →
dust2	14–16	-0.08	9%	23 Aug →
anubis	7–13	-0.13	9%	23 Aug →
anubis	2–13	-0.12	11%	22 Aug →
inferno	3–13	-0.12	13%	22 Aug →
cache	1–13	-0.06	9%	22 Aug →
train	13–8	-0.04	18%	21 Aug →
office	12–12	-0.09	5%	19 Aug →
shelter	12–12	-0.13	6%	19 Aug →
overpass	11–13	-0.07	17%	19 Aug →
nuke	7–13	-0.07	4%	12 Aug →
italy	2–13	-0.03	0%	12 Aug →
train	10–13	-0.06	9%	12 Aug →
cache	9–13	-0.12	2%	11 Aug →
anubis	4–13	-0.12	0%	8 Aug →
inferno	13–9	-0.08	7%	8 Aug →
cache	13–7	-0.10	6%	6 Aug →
anubis	5–13	-0.10	10%	6 Aug →
vertigo	13–6	-0.09	7%	6 Aug →
ancient	5–13	-0.11	30%	5 Aug →
anubis	1–13	-0.16	14%	5 Aug →
vertigo	13–8	-0.10	7%	5 Aug →
nuke	13–10	-0.09	0%	5 Aug →
vertigo	13–7	-0.00	14%	29 Jul →
vertigo	13–0	-0.04	3%	28 Jul →
dust2	13–5	0.01	5%	28 Jul →
ancient	3–13	-0.10	0%	26 Jul →
overpass	13–9	-0.11	8%	26 Jul →
boulder	13–10	-0.08	5%	24 Jul →
inferno	12–12	-0.09	7%	24 Jul →
inferno	8–8	-0.02	15%	23 Jul →
anubis	13–10	-0.06	8%	23 Jul →
anubis	11–13	-0.03	13%	23 Jul →
mirage	5–13	-0.09	6%	23 Jul →
boulder	13–4	-0.04	0%	20 Jul →
train	10–13	-0.07	3%	20 Jul →
nuke	13–5	-0.07	4%	20 Jul →
italy	2–13	-0.13	0%	16 Jul →
office	13–5	-0.07	7%	16 Jul →
cache	2–13	-0.08	8%	16 Jul →
inferno	6–13	-0.07	11%	14 Jul →
office	13–10	-0.05	13%	14 Jul →
train	9–13	-0.13	12%	14 Jul →
shelter	13–8	-0.11	8%	10 Jul →
office	9–13	-0.12	4%	10 Jul →
fachwerk	8–13	-0.10	19%	10 Jul →
ancient	13–9	0.03	7%	1 Jul →
cache	6–13	-0.12	18%	1 Jul →
cache	13–10	-0.10	25%	1 Jul →
inferno	3–13	-0.10	10%	29 Jun →
cache	13–8	-0.06	4%	29 Jun →
cache	7–13	-0.06	8%	26 Jun →
nuke	3–13	-0.12	15%	26 Jun →
mirage	13–9	-0.06	17%	23 Jun →
train	12–12	-0.12	5%	21 Jun →
train	12–12	-0.08	17%	21 Jun →
ancient	13–4	-0.06	11%	12 Jun →
inferno	13–1	-0.06	23%	12 Jun →
italy	7–13	-0.08	7%	12 Jun →
dust2	13–4	-0.09	0%	12 Jun →
overpass	13–7	-0.14	0%	10 Jun →
vertigo	12–12	-0.07	9%	3 Jun →
vertigo	10–13	-0.11	0%	3 Jun →
inferno	12–12	-0.13	4%	1 Jun →
cache	4–13	-0.08	11%	1 Jun →
overpass	13–11	-0.12	8%	1 Jun →