Dimaoneshot — CS2 Stats
PRO“Дима”Free AgentRussiaIGLSettings & gear profile →
RU76561198011984495[U:1:51718767]Steam profile ↗✓ No bans
What changed since last observed
CSDB last observed this profile on 28 Oct 2026 (4 days ago). Ranks are recorded once per day this page is viewed.
Rating over time
Faceit ELO
- At peak — 3,647
- 0 over 30 days · steady
- Next: 4,000 ELO at 4,000 — 353 to go
- Reached: 3,500 ELO · 3,000 ELO · 2,500 ELO · Level 10
- 3,500 ELO first seen 2026-08-28
- 3,000 ELO first seen 2026-08-28
Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do. Faceit ELO is CSDB’s own observation — no feed exposes ELO per match, so that line only has the days the profile was viewed and cannot be backfilled.
How this compares with the same rank
Median values for Level 10 among CSDB-tracked players (n=30,986), from Valve's own lifetime stats. Aim, positioning and utility scores are deliberately not benchmarked here — those are a third-party provider's derived metrics, which CSDB does not store.
| Metric | This player | Level 10 median |
|---|---|---|
| Headshot rate | 70.8% | 50.9% |
| Shot accuracy | 18.6% | 13.7% |
| Kill/death ratio | 1.11 | 1.09 |
| Match win rate | 50.5% | 48.4% |
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Performance scores
0–100 skill scores via Leetify.
Recent form
Last 10 vs previous 10: +10pp win rate · +0.02 avg rating · +0.6pp headshot accuracy · −19ms reaction
Win rate +10pp 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 0.07
- Avg headshot accuracy 29%
- Avg reaction 512ms
Last 10
- 6–4 · 60% win rate
- Avg rating 0.07
- Avg headshot accuracy 28%
- Avg reaction 495ms
Last 20
- 11–9 · 55% win rate
- Avg rating 0.06
- Avg headshot accuracy 27%
- Avg reaction 505ms
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: Opening Duellist — Winning opening duels is what stands out in this profile (+1 against its own average). How often you WIN the first duel — CSDB cannot yet see how often you take it.
Sharp aimerExcellent counter-strafing
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 device 90% playstyle similarity
Most alike: opening-duel success, aim profile.
Where you differ: lower utility contribution.
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
Strengths
Aim. Aim score of 94 — the mechanical foundation is a clear strength.
Areas to improve
Positioning. Positioning trails aim by 31 points — deaths here waste a strong aim profile.
Utility. Utility contribution runs far behind the mechanical game — the cheapest rating gain on this profile is thrown, not aimed.
T-side openings. Opening success drops from 57% on CT to 42% on T — the same duels are being taken with worse setups on the attacking side.
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
Composite 8.0/10 (Strong), a weighted mean of the bars with a small opposition adjustment (×1.05 for this rank band). Formula versioned (v1) and documented in code.
Trends
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
Across the last 100 tracked matches.
Highlights
Map breakdown
| Map | Grade | Played | Record | Win rate | Avg rating |
|---|---|---|---|---|---|
 dust2 | S | 25 | 18–7 | 72% | 0.05 |
 mirage | A | 16 | 9–7 | 56% | 0.03 |
 nuke | B | 15 | 8–7 | 53% | 0.05 |
 anubis | S | 10 | 8–2 | 80% | 0.06 |
 ancient | S | 9 | 7–2 | 78% | 0.02 |
 train | S | 7 | 6–1 | 86% | 0.04 |
 overpass | A | 7 | 4–3 | 57% | 0.06 |
 cache | S | 6 | 4–2 | 67% | 0.06 |
| office | — | 3 | 1–2 | 33% | 0.12 |
 vertigo | — | 1 | 1–0 | 100% | 0.19 |
 inferno | — | 1 | 0–1 | 0% | 0.05 |
Across the last 100 tracked matches.
Nuke is currently your weakest sufficiently-sampled map (53% over 15). Start with the 6 essential Nuke lineups, review the callouts, then spin up a practice server.
Lifetime stats
Most-used weapons
Lifetime map wins
Lifetime totals via Steam — visible because this profile's game details are public. Spans CS:GO and CS2.
Faceit stats
Combat
Clutches & streaks
Recent Faceit resultsWLLWW
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Mirage | 920 | 54% | 1.15 | 17.5 |
| Dust2 | 768 | 55% | 1.17 | 17.7 |
| Ancient | 477 | 51% | 1.12 | 17.3 |
| Nuke | 353 | 58% | 1.22 | 18.2 |
| Anubis | 248 | 55% | 1.09 | 16.7 |
| Overpass | 130 | 59% | 1.34 | 19.6 |
| Train | 64 | 72% | 1.34 | 18.7 |
| Inferno | 60 | 48% | 1.10 | 17.7 |
Faceit-match stats via the FACEIT Data API — a separate match pool from the sections above.
Inventory
$1,666
$1,433
$1,025
$636
$508
$408Skill profile
Aggregate performance across tracked matches — stats via Leetify. Percentile context against other CSDB-tracked players arrives as our own benchmark data accumulates.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| mirage | 6–13 | -0.03 | 23% | 30 Oct → | |
| office | 13–10 | 0.21 | 35% | 28 Oct → | |
| office | 10–13 | 0.12 | 38% | 28 Oct → | |
| cache | 13–1 | 0.09 | 29% | 22 Oct → | |
| mirage | 8–13 | -0.02 | 20% | 18 Oct → | |
| cache | 13–3 | 0.00 | 33% | 15 Oct → | |
| train | 13–4 | 0.05 | 27% | 7 Oct → | |
| cache | 13–8 | 0.08 | 25% | 27 Sept → | |
| anubis | 13–11 | 0.11 | 22% | 22 Sept → | |
| dust2 | 2–3 | 0.05 | 25% | 12 Sept → | |
| mirage | 13–0 | 0.12 | 34% | 12 Sept → | |
| train | 13–4 | 0.11 | 24% | 17 Aug → | |
| train | 13–10 | 0.12 | 17% | 17 Aug → | |
| nuke | 8–13 | -0.01 | 29% | 14 Aug → | |
| nuke | 7–13 | -0.04 | 28% | 12 Aug → | |
| dust2 | 13–3 | 0.01 | 29% | 8 Aug → | |
| nuke | 9–13 | -0.04 | 14% | 8 Aug → | |
| cache | 8–13 | 0.11 | 35% | 22 Jul → | |
| anubis | 8–13 | -0.05 | 29% | 22 Jul → | |
| anubis | 13–3 | 0.16 | 33% | 22 Jul → | |
| office | 8–13 | 0.01 | 25% | 9 Jul → | |
| cache | 10–13 | 0.02 | 26% | 6 Jul → | |
| ancient | 9–7 | -0.07 | 23% | 3 Jul → | |
| dust2 | 13–2 | 0.04 | 9% | 3 Jul → | |
| overpass | 8–13 | -0.03 | 13% | 3 Jul → | |
| dust2 | 13–5 | 0.01 | 20% | 30 Jun → | |
| dust2 | 13–5 | 0.09 | 40% | 28 Jun → | |
| cache | 13–11 | 0.04 | 27% | 17 Jun → | |
| dust2 | 13–4 | 0.02 | 29% | 17 Jun → | |
| nuke | 13–8 | 0.07 | 27% | 16 Jun → | |
| anubis | 13–3 | 0.11 | 31% | 1 Jun → | |
| nuke | 13–11 | 0.08 | 24% | 29 May → | |
| dust2 | 13–9 | 0.07 | 36% | 28 May → | |
| anubis | 13–6 | 0.01 | 15% | 2 May → | |
| anubis | 13–9 | 0.02 | 23% | 28 Apr → | |
| ancient | 13–10 | 0.02 | 23% | 27 Apr → | |
| mirage | 10–13 | -0.01 | 32% | 22 Apr → | |
| mirage | 11–13 | 0.05 | 18% | 22 Apr → | |
| mirage | 13–11 | 0.09 | 26% | 16 Apr → | |
| dust2 | 5–13 | -0.02 | 36% | 16 Apr → | |
| ancient | 13–1 | 0.08 | 22% | 16 Apr → | |
| anubis | 13–6 | 0.05 | 26% | 15 Apr → | |
| nuke | 13–10 | 0.11 | 38% | 26 Mar → | |
| overpass | 13–10 | 0.04 | 23% | 26 Mar → | |
| dust2 | 13–1 | 0.04 | 32% | 26 Mar → | |
| overpass | 6–13 | -0.01 | 19% | 13 Mar → | |
| overpass | 13–7 | 0.10 | 28% | 13 Mar → | |
| dust2 | 13–2 | 0.23 | 36% | 13 Mar → | |
| anubis | 13–10 | -0.01 | 19% | 24 Feb → | |
| dust2 | 13–5 | 0.06 | 20% | 23 Feb → | |
| nuke | 13–6 | 0.05 | 19% | 19 Feb → | |
| overpass | 13–9 | 0.12 | 19% | 18 Feb → | |
| dust2 | 13–2 | 0.07 | 36% | 16 Feb → | |
| mirage | 13–4 | 0.05 | 19% | 11 Feb → | |
| ancient | 13–2 | -0.01 | 16% | 11 Feb → | |
| dust2 | 13–10 | -0.01 | 19% | 10 Feb → | |
| mirage | 9–1 | -0.01 | 21% | 9 Feb → | |
| anubis | 9–13 | 0.11 | 20% | 6 Feb → | |
| dust2 | 13–1 | 0.08 | 42% | 26 Jan → | |
| dust2 | 13–10 | 0.06 | 27% | 18 Jan → | |
| nuke | 13–2 | 0.21 | 43% | 17 Jan → | |
| ancient | 13–10 | 0.04 | 17% | 16 Jan → | |
| anubis | 13–1 | 0.07 | 37% | 14 Jan → | |
| dust2 | 13–9 | 0.07 | 22% | 29 Dec → | |
| dust2 | 12–12 | 0.17 | 21% | 28 Dec → | |
| dust2 | 13–6 | 0.01 | 33% | 23 Dec → | |
| ancient | 13–9 | 0.14 | 36% | 23 Dec → | |
| train | 13–9 | 0.03 | 35% | 14 Dec → | |
| dust2 | 7–13 | 0.01 | 26% | 11 Dec → | |
| dust2 | 12–12 | 0.12 | 20% | 8 Dec → | |
| mirage | 13–11 | 0.05 | 22% | 8 Dec → | |
| mirage | 3–13 | -0.02 | 26% | 8 Dec → | |
| dust2 | 13–5 | -0.04 | 9% | 7 Dec → | |
| nuke | 10–13 | 0.01 | 15% | 7 Dec → | |
| ancient | 11–13 | -0.08 | 12% | 7 Dec → | |
| vertigo | 13–3 | 0.19 | 32% | 4 Dec → | |
| nuke | 13–10 | 0.07 | 39% | 27 Nov → | |
| dust2 | 13–4 | 0.08 | 30% | 27 Nov → | |
| nuke | 13–2 | 0.11 | 50% | 20 Nov → | |
| overpass | 13–4 | 0.10 | 35% | 20 Nov → | |
| overpass | 6–13 | 0.10 | 28% | 20 Nov → | |
| nuke | 13–7 | 0.14 | 33% | 13 Nov → | |
| mirage | 7–13 | 0.03 | 25% | 7 Oct → | |
| nuke | 9–13 | 0.01 | 19% | 1 Sept → | |
| train | 13–3 | 0.03 | 16% | 1 Sept → | |
| mirage | 13–10 | 0.07 | 20% | 1 Sept → | |
| dust2 | 9–13 | 0.00 | 17% | 21 Aug → | |
| train | 6–13 | -0.04 | 28% | 20 Aug → | |
| mirage | 2–13 | -0.03 | 33% | 20 Aug → | |
| dust2 | 9–2 | 0.03 | 20% | 13 Aug → | |
| inferno | 11–13 | 0.05 | 33% | 5 Aug → | |
| ancient | 13–5 | 0.10 | 40% | 1 Aug → | |
| nuke | 7–13 | 0.04 | 32% | 29 Jul → | |
| nuke | 7–13 | -0.00 | 34% | 29 Jul → | |
| train | 13–8 | -0.02 | 24% | 29 Jul → | |
| ancient | 11–13 | -0.05 | 29% | 29 Jul → | |
| dust2 | 4–13 | 0.04 | 33% | 29 Jul → | |
| mirage | 6–0 | 0.11 | 3% | 29 Jul → | |
| mirage | 13–9 | 0.05 | 25% | 29 Jul → | |
| mirage | 13–3 | 0.01 | 18% | 29 Jul → |
Match data via Leetify.
Recent teammates
Milestones
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