aiaiiia — CS2 Stats
NO76561199465661986[U:1:1505396258]Steam profile ↗✓ No bans
What changed since last observed
CSDB last observed this profile on 16 Sep 2026 (3 days ago). Ranks are recorded once per day this page is viewed.
No change since then — still 735 tracked matches. Play, then come back: the next observation lands here.
CSDB reads this profile's Premier rating from its tracked match history, and records Faceit ELO once on every day the page is viewed. 36 days played since 6 May 2026. Come back after the next session and the change shows above.
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Rating over time
Premier CS Rating
- 854 below peak (28,274)
- +367 over 30 days · steady
- +418 over 90 days
- Next: Gold band at 30,000 — 2,580 to go
- Reached: Red band · Pink band · Purple band · Blue band
- Red band first seen 2026-08-30
- Pink band first seen 2026-08-30
Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do.
How this compares with the same rank
Median values for Red band among CSDB-tracked players (n=12,855), 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 | Red band median | Gold band median | vs Gold band |
|---|---|---|---|---|
| Headshot rate | 55.4% | 49.2% | 50.2% | above |
| Shot accuracy | 8.2% | 13.3% | 11.7% | 3.5% short |
| Kill/death ratio | 1.36 | 1.12 | 1.18 | above |
| Match win rate | 63.1% | 48.5% | 51.9% | above |
This profile matches the typical Gold band player on 3 of 4 comparable metrics.
Widest gap: Shot accuracy. That is the metric furthest from the Gold band median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.
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Performance scores
0–100 skill scores via Leetify.
Recent form
Last 10 vs previous 10: +10pp win rate · +0.01 avg rating · −0.2pp headshot accuracy · +2ms 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
- 2–2–1 · 40% win rate
- Avg rating 0.03
- Avg headshot accuracy 29%
- Avg reaction 635ms
Last 10
- 6–3–1 · 60% win rate
- Avg rating 0.01
- Avg headshot accuracy 23%
- Avg reaction 563ms
Last 20
- 11–8–1 · 55% win rate
- Avg rating 0.01
- Avg headshot accuracy 24%
- Avg reaction 562ms
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: Support — Utility contribution stands above the rest of this profile (+1.4 against its own average).
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 NiKo 91% playstyle similarity
Most alike: aim profile, utility contribution.
Where you differ: lower positioning 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
Positioning. Positioning trails aim by 35 points — deaths here waste a strong aim profile.
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 6.7/10 (Solid), 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 |
|---|---|---|---|---|---|
 nuke | S | 21 | 14–7 | 67% | 0.01 |
 ancient | S | 17 | 11–6 | 65% | 0.02 |
 cache | S | 17 | 11–6 | 65% | 0.04 |
 mirage | A | 11 | 6–5 | 55% | 0.01 |
 anubis | A | 11 | 7–4 | 64% | 0.03 |
 vertigo | S | 9 | 9–0 | 100% | 0.04 |
| office | S | 6 | 6–0 | 100% | 0.09 |
 inferno | — | 4 | 2–2 | 50% | -0.00 |
 overpass | — | 2 | 1–1 | 50% | -0.04 |
 train | — | 1 | 1–0 | 100% | 0.01 |
 dust2 | — | 1 | 0–1 | 0% | -0.10 |
Across the last 100 tracked matches.
Mirage is currently your weakest sufficiently-sampled map (55% over 11). Start with the 6 essential Mirage 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 resultsWLLLW
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Mirage | 87 | 55% | 0.99 | 13.8 |
| Ancient | 75 | 61% | 1.05 | 15.1 |
| Anubis | 47 | 66% | 0.97 | 14.6 |
| Nuke | 31 | 61% | 1.16 | 16.7 |
| Vertigo | 27 | 59% | 0.96 | 16.4 |
| Overpass | 10 | 60% | 1.16 | 15.7 |
| Inferno | 5 | 80% | 1.42 | 14.0 |
| Dust2 | 3 | 33% | 0.87 | 15.3 |
Faceit-match stats via the FACEIT Data API — a separate match pool from the sections above.
Skill 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 | 0–13 | -0.06 | 7% | 13 Sept → | |
| nuke | 13–0 | -0.01 | 0% | 13 Sept → | |
| ancient | 13–7 | 0.07 | 11% | 13 Sept → | |
| nuke | 0–2 | 0.02 | 100% | 10 Sept → | |
| cache | 12–12 | 0.13 | 26% | 9 Sept → | |
| nuke | 13–6 | -0.01 | 21% | 5 Sept → | |
| cache | 13–9 | 0.04 | 23% | 22 Aug → | |
| nuke | 3–2 | 0.02 | 18% | 22 Aug → | |
| anubis | 0–13 | -0.08 | 15% | 20 Aug → | |
| nuke | 13–7 | 0.01 | 13% | 20 Aug → | |
| nuke | 1–8 | -0.08 | 32% | 20 Aug → | |
| nuke | 13–2 | 0.09 | 12% | 16 Aug → | |
| cache | 8–13 | -0.05 | 10% | 16 Aug → | |
| mirage | 13–1 | 0.02 | 38% | 15 Aug → | |
| ancient | 13–9 | 0.09 | 17% | 14 Aug → | |
| cache | 9–13 | 0.00 | 30% | 14 Aug → | |
| mirage | 13–10 | 0.02 | 22% | 10 Aug → | |
| ancient | 8–13 | -0.13 | 20% | 10 Aug → | |
| nuke | 0–2 | -0.01 | 33% | 10 Aug → | |
| vertigo | 9–2 | 0.06 | 23% | 7 Aug → | |
| nuke | 9–0 | 0.16 | 25% | 7 Aug → | |
| nuke | 9–6 | -0.06 | 33% | 7 Aug → | |
| vertigo | 9–3 | 0.10 | 23% | 7 Aug → | |
| ancient | 5–13 | -0.00 | 14% | 7 Aug → | |
| mirage | 2–13 | -0.11 | 33% | 7 Aug → | |
| cache | 8–13 | 0.04 | 19% | 7 Aug → | |
| anubis | 13–8 | -0.03 | 32% | 2 Aug → | |
| anubis | 0–13 | -0.10 | 0% | 2 Aug → | |
| cache | 10–13 | 0.01 | 21% | 1 Aug → | |
| anubis | 13–6 | 0.02 | 31% | 1 Aug → | |
| ancient | 13–8 | 0.05 | 18% | 1 Aug → | |
| mirage | 13–8 | 0.10 | 31% | 1 Aug → | |
| anubis | 13–4 | 0.13 | 27% | 1 Aug → | |
| anubis | 13–4 | 0.10 | 13% | 31 Jul → | |
| vertigo | 13–11 | 0.06 | 27% | 31 Jul → | |
| vertigo | 6–2 | -0.03 | 9% | 31 Jul → | |
| mirage | 13–0 | 0.12 | 27% | 26 Jul → | |
| anubis | 13–9 | 0.06 | 20% | 12 Jul → | |
| ancient | 13–10 | 0.01 | 15% | 8 Jul → | |
| cache | 4–13 | 0.05 | 21% | 8 Jul → | |
| mirage | 6–0 | 0.07 | 23% | 3 Jul → | |
| nuke | 13–8 | 0.06 | 26% | 3 Jul → | |
| nuke | 13–3 | -0.04 | 13% | 3 Jul → | |
| nuke | 3–13 | -0.09 | 17% | 1 Jul → | |
| ancient | 13–8 | -0.03 | 19% | 25 Jun → | |
| anubis | 1–13 | -0.02 | 44% | 25 Jun → | |
| ancient | 13–0 | 0.12 | 17% | 25 Jun → | |
| ancient | 7–13 | -0.03 | 19% | 25 Jun → | |
| ancient | 6–13 | -0.03 | 13% | 24 Jun → | |
| nuke | 13–8 | -0.00 | 8% | 24 Jun → | |
| nuke | 13–7 | -0.05 | 17% | 24 Jun → | |
| office | 13–4 | 0.02 | 14% | 21 Jun → | |
| ancient | 13–6 | 0.06 | 12% | 21 Jun → | |
| train | 13–8 | 0.01 | 19% | 21 Jun → | |
| cache | 13–3 | -0.01 | 24% | 5 Jun → | |
| vertigo | 13–4 | 0.07 | 9% | 5 Jun → | |
| vertigo | 13–10 | -0.02 | 13% | 5 Jun → | |
| ancient | 14–16 | 0.07 | 12% | 2 Jun → | |
| inferno | 13–6 | 0.08 | 20% | 2 Jun → | |
| ancient | 16–13 | 0.00 | 12% | 29 May → | |
| office | 13–3 | 0.04 | 11% | 28 May → | |
| nuke | 7–13 | 0.00 | 19% | 27 May → | |
| nuke | 0–13 | -0.13 | 0% | 27 May → | |
| overpass | 1–1 | -0.07 | 0% | 27 May → | |
| inferno | 6–13 | -0.11 | 25% | 27 May → | |
| mirage | 6–13 | -0.04 | 13% | 27 May → | |
| nuke | 9–13 | 0.06 | 11% | 27 May → | |
| anubis | 11–13 | -0.01 | 17% | 24 May → | |
| dust2 | 2–13 | -0.10 | 0% | 24 May → | |
| inferno | 8–13 | -0.06 | 15% | 24 May → | |
| ancient | 5–13 | -0.02 | 15% | 24 May → | |
| cache | 13–4 | 0.00 | 11% | 24 May → | |
| mirage | 0–13 | -0.06 | 0% | 24 May → | |
| anubis | 13–2 | 0.04 | 14% | 24 May → | |
| overpass | 13–10 | -0.01 | 9% | 24 May → | |
| nuke | 6–0 | 0.13 | 17% | 23 May → | |
| mirage | 13–5 | 0.08 | 14% | 23 May → | |
| nuke | 13–3 | 0.07 | 32% | 23 May → | |
| inferno | 13–5 | 0.09 | 16% | 23 May → | |
| ancient | 13–7 | 0.06 | 21% | 23 May → | |
| vertigo | 13–5 | -0.03 | 12% | 23 May → | |
| cache | 13–3 | 0.12 | 23% | 22 May → | |
| vertigo | 10–1 | 0.11 | 15% | 22 May → | |
| cache | 13–9 | 0.03 | 11% | 20 May → | |
| office | 13–7 | 0.30 | 24% | 20 May → | |
| ancient | 13–5 | -0.02 | 21% | 20 May → | |
| office | 13–7 | 0.04 | 23% | 19 May → | |
| vertigo | 13–7 | 0.05 | 20% | 19 May → | |
| cache | 13–4 | -0.01 | 18% | 19 May → | |
| office | 13–5 | 0.05 | 25% | 18 May → | |
| nuke | 6–0 | -0.02 | 50% | 18 May → | |
| office | 13–0 | 0.08 | 31% | 18 May → | |
| cache | 13–11 | -0.09 | 13% | 18 May → | |
| cache | 13–2 | 0.25 | 31% | 16 May → | |
| mirage | 2–13 | -0.01 | 23% | 16 May → | |
| cache | 13–6 | 0.07 | 23% | 16 May → | |
| anubis | 13–1 | 0.17 | 19% | 16 May → | |
| cache | 13–6 | 0.07 | 16% | 13 May → | |
| ancient | 13–4 | 0.09 | 13% | 13 May → | |
| cache | 13–5 | 0.04 | 20% | 6 May → |
Match data via Leetify.
