shyyne — CS2 Stats
NO76561198027020808[U:1:66755080]Steam profile ↗✓ No bans
What changed since last observed
CSDB last observed this profile on 20 Sep 2026 (2 days ago). Ranks are recorded once per day this page is viewed.
No change since then — still 2,729 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. 71 days played since 17 Apr 2024. Come back after the next session and the change shows above.
Is this you? Sign in with Steam to claim it and connect match tracking →
Rating over time
Faceit ELO
- At peak — 3,485
- Next: 3,500 ELO at 3,500 — 15 to go
- Reached: 3,000 ELO · 2,500 ELO · Level 10 · Level 9
- 3,000 ELO first seen 2026-08-31
- 2,500 ELO first seen 2026-08-31
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.
Share this profile
The image is a snapshot; the link keeps updating. Nothing here is published anywhere — it is generated in your browser when you click.
Performance scores
0–100 skill scores via Leetify.
Recent form
Last 10 vs previous 10: +70pp win rate · +0.07 avg rating · +6.0pp headshot accuracy · +10ms reaction
Win rate up 70pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp).
Last 5 · 10 · 20 matches
Last 5
- 5–0 · 100% win rate
- Avg rating 0.04
- Avg headshot accuracy 24%
- Avg reaction 500ms
Last 10
- 8–2 · 80% win rate
- Avg rating 0.03
- Avg headshot accuracy 27%
- Avg reaction 513ms
Last 20
- 9–11 · 45% win rate
- Avg rating -0.01
- Avg headshot accuracy 24%
- Avg reaction 508ms
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 (+1.8 against its own average).
Sharp aimerEffective flashes
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: positioning profile, 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 88 — the mechanical foundation is a clear strength.
Areas to improve
Positioning. Positioning trails aim by 30 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 6.8/10 (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 |
|---|---|---|---|---|---|
 ancient | B | 26 | 12–14 | 46% | 0.04 |
 nuke | A | 18 | 11–7 | 61% | 0.01 |
 anubis | S | 15 | 10–5 | 67% | 0.04 |
 inferno | C | 14 | 6–8 | 43% | 0.01 |
 mirage | S | 9 | 6–3 | 67% | 0.01 |
 dust2 | A | 7 | 4–3 | 57% | 0.04 |
 train | — | 4 | 2–2 | 50% | 0.01 |
 overpass | — | 3 | 1–2 | 33% | 0.00 |
 vertigo | — | 2 | 1–1 | 50% | 0.02 |
| thera | — | 1 | 0–1 | 0% | -0.05 |
| mills | — | 1 | 1–0 | 100% | 0.14 |
Across the last 100 tracked matches.
Inferno is currently your weakest sufficiently-sampled map (43% over 14). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.
Faceit stats
Combat
Clutches & streaks
Recent Faceit resultsWLWWL
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Ancient | 475 | 59% | 1.33 | 17.8 |
| Anubis | 321 | 57% | 1.35 | 18.1 |
| Nuke | 153 | 55% | 1.24 | 17.2 |
| Mirage | 120 | 47% | 1.14 | 16.0 |
| Inferno | 115 | 60% | 1.36 | 17.1 |
| Vertigo | 106 | 60% | 1.33 | 17.4 |
| Overpass | 58 | 62% | 1.38 | 17.3 |
| Train | 46 | 48% | 1.21 | 17.1 |
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.
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
You are losing most of the first duels you take on T side, which is usually a peeking and spacing problem, not aim.
T opening duels 36.302% — below the 40% mark we flag
Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.
43% win rate across 14 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| nuke | 13–8 | -0.06 | 24% | 14 Jun → | |
| inferno | 13–5 | 0.04 | 18% | 14 Jun → | |
| dust2 | 13–3 | 0.11 | 35% | 1 Jun → | |
| ancient | 22–20 | 0.01 | 20% | 30 May → | |
| nuke | 13–11 | 0.12 | 22% | 28 May → | |
| nuke | 7–13 | -0.06 | 23% | 19 Apr → | |
| inferno | 13–2 | 0.06 | 44% | 19 Apr → | |
| nuke | 8–13 | -0.06 | 33% | 18 Apr → | |
| inferno | 13–4 | 0.07 | 35% | 18 Apr → | |
| dust2 | 13–5 | 0.06 | 19% | 27 Mar → | |
| dust2 | 13–9 | -0.01 | 24% | 25 Mar → | |
| ancient | 10–13 | 0.02 | 22% | 25 Mar → | |
| inferno | 11–13 | -0.03 | 29% | 25 Mar → | |
| dust2 | 10–13 | -0.07 | 27% | 20 Feb → | |
| ancient | 1–13 | 0.00 | 25% | 20 Feb → | |
| inferno | 11–13 | -0.01 | 25% | 20 Feb → | |
| nuke | 2–13 | -0.09 | 33% | 20 Feb → | |
| inferno | 6–13 | -0.08 | 21% | 23 Jan → | |
| inferno | 6–13 | -0.08 | 0% | 23 Jan → | |
| nuke | 4–13 | -0.07 | 7% | 23 Jan → | |
| nuke | 4–13 | -0.07 | 0% | 23 Jan → | |
| nuke | 13–8 | 0.02 | 22% | 23 Jan → | |
| nuke | 13–8 | 0.02 | 0% | 23 Jan → | |
| train | 13–7 | 0.06 | 33% | 6 Jan → | |
| overpass | 8–13 | 0.00 | 19% | 3 Jan → | |
| ancient | 10–13 | 0.01 | 40% | 13 Nov → | |
| train | 7–13 | 0.00 | 27% | 13 Nov → | |
| overpass | 13–7 | -0.01 | 16% | 13 Nov → | |
| nuke | 13–5 | 0.06 | 21% | 11 Nov → | |
| inferno | 13–3 | 0.03 | 27% | 6 Nov → | |
| mirage | 13–11 | -0.03 | 17% | 27 Oct → | |
| train | 13–9 | -0.06 | 18% | 19 Oct → | |
| ancient | 9–13 | -0.02 | 15% | 6 Oct → | |
| ancient | 5–13 | -0.01 | 17% | 17 Jul → | |
| anubis | 9–13 | 0.00 | 19% | 17 Jul → | |
| ancient | 13–9 | -0.01 | 33% | 8 May → | |
| mirage | 9–13 | -0.02 | 18% | 24 Apr → | |
| ancient | 13–5 | 0.13 | 19% | 12 Apr → | |
| nuke | 9–13 | -0.01 | 43% | 6 Apr → | |
| mirage | 16–13 | 0.08 | 44% | 5 Apr → | |
| dust2 | 6–13 | -0.00 | 19% | 5 Apr → | |
| inferno | 5–13 | -0.04 | 35% | 5 Apr → | |
| anubis | 13–5 | 0.10 | 31% | 1 Apr → | |
| nuke | 12–8 | 0.13 | 34% | 30 Mar → | |
| ancient | 13–3 | 0.06 | 26% | 30 Mar → | |
| anubis | 7–13 | 0.02 | 23% | 30 Mar → | |
| anubis | 11–13 | -0.03 | 19% | 28 Mar → | |
| train | 7–13 | 0.03 | 23% | 25 Mar → | |
| anubis | 13–1 | 0.17 | 34% | 24 Mar → | |
| mirage | 13–9 | 0.03 | 27% | 5 Mar → | |
| dust2 | 12–12 | 0.04 | 16% | 15 Feb → | |
| ancient | 13–7 | 0.26 | 32% | 15 Feb → | |
| mirage | 13–7 | 0.06 | 24% | 15 Feb → | |
| anubis | 13–11 | 0.07 | 21% | 10 Feb → | |
| nuke | 11–13 | 0.08 | 25% | 3 Feb → | |
| anubis | 13–16 | 0.03 | 14% | 17 Jan → | |
| ancient | 7–13 | 0.02 | 10% | 2 Dec → | |
| ancient | 13–4 | 0.21 | 21% | 12 Nov → | |
| nuke | 13–4 | 0.11 | 36% | 10 Nov → | |
| nuke | 13–3 | 0.07 | 18% | 30 Oct → | |
| inferno | 8–13 | -0.00 | 21% | 28 Oct → | |
| ancient | 10–13 | -0.03 | 17% | 11 Oct → | |
| ancient | 10–13 | 0.11 | 25% | 11 Oct → | |
| nuke | 13–10 | -0.00 | 40% | 22 Sept → | |
| mirage | 16–13 | -0.00 | 38% | 18 Sept → | |
| ancient | 6–13 | -0.07 | 13% | 16 Sept → | |
| anubis | 13–8 | 0.11 | 23% | 14 Sept → | |
| ancient | 13–7 | 0.07 | 32% | 13 Sept → | |
| dust2 | 13–11 | 0.13 | 26% | 9 Sept → | |
| inferno | 13–11 | 0.09 | 31% | 9 Sept → | |
| ancient | 14–16 | 0.01 | 27% | 26 Aug → | |
| anubis | 13–10 | -0.02 | 16% | 25 Aug → | |
| anubis | 13–5 | 0.05 | 11% | 22 Aug → | |
| anubis | 13–8 | 0.03 | 15% | 17 Aug → | |
| anubis | 13–11 | 0.01 | 13% | 9 Aug → | |
| ancient | 10–13 | 0.02 | 19% | 9 Aug → | |
| nuke | 13–6 | 0.08 | 30% | 8 Aug → | |
| ancient | 13–9 | 0.00 | 25% | 6 Aug → | |
| mirage | 11–13 | -0.02 | 14% | 2 Aug → | |
| inferno | 22–19 | 0.10 | 20% | 30 Jul → | |
| anubis | 8–13 | -0.06 | 20% | 30 Jul → | |
| inferno | 8–13 | -0.01 | 26% | 29 Jul → | |
| mirage | 6–13 | -0.01 | 24% | 21 Jul → | |
| ancient | 14–16 | -0.00 | 19% | 20 Jul → | |
| ancient | 13–9 | 0.14 | 31% | 5 Jul → | |
| thera | 11–13 | -0.05 | 22% | 27 Jun → | |
| mills | 13–9 | 0.14 | 35% | 27 Jun → | |
| anubis | 13–9 | 0.03 | 38% | 21 Jun → | |
| ancient | 13–5 | 0.05 | 34% | 5 May → | |
| ancient | 9–13 | -0.03 | 21% | 1 May → | |
| vertigo | 16–19 | 0.00 | 19% | 30 Apr → | |
| overpass | 10–13 | 0.02 | 29% | 29 Apr → | |
| mirage | 13–8 | 0.04 | 18% | 28 Apr → | |
| ancient | 13–8 | 0.04 | 15% | 28 Apr → | |
| anubis | 19–16 | 0.03 | 23% | 26 Apr → | |
| vertigo | 13–8 | 0.03 | 21% | 25 Apr → | |
| ancient | 13–6 | -0.02 | 33% | 24 Apr → | |
| nuke | 13–11 | 0.01 | 14% | 24 Apr → | |
| ancient | 11–13 | 0.04 | 26% | 22 Apr → | |
| inferno | 5–13 | -0.05 | 8% | 17 Apr → |
Match data via Leetify.
