edan — CS2 Stats
FI76561198412102200[U:1:451836472]Steam profile ↗✓ No bans
What changed since last observed
CSDB last observed this profile on 18 Sep 2026 (yesterday). Ranks are recorded once per day this page is viewed.
CSDB reads this profile's Premier rating from its tracked match history, and records Faceit ELO once on every day the page is viewed. 7 days observed since 4 Sep 2026. Come back after the next session and the change shows above.
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Rating over time
Premier CS Rating
- 520 below peak (27,585)
- Next: Gold band at 30,000 — 2,935 to go
- Reached: Red band · Pink band · Purple band · Blue band
- Red band first seen 2026-09-04
- Pink band first seen 2026-09-04
Premier is CSDB’s own observation — it builds from the day a profile is first viewed and cannot be backfilled. 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=26,971), 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 | 49.7% | 51.0% |
| Shot accuracy | 15.3% | 13.7% |
| Kill/death ratio | 1.02 | 1.09 |
| Match win rate | 48.8% | 48.4% |
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Performance scores
0–100 skill scores via Leetify.
Player DNA
Primary style: Positional Player — Positioning stands above the rest of this profile (+1.8 against its own average).
Sharp aimerExcellent counter-strafingStrong positioning
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 iM 89% playstyle similarity
Most alike: utility contribution, aim profile.
Where you differ: higher opening-duel success.
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 95 — the mechanical foundation is a clear strength.
CT openings. 61% CT opening-duel success — winning the first fight on the defending side is rare and valuable.
T openings. 61% T opening-duel success — entries that actually open the round.
Areas to improve
Utility. Utility contribution runs far behind the mechanical game — the cheapest rating gain on this profile is thrown, not aimed.
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 8.9/10 (Elite), a weighted mean of the bars with a small opposition adjustment (×1.05 for this rank band). Formula versioned (v1) and documented in code.
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 resultsLWWLL
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Mirage | 169 | 57% | 1.25 | 17.4 |
| Dust2 | 64 | 58% | 1.24 | 18.2 |
| Nuke | 63 | 57% | 1.23 | 17.6 |
| Ancient | 41 | 51% | 1.26 | 17.1 |
| Inferno | 37 | 57% | 1.30 | 18.2 |
| Anubis | 29 | 62% | 1.31 | 16.7 |
| Vertigo | 16 | 75% | 1.35 | 18.0 |
| Train | 16 | 50% | 1.12 | 16.4 |
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.