Eem3l1 — CS2 Stats
76561198272287552[U:1:312021824]
Performance scores
0–100 skill scores via Leetify.
Recent form
Last 10 vs previous 10: -10pp win rate · +0.01 avg rating
Player DNA
Primary style: Passive Rifler — Aim-led profile without a single dominant tendency.
Strong CT-side opener
Style profile from tracked-match aggregates — how this player plays, not how good they are. Classification rules are deterministic and documented in code.
Your pro match
Plays most like NiKo 89% playstyle similarity
Most alike: utility contribution, opening-fight frequency.
Where you differ: 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
T-side openings. Opening success drops from 46% on CT to 19% 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 5.9/10 (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.
Highlights
Map breakdown
| Map | Grade | Played | Record | Win rate | Avg rating |
|---|---|---|---|---|---|
 mirage | S | 19 | 14–5 | 74% | 0.02 |
 ancient | S | 17 | 11–6 | 65% | 0.00 |
 nuke | A | 16 | 9–7 | 56% | -0.01 |
 inferno | C | 7 | 3–4 | 43% | 0.02 |
 cache | C | 7 | 3–4 | 43% | -0.01 |
 overpass | S | 7 | 6–1 | 86% | 0.01 |
 dust2 | S | 6 | 5–1 | 83% | 0.03 |
 anubis | — | 4 | 1–3 | 25% | -0.04 |
| office | — | 4 | 3–1 | 75% | 0.05 |
 train | — | 3 | 3–0 | 100% | 0.09 |
| poseidon | — | 2 | 0–2 | 0% | -0.06 |
| stronghold | — | 2 | 0–2 | 0% | 0.01 |
| lake | — | 2 | 1–1 | 50% | 0.06 |
| debris | — | 1 | 0–1 | 0% | -0.06 |
 vertigo | — | 1 | 0–1 | 0% | -0.05 |
| alpine | — | 1 | 1–0 | 100% | 0.19 |
| cache_b | — | 1 | 0–1 | 0% | -0.02 |
Across the last 100 tracked matches.
Inferno is currently your weakest sufficiently-sampled map (43% over 7). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.
Faceit stats
Combat
Clutches & streaks
Recent Faceit resultsLLLWW
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Mirage | 722 | 52% | 1.05 | 15.4 |
| Ancient | 528 | 56% | 1.11 | 15.9 |
| Anubis | 301 | 57% | 1.08 | 15.4 |
| Nuke | 236 | 49% | 1.00 | 15.0 |
| Dust2 | 192 | 46% | 0.98 | 14.7 |
| Train | 109 | 62% | 1.15 | 16.1 |
| Vertigo | 86 | 57% | 1.20 | 18.3 |
| Inferno | 70 | 36% | 0.94 | 15.1 |
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 MechanicsAim Training →
Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.
Preaim 12.8371° — above the 12° 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 7 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| anubis | 3–13 | -0.02 | 25% | 15 Aug → | |
| dust2 | 1–0 | 0.11 | 100% | 15 Aug → | |
| nuke | 13–6 | 0.04 | 26% | 14 Aug → | |
| ancient | 6–13 | -0.02 | 13% | 14 Aug → | |
| inferno | 0–16 | -0.05 | 41% | 14 Aug → | |
| mirage | 13–11 | 0.02 | 24% | 6 Aug → | |
| mirage | 13–3 | 0.06 | 16% | 5 Aug → | |
| ancient | 8–13 | -0.04 | 11% | 2 Aug → | |
| nuke | 2–13 | -0.07 | 18% | 1 Aug → | |
| cache | 10–13 | -0.04 | 13% | 1 Aug → | |
| cache | 13–5 | -0.08 | 29% | 30 Jul → | |
| cache | 9–13 | -0.05 | 30% | 30 Jul → | |
| nuke | 16–13 | -0.03 | 22% | 30 Jul → | |
| ancient | 13–1 | -0.01 | 15% | 23 Jul → | |
| office | 0–3 | -0.08 | 0% | 18 Jul → | |
| mirage | 7–13 | -0.01 | 26% | 18 Jul → | |
| cache | 13–9 | 0.08 | 23% | 17 Jul → | |
| cache | 8–13 | -0.01 | 39% | 17 Jul → | |
| mirage | 5–13 | 0.05 | 25% | 17 Jul → | |
| dust2 | 13–5 | -0.01 | 21% | 14 Jul → | |
| debris | 4–8 | -0.06 | 14% | 10 Jul → | |
| mirage | 7–13 | -0.01 | 25% | 29 Jun → | |
| anubis | 5–13 | -0.03 | 29% | 29 Jun → | |
| nuke | 5–13 | -0.03 | 19% | 29 Jun → | |
| ancient | 10–13 | -0.06 | 13% | 29 Jun → | |
| dust2 | 13–8 | -0.00 | 20% | 29 Jun → | |
| overpass | 13–11 | -0.01 | 21% | 29 Jun → | |
| inferno | 6–13 | 0.03 | 31% | 28 Jun → | |
| nuke | 19–17 | -0.01 | 24% | 28 Jun → | |
| nuke | 7–13 | -0.03 | 33% | 28 Jun → | |
| poseidon | 3–9 | -0.07 | 20% | 15 Jun → | |
| mirage | 11–13 | -0.08 | 13% | 14 Jun → | |
| inferno | 5–13 | -0.04 | 7% | 14 Jun → | |
| ancient | 13–8 | 0.03 | 31% | 9 Jun → | |
| stronghold | 3–4 | -0.01 | 22% | 7 Jun → | |
| stronghold | 10–13 | 0.03 | 33% | 7 Jun → | |
| office | 13–2 | 0.19 | 34% | 7 Jun → | |
| vertigo | 0–3 | -0.05 | 0% | 4 Jun → | |
| cache | 3–13 | -0.03 | 29% | 2 Jun → | |
| mirage | 16–14 | 0.02 | 22% | 1 Jun → | |
| mirage | 11–13 | -0.01 | 20% | 31 May → | |
| mirage | 13–6 | 0.08 | 27% | 27 May → | |
| overpass | 13–5 | -0.00 | 24% | 21 May → | |
| nuke | 13–10 | -0.06 | 30% | 16 May → | |
| ancient | 13–11 | -0.02 | 23% | 16 May → | |
| overpass | 14–16 | -0.01 | 28% | 16 May → | |
| overpass | 13–5 | 0.01 | 17% | 30 Apr → | |
| nuke | 13–4 | 0.04 | 28% | 30 Apr → | |
| alpine | 13–9 | 0.19 | 24% | 29 Apr → | |
| poseidon | 2–9 | -0.05 | 27% | 29 Apr → | |
| cache | 13–5 | 0.01 | 19% | 29 Apr → | |
| mirage | 25–23 | 0.00 | 18% | 23 Apr → | |
| ancient | 13–6 | 0.06 | 14% | 22 Apr → | |
| overpass | 13–4 | 0.00 | 30% | 21 Apr → | |
| mirage | 13–4 | -0.02 | 38% | 20 Apr → | |
| ancient | 16–14 | -0.04 | 19% | 16 Apr → | |
| dust2 | 13–3 | 0.07 | 41% | 16 Apr → | |
| mirage | 13–4 | 0.01 | 22% | 9 Apr → | |
| ancient | 13–9 | 0.02 | 29% | 9 Apr → | |
| ancient | 13–16 | 0.07 | 19% | 4 Apr → | |
| overpass | 13–5 | 0.06 | 27% | 2 Apr → | |
| inferno | 13–8 | 0.06 | 19% | 2 Apr → | |
| nuke | 11–13 | -0.01 | 29% | 20 Mar → | |
| inferno | 13–11 | 0.01 | 30% | 20 Mar → | |
| mirage | 13–4 | 0.03 | 18% | 19 Mar → | |
| ancient | 13–2 | 0.01 | 24% | 19 Mar → | |
| ancient | 13–3 | 0.06 | 16% | 17 Mar → | |
| nuke | 13–5 | 0.03 | 11% | 10 Mar → | |
| cache_b | 3–13 | -0.02 | 29% | 10 Mar → | |
| train | 13–7 | 0.17 | 37% | 10 Mar → | |
| overpass | 13–10 | 0.02 | 21% | 10 Mar → | |
| mirage | 13–5 | 0.14 | 40% | 9 Mar → | |
| mirage | 13–3 | 0.01 | 15% | 28 Feb → | |
| ancient | 13–4 | 0.04 | 27% | 28 Feb → | |
| anubis | 9–13 | -0.06 | 20% | 27 Feb → | |
| nuke | 13–8 | 0.03 | 28% | 27 Feb → | |
| inferno | 13–4 | 0.07 | 22% | 27 Feb → | |
| mirage | 13–11 | 0.05 | 21% | 25 Feb → | |
| mirage | 13–11 | -0.04 | 18% | 22 Feb → | |
| nuke | 3–13 | -0.06 | 14% | 21 Feb → | |
| nuke | 6–13 | 0.00 | 16% | 15 Feb → | |
| ancient | 13–10 | -0.02 | 19% | 14 Feb → | |
| dust2 | 13–9 | -0.03 | 24% | 11 Feb → | |
| mirage | 13–6 | 0.04 | 19% | 9 Feb → | |
| anubis | 13–10 | -0.04 | 14% | 3 Feb → | |
| nuke | 13–6 | 0.07 | 25% | 31 Jan → | |
| ancient | 13–5 | -0.03 | 22% | 28 Jan → | |
| ancient | 9–13 | -0.01 | 30% | 21 Jan → | |
| mirage | 13–9 | 0.03 | 18% | 14 Jan → | |
| nuke | 7–13 | -0.07 | 30% | 4 Jan → | |
| dust2 | 11–13 | 0.02 | 16% | 2 Jan → | |
| lake | 6–9 | -0.03 | 34% | 28 Dec → | |
| lake | 9–3 | 0.15 | 26% | 28 Dec → | |
| train | 13–8 | 0.05 | 24% | 13 Dec → | |
| ancient | 11–13 | -0.00 | 17% | 13 Dec → | |
| inferno | 11–13 | 0.03 | 20% | 13 Dec → | |
| nuke | 13–3 | 0.03 | 22% | 8 Dec → | |
| train | 13–9 | 0.05 | 32% | 27 Nov → | |
| office | 13–8 | 0.12 | 36% | 26 Nov → | |
| office | 13–5 | -0.02 | 41% | 26 Nov → |
Match data via Leetify.