MUJKA — CS2 Stats
76561198262415530[U:1:302149802]
How this compares with the same rank
Median values for Level 10 among CSDB-tracked players (n=218), 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 | vs Level 10 |
|---|---|---|---|
| Headshot rate | 48.4% | 52.8% | 4.4% short |
| Shot accuracy | 11.3% | 13.7% | 2.4% short |
| Kill/death ratio | 1.11 | 1.08 | above |
| Match win rate | 49.1% | 49.2% | meets |
This profile matches the typical Level 10 player on 2 of 4 comparable metrics.
Widest gap: Shot accuracy. That is the metric furthest from the Level 10 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 · +0.02 avg rating
Player DNA
Primary style: Entry Fragger — High opening-fight frequency with above-par success in them.
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.
Your pro match

Plays most like s1mple 98% playstyle similarity
Most alike: opening-fight frequency, 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
Strengths
Aim. Aim score of 99 — the mechanical foundation is a clear strength.
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 8.1/10 (Strong), a weighted mean of the bars with a small opposition adjustment (×0.95 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 |
|---|---|---|---|---|---|
| S | 26 | 17–9 | 65% | 0.08 | |
| S | 19 | 14–5 | 74% | 0.06 | |
| C | 16 | 7–9 | 44% | 0.03 | |
| S | 12 | 8–4 | 67% | 0.09 | |
| S | 12 | 8–4 | 67% | 0.07 | |
| A | 5 | 3–2 | 60% | -0.03 | |
| — | 4 | 2–2 | 50% | 0.13 | |
| — | 3 | 3–0 | 100% | 0.06 | |
| — | 2 | 1–1 | 50% | 0.05 | |
| — | 1 | 1–0 | 100% | -0.02 |
Across the last 100 tracked matches.
Dust 2 is currently your weakest sufficiently-sampled map (44% over 16). Start with the 6 essential Dust 2 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 resultsLLWLL
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Ancient | 391 | 54% | 1.11 | 17.4 |
| Mirage | 380 | 53% | 1.15 | 17.8 |
| Inferno | 249 | 52% | 1.12 | 17.2 |
| Dust2 | 216 | 38% | 1.04 | 16.5 |
| Nuke | 133 | 49% | 1.07 | 17.4 |
| Train | 94 | 45% | 1.16 | 17.2 |
| Anubis | 84 | 43% | 1.09 | 16.5 |
| Overpass | 52 | 60% | 1.15 | 18.4 |
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.
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.
- Grenades & UtilityGrenade Lineups →
Most of your flashes are blinding nobody. A handful of reliable pop-flash lineups fixes this faster than anything else.
Enemies flashed per flash 0.496 — below the 0.5 mark we flag
Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.
44% win rate across 16 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| 13–6 | 0.07 | 16% | 11 Jul → | ||
| 13–5 | 0.24 | 45% | 11 Jul → | ||
| 13–8 | 0.16 | 28% | 10 Jul → | ||
| 13–4 | 0.08 | 31% | 10 Jul → | ||
| 16–13 | 0.16 | 24% | 10 Jul → | ||
| 13–3 | 0.05 | 10% | 10 Jul → | ||
| 10–13 | 0.17 | 18% | 9 Jul → | ||
| 13–0 | 0.39 | 68% | 9 Jul → | ||
| 13–6 | 0.21 | 47% | 7 Jul → | ||
| 13–8 | 0.07 | 53% | 6 Jul → | ||
| 13–7 | 0.07 | 26% | 6 Jul → | ||
| 13–7 | 0.04 | 42% | 4 Jul → | ||
| 13–4 | 0.14 | 36% | 4 Jul → | ||
| 7–13 | 0.19 | 44% | 2 Jul → | ||
| 13–1 | 0.23 | 39% | 1 Jul → | ||
| 13–8 | 0.16 | 37% | 1 Jul → | ||
| 13–0 | 0.33 | 38% | 1 Jul → | ||
| 6–13 | 0.15 | 28% | 30 Jun → | ||
| 13–3 | 0.11 | 27% | 30 Jun → | ||
| 13–6 | 0.01 | 33% | 30 Jun → | ||
| 13–5 | 0.06 | 21% | 29 Jun → | ||
| 13–6 | 0.18 | 41% | 29 Jun → | ||
| 13–4 | 0.00 | 46% | 29 Jun → | ||
| 13–4 | 0.13 | 52% | 29 Jun → | ||
| 13–6 | 0.09 | 50% | 29 Jun → | ||
| 13–2 | 0.21 | 61% | 29 Jun → | ||
| 13–8 | 0.20 | 30% | 29 Jun → | ||
| 13–6 | 0.10 | 38% | 28 Jun → | ||
| 13–4 | 0.23 | 30% | 28 Jun → | ||
| 9–6 | 0.06 | 23% | 24 Jun → | ||
| 13–9 | 0.04 | 16% | 24 Jun → | ||
| 13–4 | 0.12 | 25% | 22 Jun → | ||
| 13–7 | 0.06 | 19% | 22 Jun → | ||
| 13–2 | 0.04 | 24% | 22 Jun → | ||
| 5–13 | -0.01 | 43% | 21 Jun → | ||
| 13–9 | 0.10 | 39% | 21 Jun → | ||
| 13–2 | 0.04 | 28% | 21 Jun → | ||
| 8–13 | -0.05 | 19% | 13 Jun → | ||
| 5–6 | 0.03 | 24% | 13 Jun → | ||
| 13–8 | -0.02 | 26% | 13 Jun → | ||
| 13–5 | 0.11 | 30% | 17 May → | ||
| 12–12 | 0.06 | 29% | 16 May → | ||
| 16–14 | 0.02 | 27% | 8 May → | ||
| 11–11 | 0.08 | 29% | 2 May → | ||
| 13–3 | 0.05 | 19% | 2 May → | ||
| 0–13 | -0.08 | 20% | 30 Apr → | ||
| 13–6 | 0.18 | 26% | 30 Apr → | ||
| 6–13 | 0.01 | 23% | 29 Apr → | ||
| 5–13 | 0.03 | 30% | 6 Apr → | ||
| 11–13 | 0.02 | 33% | 3 Apr → | ||
| 13–8 | 0.09 | 21% | 26 Mar → | ||
| 6–13 | -0.06 | 8% | 26 Mar → | ||
| 7–13 | 0.04 | 22% | 26 Mar → | ||
| 10–13 | 0.02 | 16% | 22 Mar → | ||
| 13–8 | 0.09 | 15% | 20 Mar → | ||
| 13–6 | 0.05 | 12% | 8 Mar → | ||
| 5–13 | -0.02 | 22% | 8 Mar → | ||
| 16–14 | 0.03 | 18% | 8 Mar → | ||
| 7–13 | 0.01 | 23% | 7 Mar → | ||
| 11–3 | 0.03 | 19% | 7 Mar → | ||
| 13–5 | 0.17 | 28% | 7 Mar → | ||
| 5–13 | -0.04 | 38% | 1 Mar → | ||
| 13–9 | 0.08 | 21% | 1 Mar → | ||
| 16–12 | 0.13 | 31% | 1 Mar → | ||
| 13–5 | 0.06 | 41% | 28 Feb → | ||
| 13–2 | 0.09 | 38% | 28 Feb → | ||
| 13–6 | 0.10 | 14% | 25 Feb → | ||
| 13–9 | -0.02 | 12% | 25 Feb → | ||
| 13–4 | 0.04 | 29% | 24 Feb → | ||
| 13–5 | 0.00 | 23% | 24 Feb → | ||
| 12–12 | -0.01 | 30% | 24 Feb → | ||
| 11–13 | 0.06 | 31% | 22 Feb → | ||
| 13–8 | 0.12 | 26% | 22 Feb → | ||
| 13–16 | 0.02 | 28% | 22 Jan → | ||
| 6–13 | 0.02 | 14% | 16 Jan → | ||
| 13–3 | 0.01 | 22% | 11 Jan → | ||
| 13–9 | -0.02 | 11% | 8 Jan → | ||
| 13–10 | -0.01 | 18% | 8 Jan → | ||
| 7–13 | -0.06 | 20% | 29 Dec → | ||
| 16–19 | -0.01 | 18% | 26 Dec → | ||
| 13–7 | -0.06 | 19% | 20 Dec → | ||
| 0–10 | -0.11 | 23% | 18 Dec → | ||
| 9–13 | -0.06 | 35% | 17 Dec → | ||
| 11–13 | -0.06 | 55% | 17 Dec → | ||
| 12–12 | 0.15 | 29% | 11 Dec → | ||
| 5–13 | 0.08 | 16% | 11 Dec → | ||
| 13–8 | 0.07 | 32% | 11 Dec → | ||
| 13–11 | -0.02 | 16% | 10 Dec → | ||
| 16–14 | 0.07 | 28% | 6 Dec → | ||
| 5–13 | -0.02 | 17% | 6 Dec → | ||
| 10–13 | -0.10 | 36% | 5 Dec → | ||
| 10–13 | -0.04 | 28% | 28 Nov → | ||
| 13–8 | 0.08 | 23% | 25 Nov → | ||
| 15–15 | -0.04 | 29% | 25 Nov → | ||
| 10–13 | 0.03 | 32% | 24 Nov → | ||
| 13–10 | 0.05 | 23% | 24 Nov → | ||
| 9–13 | 0.04 | 16% | 16 Nov → | ||
| 9–13 | 0.07 | 19% | 15 Nov → | ||
| 5–13 | -0.01 | 20% | 15 Nov → | ||
| 13–9 | 0.04 | 21% | 13 Nov → |
Match data via Leetify.