Chinese ropz — CS2 Stats
76561198200533378[U:1:240267650]
How this compares with the same rank
Median values for Level 4 among CSDB-tracked players (n=1,711), 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 4 median | Level 5 median | vs Level 5 |
|---|---|---|---|---|
| Headshot rate | 38.1% | 42.7% | 44.0% | 5.9% short |
| Shot accuracy | 10.7% | 10.8% | 11.6% | 0.9% short |
| Kill/death ratio | 0.95 | 1.00 | 1.03 | 0.07 short |
| Match win rate | 40.8% | 44.1% | 45.1% | 4.3% short |
This profile sits below the typical Level 5 player on every metric we can compare.
Widest gap: Headshot rate. That is the metric furthest from the Level 5 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: -30pp win rate · -0.05 avg rating
Player DNA
Primary style: Aggressive Rifler — Takes opening fights often, backed by a strong aim profile.
Sharp aimerExcellent counter-strafingReliable in 1v1s
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 donk 95% playstyle similarity
Most alike: positioning profile, opening-fight frequency.
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 86 — the mechanical foundation is a clear strength.
Counter-strafing. 91% of shots taken properly stopped — movement discipline most players never reach.
Areas to improve
Preaim. Mechanical aim is strong but the crosshair sits 10.2° off target when enemies appear — placement, not flicking, is the bigger win available.
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 7.1/10 (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 |
|---|---|---|---|---|---|
| A | 20 | 12–8 | 60% | -0.00 | |
| B | 19 | 10–9 | 53% | 0.00 | |
| A | 15 | 9–6 | 60% | 0.03 | |
| C | 14 | 6–8 | 43% | 0.01 | |
| B | 8 | 4–4 | 50% | -0.01 | |
| B | 8 | 4–4 | 50% | -0.00 | |
| C | 7 | 3–4 | 43% | 0.01 | |
| shelter | — | 3 | 1–2 | 33% | -0.01 |
| — | 2 | 0–2 | 0% | -0.02 | |
| office | — | 2 | 2–0 | 100% | 0.03 |
| — | 1 | 1–0 | 100% | 0.05 | |
| — | 1 | 1–0 | 100% | 0.03 |
Across the last 100 tracked matches.
Nuke is currently your weakest sufficiently-sampled map (43% over 14). Start with the 6 essential Nuke 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 resultsLWLWL
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Mirage | 6 | 17% | 0.86 | 12.7 |
| Nuke | 5 | 80% | 1.60 | 15.6 |
| Ancient | 3 | 33% | 0.44 | 9.0 |
| Cache | 2 | 0% | 0.76 | 12.5 |
| Dust2 | 2 | 0% | 0.49 | 8.5 |
| Inferno | 1 | 100% | 1.08 | 14.0 |
| Anubis | 1 | 0% | 0.69 | 9.0 |
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
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 | |
|---|---|---|---|---|---|
| 8–13 | -0.08 | 35% | 30 Aug → | ||
| 6–13 | -0.04 | 17% | 30 Aug → | ||
| 13–6 | -0.03 | 16% | 30 Aug → | ||
| 6–13 | -0.06 | 24% | 30 Aug → | ||
| 12–12 | 0.10 | 29% | 30 Aug → | ||
| shelter | 4–13 | -0.06 | 17% | 28 Aug → | |
| 12–12 | 0.01 | 17% | 28 Aug → | ||
| 13–8 | 0.01 | 11% | 21 Aug → | ||
| 13–9 | -0.01 | 15% | 19 Aug → | ||
| 13–8 | -0.06 | 10% | 16 Aug → | ||
| 15–15 | -0.01 | 8% | 16 Aug → | ||
| 13–6 | -0.03 | 15% | 16 Aug → | ||
| 13–11 | -0.05 | 14% | 16 Aug → | ||
| 13–1 | 0.06 | 12% | 16 Aug → | ||
| 13–2 | 0.08 | 16% | 16 Aug → | ||
| 7–13 | -0.01 | 14% | 16 Aug → | ||
| 13–8 | 0.03 | 20% | 16 Aug → | ||
| 8–13 | 0.05 | 19% | 16 Aug → | ||
| 13–5 | 0.12 | 21% | 16 Aug → | ||
| 13–4 | 0.04 | 9% | 16 Aug → | ||
| 13–10 | 0.08 | 19% | 14 Aug → | ||
| 6–13 | -0.00 | 19% | 14 Aug → | ||
| 11–13 | 0.01 | 10% | 14 Aug → | ||
| 13–11 | -0.05 | 6% | 14 Aug → | ||
| 13–10 | 0.02 | 18% | 12 Aug → | ||
| 3–9 | 0.10 | 18% | 11 Aug → | ||
| 6–13 | 0.05 | 20% | 11 Aug → | ||
| 13–8 | 0.01 | 7% | 11 Aug → | ||
| 5–13 | 0.01 | 14% | 10 Aug → | ||
| 13–8 | 0.00 | 15% | 10 Aug → | ||
| 12–16 | 0.02 | 18% | 9 Aug → | ||
| 13–7 | -0.02 | 11% | 9 Aug → | ||
| 9–2 | 0.07 | 13% | 9 Aug → | ||
| 8–13 | 0.00 | 20% | 9 Aug → | ||
| 13–3 | 0.02 | 7% | 9 Aug → | ||
| 13–0 | 0.01 | 15% | 9 Aug → | ||
| 11–13 | -0.03 | 14% | 9 Aug → | ||
| 13–16 | -0.03 | 13% | 8 Aug → | ||
| 7–13 | -0.02 | 19% | 8 Aug → | ||
| 9–13 | -0.01 | 20% | 8 Aug → | ||
| 5–13 | -0.02 | 6% | 8 Aug → | ||
| shelter | 9–13 | 0.03 | 12% | 5 Aug → | |
| 13–7 | 0.03 | 14% | 5 Aug → | ||
| 2–13 | -0.05 | 3% | 5 Aug → | ||
| 5–13 | -0.05 | 5% | 5 Aug → | ||
| 13–8 | 0.08 | 14% | 3 Aug → | ||
| 13–11 | 0.01 | 9% | 3 Aug → | ||
| 5–13 | -0.04 | 18% | 3 Aug → | ||
| 13–2 | 0.05 | 33% | 2 Aug → | ||
| 13–8 | 0.09 | 18% | 2 Aug → | ||
| 13–3 | 0.04 | 15% | 2 Aug → | ||
| 13–3 | 0.04 | 17% | 1 Aug → | ||
| 13–11 | -0.02 | 15% | 1 Aug → | ||
| 6–13 | 0.01 | 21% | 1 Aug → | ||
| 13–11 | -0.00 | 19% | 28 Jul → | ||
| 13–9 | -0.04 | 18% | 27 Jul → | ||
| 13–11 | 0.03 | 19% | 27 Jul → | ||
| 13–11 | 0.04 | 18% | 26 Jul → | ||
| 13–4 | 0.02 | 8% | 26 Jul → | ||
| 13–11 | -0.03 | 8% | 26 Jul → | ||
| 13–7 | 0.09 | 11% | 26 Jul → | ||
| 7–13 | 0.05 | 11% | 26 Jul → | ||
| 6–13 | -0.05 | 12% | 26 Jul → | ||
| 13–2 | 0.06 | 29% | 26 Jul → | ||
| 13–10 | 0.05 | 14% | 25 Jul → | ||
| 4–13 | -0.03 | 15% | 24 Jul → | ||
| 1–13 | -0.06 | 11% | 24 Jul → | ||
| 4–13 | -0.08 | 22% | 24 Jul → | ||
| 12–12 | -0.01 | 8% | 23 Jul → | ||
| 4–13 | -0.01 | 15% | 23 Jul → | ||
| 13–16 | 0.03 | 14% | 14 Jul → | ||
| 9–13 | -0.04 | 26% | 14 Jul → | ||
| 9–13 | -0.08 | 20% | 13 Jul → | ||
| 13–10 | -0.04 | 17% | 12 Jul → | ||
| 6–13 | -0.01 | 25% | 12 Jul → | ||
| 13–5 | -0.01 | 17% | 12 Jul → | ||
| shelter | 13–7 | 0.01 | 17% | 12 Jul → | |
| 7–13 | -0.02 | 13% | 12 Jul → | ||
| 10–13 | 0.03 | 20% | 12 Jul → | ||
| 14–16 | -0.03 | 17% | 10 Jul → | ||
| 4–13 | -0.10 | 10% | 9 Jul → | ||
| office | 13–8 | 0.08 | 26% | 8 Jul → | |
| office | 13–10 | -0.02 | 12% | 8 Jul → | |
| 7–9 | 0.02 | 20% | 7 Jul → | ||
| 16–13 | 0.05 | 23% | 5 Jul → | ||
| 13–11 | 0.02 | 18% | 4 Jul → | ||
| 13–2 | 0.05 | 27% | 4 Jul → | ||
| 13–2 | 0.09 | 29% | 4 Jul → | ||
| 13–7 | 0.08 | 19% | 4 Jul → | ||
| 13–4 | 0.02 | 25% | 4 Jul → | ||
| 13–4 | 0.00 | 16% | 29 Jun → | ||
| 9–1 | 0.12 | 19% | 29 Jun → | ||
| 13–1 | -0.03 | 27% | 29 Jun → | ||
| 7–13 | -0.04 | 26% | 29 Jun → | ||
| 11–13 | -0.05 | 45% | 27 Jun → | ||
| 5–13 | -0.01 | 45% | 27 Jun → | ||
| 10–13 | -0.07 | 27% | 27 Jun → | ||
| 13–4 | 0.02 | 42% | 27 Jun → | ||
| 13–2 | 0.03 | 16% | 27 Jun → | ||
| 9–13 | -0.01 | 6% | 27 Jun → |
Match data via Leetify.