Greenz — CS2 Stats
CA76561198127442111[U:1:167176383]Steam profile ↗✓ No bans
How this compares with the same rank
Median values for Blue band among CSDB-tracked players (n=2,468), 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 | Blue band median | Purple band median | vs Purple band |
|---|---|---|---|---|
| Headshot rate | 36.4% | 41.4% | 44.2% | 7.8% short |
| Shot accuracy | 5.1% | 9.0% | 11.6% | 6.5% short |
| Kill/death ratio | 0.56 | 0.97 | 1.03 | 0.47 short |
| Match win rate | 46.2% | 43.7% | 45.2% | above |
This profile matches the typical Purple band player on 1 of 4 comparable metrics.
Widest gap: Shot accuracy. That is the metric furthest from the Purple band 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
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 60% playstyle similarity
Most alike: opening-duel success, positioning profile.
Where you differ: lower utility contribution; 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 35% on CT to 20% on T — the same duels are being taken with worse setups on the attacking side.
Reaction time. 681ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.
Counter-strafing. Only 67% of shots are taken properly stopped — moving-shot inaccuracy is quietly taxing every duel.
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 2.6/10 (Learning), 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 |
|---|---|---|---|---|---|
| D | 18 | 4–14 | 22% | -0.07 | |
| C | 16 | 6–10 | 38% | -0.04 | |
| A | 15 | 9–6 | 60% | -0.06 | |
| A | 13 | 8–5 | 62% | -0.08 | |
| B | 11 | 5–6 | 45% | -0.07 | |
| A | 11 | 6–5 | 55% | -0.06 | |
| D | 7 | 2–5 | 29% | -0.06 | |
| A | 5 | 3–2 | 60% | -0.05 | |
| — | 4 | 1–3 | 25% | -0.09 |
Across the last 100 tracked matches.
Dust 2 is currently your weakest sufficiently-sampled map (22% over 18). 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.
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 12.2028° — above the 12° mark we flag
- Best CS2 CrosshairAim Training →
Your shots are landing on bodies more often than heads — usually a crosshair-height and placement habit rather than raw aim.
Headshot accuracy 10.9891% — below the 15% mark we flag
Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.
22% win rate across 18 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| 2–9 | -0.15 | 10% | 13 Jul → | ||
| 13–7 | -0.01 | 3% | 12 Jul → | ||
| 13–2 | -0.07 | 19% | 7 Jul → | ||
| 8–13 | -0.06 | 9% | 7 Jul → | ||
| 13–1 | -0.02 | 13% | 29 May → | ||
| 13–7 | 0.03 | 22% | 27 May → | ||
| 7–13 | -0.11 | 10% | 26 May → | ||
| 10–13 | -0.09 | 3% | 25 May → | ||
| 9–13 | -0.06 | 7% | 25 May → | ||
| 12–12 | -0.10 | 2% | 24 May → | ||
| 10–3 | -0.09 | 0% | 24 May → | ||
| 13–10 | 0.02 | 14% | 24 May → | ||
| 12–12 | -0.05 | 10% | 24 May → | ||
| 3–13 | -0.03 | 14% | 23 May → | ||
| 13–6 | -0.09 | 27% | 23 May → | ||
| 6–13 | -0.05 | 7% | 22 May → | ||
| 11–13 | -0.02 | 17% | 21 May → | ||
| 12–12 | -0.06 | 8% | 20 May → | ||
| 9–13 | -0.07 | 8% | 19 May → | ||
| 8–13 | -0.05 | 18% | 19 May → | ||
| 12–12 | -0.04 | 13% | 19 May → | ||
| 13–1 | 0.02 | 17% | 18 May → | ||
| 13–8 | -0.08 | 6% | 17 May → | ||
| 2–13 | -0.13 | 5% | 17 May → | ||
| 13–8 | -0.02 | 10% | 16 May → | ||
| 9–13 | -0.04 | 11% | 15 May → | ||
| 3–2 | -0.02 | 13% | 15 May → | ||
| 7–13 | -0.08 | 17% | 11 May → | ||
| 13–8 | -0.04 | 3% | 9 May → | ||
| 8–13 | -0.10 | 10% | 8 May → | ||
| 13–6 | -0.03 | 19% | 8 May → | ||
| 6–13 | -0.09 | 3% | 4 May → | ||
| 13–10 | -0.04 | 4% | 4 May → | ||
| 13–9 | -0.10 | 3% | 4 May → | ||
| 2–9 | -0.06 | 0% | 3 May → | ||
| 6–13 | -0.07 | 17% | 3 May → | ||
| 2–13 | -0.09 | 6% | 3 May → | ||
| 9–13 | -0.05 | 5% | 3 May → | ||
| 13–3 | 0.01 | 18% | 3 May → | ||
| 15–15 | -0.08 | 8% | 2 May → | ||
| 7–2 | -0.07 | 10% | 2 May → | ||
| 8–13 | -0.08 | 12% | 1 May → | ||
| 7–13 | -0.08 | 19% | 1 May → | ||
| 16–12 | -0.04 | 16% | 30 Apr → | ||
| 7–13 | -0.09 | 16% | 30 Apr → | ||
| 10–13 | -0.05 | 9% | 27 Apr → | ||
| 13–11 | -0.08 | 17% | 27 Apr → | ||
| 9–13 | -0.05 | 5% | 26 Apr → | ||
| 13–3 | -0.00 | 11% | 26 Apr → | ||
| 15–15 | -0.04 | 16% | 26 Apr → | ||
| 4–13 | -0.08 | 11% | 26 Apr → | ||
| 11–13 | -0.03 | 14% | 26 Apr → | ||
| 13–8 | -0.12 | 3% | 25 Apr → | ||
| 2–13 | -0.14 | 6% | 25 Apr → | ||
| 1–13 | -0.10 | 0% | 23 Apr → | ||
| 7–13 | -0.07 | 21% | 23 Apr → | ||
| 13–7 | -0.05 | 19% | 22 Apr → | ||
| 13–7 | -0.08 | 5% | 22 Apr → | ||
| 13–8 | -0.01 | 7% | 22 Apr → | ||
| 14–16 | -0.10 | 6% | 4 Apr → | ||
| 13–10 | -0.06 | 9% | 4 Apr → | ||
| 16–13 | -0.08 | 17% | 4 Apr → | ||
| 13–9 | -0.05 | 6% | 4 Apr → | ||
| 11–13 | -0.03 | 11% | 3 Apr → | ||
| 6–13 | -0.04 | 9% | 3 Apr → | ||
| 9–13 | -0.02 | 17% | 3 Apr → | ||
| 13–8 | -0.06 | 5% | 3 Apr → | ||
| 13–11 | -0.08 | 14% | 3 Apr → | ||
| 10–13 | -0.08 | 3% | 1 Apr → | ||
| 11–13 | -0.10 | 9% | 1 Apr → | ||
| 13–8 | -0.07 | 10% | 1 Apr → | ||
| 13–11 | -0.04 | 4% | 1 Apr → | ||
| 13–8 | -0.12 | 4% | 1 Apr → | ||
| 13–9 | -0.02 | 9% | 1 Apr → | ||
| 4–13 | -0.07 | 10% | 1 Apr → | ||
| 7–13 | -0.06 | 14% | 31 Mar → | ||
| 9–13 | -0.11 | 43% | 31 Mar → | ||
| 5–13 | -0.05 | 17% | 31 Mar → | ||
| 13–6 | -0.07 | 5% | 31 Mar → | ||
| 2–13 | -0.09 | 12% | 31 Mar → | ||
| 4–13 | -0.08 | 3% | 31 Mar → | ||
| 10–13 | -0.07 | 14% | 30 Mar → | ||
| 13–7 | -0.06 | 10% | 30 Mar → | ||
| 13–11 | -0.08 | 10% | 30 Mar → | ||
| 8–13 | -0.07 | 10% | 29 Mar → | ||
| 16–12 | -0.02 | 8% | 29 Mar → | ||
| 7–13 | -0.01 | 10% | 29 Mar → | ||
| 11–13 | -0.10 | 5% | 28 Mar → | ||
| 16–14 | -0.11 | 5% | 28 Mar → | ||
| 3–13 | -0.10 | 8% | 28 Mar → | ||
| 16–13 | -0.10 | 7% | 28 Mar → | ||
| 13–5 | -0.06 | 9% | 28 Mar → | ||
| 10–13 | -0.06 | 2% | 3 Mar → | ||
| 13–9 | -0.05 | 9% | 3 Mar → | ||
| 13–1 | -0.06 | 6% | 3 Mar → | ||
| 5–13 | -0.12 | 3% | 3 Mar → | ||
| 6–13 | -0.10 | 12% | 3 Mar → | ||
| 13–4 | -0.04 | 9% | 3 Mar → | ||
| 13–4 | -0.07 | 19% | 3 Mar → | ||
| 3–13 | -0.10 | 14% | 3 Mar → |
Match data via Leetify.