wolfyoy — CS2 Stats
CZ76561199115196610[U:1:1154930882]Steam profile ↗✓ No bans
How this compares with the same rank
Median values for Light Blue band among CSDB-tracked players (n=404), 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 | Light Blue band median | Blue band median | vs Blue band |
|---|---|---|---|---|
| Headshot rate | 34.5% | 40.1% | 41.7% | 7.2% short |
| Shot accuracy | 13.0% | 7.6% | 8.8% | above |
| Kill/death ratio | 0.96 | 0.92 | 0.97 | meets |
| Match win rate | 44.6% | 42.2% | 43.8% | meets |
This profile matches the typical Blue band player on 3 of 4 comparable metrics.
Widest gap: Headshot rate. That is the metric furthest from the Blue 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 · -0.03 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 ropz 74% playstyle similarity
Most alike: opening-fight frequency, opening-duel success.
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 59% on CT to 34% on T — the same duels are being taken with worse setups on the attacking side.
Reaction time. 717ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.
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 3.5/10 (Learning), a weighted mean of the bars with a small opposition adjustment (×0.90 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 | 32 | 19–13 | 59% | -0.01 | |
| C | 23 | 10–13 | 43% | 0.02 | |
| D | 17 | 5–12 | 29% | -0.02 | |
| A | 15 | 9–6 | 60% | 0.00 | |
| D | 6 | 2–4 | 33% | -0.02 | |
| — | 3 | 2–1 | 67% | -0.04 | |
| — | 2 | 1–1 | 50% | -0.03 | |
| — | 1 | 0–1 | 0% | -0.06 | |
| — | 1 | 0–1 | 0% | -0.01 |
Across the last 100 tracked matches.
Mirage is currently your weakest sufficiently-sampled map (29% over 17). Start with the 6 essential Mirage 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 12.2739° — 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 11.585% — below the 15% mark we flag
- Best CS2 SettingsAim Training →
Slow first shots are as often a setup problem as a reflex one — framerate, sensitivity and crosshair visibility all move this number.
Reaction time 716.6176ms — above the 700ms mark we flag
Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.
29% win rate across 17 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| 8–8 | -0.08 | 11% | 15 Jun → | ||
| 5–13 | -0.02 | 18% | 27 May → | ||
| 8–13 | -0.08 | 20% | 23 May → | ||
| 13–11 | -0.03 | 15% | 18 May → | ||
| 13–8 | -0.04 | 10% | 14 May → | ||
| 13–6 | -0.04 | 16% | 14 May → | ||
| 10–13 | -0.04 | 10% | 14 May → | ||
| 9–7 | 0.02 | 8% | 12 May → | ||
| 16–13 | 0.01 | 11% | 11 May → | ||
| 13–9 | -0.01 | 9% | 7 May → | ||
| 13–8 | 0.07 | 7% | 6 May → | ||
| 13–9 | 0.04 | 12% | 6 May → | ||
| 5–13 | -0.09 | 12% | 4 May → | ||
| 2–13 | -0.08 | 11% | 4 May → | ||
| 9–1 | 0.12 | 3% | 30 Apr → | ||
| 13–7 | 0.00 | 8% | 30 Apr → | ||
| 13–4 | -0.01 | 13% | 29 Apr → | ||
| 4–13 | 0.03 | 8% | 29 Apr → | ||
| 7–13 | -0.06 | 6% | 28 Apr → | ||
| 6–9 | 0.01 | 7% | 28 Apr → | ||
| 9–7 | 0.05 | 10% | 28 Apr → | ||
| 9–3 | 0.11 | 15% | 28 Apr → | ||
| 8–8 | 0.10 | 13% | 28 Apr → | ||
| 3–9 | -0.14 | 5% | 28 Apr → | ||
| 9–7 | 0.06 | 17% | 28 Apr → | ||
| 13–5 | -0.03 | 12% | 27 Apr → | ||
| 13–6 | 0.01 | 6% | 27 Apr → | ||
| 3–9 | -0.03 | 12% | 27 Apr → | ||
| 3–9 | -0.15 | 8% | 27 Apr → | ||
| 13–5 | -0.03 | 11% | 24 Apr → | ||
| 5–13 | -0.04 | 8% | 23 Apr → | ||
| 13–2 | 0.02 | 6% | 23 Apr → | ||
| 13–1 | 0.03 | 11% | 23 Apr → | ||
| 9–3 | 0.13 | 10% | 22 Apr → | ||
| 1–13 | -0.02 | 11% | 22 Apr → | ||
| 13–5 | 0.14 | 10% | 22 Apr → | ||
| 4–13 | -0.06 | 15% | 21 Apr → | ||
| 13–5 | 0.04 | 7% | 21 Apr → | ||
| 13–8 | -0.03 | 12% | 17 Apr → | ||
| 9–7 | 0.03 | 24% | 17 Apr → | ||
| 9–13 | 0.07 | 19% | 15 Apr → | ||
| 13–11 | 0.00 | 20% | 15 Apr → | ||
| 4–13 | -0.04 | 17% | 10 Apr → | ||
| 13–7 | 0.01 | 18% | 8 Apr → | ||
| 13–7 | -0.01 | 12% | 8 Apr → | ||
| 8–8 | -0.02 | 4% | 1 Apr → | ||
| 5–13 | 0.01 | 5% | 1 Apr → | ||
| 13–7 | -0.02 | 12% | 1 Apr → | ||
| 11–13 | -0.03 | 15% | 1 Apr → | ||
| 7–13 | -0.01 | 11% | 31 Mar → | ||
| 0–9 | -0.24 | 6% | 31 Mar → | ||
| 9–4 | 0.27 | 16% | 31 Mar → | ||
| 9–5 | 0.23 | 26% | 31 Mar → | ||
| 13–11 | 0.06 | 31% | 30 Mar → | ||
| 6–13 | -0.01 | 11% | 28 Mar → | ||
| 9–13 | 0.03 | 18% | 28 Mar → | ||
| 5–13 | 0.00 | 16% | 28 Mar → | ||
| 10–13 | -0.00 | 10% | 26 Mar → | ||
| 16–12 | -0.00 | 8% | 26 Mar → | ||
| 13–11 | -0.00 | 7% | 25 Mar → | ||
| 12–8 | -0.02 | 9% | 25 Mar → | ||
| 6–13 | -0.08 | 12% | 25 Mar → | ||
| 13–11 | 0.04 | 13% | 24 Mar → | ||
| 7–9 | -0.01 | 13% | 24 Mar → | ||
| 4–13 | 0.02 | 15% | 22 Mar → | ||
| 10–13 | 0.07 | 11% | 22 Mar → | ||
| 13–10 | -0.09 | 9% | 21 Mar → | ||
| 13–10 | -0.01 | 18% | 21 Mar → | ||
| 4–9 | -0.09 | 13% | 21 Mar → | ||
| 1–13 | -0.09 | 6% | 20 Mar → | ||
| 13–11 | -0.01 | 15% | 20 Mar → | ||
| 5–13 | -0.08 | 13% | 19 Mar → | ||
| 9–6 | 0.20 | 16% | 19 Mar → | ||
| 11–13 | 0.07 | 11% | 18 Mar → | ||
| 1–13 | -0.06 | 14% | 17 Mar → | ||
| 13–9 | 0.04 | 15% | 17 Mar → | ||
| 13–11 | 0.01 | 11% | 16 Mar → | ||
| 13–16 | -0.00 | 14% | 16 Mar → | ||
| 1–9 | -0.07 | 25% | 15 Mar → | ||
| 2–13 | -0.12 | 0% | 15 Mar → | ||
| 13–9 | 0.01 | 10% | 14 Mar → | ||
| 9–13 | -0.04 | 10% | 14 Mar → | ||
| 8–8 | -0.02 | 3% | 14 Mar → | ||
| 3–9 | -0.11 | 17% | 13 Mar → | ||
| 9–1 | 0.17 | 13% | 13 Mar → | ||
| 6–13 | -0.08 | 12% | 12 Mar → | ||
| 13–11 | -0.04 | 22% | 12 Mar → | ||
| 15–15 | 0.04 | 20% | 11 Mar → | ||
| 1–13 | 0.00 | 4% | 11 Mar → | ||
| 13–5 | 0.02 | 12% | 11 Mar → | ||
| 13–4 | 0.01 | 18% | 11 Mar → | ||
| 5–13 | -0.04 | 22% | 10 Mar → | ||
| 3–13 | -0.01 | 6% | 10 Mar → | ||
| 15–15 | 0.04 | 13% | 10 Mar → | ||
| 10–13 | -0.02 | 11% | 9 Mar → | ||
| 9–13 | -0.04 | 12% | 9 Mar → | ||
| 13–6 | -0.03 | 13% | 9 Mar → | ||
| 13–8 | -0.03 | 7% | 9 Mar → | ||
| 5–13 | -0.08 | 6% | 8 Mar → | ||
| 11–13 | 0.01 | 13% | 8 Mar → |
Match data via Leetify.