Pops — CS2 Stats
76561198200115782[U:1:239850054]✓ No bans
How this compares with the same rank
Median values for Level 2 among CSDB-tracked players (n=338), 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 2 median | vs Level 2 |
|---|---|---|---|
| Headshot rate | 38.2% | 42.1% | 4.0% short |
| Shot accuracy | 12.1% | 12.9% | 0.8% short |
| Kill/death ratio | 0.87 | 1.00 | 0.13 short |
| Match win rate | 44.3% | 42.9% | above |
This profile matches the typical Level 2 player on 1 of 4 comparable metrics.
Widest gap: Kill/death ratio. That is the metric furthest from the Level 2 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 · -0.04 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 76% 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
Reaction time. 664ms 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 4.0/10 (Developing), a weighted mean of the bars with a small opposition adjustment (×1.00 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 | 33 | 19–14 | 58% | -0.02 | |
| C | 20 | 8–12 | 40% | -0.03 | |
| A | 18 | 11–7 | 61% | -0.03 | |
| A | 8 | 5–3 | 63% | -0.03 | |
| D | 7 | 1–6 | 14% | -0.02 | |
| D | 7 | 2–5 | 29% | -0.03 | |
| D | 5 | 1–4 | 20% | -0.06 | |
| office | — | 1 | 1–0 | 100% | -0.01 |
| — | 1 | 1–0 | 100% | -0.07 |
Across the last 100 tracked matches.
Inferno is currently your weakest sufficiently-sampled map (14% over 7). Start with the 6 essential Inferno 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 resultsLWLWW
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Ancient | 2 | 100% | 1.00 | 15.0 |
| Cache | 1 | 100% | 0.57 | 8.0 |
| Inferno | 1 | 0% | 0.65 | 20.0 |
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
Chosen by comparing your tracked metrics against the thresholds we flag — the measurement behind each one is shown, so you can disagree with it.
- 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 14.3485% — below the 15% mark we flag
- 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 0.3374 — below the 0.5 mark we flag
- Advanced Mechanics
Losing the first CT duel repeatedly usually means holding angles that favour the peeker.
CT opening duels 26.6152% — below the 40% mark we flag
Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.
14% win rate across 7 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| 5–13 | -0.04 | 15% | 28 Aug → | ||
| 1–13 | -0.04 | 3% | 27 Aug → | ||
| 10–13 | -0.05 | 10% | 23 Aug → | ||
| 15–15 | -0.08 | 13% | 16 Aug → | ||
| 9–13 | -0.03 | 10% | 29 Jul → | ||
| 13–10 | -0.05 | 8% | 29 Jul → | ||
| 3–13 | -0.11 | 19% | 29 Jul → | ||
| 9–13 | -0.00 | 18% | 22 Jul → | ||
| 13–2 | 0.03 | 15% | 22 Jul → | ||
| 1–13 | -0.13 | 5% | 21 Jul → | ||
| 13–4 | 0.04 | 16% | 19 Jul → | ||
| 13–6 | -0.00 | 15% | 19 Jul → | ||
| 9–13 | -0.02 | 21% | 18 Jul → | ||
| 11–13 | -0.02 | 18% | 14 Jul → | ||
| 2–13 | -0.08 | 4% | 14 Jul → | ||
| 7–13 | -0.09 | 4% | 11 Jul → | ||
| 3–13 | -0.03 | 25% | 11 Jul → | ||
| 13–5 | 0.04 | 8% | 11 Jul → | ||
| 13–9 | 0.02 | 24% | 11 Jul → | ||
| 9–1 | 0.03 | 7% | 11 Jul → | ||
| 13–4 | -0.00 | 17% | 8 Jul → | ||
| 11–13 | 0.01 | 17% | 8 Jul → | ||
| 13–9 | -0.04 | 17% | 5 Jul → | ||
| 13–5 | 0.02 | 20% | 4 Jul → | ||
| 13–6 | -0.03 | 11% | 4 Jul → | ||
| 13–10 | -0.06 | 14% | 1 Jul → | ||
| 13–9 | 0.01 | 15% | 1 Jul → | ||
| 13–4 | -0.00 | 23% | 1 Jul → | ||
| 9–13 | -0.05 | 18% | 1 Jul → | ||
| 13–11 | -0.05 | 13% | 26 Jun → | ||
| 3–13 | -0.08 | 16% | 26 Jun → | ||
| 11–13 | -0.03 | 14% | 26 Jun → | ||
| 13–8 | 0.04 | 5% | 26 Jun → | ||
| 13–4 | 0.01 | 17% | 26 Jun → | ||
| 1–13 | -0.11 | 6% | 26 Jun → | ||
| 13–3 | -0.02 | 26% | 25 Jun → | ||
| 11–13 | -0.04 | 9% | 23 Jun → | ||
| 16–14 | -0.01 | 9% | 23 Jun → | ||
| 16–12 | -0.05 | 9% | 23 Jun → | ||
| 3–13 | -0.07 | 20% | 18 Jun → | ||
| 5–13 | -0.08 | 20% | 13 Jun → | ||
| 7–13 | -0.05 | 17% | 13 Jun → | ||
| 2–13 | -0.12 | 25% | 12 Jun → | ||
| 13–10 | -0.01 | 9% | 12 Jun → | ||
| 13–5 | 0.03 | 20% | 1 Jun → | ||
| 11–13 | -0.04 | 18% | 1 Jun → | ||
| 13–6 | 0.04 | 13% | 1 Jun → | ||
| 13–11 | -0.03 | 17% | 1 Jun → | ||
| 4–13 | -0.10 | 12% | 29 May → | ||
| 16–12 | -0.02 | 14% | 29 May → | ||
| 13–8 | -0.03 | 26% | 28 May → | ||
| 13–8 | -0.05 | 11% | 28 May → | ||
| 1–12 | -0.02 | 28% | 25 May → | ||
| 9–13 | -0.02 | 14% | 25 May → | ||
| 10–13 | -0.04 | 22% | 25 May → | ||
| 11–13 | -0.06 | 21% | 25 May → | ||
| 7–13 | -0.04 | 14% | 22 May → | ||
| 13–5 | -0.04 | 18% | 21 May → | ||
| 9–13 | -0.09 | 18% | 20 May → | ||
| 13–5 | 0.08 | 16% | 20 May → | ||
| 9–13 | -0.01 | 6% | 20 May → | ||
| 16–14 | -0.01 | 19% | 20 May → | ||
| 11–8 | -0.04 | 14% | 19 May → | ||
| office | 13–3 | -0.01 | 9% | 19 May → | |
| 13–5 | 0.01 | 14% | 19 May → | ||
| 7–13 | -0.01 | 3% | 19 May → | ||
| 6–13 | -0.07 | 5% | 19 May → | ||
| 10–13 | -0.06 | 5% | 19 May → | ||
| 11–13 | -0.02 | 10% | 17 May → | ||
| 7–13 | -0.07 | 10% | 17 May → | ||
| 16–14 | -0.05 | 8% | 16 May → | ||
| 13–5 | -0.00 | 33% | 16 May → | ||
| 13–10 | -0.00 | 8% | 16 May → | ||
| 13–11 | -0.02 | 20% | 13 May → | ||
| 13–2 | 0.03 | 13% | 13 May → | ||
| 6–13 | -0.01 | 12% | 13 May → | ||
| 13–10 | -0.02 | 22% | 13 May → | ||
| 13–4 | -0.07 | 11% | 12 May → | ||
| 6–13 | -0.08 | 17% | 12 May → | ||
| 13–11 | -0.01 | 12% | 12 May → | ||
| 5–13 | -0.06 | 10% | 11 May → | ||
| 4–13 | -0.03 | 17% | 11 May → | ||
| 10–13 | -0.07 | 24% | 11 May → | ||
| 13–5 | -0.01 | 16% | 11 May → | ||
| 7–13 | -0.03 | 16% | 11 May → | ||
| 16–13 | -0.04 | 20% | 10 May → | ||
| 4–13 | -0.04 | 22% | 10 May → | ||
| 13–7 | 0.04 | 8% | 10 May → | ||
| 13–8 | -0.03 | 13% | 10 May → | ||
| 11–13 | -0.03 | 10% | 9 May → | ||
| 8–13 | -0.09 | 9% | 8 May → | ||
| 12–16 | -0.06 | 12% | 8 May → | ||
| 9–13 | -0.03 | 12% | 8 May → | ||
| 11–13 | -0.07 | 11% | 8 May → | ||
| 13–3 | -0.02 | 12% | 8 May → | ||
| 4–13 | -0.01 | 13% | 8 May → | ||
| 13–10 | 0.04 | 19% | 7 May → | ||
| 13–8 | -0.05 | 7% | 7 May → | ||
| 13–5 | 0.05 | 14% | 7 May → | ||
| 7–13 | -0.04 | 20% | 7 May → |
Match data via Leetify.