♛ZiKe♛ — CS2 Stats
76561198978932258[U:1:1018666530]
How this compares with the same rank
Median values for Level 3 among CSDB-tracked players (n=1,149), 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 3 median | Level 4 median | vs Level 4 |
|---|---|---|---|---|
| Headshot rate | 48.7% | 42.2% | 42.7% | above |
| Shot accuracy | 21.8% | 11.9% | 10.8% | above |
| Kill/death ratio | 1.23 | 0.98 | 1.00 | above |
| Match win rate | 50.8% | 43.6% | 44.1% | above |
Across every metric we can compare, this profile already matches the typical Level 4 player. Rank still comes from winning matches — this is a performance comparison, not a prediction.
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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 m0NESY 92% playstyle similarity
Most alike: opening-fight frequency, aim profile.
Where you differ: lower utility contribution.
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.
CT openings. 67% CT opening-duel success — winning the first fight on the defending side is rare and valuable.
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 7.6/10 (Strong), 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 |
|---|---|---|---|---|---|
| D | 28 | 9–19 | 32% | 0.06 | |
| C | 21 | 9–12 | 43% | 0.06 | |
| C | 16 | 7–9 | 44% | 0.09 | |
| A | 14 | 8–6 | 57% | 0.05 | |
| A | 9 | 5–4 | 56% | 0.07 | |
| C | 5 | 2–3 | 40% | 0.05 | |
| C | 5 | 2–3 | 40% | 0.06 | |
| — | 2 | 2–0 | 100% | 0.11 |
Across the last 100 tracked matches.
Dust 2 is currently your weakest sufficiently-sampled map (32% over 28). 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 resultsWWWLW
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 0.475 — 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.
32% win rate across 28 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| 13–9 | -0.08 | 50% | 15 Aug → | ||
| 2–13 | 0.02 | 11% | 15 Aug → | ||
| 2–13 | -0.11 | 46% | 15 Aug → | ||
| 3–13 | 0.03 | 56% | 15 Aug → | ||
| 13–4 | 0.14 | 32% | 15 Aug → | ||
| 13–9 | 0.07 | 41% | 14 Aug → | ||
| 10–13 | 0.08 | 53% | 10 Aug → | ||
| 6–13 | 0.04 | 68% | 10 Aug → | ||
| 13–8 | 0.11 | 48% | 8 Aug → | ||
| 13–1 | 0.04 | 60% | 8 Aug → | ||
| 3–13 | -0.01 | 50% | 8 Aug → | ||
| 2–13 | 0.02 | 47% | 4 Aug → | ||
| 13–6 | 0.01 | 30% | 2 Aug → | ||
| 4–13 | -0.07 | 38% | 4 May → | ||
| 6–13 | 0.12 | 50% | 4 May → | ||
| 10–13 | 0.15 | 60% | 3 May → | ||
| 13–3 | 0.08 | 26% | 3 May → | ||
| 7–13 | 0.05 | 73% | 2 May → | ||
| 6–0 | 0.13 | 100% | 2 May → | ||
| 16–14 | 0.08 | 32% | 2 May → | ||
| 13–4 | 0.10 | 40% | 1 May → | ||
| 10–13 | 0.02 | 54% | 1 May → | ||
| 9–13 | 0.03 | 50% | 1 May → | ||
| 11–13 | 0.12 | 31% | 1 May → | ||
| 11–13 | 0.05 | 38% | 19 Apr → | ||
| 13–12 | 0.19 | 45% | 19 Apr → | ||
| 13–9 | 0.04 | 48% | 19 Apr → | ||
| 13–11 | 0.16 | 61% | 18 Apr → | ||
| 13–11 | 0.06 | 43% | 18 Apr → | ||
| 13–11 | 0.08 | 40% | 18 Apr → | ||
| 7–13 | 0.09 | 45% | 17 Apr → | ||
| 2–13 | -0.00 | 50% | 12 Apr → | ||
| 3–13 | 0.15 | 41% | 12 Apr → | ||
| 5–13 | 0.05 | 17% | 12 Apr → | ||
| 2–13 | -0.04 | 41% | 12 Apr → | ||
| 7–13 | 0.03 | 28% | 12 Apr → | ||
| 13–6 | 0.11 | 40% | 11 Apr → | ||
| 13–5 | 0.04 | 33% | 11 Apr → | ||
| 9–13 | -0.02 | 25% | 11 Apr → | ||
| 16–14 | 0.06 | 41% | 11 Apr → | ||
| 11–13 | 0.06 | 48% | 11 Apr → | ||
| 6–13 | 0.06 | 42% | 10 Apr → | ||
| 4–13 | 0.11 | 48% | 10 Apr → | ||
| 16–13 | -0.01 | 30% | 7 Apr → | ||
| 13–8 | 0.13 | 38% | 7 Apr → | ||
| 10–13 | -0.04 | 45% | 6 Apr → | ||
| 13–3 | 0.02 | 43% | 6 Apr → | ||
| 15–15 | 0.02 | 17% | 6 Apr → | ||
| 16–13 | 0.10 | 46% | 29 Mar → | ||
| 13–8 | 0.08 | 34% | 29 Mar → | ||
| 13–7 | 0.09 | 56% | 29 Mar → | ||
| 8–13 | 0.08 | 42% | 29 Mar → | ||
| 3–13 | -0.00 | 26% | 28 Mar → | ||
| 9–13 | 0.15 | 29% | 24 Mar → | ||
| 9–13 | 0.07 | 53% | 24 Mar → | ||
| 13–3 | 0.03 | 50% | 24 Mar → | ||
| 6–13 | 0.04 | 50% | 22 Mar → | ||
| 15–15 | 0.17 | 45% | 21 Mar → | ||
| 9–13 | 0.02 | 39% | 21 Mar → | ||
| 13–8 | 0.14 | 52% | 21 Mar → | ||
| 5–13 | 0.02 | 50% | 21 Mar → | ||
| 13–6 | 0.04 | 76% | 21 Mar → | ||
| 10–13 | 0.09 | 23% | 14 Mar → | ||
| 13–4 | 0.04 | 42% | 14 Mar → | ||
| 8–13 | 0.10 | 23% | 2 Mar → | ||
| 13–9 | 0.09 | 46% | 1 Mar → | ||
| 9–13 | 0.07 | 44% | 23 Feb → | ||
| 6–13 | 0.07 | 43% | 20 Feb → | ||
| 11–13 | 0.05 | 27% | 20 Feb → | ||
| 13–8 | 0.05 | 46% | 20 Feb → | ||
| 4–13 | 0.10 | 38% | 15 Feb → | ||
| 8–13 | 0.14 | 33% | 15 Feb → | ||
| 9–13 | 0.09 | 20% | 13 Feb → | ||
| 6–13 | 0.11 | 39% | 10 Feb → | ||
| 13–16 | 0.06 | 26% | 10 Feb → | ||
| 9–13 | 0.09 | 45% | 7 Feb → | ||
| 9–13 | 0.05 | 30% | 7 Feb → | ||
| 13–10 | 0.05 | 44% | 7 Feb → | ||
| 13–5 | 0.14 | 28% | 7 Feb → | ||
| 9–13 | 0.11 | 45% | 7 Feb → | ||
| 12–12 | 0.19 | 39% | 6 Feb → | ||
| 8–8 | 0.05 | 60% | 6 Feb → | ||
| 13–7 | 0.07 | 34% | 5 Feb → | ||
| 4–13 | 0.09 | 33% | 4 Feb → | ||
| 7–13 | 0.03 | 20% | 4 Feb → | ||
| 9–13 | 0.02 | 48% | 4 Feb → | ||
| 13–7 | 0.21 | 26% | 31 Jan → | ||
| 13–2 | -0.04 | 33% | 30 Jan → | ||
| 8–13 | 0.00 | 22% | 27 Jan → | ||
| 13–1 | 0.06 | 36% | 27 Jan → | ||
| 11–13 | 0.08 | 23% | 27 Jan → | ||
| 13–7 | 0.05 | 38% | 27 Jan → | ||
| 13–3 | 0.29 | 37% | 26 Jan → | ||
| 13–9 | 0.07 | 48% | 26 Jan → | ||
| 13–7 | 0.04 | 46% | 26 Jan → | ||
| 13–4 | 0.02 | 67% | 26 Jan → | ||
| 13–10 | 0.03 | 39% | 25 Jan → | ||
| 13–4 | 0.07 | 41% | 25 Jan → | ||
| 2–13 | 0.01 | 24% | 25 Jan → | ||
| 5–3 | -0.01 | 27% | 25 Jan → |
Match data via Leetify.
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