Flipykidd — CS2 Stats
76561199276973714[U:1:1316707986]1 game ban
How this compares with the same rank
Median values for Pink band among CSDB-tracked players (n=3,917), 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 | Pink band median | Red band median | vs Red band |
|---|---|---|---|---|
| Headshot rate | 47.5% | 47.2% | 49.9% | 2.5% short |
| Shot accuracy | 9.9% | 12.7% | 13.4% | 3.4% short |
| Kill/death ratio | 1.05 | 1.09 | 1.12 | 0.07 short |
| Match win rate | 51.2% | 46.6% | 48.4% | above |
This profile matches the typical Red band player on 1 of 4 comparable metrics.
Widest gap: Shot accuracy. That is the metric furthest from the Red 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: +20pp win rate · +0.03 avg rating
Player DNA
Primary style: Aggressive Rifler — Takes opening fights often, backed by a strong aim profile.
Sharp aimerLimited utility dependence
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 88% playstyle similarity
Most alike: positioning profile, opening-duel success.
Where you differ: lower utility contribution; higher 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 91 — the mechanical foundation is a clear strength.
Areas to improve
Positioning. Positioning trails aim by 34 points — deaths here waste a strong aim profile.
Utility. Utility contribution runs far behind the mechanical game — the cheapest rating gain on this profile is thrown, not aimed.
Reaction time. 590ms 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 6.1/10 (Solid), a weighted mean of the bars with a small opposition adjustment (×1.02 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 | 26 | 16–10 | 62% | 0.05 | |
| C | 18 | 7–11 | 39% | 0.06 | |
| C | 18 | 8–10 | 44% | 0.04 | |
| B | 8 | 4–4 | 50% | 0.04 | |
| B | 8 | 4–4 | 50% | 0.04 | |
| S | 6 | 4–2 | 67% | 0.06 | |
| office | C | 5 | 2–3 | 40% | 0.00 |
| italy | — | 3 | 3–0 | 100% | 0.11 |
| alpine | — | 2 | 1–1 | 50% | 0.14 |
| — | 2 | 0–2 | 0% | 0.13 | |
| — | 1 | 1–0 | 100% | 0.04 | |
| — | 1 | 0–1 | 0% | 0.07 | |
| warden | — | 1 | 0–1 | 0% | 0.14 |
| — | 1 | 1–0 | 100% | 0.06 |
Across the last 100 tracked matches.
Nuke is currently your weakest sufficiently-sampled map (39% over 18). 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.
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 0.4647 — 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.
39% win rate across 18 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| 8–13 | 0.03 | 27% | 14 May → | ||
| 16–14 | 0.08 | 33% | 13 May → | ||
| 16–14 | 0.00 | 36% | 12 May → | ||
| 13–7 | 0.02 | 30% | 11 May → | ||
| 13–6 | 0.18 | 53% | 11 May → | ||
| 6–13 | -0.05 | 33% | 10 May → | ||
| 2–13 | -0.00 | 40% | 10 May → | ||
| 11–13 | -0.03 | 38% | 10 May → | ||
| 5–13 | -0.02 | 41% | 10 May → | ||
| 13–8 | 0.04 | 42% | 10 May → | ||
| office | 2–13 | -0.15 | 20% | 8 May → | |
| 12–12 | 0.07 | 20% | 6 May → | ||
| 7–13 | -0.05 | 15% | 2 May → | ||
| 13–7 | 0.07 | 21% | 30 Apr → | ||
| italy | 8–10 | 0.06 | 45% | 26 Apr → | |
| 10–13 | 0.01 | 18% | 25 Apr → | ||
| 13–11 | -0.04 | 11% | 19 Apr → | ||
| 2–13 | -0.10 | 33% | 19 Apr → | ||
| 11–13 | -0.05 | 18% | 19 Apr → | ||
| 14–16 | 0.07 | 47% | 19 Apr → | ||
| 13–6 | 0.13 | 49% | 19 Apr → | ||
| 1–4 | 0.06 | 50% | 18 Apr → | ||
| 7–13 | -0.06 | 20% | 18 Apr → | ||
| 7–1 | -0.01 | 27% | 14 Apr → | ||
| 4–13 | -0.06 | 43% | 14 Apr → | ||
| office | 8–13 | -0.05 | 40% | 14 Apr → | |
| 10–13 | 0.06 | 21% | 12 Apr → | ||
| 3–13 | 0.02 | 45% | 11 Apr → | ||
| 15–15 | -0.00 | 48% | 2 Apr → | ||
| 1–13 | 0.16 | 44% | 1 Apr → | ||
| 13–8 | 0.09 | 29% | 30 Mar → | ||
| 12–12 | 0.02 | 30% | 30 Mar → | ||
| 5–13 | -0.01 | 38% | 29 Mar → | ||
| 13–11 | 0.12 | 41% | 29 Mar → | ||
| 11–13 | 0.05 | 18% | 25 Mar → | ||
| 4–13 | -0.08 | 50% | 23 Mar → | ||
| italy | 13–8 | 0.05 | 14% | 22 Mar → | |
| 13–9 | -0.10 | 24% | 22 Mar → | ||
| 13–8 | 0.10 | 26% | 21 Mar → | ||
| 13–7 | -0.02 | 25% | 21 Mar → | ||
| 13–9 | 0.10 | 33% | 21 Mar → | ||
| office | 1–13 | -0.05 | 45% | 18 Mar → | |
| office | 13–6 | 0.07 | 50% | 18 Mar → | |
| 13–2 | 0.01 | 38% | 17 Mar → | ||
| 16–13 | 0.00 | 28% | 16 Mar → | ||
| 8–13 | 0.02 | 24% | 16 Mar → | ||
| 10–13 | 0.04 | 30% | 15 Mar → | ||
| 13–7 | 0.14 | 44% | 15 Mar → | ||
| 4–13 | -0.05 | 57% | 15 Mar → | ||
| 8–13 | 0.12 | 68% | 14 Mar → | ||
| 13–6 | 0.09 | 23% | 14 Mar → | ||
| 10–13 | -0.01 | 34% | 10 Mar → | ||
| italy | 13–5 | 0.23 | 12% | 8 Mar → | |
| 13–3 | 0.14 | 15% | 8 Mar → | ||
| 13–7 | 0.01 | 10% | 8 Mar → | ||
| office | 13–7 | 0.21 | 11% | 8 Mar → | |
| 13–9 | 0.23 | 45% | 7 Mar → | ||
| 13–8 | 0.10 | 23% | 7 Mar → | ||
| 9–13 | 0.23 | 32% | 7 Mar → | ||
| 13–5 | 0.17 | 13% | 7 Mar → | ||
| 10–13 | 0.16 | 21% | 7 Mar → | ||
| 13–3 | 0.03 | 16% | 7 Mar → | ||
| 13–3 | 0.12 | 13% | 7 Mar → | ||
| alpine | 4–13 | 0.12 | 23% | 6 Mar → | |
| 16–12 | 0.10 | 49% | 4 Mar → | ||
| warden | 9–13 | 0.14 | 24% | 4 Mar → | |
| 13–10 | 0.05 | 39% | 3 Mar → | ||
| 13–4 | -0.00 | 45% | 3 Mar → | ||
| 6–13 | 0.05 | 27% | 2 Mar → | ||
| 4–13 | 0.06 | 36% | 2 Mar → | ||
| 15–15 | 0.16 | 22% | 2 Mar → | ||
| 13–8 | 0.09 | 15% | 1 Mar → | ||
| 13–9 | 0.18 | 16% | 1 Mar → | ||
| 13–9 | 0.06 | 19% | 1 Mar → | ||
| 6–13 | -0.02 | 20% | 1 Mar → | ||
| alpine | 13–8 | 0.17 | 31% | 28 Feb → | |
| 16–13 | 0.07 | 32% | 28 Feb → | ||
| 10–13 | -0.01 | 30% | 28 Feb → | ||
| 5–13 | 0.04 | 53% | 28 Feb → | ||
| 13–7 | 0.06 | 52% | 28 Feb → | ||
| 13–8 | 0.20 | 33% | 28 Feb → | ||
| 7–13 | 0.07 | 45% | 28 Feb → | ||
| 13–8 | 0.07 | 46% | 28 Feb → | ||
| 16–14 | 0.15 | 78% | 27 Feb → | ||
| 13–6 | 0.08 | 53% | 27 Feb → | ||
| 13–10 | 0.10 | 58% | 27 Feb → | ||
| 8–13 | 0.08 | 41% | 26 Feb → | ||
| 9–2 | 0.06 | 35% | 26 Feb → | ||
| 9–11 | 0.09 | 26% | 26 Feb → | ||
| 13–8 | 0.03 | 37% | 26 Feb → | ||
| 10–13 | 0.01 | 22% | 24 Feb → | ||
| 7–13 | 0.01 | 54% | 24 Feb → | ||
| 8–13 | 0.01 | 20% | 23 Feb → | ||
| 13–9 | 0.03 | 35% | 23 Feb → | ||
| 16–14 | 0.06 | 13% | 22 Feb → | ||
| 13–10 | 0.04 | 23% | 22 Feb → | ||
| 9–13 | -0.08 | 29% | 22 Feb → | ||
| 13–10 | 0.08 | 34% | 22 Feb → | ||
| 13–16 | 0.01 | 19% | 22 Feb → | ||
| 8–13 | 0.06 | 15% | 21 Feb → |
Match data via Leetify.