chesegoboomboom — CS2 Stats
76561198144632066[U:1:184366338]
Performance scores
0–100 skill scores via Leetify.
Recent form
Last 10 vs previous 10: +10pp win rate · -0.00 avg rating
Player DNA
Primary style: Hybrid Rifler — Aim-led profile without a single dominant tendency.
Excellent counter-strafingStrong CT-side openerLimited 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 Twistzz 90% playstyle similarity
Most alike: opening-fight frequency, positioning 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
Areas to improve
Preaim. Mechanical aim is strong but the crosshair sits 10.1° off target when enemies appear — placement, not flicking, is the bigger win available.
Utility. Utility contribution runs far behind the mechanical game — the cheapest rating gain on this profile is thrown, not aimed.
T-side openings. Opening success drops from 51% on CT to 25% on T — the same duels are being taken with worse setups on the attacking side.
Reaction time. 569ms 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 6.6/10 (Solid), 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 |
|---|---|---|---|---|---|
| B | 19 | 10–9 | 53% | 0.03 | |
| A | 19 | 12–7 | 63% | 0.01 | |
| A | 17 | 10–7 | 59% | 0.03 | |
| office | B | 11 | 5–6 | 45% | 0.01 |
| C | 9 | 4–5 | 44% | 0.01 | |
| B | 8 | 4–4 | 50% | 0.04 | |
| B | 6 | 3–3 | 50% | 0.01 | |
| B | 6 | 3–3 | 50% | 0.02 | |
| — | 3 | 2–1 | 67% | 0.03 | |
| — | 1 | 1–0 | 100% | 0.03 | |
| shelter | — | 1 | 0–1 | 0% | 0.12 |
Across the last 100 tracked matches.
Anubis is currently your weakest sufficiently-sampled map (44% over 9). Start with the 6 essential Anubis 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 resultsWLWWW
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Anubis | 2 | 100% | 1.76 | 20.5 |
| Dust2 | 2 | 100% | 2.34 | 19.0 |
| Mirage | 2 | 50% | 0.96 | 14.5 |
| Nuke | 1 | 100% | 1.46 | 19.0 |
| Ancient | 1 | 0% | 0.80 | 12.0 |
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.
- Advanced Mechanics
You are losing most of the first duels you take on T side, which is usually a peeking and spacing problem, not aim.
T opening duels 25.017% — 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.
44% win rate across 9 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| 13–4 | 0.02 | 12% | 13 Aug → | ||
| 8–13 | -0.03 | 23% | 13 Aug → | ||
| 13–8 | 0.04 | 22% | 6 Aug → | ||
| 13–11 | 0.07 | 13% | 6 Aug → | ||
| 7–13 | 0.04 | 23% | 5 Aug → | ||
| 10–13 | -0.02 | 11% | 4 Aug → | ||
| 13–8 | 0.04 | 14% | 27 Jul → | ||
| 5–3 | 0.05 | 80% | 27 Jul → | ||
| office | 9–13 | -0.05 | 22% | 25 Jul → | |
| 0–13 | -0.07 | 14% | 17 Jul → | ||
| 9–5 | 0.03 | 20% | 13 Jul → | ||
| shelter | 12–12 | 0.12 | 21% | 12 Jul → | |
| 13–11 | 0.02 | 23% | 11 Jul → | ||
| 13–6 | 0.07 | 19% | 11 Jul → | ||
| 5–13 | -0.08 | 22% | 8 Jul → | ||
| 5–13 | -0.05 | 10% | 7 Jul → | ||
| 13–7 | 0.01 | 28% | 3 Jul → | ||
| 9–13 | 0.04 | 25% | 2 Jul → | ||
| office | 8–13 | -0.05 | 13% | 1 Jul → | |
| office | 12–12 | -0.00 | 17% | 29 Jun → | |
| office | 13–10 | 0.00 | 15% | 28 Jun → | |
| 7–4 | -0.04 | 12% | 27 Jun → | ||
| 11–13 | -0.01 | 15% | 27 Jun → | ||
| 13–3 | 0.05 | 13% | 23 Jun → | ||
| 13–3 | 0.12 | 25% | 21 Jun → | ||
| 13–7 | 0.03 | 16% | 21 Jun → | ||
| 13–4 | -0.02 | 17% | 21 Jun → | ||
| 12–12 | 0.08 | 25% | 20 Jun → | ||
| 13–4 | 0.04 | 23% | 20 Jun → | ||
| 8–13 | -0.01 | 33% | 19 Jun → | ||
| 13–7 | -0.02 | 30% | 19 Jun → | ||
| 13–5 | -0.01 | 21% | 18 Jun → | ||
| 13–8 | 0.05 | 19% | 13 Jun → | ||
| 13–3 | 0.05 | 15% | 13 Jun → | ||
| 13–3 | 0.11 | 10% | 13 Jun → | ||
| 5–13 | -0.05 | 14% | 13 Jun → | ||
| 13–4 | 0.08 | 13% | 13 Jun → | ||
| 12–12 | 0.04 | 14% | 13 Jun → | ||
| 11–13 | 0.04 | 20% | 13 Jun → | ||
| 10–13 | -0.03 | 14% | 12 Jun → | ||
| 13–3 | 0.07 | 11% | 11 Jun → | ||
| 11–13 | 0.02 | 14% | 9 Jun → | ||
| 13–11 | 0.06 | 18% | 6 Jun → | ||
| 11–13 | 0.01 | 20% | 6 Jun → | ||
| 10–13 | 0.02 | 16% | 6 Jun → | ||
| 13–9 | -0.04 | 20% | 6 Jun → | ||
| 9–13 | -0.06 | 14% | 5 Jun → | ||
| 8–13 | -0.02 | 29% | 5 Jun → | ||
| 13–6 | 0.07 | 28% | 5 Jun → | ||
| 12–12 | 0.01 | 14% | 5 Jun → | ||
| 13–2 | -0.02 | 20% | 5 Jun → | ||
| 13–11 | 0.09 | 23% | 4 Jun → | ||
| 13–4 | 0.11 | 23% | 4 Jun → | ||
| 6–13 | 0.09 | 22% | 2 Jun → | ||
| 6–13 | -0.05 | 13% | 2 Jun → | ||
| 12–12 | -0.01 | 27% | 2 Jun → | ||
| office | 13–5 | 0.12 | 25% | 30 May → | |
| 13–8 | 0.01 | 29% | 30 May → | ||
| 13–7 | 0.03 | 19% | 30 May → | ||
| 8–13 | 0.01 | 13% | 30 May → | ||
| 13–4 | 0.05 | 19% | 28 May → | ||
| 13–10 | -0.02 | 23% | 27 May → | ||
| 5–13 | 0.01 | 17% | 24 May → | ||
| 10–13 | -0.00 | 20% | 24 May → | ||
| 9–13 | -0.02 | 12% | 24 May → | ||
| 11–13 | -0.02 | 18% | 23 May → | ||
| 4–13 | -0.03 | 21% | 23 May → | ||
| 5–13 | 0.03 | 33% | 23 May → | ||
| 13–5 | 0.15 | 39% | 22 May → | ||
| office | 9–13 | -0.01 | 27% | 22 May → | |
| 13–2 | -0.02 | 22% | 22 May → | ||
| 13–11 | 0.04 | 16% | 21 May → | ||
| 13–9 | 0.01 | 5% | 21 May → | ||
| office | 7–13 | 0.00 | 17% | 20 May → | |
| 13–10 | -0.01 | 16% | 19 May → | ||
| 12–12 | 0.01 | 14% | 19 May → | ||
| 8–13 | 0.03 | 34% | 16 May → | ||
| 8–13 | 0.03 | 24% | 16 May → | ||
| 13–1 | 0.04 | 13% | 16 May → | ||
| 13–8 | 0.06 | 21% | 14 May → | ||
| office | 13–6 | 0.01 | 21% | 14 May → | |
| 13–5 | -0.07 | 14% | 11 May → | ||
| 13–2 | 0.09 | 26% | 10 May → | ||
| office | 13–7 | -0.04 | 11% | 9 May → | |
| 13–11 | 0.04 | 19% | 9 May → | ||
| 8–13 | 0.04 | 20% | 9 May → | ||
| 13–10 | 0.03 | 18% | 9 May → | ||
| 12–12 | 0.08 | 26% | 8 May → | ||
| office | 13–0 | 0.07 | 22% | 7 May → | |
| 6–13 | 0.08 | 16% | 6 May → | ||
| 13–11 | 0.12 | 20% | 6 May → | ||
| 11–13 | 0.01 | 29% | 6 May → | ||
| 13–5 | 0.05 | 18% | 5 May → | ||
| 2–0 | 0.03 | 50% | 5 May → | ||
| office | 12–12 | 0.01 | 29% | 5 May → | |
| 13–9 | 0.13 | 20% | 3 May → | ||
| 13–0 | 0.04 | 41% | 3 May → | ||
| 13–7 | -0.02 | 28% | 3 May → | ||
| 10–13 | -0.08 | 8% | 3 May → | ||
| 11–13 | -0.01 | 22% | 3 May → |
Match data via Leetify.