yay — CS2 Stats
76561198102388116[U:1:142122388]
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Performance scores
0–100 skill scores via Leetify.
Recent form
Last 10 vs previous 10: +0pp win rate · -0.03 avg rating
Player DNA
Primary style: Entry Fragger — High opening-fight frequency with above-par success in them.
Limited 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 sh1ro 89% playstyle similarity
Most alike: opening-fight frequency, positioning profile.
Where you differ: lower aim profile; 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
CT openings. 62% 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.
Reaction time. 602ms 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 6.8/10 (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 |
|---|---|---|---|---|---|
 dust2 | D | 32 | 11–21 | 34% | 0.03 |
| office | S | 20 | 13–7 | 65% | 0.07 |
 mirage | C | 16 | 6–10 | 38% | 0.04 |
 cache | A | 11 | 7–4 | 64% | 0.02 |
| italy | S | 6 | 6–0 | 100% | 0.10 |
 train | C | 5 | 2–3 | 40% | 0.01 |
 anubis | — | 3 | 0–3 | 0% | 0.04 |
 overpass | — | 2 | 1–1 | 50% | -0.02 |
 nuke | — | 2 | 1–1 | 50% | 0.03 |
 inferno | — | 1 | 0–1 | 0% | 0.00 |
| warden | — | 1 | 0–1 | 0% | 0.13 |
 vertigo | — | 1 | 0–1 | 0% | -0.01 |
Across the last 100 tracked matches.
Dust 2 is currently your weakest sufficiently-sampled map (34% over 32). Start with the 6 essential Dust 2 lineups, review the callouts, then spin up a practice server.
Faceit stats
Combat
Clutches & streaks
Recent Faceit resultsWLLLL
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Mirage | 14 | 93% | 1.58 | 19.4 |
| Dust2 | 7 | 29% | 0.88 | 14.9 |
| Anubis | 5 | 60% | 1.07 | 17.2 |
| Vertigo | 4 | 75% | 1.35 | 20.0 |
| Inferno | 3 | 33% | 1.03 | 16.0 |
| Ancient | 1 | 100% | 0.65 | 13.0 |
| Nuke | 1 | 100% | 0.88 | 15.0 |
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.
- 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 0.4824 — 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.
34% win rate across 32 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| dust2 | 13–11 | 0.01 | 16% | 25 Aug → | |
| mirage | 7–13 | -0.06 | 15% | 18 Aug → | |
| train | 13–9 | 0.11 | 19% | 18 Aug → | |
| office | 4–13 | -0.07 | 8% | 11 Aug → | |
| office | 6–13 | 0.04 | 15% | 11 Aug → | |
| office | 13–0 | 0.08 | 40% | 4 Aug → | |
| dust2 | 6–13 | 0.05 | 38% | 13 Jul → | |
| dust2 | 13–3 | 0.06 | 14% | 13 Jul → | |
| cache | 13–11 | -0.01 | 24% | 25 Jun → | |
| cache | 13–3 | -0.06 | 13% | 15 Jun → | |
| office | 13–10 | 0.11 | 30% | 15 Jun → | |
| italy | 13–8 | 0.07 | 24% | 15 Jun → | |
| dust2 | 9–13 | 0.04 | 18% | 15 Jun → | |
| mirage | 2–13 | -0.02 | 17% | 15 Jun → | |
| cache | 13–5 | 0.11 | 23% | 4 Jun → | |
| dust2 | 13–7 | 0.10 | 36% | 2 Jun → | |
| cache | 3–8 | 0.04 | 12% | 2 Jun → | |
| dust2 | 13–7 | 0.08 | 16% | 28 May → | |
| dust2 | 3–13 | -0.06 | 36% | 28 May → | |
| dust2 | 13–9 | 0.04 | 15% | 28 May → | |
| mirage | 11–2 | 0.14 | 21% | 28 May → | |
| cache | 13–1 | 0.06 | 11% | 28 May → | |
| dust2 | 5–13 | 0.00 | 17% | 19 May → | |
| dust2 | 9–13 | 0.09 | 12% | 18 May → | |
| mirage | 13–9 | 0.02 | 15% | 15 May → | |
| cache | 11–13 | -0.01 | 15% | 15 May → | |
| dust2 | 11–13 | 0.05 | 15% | 13 May → | |
| cache | 3–13 | 0.01 | 12% | 13 May → | |
| mirage | 12–12 | 0.06 | 12% | 13 May → | |
| office | 13–4 | 0.05 | 27% | 13 May → | |
| dust2 | 13–2 | 0.09 | 20% | 13 May → | |
| cache | 12–12 | 0.02 | 28% | 12 May → | |
| dust2 | 12–12 | 0.03 | 19% | 12 May → | |
| cache | 13–11 | 0.07 | 24% | 12 May → | |
| italy | 13–11 | 0.06 | 19% | 8 May → | |
| mirage | 7–13 | -0.01 | 21% | 8 May → | |
| dust2 | 13–6 | 0.10 | 26% | 8 May → | |
| cache | 13–11 | 0.04 | 23% | 8 May → | |
| dust2 | 11–13 | -0.03 | 14% | 1 May → | |
| cache | 13–11 | -0.02 | 8% | 29 Apr → | |
| office | 13–8 | 0.05 | 20% | 21 Apr → | |
| italy | 13–11 | 0.12 | 18% | 15 Apr → | |
| office | 13–11 | 0.16 | 16% | 15 Apr → | |
| mirage | 3–13 | 0.03 | 18% | 15 Apr → | |
| dust2 | 7–13 | -0.09 | 18% | 15 Apr → | |
| office | 9–13 | 0.04 | 21% | 14 Apr → | |
| dust2 | 12–12 | 0.12 | 17% | 14 Apr → | |
| mirage | 12–3 | 0.06 | 22% | 14 Apr → | |
| dust2 | 12–12 | 0.08 | 15% | 8 Apr → | |
| office | 13–7 | 0.08 | 22% | 8 Apr → | |
| italy | 13–3 | 0.14 | 26% | 8 Apr → | |
| mirage | 2–13 | 0.13 | 31% | 8 Apr → | |
| dust2 | 11–13 | 0.08 | 26% | 8 Apr → | |
| office | 13–1 | 0.13 | 16% | 7 Apr → | |
| mirage | 13–8 | 0.09 | 16% | 7 Apr → | |
| anubis | 12–12 | -0.04 | 16% | 2 Apr → | |
| mirage | 6–13 | 0.03 | 28% | 2 Apr → | |
| office | 13–1 | 0.23 | 30% | 2 Apr → | |
| dust2 | 0–13 | -0.07 | 23% | 2 Apr → | |
| italy | 13–5 | 0.14 | 14% | 1 Apr → | |
| office | 13–8 | 0.06 | 23% | 1 Apr → | |
| dust2 | 11–13 | -0.01 | 31% | 1 Apr → | |
| overpass | 13–7 | -0.04 | 20% | 1 Apr → | |
| dust2 | 10–13 | 0.03 | 36% | 31 Mar → | |
| mirage | 13–6 | 0.05 | 32% | 28 Mar → | |
| mirage | 10–13 | 0.03 | 39% | 26 Mar → | |
| office | 13–10 | 0.05 | 21% | 26 Mar → | |
| dust2 | 8–13 | 0.02 | 26% | 26 Mar → | |
| inferno | 10–13 | 0.00 | 34% | 30 Jan → | |
| dust2 | 13–2 | 0.08 | 35% | 30 Jan → | |
| office | 9–13 | 0.04 | 8% | 28 Jan → | |
| warden | 10–13 | 0.13 | 19% | 28 Jan → | |
| dust2 | 3–13 | -0.01 | 31% | 21 Jan → | |
| office | 10–13 | -0.03 | 25% | 21 Jan → | |
| office | 13–11 | 0.08 | 38% | 21 Jan → | |
| dust2 | 9–13 | -0.04 | 2% | 17 Jan → | |
| office | 13–8 | 0.09 | 19% | 17 Jan → | |
| italy | 13–7 | 0.08 | 27% | 8 Jan → | |
| office | 12–12 | 0.01 | 22% | 8 Jan → | |
| mirage | 7–13 | 0.07 | 20% | 8 Jan → | |
| dust2 | 13–4 | 0.05 | 28% | 8 Jan → | |
| nuke | 3–13 | -0.04 | 48% | 1 Jan → | |
| mirage | 3–13 | 0.03 | 18% | 22 Dec → | |
| dust2 | 13–10 | 0.10 | 25% | 22 Dec → | |
| nuke | 13–2 | 0.11 | 26% | 22 Dec → | |
| overpass | 8–13 | -0.01 | 29% | 16 Dec → | |
| anubis | 10–13 | 0.08 | 28% | 12 Dec → | |
| dust2 | 10–13 | 0.08 | 22% | 7 Nov → | |
| train | 2–13 | -0.00 | 27% | 7 Nov → | |
| dust2 | 10–13 | -0.02 | 19% | 5 Nov → | |
| office | 1–13 | 0.05 | 36% | 23 Oct → | |
| anubis | 12–12 | 0.07 | 16% | 21 Oct → | |
| vertigo | 8–13 | -0.01 | 20% | 14 Oct → | |
| dust2 | 4–13 | -0.05 | 14% | 13 Oct → | |
| train | 5–13 | 0.00 | 21% | 1 Oct → | |
| dust2 | 13–9 | 0.07 | 9% | 24 Sept → | |
| office | 13–10 | 0.08 | 31% | 24 Sept → | |
| train | 4–13 | -0.03 | 12% | 16 Sept → | |
| train | 10–5 | -0.03 | 26% | 16 Sept → | |
| mirage | 13–11 | -0.03 | 11% | 9 Sept → |
Match data via Leetify.