In my heart forever Alina <3 — CS2 Stats
76561198844607250[U:1:884341522]
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Performance scores
0–100 skill scores via Leetify.
Recent form
Last 10 vs previous 10: -10pp win rate · +0.03 avg rating
Player DNA
Primary style: Entry Fragger — High opening-fight frequency with above-par success in them.
Sharp aimerStrong CT-side openerEffective flashes
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 s1mple 94% playstyle similarity
Most alike: opening-fight frequency, positioning 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
Strengths
Aim. Aim score of 86 — the mechanical foundation is a clear strength.
CT openings. 71% CT opening-duel success — winning the first fight on the defending side is rare and valuable.
Areas to improve
Preaim. Mechanical aim is strong but the crosshair sits 11.6° 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 71% on CT to 40% on T — the same duels are being taken with worse setups on the attacking side.
Reaction time. 595ms 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 7.2/10 (Strong), 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 |
|---|---|---|---|---|---|
| B | 38 | 18–20 | 47% | 0.05 | |
| A | 20 | 12–8 | 60% | 0.05 | |
| D | 14 | 4–10 | 29% | 0.05 | |
| C | 9 | 4–5 | 44% | -0.01 | |
| A | 8 | 5–3 | 63% | 0.03 | |
| A | 5 | 3–2 | 60% | 0.02 | |
| — | 3 | 2–1 | 67% | 0.09 | |
| — | 2 | 1–1 | 50% | 0.02 | |
| — | 1 | 1–0 | 100% | 0.07 |
Across the last 100 tracked matches.
Inferno is currently your weakest sufficiently-sampled map (29% over 14). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.
Faceit stats
Combat
Clutches & streaks
Recent Faceit resultsWWLWL
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Mirage | 467 | 54% | 1.23 | 17.2 |
| Ancient | 306 | 52% | 1.25 | 16.8 |
| Anubis | 259 | 53% | 1.20 | 15.9 |
| Dust2 | 190 | 51% | 1.30 | 16.8 |
| Nuke | 122 | 54% | 1.25 | 15.9 |
| Overpass | 78 | 63% | 1.45 | 19.6 |
| Inferno | 58 | 66% | 1.34 | 15.7 |
| Vertigo | 54 | 52% | 1.23 | 16.3 |
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
Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.
29% win rate across 14 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| 10–13 | 0.09 | 15% | 26 Aug → | ||
| 13–8 | 0.15 | 28% | 19 Aug → | ||
| 15–15 | 0.09 | 27% | 15 Aug → | ||
| 15–15 | 0.00 | 28% | 14 Aug → | ||
| 13–9 | 0.09 | 34% | 10 Aug → | ||
| 13–3 | 0.04 | 30% | 10 Aug → | ||
| 3–13 | 0.12 | 27% | 10 Aug → | ||
| 7–13 | 0.11 | 26% | 30 Jul → | ||
| 5–2 | 0.12 | 54% | 30 Jul → | ||
| 1–13 | -0.02 | 24% | 30 Jul → | ||
| 10–13 | 0.04 | 38% | 26 Jul → | ||
| 6–13 | 0.10 | 31% | 26 Jul → | ||
| 13–8 | 0.12 | 30% | 23 Jul → | ||
| 13–9 | 0.00 | 31% | 22 Jul → | ||
| 1–9 | -0.12 | 23% | 22 Jul → | ||
| 1–9 | -0.05 | 20% | 22 Jul → | ||
| 13–0 | 0.11 | 46% | 22 Jul → | ||
| 9–7 | 0.07 | 32% | 22 Jul → | ||
| 13–8 | 0.08 | 29% | 22 Jul → | ||
| 7–13 | 0.10 | 31% | 21 Jul → | ||
| 7–13 | -0.03 | 23% | 21 Jul → | ||
| 16–13 | 0.00 | 29% | 20 Jul → | ||
| 13–7 | 0.01 | 39% | 20 Jul → | ||
| 7–13 | 0.06 | 34% | 20 Jul → | ||
| 9–13 | 0.12 | 22% | 20 Jul → | ||
| 13–7 | 0.04 | 34% | 20 Jul → | ||
| 15–15 | 0.09 | 33% | 19 Jul → | ||
| 2–13 | -0.03 | 30% | 19 Jul → | ||
| 13–6 | 0.08 | 48% | 19 Jul → | ||
| 0–9 | -0.13 | 0% | 19 Jul → | ||
| 13–8 | 0.16 | 32% | 24 Jun → | ||
| 2–4 | 0.09 | 38% | 24 Jun → | ||
| 1–13 | 0.01 | 22% | 24 Jun → | ||
| 7–13 | 0.12 | 24% | 24 Jun → | ||
| 13–11 | 0.17 | 23% | 21 Jun → | ||
| 13–6 | 0.10 | 35% | 21 Jun → | ||
| 9–3 | 0.37 | 50% | 21 Jun → | ||
| 12–12 | 0.08 | 43% | 20 Jun → | ||
| 13–10 | 0.17 | 46% | 20 Jun → | ||
| 10–13 | 0.07 | 24% | 20 Jun → | ||
| 9–5 | 0.19 | 29% | 20 Jun → | ||
| 9–13 | 0.09 | 26% | 20 Jun → | ||
| 10–13 | 0.10 | 29% | 19 Jun → | ||
| 0–9 | -0.30 | 50% | 19 Jun → | ||
| 9–13 | 0.02 | 19% | 18 Jun → | ||
| 13–7 | -0.09 | 37% | 1 Jun → | ||
| 16–14 | 0.08 | 41% | 18 May → | ||
| 6–13 | 0.01 | 41% | 18 May → | ||
| 13–10 | 0.03 | 22% | 17 May → | ||
| 13–3 | -0.00 | 26% | 17 May → | ||
| 13–3 | -0.02 | 23% | 17 May → | ||
| 3–13 | -0.08 | 36% | 17 May → | ||
| 13–3 | 0.09 | 25% | 17 May → | ||
| 2–13 | 0.04 | 29% | 17 May → | ||
| 9–13 | -0.01 | 24% | 16 May → | ||
| 13–5 | 0.01 | 18% | 16 May → | ||
| 13–7 | 0.11 | 29% | 16 May → | ||
| 13–6 | 0.05 | 18% | 16 May → | ||
| 13–7 | 0.01 | 8% | 16 May → | ||
| 13–11 | -0.05 | 28% | 16 May → | ||
| 13–11 | 0.06 | 30% | 16 May → | ||
| 5–13 | -0.01 | 42% | 16 May → | ||
| 10–13 | 0.01 | 46% | 16 May → | ||
| 13–4 | -0.01 | 26% | 15 May → | ||
| 3–13 | -0.03 | 23% | 15 May → | ||
| 13–8 | 0.13 | 38% | 15 May → | ||
| 2–13 | -0.05 | 29% | 15 May → | ||
| 13–4 | 0.07 | 38% | 15 May → | ||
| 6–13 | -0.04 | 26% | 15 May → | ||
| 13–5 | 0.04 | 46% | 15 May → | ||
| 13–8 | 0.09 | 31% | 14 May → | ||
| 10–13 | 0.05 | 13% | 14 May → | ||
| 13–6 | 0.06 | 29% | 14 May → | ||
| 13–7 | 0.08 | 23% | 14 May → | ||
| 13–11 | 0.01 | 31% | 14 May → | ||
| 14–16 | 0.05 | 36% | 13 May → | ||
| 15–15 | 0.07 | 37% | 13 May → | ||
| 3–13 | -0.02 | 27% | 13 May → | ||
| 13–10 | 0.04 | 21% | 13 May → | ||
| 2–13 | -0.10 | 30% | 4 May → | ||
| 16–14 | 0.09 | 25% | 2 May → | ||
| 10–13 | 0.01 | 16% | 2 May → | ||
| 8–13 | 0.02 | 15% | 28 Apr → | ||
| 13–6 | 0.06 | 27% | 28 Apr → | ||
| 13–1 | 0.00 | 28% | 12 Apr → | ||
| 13–8 | 0.01 | 17% | 12 Apr → | ||
| 13–9 | 0.06 | 29% | 12 Apr → | ||
| 13–8 | 0.15 | 28% | 12 Apr → | ||
| 10–13 | 0.09 | 28% | 12 Apr → | ||
| 13–9 | 0.03 | 23% | 12 Apr → | ||
| 5–13 | -0.01 | 20% | 11 Apr → | ||
| 8–13 | -0.01 | 13% | 10 Apr → | ||
| 11–13 | 0.13 | 31% | 10 Apr → | ||
| 5–13 | -0.04 | 24% | 10 Apr → | ||
| 8–13 | -0.02 | 33% | 5 Apr → | ||
| 15–15 | -0.07 | 38% | 3 Apr → | ||
| 13–7 | 0.03 | 5% | 3 Apr → | ||
| 12–16 | -0.05 | 20% | 31 Mar → | ||
| 13–9 | -0.02 | 34% | 31 Mar → | ||
| 13–4 | 0.09 | 16% | 31 Mar → |
Match data via Leetify.