VIGGO — CS2 Stats
76561197997319593[U:1:37053865]Steam profile ↗
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 | 46.1% | 47.2% | 49.9% | 3.8% short |
| Shot accuracy | 15.7% | 12.7% | 13.4% | above |
| Kill/death ratio | 1.09 | 1.09 | 1.12 | 0.03 short |
| Match win rate | 48.2% | 46.6% | 48.4% | meets |
This profile matches the typical Red band player on 2 of 4 comparable metrics.
Widest gap: Headshot rate. 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: -10pp win rate · -0.01 avg rating
Player DNA
Primary style: Hybrid Rifler — Aim-led profile without a single dominant tendency.
Strong CT-side opener
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 87% playstyle similarity
Most alike: opening-fight frequency, opening-duel success.
Where you differ: lower utility contribution; lower aim 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
Areas to improve
T-side openings. Opening success drops from 57% on CT to 41% on T — the same duels are being taken with worse setups on the attacking side.
Reaction time. 622ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.
Counter-strafing. Only 67% of shots are taken properly stopped — moving-shot inaccuracy is quietly taxing every duel.
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 5.8/10 (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 68 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 |
|---|---|---|---|---|---|
 nuke | B | 15 | 7–8 | 47% | -0.01 |
 ancient | S | 12 | 8–4 | 67% | -0.00 |
 inferno | A | 11 | 6–5 | 55% | -0.00 |
 anubis | D | 7 | 2–5 | 29% | 0.00 |
 overpass | C | 7 | 3–4 | 43% | 0.02 |
 dust2 | A | 5 | 3–2 | 60% | 0.02 |
 train | — | 4 | 2–2 | 50% | 0.01 |
 mirage | — | 3 | 1–2 | 33% | -0.02 |
 cache | — | 1 | 0–1 | 0% | -0.03 |
| cache_b | — | 1 | 0–1 | 0% | 0.01 |
| italy | — | 1 | 0–1 | 0% | -0.01 |
| office | — | 1 | 0–1 | 0% | -0.03 |
Across the last 68 tracked matches.
Anubis is currently your weakest sufficiently-sampled map (29% over 7). 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 resultsWLWLL
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Nuke | 126 | 56% | 1.04 | 17.4 |
| Inferno | 111 | 41% | 1.05 | 17.3 |
| Ancient | 108 | 51% | 1.00 | 16.7 |
| Vertigo | 81 | 56% | 1.15 | 18.7 |
| Anubis | 78 | 46% | 1.04 | 18.1 |
| Mirage | 39 | 51% | 0.95 | 15.2 |
| Overpass | 34 | 56% | 1.07 | 18.7 |
| Dust2 | 20 | 45% | 1.20 | 20.8 |
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 7 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| cache | 5–13 | -0.03 | 24% | 24 Aug → | |
| ancient | 13–8 | -0.02 | 18% | 24 Aug → | |
| nuke | 2–9 | 0.01 | 20% | 18 Aug → | |
| inferno | 4–9 | -0.04 | 9% | 18 Aug → | |
| anubis | 16–13 | 0.05 | 29% | 15 Aug → | |
| ancient | 13–8 | -0.03 | 12% | 3 May → | |
| overpass | 16–12 | 0.04 | 18% | 3 May → | |
| nuke | 13–10 | 0.01 | 14% | 27 Apr → | |
| ancient | 13–7 | -0.02 | 21% | 26 Apr → | |
| overpass | 10–13 | 0.04 | 27% | 21 Apr → | |
| cache_b | 14–16 | 0.01 | 19% | 17 Apr → | |
| overpass | 13–9 | -0.03 | 18% | 16 Apr → | |
| nuke | 13–11 | 0.03 | 21% | 25 Mar → | |
| dust2 | 13–2 | 0.07 | 19% | 23 Mar → | |
| inferno | 13–6 | 0.02 | 13% | 19 Mar → | |
| inferno | 13–11 | -0.02 | 22% | 12 Mar → | |
| nuke | 2–13 | -0.04 | 11% | 6 Mar → | |
| nuke | 16–14 | 0.01 | 28% | 6 Mar → | |
| mirage | 13–8 | 0.03 | 17% | 2 Mar → | |
| overpass | 4–13 | 0.03 | 10% | 1 Mar → | |
| inferno | 13–7 | -0.03 | 13% | 1 Mar → | |
| dust2 | 3–11 | -0.01 | 27% | 28 Feb → | |
| italy | 11–13 | -0.01 | 20% | 28 Feb → | |
| anubis | 12–12 | -0.01 | 11% | 28 Feb → | |
| nuke | 13–5 | 0.07 | 38% | 28 Feb → | |
| dust2 | 13–9 | -0.03 | 21% | 26 Feb → | |
| anubis | 8–1 | -0.02 | 17% | 24 Feb → | |
| overpass | 4–13 | 0.01 | 19% | 25 Jan → | |
| ancient | 13–7 | 0.06 | 25% | 12 Nov → | |
| overpass | 9–13 | -0.01 | 8% | 17 Oct → | |
| ancient | 9–13 | -0.05 | 18% | 6 Oct → | |
| inferno | 6–13 | -0.04 | 27% | 6 Oct → | |
| train | 13–7 | 0.02 | 35% | 6 Oct → | |
| dust2 | 8–13 | -0.00 | 32% | 11 Sept → | |
| dust2 | 13–6 | 0.09 | 27% | 11 Sept → | |
| ancient | 6–13 | 0.01 | 21% | 11 Sept → | |
| inferno | 3–13 | -0.11 | 29% | 17 Aug → | |
| overpass | 13–9 | 0.04 | 20% | 16 Jul → | |
| nuke | 2–13 | 0.00 | 15% | 26 Jun → | |
| ancient | 16–12 | 0.01 | 35% | 26 Jun → | |
| train | 14–16 | 0.03 | 17% | 24 Jun → | |
| ancient | 5–13 | -0.03 | 13% | 23 Jun → | |
| nuke | 4–13 | -0.04 | 9% | 22 Jun → | |
| ancient | 13–7 | -0.02 | 16% | 22 Jun → | |
| nuke | 13–8 | 0.12 | 23% | 19 Jun → | |
| inferno | 13–9 | 0.00 | 21% | 18 Jun → | |
| nuke | 6–13 | -0.06 | 16% | 18 Jun → | |
| train | 13–11 | -0.02 | 16% | 18 Jun → | |
| inferno | 13–2 | 0.16 | 20% | 18 Jun → | |
| anubis | 7–13 | -0.03 | 23% | 3 Jun → | |
| ancient | 13–3 | 0.07 | 18% | 3 Jun → | |
| ancient | 13–10 | 0.06 | 33% | 25 May → | |
| office | 9–13 | -0.03 | 28% | 19 May → | |
| inferno | 3–13 | -0.01 | 40% | 17 May → | |
| anubis | 6–13 | 0.02 | 18% | 17 May → | |
| inferno | 9–13 | -0.01 | 20% | 2 Apr → | |
| nuke | 3–13 | -0.05 | 20% | 1 Apr → | |
| nuke | 13–4 | -0.01 | 18% | 1 Apr → | |
| nuke | 7–13 | -0.05 | 12% | 17 Mar → | |
| anubis | 9–13 | 0.00 | 23% | 10 Mar → | |
| nuke | 11–13 | -0.07 | 20% | 10 Mar → | |
| nuke | 13–11 | -0.02 | 38% | 28 Feb → | |
| train | 11–13 | 0.02 | 18% | 20 Feb → | |
| anubis | 10–13 | -0.01 | 22% | 16 Feb → | |
| inferno | 13–3 | 0.03 | 24% | 16 Feb → | |
| mirage | 2–10 | -0.11 | 14% | 7 Feb → | |
| ancient | 5–13 | -0.07 | 10% | 28 Jan → | |
| mirage | 10–13 | 0.01 | 16% | 26 Jan → |
Match data via Leetify.
