Vizion — CS2 Stats
MG76561198061409238[U:1:101143510]Steam profile ↗✓ No bans
CSDB reads this profile's Premier rating from its tracked match history, and records Faceit ELO once on every day the page is viewed. 59 days played since 17 Nov 2023. Come back after the next session and the change shows above.
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Rating over time
Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do. Faceit ELO is CSDB’s own observation — no feed exposes ELO per match, so that line only has the days the profile was viewed and cannot be backfilled.
Performance scores
0–100 skill scores via Leetify.
Recent form
Last 10 vs previous 10: −40pp win rate · +0.02 avg rating · +6.9pp headshot accuracy · +87ms reaction
Win rate down 40pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp). Average match rating moved the other way (+0.02), so results shifted more than performance did.
Last 5 · 10 · 20 matches
Last 5
- 0–5 · 0% win rate
- Avg rating 0.07
- Avg headshot accuracy 38%
- Avg reaction 661ms
Last 10
- 3–5–2 · 30% win rate
- Avg rating 0.07
- Avg headshot accuracy 36%
- Avg reaction 671ms
Last 20
- 10–7–3 · 50% win rate
- Avg rating 0.06
- Avg headshot accuracy 32%
- Avg reaction 628ms
Newest first, from the last 100 tracked matches. Each block is its own sample — one result moves a 5-match win rate by 20 points.
Player DNA
Primary style: Opening Duellist — Winning opening duels is what stands out in this profile (+2.1 against its own average). How often you WIN the first duel — CSDB cannot yet see how often you take it.
Sharp aimerExcellent counter-strafing
Style profile from tracked-match aggregates — how this player plays, not how good they are. Classification rules are deterministic and documented in code.
What this cannot see yet: which weapons you use — so CSDB cannot identify an AWPer, and no style here implies a rifle or a sniper. It also cannot see how often you take opening duels, only how often you win them, nor where you hold, so roles that depend on those (entry, lurk, anchor) are deliberately absent rather than guessed. All of it needs round-by-round demo data, which is the next thing being built.
Your pro match

Plays most like s1mple 85% playstyle similarity
Most alike: opening-duel success, 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
Strengths
Aim. Aim score of 90 — the mechanical foundation is a clear strength.
Counter-strafing. 91% of shots taken properly stopped — movement discipline most players never reach.
CT openings. 63% 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. 631ms 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 7.8/10 (Strong), 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.
Personal bests
Across the last 100 tracked matches.
Highlights
Map breakdown
| Map | Grade | Played | Record | Win rate | Avg rating |
|---|---|---|---|---|---|
| A | 20 | 11–9 | 55% | 0.03 | |
| A | 14 | 8–6 | 57% | -0.00 | |
| A | 14 | 8–6 | 57% | -0.01 | |
| S | 13 | 9–4 | 69% | 0.03 | |
| D | 12 | 3–9 | 25% | 0.02 | |
| D | 10 | 2–8 | 20% | 0.01 | |
| D | 9 | 3–6 | 33% | 0.06 | |
| B | 6 | 3–3 | 50% | 0.04 | |
| mills | — | 1 | 1–0 | 100% | -0.02 |
| office | — | 1 | 0–1 | 0% | 0.04 |
Across the last 100 tracked matches.
Nuke is currently your weakest sufficiently-sampled map (20% over 10). 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.
Faceit stats
Combat
Clutches & streaks
Recent Faceit resultsWLWLL
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Ancient | 51 | 53% | 1.05 | 16.3 |
| Mirage | 51 | 39% | 1.04 | 16.6 |
| Nuke | 38 | 45% | 1.08 | 15.7 |
| Anubis | 35 | 46% | 1.09 | 15.4 |
| Vertigo | 34 | 53% | 1.24 | 17.0 |
| Overpass | 23 | 52% | 1.05 | 18.4 |
| Dust2 | 21 | 43% | 1.01 | 16.8 |
| Inferno | 16 | 44% | 1.07 | 16.7 |
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.
20% win rate across 10 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| 7–13 | 0.11 | 34% | 15 Sept → | ||
| 11–13 | 0.04 | 33% | 12 Aug → | ||
| 2–13 | -0.01 | 38% | 8 Aug → | ||
| 9–13 | 0.08 | 40% | 14 Apr → | ||
| 8–13 | 0.14 | 47% | 14 Mar → | ||
| 13–7 | 0.03 | 32% | 12 Mar → | ||
| 12–12 | 0.09 | 42% | 11 Mar → | ||
| 13–5 | 0.19 | 33% | 11 Mar → | ||
| 15–15 | 0.03 | 26% | 4 Jan → | ||
| 13–8 | 0.00 | 31% | 7 Sept → | ||
| 3–1 | -0.03 | 0% | 7 Sept → | ||
| 12–12 | 0.03 | 30% | 4 Sept → | ||
| 13–8 | 0.07 | 33% | 4 Sept → | ||
| 13–8 | 0.06 | 25% | 2 Sept → | ||
| 11–13 | -0.02 | 31% | 29 Aug → | ||
| 11–13 | 0.03 | 33% | 20 Aug → | ||
| 13–10 | 0.14 | 29% | 18 Aug → | ||
| 13–6 | 0.10 | 37% | 18 Aug → | ||
| 13–10 | 0.07 | 51% | 18 Aug → | ||
| 16–14 | 0.00 | 19% | 13 Aug → | ||
| 13–9 | 0.03 | 33% | 13 Aug → | ||
| 13–9 | 0.00 | 64% | 9 Aug → | ||
| 9–7 | -0.02 | 17% | 6 Aug → | ||
| 13–3 | 0.03 | 20% | 4 Aug → | ||
| 13–6 | 0.08 | 22% | 2 Aug → | ||
| 11–13 | -0.02 | 31% | 22 Jul → | ||
| mills | 13–9 | -0.02 | 37% | 12 Jul → | |
| office | 6–13 | 0.04 | 26% | 12 Jul → | |
| 13–11 | 0.04 | 39% | 12 Jul → | ||
| 13–6 | 0.00 | 29% | 11 Jul → | ||
| 9–13 | 0.16 | 27% | 7 Jul → | ||
| 13–8 | 0.04 | 22% | 24 Jun → | ||
| 9–13 | -0.04 | 31% | 23 Jun → | ||
| 6–13 | -0.02 | 26% | 20 Jun → | ||
| 13–16 | -0.05 | 24% | 19 Jun → | ||
| 13–9 | -0.05 | 12% | 19 Jun → | ||
| 10–13 | -0.01 | 43% | 3 Jun → | ||
| 14–16 | 0.05 | 36% | 3 Jun → | ||
| 15–19 | -0.04 | 36% | 3 May → | ||
| 13–10 | 0.04 | 48% | 26 Apr → | ||
| 18–22 | -0.01 | 27% | 26 Apr → | ||
| 10–13 | 0.16 | 39% | 26 Apr → | ||
| 9–6 | 0.18 | 32% | 24 Apr → | ||
| 8–13 | -0.00 | 28% | 3 Mar → | ||
| 16–14 | 0.09 | 32% | 3 Mar → | ||
| 10–13 | -0.07 | 42% | 21 Feb → | ||
| 13–8 | 0.07 | 36% | 19 Feb → | ||
| 13–10 | 0.03 | 36% | 19 Feb → | ||
| 22–20 | 0.02 | 23% | 19 Feb → | ||
| 19–17 | -0.02 | 30% | 18 Feb → | ||
| 13–6 | -0.01 | 41% | 18 Feb → | ||
| 13–8 | 0.06 | 37% | 18 Feb → | ||
| 13–16 | -0.05 | 35% | 16 Feb → | ||
| 13–10 | -0.02 | 24% | 15 Feb → | ||
| 10–13 | -0.05 | 34% | 14 Feb → | ||
| 7–13 | -0.04 | 35% | 14 Feb → | ||
| 13–9 | -0.00 | 32% | 14 Feb → | ||
| 11–13 | -0.02 | 26% | 14 Feb → | ||
| 4–13 | -0.04 | 36% | 14 Feb → | ||
| 7–13 | 0.00 | 22% | 11 Feb → | ||
| 11–13 | 0.03 | 27% | 11 Feb → | ||
| 13–3 | -0.00 | 43% | 11 Feb → | ||
| 13–7 | 0.02 | 27% | 11 Feb → | ||
| 13–11 | 0.07 | 31% | 11 Feb → | ||
| 1–13 | -0.03 | 40% | 10 Feb → | ||
| 10–13 | 0.05 | 31% | 10 Feb → | ||
| 7–13 | 0.02 | 24% | 10 Feb → | ||
| 13–3 | 0.07 | 33% | 10 Feb → | ||
| 13–9 | 0.02 | 25% | 9 Feb → | ||
| 10–13 | 0.01 | 14% | 9 Feb → | ||
| 6–13 | 0.00 | 29% | 9 Feb → | ||
| 11–13 | -0.02 | 33% | 9 Feb → | ||
| 2–13 | 0.00 | 30% | 6 Feb → | ||
| 13–6 | 0.06 | 36% | 1 Feb → | ||
| 13–11 | 0.05 | 52% | 25 Jan → | ||
| 10–13 | -0.00 | 48% | 23 Jan → | ||
| 13–16 | 0.02 | 38% | 21 Jan → | ||
| 5–13 | -0.04 | 36% | 11 Jan → | ||
| 13–10 | 0.01 | 31% | 8 Jan → | ||
| 13–4 | 0.02 | 27% | 5 Dec → | ||
| 4–13 | -0.06 | 24% | 5 Dec → | ||
| 6–13 | -0.07 | 18% | 3 Dec → | ||
| 13–9 | 0.05 | 21% | 1 Dec → | ||
| 13–8 | 0.11 | 36% | 1 Dec → | ||
| 13–4 | 0.10 | 32% | 1 Dec → | ||
| 7–13 | -0.03 | 18% | 28 Nov → | ||
| 5–13 | -0.06 | 29% | 28 Nov → | ||
| 13–8 | 0.02 | 37% | 27 Nov → | ||
| 16–13 | -0.04 | 16% | 27 Nov → | ||
| 13–16 | 0.07 | 29% | 24 Nov → | ||
| 7–13 | 0.03 | 31% | 23 Nov → | ||
| 13–16 | -0.02 | 20% | 21 Nov → | ||
| 13–11 | 0.10 | 33% | 20 Nov → | ||
| 9–13 | -0.05 | 26% | 20 Nov → | ||
| 13–9 | -0.02 | 24% | 20 Nov → | ||
| 10–13 | 0.03 | 32% | 19 Nov → | ||
| 10–13 | -0.05 | 29% | 19 Nov → | ||
| 9–13 | -0.04 | 15% | 19 Nov → | ||
| 13–10 | -0.01 | 17% | 17 Nov → | ||
| 8–13 | -0.01 | 37% | 17 Nov → |
Match data via Leetify.