Shifto — CS2 Stats
TV76561198031880331[U:1:71614603]Steam profile ↗✓ No bans
What changed since last observed
CSDB last observed this profile on 7 Sep 2026 (10 days ago). Ranks are recorded once per day this page is viewed.
CSDB reads this profile's Premier rating from its tracked match history, and records Faceit ELO once on every day the page is viewed. 74 days played since 29 Dec 2025. Come back after the next session and the change shows above.
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Rating over time
Faceit ELO
- At peak — 2,683
- Next: 3,000 ELO at 3,000 — 317 to go
- Reached: 2,500 ELO · Level 10 · Level 9 · Level 8
- 2,500 ELO first seen 2026-09-01
- Level 10 first seen 2026-09-01
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.
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Performance scores
0–100 skill scores via Leetify.
Recent form
Last 10 vs previous 10: −20pp win rate · +0.04 avg rating · +7.5pp headshot accuracy · −24ms reaction
Win rate down 20pp 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 (+0.04), so results shifted more than performance did.
Last 5 · 10 · 20 matches
Last 5
- 1–4 · 20% win rate
- Avg rating 0.08
- Avg headshot accuracy 32%
- Avg reaction 473ms
Last 10
- 3–7 · 30% win rate
- Avg rating 0.07
- Avg headshot accuracy 30%
- Avg reaction 549ms
Last 20
- 8–11–1 · 40% win rate
- Avg rating 0.05
- Avg headshot accuracy 26%
- Avg reaction 561ms
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 (+2.2 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.
CT openings. 67% 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.
T-side openings. Opening success drops from 67% on CT to 46% on T — the same duels are being taken with worse setups on the attacking side.
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 7.4/10 (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.
Personal bests
Across the last 100 tracked matches.
Highlights
Map breakdown
| Map | Grade | Played | Record | Win rate | Avg rating |
|---|---|---|---|---|---|
 mirage | S | 17 | 11–6 | 65% | 0.02 |
 dust2 | A | 14 | 9–5 | 64% | 0.02 |
 cache | C | 10 | 4–6 | 40% | 0.05 |
 anubis | S | 10 | 8–2 | 80% | 0.06 |
 nuke | C | 9 | 4–5 | 44% | 0.01 |
 overpass | A | 9 | 5–4 | 56% | 0.03 |
 inferno | S | 9 | 6–3 | 67% | 0.03 |
| office | A | 7 | 4–3 | 57% | 0.08 |
 vertigo | A | 7 | 4–3 | 57% | 0.08 |
 train | — | 4 | 3–1 | 75% | 0.09 |
 ancient | — | 4 | 3–1 | 75% | 0.03 |
Across the last 100 tracked matches.
Faceit stats
Combat
Clutches & streaks
Recent Faceit resultsWLWLL
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Mirage | 255 | 53% | 1.18 | 19.0 |
| Dust2 | 143 | 48% | 1.17 | 18.3 |
| Nuke | 96 | 56% | 1.21 | 18.6 |
| Anubis | 96 | 55% | 1.14 | 17.0 |
| Inferno | 66 | 55% | 1.16 | 17.4 |
| Overpass | 51 | 59% | 1.19 | 18.1 |
| Ancient | 38 | 53% | 1.18 | 17.6 |
| Train | 28 | 68% | 1.21 | 17.9 |
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.
40% win rate across 10 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| office | 2–13 | -0.04 | 33% | 9 Sept → | |
| office | 10–13 | 0.09 | 30% | 7 Sept → | |
| cache | 13–10 | 0.25 | 29% | 6 Sept → | |
| office | 8–13 | 0.10 | 41% | 6 Sept → | |
| mirage | 10–13 | 0.01 | 27% | 25 Aug → | |
| mirage | 0–3 | -0.01 | 40% | 15 Aug → | |
| nuke | 11–13 | 0.04 | 26% | 12 Aug → | |
| mirage | 13–3 | 0.14 | 15% | 31 Jul → | |
| vertigo | 13–8 | 0.10 | 23% | 29 Jul → | |
| vertigo | 8–13 | 0.05 | 36% | 29 Jul → | |
| dust2 | 13–7 | 0.05 | 33% | 27 Jul → | |
| anubis | 13–6 | 0.12 | 25% | 27 Jul → | |
| anubis | 9–13 | 0.11 | 21% | 22 Jul → | |
| office | 13–9 | 0.06 | 18% | 13 Jul → | |
| cache | 13–4 | -0.00 | 29% | 6 Jul → | |
| cache | 4–13 | -0.02 | 26% | 6 Jul → | |
| mirage | 13–9 | 0.03 | 17% | 26 Jun → | |
| overpass | 3–13 | 0.05 | 26% | 24 Jun → | |
| mirage | 1–13 | -0.05 | 9% | 24 Jun → | |
| mirage | 15–15 | -0.03 | 21% | 23 Jun → | |
| dust2 | 9–13 | 0.01 | 22% | 21 Jun → | |
| dust2 | 13–7 | -0.01 | 13% | 19 Jun → | |
| train | 13–3 | 0.13 | 19% | 19 Jun → | |
| train | 13–10 | 0.11 | 27% | 17 Jun → | |
| inferno | 13–9 | 0.07 | 25% | 16 Jun → | |
| nuke | 0–13 | -0.06 | 0% | 15 Jun → | |
| ancient | 16–12 | -0.03 | 19% | 15 Jun → | |
| dust2 | 13–5 | 0.05 | 14% | 9 Jun → | |
| vertigo | 13–4 | 0.12 | 27% | 9 Jun → | |
| vertigo | 6–13 | 0.06 | 29% | 9 Jun → | |
| cache | 13–8 | 0.03 | 20% | 8 Jun → | |
| vertigo | 5–13 | 0.07 | 25% | 6 Jun → | |
| cache | 13–2 | 0.13 | 25% | 1 Jun → | |
| anubis | 13–2 | 0.05 | 34% | 1 Jun → | |
| anubis | 2–13 | -0.05 | 26% | 1 Jun → | |
| cache | 6–13 | 0.10 | 20% | 26 May → | |
| cache | 12–12 | 0.02 | 21% | 24 May → | |
| dust2 | 11–13 | 0.02 | 25% | 22 May → | |
| anubis | 13–5 | 0.09 | 22% | 22 May → | |
| cache | 4–13 | -0.05 | 17% | 18 May → | |
| office | 13–6 | 0.07 | 18% | 15 May → | |
| cache | 12–12 | 0.05 | 30% | 12 May → | |
| nuke | 5–13 | -0.14 | 4% | 7 May → | |
| nuke | 16–14 | 0.12 | 33% | 4 May → | |
| mirage | 9–13 | -0.00 | 28% | 3 May → | |
| dust2 | 13–1 | 0.03 | 17% | 2 May → | |
| cache | 6–13 | -0.03 | 23% | 1 May → | |
| mirage | 13–8 | 0.08 | 24% | 25 Apr → | |
| train | 13–1 | 0.07 | 31% | 24 Apr → | |
| dust2 | 14–16 | -0.00 | 22% | 17 Apr → | |
| inferno | 11–13 | 0.04 | 33% | 15 Apr → | |
| dust2 | 13–9 | 0.04 | 23% | 15 Apr → | |
| inferno | 0–7 | -0.01 | 30% | 10 Apr → | |
| nuke | 13–8 | 0.06 | 30% | 10 Apr → | |
| vertigo | 13–7 | 0.07 | 34% | 8 Apr → | |
| overpass | 19–22 | 0.00 | 26% | 7 Apr → | |
| anubis | 13–6 | 0.07 | 23% | 2 Apr → | |
| anubis | 13–3 | 0.13 | 42% | 2 Apr → | |
| overpass | 13–8 | -0.08 | 21% | 28 Mar → | |
| mirage | 13–2 | -0.07 | 34% | 25 Mar → | |
| office | 9–1 | 0.18 | 21% | 23 Mar → | |
| office | 8–3 | 0.11 | 41% | 23 Mar → | |
| inferno | 13–10 | 0.10 | 23% | 10 Mar → | |
| nuke | 14–16 | -0.02 | 27% | 7 Mar → | |
| train | 6–13 | 0.03 | 26% | 3 Mar → | |
| mirage | 16–14 | 0.09 | 29% | 28 Feb → | |
| nuke | 13–7 | 0.05 | 26% | 27 Feb → | |
| overpass | 13–9 | 0.06 | 15% | 26 Feb → | |
| mirage | 13–11 | 0.03 | 33% | 19 Feb → | |
| anubis | 13–8 | -0.01 | 4% | 18 Feb → | |
| dust2 | 13–10 | 0.05 | 29% | 18 Feb → | |
| vertigo | 13–5 | 0.12 | 18% | 11 Feb → | |
| inferno | 13–9 | -0.02 | 17% | 10 Feb → | |
| mirage | 13–6 | 0.02 | 26% | 8 Feb → | |
| anubis | 13–9 | 0.03 | 39% | 5 Feb → | |
| dust2 | 3–13 | 0.00 | 0% | 2 Feb → | |
| inferno | 10–13 | 0.03 | 24% | 30 Jan → | |
| mirage | 13–3 | 0.01 | 28% | 30 Jan → | |
| anubis | 13–5 | 0.02 | 22% | 24 Jan → | |
| dust2 | 15–15 | 0.01 | 14% | 23 Jan → | |
| dust2 | 16–12 | 0.02 | 25% | 23 Jan → | |
| ancient | 13–3 | 0.10 | 35% | 20 Jan → | |
| inferno | 5–4 | 0.04 | 41% | 18 Jan → | |
| ancient | 7–13 | -0.05 | 15% | 18 Jan → | |
| nuke | 13–4 | 0.02 | 20% | 16 Jan → | |
| overpass | 13–7 | 0.05 | 18% | 15 Jan → | |
| overpass | 10–13 | 0.03 | 25% | 15 Jan → | |
| overpass | 13–6 | 0.03 | 15% | 14 Jan → | |
| ancient | 13–5 | 0.08 | 23% | 12 Jan → | |
| inferno | 13–6 | 0.10 | 29% | 11 Jan → | |
| overpass | 9–13 | -0.02 | 16% | 8 Jan → | |
| mirage | 16–13 | 0.02 | 29% | 5 Jan → | |
| mirage | 13–10 | 0.02 | 25% | 5 Jan → | |
| dust2 | 13–7 | -0.00 | 15% | 5 Jan → | |
| inferno | 13–10 | -0.03 | 26% | 3 Jan → | |
| mirage | 7–13 | 0.07 | 27% | 3 Jan → | |
| mirage | 13–10 | -0.01 | 25% | 2 Jan → | |
| dust2 | 13–6 | 0.03 | 37% | 29 Dec → | |
| overpass | 13–1 | 0.14 | 33% | 29 Dec → | |
| nuke | 4–13 | -0.01 | 38% | 29 Dec → |
Match data via Leetify.
