joeiff ツ — CS2 Stats
76561198003122437[U:1:42856709]✓ No bans
Performance scores
0–100 skill scores via Leetify.
Recent form
Last 10 vs previous 10: +30pp win rate · -0.01 avg rating
Player DNA
Primary style: Passive Rifler — Aim-led profile without a single dominant tendency.
Effective 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 NiKo 80% playstyle similarity
Most alike: opening-fight frequency, opening-duel success.
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
Flashes. 0.79 enemies blinded per flash — utility that consistently lands.
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. 613ms 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 5.5/10 (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 |
|---|---|---|---|---|---|
 mirage | B | 21 | 11–10 | 52% | -0.02 |
 inferno | A | 16 | 10–6 | 63% | 0.01 |
 nuke | A | 14 | 8–6 | 57% | 0.01 |
 ancient | D | 14 | 4–10 | 29% | -0.03 |
 dust2 | C | 12 | 5–7 | 42% | -0.02 |
 anubis | D | 9 | 2–7 | 22% | -0.03 |
 train | S | 5 | 4–1 | 80% | -0.01 |
 overpass | — | 4 | 2–2 | 50% | 0.00 |
 vertigo | — | 4 | 2–2 | 50% | -0.01 |
| office | — | 1 | 1–0 | 100% | -0.02 |
Across the last 100 tracked matches.
Anubis is currently your weakest sufficiently-sampled map (22% over 9). Start with the 6 essential Anubis lineups, review the callouts, then spin up a practice server.
Faceit stats
Combat
Clutches & streaks
Recent Faceit resultsWWWWL
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Mirage | 21 | 57% | 0.73 | 11.5 |
| Ancient | 15 | 40% | 0.70 | 12.0 |
| Anubis | 10 | 50% | 0.71 | 10.7 |
| Inferno | 7 | 57% | 0.95 | 12.4 |
| Nuke | 6 | 83% | 1.00 | 18.3 |
| Vertigo | 6 | 83% | 0.78 | 10.2 |
| Cache | 3 | 100% | 0.97 | 16.7 |
| Dust2 | 3 | 33% | 0.83 | 12.0 |
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.
- Advanced Mechanics
You are losing most of the first duels you take on T side, which is usually a peeking and spacing problem, not aim.
T opening duels 25.8501% — below the 40% mark we flag
Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.
22% win rate across 9 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| dust2 | 13–7 | 0.05 | 22% | 18 Jul → | |
| mirage | 1–13 | -0.10 | 27% | 18 Jul → | |
| dust2 | 13–10 | -0.09 | 13% | 18 Jul → | |
| nuke | 13–7 | 0.01 | 21% | 17 Jul → | |
| anubis | 5–13 | -0.00 | 9% | 17 Jul → | |
| mirage | 13–5 | 0.02 | 47% | 21 Mar → | |
| office | 13–9 | -0.02 | 25% | 16 Mar → | |
| mirage | 13–1 | -0.05 | 15% | 19 Oct → | |
| dust2 | 13–10 | 0.04 | 19% | 19 Oct → | |
| overpass | 13–4 | 0.02 | 24% | 19 Oct → | |
| inferno | 13–5 | 0.03 | 21% | 31 Aug → | |
| vertigo | 7–2 | -0.04 | 0% | 29 Aug → | |
| overpass | 9–13 | -0.01 | 23% | 28 Aug → | |
| inferno | 13–10 | 0.04 | 15% | 27 Aug → | |
| vertigo | 7–13 | -0.02 | 24% | 27 Aug → | |
| inferno | 13–7 | 0.06 | 14% | 25 Aug → | |
| inferno | 6–13 | 0.01 | 19% | 25 Aug → | |
| ancient | 13–11 | -0.06 | 17% | 25 Aug → | |
| mirage | 6–13 | -0.03 | 27% | 24 Aug → | |
| dust2 | 2–13 | -0.04 | 23% | 24 Aug → | |
| dust2 | 15–15 | -0.00 | 21% | 24 Aug → | |
| nuke | 9–13 | 0.04 | 25% | 23 Aug → | |
| inferno | 13–2 | 0.12 | 23% | 23 Aug → | |
| mirage | 6–13 | -0.03 | 28% | 23 Aug → | |
| overpass | 12–12 | -0.07 | 19% | 22 Aug → | |
| dust2 | 11–13 | -0.09 | 6% | 22 Aug → | |
| inferno | 13–10 | -0.01 | 22% | 22 Aug → | |
| train | 13–10 | 0.04 | 35% | 22 Aug → | |
| ancient | 13–10 | 0.08 | 12% | 19 Aug → | |
| mirage | 4–13 | -0.04 | 17% | 16 Aug → | |
| train | 13–6 | 0.06 | 21% | 15 Aug → | |
| mirage | 6–13 | 0.03 | 21% | 15 Aug → | |
| mirage | 13–7 | -0.07 | 24% | 14 Aug → | |
| nuke | 1–13 | -0.05 | 30% | 10 Aug → | |
| inferno | 13–5 | 0.10 | 17% | 10 Aug → | |
| vertigo | 12–12 | -0.00 | 26% | 10 Aug → | |
| ancient | 8–13 | -0.01 | 32% | 9 Aug → | |
| train | 11–13 | -0.04 | 17% | 8 Aug → | |
| dust2 | 13–6 | -0.02 | 15% | 25 Jul → | |
| inferno | 10–13 | 0.03 | 15% | 25 Jul → | |
| mirage | 13–10 | 0.03 | 37% | 18 Jul → | |
| overpass | 13–5 | 0.06 | 9% | 18 Jul → | |
| nuke | 13–7 | -0.04 | 25% | 13 Jul → | |
| dust2 | 2–13 | -0.07 | 29% | 13 Jul → | |
| nuke | 13–6 | 0.00 | 18% | 11 Jun → | |
| nuke | 13–9 | -0.04 | 31% | 2 May → | |
| nuke | 13–6 | 0.10 | 38% | 27 Apr → | |
| ancient | 8–13 | -0.11 | 13% | 27 Apr → | |
| mirage | 13–4 | 0.08 | 17% | 21 Apr → | |
| anubis | 5–13 | -0.11 | 20% | 21 Apr → | |
| inferno | 0–2 | -0.09 | 0% | 21 Apr → | |
| mirage | 13–7 | -0.02 | 22% | 20 Apr → | |
| mirage | 13–10 | 0.02 | 20% | 20 Apr → | |
| nuke | 13–4 | 0.15 | 28% | 20 Apr → | |
| inferno | 10–13 | -0.01 | 23% | 4 Apr → | |
| dust2 | 12–12 | -0.04 | 24% | 4 Apr → | |
| train | 13–5 | -0.03 | 22% | 4 Apr → | |
| ancient | 10–13 | 0.01 | 27% | 4 Apr → | |
| vertigo | 13–8 | 0.01 | 25% | 4 Apr → | |
| mirage | 13–6 | 0.03 | 26% | 30 Mar → | |
| anubis | 7–13 | -0.04 | 24% | 30 Mar → | |
| ancient | 11–13 | -0.01 | 27% | 30 Mar → | |
| nuke | 14–16 | -0.03 | 26% | 29 Mar → | |
| inferno | 16–14 | -0.00 | 18% | 29 Mar → | |
| anubis | 9–13 | -0.04 | 38% | 29 Mar → | |
| mirage | 8–13 | -0.05 | 36% | 29 Mar → | |
| train | 16–14 | -0.05 | 20% | 24 Mar → | |
| ancient | 9–13 | -0.02 | 26% | 24 Mar → | |
| ancient | 13–11 | -0.05 | 24% | 24 Mar → | |
| dust2 | 8–13 | -0.02 | 23% | 22 Mar → | |
| ancient | 15–15 | -0.02 | 16% | 16 Mar → | |
| inferno | 13–2 | 0.02 | 30% | 16 Mar → | |
| ancient | 8–13 | -0.03 | 19% | 15 Mar → | |
| mirage | 11–13 | 0.01 | 23% | 15 Mar → | |
| ancient | 3–13 | -0.06 | 23% | 14 Mar → | |
| nuke | 12–12 | -0.03 | 15% | 14 Mar → | |
| mirage | 10–13 | -0.08 | 16% | 14 Mar → | |
| mirage | 13–8 | -0.03 | 18% | 14 Mar → | |
| nuke | 13–4 | -0.03 | 38% | 11 Mar → | |
| anubis | 13–6 | 0.02 | 30% | 10 Mar → | |
| anubis | 13–11 | -0.01 | 17% | 9 Mar → | |
| mirage | 13–11 | -0.02 | 13% | 8 Mar → | |
| dust2 | 11–13 | 0.01 | 29% | 8 Mar → | |
| anubis | 10–13 | -0.04 | 21% | 8 Mar → | |
| nuke | 5–13 | -0.03 | 26% | 8 Mar → | |
| nuke | 13–6 | 0.10 | 35% | 7 Mar → | |
| anubis | 10–13 | -0.01 | 15% | 6 Mar → | |
| inferno | 4–9 | -0.01 | 44% | 4 Mar → | |
| anubis | 14–16 | -0.08 | 14% | 2 Mar → | |
| inferno | 16–12 | -0.06 | 29% | 2 Mar → | |
| inferno | 13–10 | -0.01 | 38% | 2 Mar → | |
| mirage | 19–16 | 0.02 | 26% | 27 Feb → | |
| inferno | 6–13 | -0.02 | 28% | 27 Feb → | |
| ancient | 5–13 | -0.04 | 25% | 25 Feb → | |
| ancient | 16–12 | -0.05 | 15% | 25 Feb → | |
| mirage | 9–13 | -0.06 | 14% | 22 Feb → | |
| ancient | 7–13 | -0.08 | 19% | 22 Feb → | |
| nuke | 8–13 | -0.03 | 29% | 22 Feb → | |
| mirage | 10–13 | -0.07 | 23% | 22 Feb → | |
| dust2 | 13–10 | 0.04 | 33% | 21 Feb → |
Match data via Leetify.