Kantsi — CS2 Stats
76561198874207139[U:1:913941411]✓ No bans
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Performance scores
0–100 skill scores via Leetify.
Recent form
Last 10 vs previous 10: +0pp win rate · +0.03 avg rating
Player DNA
Primary style: Passive Rifler — Aim-led profile without a single dominant tendency.
Limited utility dependence
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 75% playstyle similarity
Most alike: opening-duel success, positioning profile.
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
Reaction time. 601ms 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 3.9/10 (Learning), a weighted mean of the bars with a small opposition adjustment (×0.95 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 | C | 30 | 12–18 | 40% | -0.02 |
 nuke | B | 20 | 10–10 | 50% | -0.03 |
 train | A | 16 | 9–7 | 56% | 0.01 |
 inferno | C | 7 | 3–4 | 43% | -0.02 |
 ancient | C | 7 | 3–4 | 43% | -0.04 |
 dust2 | B | 6 | 3–3 | 50% | -0.01 |
 anubis | D | 5 | 1–4 | 20% | -0.03 |
 vertigo | — | 2 | 1–1 | 50% | 0.05 |
 overpass | — | 2 | 1–1 | 50% | 0.01 |
| agency | — | 2 | 1–1 | 50% | -0.02 |
 cache | — | 1 | 1–0 | 100% | 0.01 |
| grail | — | 1 | 0–1 | 0% | -0.02 |
| thera | — | 1 | 1–0 | 100% | -0.07 |
Across the last 100 tracked matches.
Anubis is currently your weakest sufficiently-sampled map (20% over 5). 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 resultsLLWWW
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Mirage | 190 | 53% | 0.91 | 12.5 |
| Nuke | 79 | 48% | 0.86 | 13.3 |
| Train | 55 | 62% | 0.87 | 13.7 |
| Anubis | 42 | 43% | 0.80 | 13.4 |
| Dust2 | 18 | 44% | 0.83 | 12.9 |
| Inferno | 18 | 22% | 0.66 | 10.9 |
| Ancient | 18 | 50% | 0.94 | 13.8 |
| Vertigo | 8 | 75% | 1.48 | 19.0 |
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 23.3586% — 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.
20% win rate across 5 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| mirage | 11–13 | -0.04 | 19% | 10 Jun → | |
| mirage | 11–13 | -0.03 | 25% | 24 May → | |
| mirage | 13–11 | -0.03 | 23% | 24 May → | |
| nuke | 3–13 | -0.04 | 31% | 22 May → | |
| nuke | 10–13 | -0.05 | 11% | 22 May → | |
| anubis | 9–13 | -0.04 | 20% | 22 May → | |
| cache | 19–17 | 0.01 | 26% | 13 May → | |
| vertigo | 8–8 | 0.11 | 21% | 13 Mar → | |
| nuke | 10–13 | -0.01 | 20% | 26 Feb → | |
| mirage | 11–13 | -0.01 | 12% | 26 Feb → | |
| mirage | 7–13 | -0.00 | 19% | 26 Feb → | |
| inferno | 8–13 | -0.06 | 20% | 21 Feb → | |
| mirage | 11–13 | -0.04 | 12% | 19 Feb → | |
| mirage | 13–8 | 0.00 | 18% | 16 Feb → | |
| nuke | 8–13 | -0.06 | 21% | 13 Feb → | |
| mirage | 13–11 | -0.02 | 21% | 29 Jan → | |
| dust2 | 6–13 | -0.08 | 5% | 26 Jan → | |
| mirage | 5–13 | -0.09 | 12% | 23 Jan → | |
| mirage | 6–13 | -0.04 | 33% | 22 Jan → | |
| mirage | 9–13 | 0.00 | 33% | 18 Jan → | |
| train | 13–7 | 0.04 | 13% | 16 Jan → | |
| nuke | 16–13 | -0.02 | 15% | 16 Jan → | |
| train | 13–4 | 0.02 | 8% | 16 Jan → | |
| mirage | 1–13 | 0.03 | 19% | 15 Jan → | |
| ancient | 9–13 | -0.06 | 9% | 15 Jan → | |
| anubis | 8–13 | 0.01 | 28% | 13 Jan → | |
| train | 13–4 | 0.02 | 17% | 9 Jan → | |
| mirage | 13–9 | -0.01 | 19% | 8 Jan → | |
| mirage | 10–13 | -0.03 | 26% | 8 Jan → | |
| nuke | 13–8 | 0.01 | 21% | 4 Jan → | |
| mirage | 8–13 | 0.06 | 19% | 20 Dec → | |
| nuke | 5–13 | 0.01 | 11% | 14 Dec → | |
| ancient | 11–13 | -0.08 | 12% | 14 Dec → | |
| train | 5–13 | -0.05 | 9% | 10 Dec → | |
| inferno | 16–12 | -0.01 | 14% | 10 Dec → | |
| train | 16–14 | -0.03 | 20% | 7 Dec → | |
| nuke | 13–7 | 0.02 | 10% | 6 Dec → | |
| inferno | 15–15 | 0.03 | 22% | 5 Dec → | |
| mirage | 13–5 | 0.02 | 29% | 25 Nov → | |
| nuke | 13–7 | -0.01 | 21% | 11 Nov → | |
| mirage | 13–11 | -0.01 | 13% | 11 Nov → | |
| nuke | 13–8 | 0.00 | 12% | 11 Nov → | |
| mirage | 10–13 | -0.05 | 24% | 9 Nov → | |
| nuke | 8–13 | -0.05 | 38% | 8 Nov → | |
| train | 9–13 | 0.05 | 26% | 8 Nov → | |
| dust2 | 13–5 | 0.04 | 16% | 5 Nov → | |
| nuke | 5–13 | -0.06 | 13% | 2 Nov → | |
| mirage | 13–7 | -0.01 | 11% | 2 Nov → | |
| nuke | 10–13 | -0.01 | 20% | 1 Nov → | |
| ancient | 5–13 | -0.07 | 18% | 27 Oct → | |
| mirage | 2–13 | -0.09 | 33% | 26 Oct → | |
| overpass | 9–13 | 0.01 | 13% | 1 Oct → | |
| dust2 | 13–10 | 0.03 | 16% | 29 Aug → | |
| mirage | 9–13 | -0.06 | 13% | 29 Aug → | |
| overpass | 9–4 | 0.01 | 9% | 29 Aug → | |
| grail | 12–12 | -0.02 | 6% | 27 Aug → | |
| nuke | 16–14 | -0.07 | 10% | 25 Aug → | |
| mirage | 11–13 | -0.02 | 8% | 24 Aug → | |
| mirage | 16–13 | -0.05 | 14% | 23 Aug → | |
| agency | 13–8 | 0.01 | 11% | 8 Aug → | |
| agency | 6–13 | -0.04 | 9% | 8 Aug → | |
| dust2 | 8–13 | -0.03 | 16% | 6 Jul → | |
| nuke | 13–8 | -0.06 | 11% | 10 May → | |
| dust2 | 16–13 | 0.01 | 18% | 10 May → | |
| nuke | 13–11 | -0.02 | 18% | 10 May → | |
| train | 7–13 | -0.03 | 13% | 10 Apr → | |
| mirage | 11–13 | -0.03 | 13% | 10 Apr → | |
| nuke | 13–9 | 0.01 | 17% | 8 Apr → | |
| anubis | 13–8 | -0.04 | 10% | 7 Apr → | |
| nuke | 6–9 | -0.18 | 5% | 6 Apr → | |
| dust2 | 11–13 | -0.02 | 15% | 27 Mar → | |
| mirage | 16–14 | -0.03 | 13% | 13 Mar → | |
| inferno | 9–13 | -0.02 | 14% | 10 Mar → | |
| nuke | 13–9 | -0.05 | 28% | 3 Feb → | |
| mirage | 2–13 | -0.08 | 15% | 3 Feb → | |
| ancient | 8–13 | -0.07 | 19% | 3 Feb → | |
| anubis | 9–13 | 0.00 | 16% | 9 Jan → | |
| mirage | 10–13 | -0.05 | 20% | 9 Jan → | |
| inferno | 5–13 | -0.04 | 35% | 9 Jan → | |
| mirage | 13–9 | -0.03 | 10% | 28 Nov → | |
| train | 13–10 | -0.02 | 6% | 28 Nov → | |
| train | 13–2 | 0.10 | 18% | 28 Nov → | |
| train | 12–12 | -0.06 | 18% | 18 Nov → | |
| train | 13–11 | 0.00 | 23% | 18 Nov → | |
| train | 13–6 | 0.08 | 29% | 18 Nov → | |
| train | 12–12 | -0.01 | 16% | 17 Nov → | |
| train | 4–13 | -0.01 | 19% | 15 Nov → | |
| train | 13–10 | 0.03 | 30% | 15 Nov → | |
| train | 12–12 | 0.00 | 21% | 14 Nov → | |
| mirage | 13–7 | 0.01 | 17% | 11 Oct → | |
| nuke | 6–13 | -0.03 | 23% | 10 Oct → | |
| ancient | 13–0 | 0.01 | 18% | 10 Oct → | |
| inferno | 13–9 | -0.07 | 6% | 10 Oct → | |
| thera | 13–8 | -0.07 | 10% | 10 Oct → | |
| vertigo | 13–6 | -0.01 | 20% | 9 Oct → | |
| anubis | 6–13 | -0.05 | 14% | 9 Oct → | |
| ancient | 13–7 | 0.05 | 19% | 9 Oct → | |
| ancient | 13–9 | -0.09 | 9% | 8 Oct → | |
| mirage | 13–9 | -0.01 | 12% | 8 Oct → | |
| inferno | 13–6 | 0.02 | 11% | 8 Oct → |
Match data via Leetify.