Níko — CS2 Stats
FI76561198014450545[U:1:54184817]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. 70 days played since 19 Feb 2025. Come back after the next session and the change shows above.
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Rating over time
Faceit ELO
- At peak — 1,720
- Next: Level 9 at 1,751 — 31 to go
- Reached: Level 8 · Level 7 · Level 6 · Level 5
Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do.
How this compares with the same rank
Median values for Level 9 among CSDB-tracked players (n=13,629), 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 | Level 9 median | Level 10 median | vs Level 10 |
|---|---|---|---|---|
| Headshot rate | 51.6% | 48.3% | 51.0% | meets |
| Shot accuracy | 11.7% | 13.2% | 13.7% | 2.0% short |
| Kill/death ratio | 1.11 | 1.07 | 1.09 | above |
| Match win rate | 47.7% | 46.9% | 48.4% | meets |
This profile matches the typical Level 10 player on 3 of 4 comparable metrics.
Widest gap: Shot accuracy. That is the metric furthest from the Level 10 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: 0pp win rate · +0.01 avg rating · +4.2pp headshot accuracy · −51ms reaction
Win rate 0pp across the last 10 against the 10 before — within the normal variation of a 10-match window (±20pp), so no real shift yet.
Last 5 · 10 · 20 matches
Last 5
- 2–3 · 40% win rate
- Avg rating 0.02
- Avg headshot accuracy 27%
- Avg reaction 628ms
Last 10
- 3–7 · 30% win rate
- Avg rating 0.00
- Avg headshot accuracy 24%
- Avg reaction 582ms
Last 20
- 6–14 · 30% win rate
- Avg rating -0.00
- Avg headshot accuracy 22%
- Avg reaction 607ms
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: Support — Utility contribution stands above the rest of this profile (+1.7 against its own average).
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.
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 NiKo 79% playstyle similarity
Most alike: utility contribution, positioning profile.
Where you differ: lower opening-duel success; 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
Strengths
Flashes. 0.76 enemies blinded per flash — utility that consistently lands.
Areas to improve
Reaction time. 609ms 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 5.2/10 (Developing), 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.
Personal bests
Across the last 100 tracked matches.
Highlights
Map breakdown
| Map | Grade | Played | Record | Win rate | Avg rating |
|---|---|---|---|---|---|
 nuke | A | 31 | 17–14 | 55% | 0.01 |
 anubis | B | 21 | 10–11 | 48% | -0.00 |
 ancient | B | 16 | 8–8 | 50% | -0.00 |
 dust2 | C | 14 | 5–9 | 36% | -0.01 |
 overpass | D | 6 | 2–4 | 33% | -0.03 |
 mirage | D | 5 | 1–4 | 20% | 0.02 |
 train | S | 5 | 4–1 | 80% | 0.02 |
 inferno | — | 1 | 1–0 | 100% | -0.02 |
| office | — | 1 | 0–1 | 0% | -0.01 |
Across the last 100 tracked matches.
Mirage is currently your weakest sufficiently-sampled map (20% over 5). Start with the 6 essential Mirage 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 resultsWLLLL
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Anubis | 193 | 55% | 1.03 | 15.1 |
| Nuke | 163 | 60% | 1.13 | 15.7 |
| Ancient | 109 | 41% | 0.98 | 14.3 |
| Dust2 | 90 | 34% | 0.94 | 13.6 |
| Mirage | 41 | 39% | 0.95 | 13.8 |
| Train | 17 | 47% | 1.08 | 15.7 |
| Cache | 11 | 73% | 1.20 | 16.4 |
| Inferno | 10 | 20% | 1.04 | 16.1 |
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 5 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| ancient | 9–13 | 0.01 | 27% | 13 Sept → | |
| nuke | 8–13 | -0.00 | 28% | 13 Sept → | |
| anubis | 14–16 | -0.00 | 24% | 23 Aug → | |
| nuke | 13–5 | 0.12 | 31% | 31 Jul → | |
| dust2 | 13–9 | -0.02 | 25% | 28 Jul → | |
| nuke | 8–13 | -0.06 | 13% | 8 Jul → | |
| anubis | 11–13 | -0.06 | 18% | 26 Jun → | |
| inferno | 13–7 | -0.02 | 15% | 8 Jun → | |
| nuke | 5–13 | 0.05 | 32% | 24 May → | |
| anubis | 8–13 | 0.01 | 31% | 28 Apr → | |
| ancient | 13–8 | 0.01 | 12% | 27 Apr → | |
| overpass | 8–13 | 0.01 | 24% | 22 Apr → | |
| ancient | 5–13 | -0.04 | 26% | 21 Apr → | |
| ancient | 13–7 | 0.01 | 10% | 17 Apr → | |
| nuke | 16–19 | -0.04 | 15% | 4 Apr → | |
| dust2 | 10–13 | 0.03 | 15% | 4 Apr → | |
| dust2 | 13–4 | 0.06 | 22% | 3 Apr → | |
| anubis | 7–13 | -0.02 | 20% | 3 Apr → | |
| ancient | 3–13 | -0.04 | 26% | 29 Mar → | |
| nuke | 11–13 | -0.03 | 33% | 29 Mar → | |
| nuke | 13–6 | 0.04 | 19% | 27 Mar → | |
| dust2 | 6–13 | -0.02 | 29% | 27 Mar → | |
| ancient | 13–8 | 0.07 | 23% | 27 Mar → | |
| nuke | 3–13 | -0.07 | 26% | 22 Mar → | |
| dust2 | 2–13 | -0.05 | 41% | 19 Mar → | |
| nuke | 13–5 | 0.09 | 29% | 15 Mar → | |
| ancient | 13–9 | 0.03 | 18% | 13 Mar → | |
| dust2 | 11–13 | 0.01 | 14% | 12 Mar → | |
| mirage | 13–7 | 0.10 | 12% | 12 Mar → | |
| overpass | 7–13 | -0.07 | 8% | 12 Mar → | |
| anubis | 13–5 | 0.05 | 11% | 23 Feb → | |
| anubis | 11–13 | -0.05 | 11% | 23 Feb → | |
| nuke | 4–13 | -0.04 | 11% | 15 Feb → | |
| anubis | 13–6 | -0.02 | 6% | 15 Feb → | |
| mirage | 9–13 | 0.01 | 13% | 14 Feb → | |
| dust2 | 9–13 | -0.05 | 23% | 14 Feb → | |
| dust2 | 16–14 | -0.03 | 17% | 10 Feb → | |
| ancient | 6–13 | -0.04 | 28% | 9 Feb → | |
| nuke | 16–14 | 0.00 | 25% | 5 Feb → | |
| nuke | 10–13 | -0.07 | 25% | 2 Feb → | |
| mirage | 7–13 | -0.03 | 23% | 2 Feb → | |
| ancient | 13–9 | -0.04 | 18% | 27 Jan → | |
| office | 12–12 | -0.01 | 18% | 21 Jan → | |
| anubis | 6–13 | -0.06 | 19% | 21 Jan → | |
| dust2 | 16–12 | -0.01 | 22% | 18 Jan → | |
| nuke | 13–5 | 0.13 | 22% | 18 Jan → | |
| mirage | 4–13 | 0.01 | 32% | 17 Jan → | |
| dust2 | 8–13 | -0.07 | 24% | 17 Jan → | |
| overpass | 16–14 | -0.06 | 14% | 20 Dec → | |
| nuke | 13–4 | 0.08 | 18% | 20 Dec → | |
| dust2 | 8–13 | -0.03 | 25% | 20 Dec → | |
| nuke | 13–7 | -0.01 | 22% | 9 Dec → | |
| overpass | 13–16 | -0.06 | 15% | 12 Nov → | |
| overpass | 8–13 | 0.00 | 20% | 23 Oct → | |
| dust2 | 13–4 | 0.07 | 21% | 22 Oct → | |
| ancient | 13–3 | 0.00 | 40% | 22 Oct → | |
| mirage | 11–13 | -0.00 | 20% | 19 Oct → | |
| ancient | 13–10 | 0.01 | 22% | 30 Sept → | |
| nuke | 13–8 | -0.01 | 11% | 25 Sept → | |
| nuke | 13–10 | 0.03 | 22% | 21 Sept → | |
| nuke | 10–13 | -0.04 | 23% | 7 Sept → | |
| nuke | 2–13 | -0.02 | 32% | 18 Aug → | |
| overpass | 13–10 | -0.01 | 16% | 29 Jul → | |
| anubis | 13–6 | -0.01 | 20% | 30 Jun → | |
| dust2 | 11–13 | 0.02 | 24% | 11 Jun → | |
| nuke | 13–7 | 0.09 | 21% | 5 Jun → | |
| ancient | 11–13 | -0.01 | 13% | 27 Apr → | |
| anubis | 16–12 | -0.02 | 12% | 27 Apr → | |
| train | 5–13 | 0.03 | 22% | 20 Apr → | |
| nuke | 5–13 | -0.08 | 23% | 20 Apr → | |
| anubis | 13–5 | 0.03 | 19% | 4 Apr → | |
| train | 22–19 | 0.07 | 21% | 4 Apr → | |
| ancient | 13–10 | 0.01 | 8% | 4 Apr → | |
| anubis | 7–13 | -0.02 | 19% | 3 Apr → | |
| nuke | 13–8 | 0.08 | 20% | 3 Apr → | |
| anubis | 13–8 | 0.07 | 20% | 30 Mar → | |
| train | 13–10 | 0.02 | 17% | 25 Mar → | |
| ancient | 11–13 | -0.06 | 18% | 20 Mar → | |
| anubis | 14–16 | 0.01 | 17% | 14 Mar → | |
| nuke | 13–7 | 0.02 | 13% | 14 Mar → | |
| nuke | 7–13 | -0.02 | 27% | 12 Mar → | |
| dust2 | 5–13 | -0.08 | 12% | 12 Mar → | |
| anubis | 13–5 | -0.02 | 17% | 11 Mar → | |
| nuke | 16–13 | -0.00 | 13% | 9 Mar → | |
| anubis | 13–3 | 0.03 | 16% | 9 Mar → | |
| nuke | 13–9 | -0.00 | 16% | 9 Mar → | |
| nuke | 9–13 | -0.04 | 13% | 8 Mar → | |
| nuke | 13–6 | 0.07 | 21% | 7 Mar → | |
| anubis | 6–13 | -0.07 | 15% | 6 Mar → | |
| anubis | 11–13 | -0.01 | 23% | 5 Mar → | |
| train | 13–7 | -0.04 | 26% | 5 Mar → | |
| nuke | 10–13 | -0.03 | 24% | 28 Feb → | |
| anubis | 8–13 | -0.07 | 13% | 28 Feb → | |
| ancient | 11–13 | -0.03 | 18% | 26 Feb → | |
| ancient | 9–13 | 0.03 | 29% | 23 Feb → | |
| anubis | 13–10 | 0.10 | 24% | 23 Feb → | |
| nuke | 13–11 | 0.01 | 21% | 22 Feb → | |
| train | 13–9 | 0.03 | 12% | 21 Feb → | |
| anubis | 13–9 | 0.05 | 14% | 20 Feb → | |
| nuke | 19–17 | -0.00 | 18% | 19 Feb → |
Match data via Leetify.
