Bestefart — CS2 Stats
NO76561198378213016[U:1:417947288]Steam profile ↗✓ No bans
Performance scores
0–100 skill scores via Leetify.
Recent form
Last 10 vs previous 10: +0pp win rate · -0.00 avg rating
Player DNA
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 69% playstyle similarity
Most alike: opening-fight frequency, opening-duel success.
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
CT openings. Opening success drops from 37% on T to 18% on CT — first contacts on the defending side are being lost.
Reaction time. 714ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.
Counter-strafing. Only 68% of shots are taken properly stopped — moving-shot inaccuracy is quietly taxing every duel.
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 2.8/10 (Learning), 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 | C | 19 | 8–11 | 42% | -0.03 |
 inferno | B | 16 | 8–8 | 50% | 0.00 |
 dust2 | C | 14 | 5–9 | 36% | -0.03 |
 nuke | C | 13 | 5–8 | 38% | -0.05 |
 cache | C | 11 | 4–7 | 36% | -0.04 |
 anubis | D | 8 | 2–6 | 25% | -0.02 |
 ancient | D | 6 | 1–5 | 17% | -0.05 |
 overpass | D | 5 | 0–5 | 0% | -0.03 |
| office | — | 3 | 1–2 | 33% | -0.08 |
 train | — | 3 | 2–1 | 67% | -0.04 |
 vertigo | — | 2 | 0–2 | 0% | -0.06 |
Across the last 100 tracked matches.
Overpass is currently your weakest sufficiently-sampled map (0% over 5). Start with the 6 essential Overpass 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.
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.
- Best CS2 CrosshairAim Training →
Your shots are landing on bodies more often than heads — usually a crosshair-height and placement habit rather than raw aim.
Headshot accuracy 13.8862% — below the 15% mark we flag
- Best CS2 SettingsAim Training →
Slow first shots are as often a setup problem as a reflex one — framerate, sensitivity and crosshair visibility all move this number.
Reaction time 713.5214ms — above the 700ms mark we flag
- Grenades & UtilityGrenade Lineups →
Most of your flashes are blinding nobody. A handful of reliable pop-flash lineups fixes this faster than anything else.
Enemies flashed per flash 0.3451 — below the 0.5 mark we flag
Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.
0% win rate across 5 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| cache | 6–13 | -0.09 | 3% | 26 Aug → | |
| inferno | 13–3 | -0.02 | 5% | 26 Aug → | |
| dust2 | 10–13 | -0.09 | 21% | 26 Aug → | |
| cache | 13–6 | -0.02 | 11% | 11 Aug → | |
| inferno | 1–13 | -0.00 | 26% | 7 Aug → | |
| mirage | 13–6 | -0.10 | 14% | 6 Aug → | |
| dust2 | 7–13 | 0.01 | 7% | 6 Aug → | |
| ancient | 13–3 | 0.00 | 7% | 6 Aug → | |
| anubis | 12–12 | 0.00 | 6% | 3 Aug → | |
| inferno | 8–13 | 0.00 | 10% | 26 Jul → | |
| ancient | 3–13 | -0.11 | 4% | 25 Jul → | |
| anubis | 3–13 | -0.09 | 67% | 23 Jul → | |
| mirage | 3–13 | 0.03 | 4% | 23 Jul → | |
| mirage | 13–9 | 0.03 | 12% | 23 Jul → | |
| dust2 | 8–13 | 0.03 | 29% | 22 Jul → | |
| mirage | 3–13 | -0.08 | 3% | 22 Jul → | |
| inferno | 13–3 | 0.09 | 28% | 20 Jul → | |
| mirage | 13–6 | -0.07 | 13% | 17 Jul → | |
| anubis | 13–9 | -0.05 | 16% | 16 Jul → | |
| inferno | 8–13 | -0.09 | 17% | 16 Jul → | |
| mirage | 10–13 | -0.06 | 23% | 16 Jul → | |
| nuke | 14–16 | -0.09 | 6% | 15 Jul → | |
| mirage | 2–13 | -0.06 | 6% | 12 Jul → | |
| cache | 10–13 | -0.07 | 6% | 12 Jul → | |
| mirage | 6–13 | -0.07 | 13% | 12 Jul → | |
| inferno | 13–5 | 0.07 | 8% | 12 Jul → | |
| cache | 12–16 | -0.02 | 8% | 12 Jul → | |
| inferno | 3–13 | -0.02 | 19% | 12 Jul → | |
| inferno | 13–4 | 0.07 | 18% | 3 Jul → | |
| mirage | 13–7 | 0.03 | 19% | 3 Jul → | |
| dust2 | 2–10 | -0.03 | 42% | 3 Jul → | |
| nuke | 8–13 | -0.11 | 0% | 1 Jul → | |
| nuke | 13–3 | 0.00 | 7% | 1 Jul → | |
| ancient | 11–13 | 0.01 | 5% | 1 Jul → | |
| mirage | 2–13 | -0.08 | 22% | 1 Jul → | |
| office | 12–12 | -0.07 | 16% | 1 Jul → | |
| overpass | 11–13 | -0.06 | 3% | 30 Jun → | |
| dust2 | 6–7 | -0.10 | 11% | 30 Jun → | |
| mirage | 13–3 | 0.03 | 28% | 30 Jun → | |
| nuke | 13–8 | -0.07 | 25% | 20 Jun → | |
| train | 13–7 | 0.01 | 5% | 17 Jun → | |
| anubis | 5–13 | 0.02 | 15% | 16 Jun → | |
| inferno | 13–4 | 0.03 | 18% | 15 Jun → | |
| overpass | 2–13 | -0.04 | 12% | 14 Jun → | |
| inferno | 2–13 | -0.00 | 8% | 13 Jun → | |
| train | 13–6 | -0.06 | 18% | 13 Jun → | |
| overpass | 5–13 | -0.04 | 10% | 13 Jun → | |
| ancient | 8–13 | -0.03 | 3% | 13 Jun → | |
| cache | 4–13 | -0.07 | 9% | 13 Jun → | |
| overpass | 6–13 | 0.08 | 15% | 12 Jun → | |
| mirage | 10–13 | 0.04 | 13% | 12 Jun → | |
| dust2 | 13–6 | -0.02 | 6% | 12 Jun → | |
| anubis | 5–13 | 0.07 | 15% | 12 Jun → | |
| inferno | 10–13 | 0.01 | 6% | 12 Jun → | |
| dust2 | 5–2 | 0.10 | 14% | 12 Jun → | |
| mirage | 7–13 | -0.06 | 12% | 12 Jun → | |
| cache | 4–1 | 0.08 | 7% | 12 Jun → | |
| anubis | 8–13 | -0.04 | 11% | 11 Jun → | |
| dust2 | 13–7 | 0.06 | 4% | 11 Jun → | |
| mirage | 13–11 | -0.05 | 8% | 11 Jun → | |
| anubis | 13–7 | -0.06 | 8% | 11 Jun → | |
| nuke | 3–13 | -0.10 | 11% | 11 Jun → | |
| nuke | 13–8 | -0.02 | 18% | 11 Jun → | |
| cache | 13–7 | -0.03 | 12% | 10 Jun → | |
| nuke | 13–6 | 0.09 | 15% | 9 Jun → | |
| mirage | 13–7 | 0.08 | 19% | 8 Jun → | |
| nuke | 12–2 | 0.01 | 5% | 8 Jun → | |
| inferno | 13–8 | 0.00 | 7% | 8 Jun → | |
| office | 13–4 | -0.05 | 8% | 8 Jun → | |
| dust2 | 13–9 | 0.07 | 10% | 8 Jun → | |
| cache | 3–13 | 0.00 | 13% | 8 Jun → | |
| dust2 | 10–13 | -0.06 | 9% | 7 Jun → | |
| mirage | 12–12 | 0.02 | 11% | 7 Jun → | |
| inferno | 13–8 | 0.04 | 14% | 7 Jun → | |
| cache | 2–8 | -0.05 | 0% | 7 Jun → | |
| office | 8–13 | -0.12 | 8% | 6 Jun → | |
| dust2 | 2–13 | -0.07 | 9% | 5 Jun → | |
| anubis | 6–13 | -0.01 | 18% | 4 Jun → | |
| nuke | 3–13 | -0.04 | 25% | 4 Jun → | |
| vertigo | 8–13 | -0.07 | 14% | 3 Jun → | |
| vertigo | 5–13 | -0.06 | 19% | 3 Jun → | |
| inferno | 11–13 | -0.06 | 26% | 3 Jun → | |
| dust2 | 13–11 | -0.11 | 22% | 2 Jun → | |
| inferno | 4–13 | -0.01 | 6% | 28 May → | |
| cache | 10–13 | -0.07 | 7% | 28 May → | |
| nuke | 4–13 | -0.07 | 10% | 27 May → | |
| mirage | 5–13 | 0.02 | 26% | 27 May → | |
| nuke | 12–12 | -0.09 | 8% | 27 May → | |
| dust2 | 7–13 | -0.12 | 7% | 27 May → | |
| dust2 | 1–13 | -0.10 | 0% | 25 May → | |
| nuke | 10–13 | -0.07 | 7% | 25 May → | |
| ancient | 4–13 | -0.10 | 9% | 25 May → | |
| mirage | 11–13 | -0.10 | 0% | 25 May → | |
| cache | 13–9 | -0.06 | 8% | 24 May → | |
| ancient | 10–13 | -0.08 | 11% | 23 Oct → | |
| mirage | 13–6 | -0.05 | 7% | 23 Oct → | |
| inferno | 16–13 | -0.08 | 29% | 23 Oct → | |
| nuke | 7–13 | -0.11 | 5% | 10 Oct → | |
| overpass | 9–13 | -0.08 | 3% | 10 Oct → | |
| train | 3–13 | -0.06 | 12% | 8 Oct → |
Match data via Leetify.