Nova Pork — CS2 Stats
ST76561198245671118[U:1:285405390]Steam profile ↗✓ No bans
How this compares with the same rank
Median values for Blue band among CSDB-tracked players (n=1,240), 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 | Blue band median | Purple band median | vs Purple band |
|---|---|---|---|---|
| Headshot rate | 37.3% | 41.7% | 44.1% | 6.7% short |
| Shot accuracy | 7.7% | 8.8% | 11.4% | 3.7% short |
| Kill/death ratio | 0.93 | 0.97 | 1.03 | 0.10 short |
| Match win rate | 43.9% | 43.8% | 45.2% | 1.3% short |
This profile sits below the typical Purple band player on every metric we can compare.
Widest gap: Shot accuracy. That is the metric furthest from the Purple band 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 · -0.02 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 75% playstyle similarity
Most alike: opening-fight frequency, opening-duel success.
Where you differ: lower positioning profile; 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. 585ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.
Counter-strafing. Only 56% 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 (×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 |
|---|---|---|---|---|---|
 dust2 | B | 85 | 41–44 | 48% | -0.02 |
 mirage | D | 6 | 1–5 | 17% | -0.04 |
 inferno | — | 4 | 1–3 | 25% | -0.03 |
 nuke | — | 3 | 0–3 | 0% | -0.04 |
 cache | — | 1 | 0–1 | 0% | -0.02 |
 ancient | — | 1 | 1–0 | 100% | -0.05 |
Across the last 100 tracked matches.
Mirage is currently your weakest sufficiently-sampled map (17% over 6). 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 resultsLLWLL
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Anubis | 3 | 33% | 0.56 | 11.0 |
| Nuke | 1 | 0% | 0.27 | 4.0 |
| Dust2 | 1 | 0% | 0.31 | 5.0 |
| Mirage | 1 | 0% | 0.50 | 10.0 |
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 MechanicsAim Training →
Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.
Preaim 13.2351° — above the 12° mark we flag
- 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 12.3441% — below the 15% mark we flag
Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.
17% win rate across 6 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| nuke | 11–13 | -0.04 | 4% | 26 Aug → | |
| nuke | 4–13 | -0.05 | 5% | 23 Aug → | |
| mirage | 15–15 | -0.04 | 20% | 23 Aug → | |
| dust2 | 13–10 | -0.04 | 13% | 19 Aug → | |
| dust2 | 13–9 | -0.02 | 14% | 19 Aug → | |
| inferno | 16–13 | 0.01 | 12% | 19 Aug → | |
| dust2 | 9–4 | -0.06 | 7% | 19 Aug → | |
| cache | 8–13 | -0.02 | 18% | 18 Aug → | |
| mirage | 3–13 | -0.06 | 16% | 18 Aug → | |
| inferno | 7–13 | -0.08 | 9% | 16 Aug → | |
| ancient | 16–14 | -0.05 | 12% | 16 Aug → | |
| dust2 | 13–9 | -0.08 | 11% | 16 Aug → | |
| mirage | 8–13 | 0.02 | 9% | 16 Aug → | |
| dust2 | 6–13 | 0.00 | 18% | 10 Aug → | |
| dust2 | 13–10 | 0.01 | 10% | 10 Aug → | |
| inferno | 14–16 | -0.02 | 13% | 9 Aug → | |
| dust2 | 13–9 | 0.01 | 23% | 9 Aug → | |
| inferno | 9–13 | -0.05 | 10% | 4 Aug → | |
| nuke | 6–13 | -0.04 | 12% | 4 Aug → | |
| dust2 | 5–13 | -0.04 | 10% | 4 Aug → | |
| mirage | 1–13 | -0.10 | 4% | 3 Aug → | |
| dust2 | 3–13 | -0.08 | 18% | 3 Aug → | |
| mirage | 13–10 | -0.02 | 12% | 1 Aug → | |
| mirage | 1–13 | -0.06 | 5% | 1 Aug → | |
| dust2 | 13–10 | -0.08 | 17% | 31 Jul → | |
| dust2 | 0–13 | -0.11 | 0% | 18 Jul → | |
| dust2 | 13–7 | -0.02 | 13% | 11 Jul → | |
| dust2 | 8–13 | -0.04 | 21% | 11 Jul → | |
| dust2 | 10–13 | -0.06 | 10% | 11 Jul → | |
| dust2 | 13–7 | -0.01 | 11% | 11 Jul → | |
| dust2 | 13–9 | -0.06 | 15% | 11 Jul → | |
| dust2 | 13–1 | 0.08 | 29% | 11 Jul → | |
| dust2 | 2–0 | 0.00 | 0% | 10 Jul → | |
| dust2 | 13–10 | -0.04 | 17% | 9 Jul → | |
| dust2 | 13–3 | -0.02 | 12% | 9 Jul → | |
| dust2 | 5–13 | -0.07 | 17% | 4 Jul → | |
| dust2 | 12–12 | -0.02 | 6% | 4 Jul → | |
| dust2 | 10–13 | -0.04 | 25% | 3 Jul → | |
| dust2 | 6–13 | -0.03 | 0% | 2 Jul → | |
| dust2 | 13–3 | -0.06 | 0% | 2 Jul → | |
| dust2 | 4–13 | -0.05 | 13% | 2 Jul → | |
| dust2 | 7–13 | -0.07 | 18% | 2 Jul → | |
| dust2 | 13–11 | -0.04 | 9% | 2 Jul → | |
| dust2 | 9–13 | -0.01 | 18% | 2 Jul → | |
| dust2 | 12–12 | -0.06 | 15% | 2 Jul → | |
| dust2 | 12–12 | -0.07 | 8% | 2 Jul → | |
| dust2 | 8–13 | -0.06 | 17% | 2 Jul → | |
| dust2 | 8–13 | 0.03 | 18% | 2 Jul → | |
| dust2 | 13–11 | -0.04 | 13% | 30 Jun → | |
| dust2 | 13–9 | -0.02 | 15% | 28 Jun → | |
| dust2 | 13–7 | -0.02 | 11% | 28 Jun → | |
| dust2 | 3–13 | 0.01 | 13% | 28 Jun → | |
| dust2 | 12–12 | -0.08 | 9% | 28 Jun → | |
| dust2 | 4–13 | -0.09 | 8% | 23 Jun → | |
| dust2 | 7–13 | -0.06 | 11% | 23 Jun → | |
| dust2 | 7–13 | -0.03 | 16% | 20 Jun → | |
| dust2 | 13–3 | 0.04 | 10% | 20 Jun → | |
| dust2 | 13–8 | 0.03 | 13% | 18 Jun → | |
| dust2 | 12–12 | 0.04 | 16% | 18 Jun → | |
| dust2 | 12–12 | 0.00 | 11% | 18 Jun → | |
| dust2 | 9–13 | -0.02 | 11% | 18 Jun → | |
| dust2 | 13–5 | -0.03 | 1% | 12 Jun → | |
| dust2 | 13–11 | 0.10 | 8% | 12 Jun → | |
| dust2 | 6–13 | -0.04 | 12% | 12 Jun → | |
| dust2 | 13–7 | 0.05 | 5% | 12 Jun → | |
| dust2 | 7–13 | -0.07 | 12% | 12 Jun → | |
| dust2 | 13–8 | 0.05 | 8% | 9 Jun → | |
| dust2 | 13–9 | 0.04 | 8% | 9 Jun → | |
| dust2 | 8–1 | 0.07 | 4% | 9 Jun → | |
| dust2 | 11–2 | -0.05 | 10% | 9 Jun → | |
| dust2 | 11–13 | -0.05 | 12% | 8 Jun → | |
| dust2 | 13–7 | -0.04 | 11% | 8 Jun → | |
| dust2 | 10–13 | -0.06 | 10% | 8 Jun → | |
| dust2 | 8–13 | -0.09 | 12% | 8 Jun → | |
| dust2 | 13–9 | -0.02 | 4% | 1 Jun → | |
| dust2 | 13–11 | -0.01 | 11% | 1 Jun → | |
| dust2 | 13–3 | -0.02 | 0% | 29 May → | |
| dust2 | 13–9 | 0.00 | 11% | 29 May → | |
| dust2 | 12–12 | 0.02 | 12% | 29 May → | |
| dust2 | 0–4 | -0.04 | 25% | 29 May → | |
| dust2 | 9–13 | 0.01 | 11% | 29 May → | |
| dust2 | 12–12 | -0.07 | 11% | 25 May → | |
| dust2 | 7–13 | -0.06 | 13% | 25 May → | |
| dust2 | 13–6 | 0.00 | 12% | 24 May → | |
| dust2 | 2–13 | -0.00 | 10% | 24 May → | |
| dust2 | 13–11 | -0.03 | 17% | 24 May → | |
| dust2 | 13–0 | 0.01 | 15% | 24 May → | |
| dust2 | 7–13 | -0.08 | 8% | 9 May → | |
| dust2 | 12–12 | -0.04 | 9% | 9 May → | |
| dust2 | 10–13 | -0.03 | 21% | 9 May → | |
| dust2 | 13–11 | -0.04 | 16% | 9 May → | |
| dust2 | 12–12 | 0.03 | 5% | 9 May → | |
| dust2 | 7–13 | -0.01 | 13% | 9 May → | |
| dust2 | 13–9 | 0.01 | 7% | 9 May → | |
| dust2 | 8–13 | -0.07 | 11% | 9 May → | |
| dust2 | 11–13 | -0.07 | 14% | 4 May → | |
| dust2 | 13–6 | -0.01 | 10% | 4 May → | |
| dust2 | 12–12 | 0.05 | 16% | 4 May → | |
| dust2 | 13–10 | -0.03 | 5% | 4 May → | |
| dust2 | 13–4 | 0.01 | 7% | 18 Apr → |
Match data via Leetify.
