Appalachiangrabber — CS2 Stats
GS76561199564161021[U:1:1603895293]Steam profile ↗✓ No bans
How this compares with the same rank
Median values for Light Blue band among CSDB-tracked players (n=404), 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 | Light Blue band median | vs Light Blue band |
|---|---|---|---|
| Headshot rate | 30.4% | 40.1% | 9.6% short |
| Shot accuracy | 0.0% | 7.6% | 7.6% short |
| Kill/death ratio | 0.66 | 0.92 | 0.26 short |
| Match win rate | 37.8% | 42.2% | 4.3% short |
This profile sits below the typical Light Blue band player on every metric we can compare.
Widest gap: Shot accuracy. That is the metric furthest from the Light Blue 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: +10pp 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 80% playstyle similarity
Most alike: utility contribution, opening-fight frequency.
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
T-side openings. Opening success drops from 48% on CT to 30% on T — the same duels are being taken with worse setups on the attacking side.
Reaction time. 683ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.
Counter-strafing. Only 58% 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 2.5/10 (Learning), a weighted mean of the bars with a small opposition adjustment (×0.90 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 |
|---|---|---|---|---|---|
| C | 29 | 12–17 | 41% | -0.02 | |
| C | 27 | 10–17 | 37% | -0.03 | |
| D | 16 | 4–12 | 25% | -0.04 | |
| D | 15 | 5–10 | 33% | -0.04 | |
| C | 9 | 4–5 | 44% | -0.02 | |
| — | 2 | 1–1 | 50% | -0.08 | |
| — | 1 | 0–1 | 0% | -0.08 | |
| office | — | 1 | 1–0 | 100% | -0.06 |
Across the last 100 tracked matches.
Inferno is currently your weakest sufficiently-sampled map (25% over 16). Start with the 6 essential Inferno 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.
- 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 17.0352° — 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 12.021% — 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.
25% win rate across 16 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| 13–6 | -0.04 | 8% | 26 Aug → | ||
| 0–13 | -0.11 | 0% | 20 Aug → | ||
| 6–5 | -0.01 | 14% | 20 Aug → | ||
| 13–8 | 0.01 | 21% | 8 Aug → | ||
| 4–13 | -0.08 | 18% | 3 Aug → | ||
| 13–9 | -0.09 | 11% | 3 Aug → | ||
| 1–13 | -0.09 | 4% | 3 Aug → | ||
| 6–13 | -0.07 | 10% | 3 Aug → | ||
| 5–8 | 0.03 | 7% | 2 Aug → | ||
| 6–13 | -0.03 | 18% | 2 Aug → | ||
| 13–11 | 0.02 | 9% | 2 Aug → | ||
| 11–13 | -0.05 | 8% | 31 Jul → | ||
| 9–13 | -0.08 | 17% | 31 Jul → | ||
| 13–5 | 0.07 | 12% | 31 Jul → | ||
| 12–12 | 0.05 | 16% | 30 Jul → | ||
| 3–13 | -0.05 | 14% | 30 Jul → | ||
| 4–13 | -0.06 | 8% | 30 Jul → | ||
| 1–13 | -0.06 | 26% | 29 Jul → | ||
| 13–3 | -0.01 | 23% | 29 Jul → | ||
| 10–13 | -0.08 | 14% | 29 Jul → | ||
| 5–13 | -0.06 | 21% | 28 Jul → | ||
| 6–13 | -0.08 | 10% | 28 Jul → | ||
| 2–13 | -0.05 | 9% | 28 Jul → | ||
| 12–12 | 0.01 | 9% | 27 Jul → | ||
| 13–6 | 0.03 | 13% | 15 Jul → | ||
| 4–13 | -0.09 | 5% | 15 Jul → | ||
| 7–13 | -0.01 | 15% | 25 Jun → | ||
| 4–13 | -0.01 | 7% | 24 Jun → | ||
| 13–9 | 0.01 | 8% | 23 Jun → | ||
| 13–10 | -0.04 | 6% | 23 Jun → | ||
| 13–6 | -0.02 | 12% | 20 Jun → | ||
| 13–2 | 0.04 | 4% | 20 Jun → | ||
| 5–13 | -0.09 | 12% | 15 Jun → | ||
| 13–2 | -0.03 | 3% | 15 Jun → | ||
| 7–13 | -0.02 | 11% | 14 Jun → | ||
| 9–13 | -0.04 | 13% | 14 Jun → | ||
| 12–12 | -0.02 | 32% | 13 Jun → | ||
| 13–6 | -0.01 | 5% | 13 Jun → | ||
| 13–3 | -0.02 | 20% | 1 Jun → | ||
| 8–13 | -0.00 | 12% | 1 Jun → | ||
| 7–3 | -0.04 | 17% | 26 May → | ||
| 13–6 | 0.03 | 23% | 26 May → | ||
| 12–12 | -0.05 | 10% | 26 May → | ||
| 0–13 | -0.09 | 9% | 25 May → | ||
| 5–13 | -0.08 | 11% | 25 May → | ||
| 9–13 | -0.05 | 8% | 25 May → | ||
| 8–13 | -0.08 | 14% | 23 May → | ||
| 5–13 | -0.13 | 4% | 22 May → | ||
| 13–11 | -0.06 | 16% | 22 May → | ||
| 1–13 | -0.09 | 50% | 21 May → | ||
| 5–13 | -0.04 | 5% | 21 May → | ||
| 5–13 | -0.07 | 8% | 21 May → | ||
| 5–13 | -0.06 | 5% | 21 May → | ||
| 12–12 | 0.03 | 10% | 21 May → | ||
| 1–8 | -0.01 | 44% | 21 May → | ||
| 13–10 | -0.01 | 6% | 20 May → | ||
| 11–13 | -0.08 | 5% | 20 May → | ||
| 12–12 | -0.05 | 13% | 19 May → | ||
| 9–13 | 0.03 | 17% | 19 May → | ||
| 13–8 | 0.03 | 14% | 19 May → | ||
| 9–13 | -0.03 | 8% | 18 May → | ||
| 2–8 | -0.09 | 13% | 14 May → | ||
| 5–13 | -0.07 | 7% | 14 May → | ||
| 5–13 | -0.03 | 5% | 14 May → | ||
| 4–13 | -0.11 | 11% | 14 May → | ||
| 9–13 | -0.08 | 11% | 13 May → | ||
| 11–13 | -0.02 | 14% | 13 May → | ||
| 5–13 | -0.05 | 10% | 13 May → | ||
| 13–10 | 0.09 | 14% | 13 May → | ||
| 12–12 | -0.02 | 5% | 13 May → | ||
| 13–7 | -0.00 | 11% | 13 May → | ||
| 13–7 | -0.01 | 9% | 10 May → | ||
| 13–9 | -0.02 | 23% | 10 May → | ||
| 4–13 | -0.07 | 14% | 7 May → | ||
| 12–12 | -0.02 | 8% | 7 May → | ||
| 7–13 | -0.01 | 5% | 6 May → | ||
| 7–13 | -0.04 | 9% | 6 May → | ||
| 12–12 | -0.01 | 10% | 6 May → | ||
| 3–13 | -0.06 | 8% | 5 May → | ||
| 12–12 | 0.03 | 15% | 5 May → | ||
| 7–13 | -0.12 | 3% | 5 May → | ||
| 13–4 | -0.08 | 10% | 5 May → | ||
| 11–13 | -0.09 | 5% | 2 May → | ||
| 13–6 | -0.02 | 13% | 28 Apr → | ||
| 13–7 | 0.01 | 28% | 28 Apr → | ||
| 5–13 | -0.09 | 11% | 27 Apr → | ||
| 11–13 | -0.07 | 9% | 27 Apr → | ||
| 10–0 | -0.01 | 8% | 27 Apr → | ||
| 13–8 | -0.02 | 16% | 27 Apr → | ||
| 13–6 | -0.01 | 13% | 27 Apr → | ||
| office | 13–9 | -0.06 | 7% | 24 Apr → | |
| 13–4 | 0.03 | 10% | 24 Apr → | ||
| 6–13 | 0.03 | 11% | 24 Apr → | ||
| 13–6 | -0.00 | 18% | 15 Apr → | ||
| 13–5 | -0.05 | 10% | 15 Apr → | ||
| 4–13 | -0.05 | 4% | 15 Apr → | ||
| 13–6 | -0.02 | 10% | 15 Apr → | ||
| 13–1 | 0.07 | 12% | 13 Apr → | ||
| 13–9 | -0.02 | 16% | 13 Apr → | ||
| 13–9 | 0.03 | 8% | 10 Apr → |
Match data via Leetify.