SkemeL — CS2 Stats
FI76561198204121352[U:1:243855624]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. Today is the first observation — history builds from here and cannot be backfilled. Come back after the next session and the change shows above.
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Performance scores
0–100 skill scores via Leetify.
Recent form
Last 10 vs previous 10: +20pp win rate · +0.02 avg rating · −0.8pp headshot accuracy · −45ms reaction
Win rate up 20pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp).
Last 5 · 10 · 20 matches
Last 5
- 4–0–1 · 80% win rate
- Avg rating -0.02
- Avg headshot accuracy 24%
- Avg reaction 675ms
Last 10
- 6–3–1 · 60% win rate
- Avg rating -0.03
- Avg headshot accuracy 23%
- Avg reaction 672ms
Last 20
- 10–9–1 · 50% win rate
- Avg rating -0.04
- Avg headshot accuracy 23%
- Avg reaction 695ms
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 (+1.3 against its own average).
Strong T-side openerLimited 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.
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 device 91% playstyle similarity
Most alike: positioning profile, utility contribution.
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
Areas to improve
Reaction time. 711ms 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 4.9/10 (Developing), 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.
Personal bests
Across the last 100 tracked matches.
Highlights
Map breakdown
| Map | Grade | Played | Record | Win rate | Avg rating |
|---|---|---|---|---|---|
 cache | A | 36 | 20–16 | 56% | -0.04 |
 train | B | 33 | 16–17 | 48% | -0.03 |
 inferno | B | 15 | 7–8 | 47% | -0.04 |
 vertigo | B | 6 | 3–3 | 50% | -0.02 |
 dust2 | — | 4 | 3–1 | 75% | -0.00 |
 mirage | — | 3 | 2–1 | 67% | -0.03 |
 nuke | — | 2 | 2–0 | 100% | -0.03 |
 anubis | — | 1 | 0–1 | 0% | -0.08 |
Across the last 100 tracked matches.
Inferno is currently your weakest sufficiently-sampled map (47% over 15). 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.
- 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 710.7551ms — 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 0.4518 — below the 0.5 mark we flag
- 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 33.277% — 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.
47% win rate across 15 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| train | 13–7 | -0.00 | 18% | 14 Sept → | |
| cache | 13–8 | 0.05 | 23% | 14 Sept → | |
| train | 12–12 | -0.08 | 30% | 13 Sept → | |
| cache | 13–4 | -0.03 | 32% | 13 Sept → | |
| inferno | 13–6 | -0.04 | 17% | 13 Sept → | |
| cache | 5–13 | -0.14 | 27% | 13 Sept → | |
| train | 13–8 | -0.01 | 32% | 13 Sept → | |
| train | 13–3 | 0.00 | 23% | 12 Sept → | |
| cache | 8–13 | 0.00 | 13% | 12 Sept → | |
| cache | 2–13 | -0.03 | 13% | 30 Aug → | |
| dust2 | 9–13 | -0.01 | 17% | 30 Aug → | |
| cache | 13–4 | -0.04 | 50% | 30 Aug → | |
| inferno | 8–13 | -0.08 | 21% | 28 Aug → | |
| nuke | 13–6 | -0.06 | 16% | 28 Aug → | |
| cache | 8–13 | -0.04 | 29% | 28 Aug → | |
| cache | 6–13 | -0.03 | 23% | 22 Aug → | |
| cache | 13–5 | -0.01 | 23% | 21 Aug → | |
| inferno | 1–13 | -0.06 | 13% | 21 Aug → | |
| train | 13–9 | -0.07 | 12% | 21 Aug → | |
| cache | 9–13 | -0.06 | 30% | 21 Aug → | |
| inferno | 1–13 | -0.14 | 14% | 7 Aug → | |
| cache | 13–11 | 0.02 | 12% | 7 Aug → | |
| cache | 3–13 | -0.07 | 7% | 7 Aug → | |
| dust2 | 13–7 | -0.08 | 23% | 6 Aug → | |
| cache | 5–13 | -0.06 | 24% | 6 Aug → | |
| mirage | 16–13 | -0.03 | 13% | 2 Aug → | |
| cache | 13–7 | -0.04 | 27% | 2 Aug → | |
| dust2 | 16–13 | -0.00 | 31% | 2 Aug → | |
| anubis | 6–13 | -0.08 | 8% | 2 Aug → | |
| inferno | 13–5 | 0.02 | 24% | 2 Aug → | |
| train | 11–13 | -0.05 | 23% | 1 Aug → | |
| cache | 4–13 | -0.03 | 27% | 1 Aug → | |
| cache | 13–6 | -0.04 | 50% | 1 Aug → | |
| cache | 13–10 | -0.03 | 17% | 1 Aug → | |
| train | 5–13 | -0.06 | 33% | 31 Jul → | |
| cache | 10–2 | -0.05 | 13% | 31 Jul → | |
| train | 13–2 | -0.02 | 18% | 31 Jul → | |
| cache | 6–13 | -0.09 | 13% | 30 Jul → | |
| mirage | 8–13 | -0.07 | 19% | 19 Jul → | |
| cache | 13–5 | -0.02 | 25% | 19 Jul → | |
| train | 13–5 | -0.04 | 28% | 19 Jul → | |
| train | 13–9 | -0.02 | 20% | 19 Jul → | |
| train | 3–13 | -0.06 | 26% | 18 Jul → | |
| cache | 13–4 | 0.00 | 21% | 18 Jul → | |
| cache | 13–10 | -0.04 | 35% | 18 Jul → | |
| cache | 13–10 | -0.04 | 34% | 15 Jul → | |
| train | 13–8 | -0.04 | 12% | 12 Jul → | |
| cache | 8–13 | -0.07 | 23% | 12 Jul → | |
| cache | 13–11 | -0.12 | 19% | 12 Jul → | |
| train | 7–13 | -0.07 | 46% | 10 Jul → | |
| cache | 13–7 | -0.01 | 33% | 10 Jul → | |
| cache | 2–13 | -0.09 | 38% | 14 Jun → | |
| train | 13–10 | -0.01 | 17% | 14 Jun → | |
| vertigo | 1–13 | -0.07 | 27% | 13 Jun → | |
| cache | 13–8 | 0.09 | 17% | 13 Jun → | |
| inferno | 13–6 | -0.01 | 18% | 13 Jun → | |
| train | 1–13 | -0.04 | 15% | 7 Jun → | |
| cache | 13–11 | -0.06 | 16% | 7 Jun → | |
| cache | 5–13 | -0.08 | 29% | 24 May → | |
| train | 13–3 | 0.05 | 55% | 24 May → | |
| inferno | 13–8 | -0.01 | 17% | 24 May → | |
| cache | 6–13 | -0.09 | 22% | 24 May → | |
| cache | 12–12 | -0.10 | 18% | 23 May → | |
| train | 13–3 | -0.14 | 14% | 23 May → | |
| train | 6–0 | 0.00 | 30% | 14 May → | |
| cache | 8–13 | -0.08 | 25% | 14 May → | |
| train | 12–12 | -0.01 | 23% | 10 May → | |
| cache | 13–6 | -0.01 | 15% | 10 May → | |
| train | 13–5 | 0.01 | 50% | 6 May → | |
| cache | 13–7 | -0.03 | 16% | 6 May → | |
| train | 13–7 | -0.02 | 22% | 6 May → | |
| cache | 13–9 | -0.08 | 14% | 6 May → | |
| vertigo | 13–7 | 0.01 | 27% | 26 Apr → | |
| train | 11–13 | -0.02 | 29% | 26 Apr → | |
| inferno | 10–13 | -0.08 | 13% | 26 Apr → | |
| train | 6–13 | -0.06 | 29% | 26 Apr → | |
| mirage | 13–9 | 0.01 | 32% | 25 Apr → | |
| train | 8–13 | 0.00 | 33% | 25 Apr → | |
| vertigo | 6–13 | -0.05 | 18% | 20 Apr → | |
| train | 7–13 | -0.04 | 32% | 20 Apr → | |
| inferno | 4–13 | -0.08 | 22% | 20 Apr → | |
| vertigo | 13–7 | -0.02 | 19% | 19 Apr → | |
| train | 12–12 | -0.01 | 17% | 19 Apr → | |
| nuke | 13–10 | -0.01 | 28% | 17 Apr → | |
| inferno | 13–3 | -0.01 | 19% | 17 Apr → | |
| vertigo | 12–12 | -0.02 | 23% | 17 Apr → | |
| inferno | 9–13 | -0.03 | 21% | 17 Apr → | |
| train | 12–12 | -0.13 | 24% | 17 Apr → | |
| dust2 | 13–10 | 0.09 | 25% | 3 Apr → | |
| inferno | 1–7 | -0.10 | 100% | 1 Mar → | |
| train | 12–12 | -0.02 | 27% | 1 Mar → | |
| train | 10–13 | -0.08 | 45% | 28 Feb → | |
| train | 1–4 | -0.00 | 36% | 28 Feb → | |
| inferno | 13–7 | 0.02 | 19% | 28 Feb → | |
| train | 13–9 | 0.04 | 25% | 28 Feb → | |
| inferno | 7–13 | 0.00 | 24% | 28 Feb → | |
| vertigo | 13–3 | 0.01 | 31% | 28 Feb → | |
| train | 13–1 | -0.03 | 50% | 25 Feb → | |
| inferno | 13–4 | -0.05 | 38% | 25 Feb → | |
| train | 4–13 | -0.11 | 44% | 25 Feb → |
Match data via Leetify.
