iZnoGoud — CS2 Stats
DE76561198033045623[U:1:72779895]Steam profile ↗VAC banned
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: −10pp win rate · +0.01 avg rating · +5.3pp headshot accuracy · +31ms reaction
Win rate −10pp 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
- 3–1–1 · 60% win rate
- Avg rating 0.06
- Avg headshot accuracy 23%
- Avg reaction 587ms
Last 10
- 5–4–1 · 50% win rate
- Avg rating 0.05
- Avg headshot accuracy 23%
- Avg reaction 595ms
Last 20
- 11–7–2 · 55% win rate
- Avg rating 0.05
- Avg headshot accuracy 21%
- Avg reaction 580ms
Newest first, from the last 33 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 (+0.9 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 jL 76% playstyle similarity
Most alike: opening-duel success, utility contribution.
Where you differ: 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. 606ms 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 6.5/10 (Solid), 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 33 tracked matches, oldest to newest. The delta compares the first third of the window with the last.
Personal bests
Across the last 33 tracked matches.
Highlights
Map breakdown
| Map | Grade | Played | Record | Win rate | Avg rating |
|---|---|---|---|---|---|
 inferno | B | 11 | 5–6 | 45% | 0.06 |
 nuke | S | 6 | 4–2 | 67% | 0.05 |
 anubis | C | 5 | 2–3 | 40% | 0.03 |
 dust2 | — | 4 | 3–1 | 75% | 0.02 |
 mirage | — | 4 | 2–2 | 50% | 0.09 |
 ancient | — | 3 | 1–2 | 33% | 0.07 |
Across the last 33 tracked matches.
Anubis is currently your weakest sufficiently-sampled map (40% over 5). Start with the 6 essential Anubis lineups, review the callouts, then spin up a practice server.
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.
40% win rate across 5 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| inferno | 15–15 | 0.10 | 21% | 21 Aug → | |
| anubis | 13–8 | 0.08 | 28% | 31 Jul → | |
| ancient | 13–10 | 0.08 | 21% | 31 Jul → | |
| anubis | 4–13 | -0.06 | 31% | 31 Jul → | |
| inferno | 13–9 | 0.11 | 14% | 11 Apr → | |
| anubis | 8–13 | 0.03 | 24% | 11 Apr → | |
| nuke | 6–13 | 0.03 | 25% | 6 Apr → | |
| dust2 | 7–13 | -0.02 | 35% | 2 Apr → | |
| nuke | 16–13 | 0.11 | 14% | 2 Apr → | |
| inferno | 13–6 | 0.07 | 20% | 27 Mar → | |
| nuke | 13–11 | -0.02 | 17% | 27 Mar → | |
| ancient | 9–13 | 0.08 | 22% | 7 Mar → | |
| nuke | 15–15 | -0.01 | 13% | 7 Mar → | |
| dust2 | 13–4 | 0.04 | 18% | 6 Mar → | |
| inferno | 10–13 | -0.01 | 21% | 13 Feb → | |
| inferno | 13–4 | 0.18 | 17% | 10 Jan → | |
| nuke | 13–9 | 0.06 | 16% | 10 Jan → | |
| inferno | 6–13 | -0.03 | 15% | 9 Jan → | |
| inferno | 13–10 | 0.08 | 20% | 12 Dec → | |
| inferno | 13–9 | 0.04 | 21% | 12 Dec → | |
| dust2 | 16–13 | 0.02 | 10% | 10 Dec → | |
| ancient | 7–13 | 0.05 | 11% | 18 Oct → | |
| inferno | 11–13 | -0.01 | 14% | 15 Oct → | |
| mirage | 13–8 | 0.07 | 13% | 15 Oct → | |
| mirage | 13–9 | -0.00 | 14% | 12 Jul → | |
| anubis | 8–13 | 0.00 | 11% | 12 Jul → | |
| anubis | 13–9 | 0.12 | 13% | 28 May → | |
| nuke | 13–1 | 0.14 | 12% | 2 Jan → | |
| mirage | 11–13 | 0.16 | 24% | 2 Jan → | |
| inferno | 11–13 | 0.21 | 14% | 1 Jan → | |
| dust2 | 13–2 | 0.02 | 17% | 1 Jan → | |
| mirage | 8–13 | 0.12 | 15% | 1 Jan → | |
| inferno | 3–13 | -0.10 | 11% | 17 Nov → |
Match data via Leetify.
