tigeR. — CS2 Stats
LT76561198840650369[U:1:880384641]Steam profile ↗
Share this profile
The image is a snapshot; the link keeps updating. Nothing here is published anywhere — it is generated in your browser when you click.
Performance scores
0–100 skill scores via Leetify.
Recent form
Last 10 vs previous 10: +40pp win rate · -0.06 avg rating
Player DNA
Primary style: Entry Fragger — High opening-fight frequency with above-par success in them.
Sharp aimer
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 s1mple 99% playstyle similarity
Most alike: opening-fight frequency, utility contribution.
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
Strengths
Aim. Aim score of 96 — the mechanical foundation is a clear strength.
CT openings. 64% CT opening-duel success — winning the first fight on the defending side is rare and valuable.
Areas to improve
Utility. Utility contribution runs far behind the mechanical game — the cheapest rating gain on this profile is thrown, not aimed.
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 8.6/10 (Elite), a weighted mean of the bars with a small opposition adjustment (×1.05 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 |
|---|---|---|---|---|---|
| B | 28 | 13–15 | 46% | 0.05 | |
| A | 19 | 12–7 | 63% | 0.03 | |
| B | 13 | 7–6 | 54% | 0.05 | |
| A | 11 | 7–4 | 64% | 0.05 | |
| C | 9 | 4–5 | 44% | 0.05 | |
| S | 9 | 6–3 | 67% | 0.05 | |
| — | 3 | 1–2 | 33% | 0.05 | |
| — | 3 | 0–3 | 0% | 0.00 | |
| office | — | 2 | 2–0 | 100% | 0.03 |
| sanctum | — | 1 | 0–1 | 0% | 0.08 |
| — | 1 | 0–1 | 0% | 0.00 | |
| golden | — | 1 | 0–1 | 0% | 0.08 |
Across the last 100 tracked matches.
Inferno is currently your weakest sufficiently-sampled map (44% over 9). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.
Faceit stats
Combat
Clutches & streaks
Recent Faceit resultsWWWLL
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Mirage | 696 | 54% | 1.36 | 19.2 |
| Anubis | 322 | 61% | 1.32 | 18.6 |
| Dust2 | 305 | 51% | 1.27 | 18.4 |
| Ancient | 231 | 57% | 1.35 | 19.0 |
| Inferno | 175 | 54% | 1.30 | 17.9 |
| Nuke | 156 | 52% | 1.30 | 18.8 |
| Vertigo | 46 | 46% | 1.33 | 16.7 |
| Overpass | 36 | 44% | 1.29 | 18.1 |
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
Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.
44% win rate across 9 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| 13–5 | 0.05 | 28% | 28 Aug → | ||
| 4–13 | -0.03 | 15% | 21 Aug → | ||
| 13–4 | 0.06 | 25% | 16 Aug → | ||
| 16–14 | 0.00 | 25% | 12 Aug → | ||
| 13–9 | 0.10 | 32% | 8 Aug → | ||
| 13–7 | 0.02 | 37% | 4 Aug → | ||
| office | 13–10 | -0.04 | 18% | 22 Jul → | |
| 9–13 | 0.02 | 27% | 17 Jul → | ||
| 13–11 | -0.02 | 18% | 15 Jul → | ||
| 13–3 | 0.09 | 33% | 12 Jul → | ||
| 17–19 | 0.01 | 17% | 11 Jul → | ||
| 13–10 | 0.07 | 31% | 2 Jul → | ||
| 13–16 | 0.07 | 34% | 25 Jun → | ||
| 12–16 | 0.19 | 21% | 24 Jun → | ||
| 13–16 | 0.08 | 30% | 17 Jun → | ||
| 13–9 | 0.08 | 24% | 14 Jun → | ||
| 7–13 | 0.00 | 22% | 14 Jun → | ||
| 13–8 | 0.07 | 38% | 12 Jun → | ||
| 13–5 | 0.18 | 28% | 12 Jun → | ||
| 5–13 | 0.07 | 33% | 12 Jun → | ||
| 13–10 | 0.03 | 31% | 11 Jun → | ||
| 13–4 | 0.13 | 27% | 11 Jun → | ||
| 8–13 | -0.04 | 35% | 8 Jun → | ||
| 13–6 | 0.16 | 47% | 8 Jun → | ||
| 10–13 | 0.02 | 33% | 8 Jun → | ||
| 7–13 | 0.06 | 46% | 31 May → | ||
| 13–2 | 0.03 | 21% | 28 May → | ||
| 13–6 | 0.13 | 33% | 28 May → | ||
| 9–1 | 0.03 | 29% | 24 May → | ||
| 13–9 | -0.04 | 36% | 23 May → | ||
| 19–15 | 0.02 | 22% | 23 May → | ||
| 13–9 | 0.07 | 30% | 22 May → | ||
| 13–11 | 0.04 | 18% | 13 May → | ||
| 13–2 | 0.07 | 29% | 10 May → | ||
| 4–6 | -0.08 | 19% | 9 May → | ||
| 10–13 | 0.03 | 19% | 6 May → | ||
| 13–10 | 0.01 | 33% | 4 May → | ||
| 5–13 | -0.02 | 6% | 1 May → | ||
| sanctum | 7–9 | 0.08 | 25% | 30 Apr → | |
| 5–13 | 0.12 | 16% | 30 Apr → | ||
| 13–6 | 0.07 | 16% | 25 Apr → | ||
| 8–2 | 0.02 | 27% | 23 Apr → | ||
| 11–13 | 0.01 | 28% | 20 Apr → | ||
| 13–6 | 0.07 | 26% | 20 Apr → | ||
| 13–10 | 0.12 | 38% | 19 Apr → | ||
| 13–10 | 0.07 | 21% | 19 Apr → | ||
| 13–16 | -0.04 | 18% | 17 Apr → | ||
| 14–16 | 0.06 | 17% | 17 Apr → | ||
| 13–4 | 0.04 | 48% | 15 Apr → | ||
| office | 13–10 | 0.11 | 42% | 10 Apr → | |
| 13–11 | 0.07 | 44% | 10 Apr → | ||
| 13–6 | 0.07 | 22% | 7 Apr → | ||
| 13–10 | -0.02 | 10% | 6 Apr → | ||
| 13–8 | 0.02 | 32% | 4 Apr → | ||
| 10–13 | 0.01 | 23% | 4 Apr → | ||
| 14–16 | 0.04 | 39% | 14 Mar → | ||
| 13–16 | 0.05 | 26% | 11 Mar → | ||
| 16–13 | 0.05 | 40% | 5 Mar → | ||
| 6–13 | -0.05 | 17% | 2 Mar → | ||
| 13–16 | 0.03 | 23% | 1 Mar → | ||
| 5–13 | 0.01 | 19% | 26 Feb → | ||
| 13–9 | 0.08 | 22% | 26 Feb → | ||
| 3–13 | -0.02 | 22% | 25 Feb → | ||
| 6–13 | 0.03 | 33% | 19 Feb → | ||
| 13–5 | 0.11 | 29% | 4 Feb → | ||
| 19–16 | 0.05 | 37% | 3 Feb → | ||
| 13–9 | 0.05 | 32% | 26 Jan → | ||
| 13–10 | 0.10 | 38% | 25 Jan → | ||
| 5–13 | 0.02 | 29% | 19 Jan → | ||
| 10–13 | 0.10 | 23% | 17 Jan → | ||
| 14–16 | 0.02 | 22% | 14 Jan → | ||
| 7–13 | -0.02 | 36% | 11 Jan → | ||
| 13–3 | 0.13 | 20% | 11 Jan → | ||
| 11–13 | 0.13 | 34% | 9 Jan → | ||
| 13–3 | 0.07 | 32% | 8 Jan → | ||
| 13–4 | 0.02 | 28% | 7 Jan → | ||
| 8–13 | 0.08 | 13% | 7 Jan → | ||
| 13–7 | 0.13 | 22% | 6 Jan → | ||
| 4–13 | 0.03 | 34% | 22 Dec → | ||
| 9–13 | 0.00 | 40% | 19 Dec → | ||
| 1–13 | 0.00 | 28% | 18 Dec → | ||
| 2–13 | 0.02 | 45% | 17 Dec → | ||
| 10–13 | 0.17 | 41% | 17 Dec → | ||
| 13–16 | -0.01 | 37% | 17 Dec → | ||
| 6–13 | 0.04 | 21% | 10 Dec → | ||
| 13–5 | 0.10 | 38% | 9 Dec → | ||
| 13–6 | 0.02 | 32% | 8 Dec → | ||
| 6–13 | -0.00 | 25% | 8 Dec → | ||
| golden | 6–13 | 0.08 | 27% | 8 Dec → | |
| 15–15 | -0.03 | 20% | 3 Dec → | ||
| 13–10 | 0.02 | 33% | 2 Dec → | ||
| 13–2 | 0.04 | 29% | 1 Dec → | ||
| 4–13 | 0.00 | 45% | 27 Nov → | ||
| 16–14 | 0.08 | 29% | 23 Nov → | ||
| 7–13 | 0.00 | 44% | 18 Sept → | ||
| 13–7 | 0.03 | 27% | 16 Sept → | ||
| 0–4 | 0.01 | 25% | 16 Sept → | ||
| 15–15 | 0.04 | 32% | 12 Sept → | ||
| 8–13 | 0.00 | 28% | 11 Sept → | ||
| 13–7 | 0.04 | 47% | 10 Sept → |
Match data via Leetify.