Jabalaye — CS2 Stats
76561197965212496[U:1:4946768]
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Performance scores
0–100 skill scores via Leetify.
Recent form
Last 10 vs previous 10: -20pp win rate · -0.01 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 ropz 83% playstyle similarity
Most alike: opening-fight frequency, 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. 568ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.
Counter-strafing. Only 68% 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 4.1/10 (Developing), 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 |
|---|---|---|---|---|---|
 nuke | S | 18 | 12–6 | 67% | 0.02 |
 dust2 | A | 14 | 9–5 | 64% | 0.03 |
 anubis | S | 14 | 10–4 | 71% | 0.02 |
 ancient | A | 14 | 9–5 | 64% | 0.01 |
 mirage | C | 12 | 5–7 | 42% | -0.01 |
 inferno | D | 11 | 2–9 | 18% | 0.01 |
 vertigo | B | 8 | 4–4 | 50% | 0.00 |
 overpass | S | 6 | 4–2 | 67% | 0.02 |
 train | — | 2 | 0–2 | 0% | 0.02 |
 cache | — | 1 | 0–1 | 0% | -0.08 |
Across the last 100 tracked matches.
Inferno is currently your weakest sufficiently-sampled map (18% over 11). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.
Faceit stats
Combat
Clutches & streaks
Recent Faceit resultsWWWWL
| Map | Matches | Win rate | Avg K/D | Avg kills |
|---|---|---|---|---|
| Anubis | 79 | 53% | 1.10 | 16.5 |
| Nuke | 73 | 60% | 1.33 | 17.1 |
| Ancient | 69 | 51% | 1.03 | 16.2 |
| Vertigo | 46 | 57% | 1.18 | 17.2 |
| Dust2 | 45 | 36% | 1.17 | 17.3 |
| Inferno | 42 | 38% | 0.91 | 15.4 |
| Mirage | 41 | 44% | 1.00 | 15.2 |
| Train | 20 | 60% | 1.10 | 17.6 |
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.
- 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 0.3452 — 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 33.9245% — 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.
18% win rate across 11 tracked games — your weakest map with enough games to be worth reading into.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| inferno | 14–16 | 0.05 | 16% | 27 Aug → | |
| dust2 | 9–13 | 0.02 | 20% | 27 Aug → | |
| anubis | 7–13 | 0.01 | 16% | 15 Aug → | |
| vertigo | 10–13 | -0.02 | 17% | 8 Aug → | |
| nuke | 6–13 | -0.03 | 6% | 8 Aug → | |
| ancient | 13–10 | 0.12 | 14% | 8 Aug → | |
| ancient | 20–22 | 0.01 | 17% | 3 Aug → | |
| anubis | 16–14 | -0.01 | 16% | 30 Jul → | |
| dust2 | 6–13 | -0.11 | 11% | 25 Jul → | |
| ancient | 13–10 | 0.02 | 12% | 24 Jul → | |
| inferno | 13–16 | -0.08 | 14% | 23 Jul → | |
| overpass | 13–5 | 0.04 | 13% | 2 Jul → | |
| cache | 3–13 | -0.08 | 20% | 2 May → | |
| inferno | 11–13 | 0.10 | 22% | 2 May → | |
| nuke | 13–8 | 0.05 | 4% | 2 May → | |
| dust2 | 13–6 | 0.02 | 24% | 1 Mar → | |
| overpass | 6–13 | 0.03 | 24% | 1 Mar → | |
| anubis | 16–19 | -0.02 | 13% | 25 Jan → | |
| mirage | 13–4 | 0.07 | 20% | 24 Jan → | |
| dust2 | 13–5 | -0.01 | 16% | 15 Jan → | |
| overpass | 13–7 | 0.01 | 21% | 12 Jan → | |
| anubis | 13–9 | -0.01 | 12% | 11 Jan → | |
| ancient | 7–13 | -0.00 | 14% | 10 Jan → | |
| mirage | 11–13 | -0.01 | 11% | 21 Dec → | |
| mirage | 5–13 | 0.07 | 23% | 16 Dec → | |
| inferno | 7–13 | -0.02 | 13% | 16 Dec → | |
| overpass | 13–10 | 0.08 | 15% | 10 Dec → | |
| mirage | 8–13 | -0.04 | 14% | 9 Dec → | |
| nuke | 13–3 | 0.09 | 17% | 9 Dec → | |
| nuke | 9–13 | -0.00 | 11% | 9 Dec → | |
| mirage | 13–11 | 0.01 | 13% | 9 Dec → | |
| dust2 | 13–8 | 0.12 | 18% | 8 Dec → | |
| mirage | 13–8 | -0.03 | 24% | 24 Oct → | |
| mirage | 9–13 | 0.00 | 18% | 17 Sept → | |
| dust2 | 13–11 | 0.00 | 32% | 17 Sept → | |
| inferno | 14–16 | 0.00 | 19% | 15 Sept → | |
| train | 9–13 | 0.03 | 30% | 14 Sept → | |
| ancient | 7–1 | 0.08 | 31% | 12 Sept → | |
| overpass | 13–2 | 0.03 | 29% | 10 Sept → | |
| overpass | 10–13 | -0.05 | 22% | 6 Sept → | |
| ancient | 9–13 | -0.05 | 19% | 6 Sept → | |
| nuke | 8–13 | -0.01 | 31% | 4 Sept → | |
| ancient | 13–11 | -0.06 | 26% | 8 Jul → | |
| dust2 | 4–13 | -0.00 | 23% | 8 Jul → | |
| ancient | 13–10 | -0.06 | 17% | 7 Jul → | |
| mirage | 9–13 | -0.02 | 24% | 27 Apr → | |
| dust2 | 2–13 | 0.02 | 31% | 27 Apr → | |
| mirage | 10–13 | -0.05 | 32% | 26 Apr → | |
| ancient | 13–10 | 0.05 | 29% | 26 Apr → | |
| nuke | 13–3 | 0.03 | 22% | 26 Apr → | |
| mirage | 0–13 | -0.05 | 17% | 26 Apr → | |
| nuke | 4–13 | 0.00 | 32% | 26 Apr → | |
| anubis | 7–13 | 0.05 | 14% | 26 Apr → | |
| inferno | 4–13 | -0.00 | 11% | 25 Apr → | |
| inferno | 8–13 | 0.01 | 13% | 25 Apr → | |
| nuke | 13–7 | 0.09 | 16% | 25 Apr → | |
| dust2 | 13–2 | 0.03 | 24% | 25 Apr → | |
| mirage | 16–12 | -0.01 | 16% | 25 Apr → | |
| anubis | 16–13 | 0.05 | 28% | 24 Apr → | |
| ancient | 4–13 | -0.04 | 24% | 24 Apr → | |
| dust2 | 13–5 | 0.06 | 14% | 24 Apr → | |
| nuke | 13–11 | 0.02 | 23% | 24 Apr → | |
| dust2 | 13–3 | 0.00 | 22% | 24 Apr → | |
| nuke | 10–13 | 0.03 | 26% | 24 Apr → | |
| mirage | 13–8 | -0.04 | 24% | 22 Apr → | |
| dust2 | 9–13 | -0.02 | 25% | 22 Apr → | |
| anubis | 13–6 | -0.00 | 22% | 22 Apr → | |
| inferno | 13–5 | 0.06 | 25% | 19 Apr → | |
| anubis | 11–13 | 0.03 | 26% | 14 Mar → | |
| inferno | 3–13 | -0.00 | 15% | 3 Mar → | |
| nuke | 13–9 | 0.04 | 13% | 15 Feb → | |
| nuke | 13–8 | 0.03 | 13% | 15 Feb → | |
| train | 10–13 | 0.01 | 14% | 16 Nov → | |
| dust2 | 13–5 | 0.12 | 23% | 7 Nov → | |
| anubis | 13–3 | 0.07 | 27% | 1 Nov → | |
| nuke | 13–8 | 0.03 | 6% | 31 Oct → | |
| anubis | 13–7 | 0.04 | 16% | 30 Oct → | |
| vertigo | 10–13 | -0.04 | 11% | 25 Oct → | |
| ancient | 13–10 | 0.02 | 15% | 25 Oct → | |
| nuke | 13–5 | -0.01 | 11% | 11 Oct → | |
| nuke | 19–16 | 0.04 | 17% | 3 Oct → | |
| nuke | 13–8 | 0.04 | 17% | 23 Sept → | |
| ancient | 13–11 | 0.04 | 13% | 22 Sept → | |
| vertigo | 13–7 | 0.05 | 11% | 22 Sept → | |
| inferno | 16–14 | 0.06 | 24% | 14 Sept → | |
| vertigo | 13–10 | -0.03 | 26% | 14 Sept → | |
| nuke | 9–13 | -0.01 | 15% | 13 Sept → | |
| vertigo | 13–3 | 0.12 | 26% | 13 Sept → | |
| anubis | 13–10 | -0.05 | 11% | 13 Sept → | |
| ancient | 13–7 | 0.07 | 12% | 8 Sept → | |
| anubis | 16–13 | -0.01 | 16% | 7 Sept → | |
| vertigo | 12–16 | -0.08 | 17% | 6 Sept → | |
| anubis | 13–2 | 0.10 | 30% | 2 Sept → | |
| dust2 | 13–7 | 0.13 | 33% | 30 Aug → | |
| anubis | 13–7 | 0.05 | 23% | 30 Aug → | |
| ancient | 12–16 | -0.00 | 16% | 30 Aug → | |
| nuke | 13–9 | -0.02 | 18% | 30 Aug → | |
| vertigo | 10–13 | -0.02 | 13% | 29 Aug → | |
| vertigo | 13–1 | 0.05 | 17% | 24 Aug → | |
| inferno | 7–13 | -0.07 | 28% | 23 Aug → |
Match data via Leetify.