Mr. Saggynuts — CS2 Stats
AF76561198981883749[U:1:1021618021]Steam profile ↗✓ No bans
Rating over time
Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do.
Performance scores
0–100 skill scores via Leetify.
Recent form
Last 10 vs previous 10: −30pp win rate · +0.03 avg rating · +4.6pp headshot accuracy · −114ms reaction
Win rate down 30pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp). Average match rating moved the other way (+0.03), so results shifted more than performance did.
Last 5 · 10 · 20 matches
Last 5
- 4–1 · 80% win rate
- Avg rating 0.05
- Avg headshot accuracy 35%
- Avg reaction 499ms
Last 10
- 6–4 · 60% win rate
- Avg rating 0.05
- Avg headshot accuracy 30%
- Avg reaction 496ms
Last 20
- 15–5 · 75% win rate
- Avg rating 0.03
- Avg headshot accuracy 27%
- Avg reaction 553ms
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: Positional Player — Positioning stands above the rest of this profile (+0.9 against its own average).
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.
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 96% playstyle similarity
Most alike: utility contribution, opening-duel success.
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 86 — the mechanical foundation is a clear strength.
Areas to improve
Reaction time. 575ms 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 7.2/10 (Strong), 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 |
|---|---|---|---|---|---|
| office | A | 21 | 13–8 | 62% | 0.03 |
 mirage | S | 20 | 13–7 | 65% | 0.03 |
 dust2 | S | 17 | 11–6 | 65% | 0.03 |
 nuke | A | 16 | 9–7 | 56% | 0.05 |
 inferno | B | 13 | 7–6 | 54% | 0.03 |
 ancient | S | 5 | 5–0 | 100% | 0.05 |
 anubis | — | 3 | 2–1 | 67% | 0.03 |
 overpass | — | 2 | 2–0 | 100% | 0.11 |
 cache | — | 1 | 1–0 | 100% | 0.02 |
| agency | — | 1 | 1–0 | 100% | 0.07 |
 train | — | 1 | 0–1 | 0% | 0.05 |
Across the last 100 tracked matches.
Inferno is currently your weakest sufficiently-sampled map (54% over 13). Start with the 6 essential Inferno 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.
Recent matches
| Map | Score | Rating | HS% | Date | |
|---|---|---|---|---|---|
| office | 13–8 | 0.04 | 36% | 28 Oct → | |
| office | 13–8 | 0.05 | 47% | 28 Oct → | |
| office | 13–8 | 0.05 | 41% | 28 Oct → | |
| cache | 13–6 | 0.02 | 22% | 25 Oct → | |
| inferno | 11–13 | 0.10 | 31% | 20 Oct → | |
| inferno | 13–2 | 0.08 | 27% | 20 Oct → | |
| dust2 | 9–13 | 0.02 | 24% | 20 Oct → | |
| inferno | 14–16 | 0.03 | 26% | 20 Oct → | |
| dust2 | 5–13 | -0.01 | 28% | 9 Oct → | |
| anubis | 13–2 | 0.11 | 16% | 9 Oct → | |
| anubis | 13–0 | 0.01 | 23% | 9 Oct → | |
| office | 13–3 | 0.08 | 29% | 10 Apr → | |
| mirage | 5–13 | -0.02 | 25% | 3 Mar → | |
| dust2 | 13–6 | 0.03 | 15% | 3 Mar → | |
| nuke | 13–6 | 0.01 | 28% | 27 Feb → | |
| mirage | 13–6 | 0.02 | 17% | 27 Feb → | |
| mirage | 13–1 | 0.05 | 31% | 27 Feb → | |
| office | 13–8 | -0.06 | 28% | 29 Nov → | |
| mirage | 13–7 | 0.05 | 32% | 7 Nov → | |
| dust2 | 13–2 | -0.02 | 23% | 7 Nov → | |
| ancient | 13–3 | 0.01 | 27% | 7 Nov → | |
| nuke | 13–5 | 0.01 | 24% | 7 Nov → | |
| inferno | 13–9 | 0.04 | 23% | 7 Nov → | |
| inferno | 13–9 | 0.11 | 14% | 2 Nov → | |
| mirage | 15–15 | 0.01 | 18% | 2 Nov → | |
| overpass | 13–7 | 0.18 | 17% | 2 Nov → | |
| dust2 | 10–13 | -0.02 | 33% | 2 Nov → | |
| mirage | 13–10 | 0.03 | 28% | 2 Nov → | |
| dust2 | 13–7 | 0.07 | 31% | 2 Nov → | |
| ancient | 16–13 | 0.02 | 25% | 2 Nov → | |
| office | 12–12 | -0.01 | 41% | 2 Nov → | |
| nuke | 2–13 | -0.03 | 17% | 30 Oct → | |
| inferno | 13–11 | -0.02 | 22% | 23 Oct → | |
| overpass | 13–10 | 0.05 | 17% | 21 Oct → | |
| inferno | 8–13 | -0.07 | 22% | 16 Oct → | |
| mirage | 13–3 | 0.04 | 26% | 15 Oct → | |
| dust2 | 13–3 | 0.16 | 28% | 15 Oct → | |
| mirage | 13–7 | 0.02 | 32% | 15 Oct → | |
| inferno | 7–13 | 0.08 | 33% | 27 Sept → | |
| nuke | 13–5 | 0.07 | 31% | 27 Sept → | |
| nuke | 16–13 | 0.03 | 23% | 20 Sept → | |
| nuke | 13–3 | 0.07 | 29% | 20 Sept → | |
| mirage | 6–13 | -0.02 | 39% | 17 Sept → | |
| dust2 | 16–14 | 0.01 | 23% | 17 Sept → | |
| dust2 | 11–13 | 0.06 | 21% | 9 Sept → | |
| nuke | 13–6 | 0.10 | 25% | 9 Sept → | |
| dust2 | 13–10 | 0.01 | 31% | 9 Sept → | |
| office | 13–7 | 0.05 | 38% | 22 May → | |
| nuke | 5–8 | 0.12 | 25% | 15 May → | |
| agency | 13–3 | 0.07 | 21% | 11 May → | |
| office | 10–11 | -0.04 | 23% | 11 May → | |
| office | 3–13 | 0.11 | 45% | 11 May → | |
| office | 13–8 | 0.01 | 41% | 11 May → | |
| office | 0–6 | -0.06 | 25% | 11 May → | |
| office | 6–13 | -0.01 | 28% | 3 May → | |
| office | 13–8 | 0.05 | 29% | 3 May → | |
| ancient | 13–10 | 0.04 | 19% | 3 May → | |
| inferno | 7–13 | 0.05 | 29% | 10 Apr → | |
| mirage | 8–13 | 0.03 | 22% | 10 Apr → | |
| train | 7–13 | 0.05 | 36% | 8 Apr → | |
| nuke | 0–13 | -0.09 | 11% | 8 Apr → | |
| inferno | 13–10 | -0.01 | 16% | 8 Apr → | |
| office | 8–13 | 0.05 | 58% | 6 Apr → | |
| ancient | 13–11 | 0.08 | 36% | 6 Apr → | |
| dust2 | 13–4 | -0.05 | 18% | 4 Apr → | |
| anubis | 5–13 | -0.04 | 17% | 3 Apr → | |
| mirage | 8–13 | -0.02 | 30% | 3 Apr → | |
| inferno | 13–3 | 0.01 | 42% | 3 Apr → | |
| mirage | 9–13 | -0.10 | 26% | 3 Apr → | |
| nuke | 13–3 | 0.10 | 27% | 2 Apr → | |
| dust2 | 6–13 | -0.03 | 33% | 2 Apr → | |
| dust2 | 13–8 | 0.07 | 34% | 2 Apr → | |
| dust2 | 13–9 | 0.06 | 48% | 2 Apr → | |
| mirage | 13–3 | 0.08 | 27% | 2 Apr → | |
| ancient | 13–8 | 0.10 | 27% | 27 Mar → | |
| nuke | 13–6 | 0.09 | 32% | 27 Mar → | |
| dust2 | 13–8 | -0.02 | 20% | 27 Mar → | |
| nuke | 13–8 | 0.07 | 21% | 27 Mar → | |
| mirage | 13–5 | 0.06 | 28% | 27 Mar → | |
| dust2 | 9–13 | 0.00 | 29% | 26 Mar → | |
| mirage | 13–4 | 0.01 | 25% | 26 Mar → | |
| dust2 | 13–9 | 0.12 | 30% | 26 Mar → | |
| mirage | 2–13 | -0.05 | 67% | 26 Mar → | |
| nuke | 9–13 | 0.10 | 25% | 2 Mar → | |
| mirage | 13–5 | 0.05 | 24% | 2 Mar → | |
| mirage | 13–5 | 0.22 | 36% | 27 Feb → | |
| office | 13–3 | -0.01 | 38% | 21 Jul → | |
| office | 13–5 | 0.03 | 27% | 21 Jul → | |
| office | 13–5 | 0.05 | 26% | 20 Jul → | |
| office | 13–9 | 0.10 | 29% | 20 Jul → | |
| office | 8–13 | 0.04 | 28% | 20 Jul → | |
| office | 13–1 | 0.09 | 59% | 20 Jul → | |
| office | 1–13 | -0.06 | 28% | 20 Jul → | |
| mirage | 13–5 | 0.09 | 23% | 10 Jul → | |
| inferno | 13–7 | 0.06 | 23% | 10 Jul → | |
| nuke | 10–13 | 0.12 | 30% | 10 Jul → | |
| mirage | 13–9 | 0.08 | 35% | 15 Feb → | |
| nuke | 2–13 | 0.06 | 53% | 15 Feb → | |
| nuke | 14–16 | 0.01 | 58% | 15 Feb → | |
| inferno | 3–13 | -0.01 | 36% | 9 Feb → |
Match data via Leetify.
Recent teammates
Milestones
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