G-Man

G-Man — CS2 Stats

76561199063661833[U:1:1103396105]Steam profile ↗✓ No bans

778Tracked matches46%Win rate2021Tracked since
CSDB Rating5.4 DevelopingPositional Player
Premier CS Rating3,832Grey band · top ~83.7% of ranked players (population est.)
WingmanSilver Elite
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 3 Nov 2026 (2 days ago). Ranks are recorded once per day this page is viewed.

−133Premier CS Rating · 3,965 → 3,832

Rating over time

Premier CS Rating: 3,832 -5,372 18 Oct – 5 Nov · 6 days played
3,8329,204peak 9,20418 Oct5 Nov
9,204Peak Premier in tracked matches
12Days played since 2026-10-18

Premier CS Rating

  • 5,372 below peak (9,204)
  • Next: Light Blue band at 5,000 — 1,168 to go
  • Reached: Light Blue band

Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do.

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CSDB.GGG-ManPREMIER3,832 · Grey bandcsdb.gg/stats

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Performance scores

Aim83
Positioning62
Utility14

0–100 skill scores via Leetify.

Recent form

STEADY55–40–5Last 10055%Win rateLLWLWWLWLL

Last 10 vs previous 10: +10pp win rate · +0.01 avg rating · +11.5pp headshot accuracy · +44ms 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

  • 2–3 · 40% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 29%
  • Avg reaction 612ms

Last 10

  • 4–6 · 40% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 27%
  • Avg reaction 570ms

Last 20

  • 7–11–2 · 35% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 21%
  • Avg reaction 549ms

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 (+2.3 against its own average).

Aim8.3
Utility1.4
Positioning6.2
Opening Duels4.2

Limited 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

donk

Plays most like donk 75% playstyle similarity

Most alike: aim profile, positioning profile.

Where you differ: lower opening-duel success; lower 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

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

Aim8.3
Positioning6.2
Utility1.4
Mechanics7.7
Opening Duels5.8
Win Impact3.8

Composite 5.4/10 (Developing), a weighted mean of the bars with a small opposition adjustment (×0.90 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.02−0.01
first ⅓ avg -0.00 → last ⅓ avg -0.02
Reaction time540ms+22ms
first ⅓ avg 518ms → last ⅓ avg 540ms
Headshot accuracy22.2%+0.7%
first ⅓ avg 21.5% → last ⅓ avg 22.2%

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

0.17Best match rating · 13–1 · office, 31 Oct →
60%Best headshot accuracy · 13–2 · dust2, 4 Nov →
375msFastest reaction time · 13–6 · inferno, 31 Oct →
13–0Biggest win · nuke, 2 Nov →

Across the last 100 tracked matches.

Highlights

7Longest win streak
L2Current streak
3–4In matches decided by ≤2 rounds

Map breakdown

cacheBest map · 80% over 5infernoWeakest map · 22% over 9
MapGradePlayedRecordWin rateAvg rating
mirageB179–853%-0.02
anubisA159–660%-0.00
nukeC146–843%0.01
dust2S129–375%0.01
ancientS107–370%-0.03
infernoD92–722%-0.03
overpassB84–450%-0.03
vertigoC52–340%-0.01
cacheS54–180%-0.01
train—32–167%-0.05
italy—10–10%-0.02
office—11–0100%0.17

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (22% over 9). 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.

21.6%Headshot accuracy
33.7%Accuracy (enemy spotted)
35.2%Spray accuracy
84.5%Counter-strafing
7.2°Preaim
549msReaction time
58.7%T opening success
47.8%CT opening success
0.17Enemies flashed / flash
4.5%Flash assists
6.11HE damage / grenade
2.10Flashes / match

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.

  1. Grenades & Utility

    Most of your flashes are blinding nobody. A handful of reliable pop-flash lineups fixes this faster than anything else.

    Enemies flashed per flash 0.1734 — below the 0.5 mark we flag

    Grenade Lineups →
Spend your practice time on Inferno

Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.

22% win rate across 9 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
ancient8–130.0128%5 Nov →
ancient10–13-0.0813%4 Nov →
dust213–2-0.0260%4 Nov →
italy10–13-0.0217%4 Nov →
dust213–100.0027%4 Nov →
dust213–90.1226%4 Nov →
train5–13-0.0213%4 Nov →
anubis13–9-0.0324%4 Nov →
nuke9–130.0526%4 Nov →
inferno1–13-0.0838%4 Nov →
vertigo12–12-0.0224%4 Nov →
overpass3–13-0.0914%4 Nov →
ancient13–30.025%4 Nov →
anubis11–13-0.0414%4 Nov →
nuke13–50.1213%4 Nov →
nuke4–13-0.0911%4 Nov →
overpass12–12-0.0410%3 Nov →
ancient13–3-0.0015%3 Nov →
inferno9–13-0.0214%3 Nov →
mirage5–13-0.0535%3 Nov →
overpass13–9-0.0622%3 Nov →
anubis13–10.0638%3 Nov →
overpass7–13-0.0216%3 Nov →
nuke13–30.0922%3 Nov →
anubis4–13-0.1120%3 Nov →
nuke8–30.0143%3 Nov →
inferno13–9-0.0218%3 Nov →
nuke9–130.0021%3 Nov →
inferno4–13-0.0418%3 Nov →
mirage13–5-0.0325%2 Nov →
nuke6–13-0.0419%2 Nov →
mirage9–13-0.0417%2 Nov →
nuke4–13-0.0825%2 Nov →
inferno4–13-0.0622%2 Nov →
vertigo13–5-0.0216%2 Nov →
cache13–40.0121%2 Nov →
nuke8–13-0.0530%2 Nov →
vertigo12–12-0.0116%2 Nov →
anubis11–13-0.0518%2 Nov →
dust213–2-0.010%2 Nov →
mirage13–5-0.0414%2 Nov →
mirage13–2-0.0150%2 Nov →
nuke13–00.1221%2 Nov →
mirage5–13-0.0435%2 Nov →
ancient13–4-0.0513%2 Nov →
nuke13–30.0118%2 Nov →
anubis3–13-0.0911%2 Nov →
mirage7–13-0.0616%2 Nov →
cache13–7-0.0523%1 Nov →
mirage3–130.0225%1 Nov →
dust213–7-0.0129%1 Nov →
anubis4–20.0756%1 Nov →
anubis13–10.0823%1 Nov →
mirage3–13-0.0310%1 Nov →
dust213–2-0.0328%1 Nov →
mirage13–9-0.0415%1 Nov →
overpass13–8-0.0117%1 Nov →
train13–10-0.1116%1 Nov →
anubis13–8-0.0512%31 Oct →
cache13–30.0216%31 Oct →
ancient13–3-0.0315%31 Oct →
inferno7–13-0.0217%31 Oct →
nuke8–13-0.0446%31 Oct →
office13–10.1742%31 Oct →
nuke13–90.0929%31 Oct →
inferno13–60.0431%31 Oct →
ancient13–8-0.0321%31 Oct →
ancient9–13-0.0614%31 Oct →
cache13–10.0333%31 Oct →
dust213–50.0321%31 Oct →
overpass2–13-0.1011%30 Oct →
mirage13–40.0320%30 Oct →
mirage13–10-0.0426%30 Oct →
anubis6–13-0.0425%29 Oct →
train13–10-0.019%29 Oct →
mirage12–12-0.0924%29 Oct →
inferno4–130.0128%29 Oct →
dust213–30.0530%28 Oct →
dust27–13-0.0218%20 Oct →
nuke4–5-0.099%20 Oct →
anubis13–60.0136%20 Oct →
dust24–13-0.0019%20 Oct →
overpass13–30.0918%19 Oct →
ancient13–11-0.0715%19 Oct →
vertigo13–60.0621%19 Oct →
cache12–12-0.0724%19 Oct →
mirage13–50.0124%19 Oct →
anubis13–30.0316%19 Oct →
mirage10–130.0225%19 Oct →
inferno2–13-0.1126%19 Oct →
overpass13–70.0113%19 Oct →
anubis13–70.0427%19 Oct →
dust210–13-0.0223%19 Oct →
ancient13–90.0518%19 Oct →
dust213–110.0214%18 Oct →
anubis13–70.0415%18 Oct →
anubis11–13-0.0031%18 Oct →
mirage13–00.0425%18 Oct →
vertigo5–13-0.0525%18 Oct →
mirage13–90.0329%18 Oct →

Match data via Leetify.

Recent teammates

Milestones

500 Matches
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

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