ddro`bb

ddro`bb — CS2 Stats

US76561197960391597[U:1:125869]Steam profile ↗✓ No bans

216Tracked matches34%Win rate2023Tracked since
CSDB Rating4.3 DevelopingSupport
WingmanGold Nova Master
Ladder ranks via Leetify

Performance scores

Aim58
Positioning39
Utility41

0–100 skill scores via Leetify.

Recent form

STEADY37–56–7Last 10037%Win rateLLWWWWLLLT

Last 10 vs previous 10: +10pp win rate · −0.01 avg rating · −4.0pp headshot accuracy · −155ms 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

  • 3–2 · 60% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 21%
  • Avg reaction 572ms

Last 10

  • 4–5–1 · 40% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 18%
  • Avg reaction 563ms

Last 20

  • 7–12–1 · 35% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 20%
  • Avg reaction 641ms

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: Support — Utility contribution stands above the rest of this profile (+1.2 against its own average).

Aim5.8
Utility4.1
Positioning3.9
Opening Duels0.8

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

ZywOo

Plays most like ZywOo 95% playstyle similarity

Most alike: positioning profile, 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. 650ms 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

Aim5.8
Positioning3.9
Utility4.1
Mechanics7.8
Opening Duels0.1
Win Impact0.0

Composite 4.3/10 (Developing), 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

Match rating-0.04+0.01
first ⅓ avg -0.05 → last ⅓ avg -0.04
Reaction time652ms−32ms
first ⅓ avg 684ms → last ⅓ avg 652ms
Headshot accuracy18.3%+3.2%
first ⅓ avg 15.1% → last ⅓ avg 18.3%

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

0.06Best match rating · 13–7 · ancient, 20 Sept →
38%Best headshot accuracy · 7–13 · mirage, 27 Jun →
414msFastest reaction time · 1–9 · nuke, 30 Jan →
13–1Biggest win · mirage, 1 Oct →

Across the last 100 tracked matches.

Highlights

5Longest win streak
L2Current streak
6–11In matches decided by ≤2 rounds

Map breakdown

nukeBest map · 56% over 16dust2Weakest map · 29% over 24
MapGradePlayedRecordWin rateAvg rating
dust2D247–1729%-0.04
infernoC167–944%-0.03
nukeA169–756%-0.04
ancientC135–838%-0.04
anubisD72–529%-0.06
cacheC73–443%-0.05
mirageD72–529%-0.03
trainD72–529%-0.03
overpass—20–20%-0.08
vertigo—10–10%-0.07

Across the last 100 tracked matches.

Dust 2 is currently your weakest sufficiently-sampled map (29% over 24). Start with the 6 essential Dust 2 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.

18.9%Headshot accuracy
32.1%Accuracy (enemy spotted)
33.2%Spray accuracy
85.2%Counter-strafing
11.1°Preaim
650msReaction time
27.7%T opening success
31.0%CT opening success
0.64Enemies flashed / flash
3.1%Flash assists
5.40HE damage / grenade
3.89Flashes / 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

    You are buying and holding utility rather than using it. Unthrown flashes are wasted money every round.

    Flashes per match 3.8858 — below the 4 mark we flag

    Grenade Lineups →
  2. 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 27.6724% — below the 40% mark we flag

Spend your practice time on Dust 2

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

29% win rate across 24 tracked games — your weakest map with enough games to be worth reading into.

Dust 2 callouts & strategy →Dust 2 grenade lineups →

Recent matches

MapScoreRatingHS%Date
anubis6–13-0.0722%27 Sept →
dust210–13-0.0320%23 Aug →
cache13–5-0.0626%23 Aug →
ancient13–7-0.0514%22 Aug →
inferno13–50.0124%20 Aug →
cache13–2-0.0710%20 Aug →
cache8–13-0.0420%15 Aug →
dust23–13-0.0518%15 Aug →
ancient5–13-0.057%15 Aug →
ancient12–12-0.0416%13 Aug →
nuke13–9-0.0311%13 Aug →
cache13–11-0.0712%12 Jul →
ancient7–13-0.0538%12 Jul →
inferno13–3-0.0212%12 Jul →
inferno9–130.0121%28 Jun →
inferno11–13-0.0119%27 Jun →
mirage7–13-0.0238%27 Jun →
dust27–13-0.0419%27 Jun →
nuke11–13-0.0820%24 Jun →
cache6–13-0.0329%22 Jun →
dust21–13-0.115%22 Jun →
cache11–13-0.0130%16 May →
cache9–13-0.0733%10 May →
dust211–13-0.038%25 Apr →
anubis13–8-0.107%25 Apr →
ancient13–8-0.0612%30 Mar →
overpass11–13-0.0618%29 Mar →
nuke4–13-0.0418%29 Mar →
inferno13–7-0.0120%29 Mar →
dust210–13-0.0416%28 Mar →
anubis11–13-0.0110%21 Mar →
nuke13–8-0.0312%21 Mar →
dust213–9-0.0218%21 Mar →
ancient13–6-0.0319%19 Feb →
inferno6–13-0.1024%19 Feb →
inferno13–11-0.0418%7 Feb →
anubis12–12-0.0511%7 Feb →
dust29–13-0.0035%5 Feb →
nuke13–7-0.0110%5 Feb →
inferno12–12-0.0315%31 Jan →
ancient8–13-0.0217%18 Jan →
overpass4–13-0.1022%8 Jan →
ancient4–13-0.0422%8 Jan →
nuke13–8-0.0411%4 Jan →
train13–80.0231%4 Jan →
dust26–13-0.0625%4 Jan →
train12–12-0.0323%3 Jan →
dust24–13-0.0011%3 Jan →
nuke13–9-0.0115%3 Jan →
nuke13–4-0.0321%6 Dec →
dust213–5-0.0613%6 Dec →
nuke5–7-0.0314%28 Nov →
dust213–110.0327%23 Nov →
inferno13–40.0223%23 Nov →
dust213–3-0.048%17 Nov →
nuke13–7-0.0418%13 Nov →
nuke13–50.0027%9 Nov →
dust212–120.0529%9 Nov →
ancient8–13-0.0512%1 Nov →
train10–130.0116%1 Nov →
inferno12–12-0.048%31 Oct →
dust213–4-0.0613%31 Oct →
vertigo11–13-0.0710%26 Oct →
nuke8–13-0.0315%26 Oct →
inferno3–13-0.0624%26 Oct →
nuke7–13-0.0715%25 Oct →
dust213–11-0.0422%25 Oct →
anubis13–11-0.0625%25 Oct →
dust213–50.0017%18 Oct →
mirage7–13-0.0714%11 Oct →
inferno3–13-0.097%11 Oct →
nuke4–13-0.0610%9 Oct →
inferno13–110.0023%9 Oct →
mirage10–13-0.0615%7 Oct →
dust28–13-0.0411%7 Oct →
dust26–13-0.0215%5 Oct →
mirage13–50.0317%4 Oct →
nuke13–6-0.0321%4 Oct →
dust29–13-0.073%4 Oct →
dust27–13-0.085%2 Oct →
ancient11–13-0.0716%2 Oct →
mirage9–13-0.0125%2 Oct →
mirage13–1-0.040%1 Oct →
train13–5-0.058%1 Oct →
inferno13–10-0.0419%1 Oct →
dust28–13-0.1018%1 Oct →
train9–13-0.0011%22 Sept →
dust28–13-0.059%22 Sept →
ancient13–70.0624%20 Sept →
dust25–13-0.0519%13 Sept →
train11–13-0.0810%6 Sept →
mirage6–13-0.0612%6 Sept →
ancient5–13-0.0819%12 Apr →
inferno6–13-0.0612%12 Apr →
ancient13–4-0.0416%9 Apr →
anubis2–13-0.1314%9 Apr →
train12–12-0.0426%29 Mar →
inferno11–13-0.0021%15 Feb →
anubis7–13-0.0210%15 Feb →
nuke1–9-0.1625%30 Jan →

Match data via Leetify.

Recent teammates

Milestones

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

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