Micky

Micky — CS2 Stats

DK76561198020043273[U:1:59777545]Steam profile ↗✓ No bans

111Tracked matches38%Win rate2023Tracked since
654Hours in CS
CSDB Rating2.5 LearningSupport
WingmanThe Global Elite
Ladder ranks via Leetify

Rating over time

Premier CS Rating: 10,930 -1,528 10 Jan – 14 Jul · 8 days played
10,00012,458peak 12,45810 Jan14 Jul
12,566Peak Premier in tracked matches
36Days played since 2024-01-10

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

Performance scores

Aim25
Positioning34
Utility34

0–100 skill scores via Leetify.

Recent form

COLD37–50–6Last 9340%Win rateLLWLLLLWLL

Last 10 vs previous 10: −30pp win rate · −0.02 avg rating · −0.9pp headshot accuracy · −5ms reaction

Win rate down 30pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp).

Last 5 · 10 · 20 matches

Last 5

  • 1–4 · 20% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 15%
  • Avg reaction 591ms

Last 10

  • 2–8 · 20% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 14%
  • Avg reaction 618ms

Last 20

  • 7–13 · 35% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 14%
  • Avg reaction 620ms

Newest first, from the last 93 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 (+1.1 against its own average).

Aim2.5
Utility3.4
Positioning3.4
Opening Duels0.0

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

iM

Plays most like iM 69% playstyle similarity

Most alike: utility contribution, positioning profile.

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. 590ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 63% 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

Aim2.5
Positioning3.4
Utility3.4
Mechanics2.9
Opening Duels0.0
Win Impact1.0

Composite 2.5/10 (Learning), 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+0.03
first ⅓ avg -0.07 → last ⅓ avg -0.04
Reaction time598ms−60ms
first ⅓ avg 657ms → last ⅓ avg 598ms
Headshot accuracy12.8%+1.3%
first ⅓ avg 11.5% → last ⅓ avg 12.8%

Rolling 5-match average across the last 93 tracked matches, oldest to newest. The delta compares the first third of the window with the last.

Personal bests

0.10Best match rating · 13–9 · dust2, 11 Sept →
30%Best headshot accuracy · 8–8 · dogtown, 30 Jun →
406msFastest reaction time · 15–15 · train, 19 Jul →
13–2Biggest win · inferno, 27 Sept →

Across the last 93 tracked matches.

Highlights

5Longest win streak
L2Current streak
5–7In matches decided by ≤2 rounds
7Overtime games

Map breakdown

infernoBest map · 57% over 21ancientWeakest map · 24% over 17
MapGradePlayedRecordWin rateAvg rating
infernoA2112–957%-0.04
ancientD174–1324%-0.05
dust2B105–550%-0.04
trainC104–640%-0.06
nukeC94–544%-0.06
anubisC94–544%-0.05
dogtownC73–443%-0.10
overpass—30–30%-0.06
mirage—30–30%-0.07
office—10–10%-0.05
italy—11–0100%-0.06
grail—10–10%-0.09
vertigo—10–10%-0.13

Across the last 93 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (24% over 17). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Lifetime stats

17,276Lifetime kills
0.76K/D
1,394Matches
29.3%Match win rate
33.5%Headshot %
11.1%Shot accuracy
706MVPs
654Hours (in match)
536Bombs planted
133Bombs defused

Most-used weapons

Lifetime map wins

984dust2
833inferno
372nuke
258vertigo
207train
102cbble
50ar_shoots
36ar_monastery

Lifetime totals via Steam — visible because this profile's game details are public. Spans CS:GO and CS2.

Skill profile

Aggregate performance across tracked matches — stats via Leetify. Percentile context against other CSDB-tracked players arrives as our own benchmark data accumulates.

12.9%Headshot accuracy
26.9%Accuracy (enemy spotted)
30.2%Spray accuracy
63.1%Counter-strafing
12.4°Preaim
590msReaction time
25.9%T opening success
25.7%CT opening success
0.49Enemies flashed / flash
0.9%Flash assists
11.26HE damage / grenade
2.88Flashes / 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. Advanced Mechanics

    Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.

    Preaim 12.4435° — above the 12° mark we flag

    Aim Training →
  2. Best CS2 Crosshair

    Your shots are landing on bodies more often than heads — usually a crosshair-height and placement habit rather than raw aim.

    Headshot accuracy 12.9044% — below the 15% mark we flag

    Aim Training →
  3. 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 0.4907 — below the 0.5 mark we flag

    Grenade Lineups →
Spend your practice time on Ancient

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

24% win rate across 17 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

MapScoreRatingHS%Date
office5–13-0.0513%7 Oct →
dust25–13-0.0816%27 Sept →
inferno13–2-0.0910%27 Sept →
overpass7–13-0.0524%27 Sept →
dust27–13-0.0011%26 Sept →
dust27–13-0.0312%26 Sept →
inferno9–13-0.0417%21 Sept →
dust213–8-0.0812%21 Sept →
ancient14–16-0.078%20 Sept →
dust24–13-0.0413%20 Sept →
dust26–13-0.1119%20 Sept →
inferno13–100.0611%19 Sept →
inferno10–13-0.0418%17 Sept →
dust213–90.1014%11 Sept →
dogtown2–9-0.115%10 Sept →
train7–13-0.023%10 Sept →
train13–11-0.0021%10 Sept →
dogtown9–5-0.0211%9 Sept →
inferno13–9-0.0727%24 Jul →
inferno8–13-0.0815%24 Jul →
inferno4–13-0.017%22 Jul →
nuke16–130.0415%22 Jul →
inferno6–13-0.0510%21 Jul →
inferno10–13-0.0712%20 Jul →
train10–13-0.085%20 Jul →
ancient7–13-0.053%19 Jul →
train15–15-0.0312%19 Jul →
inferno16–12-0.0819%19 Jul →
inferno13–50.0314%19 Jul →
ancient4–13-0.067%19 Jul →
inferno13–9-0.0511%19 Jul →
train13–8-0.063%19 Jul →
train15–15-0.0015%17 Jul →
nuke4–13-0.0616%16 Jul →
overpass5–13-0.0817%16 Jul →
ancient7–13-0.108%16 Jul →
inferno16–13-0.0612%16 Jul →
train13–4-0.104%16 Jul →
nuke13–3-0.0711%16 Jul →
italy13–11-0.0612%15 Jul →
ancient13–20.018%15 Jul →
train9–13-0.119%15 Jul →
nuke13–4-0.063%15 Jul →
ancient9–130.009%15 Jul →
inferno13–11-0.103%14 Jul →
ancient15–15-0.036%14 Jul →
ancient10–13-0.085%14 Jul →
ancient13–11-0.0311%14 Jul →
nuke4–13-0.074%14 Jul →
mirage10–13-0.077%14 Jul →
dust213–5-0.067%14 Jul →
dust213–8-0.0214%14 Jul →
ancient2–13-0.0712%14 Jul →
inferno13–8-0.056%14 Jul →
inferno13–3-0.0223%14 Jul →
inferno11–130.015%13 Jul →
ancient11–13-0.0213%13 Jul →
anubis10–13-0.058%13 Jul →
ancient6–13-0.096%12 Jul →
inferno11–130.0217%9 Jul →
inferno13–9-0.0613%9 Jul →
ancient5–13-0.126%9 Jul →
mirage5–13-0.0613%9 Jul →
anubis13–2-0.033%8 Jul →
anubis13–9-0.0212%8 Jul →
inferno3–13-0.0912%8 Jul →
anubis5–13-0.0514%8 Jul →
anubis13–6-0.038%8 Jul →
dust213–6-0.0612%7 Jul →
inferno13–4-0.0710%7 Jul →
train13–4-0.059%7 Jul →
train3–13-0.113%7 Jul →
ancient13–20.0315%6 Jul →
ancient4–13-0.089%6 Jul →
anubis13–7-0.054%6 Jul →
nuke3–13-0.054%6 Jul →
nuke12–12-0.039%5 Jul →
ancient11–13-0.077%5 Jul →
overpass0–13-0.0721%5 Jul →
grail10–13-0.0917%5 Jul →
dogtown4–9-0.1514%1 Jul →
dogtown8–8-0.2730%30 Jun →
dogtown9–00.0225%29 Jun →
dogtown8–8-0.175%27 Jun →
dogtown9–1-0.0212%27 Jun →
nuke13–11-0.105%9 Feb →
anubis3–13-0.0810%5 Feb →
anubis11–13-0.0417%14 Jan →
mirage5–13-0.0810%13 Jan →
vertigo9–13-0.139%11 Jan →
nuke4–13-0.128%10 Jan →
ancient13–7-0.0518%10 Jan →
anubis11–13-0.1114%10 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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