e4st

e4st — CS2 Stats

US76561197960786764[U:1:521036]Steam profile ↗✓ No bans

224Tracked matches66%Win rate2020Tracked since
CSDB Rating6.5 SolidPositional Player
Ladder ranks via Leetify

Rating over time

Premier CS Rating: 17,942 +5,082 4 May – 16 May · 12 days played
8,62817,942peak 17,9424 May16 May
17,942Peak Premier in tracked matches
27Days played since 2024-05-04

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

Performance scores

Aim68
Positioning61
Utility46

0–100 skill scores via Leetify.

Recent form

HOT58–37–5Last 10058%Win rateWWWLWLWWWL

Last 10 vs previous 10: +20pp win rate · +0.04 avg rating · −7.8pp headshot accuracy · −80ms reaction

Win rate up 20pp 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

  • 4–1 · 80% win rate
  • Avg rating 0.02
  • Avg headshot accuracy 12%
  • Avg reaction 580ms

Last 10

  • 7–3 · 70% win rate
  • Avg rating 0.03
  • Avg headshot accuracy 10%
  • Avg reaction 621ms

Last 20

  • 12–8 · 60% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 14%
  • Avg reaction 661ms

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 (+1.3 against its own average).

Aim6.8
Utility4.6
Positioning6.1
Opening Duels3.7

Excellent counter-strafingLimited 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 91% 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

Strengths

Counter-strafing. 90% of shots taken properly stopped — movement discipline most players never reach.

Areas to improve

T-side openings. Opening success drops from 49% on CT to 28% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 643ms 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

Aim6.8
Positioning6.1
Utility4.6
Mechanics8.9
Opening Duels2.4
Win Impact10.0

Composite 6.5/10 (Solid), 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 rating0.02−0.04
first ⅓ avg 0.06 → last ⅓ avg 0.02
Reaction time653ms+54ms
first ⅓ avg 599ms → last ⅓ avg 653ms
Headshot accuracy16.1%−6.3%
first ⅓ avg 22.3% → last ⅓ avg 16.1%

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

0.22Best match rating · 13–3 · ancient, 4 May →
50%Best headshot accuracy · 13–7 · inferno, 21 Sept →
438msFastest reaction time · 2–9 · inferno, 15 Mar →
13–1Biggest win · train, 3 Jan →

Across the last 100 tracked matches.

Highlights

11Longest win streak
W3Current streak
8–8In matches decided by ≤2 rounds
10Overtime games

Map breakdown

vertigoBest map · 86% over 7anubisWeakest map · 38% over 8
MapGradePlayedRecordWin rateAvg rating
nukeS2013–765%0.02
infernoB1910–953%0.03
dust2A148–657%0.04
ancientB137–654%0.05
overpassA95–456%0.02
anubisC83–538%0.02
vertigoS76–186%0.07
mirageB63–350%0.03
train—21–150%0.04
cache—11–0100%0.02
office—11–0100%0.11

Across the last 100 tracked matches.

Anubis is currently your weakest sufficiently-sampled map (38% over 8). Start with the 6 essential Anubis 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.

15.7%Headshot accuracy
37.2%Accuracy (enemy spotted)
39.6%Spray accuracy
90.1%Counter-strafing
12.0°Preaim
643msReaction time
28.1%T opening success
48.9%CT opening success
0.61Enemies flashed / flash
0.9%Flash assists
5.41HE damage / grenade
3.54Flashes / 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 3.5407 — 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 28.0501% — below the 40% mark we flag

Spend your practice time on Anubis

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

38% win rate across 8 tracked games — your weakest map with enough games to be worth reading into.

Anubis callouts & strategy →Anubis grenade lineups →

Recent matches

MapScoreRatingHS%Date
cache13–110.0214%14 Jul →
nuke13–40.029%14 Jul →
nuke13–110.069%16 May →
ancient8–13-0.0210%16 May →
inferno13–60.0317%7 May →
overpass6–130.0312%7 May →
overpass13–9-0.0110%8 Apr →
nuke13–100.0310%8 Apr →
ancient13–50.134%22 Mar →
mirage9–130.038%22 Mar →
overpass11–13-0.0018%21 Mar →
inferno13–60.0716%21 Mar →
nuke10–13-0.037%21 Mar →
nuke16–140.0418%15 Mar →
overpass13–70.0524%15 Mar →
nuke3–13-0.0722%15 Mar →
ancient13–80.0422%15 Mar →
nuke9–6-0.0519%15 Mar →
inferno2–9-0.2122%15 Mar →
inferno8–130.0412%5 Mar →
anubis13–90.0422%5 Mar →
overpass13–50.0618%5 Mar →
overpass15–150.0119%3 Mar →
nuke9–13-0.0327%3 Mar →
ancient8–13-0.018%3 Mar →
nuke13–11-0.0121%17 Feb →
ancient13–50.0320%17 Feb →
inferno13–90.0424%17 Feb →
dust214–16-0.0610%17 Feb →
nuke13–10-0.0018%17 Feb →
dust213–80.0819%16 Jan →
inferno16–140.0521%16 Jan →
ancient11–130.0921%16 Jan →
inferno3–13-0.0721%16 Jan →
inferno11–13-0.0117%10 Jan →
overpass2–130.0026%10 Jan →
dust29–130.0318%10 Jan →
nuke11–130.0520%10 Jan →
inferno13–50.1112%10 Jan →
dust23–130.0816%10 Jan →
nuke8–13-0.0219%10 Jan →
overpass13–5-0.0717%3 Jan →
train13–10.1431%3 Jan →
nuke13–70.069%3 Jan →
inferno2–00.060%3 Jan →
dust213–50.0830%3 Jan →
ancient13–11-0.0111%3 Jan →
anubis12–120.0620%3 Jan →
nuke13–20.1317%3 Jan →
overpass13–70.1014%3 Jan →
mirage10–13-0.0127%10 Oct →
inferno13–80.0414%2 Aug →
ancient15–15-0.0316%2 Aug →
mirage8–130.005%2 Aug →
anubis13–30.0421%14 Jul →
mirage13–50.0317%13 Jul →
inferno5–13-0.054%15 May →
train2–13-0.068%15 May →
nuke13–70.0714%15 May →
ancient15–150.0720%15 May →
dust210–13-0.0417%15 May →
dust216–130.0024%12 Apr →
dust213–50.0717%12 Apr →
nuke13–100.0140%12 Apr →
mirage13–30.0511%12 Apr →
inferno9–130.0113%3 Dec →
ancient13–70.0011%3 Dec →
anubis8–130.0214%3 Dec →
dust213–80.0819%3 Dec →
nuke13–50.0715%3 Dec →
anubis6–13-0.0622%2 Dec →
ancient8–130.0234%2 Dec →
vertigo13–90.1217%2 Dec →
nuke11–13-0.0212%2 Dec →
vertigo16–130.0025%28 Sept →
nuke13–60.0823%28 Sept →
dust215–150.0420%28 Sept →
anubis10–13-0.0414%28 Sept →
inferno13–40.1320%28 Sept →
ancient13–60.0738%23 Sept →
anubis2–3-0.0112%23 Sept →
vertigo13–80.0916%23 Sept →
anubis13–110.1225%21 Sept →
vertigo13–40.1230%21 Sept →
mirage13–90.0918%21 Sept →
vertigo13–40.0817%21 Sept →
inferno13–70.2050%21 Sept →
dust213–80.0842%21 Sept →
office13–60.1133%20 Sept →
dust210–60.0347%20 Sept →
inferno13–30.1825%20 Sept →
vertigo13–80.0825%20 Sept →
dust213–160.0319%20 Sept →
inferno5–13-0.0518%22 Jun →
dust213–80.0215%22 Jun →
nuke10–130.0722%22 Jun →
inferno5–13-0.106%22 Jun →
inferno11–130.0312%4 May →
ancient13–30.2214%4 May →
vertigo10–130.0318%4 May →

Match data via Leetify.

Recent teammates

Milestones

100 Matches11-Win Streak
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

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