Kekinger

Kekinger — CS2 Stats

76561198127025962[U:1:166760234]Steam profile ↗✓ No bans

463Tracked matches45%Win rate2021Tracked since
CSDB Rating4.9 DevelopingPositional Player
Ladder ranks via Leetify

Rating over time

Premier CS Rating: 9,999 +585 23 Aug – 1 Jan · 26 days played
7,56910,104peak 10,10423 Aug1 Jan
10,104Peak Premier in tracked matches
44Days played since 2025-08-23

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

Performance scores

Aim58
Positioning54
Utility20

0–100 skill scores via Leetify.

Recent form

STEADY47–48–5Last 10047%Win rateLTWWLWLWWL

Last 10 vs previous 10: +20pp win rate · +0.03 avg rating · −2.3pp headshot accuracy · −66ms 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

  • 2–2–1 · 40% win rate
  • Avg rating 0.00
  • Avg headshot accuracy 11%
  • Avg reaction 586ms

Last 10

  • 5–4–1 · 50% win rate
  • Avg rating -0.00
  • Avg headshot accuracy 12%
  • Avg reaction 610ms

Last 20

  • 8–10–2 · 40% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 13%
  • Avg reaction 643ms

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 (+1.7 against its own average).

Aim5.8
Utility2.0
Positioning5.4
Opening Duels3.6

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

s1mple

Plays most like s1mple 84% playstyle similarity

Most alike: positioning profile, aim profile.

Where you differ: lower utility contribution; lower 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

Areas to improve

Utility. Utility contribution runs far behind the mechanical game — the cheapest rating gain on this profile is thrown, not aimed.

Reaction time. 646ms 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
Positioning5.4
Utility2.0
Mechanics7.4
Opening Duels3.2
Win Impact3.3

Composite 4.9/10 (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-0.02−0.02
first ⅓ avg 0.00 → last ⅓ avg -0.02
Reaction time644ms−11ms
first ⅓ avg 655ms → last ⅓ avg 644ms
Headshot accuracy14.1%+1.4%
first ⅓ avg 12.8% → last ⅓ avg 14.1%

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

0.16Best match rating · 13–2 · nuke, 7 Dec →
30%Best headshot accuracy · 13–0 · inferno, 10 Oct →
422msFastest reaction time · 13–0 · inferno, 10 Oct →
13–0Biggest win · inferno, 10 Oct →

Across the last 100 tracked matches.

Highlights

4Longest win streak
8–7In matches decided by ≤2 rounds
5Overtime games

Map breakdown

trainBest map · 90% over 10ancientWeakest map · 11% over 18
MapGradePlayedRecordWin rateAvg rating
nukeS2617–965%0.02
ancientD182–1611%-0.01
infernoB126–650%0.02
overpassD123–925%0.01
trainS109–190%0.03
cacheC94–544%-0.01
mirage—41–325%-0.05
anubis—32–167%-0.01
office—20–20%-0.08
dust2—22–0100%0.01
vertigo—10–10%0.05
golden—11–0100%-0.01

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (11% over 18). Start with the 6 essential Ancient 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.

14.8%Headshot accuracy
35.2%Accuracy (enemy spotted)
36.8%Spray accuracy
83.4%Counter-strafing
11.6°Preaim
646msReaction time
41.6%T opening success
44.3%CT opening success
0.17Enemies flashed / flash
5.2%Flash assists
10.58HE damage / grenade
1.23Flashes / 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. 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 14.7643% — below the 15% mark we flag

    Aim Training →
  2. 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 0.1691 — 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.

11% win rate across 18 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

MapScoreRatingHS%Date
vertigo7–130.0512%22 Oct →
inferno8–8-0.1410%22 Oct →
nuke9–20.0710%22 Oct →
nuke13–110.0510%19 Oct →
overpass5–13-0.0215%19 Oct →
nuke10–1-0.0310%18 Oct →
cache9–130.0516%18 Oct →
anubis13–30.049%18 Oct →
ancient13–11-0.0410%6 Sept →
cache10–13-0.0521%23 Aug →
mirage13–11-0.069%17 Aug →
ancient10–13-0.0317%10 Aug →
inferno9–130.0214%2 Jul →
anubis12–12-0.0713%27 Jun →
inferno11–13-0.0123%27 Jun →
nuke2–13-0.087%27 Jun →
cache6–13-0.0617%23 Jun →
cache13–10-0.0515%23 Jun →
cache10–13-0.0316%6 May →
cache13–7-0.0014%6 May →
cache13–70.018%29 Apr →
cache13–20.0624%29 Apr →
cache7–13-0.0412%29 Apr →
office7–13-0.0813%1 Mar →
nuke13–11-0.0213%22 Feb →
anubis13–90.0114%6 Feb →
nuke9–130.0421%6 Feb →
nuke13–80.0619%1 Jan →
mirage7–13-0.0311%1 Jan →
overpass11–13-0.0619%15 Dec →
nuke6–13-0.0318%14 Dec →
inferno13–11-0.0119%7 Dec →
train13–8-0.028%7 Dec →
nuke13–20.168%7 Dec →
dust213–80.0111%5 Dec →
office2–3-0.090%5 Dec →
mirage6–13-0.0110%2 Dec →
train13–30.0211%2 Dec →
ancient8–13-0.037%2 Dec →
nuke13–80.0116%30 Nov →
dust213–7-0.0010%27 Nov →
inferno13–110.0513%27 Nov →
train8–130.017%26 Nov →
inferno13–70.0714%26 Nov →
overpass2–13-0.0816%10 Nov →
train13–80.1018%10 Nov →
inferno11–130.0814%1 Nov →
overpass13–90.0611%26 Oct →
train13–10-0.0017%26 Oct →
nuke13–5-0.014%20 Oct →
ancient4–13-0.0211%19 Oct →
ancient15–150.0014%19 Oct →
nuke13–30.0919%15 Oct →
train13–6-0.0110%15 Oct →
overpass15–150.1013%13 Oct →
golden13–7-0.0115%13 Oct →
ancient4–130.0216%10 Oct →
nuke13–70.1010%10 Oct →
inferno13–00.1230%10 Oct →
nuke7–13-0.0615%8 Oct →
inferno13–70.1013%8 Oct →
nuke16–140.0215%8 Oct →
ancient8–130.0414%1 Oct →
nuke10–13-0.019%1 Oct →
train13–90.0924%1 Oct →
overpass6–130.0522%30 Sept →
overpass13–60.0511%30 Sept →
nuke11–13-0.0317%30 Sept →
ancient3–130.0213%30 Sept →
overpass6–13-0.077%24 Sept →
nuke13–50.1116%24 Sept →
ancient3–13-0.0221%24 Sept →
train13–30.0622%24 Sept →
ancient11–13-0.0110%22 Sept →
overpass6–130.0412%22 Sept →
nuke13–10-0.0112%22 Sept →
overpass8–130.0112%22 Sept →
nuke13–11-0.0211%21 Sept →
ancient11–130.0713%21 Sept →
ancient13–20.019%21 Sept →
ancient6–13-0.0217%21 Sept →
nuke13–5-0.009%21 Sept →
nuke7–130.1310%19 Sept →
nuke13–80.1011%29 Aug →
train13–100.0321%29 Aug →
ancient5–13-0.0614%28 Aug →
overpass13–100.0215%28 Aug →
ancient5–130.019%28 Aug →
nuke13–90.088%28 Aug →
nuke3–13-0.0112%26 Aug →
mirage10–13-0.094%26 Aug →
inferno12–16-0.0011%26 Aug →
ancient4–13-0.036%26 Aug →
overpass15–15-0.039%25 Aug →
inferno13–40.0314%25 Aug →
train13–6-0.0114%25 Aug →
inferno6–13-0.0419%25 Aug →
ancient10–13-0.0313%25 Aug →
nuke5–13-0.0916%23 Aug →
ancient8–13-0.0313%23 Aug →

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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