ekiNDAR

ekiNDAR — CS2 Stats

FI76561198050986816[U:1:90721088]Steam profile ↗✓ No bans

1,078Tracked matches53%Win rate2020Tracked since
CSDB Rating6.0 SolidPositional Player
FaceitLevel 9 · 2,049 ELOTop 20.7% of ranked FACEIT players
WingmanLegendary Eagle
Ladder ranks via Leetify

Rating over time

14,504Peak Premier in tracked matches
2,049Highest Faceit ELO seen on CSDB
71Days played since 2024-04-27

Faceit ELO

  • At peak — 2,049
  • Next: 2,500 ELO at 2,500 — 451 to go
  • Reached: Level 10 · Level 9 · Level 8 · Level 7

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

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CSDB.GGekiNDARFACEITLevel 9 · 2,049 ELOSTANDINGTop 20.7% of rankedPEAK ELO2,049csdb.gg/stats

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Performance scores

Aim76
Positioning65
Utility48

0–100 skill scores via Leetify.

Recent form

COLD45–55Last 10045%Win rateLLLLWLWWLL

Last 10 vs previous 10: −30pp win rate · −0.02 avg rating · +0.6pp headshot accuracy · −44ms 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 0.01
  • Avg headshot accuracy 27%
  • Avg reaction 573ms

Last 10

  • 3–7 · 30% win rate
  • Avg rating 0.02
  • Avg headshot accuracy 26%
  • Avg reaction 549ms

Last 20

  • 9–11 · 45% win rate
  • Avg rating 0.03
  • Avg headshot accuracy 26%
  • Avg reaction 571ms

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 (+1.9 against its own average).

Aim7.6
Utility4.8
Positioning6.5
Opening Duels3.8
Clutch3.4

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 92% playstyle similarity

Most alike: utility contribution, positioning profile.

Where you differ: lower aim profile; 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

Reaction time. 583ms 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

Aim7.6
Positioning6.5
Utility4.8
Mechanics4.9
Opening Duels3.5
Win Impact6.1

Composite 6.0/10 (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+0.02
first ⅓ avg 0.00 → last ⅓ avg 0.02
Reaction time592ms−71ms
first ⅓ avg 663ms → last ⅓ avg 592ms
Headshot accuracy24.1%+5.6%
first ⅓ avg 18.6% → last ⅓ avg 24.1%

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

0.20Best match rating · 3–3 · anubis, 11 Apr →
41%Best headshot accuracy · 11–13 · ancient, 8 Feb →
391msFastest reaction time · 6–13 · vertigo, 4 Oct →
13–2Biggest win · ancient, 3 Oct →

Across the last 100 tracked matches.

Highlights

5Longest win streak
L4Current streak
8–14In matches decided by ≤2 rounds
11Overtime games

Map breakdown

trainBest map · 63% over 8anubisWeakest map · 20% over 15
MapGradePlayedRecordWin rateAvg rating
ancientA2715–1256%0.03
mirageC167–944%0.01
anubisD153–1220%0.00
infernoA116–555%-0.01
cacheB105–550%0.04
nukeC104–640%0.01
trainA85–363%0.01
vertigo—20–20%-0.02
dust2—10–10%-0.03

Across the last 100 tracked matches.

Anubis is currently your weakest sufficiently-sampled map (20% over 15). Start with the 6 essential Anubis lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

819Matches
52%Win rate
1.19Avg K/D
82.0ADR
52%Headshot %

Clutches & streaks

37%1v1 clutch win
24%1v2 clutch win
8Longest win streak

Recent Faceit resultsWWLLL

MapMatchesWin rateAvg K/DAvg kills
Mirage18956%1.2017.9
Ancient13954%1.1617.3
Anubis11447%1.1817.6
Nuke10556%1.3218.8
Inferno4740%0.9915.0
Vertigo3845%1.0316.3
Train3253%1.2818.9
Dust22025%0.9213.8

Faceit-match stats via the FACEIT Data API — a separate match pool from the sections above.

Skill profile

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

24.5%Headshot accuracy
34.5%Accuracy (enemy spotted)
38.5%Spray accuracy
72.2%Counter-strafing
8.8°Preaim
583msReaction time
40.2%T opening success
47.4%CT opening success
0.50Enemies flashed / flash
6.6%Flash assists
7.47HE damage / grenade
8.38Flashes / 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 0.4958 — below the 0.5 mark we flag

    Grenade Lineups →
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.

20% win rate across 15 tracked games — your weakest map with enough games to be worth reading into.

Anubis callouts & strategy →Anubis grenade lineups →

Recent matches

MapScoreRatingHS%Date
cache9–130.0533%12 Oct →
ancient8–13-0.0026%10 Oct →
mirage9–13-0.0225%5 Oct →
vertigo6–13-0.0427%4 Oct →
nuke13–90.0524%4 Oct →
mirage10–13-0.0613%3 Oct →
ancient13–20.0820%3 Oct →
ancient13–80.0736%30 Sept →
cache11–130.0131%29 Sept →
cache5–130.0127%11 Sept →
anubis13–70.078%6 Sept →
cache7–13-0.0315%6 Sept →
train6–130.0826%5 Sept →
ancient13–16-0.0125%5 Sept →
cache13–20.0733%5 Sept →
mirage5–130.0738%3 Sept →
ancient13–100.0329%23 Aug →
ancient13–60.0220%22 Aug →
cache13–90.0432%3 May →
cache13–50.0630%3 May →
cache11–130.0326%2 May →
cache13–90.0836%2 May →
cache13–90.0532%2 May →
train13–50.0137%18 Dec →
train13–11-0.0111%24 May →
ancient16–14-0.0014%24 May →
ancient3–13-0.0824%19 May →
dust25–13-0.0311%18 May →
anubis13–8-0.0312%18 May →
train19–150.0220%14 May →
anubis6–130.0418%11 May →
nuke11–13-0.0421%5 May →
anubis11–13-0.0014%5 May →
ancient12–16-0.0321%5 May →
anubis16–14-0.0215%3 May →
mirage13–110.0927%24 Apr →
train10–13-0.0318%22 Apr →
ancient3–13-0.0614%14 Apr →
ancient13–50.0613%13 Apr →
ancient13–70.0832%12 Apr →
ancient13–80.1033%11 Apr →
anubis3–30.2024%11 Apr →
anubis9–13-0.0316%4 Apr →
mirage13–100.0421%29 Mar →
anubis11–13-0.0321%28 Mar →
mirage1–13-0.0724%24 Mar →
anubis10–13-0.0113%23 Mar →
ancient13–90.0127%23 Mar →
ancient13–110.0520%23 Mar →
ancient10–13-0.0222%23 Mar →
inferno7–13-0.0127%2 Mar →
anubis11–13-0.0025%1 Mar →
mirage13–100.0324%27 Feb →
mirage13–9-0.0122%20 Feb →
ancient11–120.0320%19 Feb →
anubis13–160.0222%16 Feb →
inferno13–100.0222%10 Feb →
ancient11–130.0616%10 Feb →
ancient13–80.0425%8 Feb →
ancient13–80.1226%8 Feb →
ancient11–130.0341%8 Feb →
train7–13-0.0338%3 Feb →
mirage13–60.0425%3 Feb →
inferno13–30.0326%25 Jan →
inferno5–130.0226%25 Jan →
inferno13–9-0.0317%23 Jan →
mirage4–13-0.0314%22 Jan →
ancient13–90.0417%21 Jan →
nuke19–170.0430%17 Jan →
ancient11–13-0.0113%17 Jan →
inferno13–80.0315%16 Jan →
mirage4–13-0.024%15 Jan →
anubis7–13-0.0816%13 Jan →
train16–13-0.0116%12 Jan →
nuke13–11-0.0414%12 Jan →
ancient13–90.0120%11 Jan →
mirage13–7-0.0013%5 Jan →
mirage3–13-0.0022%5 Jan →
inferno12–16-0.0823%3 Jan →
nuke16–140.0535%2 Jan →
inferno13–90.0316%19 Dec →
nuke10–130.0115%19 Dec →
nuke9–130.0210%19 Dec →
mirage11–130.0020%19 Dec →
anubis8–130.0415%18 Dec →
ancient16–130.0410%18 Dec →
ancient11–13-0.009%17 Dec →
nuke11–130.0827%12 Dec →
mirage9–13-0.0227%11 Dec →
inferno3–13-0.0521%2 Dec →
mirage13–80.1121%22 Nov →
train7–00.0425%19 Nov →
anubis7–12-0.0822%5 Jun →
nuke7–130.0430%31 May →
inferno4–13-0.0124%30 May →
vertigo10–13-0.0122%14 May →
ancient8–130.0916%14 May →
inferno13–9-0.0411%11 May →
nuke3–9-0.1321%1 May →
anubis10–13-0.0513%27 Apr →

Match data via Leetify.

Recent teammates

Milestones

2K ELO1,000 Matches
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

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