ounailija lapa

ounailija lapa — CS2 Stats

FI76561198225377128[U:1:265111400]Steam profile ↗✓ No bans

1,162Tracked matches43%Win rate2020Tracked since
4,489Hours in CS2Hrs last 2 wks
CSDB Rating4.1 DevelopingSupport
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 20 Sep 2026 (45 days ago). Ranks are recorded once per day this page is viewed.

0Faceit ELO · 966 → 966
+23Tracked matches · now 1,162
−15.3ppWin rate · 58.6% → 43.3%

Rating over time

966Highest Faceit ELO seen on CSDB
51Days played since 2025-04-20

Faceit ELO

  • At peak — 966
  • Next: Level 5 at 1,051 — 85 to go
  • Reached: Level 4 · Level 3 · Level 2
  • Level 4 first seen 2026-09-20
  • Level 3 first seen 2026-09-20

Premier is CSDB’s own observation — it builds from the day a profile is first viewed and cannot be backfilled. Faceit ELO is CSDB’s own observation — no feed exposes ELO per match, so that line only has the days the profile was viewed and cannot be backfilled.

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CSDB.GGounailija lapaPEAK ELO966csdb.gg/stats

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Performance scores

Aim48
Positioning50
Utility51

0–100 skill scores via Leetify.

Recent form

STEADY46–52–2Last 10046%Win rateLWLLWWLLWL

Last 10 vs previous 10: +20pp win rate · +0.02 avg rating · +4.0pp headshot accuracy · +38ms 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

  • 2–3 · 40% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 14%
  • Avg reaction 547ms

Last 10

  • 4–6 · 40% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 17%
  • Avg reaction 562ms

Last 20

  • 6–14 · 30% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 15%
  • Avg reaction 543ms

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 (+1.8 against its own average).

Aim4.8
Utility5.1
Positioning5.0
Opening Duels0.3
Clutch2.6

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

iM

Plays most like iM 73% 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

CT openings. Opening success drops from 53% on T to 26% on CT — first contacts on the defending side are being lost.

Reaction time. 566ms 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

Aim4.8
Positioning5.0
Utility5.1
Mechanics5.2
Opening Duels2.8
Win Impact2.8

Composite 4.1/10 (Developing), a weighted mean of the bars with a small opposition adjustment (×0.90 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.02+0.01
first ⅓ avg -0.03 → last ⅓ avg -0.02
Reaction time557ms−91ms
first ⅓ avg 648ms → last ⅓ avg 557ms
Headshot accuracy14.4%+2.1%
first ⅓ avg 12.3% → last ⅓ avg 14.4%

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 · 13–8 · anubis, 18 Feb →
29%Best headshot accuracy · 13–6 · dust2, 9 Sept →
422msFastest reaction time · 13–10 · ancient, 12 Oct →
13–3Biggest win · anubis, 18 Aug →

Across the last 100 tracked matches.

Highlights

3Longest win streak
6–8In matches decided by ≤2 rounds
9Overtime games

Map breakdown

anubisBest map · 67% over 18ancientWeakest map · 20% over 20
MapGradePlayedRecordWin rateAvg rating
ancientD204–1620%-0.03
nukeA1911–858%-0.02
anubisS1812–667%-0.02
infernoB178–947%-0.03
dust2B115–645%-0.03
overpassA74–357%-0.03
mirage—31–233%-0.01
cache—31–233%-0.02
train—20–20%-0.04

Across the last 100 tracked matches.

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

Lifetime stats

65,942Lifetime kills
0.76K/D
4,270Matches
43.4%Match win rate
34.9%Headshot %
14.7%Shot accuracy · Top 50% of tracked players
6,229MVPs
2,030Hours (in match)
5,900Bombs planted
981Bombs defused

Most-used weapons

AK-4721,622
UMP-453,478
P2502,303
AWP1,953
P901,794
Knife1,134

Lifetime map wins

9,253inferno
4,832nuke
4,528dust2
1,642train
997vertigo
174cbble
43lake
21ar_shoots

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

Faceit stats

Combat

1,218Matches
48%Win rate
0.76Avg K/D
65.2ADR
34%Headshot %

Clutches & streaks

33%1v1 clutch win
16%1v2 clutch win
7Longest win streak

Recent Faceit resultsWLLWL

MapMatchesWin rateAvg K/DAvg kills
Ancient6835%0.8412.6
Inferno6263%1.0713.5
Anubis5847%0.9313.3
Nuke5553%0.8712.0
Dust23132%0.7812.0
Mirage2143%0.7510.7
Overpass1540%0.7811.3
Train825%0.9114.3

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.

15.0%Headshot accuracy
32.3%Accuracy (enemy spotted)
38.4%Spray accuracy
73.4%Counter-strafing
13.6°Preaim
566msReaction time
52.7%T opening success
26.1%CT opening success
0.50Enemies flashed / flash
3.4%Flash assists
11.94HE damage / grenade
4.16Flashes / 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 13.6203° — above the 12° 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 0.4984 — 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.

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

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

MapScoreRatingHS%Date
mirage10–13-0.0121%25 Oct →
inferno13–9-0.0310%25 Oct →
nuke8–13-0.0112%21 Oct →
anubis16–19-0.0213%21 Oct →
nuke13–10-0.0216%18 Oct →
anubis13–10-0.0213%18 Oct →
anubis11–13-0.0228%14 Oct →
nuke13–16-0.0216%14 Oct →
anubis13–6-0.0621%14 Oct →
inferno6–130.0118%11 Oct →
anubis11–13-0.0314%11 Oct →
nuke13–7-0.0412%30 Sept →
dust21–2-0.090%30 Sept →
inferno11–13-0.0121%27 Sept →
dust24–13-0.0814%27 Sept →
dust210–130.0118%27 Sept →
ancient1–13-0.1613%23 Sept →
cache8–13-0.0316%23 Sept →
cache13–40.009%23 Sept →
nuke7–13-0.0211%20 Sept →
nuke13–11-0.0210%16 Sept →
anubis13–70.0116%16 Sept →
nuke13–80.048%13 Sept →
inferno6–13-0.0316%13 Sept →
anubis13–80.0110%13 Sept →
dust213–60.0029%9 Sept →
nuke13–110.0016%9 Sept →
cache7–13-0.0322%9 Sept →
inferno13–70.049%6 Sept →
dust211–13-0.038%6 Sept →
nuke13–50.0615%6 Sept →
inferno16–140.0015%2 Sept →
ancient9–13-0.069%2 Sept →
anubis13–80.0213%2 Sept →
dust213–6-0.0618%30 Aug →
inferno6–13-0.0712%30 Aug →
inferno3–13-0.1122%30 Aug →
ancient3–13-0.054%26 Aug →
anubis13–10-0.0416%26 Aug →
ancient13–16-0.0420%26 Aug →
ancient13–16-0.0410%19 Aug →
inferno13–10-0.0616%19 Aug →
ancient15–150.0014%19 Aug →
anubis13–3-0.0224%18 Aug →
overpass13–60.0212%8 Apr →
ancient9–130.0212%7 Apr →
train8–13-0.0317%2 Apr →
anubis13–9-0.068%2 Apr →
anubis13–11-0.0114%31 Mar →
ancient9–13-0.0220%31 Mar →
inferno10–13-0.0325%31 Mar →
inferno10–130.0026%23 Mar →
overpass13–3-0.0312%23 Mar →
nuke2–13-0.0227%23 Mar →
anubis8–13-0.0115%23 Mar →
ancient4–13-0.0320%25 Feb →
overpass9–13-0.0621%23 Feb →
inferno5–13-0.0624%23 Feb →
anubis13–80.0614%18 Feb →
anubis16–19-0.0210%11 Feb →
dust223–25-0.0426%28 Jan →
nuke13–80.0118%26 Jan →
dust213–10-0.0412%9 Nov →
inferno13–100.0027%9 Nov →
overpass9–13-0.0713%20 Oct →
ancient13–10-0.0411%12 Oct →
nuke8–13-0.0418%9 Oct →
nuke13–7-0.0119%9 Oct →
inferno7–13-0.0421%1 Oct →
ancient13–11-0.0512%1 Oct →
nuke13–8-0.0020%25 Sept →
inferno13–9-0.028%24 Sept →
nuke9–13-0.0419%24 Sept →
ancient13–16-0.019%15 Sept →
overpass13–9-0.0310%15 Sept →
ancient6–13-0.0812%15 Sept →
overpass13–6-0.0310%14 Sept →
inferno13–80.0115%10 Sept →
ancient7–13-0.048%10 Sept →
nuke1–13-0.1011%10 Sept →
overpass11–13-0.0415%9 Sept →
nuke13–5-0.0118%3 Sept →
nuke13–7-0.0127%31 Aug →
ancient4–13-0.026%31 Aug →
mirage13–11-0.054%27 Aug →
ancient10–13-0.015%27 Aug →
inferno13–6-0.056%24 Aug →
ancient13–4-0.033%24 Aug →
dust212–120.0313%24 Aug →
dust213–10-0.0616%17 Aug →
train2–13-0.0419%25 May →
dust213–10-0.0018%25 May →
mirage8–130.0213%15 May →
ancient13–100.0113%11 May →
nuke3–13-0.0913%11 May →
ancient11–13-0.0610%4 May →
anubis13–5-0.044%27 Apr →
ancient7–130.0414%27 Apr →
anubis13–3-0.0210%27 Apr →
anubis1–13-0.074%20 Apr →

Match data via Leetify.

Recent teammates

Milestones

1,000 Matches
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

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