Runa

Runa — CS2 Stats

US76561198011608823[U:1:51343095]Steam profile ↗✓ No bans

590Tracked matches33%Win rate2024Tracked since
413Hours in CS2Hrs last 2 wks
CSDB Rating2.0 LearningPositional Player
Ladder ranks via Leetify

Performance scores

Aim15
Positioning30
Utility28

0–100 skill scores via Leetify.

Recent form

COLD39–55–6Last 10039%Win rateLLLTLWLWLL

Last 10 vs previous 10: −20pp win rate · −0.05 avg rating · −0.7pp headshot accuracy · −10ms reaction

Win rate down 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

  • 0–4–1 · 0% win rate
  • Avg rating -0.07
  • Avg headshot accuracy 7%
  • Avg reaction 641ms

Last 10

  • 2–7–1 · 20% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 8%
  • Avg reaction 653ms

Last 20

  • 6–12–2 · 30% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 9%
  • Avg reaction 658ms

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 (+1.1 against its own average).

Aim1.5
Utility2.8
Positioning3.0
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

NiKo

Plays most like NiKo 67% 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. 699ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 67% 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

Aim1.5
Positioning3.0
Utility2.8
Mechanics3.8
Opening Duels0.0
Win Impact0.0

Composite 2.0/10 (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-0.03−0.02
first ⅓ avg -0.01 → last ⅓ avg -0.03
Reaction time649ms−3ms
first ⅓ avg 652ms → last ⅓ avg 649ms
Headshot accuracy8.1%−2.1%
first ⅓ avg 10.1% → last ⅓ avg 8.1%

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

0.12Best match rating · 13–3 · cache, 8 May →
34%Best headshot accuracy · 11–4 · office, 14 Apr →
469msFastest reaction time · 12–12 · ancient, 10 Aug →
13–2Biggest win · mirage, 6 May →

Across the last 100 tracked matches.

Highlights

4Longest win streak
L3Current streak
6–4In matches decided by ≤2 rounds

Map breakdown

infernoBest map · 55% over 11trainWeakest map · 9% over 11
MapGradePlayedRecordWin rateAvg rating
nukeB179–853%-0.01
ancientB136–746%-0.03
cacheD124–833%-0.01
vertigoB115–645%-0.01
trainD111–109%-0.04
infernoA116–555%-0.03
mirageC52–340%-0.02
dust2C52–340%-0.04
overpass—41–325%0.00
anubis—30–30%-0.08
office—31–233%-0.05
italy—31–233%-0.01
alpine—21–150%0.01

Across the last 100 tracked matches.

Lifetime stats

8,399Lifetime kills
0.81K/D
711Matches
42.1%Match win rate
34.9%Headshot %
0.9%Shot accuracy
973MVPs
236Hours (in match)
703Bombs planted
111Bombs defused

Most-used weapons

Lifetime map wins

1,031inferno
764nuke
555vertigo
511train
510office
349dust2
159italy
25lake

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.

8.5%Headshot accuracy
29.6%Accuracy (enemy spotted)
31.5%Spray accuracy
66.9%Counter-strafing
14.4°Preaim
699msReaction time
24.8%T opening success
27.0%CT opening success
0.34Enemies flashed / flash
5.3%Flash assists
6.60HE damage / grenade
2.72Flashes / 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 14.4063° — 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 8.5121% — 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 0.3433 — below the 0.5 mark we flag

    Grenade Lineups →
Spend your practice time on Train

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

9% win rate across 11 tracked games — your weakest map with enough games to be worth reading into.

Train callouts & strategy →

Recent matches

MapScoreRatingHS%Date
mirage8–13-0.0710%5 Oct →
vertigo8–13-0.108%5 Oct →
train5–13-0.075%4 Oct →
ancient12–12-0.048%10 Aug →
train11–13-0.067%15 Jun →
ancient13–11-0.046%14 Jun →
anubis5–13-0.075%28 May →
ancient13–3-0.015%28 May →
nuke5–13-0.009%23 May →
mirage6–13-0.0820%23 May →
train0–20.000%17 May →
cache8–13-0.0712%8 May →
cache13–30.1212%8 May →
cache12–12-0.0811%7 May →
vertigo5–13-0.006%7 May →
anubis7–13-0.056%6 May →
inferno13–40.025%6 May →
mirage13–20.095%6 May →
nuke13–9-0.046%5 May →
cache3–13-0.0326%5 May →
dust23–13-0.1111%4 May →
dust25–13-0.056%4 May →
train6–13-0.117%4 May →
nuke13–50.0316%3 May →
inferno12–120.0411%2 May →
dust22–1-0.060%2 May →
nuke9–13-0.076%2 May →
cache4–13-0.045%2 May →
cache13–4-0.006%2 May →
inferno1–13-0.103%2 May →
overpass3–130.0217%2 May →
nuke9–13-0.028%2 May →
nuke11–30.010%2 May →
inferno4–13-0.1010%2 May →
cache8–13-0.0711%2 May →
nuke13–50.013%2 May →
cache13–50.0115%2 May →
cache10–13-0.048%1 May →
cache8–13-0.028%29 Apr →
mirage12–12-0.0812%29 Apr →
nuke9–13-0.0210%29 Apr →
ancient2–130.054%29 Apr →
cache5–130.029%29 Apr →
cache13–70.127%29 Apr →
inferno8–13-0.087%27 Apr →
overpass10–13-0.0315%27 Apr →
vertigo13–8-0.0016%27 Apr →
ancient11–13-0.059%27 Apr →
dust213–80.084%25 Apr →
ancient3–13-0.083%25 Apr →
nuke9–13-0.098%25 Apr →
nuke6–13-0.039%25 Apr →
mirage13–70.0423%25 Apr →
nuke1–50.0114%24 Apr →
train3–13-0.029%24 Apr →
vertigo8–13-0.0620%24 Apr →
train6–13-0.0511%23 Apr →
inferno13–11-0.035%22 Apr →
ancient7–13-0.064%15 Apr →
anubis7–13-0.1110%15 Apr →
office12–12-0.0015%14 Apr →
dust20–11-0.0310%14 Apr →
alpine2–80.018%14 Apr →
office11–4-0.0634%14 Apr →
inferno13–70.028%14 Apr →
nuke13–90.0414%13 Apr →
ancient6–13-0.084%13 Apr →
train1–10-0.0612%13 Apr →
ancient13–80.0213%9 Apr →
vertigo6–13-0.0222%9 Apr →
ancient13–5-0.023%8 Apr →
nuke13–4-0.0011%8 Apr →
ancient0–8-0.0014%7 Apr →
office8–13-0.089%7 Apr →
ancient13–7-0.050%7 Apr →
inferno4–13-0.1011%7 Apr →
italy12–12-0.0413%7 Apr →
vertigo13–110.006%6 Apr →
nuke13–7-0.0211%6 Apr →
nuke9–130.0611%5 Apr →
vertigo9–13-0.0315%3 Apr →
ancient13–100.0020%3 Apr →
train11–13-0.0111%2 Apr →
vertigo13–110.059%2 Apr →
inferno13–40.056%2 Apr →
italy8–130.0610%2 Apr →
nuke13–50.019%2 Apr →
alpine13–100.0013%30 Mar →
vertigo9–130.027%30 Mar →
train8–13-0.0511%30 Mar →
vertigo13–7-0.015%29 Mar →
overpass13–100.028%29 Mar →
overpass9–130.0014%27 Mar →
train13–11-0.064%27 Mar →
nuke13–90.0210%27 Mar →
vertigo13–90.036%26 Mar →
inferno13–40.025%26 Mar →
train4–130.0515%25 Mar →
italy13–2-0.0712%25 Mar →
inferno13–7-0.0210%25 Mar →

Match data via Leetify.

Recent teammates

Milestones

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

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