Stefan #refrag

Stefan #refrag — CS2 Stats

NL76561198020916255[U:1:60650527]Steam profile ↗✓ No bans

464Tracked matches52%Win rate2023Tracked since
CSDB Rating6.0 SolidAll-Rounder
FaceitLevel 6Top 54.0% of ranked FACEIT players
Ladder ranks via Leetify

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CSDB.GGStefan #refragFACEITLevel 6STANDINGTop 54.0% of rankedcsdb.gg/stats

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Performance scores

Aim64
Positioning56
Utility56

0–100 skill scores via Leetify.

Recent form

STEADY50–47–3Last 10050%Win rateWLLTWWLWWL

Last 10 vs previous 10: +40pp win rate · +0.03 avg rating · +0.3pp headshot accuracy · +43ms reaction

Win rate up 40pp 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 -0.01
  • Avg headshot accuracy 13%
  • Avg reaction 674ms

Last 10

  • 5–4–1 · 50% win rate
  • Avg rating -0.00
  • Avg headshot accuracy 14%
  • Avg reaction 664ms

Last 20

  • 6–11–3 · 30% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 14%
  • 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: All-Rounder — No style dimension stands clear of the others in this profile.

Aim6.4
Utility5.6
Positioning5.6
Opening Duels4.5

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

jL

Plays most like jL 86% playstyle similarity

Most alike: utility contribution, opening-duel success.

Where you differ: lower positioning profile; 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. 615ms 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

Aim6.4
Positioning5.6
Utility5.6
Mechanics7.2
Opening Duels4.5
Win Impact5.6

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 rating-0.01−0.00
first ⅓ avg -0.01 → last ⅓ avg -0.01
Reaction time626ms−9ms
first ⅓ avg 636ms → last ⅓ avg 626ms
Headshot accuracy13.7%−0.4%
first ⅓ avg 14.0% → last ⅓ avg 13.7%

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

0.15Best match rating · 13–8 · train, 20 Nov →
43%Best headshot accuracy · 3–2 · mirage, 24 Aug →
438msFastest reaction time · 11–13 · train, 14 Jun →
13–1Biggest win · nuke, 22 Aug →

Across the last 100 tracked matches.

Highlights

7Longest win streak
7–9In matches decided by ≤2 rounds
1Overtime games

Map breakdown

ancientBest map · 63% over 8anubisWeakest map · 33% over 6
MapGradePlayedRecordWin rateAvg rating
mirageC187–1139%-0.01
dust2C188–1044%-0.01
nukeA159–660%0.01
infernoB136–746%-0.03
trainB105–550%0.04
ancientA85–363%-0.00
anubisD62–433%-0.04
vertigo—32–167%-0.05
overpass—32–167%-0.04
grail—11–0100%0.09
jura—11–0100%0.01
whistle—11–0100%0.02
palais—10–10%-0.04
edin—10–10%0.01
basalt—11–0100%0.06

Across the last 100 tracked matches.

Anubis is currently your weakest sufficiently-sampled map (33% over 6). Start with the 6 essential Anubis lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

27Matches
52%Win rate
1.31Avg K/D
—ADR
23%Headshot %

Clutches & streaks

—1v1 clutch win
—1v2 clutch win
3Longest win streak

Recent Faceit resultsWLWLL

MapMatchesWin rateAvg K/DAvg kills
Mirage333%0.8112.3
Inferno10%1.1416.0

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.

13.9%Headshot accuracy
36.1%Accuracy (enemy spotted)
33.9%Spray accuracy
82.3%Counter-strafing
9.7°Preaim
615msReaction time
45.7%T opening success
49.9%CT opening success
0.54Enemies flashed / flash
7.5%Flash assists
7.71HE damage / grenade
13.57Flashes / 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 13.9019% — below the 15% mark we flag

    Aim Training →
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.

33% win rate across 6 tracked games — your weakest map with enough games to be worth reading into.

Anubis callouts & strategy →Anubis grenade lineups →

Recent matches

MapScoreRatingHS%Date
mirage13–6-0.0220%20 Jan →
anubis2–7-0.0910%20 Jan →
nuke6–13-0.0212%5 Dec →
mirage12–12-0.017%5 Dec →
train13–30.0916%5 Dec →
dust213–40.029%29 Nov →
mirage9–130.0816%29 Nov →
inferno13–6-0.0115%28 Nov →
train13–60.0221%28 Nov →
nuke4–9-0.1213%28 Nov →
dust213–60.0117%28 Nov →
mirage12–12-0.0320%28 Nov →
inferno6–130.0310%27 Nov →
dust211–13-0.0517%27 Nov →
mirage12–120.0113%24 Nov →
dust211–13-0.0416%24 Nov →
mirage11–13-0.027%21 Nov →
dust211–13-0.0215%21 Nov →
vertigo3–9-0.1413%21 Nov →
inferno4–9-0.078%21 Nov →
overpass8–13-0.0118%21 Nov →
mirage13–10-0.0410%21 Nov →
mirage13–90.0412%20 Nov →
train13–80.1514%20 Nov →
dust27–10.0618%20 Nov →
inferno5–13-0.0823%19 Nov →
mirage6–13-0.016%19 Nov →
inferno2–9-0.1815%17 Sept →
ancient10–40.0614%31 Aug →
nuke11–130.0421%31 Aug →
ancient3–13-0.078%30 Aug →
overpass13–11-0.039%30 Aug →
vertigo13–8-0.059%30 Aug →
grail13–20.0916%30 Aug →
nuke13–70.0021%30 Aug →
inferno13–80.0218%30 Aug →
ancient13–60.0523%29 Aug →
train13–110.0718%29 Aug →
dust20–9-0.0813%29 Aug →
mirage13–20.0631%29 Aug →
nuke13–80.0419%27 Aug →
dust212–16-0.0517%27 Aug →
ancient13–5-0.0114%27 Aug →
nuke13–40.0012%27 Aug →
train13–60.0511%27 Aug →
train10–13-0.0221%26 Aug →
inferno13–60.0212%26 Aug →
overpass13–11-0.0723%26 Aug →
nuke10–130.0814%26 Aug →
ancient13–50.0611%26 Aug →
mirage1–13-0.1018%26 Aug →
dust213–90.0811%26 Aug →
nuke13–40.0711%25 Aug →
nuke13–110.0119%24 Aug →
dust213–90.0120%24 Aug →
mirage3–20.0243%24 Aug →
nuke13–30.0222%22 Aug →
mirage9–13-0.0220%22 Aug →
nuke13–10.0711%22 Aug →
dust211–13-0.0023%22 Aug →
mirage11–13-0.014%18 Aug →
train9–13-0.0118%18 Aug →
dust213–6-0.0615%18 Aug →
dust28–130.0225%17 Aug →
nuke6–13-0.027%17 Aug →
mirage5–13-0.0410%17 Aug →
ancient13–8-0.0512%21 Jun →
dust213–4-0.029%21 Jun →
anubis7–11-0.0711%21 Jun →
nuke13–100.0114%21 Jun →
inferno13–8-0.007%20 Jun →
anubis6–13-0.0214%20 Jun →
anubis13–10-0.0010%20 Jun →
dust24–13-0.0819%19 Jun →
anubis13–8-0.025%19 Jun →
vertigo13–30.0212%19 Jun →
nuke6–12-0.0429%19 Jun →
train3–130.039%17 Jun →
inferno6–9-0.098%17 Jun →
mirage9–13-0.0011%17 Jun →
dust26–13-0.0310%16 Jun →
anubis2–6-0.047%16 Jun →
nuke8–13-0.018%14 Jun →
inferno6–13-0.0228%14 Jun →
train11–130.0616%14 Jun →
dust213–60.0221%14 Jun →
mirage13–70.0012%14 Jun →
dust24–13-0.0224%10 Jun →
ancient7–13-0.0310%10 Jun →
inferno8–13-0.0410%9 Jun →
inferno13–5-0.027%9 Jun →
inferno13–6-0.0014%7 Jun →
mirage13–11-0.0521%7 Jun →
jura13–100.0118%5 Jun →
whistle9–70.0220%27 Nov →
palais6–9-0.0411%27 Nov →
edin11–130.0114%27 Nov →
ancient7–13-0.0321%27 Nov →
basalt13–90.0610%20 Nov →
train7–13-0.0323%20 Nov →

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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