serbanito

serbanito — CS2 Stats

76561198230319232[U:1:270053504]Steam profile ↗✓ No bans

879Tracked matches60%Win rate2020Tracked since
887Hours in CS
CSDB Rating4.7 DevelopingPositional Player
FaceitLevel 8 · 1,903 ELOTop 31.2% of ranked FACEIT players
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 10 Sep 2026 (65 days ago). Ranks are recorded once per day this page is viewed.

0Faceit ELO · 1,903 → 1,903
+9Tracked matches · now 879
0.0ppWin rate · 60.0% → 60.0%

Rating over time

Premier CS Rating: 19,136 -143 20 Dec – 24 Apr · 19 days played
18,26620,532peak 20,53220 Dec24 Apr
20,975Peak Premier in tracked matches
1,903Highest Faceit ELO seen on CSDB
72Days played since 2025-09-16

Faceit ELO

  • At peak — 1,903
  • Next: Level 10 at 2,001 — 98 to go
  • Reached: Level 9 · Level 8 · Level 7 · Level 6
  • Level 9 first seen 2026-09-10
  • Level 8 first seen 2026-09-10

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

How this compares with the same rank

Median values for Level 8 among CSDB-tracked players (n=15,525), from Valve's own lifetime stats. Aim, positioning and utility scores are deliberately not benchmarked here — those are a third-party provider's derived metrics, which CSDB does not store.

MetricThis playerLevel 8 medianLevel 9 medianvs Level 9
Headshot rate52.4%46.8%48.2%above
Shot accuracy7.7%13.0%13.2%5.5% short
Kill/death ratio1.191.061.07above
Match win rate48.5%46.3%46.9%above

This profile matches the typical Level 9 player on 3 of 4 comparable metrics.

Widest gap: Shot accuracy. That is the metric furthest from the Level 9 median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.

Share this profile

CSDB.GGserbanitoFACEITLevel 8 · 1,903 ELOSTANDINGTop 31.2% of rankedPEAK ELO1,903csdb.gg/stats

The image is a snapshot; the link keeps updating. Nothing here is published anywhere — it is generated in your browser when you click.

Performance scores

Aim61
Positioning42
Utility36

0–100 skill scores via Leetify.

Recent form

STEADY49–47–4Last 10049%Win rateLWLWWLWLWW

Last 10 vs previous 10: 0pp win rate · +0.01 avg rating · +6.1pp headshot accuracy · +12ms reaction

Win rate 0pp across the last 10 against the 10 before — within the normal variation of a 10-match window (±20pp), so no real shift yet.

Last 5 · 10 · 20 matches

Last 5

  • 3–2 · 60% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 15%
  • Avg reaction 625ms

Last 10

  • 6–4 · 60% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 18%
  • Avg reaction 614ms

Last 20

  • 12–8 · 60% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 15%
  • Avg reaction 608ms

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).

Aim6.1
Utility3.6
Positioning4.2
Opening Duels0.8
Clutch3.8

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

Twistzz

Plays most like Twistzz 93% playstyle similarity

Most alike: positioning profile, utility contribution.

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. 630ms 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

Aim6.1
Positioning4.2
Utility3.6
Mechanics5.5
Opening Duels0.7
Win Impact8.3

Composite 4.7/10 (Developing), a weighted mean of the bars with a small opposition adjustment (×0.95 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.02+0.02
first ⅓ avg -0.04 → last ⅓ avg -0.02
Reaction time618ms−63ms
first ⅓ avg 681ms → last ⅓ avg 618ms
Headshot accuracy15.9%−3.4%
first ⅓ avg 19.3% → last ⅓ avg 15.9%

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

0.09Best match rating · 13–2 · train, 4 Jan →
43%Best headshot accuracy · 13–16 · inferno, 15 Mar →
469msFastest reaction time · 13–10 · anubis, 26 Jan →
13–1Biggest win · dust2, 16 Mar →

Across the last 100 tracked matches.

Highlights

5Longest win streak
5–13In matches decided by ≤2 rounds
12Overtime games

Map breakdown

ancientBest map · 78% over 9infernoWeakest map · 17% over 6
MapGradePlayedRecordWin rateAvg rating
dust2B3717–2046%-0.03
nukeC145–936%-0.03
mirageB136–746%-0.05
ancientS97–278%-0.01
anubisB84–450%-0.03
infernoD61–517%-0.03
trainS64–267%-0.03
overpassA53–260%-0.03
vertigo—11–0100%0.02
cache—11–0100%-0.06

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (17% over 6). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

55,254Lifetime kills
1.19K/D · Top 25% of Level 8 players
1,733Matches
48.5%Match win rate · Top 50% of Level 8 players
52.4%Headshot % · Top 25% of Level 8 players
7.7%Shot accuracy
3,552MVPs
887Hours (in match)
1,933Bombs planted
390Bombs defused

Most-used weapons

Lifetime map wins

9,114dust2
912nuke
724inferno
505train
310vertigo
113italy
113office
103dust

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

Faceit stats

Combat

640Matches
52%Win rate
0.92Avg K/D
71.0ADR
46%Headshot %

Clutches & streaks

39%1v1 clutch win
17%1v2 clutch win
15Longest win streak

Recent Faceit resultsLWLWL

MapMatchesWin rateAvg K/DAvg kills
Dust218561%1.0113.7
Mirage11252%0.9213.4
Anubis8450%0.9014.4
Ancient7654%1.0514.2
Nuke3845%0.8713.7
Train2730%0.6811.1
Inferno1844%0.8813.9
Overpass667%0.8412.2

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.

16.7%Headshot accuracy
34.2%Accuracy (enemy spotted)
38.7%Spray accuracy
74.5%Counter-strafing
9.8°Preaim
630msReaction time
34.2%T opening success
31.4%CT opening success
0.55Enemies flashed / flash
3.1%Flash assists
5.68HE damage / grenade
5.54Flashes / 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

    You are losing most of the first duels you take on T side, which is usually a peeking and spacing problem, not aim.

    T opening duels 34.1557% — below the 40% mark we flag

Spend your practice time on Inferno

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

17% win rate across 6 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
inferno11–13-0.028%17 Oct →
ancient13–4-0.0310%2 Oct →
dust212–16-0.078%19 Aug →
vertigo13–40.0218%6 Aug →
dust213–4-0.0630%6 Aug →
dust210–13-0.0019%6 Aug →
dust213–70.0330%6 Aug →
inferno11–13-0.0129%26 Jul →
mirage13–90.0119%26 Jul →
dust213–9-0.037%25 Jul →
mirage16–120.0615%25 Jul →
cache13–7-0.0614%6 May →
dust29–130.0214%24 Apr →
overpass13–4-0.0118%22 Apr →
dust26–13-0.1011%21 Apr →
nuke13–9-0.049%16 Apr →
mirage11–13-0.0411%15 Apr →
dust213–100.000%9 Apr →
anubis9–13-0.0715%1 Apr →
dust213–10-0.0310%26 Mar →
dust213–1-0.0313%16 Mar →
inferno13–160.0343%15 Mar →
overpass6–13-0.079%12 Mar →
anubis13–11-0.0216%8 Mar →
overpass13–60.0516%5 Mar →
nuke13–40.0416%25 Feb →
dust25–13-0.0226%23 Feb →
ancient13–30.0020%22 Feb →
inferno9–13-0.0417%21 Feb →
overpass13–8-0.0613%19 Feb →
ancient13–8-0.038%19 Feb →
anubis9–13-0.0419%13 Feb →
mirage13–9-0.0114%11 Feb →
ancient13–5-0.0417%10 Feb →
mirage8–13-0.058%9 Feb →
dust211–13-0.067%9 Feb →
dust210–13-0.0522%8 Feb →
inferno13–9-0.0613%8 Feb →
anubis13–6-0.0414%4 Feb →
nuke13–9-0.0711%30 Jan →
dust29–13-0.0630%28 Jan →
inferno9–13-0.0516%27 Jan →
dust213–30.0112%26 Jan →
anubis13–100.0222%26 Jan →
mirage13–11-0.077%25 Jan →
dust213–50.019%25 Jan →
anubis13–80.0313%25 Jan →
dust210–13-0.0320%25 Jan →
nuke6–13-0.0219%21 Jan →
dust213–9-0.0017%19 Jan →
anubis0–13-0.070%15 Jan →
dust24–13-0.0214%8 Jan →
nuke11–13-0.0616%6 Jan →
train13–20.0924%4 Jan →
train11–13-0.0110%3 Jan →
train16–14-0.054%2 Jan →
dust213–10-0.0621%31 Dec →
mirage17–19-0.0219%31 Dec →
train3–13-0.1118%31 Dec →
nuke8–13-0.0511%30 Dec →
dust211–13-0.0515%30 Dec →
dust213–5-0.0018%29 Dec →
dust23–13-0.0920%27 Dec →
dust216–13-0.0219%27 Dec →
dust211–130.0317%27 Dec →
nuke13–80.0016%26 Dec →
ancient16–13-0.0118%26 Dec →
dust213–6-0.0313%24 Dec →
nuke15–15-0.0418%24 Dec →
mirage3–13-0.0620%23 Dec →
train13–5-0.0436%22 Dec →
dust213–110.0130%22 Dec →
nuke15–15-0.0524%21 Dec →
dust23–13-0.0715%21 Dec →
mirage9–13-0.0619%21 Dec →
dust29–13-0.0018%20 Dec →
nuke13–50.0521%20 Dec →
nuke15–15-0.0242%17 Dec →
anubis4–10-0.0725%15 Dec →
mirage6–13-0.118%15 Dec →
ancient11–130.0217%14 Dec →
nuke15–15-0.0629%12 Dec →
dust28–130.0216%11 Dec →
mirage13–11-0.0727%11 Dec →
dust22–6-0.0420%10 Dec →
dust211–13-0.074%8 Dec →
dust213–7-0.0227%4 Dec →
dust213–40.0219%4 Dec →
ancient13–90.0117%4 Dec →
nuke14–16-0.0530%24 Nov →
dust23–13-0.0619%23 Nov →
mirage0–3-0.160%22 Nov →
overpass8–13-0.0815%21 Nov →
mirage13–10-0.0412%15 Nov →
ancient4–13-0.0214%14 Nov →
nuke11–13-0.0518%8 Oct →
dust213–50.0124%4 Oct →
dust28–13-0.138%17 Sept →
ancient13–4-0.046%17 Sept →
train13–9-0.0429%16 Sept →

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 →

This profile is built from public Steam data. If it is yours, you can remove it, or delete your CSDB account.