SUKSI VITTUUN

SUKSI VITTUUN — CS2 Stats

FI76561199088546085[U:1:1128280357]Steam profile ↗✓ No bans

637Tracked matches59%Win rate2024Tracked since
1,012Hours in CS44Hrs last 2 wks
CSDB Rating5.1 DevelopingPositional Player
WingmanSilver Elite Master
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 2 Nov 2026 (3 days ago). Ranks are recorded once per day this page is viewed.

+41Tracked matches · now 637
+1.5ppWin rate · 57.1% → 58.6%

Rating over time

12Days played since 2026-10-05

Premier is CSDB’s own observation — it builds from the day a profile is first viewed and cannot be backfilled.

Compare periods

Last 7 days vs previous 7

MetricLast 7Previous 7Change
Matches62——
Win rate44%——
K/D1.17——
Headshot %44%——

Measured 27 Oct 2026 → 5 Nov 2026; no observation near 14 days ago, so there is no previous window yet.

Last 30 days vs previous 30

MetricLast 30Previous 30Change
Matches161——
Win rate41%——
K/D1.21——
Headshot %53%——

Measured 5 Oct 2026 → 5 Nov 2026; no observation near 60 days ago, so there is no previous window yet.

Differences between Valve's lifetime totals on the days CSDB observed this profile — every mode Valve counts, not only ranked. A window appears only when an observation sits within a few days of each end.

Performance scores

Aim48
Positioning58
Utility48

0–100 skill scores via Leetify.

Recent form

HOT49–42–9Last 10049%Win rateWLWWLWLWWW

Last 10 vs previous 10: +10pp win rate · −0.05 avg rating · −5.1pp headshot accuracy · −42ms reaction

Win rate +10pp 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 0.08
  • Avg headshot accuracy 9%
  • Avg reaction 499ms

Last 10

  • 7–3 · 70% win rate
  • Avg rating 0.05
  • Avg headshot accuracy 9%
  • Avg reaction 493ms

Last 20

  • 13–5–2 · 65% win rate
  • Avg rating 0.07
  • Avg headshot accuracy 12%
  • Avg reaction 514ms

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 (+2.2 against its own average).

Aim4.8
Utility4.8
Positioning5.8
Opening Duels2.7
Clutch1.0

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

jL

Plays most like jL 73% playstyle similarity

Most alike: positioning profile, utility contribution.

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

Strengths

T openings. 70% T opening-duel success — entries that actually open the round.

Areas to improve

CT openings. Opening success drops from 70% on T to 36% on CT — first contacts on the defending side are being lost.

Counter-strafing. Only 62% 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

Aim4.8
Positioning5.8
Utility4.8
Mechanics2.6
Opening Duels5.7
Win Impact7.9

Composite 5.1/10 (Developing), 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 rating0.04+0.05
first ⅓ avg -0.01 → last ⅓ avg 0.04
Reaction time496ms−39ms
first ⅓ avg 536ms → last ⅓ avg 496ms
Headshot accuracy11.0%−1.6%
first ⅓ avg 12.6% → last ⅓ avg 11.0%

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

0.35Best match rating · 9–0 · inferno, 4 Nov →
44%Best headshot accuracy · 3–9 · inferno, 2 Nov →
398msFastest reaction time · 9–7 · inferno, 4 Nov →
13–3Biggest win · inferno, 2 Nov →

Across the last 100 tracked matches.

Highlights

4Longest win streak
12–7In matches decided by ≤2 rounds
3Overtime games

Map breakdown

nukeBest map · 56% over 9overpassWeakest map · 20% over 5
MapGradePlayedRecordWin rateAvg rating
infernoB5529–2653%0.01
nukeA95–456%-0.00
cacheC73–443%-0.02
overpassD51–420%-0.02
poseidon—40–40%-0.04
vertigo—32–167%-0.04
office—32–167%0.06
debris—21–150%0.14
mirage—21–150%-0.08
ancient—22–0100%0.00
anubis—20–20%-0.01
dust2—21–150%-0.04
train—10–10%-0.03
eldorado—10–10%0.13
shelter—11–0100%-0.03
boulder—11–0100%-0.02

Across the last 100 tracked matches.

Overpass is currently your weakest sufficiently-sampled map (20% over 5). Start with the 6 essential Overpass lineups, review the callouts, then spin up a practice server.

Lifetime stats

38,161Lifetime kills
0.89K/D
2,282Matches
32.6%Match win rate
22.6%Headshot %
8.6%Shot accuracy
3,088MVPs
606Hours (in match)
1,028Bombs planted
212Bombs defused

Most-used weapons

Lifetime map wins

2,219inferno
2,137dust2
1,600nuke
923vertigo
902train
393office
165italy
30lake

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

Faceit stats

Combat

3Matches
33%Win rate
1.32Avg K/D
84.8ADR
24%Headshot %

Clutches & streaks

25%1v1 clutch win
0%1v2 clutch win
1Longest win streak

Recent Faceit resultsLLLWL

MapMatchesWin rateAvg K/DAvg kills
Inferno1100%2.1935.0
Nuke10%0.8426.0
Train10%0.9314.0

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.

11.0%Headshot accuracy
30.8%Accuracy (enemy spotted)
43.3%Spray accuracy
61.8%Counter-strafing
10.6°Preaim
529msReaction time
70.0%T opening success
35.6%CT opening success
0.53Enemies flashed / flash
8.3%Flash assists
9.27HE damage / grenade
4.40Flashes / 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 11.049% — below the 15% mark we flag

    Aim Training →
  2. Advanced Mechanics

    Losing the first CT duel repeatedly usually means holding angles that favour the peeker.

    CT opening duels 35.6255% — below the 40% mark we flag

Spend your practice time on Overpass

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 5 tracked games — your weakest map with enough games to be worth reading into.

Overpass callouts & strategy →Overpass grenade lineups →

Recent matches

MapScoreRatingHS%Date
nuke13–8-0.025%4 Nov →
poseidon7–90.033%4 Nov →
inferno9–00.3514%4 Nov →
inferno9–70.0811%4 Nov →
nuke5–13-0.0413%4 Nov →
nuke13–80.0612%4 Nov →
overpass2–13-0.127%4 Nov →
inferno9–70.056%4 Nov →
nuke9–50.069%4 Nov →
inferno9–50.0211%4 Nov →
inferno7–90.0516%4 Nov →
inferno9–10.266%4 Nov →
inferno9–50.0312%4 Nov →
train5–13-0.039%3 Nov →
debris9–10.3230%3 Nov →
poseidon8–8-0.0212%3 Nov →
inferno9–30.1724%3 Nov →
overpass8–80.0414%3 Nov →
nuke9–50.054%3 Nov →
vertigo13–60.1114%3 Nov →
nuke13–50.129%3 Nov →
mirage6–13-0.0911%3 Nov →
inferno6–9-0.186%3 Nov →
inferno9–50.2112%2 Nov →
inferno0–3-0.0814%2 Nov →
inferno9–50.0513%2 Nov →
inferno3–9-0.1544%2 Nov →
inferno8–80.078%2 Nov →
inferno4–9-0.100%2 Nov →
inferno9–4-0.023%2 Nov →
cache12–12-0.035%2 Nov →
inferno13–30.002%2 Nov →
ancient13–10-0.004%2 Nov →
office13–40.1020%2 Nov →
office13–60.108%2 Nov →
anubis14–160.019%2 Nov →
inferno9–50.0610%2 Nov →
inferno9–50.058%2 Nov →
inferno9–20.128%2 Nov →
inferno9–50.0913%2 Nov →
office7–13-0.019%2 Nov →
inferno9–50.038%1 Nov →
inferno0–9-0.246%1 Nov →
inferno1–9-0.0420%1 Nov →
inferno5–9-0.0920%1 Nov →
inferno9–60.1035%1 Nov →
poseidon5–9-0.018%1 Nov →
overpass7–90.048%1 Nov →
inferno9–70.0024%1 Nov →
debris4–9-0.0514%1 Nov →
overpass5–5-0.050%1 Nov →
vertigo5–9-0.169%1 Nov →
nuke7–90.0317%1 Nov →
eldorado8–80.1320%1 Nov →
poseidon6–9-0.154%1 Nov →
inferno5–9-0.0312%1 Nov →
inferno3–9-0.1314%1 Nov →
inferno4–9-0.033%1 Nov →
inferno2–9-0.0314%31 Oct →
inferno2–9-0.115%31 Oct →
inferno4–9-0.1519%31 Oct →
inferno9–7-0.188%31 Oct →
inferno5–9-0.069%31 Oct →
cache13–11-0.0318%28 Oct →
cache13–100.0214%28 Oct →
overpass8–8-0.015%28 Oct →
inferno5–9-0.1210%28 Oct →
inferno9–10.1318%28 Oct →
cache11–13-0.006%25 Oct →
inferno9–10.2715%25 Oct →
shelter13–9-0.036%25 Oct →
boulder13–3-0.028%25 Oct →
dust24–13-0.067%24 Oct →
anubis14–16-0.0325%24 Oct →
inferno13–5-0.0111%24 Oct →
nuke9–13-0.046%24 Oct →
mirage13–9-0.0715%24 Oct →
inferno4–9-0.0613%23 Oct →
inferno5–13-0.0414%23 Oct →
cache13–110.0614%23 Oct →
vertigo9–4-0.074%23 Oct →
nuke0–9-0.268%23 Oct →
ancient13–110.0121%22 Oct →
dust216–14-0.0216%22 Oct →
inferno3–9-0.137%22 Oct →
inferno8–80.0413%22 Oct →
inferno9–10.0923%22 Oct →
inferno9–10.137%22 Oct →
inferno9–50.1118%22 Oct →
inferno4–9-0.0817%21 Oct →
inferno9–70.1028%21 Oct →
cache7–13-0.072%21 Oct →
cache8–13-0.085%21 Oct →
inferno4–9-0.1714%21 Oct →
inferno9–6-0.0614%21 Oct →
inferno9–70.1116%19 Oct →
inferno9–7-0.1113%19 Oct →
inferno8–80.049%19 Oct →
inferno8–8-0.048%19 Oct →
inferno5–9-0.0713%19 Oct →

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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