Sog

Sog — CS2 Stats

76561197985972514[U:1:25706786]Steam profile ↗✓ No bans

157Tracked matches34%Win rate2020Tracked since
CSDB Rating2.1 LearningSupport
WingmanSilver I
Ladder ranks via Leetify

Performance scores

Aim15
Positioning34
Utility36

0–100 skill scores via Leetify.

Recent form

COLD34–57–9Last 10034%Win rateLLLLLWLLTW

Last 10 vs previous 10: −20pp win rate · +0.01 avg rating · +1.5pp headshot accuracy · +64ms reaction

Win rate down 20pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp). Average match rating moved the other way (+0.01), so results shifted more than performance did.

Last 5 · 10 · 20 matches

Last 5

  • 0–5 · 0% win rate
  • Avg rating -0.08
  • Avg headshot accuracy 14%
  • Avg reaction 730ms

Last 10

  • 2–7–1 · 20% win rate
  • Avg rating -0.07
  • Avg headshot accuracy 17%
  • Avg reaction 753ms

Last 20

  • 6–13–1 · 30% win rate
  • Avg rating -0.07
  • Avg headshot accuracy 16%
  • Avg reaction 721ms

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 (+0.9 against its own average).

Aim1.5
Utility3.6
Positioning3.4
Opening Duels1.2

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 64% 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. 706ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 60% 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
Positioning3.4
Utility3.6
Mechanics2.2
Opening Duels1.4
Win Impact0.0

Composite 2.1/10 (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-0.06−0.02
first ⅓ avg -0.04 → last ⅓ avg -0.06
Reaction time702ms+40ms
first ⅓ avg 662ms → last ⅓ avg 702ms
Headshot accuracy14.6%−0.2%
first ⅓ avg 14.8% → last ⅓ avg 14.6%

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

0.11Best match rating · 16–7 · nuke, 16 Mar →
40%Best headshot accuracy · 13–4 · inferno, 2 Nov →
375msFastest reaction time · 9–6 · inferno, 23 Sept →
13–0Biggest win · mirage, 11 Sept →

Across the last 100 tracked matches.

Highlights

3Longest win streak
L5Current streak
4–9In matches decided by ≤2 rounds
13Overtime games

Map breakdown

dust2Best map · 54% over 13vertigoWeakest map · 14% over 7
MapGradePlayedRecordWin rateAvg rating
infernoD256–1924%-0.07
mirageC167–944%-0.04
nukeC166–1038%-0.03
dust2B137–654%-0.05
overpassC83–538%-0.01
vertigoD71–614%-0.03
ancientC52–340%-0.06
cache—31–233%-0.05
anubis—30–30%-0.08
train—31–233%-0.01
insertion2—10–10%-0.06

Across the last 100 tracked matches.

Vertigo is currently your weakest sufficiently-sampled map (14% over 7). Start with the 6 essential Vertigo lineups, review the callouts, then spin up a practice server.

Skill profile

Aggregate performance across tracked matches — stats via Leetify. Percentile context against other CSDB-tracked players arrives as our own benchmark data accumulates.

14.5%Headshot accuracy
23.6%Accuracy (enemy spotted)
30.0%Spray accuracy
59.8%Counter-strafing
11.6°Preaim
706msReaction time
28.7%T opening success
41.5%CT opening success
0.50Enemies flashed / flash
4.2%Flash assists
7.14HE damage / grenade
9.66Flashes / 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 14.4808% — below the 15% mark we flag

    Aim Training →
  2. Best CS2 Settings

    Slow first shots are as often a setup problem as a reflex one — framerate, sensitivity and crosshair visibility all move this number.

    Reaction time 705.7823ms — above the 700ms mark we flag

    Aim Training →
  3. 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 28.7415% — below the 40% mark we flag

Spend your practice time on Vertigo

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

14% win rate across 7 tracked games — your weakest map with enough games to be worth reading into.

Vertigo callouts & strategy →Vertigo grenade lineups →

Recent matches

MapScoreRatingHS%Date
cache10–13-0.0519%15 Nov →
inferno5–13-0.095%14 Nov →
vertigo6–9-0.0811%14 Nov →
anubis13–16-0.1017%13 Nov →
anubis7–13-0.0819%12 Nov →
ancient13–7-0.0133%10 Nov →
mirage9–13-0.0335%9 Nov →
dust210–13-0.068%8 Nov →
inferno12–12-0.1221%6 Nov →
dust213–9-0.064%6 Nov →
cache13–2-0.096%4 Nov →
inferno10–13-0.1018%4 Nov →
ancient6–13-0.1111%3 Nov →
cache11–13-0.0111%3 Nov →
ancient8–13-0.078%3 Nov →
inferno13–4-0.1040%2 Nov →
dust213–4-0.0529%2 Nov →
dust213–5-0.0710%2 Nov →
ancient4–13-0.118%31 Oct →
inferno9–13-0.0714%30 Oct →
mirage13–10-0.0714%30 Oct →
mirage4–13-0.0911%30 Oct →
nuke7–13-0.017%18 Sept →
mirage11–13-0.0511%18 Sept →
mirage3–10-0.1122%12 Sept →
vertigo6–13-0.014%12 Sept →
ancient13–6-0.0012%12 Sept →
mirage13–0-0.0213%11 Sept →
nuke13–7-0.0812%11 Sept →
overpass6–13-0.0215%10 Sept →
inferno8–13-0.0213%10 Sept →
anubis3–13-0.0818%10 Sept →
vertigo11–13-0.066%10 Sept →
mirage4–13-0.0713%9 Sept →
nuke11–13-0.026%8 Sept →
overpass7–13-0.0031%8 Sept →
nuke13–110.0314%8 Sept →
nuke12–16-0.0615%5 Sept →
nuke13–6-0.070%3 Sept →
mirage13–11-0.023%3 Sept →
overpass13–3-0.010%1 Sept →
inferno11–13-0.0816%1 Sept →
mirage13–50.0219%1 Sept →
inferno3–9-0.0729%9 Aug →
mirage16–110.0118%9 Aug →
nuke6–9-0.113%28 Jul →
inferno9–30.048%24 Jul →
nuke9–20.0116%24 Jul →
mirage6–90.0114%18 Jul →
mirage6–9-0.0123%17 Jul →
inferno6–9-0.105%16 Jul →
train10–16-0.0613%16 Jul →
inferno16–14-0.027%16 Mar →
overpass16–11-0.0718%16 Mar →
nuke16–70.1115%16 Mar →
overpass4–90.0125%16 Mar →
train8–8-0.0419%15 Mar →
inferno1–9-0.0419%13 Nov →
dust214–16-0.0426%3 Nov →
dust29–6-0.059%6 Oct →
vertigo0–9-0.1110%23 Sept →
inferno9–6-0.0112%23 Sept →
dust29–20.0115%23 Sept →
vertigo8–8-0.0713%17 Sept →
inferno9–5-0.0021%17 Sept →
dust29–4-0.044%17 Sept →
inferno4–9-0.068%31 Aug →
nuke8–8-0.054%30 Aug →
inferno6–9-0.0520%30 Aug →
mirage2–9-0.0633%30 Aug →
inferno8–8-0.046%29 Aug →
inferno4–9-0.104%29 Aug →
mirage2–9-0.0717%29 Aug →
nuke6–9-0.080%28 Aug →
inferno6–9-0.0810%28 Aug →
overpass15–150.0617%27 Aug →
overpass16–12-0.0522%25 Aug →
overpass14–160.0223%23 Aug →
dust27–9-0.0723%18 May →
inferno2–9-0.1911%2 May →
vertigo9–70.0815%25 Apr →
inferno2–9-0.0711%11 Apr →
mirage9–4-0.0424%11 Apr →
vertigo5–90.077%27 Nov →
inferno6–9-0.0318%27 Nov →
inferno2–9-0.1413%23 Nov →
inferno4–9-0.0213%15 Nov →
nuke9–16-0.0223%11 Nov →
nuke16–7-0.083%9 Nov →
dust28–8-0.0715%8 Nov →
nuke1–9-0.0713%2 Nov →
dust22–9-0.1219%2 Nov →
insertion27–9-0.0611%2 Nov →
nuke8–80.0513%2 Nov →
train9–40.0616%1 Nov →
nuke3–9-0.0626%30 Oct →
inferno8–1-0.078%29 Oct →
mirage9–3-0.0320%27 Oct →
dust29–6-0.0414%27 Oct →
dust28–8-0.0118%22 Oct →

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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