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tjb766

tjb766 — CS2 Stats

76561198086176418[U:1:125910690]Steam profile ↗✓ No bans

1,329Tracked matches55%Win rate2025Tracked since
CSDB Rating4.8 DevelopingSupport
Ladder ranks via Leetify

Performance scores

Aim42
Positioning38
Utility50

0–100 skill scores via Leetify.

Recent form

STEADY46–50–4Last 10046%Win rateWLTWWWLWLL

Last 10 vs previous 10: +10pp win rate · +0.03 avg rating · −3.7pp headshot accuracy · −76ms 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

  • 3–1–1 · 60% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 6%
  • Avg reaction 584ms

Last 10

  • 5–4–1 · 50% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 8%
  • Avg reaction 619ms

Last 20

  • 9–10–1 · 45% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 10%
  • Avg reaction 657ms

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 (+2.1 against its own average).

Aim4.2
Utility5.0
Positioning3.8
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

iM

Plays most like iM 74% 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

CT openings. Opening success drops from 51% on T to 25% on CT — first contacts on the defending side are being lost.

Reaction time. 715ms 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

Aim4.2
Positioning3.8
Utility5.0
Mechanics7.2
Opening Duels2.6
Win Impact6.7

Composite 4.8/10 (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 rating-0.02−0.00
first ⅓ avg -0.02 → last ⅓ avg -0.02
Reaction time695ms−94ms
first ⅓ avg 789ms → last ⅓ avg 695ms
Headshot accuracy10.2%−2.4%
first ⅓ avg 12.6% → last ⅓ avg 10.2%

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

0.08Best match rating · 13–4 · mirage, 13 Sept →
38%Best headshot accuracy · 6–13 · inferno, 5 Oct →
484msFastest reaction time · 1–13 · inferno, 28 Oct →
13–0Biggest win · mirage, 25 Oct →

Across the last 100 tracked matches.

Highlights

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

Map breakdown

nukeBest map · 57% over 7infernoWeakest map · 30% over 10
MapGradePlayedRecordWin rateAvg rating
dust2B4018–2245%-0.03
mirageB2512–1348%-0.02
infernoD103–730%-0.04
nukeA74–357%-0.04
anubisA74–357%-0.02
ancientC52–340%-0.03
vertigo—42–250%-0.00
train—10–10%-0.06
overpass—11–0100%-0.09

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (30% over 10). Start with the 6 essential Inferno 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.

10.8%Headshot accuracy
32.5%Accuracy (enemy spotted)
34.8%Spray accuracy
82.3%Counter-strafing
10.6°Preaim
715msReaction time
50.6%T opening success
24.7%CT opening success
0.69Enemies flashed / flash
12.5%Flash assists
11.54HE damage / grenade
3.67Flashes / 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 10.7727% — 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 715.1088ms — above the 700ms mark we flag

    Aim Training →
  3. Grenades & Utility

    You are buying and holding utility rather than using it. Unthrown flashes are wasted money every round.

    Flashes per match 3.6735 — below the 4 mark we flag

    Grenade Lineups →
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.

30% win rate across 10 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
dust213–11-0.013%13 Nov →
dust26–13-0.0415%13 Nov →
mirage12–120.008%5 Nov →
mirage2–00.000%5 Nov →
mirage13–5-0.026%5 Nov →
ancient13–3-0.017%5 Nov →
dust28–13-0.058%5 Nov →
dust213–50.0313%2 Nov →
nuke5–13-0.0413%2 Nov →
vertigo3–13-0.0411%28 Oct →
dust211–13-0.059%28 Oct →
mirage13–10-0.069%28 Oct →
dust211–13-0.0423%28 Oct →
inferno1–13-0.090%28 Oct →
inferno13–7-0.0817%28 Oct →
mirage13–50.059%28 Oct →
dust211–13-0.018%28 Oct →
anubis13–10-0.0413%28 Oct →
inferno4–13-0.1110%27 Oct →
dust211–13-0.0223%27 Oct →
dust211–13-0.035%27 Oct →
dust213–70.025%27 Oct →
dust213–10.056%27 Oct →
dust213–7-0.0126%27 Oct →
dust213–90.026%25 Oct →
mirage13–00.082%25 Oct →
anubis11–13-0.007%20 Oct →
dust27–13-0.0416%17 Oct →
mirage13–7-0.056%17 Oct →
dust213–11-0.0821%17 Oct →
inferno13–60.057%17 Oct →
mirage4–13-0.0510%17 Oct →
anubis13–9-0.0316%13 Oct →
mirage9–13-0.079%13 Oct →
mirage4–13-0.0515%13 Oct →
dust213–10-0.0315%13 Oct →
inferno4–13-0.0214%11 Oct →
anubis12–12-0.066%11 Oct →
nuke4–13-0.0513%11 Oct →
mirage13–4-0.093%11 Oct →
mirage4–13-0.0813%11 Oct →
mirage11–13-0.073%11 Oct →
dust28–13-0.046%10 Oct →
mirage5–13-0.035%10 Oct →
mirage9–13-0.0514%6 Oct →
inferno6–13-0.1138%5 Oct →
mirage9–13-0.0213%5 Oct →
dust213–10-0.089%5 Oct →
dust213–4-0.058%5 Oct →
train10–13-0.0623%5 Oct →
dust213–5-0.0610%27 Sept →
vertigo7–00.0415%27 Sept →
ancient13–4-0.015%27 Sept →
dust211–13-0.0612%16 Sept →
inferno6–13-0.0524%16 Sept →
inferno7–13-0.0416%16 Sept →
mirage4–13-0.0327%16 Sept →
anubis13–7-0.0113%16 Sept →
dust213–10-0.0815%15 Sept →
ancient9–13-0.0716%15 Sept →
mirage4–13-0.1018%15 Sept →
dust213–30.004%15 Sept →
dust210–13-0.0611%15 Sept →
vertigo13–7-0.0418%15 Sept →
dust213–80.0314%14 Sept →
dust213–50.0710%14 Sept →
nuke13–11-0.0119%14 Sept →
dust28–130.0514%14 Sept →
dust28–13-0.0310%14 Sept →
inferno13–20.0514%13 Sept →
mirage13–40.0811%13 Sept →
anubis7–13-0.007%13 Sept →
dust211–130.0216%13 Sept →
vertigo11–130.0214%5 Sept →
ancient8–13-0.1018%5 Sept →
overpass13–5-0.0915%1 Sept →
mirage13–00.0211%1 Sept →
mirage11–130.0823%26 Aug →
anubis13–10-0.0316%26 Aug →
nuke13–9-0.069%26 Aug →
inferno11–13-0.0313%26 Aug →
dust213–80.0213%26 Aug →
mirage6–13-0.0822%26 Aug →
dust213–6-0.037%26 Aug →
dust24–13-0.0112%26 Aug →
mirage13–7-0.010%26 Aug →
ancient5–130.029%26 Aug →
dust213–8-0.0510%19 Aug →
dust29–13-0.0015%19 Aug →
dust25–13-0.1113%19 Aug →
dust27–13-0.0413%19 Aug →
nuke13–10-0.0613%19 Aug →
dust210–13-0.1210%15 Aug →
mirage13–110.0212%15 Aug →
mirage13–70.056%15 Aug →
nuke13–3-0.019%15 Aug →
nuke11–13-0.0425%5 Aug →
dust212–12-0.0312%5 Aug →
dust212–12-0.0117%5 Aug →
dust23–13-0.0610%5 Aug →

Match data via Leetify.

Recent teammates

Milestones

1,000 Matches
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

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