judd313

judd313 — CS2 Stats

76561198345271402[U:1:385005674]Steam profile ↗✓ No bans

277Tracked matches68%Win rate2024Tracked since
CSDB Rating5.3 DevelopingPositional Player
Ladder ranks via Leetify

Performance scores

Aim53
Positioning45
Utility34

0–100 skill scores via Leetify.

Recent form

HOT54–39–7Last 10054%Win rateLLWLWWWWWW

Last 10 vs previous 10: 0pp win rate · +0.00 avg rating · +3.6pp headshot accuracy · −2ms 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

  • 2–3 · 40% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 24%
  • Avg reaction 687ms

Last 10

  • 7–3 · 70% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 24%
  • Avg reaction 696ms

Last 20

  • 14–4–2 · 70% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 22%
  • Avg reaction 697ms

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 (+1.3 against its own average).

Aim5.3
Utility3.4
Positioning4.5
Opening Duels1.6

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 88% 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

Areas to improve

Reaction time. 689ms 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

Aim5.3
Positioning4.5
Utility3.4
Mechanics7.2
Opening Duels2.1
Win Impact10.0

Composite 5.3/10 (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-0.04+0.00
first ⅓ avg -0.04 → last ⅓ avg -0.04
Reaction time703ms−44ms
first ⅓ avg 746ms → last ⅓ avg 703ms
Headshot accuracy21.8%+4.3%
first ⅓ avg 17.6% → last ⅓ avg 21.8%

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

0.10Best match rating · 13–4 · mirage, 10 Aug →
67%Best headshot accuracy · 8–0 · dust2, 24 Aug →
531msFastest reaction time · 8–13 · inferno, 9 Aug →
13–0Biggest win · overpass, 25 Jun →

Across the last 100 tracked matches.

Highlights

6Longest win streak
L2Current streak
6–4In matches decided by ≤2 rounds
2Overtime games

Map breakdown

nukeBest map · 88% over 8ancientWeakest map · 44% over 9
MapGradePlayedRecordWin rateAvg rating
dust2B3619–1753%-0.04
cacheB126–650%-0.04
mirageB126–650%-0.02
anubisB115–645%-0.04
ancientC94–544%-0.06
infernoA85–363%-0.05
nukeS87–188%-0.01
train—31–233%-0.03
overpass—11–0100%-0.02

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (44% over 9). Start with the 6 essential Ancient 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.

21.4%Headshot accuracy
32.1%Accuracy (enemy spotted)
28.0%Spray accuracy
82.4%Counter-strafing
10.6°Preaim
689msReaction time
36.3%T opening success
40.4%CT opening success
0.66Enemies flashed / flash
4.3%Flash assists
9.15HE damage / grenade
3.99Flashes / 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. Grenades & Utility

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

    Flashes per match 3.9878 — below the 4 mark we flag

    Grenade Lineups →
  2. 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 36.2898% — below the 40% mark we flag

Spend your practice time on Ancient

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

44% win rate across 9 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

MapScoreRatingHS%Date
anubis4–130.0126%16 Sept →
dust23–13-0.0910%16 Sept →
dust211–9-0.0632%16 Sept →
cache0–5-0.0933%16 Sept →
dust213–7-0.0518%12 Sept →
mirage13–11-0.0225%11 Sept →
dust213–6-0.0814%11 Sept →
inferno13–6-0.0428%11 Sept →
ancient13–6-0.0227%11 Sept →
nuke13–2-0.0826%10 Sept →
dust212–12-0.0430%10 Sept →
ancient12–12-0.1117%10 Sept →
dust213–7-0.0726%10 Sept →
anubis13–9-0.0221%7 Sept →
dust213–10-0.0823%1 Sept →
dust213–11-0.0124%1 Sept →
nuke13–10-0.0720%1 Sept →
cache13–6-0.085%1 Sept →
cache8–13-0.0626%30 Aug →
nuke13–20.0110%30 Aug →
ancient13–70.0114%30 Aug →
dust25–13-0.1030%30 Aug →
dust26–13-0.0238%29 Aug →
cache13–3-0.094%29 Aug →
mirage12–00.0817%29 Aug →
cache11–13-0.0515%28 Aug →
nuke13–4-0.0114%28 Aug →
ancient13–7-0.0618%28 Aug →
anubis5–13-0.0426%28 Aug →
cache9–13-0.0725%27 Aug →
dust211–13-0.0532%27 Aug →
anubis13–110.0329%25 Aug →
dust210–13-0.0317%25 Aug →
dust213–3-0.0715%24 Aug →
dust213–30.0311%24 Aug →
dust28–0-0.0167%24 Aug →
anubis7–13-0.0410%24 Aug →
cache13–9-0.0118%23 Aug →
anubis13–4-0.1021%23 Aug →
dust213–30.0113%23 Aug →
cache9–13-0.0615%21 Aug →
dust213–7-0.0812%21 Aug →
anubis13–4-0.0229%21 Aug →
mirage13–10-0.0621%21 Aug →
mirage11–130.0526%20 Aug →
dust210–13-0.0315%20 Aug →
inferno13–7-0.0615%20 Aug →
anubis5–13-0.0522%18 Aug →
ancient9–13-0.0924%17 Aug →
dust29–13-0.0718%17 Aug →
dust213–90.0020%17 Aug →
nuke13–11-0.019%15 Aug →
dust213–90.0314%15 Aug →
dust213–10-0.0114%15 Aug →
cache13–100.0220%14 Aug →
nuke7–130.0127%14 Aug →
dust210–13-0.0911%14 Aug →
dust212–12-0.0715%13 Aug →
dust212–12-0.0724%13 Aug →
ancient13–5-0.084%13 Aug →
train13–2-0.0819%11 Aug →
mirage13–2-0.0417%11 Aug →
ancient2–13-0.0529%11 Aug →
dust23–13-0.0631%11 Aug →
mirage15–15-0.0323%10 Aug →
mirage13–40.1031%10 Aug →
nuke7–10.0519%9 Aug →
inferno8–13-0.0714%9 Aug →
dust213–8-0.0415%8 Aug →
ancient7–13-0.1115%8 Aug →
dust23–13-0.0643%7 Aug →
dust215–15-0.0524%7 Aug →
ancient7–13-0.0120%6 Aug →
mirage11–13-0.0813%6 Aug →
train6–130.0226%6 Aug →
inferno13–9-0.0318%5 Aug →
dust27–13-0.0918%3 Aug →
anubis6–13-0.0916%3 Aug →
mirage6–13-0.0619%2 Aug →
mirage13–7-0.1024%2 Aug →
cache13–10-0.0211%2 Aug →
anubis8–13-0.0617%2 Aug →
inferno13–3-0.0133%2 Aug →
dust28–13-0.0733%2 Aug →
inferno13–10-0.0933%18 Jul →
dust213–4-0.0615%18 Jul →
nuke13–100.0418%18 Jul →
dust213–4-0.0313%4 Jul →
inferno12–12-0.114%26 Jun →
dust213–2-0.0310%26 Jun →
mirage6–13-0.0514%26 Jun →
dust213–80.0210%25 Jun →
mirage5–13-0.083%25 Jun →
train5–13-0.048%25 Jun →
cache5–13-0.0113%25 Jun →
inferno3–130.0412%25 Jun →
dust24–13-0.0422%25 Jun →
cache13–70.0017%25 Jun →
overpass13–0-0.0218%25 Jun →
anubis5–3-0.0811%24 Jun →

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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