JDawg

JDawg — CS2 Stats

US76561198287030537[U:1:326764809]Steam profile ↗✓ No bans

631Tracked matches46%Win rate2020Tracked since
CSDB Rating5.4 DevelopingPositional Player
CSDB Leaderboard#94700 of 251092 tracked
FaceitLevel 4Top 81.0% of ranked FACEIT players
WingmanMaster Guardian I
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 14 Sep 2026 (24 days ago). Ranks are recorded once per day this page is viewed.

+2Tracked matches · now 631
−3.8ppWin rate · 50.0% → 46.2%

Rating over time

Premier CS Rating: 19,051 -789 21 Nov – 14 Sept · 26 days played
16,63120,112peak 20,11221 Nov14 Sept
20,112Peak Premier in tracked matches
49Days played since 2025-11-21

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

Share this profile

CSDB.GGJDawgFACEITLevel 4STANDINGTop 81.0% of rankedcsdb.gg/stats

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Performance scores

Aim65
Positioning56
Utility45

0–100 skill scores via Leetify.

Recent form

COLD47–45–8Last 10047%Win rateLLWTLTLLWL

Last 10 vs previous 10: −20pp win rate · +0.06 avg rating · +8.4pp headshot accuracy · −10ms 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 (+0.06), so results shifted more than performance did.

Last 5 · 10 · 20 matches

Last 5

  • 1–3–1 · 20% win rate
  • Avg rating 0.06
  • Avg headshot accuracy 26%
  • Avg reaction 558ms

Last 10

  • 2–6–2 · 20% win rate
  • Avg rating 0.03
  • Avg headshot accuracy 23%
  • Avg reaction 591ms

Last 20

  • 6–12–2 · 30% win rate
  • Avg rating 0.00
  • Avg headshot accuracy 19%
  • Avg reaction 597ms

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 (+0.8 against its own average).

Aim6.5
Utility4.5
Positioning5.6
Opening Duels4.2

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

donk

Plays most like donk 92% 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

Reaction time. 623ms 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

Aim6.5
Positioning5.6
Utility4.5
Mechanics6.0
Opening Duels3.7
Win Impact3.7

Composite 5.4/10 (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 rating0.01+0.01
first ⅓ avg 0.00 → last ⅓ avg 0.01
Reaction time611ms−8ms
first ⅓ avg 619ms → last ⅓ avg 611ms
Headshot accuracy18.6%−1.0%
first ⅓ avg 19.6% → last ⅓ avg 18.6%

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

0.32Best match rating · 9–1 · nuke, 23 Apr →
40%Best headshot accuracy · 13–10 · dust2, 22 Apr →
391msFastest reaction time · 13–4 · inferno, 4 Dec →
13–0Biggest win · ancient, 15 Dec →

Across the last 100 tracked matches.

Highlights

7Longest win streak
L2Current streak
5–6In matches decided by ≤2 rounds
10Overtime games

Map breakdown

nukeBest map · 70% over 10infernoWeakest map · 29% over 21
MapGradePlayedRecordWin rateAvg rating
infernoD216–1529%0.00
ancientB178–947%0.00
mirageC114–736%0.02
dust2A106–460%-0.00
nukeS107–370%0.06
anubisB105–550%0.00
overpassC94–544%-0.00
train—42–250%0.01
cache—32–167%-0.04
vertigo—22–0100%0.04
sanctum—11–0100%0.05
poseidon—10–10%-0.02
office—10–10%-0.16

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (29% over 21). 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.

18.9%Headshot accuracy
33.8%Accuracy (enemy spotted)
35.5%Spray accuracy
77.2%Counter-strafing
9.1°Preaim
623msReaction time
40.8%T opening success
48.7%CT opening success
0.53Enemies flashed / flash
0.5%Flash assists
10.42HE damage / grenade
5.07Flashes / match

Recommended for you

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.

29% win rate across 21 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
mirage10–130.0527%16 Sept →
dust21–60.0723%16 Sept →
nuke13–110.1221%14 Jul →
inferno12–120.0526%14 Jul →
inferno10–13-0.0132%14 Jul →
nuke12–12-0.0521%8 Jul →
overpass9–130.0419%8 Jul →
dust26–13-0.0014%6 Jul →
sanctum9–60.0516%2 Jul →
poseidon6–9-0.0231%2 Jul →
inferno4–9-0.1027%2 Jul →
dust22–0-0.110%30 Jun →
mirage6–13-0.005%26 Jun →
mirage16–140.0722%21 Jun →
inferno11–130.0118%21 Jun →
cache13–4-0.0212%21 Jun →
cache7–13-0.0713%20 Jun →
inferno7–13-0.0224%20 Jun →
ancient13–4-0.0711%20 Jun →
ancient7–130.0115%12 Jun →
nuke13–30.1115%12 Jun →
nuke13–30.0729%7 Jun →
dust213–7-0.0118%4 Jun →
cache13–8-0.0419%22 May →
inferno13–16-0.0219%15 May →
anubis13–70.0317%11 May →
ancient13–50.0522%11 May →
anubis6–130.0113%11 May →
mirage10–13-0.0113%5 May →
anubis13–7-0.0017%5 May →
dust215–150.0627%29 Apr →
nuke15–150.0417%28 Apr →
anubis3–13-0.0511%27 Apr →
dust216–130.0413%27 Apr →
inferno2–13-0.0221%27 Apr →
nuke13–6-0.0017%27 Apr →
inferno7–9-0.0125%23 Apr →
nuke9–10.3236%23 Apr →
nuke13–1-0.0326%22 Apr →
dust213–10-0.0140%22 Apr →
anubis4–13-0.0215%20 Apr →
anubis10–13-0.0316%20 Apr →
mirage13–90.0618%20 Apr →
overpass13–90.0115%17 Apr →
ancient13–11-0.0226%17 Apr →
ancient13–50.0525%14 Apr →
nuke14–160.0423%14 Apr →
ancient6–13-0.0418%24 Mar →
ancient11–13-0.019%22 Mar →
overpass13–10-0.0414%22 Mar →
inferno5–13-0.0317%22 Mar →
anubis6–00.0223%22 Mar →
anubis13–50.0419%11 Mar →
ancient12–20.0223%11 Mar →
anubis13–100.0423%11 Mar →
overpass6–130.0221%10 Mar →
overpass13–5-0.0121%10 Mar →
inferno8–130.0216%10 Mar →
inferno11–130.0223%9 Mar →
overpass13–90.0923%9 Mar →
inferno13–90.037%4 Mar →
ancient16–130.1021%4 Mar →
dust22–130.0220%4 Mar →
mirage4–13-0.0127%4 Mar →
overpass3–13-0.0928%4 Mar →
ancient9–13-0.0331%3 Mar →
inferno9–50.0128%18 Feb →
vertigo13–110.0126%18 Feb →
vertigo13–90.0825%18 Feb →
anubis12–12-0.0222%6 Feb →
office3–13-0.1617%25 Jan →
mirage13–80.1016%12 Jan →
mirage6–130.0318%9 Jan →
inferno13–60.0615%9 Jan →
train5–13-0.0217%7 Jan →
train13–100.0523%6 Jan →
ancient15–150.0013%29 Dec →
inferno9–130.0222%29 Dec →
inferno11–130.0220%29 Dec →
inferno7–13-0.0216%24 Dec →
mirage12–120.0021%24 Dec →
dust216–13-0.0221%22 Dec →
ancient7–13-0.0516%22 Dec →
overpass4–13-0.025%22 Dec →
inferno12–40.0128%18 Dec →
mirage6–3-0.0324%18 Dec →
ancient13–00.0617%15 Dec →
dust213–5-0.0327%15 Dec →
nuke13–80.0113%6 Dec →
inferno13–40.0819%4 Dec →
train13–40.0326%4 Dec →
ancient12–16-0.0315%4 Dec →
overpass10–13-0.0226%4 Dec →
ancient9–13-0.0214%2 Dec →
inferno13–10-0.0014%2 Dec →
ancient13–7-0.008%2 Dec →
inferno0–7-0.0933%29 Nov →
ancient12–120.0413%23 Nov →
train9–130.0028%23 Nov →
mirage6–13-0.0131%21 Nov →

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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