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Davince4

Davince4 — CS2 Stats

US76561198144445213[U:1:184179485]Steam profile ↗

479Tracked matches38%Win rate2020Tracked since
CSDB Rating4.0 DevelopingPassive Rifler
WingmanGold Nova III
Ladder ranks via Leetify

Performance scores

Aim68
Positioning36
Utility23

0–100 skill scores via Leetify.

Recent form

COLD46–42–12Last 10046%Win rateLLWWLLLLLW

Last 10 vs previous 10: +10pp win rate · -0.01 avg rating

Player DNA

Primary style: Passive Rifler — Aim-led profile without a single dominant tendency.

Aim6.8
Aggression1.6
Utility2.3
Positioning3.6
Opening Duels0.0

Strong CT-side openerLimited 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.

Your pro match

NiKo

Plays most like NiKo 72% playstyle similarity

Most alike: opening-duel success, positioning profile.

Where you differ: lower opening-fight frequency; lower utility contribution.

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

Positioning. Positioning trails aim by 33 points — deaths here waste a strong aim profile.

Utility. Utility contribution runs far behind the mechanical game — the cheapest rating gain on this profile is thrown, not aimed.

T-side openings. Opening success drops from 36% on CT to 20% on T — the same duels are being taken with worse setups on the attacking side.

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

Aim6.8
Positioning3.6
Utility2.3
Mechanics4.9
Opening Duels0.7
Win Impact1.2

Composite 4.0/10 (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-0.04−0.02
first ⅓ avg -0.02 → last ⅓ avg -0.04
Reaction time503ms−64ms
first ⅓ avg 567ms → last ⅓ avg 503ms
Headshot accuracy16.6%+6.7%
first ⅓ avg 9.9% → last ⅓ avg 16.6%

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.

Highlights

0.09Best rating — office 13–3
13–1Biggest win — office
6Longest win streak
L2Current streak
6–5In matches decided by ≤2 rounds

Map breakdown

officeBest map · 73% over 11trainWeakest map · 14% over 7
MapGradePlayedRecordWin rateAvg rating
infernoA127–558%-0.03
ancientD124–833%-0.05
officeS118–373%-0.00
dust2D102–820%-0.04
mirageC94–544%-0.02
trainD71–614%-0.04
vertigoC52–340%-0.02
cacheD51–420%-0.07
nukeA53–260%-0.02
wardenA53–260%-0.03
grail—42–250%0.01
anubis—31–233%-0.02
alpine—32–167%-0.06
italy—22–0100%0.04
agency—22–0100%-0.01
fachwerk—10–10%-0.01
stronghold—10–10%-0.02
palacio—11–0100%-0.03
golden—10–10%-0.02
overpass—11–0100%-0.02

Across the last 100 tracked matches.

Skill profile

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

16.9%Headshot accuracy
29.3%Accuracy (enemy spotted)
44.7%Spray accuracy
72.2%Counter-strafing
9.8°Preaim
501msReaction time
19.7%T opening success
35.8%CT opening success
0.25Enemies flashed / flash
3.0%Flash assists
8.34HE damage / grenade
0.60Flashes / 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

    Most of your flashes are blinding nobody. A handful of reliable pop-flash lineups fixes this faster than anything else.

    Enemies flashed per flash 0.2456 — below the 0.5 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 19.7271% — below the 40% mark we flag

Spend your practice time on Train

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.

Train callouts & strategy →

Recent matches

MapScoreRatingHS%Date
inferno8–13-0.080%24 Aug →
train4–13-0.0617%24 Aug →
mirage13–10-0.0420%23 Aug →
train13–11-0.0613%18 Aug →
train7–13-0.0617%16 Aug →
vertigo7–13-0.079%16 Aug →
ancient5–13-0.080%16 Aug →
vertigo7–130.004%16 Aug →
cache9–13-0.0840%16 Aug →
vertigo13–11-0.0343%15 Aug →
dust26–13-0.0910%15 Aug →
cache6–13-0.0510%15 Aug →
vertigo12–12-0.0533%11 Aug →
mirage2–13-0.045%11 Aug →
mirage13–11-0.013%28 Jul →
fachwerk12–12-0.010%11 Jul →
inferno8–8-0.0710%3 Jul →
office9–13-0.0223%18 Jun →
italy13–4-0.0133%14 Jun →
ancient5–13-0.0923%13 Jun →
dust23–13-0.0815%13 Jun →
train4–130.0011%11 Jun →
office13–40.0314%11 Jun →
office13–7-0.010%10 Jun →
cache10–13-0.0533%10 Jun →
office2–13-0.098%8 Jun →
office13–10.01100%8 Jun →
cache12–12-0.106%25 May →
office13–50.000%15 May →
cache13–7-0.0522%13 May →
mirage12–12-0.038%13 May →
inferno13–6-0.047%6 May →
mirage13–70.0611%19 Apr →
inferno13–6-0.010%19 Apr →
dust25–13-0.0514%18 Apr →
ancient13–10-0.060%10 Apr →
nuke13–8-0.0233%8 Apr →
ancient3–13-0.099%5 Apr →
dust213–70.0513%31 Mar →
inferno13–80.0418%28 Mar →
warden13–70.019%27 Mar →
dust22–13-0.0314%25 Mar →
office13–30.0950%24 Mar →
dust213–2-0.0314%23 Mar →
ancient10–13-0.0713%20 Mar →
dust26–130.017%17 Mar →
anubis9–00.030%16 Mar →
inferno13–1-0.030%16 Mar →
dust24–13-0.0710%14 Mar →
warden11–13-0.035%11 Mar →
warden13–8-0.057%10 Mar →
ancient11–13-0.067%9 Mar →
dust212–12-0.1027%6 Mar →
stronghold12–12-0.0213%4 Mar →
dust24–13-0.075%28 Feb →
alpine12–12-0.046%26 Feb →
mirage3–130.0325%26 Feb →
office13–90.0112%23 Feb →
warden6–13-0.047%20 Feb →
ancient13–7-0.0519%20 Feb →
office13–7-0.020%18 Feb →
warden13–4-0.026%18 Feb →
alpine13–10-0.0913%17 Feb →
inferno13–1-0.020%17 Feb →
alpine7–5-0.060%17 Feb →
inferno10–13-0.037%2 Feb →
mirage13–4-0.027%2 Feb →
nuke13–40.0215%4 Oct →
palacio13–2-0.035%4 Oct →
golden9–13-0.027%4 Oct →
train12–120.0223%30 Sept →
italy13–70.0817%30 Sept →
anubis8–13-0.0622%29 Sept →
inferno13–7-0.0317%20 Sept →
inferno13–6-0.016%9 Sept →
ancient11–130.0119%31 Aug →
grail3–70.0112%31 Aug →
nuke3–13-0.098%9 Aug →
inferno10–13-0.0319%9 Aug →
inferno9–13-0.035%22 Jul →
mirage12–12-0.064%17 Jun →
grail8–13-0.030%13 Jun →
ancient13–10-0.027%9 Jun →
ancient12–12-0.0510%28 May →
train11–13-0.0810%11 May →
grail13–90.033%11 May →
agency13–80.036%11 May →
grail13–20.0510%10 May →
agency13–10-0.053%10 May →
mirage11–13-0.0712%3 May →
vertigo13–50.0511%3 May →
ancient12–12-0.029%3 May →
nuke13–50.028%3 May →
nuke10–13-0.040%15 Apr →
overpass13–4-0.025%15 Apr →
anubis6–13-0.0415%12 Apr →
office4–13-0.055%12 Apr →
office13–11-0.000%10 Apr →
ancient13–11-0.0314%9 Apr →
train6–13-0.0222%20 Mar →

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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