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2001 Cadillac Seville

2001 Cadillac Seville — CS2 Stats

76561198292458864[U:1:332193136]Steam profile ↗✓ No bans

1,023Tracked matches38%Win rate2024Tracked since
CSDB Rating4.1 DevelopingPositional Player
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 6 Sep 2026 (41 days ago). Ranks are recorded once per day this page is viewed.

+141Tracked matches · now 1,023
−6.5ppWin rate · 44.4% → 37.9%

Rating over time

24Days played since 2026-09-06

Premier is CSDB’s own observation — it builds from the day a profile is first viewed and cannot be backfilled.

Performance scores

Aim48
Positioning56
Utility26

0–100 skill scores via Leetify.

Recent form

COLD46–49–5Last 10046%Win rateLWLLLLWLTL

Last 10 vs previous 10: −30pp win rate · −0.03 avg rating · +4.4pp headshot accuracy · +22ms reaction

Win rate down 30pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp).

Last 5 · 10 · 20 matches

Last 5

  • 1–4 · 20% win rate
  • Avg rating 0.00
  • Avg headshot accuracy 15%
  • Avg reaction 630ms

Last 10

  • 2–7–1 · 20% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 12%
  • Avg reaction 641ms

Last 20

  • 7–12–1 · 35% win rate
  • Avg rating 0.00
  • Avg headshot accuracy 10%
  • Avg reaction 630ms

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 (+2 against its own average).

Aim4.8
Utility2.6
Positioning5.6
Opening Duels2.5

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 82% playstyle similarity

Most alike: positioning profile, opening-duel success.

Where you differ: lower utility contribution; 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 57% on T to 39% on CT — first contacts on the defending side are being lost.

Reaction time. 623ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 68% 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

Aim4.8
Positioning5.6
Utility2.6
Mechanics4.0
Opening Duels4.6
Win Impact1.0

Composite 4.1/10 (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-0.01−0.01
first ⅓ avg -0.00 → last ⅓ avg -0.01
Reaction time607ms−50ms
first ⅓ avg 657ms → last ⅓ avg 607ms
Headshot accuracy10.9%−7.6%
first ⅓ avg 18.5% → last ⅓ avg 10.9%

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

0.13Best match rating · 13–10 · dust2, 11 Oct →
67%Best headshot accuracy · 13–1 · nuke, 14 Sept →
336msFastest reaction time · 3–13 · nuke, 10 Oct →
13–1Biggest win · nuke, 14 Sept →

Across the last 100 tracked matches.

Highlights

5Longest win streak
5–4In matches decided by ≤2 rounds

Map breakdown

trainBest map · 57% over 14nukeWeakest map · 35% over 23
MapGradePlayedRecordWin rateAvg rating
dust2B3115–1648%0.01
nukeC238–1535%-0.01
mirageB157–847%0.01
trainA148–657%-0.03
infernoB105–550%-0.00
anubis—31–233%0.00
ancient—20–20%-0.01
vertigo—22–0100%-0.00

Across the last 100 tracked matches.

Nuke is currently your weakest sufficiently-sampled map (35% over 23). Start with the 6 essential Nuke 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.

10.7%Headshot accuracy
35.4%Accuracy (enemy spotted)
34.1%Spray accuracy
67.9%Counter-strafing
9.4°Preaim
623msReaction time
57.5%T opening success
39.5%CT opening success
0.63Enemies flashed / flash
2.5%Flash assists
0.42HE damage / grenade
5.36Flashes / 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 10.744% — below the 15% mark we flag

    Aim Training →
  2. Advanced Mechanics

    Losing the first CT duel repeatedly usually means holding angles that favour the peeker.

    CT opening duels 39.4838% — below the 40% mark we flag

Spend your practice time on Nuke

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

35% win rate across 23 tracked games — your weakest map with enough games to be worth reading into.

Nuke callouts & strategy →Nuke grenade lineups →

Recent matches

MapScoreRatingHS%Date
dust29–13-0.007%16 Oct →
nuke13–90.0115%16 Oct →
mirage7–130.0329%15 Oct →
dust26–13-0.019%15 Oct →
ancient2–13-0.0213%15 Oct →
dust26–130.0120%15 Oct →
train13–2-0.0811%14 Oct →
nuke10–13-0.0112%14 Oct →
dust212–12-0.044%14 Oct →
nuke10–13-0.015%13 Oct →
mirage11–70.0117%13 Oct →
nuke6–130.0210%13 Oct →
dust213–6-0.020%13 Oct →
dust21–130.0811%13 Oct →
dust213–50.034%13 Oct →
nuke8–13-0.004%13 Oct →
dust210–130.107%12 Oct →
train13–50.0110%12 Oct →
nuke13–8-0.059%12 Oct →
train8–130.029%12 Oct →
inferno6–13-0.0610%12 Oct →
train13–110.0315%12 Oct →
nuke13–7-0.054%12 Oct →
mirage13–10-0.0410%12 Oct →
train9–13-0.0312%12 Oct →
mirage6–13-0.0610%12 Oct →
dust213–100.1324%11 Oct →
nuke9–13-0.1614%11 Oct →
nuke0–5-0.130%11 Oct →
mirage3–13-0.0713%11 Oct →
dust213–40.0323%11 Oct →
inferno13–110.0412%11 Oct →
dust213–60.046%11 Oct →
nuke12–120.047%11 Oct →
anubis5–10.0021%11 Oct →
dust211–13-0.074%10 Oct →
nuke3–13-0.0138%10 Oct →
nuke10–130.0411%10 Oct →
nuke4–90.0119%10 Oct →
mirage13–90.0515%10 Oct →
ancient6–13-0.006%10 Oct →
nuke6–130.066%9 Oct →
mirage10–130.0413%9 Oct →
nuke8–13-0.0218%9 Oct →
inferno4–13-0.0922%9 Oct →
train13–7-0.0214%9 Oct →
anubis7–130.0013%9 Oct →
train11–13-0.047%8 Oct →
vertigo13–30.0211%8 Oct →
train11–13-0.0511%8 Oct →
nuke3–13-0.0024%8 Oct →
dust213–20.009%8 Oct →
dust213–50.0614%8 Oct →
nuke13–80.037%1 Oct →
dust22–130.0721%1 Oct →
mirage12–120.053%30 Sept →
train13–11-0.0313%30 Sept →
inferno13–11-0.0321%30 Sept →
dust24–13-0.098%30 Sept →
nuke13–100.0116%29 Sept →
vertigo13–3-0.0318%29 Sept →
inferno13–40.0018%28 Sept →
nuke12–12-0.059%28 Sept →
dust23–13-0.0211%28 Sept →
mirage6–13-0.0025%27 Sept →
dust213–100.0216%27 Sept →
train9–13-0.0216%27 Sept →
dust27–3-0.0825%27 Sept →
dust213–70.0715%26 Sept →
mirage13–30.0913%26 Sept →
train4–13-0.0321%26 Sept →
train13–8-0.038%26 Sept →
mirage13–2-0.0528%24 Sept →
dust211–120.0010%24 Sept →
dust23–9-0.0913%24 Sept →
train13–6-0.0614%22 Sept →
dust26–13-0.0021%22 Sept →
mirage13–70.0410%22 Sept →
nuke6–13-0.0314%21 Sept →
mirage5–13-0.058%21 Sept →
nuke13–10-0.0011%21 Sept →
anubis12–12-0.0027%21 Sept →
dust29–13-0.0120%21 Sept →
dust211–13-0.0118%19 Sept →
inferno13–20.1125%18 Sept →
inferno9–13-0.0819%18 Sept →
dust213–30.107%18 Sept →
mirage13–30.0824%18 Sept →
dust213–5-0.068%18 Sept →
train13–6-0.0423%18 Sept →
dust213–30.0215%17 Sept →
dust26–13-0.0917%17 Sept →
inferno8–130.0115%16 Sept →
dust29–130.1223%16 Sept →
nuke13–40.0116%15 Sept →
inferno11–20.0110%15 Sept →
nuke13–1-0.0367%14 Sept →
inferno5–130.0928%14 Sept →
mirage5–13-0.0117%14 Sept →
dust213–80.0020%14 Sept →

Match data via Leetify.

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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