WileX

WileX — CS2 Stats

FI76561198271644025[U:1:311378297]Steam profile ↗✓ No bans

466Tracked matches56%Win rate2021Tracked since
CSDB Rating3.8 LearningPositional Player
FaceitLevel 5Top 67.6% of ranked FACEIT players
WingmanGold Nova I
Ladder ranks via Leetify

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CSDB.GGWileXFACEITLevel 5STANDINGTop 67.6% of rankedcsdb.gg/stats

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Performance scores

Aim41
Positioning35
Utility27

0–100 skill scores via Leetify.

Recent form

COLD42–47–11Last 10042%Win rateLWLLLWWLLT

Last 10 vs previous 10: −40pp win rate · −0.01 avg rating · +6.8pp headshot accuracy · +13ms reaction

Win rate down 40pp 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 -0.07
  • Avg headshot accuracy 22%
  • Avg reaction 617ms

Last 10

  • 3–6–1 · 30% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 22%
  • Avg reaction 609ms

Last 20

  • 10–8–2 · 50% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 19%
  • Avg reaction 602ms

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 (+1.9 against its own average).

Aim4.1
Utility2.7
Positioning3.5
Opening Duels0.4
Clutch0.0

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 85% 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. 612ms 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

Aim4.1
Positioning3.5
Utility2.7
Mechanics4.8
Opening Duels0.5
Win Impact6.9

Composite 3.8/10 (Learning), 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 time605ms−51ms
first ⅓ avg 656ms → last ⅓ avg 605ms
Headshot accuracy17.4%+5.0%
first ⅓ avg 12.4% → last ⅓ avg 17.4%

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

0.15Best match rating · 8–8 · overpass, 7 Jul →
50%Best headshot accuracy · 0–5 · ancient, 15 Mar →
430msFastest reaction time · 10–13 · anubis, 11 Dec →
13–2Biggest win · anubis, 8 Sept →

Across the last 100 tracked matches.

Highlights

5Longest win streak
10–9In matches decided by ≤2 rounds
1Overtime games

Map breakdown

mirageBest map · 71% over 7ancientWeakest map · 22% over 9
MapGradePlayedRecordWin rateAvg rating
nukeD186–1233%-0.06
anubisB136–746%-0.03
cacheB137–654%-0.02
infernoD133–1023%-0.04
ancientD92–722%-0.06
overpassA95–456%-0.02
dust2C73–443%-0.02
mirageS75–271%-0.04
trainC52–340%-0.05
vertigoA53–260%0.02
grail—10–10%-0.03

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (22% over 9). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

104Matches
57%Win rate
1.04Avg K/D
45.6ADR
42%Headshot %

Clutches & streaks

0%1v1 clutch win
0%1v2 clutch win
10Longest win streak

Recent Faceit resultsLLWLL

MapMatchesWin rateAvg K/DAvg kills
Mirage20%0.5911.0
Anubis20%0.253.5
Nuke250%0.6210.5
Overpass10%0.459.0
Vertigo10%0.255.0
Dust210%0.326.0
Inferno10%0.388.0

Faceit-match stats via the FACEIT Data API — a separate match pool from the sections above.

Skill profile

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

17.0%Headshot accuracy
28.6%Accuracy (enemy spotted)
28.3%Spray accuracy
71.5%Counter-strafing
10.7°Preaim
612msReaction time
27.7%T opening success
33.7%CT opening success
0.26Enemies flashed / flash
3.5%Flash assists
7.21HE damage / grenade
3.17Flashes / 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 0.261 — 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 27.6684% — 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.

22% 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
anubis9–13-0.0511%4 Oct →
nuke13–5-0.0427%4 Oct →
cache8–13-0.0715%4 Oct →
train2–13-0.0940%4 Oct →
ancient13–16-0.1119%4 Oct →
cache13–80.0423%3 Oct →
vertigo13–100.0314%21 Sept →
anubis11–13-0.0015%21 Sept →
nuke6–13-0.1036%8 Sept →
ancient12–12-0.0620%8 Sept →
overpass13–8-0.0613%8 Sept →
anubis13–2-0.0319%8 Sept →
cache13–6-0.0222%5 Sept →
dust27–13-0.0012%5 Sept →
dust213–8-0.005%3 Aug →
nuke2–13-0.1229%3 Aug →
anubis13–7-0.0223%3 Aug →
cache13–3-0.082%3 Aug →
cache13–11-0.018%11 May →
cache12–120.0319%7 May →
cache13–2-0.0514%7 May →
cache10–13-0.1016%7 May →
cache12–120.0221%5 May →
cache13–50.0120%4 May →
cache13–110.0511%3 May →
cache10–13-0.0711%29 Apr →
cache3–13-0.0720%29 Apr →
nuke6–13-0.089%26 Apr →
nuke13–7-0.0520%26 Apr →
inferno13–3-0.096%26 Apr →
dust29–13-0.0313%25 Apr →
anubis13–80.0021%25 Apr →
train2–7-0.1222%25 Apr →
anubis12–12-0.076%22 Apr →
mirage13–8-0.0316%14 Apr →
mirage13–100.0310%9 Apr →
inferno6–13-0.0139%9 Apr →
vertigo12–12-0.0313%1 Apr →
dust23–13-0.0819%30 Mar →
train8–130.0219%30 Mar →
nuke8–13-0.0512%29 Mar →
dust212–12-0.0316%29 Mar →
mirage13–6-0.036%29 Mar →
nuke11–13-0.0811%23 Mar →
nuke7–9-0.0726%23 Mar →
mirage6–13-0.0413%22 Mar →
dust213–50.0216%22 Mar →
inferno13–60.0624%22 Mar →
nuke9–7-0.0717%22 Mar →
mirage6–13-0.0919%19 Mar →
inferno12–12-0.084%19 Mar →
vertigo3–9-0.0115%19 Mar →
mirage13–11-0.0510%17 Mar →
overpass6–9-0.1019%17 Mar →
vertigo9–10.1313%17 Mar →
nuke8–80.0528%17 Mar →
inferno6–9-0.0624%17 Mar →
ancient0–5-0.0450%15 Mar →
inferno8–13-0.0620%15 Mar →
anubis10–13-0.0515%15 Mar →
nuke7–9-0.0915%15 Mar →
nuke13–11-0.0037%13 Mar →
dust213–6-0.0324%13 Mar →
inferno7–130.0214%13 Mar →
overpass10–13-0.0617%13 Mar →
ancient13–90.0220%13 Mar →
inferno11–13-0.0919%13 Mar →
nuke9–13-0.0917%12 Mar →
anubis13–9-0.0921%12 Mar →
inferno6–13-0.0610%24 Jan →
overpass0–2-0.010%20 Jan →
nuke6–13-0.0816%20 Jan →
overpass13–8-0.0117%18 Jan →
anubis13–11-0.0411%30 Dec →
anubis10–13-0.0417%30 Dec →
train13–2-0.0610%11 Dec →
anubis10–13-0.0414%11 Dec →
ancient13–7-0.0520%2 Nov →
ancient6–13-0.108%21 Oct →
inferno11–13-0.0615%21 Oct →
overpass13–5-0.012%19 Sept →
ancient3–5-0.076%19 Sept →
inferno11–13-0.0418%19 Sept →
overpass13–11-0.034%15 Sept →
nuke13–10-0.044%15 Sept →
inferno7–13-0.0813%15 Sept →
mirage11–6-0.050%15 Sept →
nuke5–9-0.119%8 Jul →
overpass8–80.1524%7 Jul →
anubis12–12-0.0722%6 Jul →
inferno13–11-0.0310%6 Jul →
nuke10–13-0.055%6 Jul →
overpass13–11-0.0510%6 Jul →
nuke9–1-0.0518%6 Jul →
train13–70.0024%6 Jul →
anubis13–100.0425%6 Jul →
vertigo13–110.016%6 Jul →
ancient12–12-0.109%6 Jul →
grail8–13-0.0313%6 Jul →
ancient5–13-0.0512%5 Jul →

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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