♛

♛ — CS2 Stats

FI76561198022517628[U:1:62251900]Steam profile ↗✓ No bans

262Tracked matches46%Win rate2021Tracked since
2,114Hours in CS25Hrs last 2 wks
CSDB Rating3.1 LearningSupport
FaceitLevel 2 · 877 ELOTop 97.9% of ranked FACEIT players
Ladder ranks via Leetify

Rating over time

Premier CS Rating: 8,246 -1,418 8 Nov – 19 Jan · 14 days played
7,89010,329peak 10,3298 Nov19 Jan
10,388Peak Premier in tracked matches
877Highest Faceit ELO seen on CSDB
56Days played since 2025-01-27

Faceit ELO

  • At peak — 877
  • Next: Level 4 at 901 — 24 to go
  • Reached: Level 3 · Level 2

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

How this compares with the same rank

Median values for Level 2 among CSDB-tracked players (n=6,128), from Valve's own lifetime stats. Aim, positioning and utility scores are deliberately not benchmarked here — those are a third-party provider's derived metrics, which CSDB does not store.

MetricThis playerLevel 2 medianLevel 3 medianvs Level 3
Headshot rate46.4%41.3%41.8%above
Shot accuracy12.8%12.7%12.2%above
Kill/death ratio0.890.950.980.09 short
Match win rate75.8%42.6%43.3%above

This profile matches the typical Level 3 player on 3 of 4 comparable metrics.

Widest gap: Kill/death ratio. That is the metric furthest from the Level 3 median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.

Share this profile

CSDB.GG♛FACEITLevel 2 · 877 ELOSTANDINGTop 97.9% of rankedPEAK ELO877csdb.gg/stats

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Performance scores

Aim26
Positioning50
Utility54

0–100 skill scores via Leetify.

Recent form

COLD46–48–6Last 10046%Win rateWWLLWLLLLL

Last 10 vs previous 10: +10pp win rate · +0.00 avg rating · +0.7pp headshot accuracy · +19ms reaction

Win rate +10pp 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

  • 3–2 · 60% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 9%
  • Avg reaction 653ms

Last 10

  • 3–7 · 30% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 13%
  • Avg reaction 638ms

Last 20

  • 5–14–1 · 25% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 13%
  • Avg reaction 628ms

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: Support — Utility contribution stands above the rest of this profile (+2.4 against its own average).

Aim2.6
Utility5.4
Positioning5.0
Opening Duels0.0
Clutch1.6

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 56% playstyle similarity

Most alike: utility contribution, positioning profile.

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. 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 61% 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

Aim2.6
Positioning5.0
Utility5.4
Mechanics2.4
Opening Duels1.4
Win Impact3.8

Composite 3.1/10 (Learning), a weighted mean of the bars with a small opposition adjustment (×0.90 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.03+0.01
first ⅓ avg -0.04 → last ⅓ avg -0.03
Reaction time619ms−55ms
first ⅓ avg 674ms → last ⅓ avg 619ms
Headshot accuracy13.2%+0.6%
first ⅓ avg 12.6% → last ⅓ avg 13.2%

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

0.11Best match rating · 13–2 · ancient, 28 Aug →
31%Best headshot accuracy · 13–2 · cache, 20 Sept →
438msFastest reaction time · 16–13 · inferno, 31 May →
13–0Biggest win · nuke, 13 Sept →

Across the last 100 tracked matches.

Highlights

5Longest win streak
W2Current streak
8–5In matches decided by ≤2 rounds
11Overtime games

Map breakdown

ancientBest map · 60% over 20mirageWeakest map · 27% over 11
MapGradePlayedRecordWin rateAvg rating
ancientA2012–860%-0.01
infernoC197–1237%-0.04
nukeA148–657%-0.02
dust2C125–742%-0.03
mirageD113–827%-0.03
anubisB84–450%-0.06
overpassC73–443%-0.00
trainC52–340%-0.03
cache—42–250%-0.01

Across the last 100 tracked matches.

Mirage is currently your weakest sufficiently-sampled map (27% over 11). Start with the 5 essential Mirage lineups, review the callouts, then spin up a practice server.

Lifetime stats

61,867Lifetime kills
0.89K/D
8,351Matches
75.8%Match win rate · Top 1% of Level 2 players
46.4%Headshot % · Top 50% of Level 2 players
12.8%Shot accuracy · Top 50% of Level 2 players
13,630MVPs
1,713Hours (in match)
2,256Bombs planted
571Bombs defused

Most-used weapons

AK-4713,204
AWP9,178
Nova5,176
P901,831
FAMAS1,468

Lifetime map wins

9,994dust2
2,165inferno
1,535nuke
717train
403italy
204office
172dust
153aztec

Lifetime totals via Steam — visible because this profile's game details are public. Spans CS:GO and CS2.

Faceit stats

Combat

108Matches
43%Win rate
0.75Avg K/D
62.4ADR
38%Headshot %

Clutches & streaks

28%1v1 clutch win
16%1v2 clutch win
7Longest win streak

Recent Faceit resultsLWLWW

MapMatchesWin rateAvg K/DAvg kills
Ancient2357%0.7912.3
Anubis1540%0.7411.1
Nuke1457%0.8513.1
Inferno1250%0.8010.3
Mirage80%0.6812.4
Dust2520%0.6310.6
Cache450%1.1211.3
Train3100%0.9410.7

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.

13.4%Headshot accuracy
29.5%Accuracy (enemy spotted)
31.0%Spray accuracy
60.8%Counter-strafing
12.2°Preaim
623msReaction time
40.9%T opening success
30.4%CT opening success
0.57Enemies flashed / flash
2.5%Flash assists
10.41HE damage / grenade
7.31Flashes / 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. Advanced Mechanics

    Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.

    Preaim 12.2379° — above the 12° mark we flag

    Aim Training →
  2. 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 13.364% — below the 15% mark we flag

    Aim Training →
Spend your practice time on Mirage

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

27% win rate across 11 tracked games — your weakest map with enough games to be worth reading into.

Mirage callouts & strategy →Mirage grenade lineups →

Recent matches

MapScoreRatingHS%Date
cache13–11-0.0412%1 Nov →
ancient13–11-0.035%1 Nov →
anubis7–13-0.078%30 Oct →
inferno5–13-0.0410%30 Oct →
inferno13–6-0.0410%25 Oct →
dust28–13-0.0314%25 Oct →
cache6–13-0.0418%24 Oct →
inferno11–13-0.0218%24 Oct →
anubis4–13-0.0526%24 Oct →
inferno14–160.0214%23 Oct →
ancient8–13-0.018%23 Oct →
inferno9–13-0.0828%18 Oct →
ancient8–13-0.079%18 Oct →
dust213–10-0.0111%18 Oct →
overpass12–120.0319%16 Oct →
nuke3–13-0.080%15 Oct →
ancient8–130.0317%15 Oct →
nuke13–11-0.0614%10 Oct →
inferno8–13-0.098%10 Oct →
ancient9–13-0.0214%8 Oct →
inferno13–70.0216%8 Oct →
ancient13–6-0.0912%8 Oct →
dust213–5-0.088%4 Oct →
anubis4–13-0.0810%27 Sept →
ancient13–80.009%20 Sept →
cache13–20.0831%20 Sept →
ancient13–3-0.0114%13 Sept →
nuke13–00.109%13 Sept →
anubis22–18-0.0313%27 Aug →
nuke15–15-0.0416%27 Aug →
dust215–15-0.0412%23 Aug →
inferno7–13-0.0513%16 Aug →
ancient13–70.0611%16 Aug →
ancient10–13-0.1019%15 Aug →
nuke6–13-0.043%10 Aug →
cache6–13-0.0611%23 Jul →
inferno11–130.0217%15 Jul →
dust213–9-0.0226%12 Jul →
mirage13–60.0526%19 Jan →
ancient8–130.0614%16 Jan →
inferno8–13-0.0613%16 Jan →
dust210–130.039%8 Jan →
nuke19–16-0.0210%6 Jan →
mirage10–13-0.0814%24 Dec →
nuke13–40.0411%23 Dec →
dust213–5-0.0024%23 Dec →
overpass10–13-0.0122%23 Dec →
ancient13–9-0.0215%22 Dec →
nuke13–90.0314%22 Dec →
overpass5–13-0.0410%21 Dec →
ancient5–13-0.0615%7 Dec →
train13–40.0223%7 Dec →
overpass4–130.0317%7 Dec →
mirage15–15-0.075%4 Dec →
overpass13–50.0510%4 Dec →
nuke11–130.0210%3 Dec →
ancient13–11-0.039%28 Nov →
ancient13–90.0310%28 Nov →
nuke9–13-0.0510%24 Nov →
inferno4–13-0.124%24 Nov →
inferno16–14-0.0123%17 Nov →
nuke13–11-0.0813%17 Nov →
dust213–9-0.0115%8 Nov →
nuke13–8-0.0116%7 Nov →
inferno5–13-0.0814%6 Nov →
mirage13–100.0415%21 Sept →
inferno13–9-0.0511%21 Sept →
ancient13–80.0620%21 Sept →
nuke8–13-0.0516%21 Sept →
mirage7–13-0.0214%11 Sept →
overpass13–10-0.0515%5 Sept →
ancient13–20.1120%28 Aug →
train6–13-0.086%28 Aug →
mirage13–4-0.0420%27 Aug →
dust210–13-0.0814%17 Aug →
overpass16–14-0.048%2 Aug →
dust25–13-0.029%26 Jul →
mirage9–130.0716%26 Jul →
ancient4–13-0.098%26 Jul →
ancient13–100.0215%26 Jul →
mirage1–13-0.109%12 Jul →
mirage9–13-0.0412%11 Jul →
dust29–13-0.099%11 Jul →
mirage3–13-0.0513%11 Jul →
inferno4–13-0.0315%11 Jul →
anubis13–11-0.0520%11 Jul →
anubis11–13-0.0717%10 Jul →
ancient13–6-0.0518%10 Jul →
mirage1–13-0.110%31 May →
dust28–13-0.0313%31 May →
inferno16–13-0.0516%31 May →
inferno15–15-0.0614%4 Feb →
anubis13–6-0.0310%4 Feb →
train13–100.0216%4 Feb →
inferno8–1-0.060%2 Feb →
nuke13–9-0.0516%30 Jan →
train9–13-0.089%30 Jan →
train12–12-0.046%30 Jan →
anubis16–13-0.0715%28 Jan →
inferno13–20.046%27 Jan →

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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