mathE2matician

mathE2matician — CS2 Stats

FI76561198055270485[U:1:95004757]Steam profile ↗✓ No bans

984Tracked matches27%Win rate2020Tracked since
804Hours in CS
CSDB Rating3.6 LearningPositional Player
Premier CS Rating15,428Purple band · top ~26.7% of ranked players (population est.)
FaceitLevel 3Top 90.6% of ranked FACEIT players
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 4 Nov 2026 (yesterday). Ranks are recorded once per day this page is viewed.

No change since then. Play, then come back: the next observation lands here.

Rating over time

Premier CS Rating: 15,428 -1,640 24 Jul – 4 Nov · 42 days played
14,67618,518peak 18,51824 Jul4 Nov
18,807Peak Premier in tracked matches
39Days played since 2026-07-24

Premier CS Rating

  • 3,090 below peak (18,518)
  • -1,604 over 30 days · declining
  • -2,543 over 90 days
  • Next: Pink band at 20,000 — 4,572 to go
  • Reached: Purple band · Blue band · Light Blue band
  • Purple band first seen 2026-09-10
  • Blue band first seen 2026-09-10

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 3 among CSDB-tracked players (n=10,505), 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 3 medianLevel 4 medianvs Level 4
Headshot rate41.9%41.8%42.7%0.9% short
Shot accuracy15.1%12.2%11.2%above
Kill/death ratio0.810.980.990.18 short
Match win rate35.6%43.3%43.9%8.4% short

This profile matches the typical Level 4 player on 1 of 4 comparable metrics.

Widest gap: Match win rate. That is the metric furthest from the Level 4 median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.

Share this profile

CSDB.GGmathE2maticianFACEITLevel 3STANDINGTop 90.6% of rankedPREMIER15,428 · Purple bandcsdb.gg/stats

The image is a snapshot; the link keeps updating. Nothing here is published anywhere — it is generated in your browser when you click.

Performance scores

Aim36
Positioning54
Utility50

0–100 skill scores via Leetify.

Recent form

STEADY30–69–1Last 10030%Win rateLWWWLLLWLW

Last 10 vs previous 10: +20pp win rate · +0.01 avg rating · −0.1pp headshot accuracy · −46ms reaction

Win rate up 20pp 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

  • 3–2 · 60% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 14%
  • Avg reaction 585ms

Last 10

  • 5–5 · 50% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 15%
  • Avg reaction 535ms

Last 20

  • 8–12 · 40% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 15%
  • Avg reaction 558ms

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 (+1.7 against its own average).

Aim3.6
Utility5.0
Positioning5.4
Opening Duels0.8

Effective flashes

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 64% 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

CT openings. Opening success drops from 55% on T to 32% on CT — first contacts on the defending side are being lost.

Reaction time. 550ms 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

Aim3.6
Positioning5.4
Utility5.0
Mechanics2.5
Opening Duels3.4
Win Impact0.0

Composite 3.6/10 (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-0.04−0.01
first ⅓ avg -0.03 → last ⅓ avg -0.04
Reaction time557ms−28ms
first ⅓ avg 585ms → last ⅓ avg 557ms
Headshot accuracy15.8%+1.4%
first ⅓ avg 14.4% → last ⅓ avg 15.8%

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

0.06Best match rating · 13–11 · ancient, 26 Jul →
38%Best headshot accuracy · 0–13 · nuke, 4 Oct →
391msFastest reaction time · 11–13 · inferno, 25 Oct →
13–4Biggest win · nuke, 29 Oct →

Across the last 100 tracked matches.

Highlights

3Longest win streak
4–6In matches decided by ≤2 rounds
10Overtime games

Map breakdown

nukeBest map · 58% over 24dust2Weakest map · 0% over 10
MapGradePlayedRecordWin rateAvg rating
nukeA2414–1058%-0.02
ancientD175–1229%-0.03
infernoD174–1324%-0.03
cacheD133–1023%-0.05
mirageD101–910%-0.03
dust2D100–100%-0.07
anubisD93–633%-0.04

Across the last 100 tracked matches.

Dust 2 is currently your weakest sufficiently-sampled map (0% over 10). Start with the 6 essential Dust 2 lineups, review the callouts, then spin up a practice server.

Lifetime stats

36,017Lifetime kills
0.81K/D
2,033Matches
35.6%Match win rate
41.9%Headshot % · Top 50% of Level 3 players
15.1%Shot accuracy · Top 50% of Level 3 players
2,537MVPs
804Hours (in match)
1,603Bombs planted
350Bombs defused

Most-used weapons

Lifetime map wins

3,702nuke
3,383inferno
1,410vertigo
740dust2
680train
59lake
44safehouse
43cbble

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

Faceit stats

Combat

49Matches
53%Win rate
0.90Avg K/D
—ADR
30%Headshot %

Clutches & streaks

—1v1 clutch win
—1v2 clutch win
4Longest win streak

Recent Faceit resultsWWLLW

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.

16.0%Headshot accuracy
25.6%Accuracy (enemy spotted)
28.4%Spray accuracy
61.4%Counter-strafing
9.8°Preaim
550msReaction time
54.8%T opening success
32.3%CT opening success
0.73Enemies flashed / flash
5.0%Flash assists
4.94HE damage / grenade
10.88Flashes / 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

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

    CT opening duels 32.2855% — below the 40% mark we flag

Spend your practice time on Dust 2

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

0% win rate across 10 tracked games — your weakest map with enough games to be worth reading into.

Dust 2 callouts & strategy →Dust 2 grenade lineups →

Recent matches

MapScoreRatingHS%Date
cache3–13-0.1311%4 Nov →
nuke13–50.0323%4 Nov →
nuke13–90.0215%3 Nov →
ancient13–80.0213%3 Nov →
cache8–13-0.087%2 Nov →
anubis5–13-0.0611%2 Nov →
mirage11–130.0115%1 Nov →
ancient13–7-0.0010%1 Nov →
mirage6–13-0.0820%1 Nov →
nuke13–6-0.0229%1 Nov →
dust210–13-0.0624%1 Nov →
dust27–13-0.0711%1 Nov →
ancient5–13-0.058%1 Nov →
cache10–13-0.0514%30 Oct →
nuke16–13-0.047%30 Oct →
nuke13–60.0119%30 Oct →
nuke9–13-0.0725%30 Oct →
inferno13–16-0.0221%29 Oct →
nuke13–4-0.078%29 Oct →
mirage8–130.0018%29 Oct →
ancient11–13-0.039%27 Oct →
ancient7–13-0.0329%27 Oct →
cache5–13-0.0424%27 Oct →
ancient13–16-0.0810%26 Oct →
mirage10–13-0.0125%26 Oct →
dust25–13-0.0217%26 Oct →
nuke5–13-0.0115%25 Oct →
inferno11–13-0.0115%25 Oct →
mirage4–13-0.059%25 Oct →
mirage5–13-0.0621%25 Oct →
ancient11–13-0.0611%25 Oct →
anubis16–13-0.0215%24 Oct →
inferno10–13-0.0712%24 Oct →
mirage4–13-0.0129%23 Oct →
nuke13–60.0124%23 Oct →
inferno8–130.0121%23 Oct →
inferno13–50.0213%22 Oct →
nuke13–9-0.0117%20 Oct →
nuke2–130.0123%20 Oct →
dust25–13-0.0717%20 Oct →
dust27–13-0.0419%14 Oct →
cache2–13-0.107%14 Oct →
ancient10–13-0.0717%12 Oct →
inferno16–13-0.0019%11 Oct →
nuke13–4-0.047%11 Oct →
dust213–16-0.0816%8 Oct →
mirage11–130.0120%8 Oct →
mirage10–13-0.0312%4 Oct →
nuke0–13-0.0738%4 Oct →
cache11–13-0.0215%3 Oct →
anubis9–13-0.039%3 Oct →
cache13–16-0.0311%1 Oct →
nuke2–13-0.0916%1 Oct →
mirage13–7-0.097%30 Sept →
dust26–13-0.125%29 Sept →
nuke4–13-0.0313%29 Sept →
dust25–13-0.0511%28 Sept →
nuke13–10-0.0217%28 Sept →
dust24–13-0.0817%23 Sept →
dust23–13-0.0813%23 Sept →
cache13–10-0.087%21 Sept →
ancient10–13-0.0616%21 Sept →
ancient10–130.0017%3 Sept →
cache4–13-0.069%3 Sept →
inferno7–13-0.0415%2 Sept →
nuke16–12-0.0111%2 Sept →
inferno3–13-0.0313%28 Aug →
nuke4–13-0.0216%28 Aug →
inferno13–7-0.017%28 Aug →
anubis2–13-0.0710%28 Aug →
ancient13–10-0.0223%25 Aug →
inferno13–90.0111%25 Aug →
inferno6–13-0.0629%18 Aug →
anubis13–8-0.0013%18 Aug →
inferno10–13-0.0311%17 Aug →
inferno5–13-0.0216%16 Aug →
cache2–130.0613%16 Aug →
inferno7–13-0.0811%15 Aug →
nuke15–15-0.0114%15 Aug →
anubis5–13-0.098%15 Aug →
ancient4–13-0.0515%15 Aug →
inferno6–13-0.0528%14 Aug →
cache8–13-0.0910%14 Aug →
nuke10–13-0.008%14 Aug →
cache13–11-0.0311%10 Aug →
nuke13–110.0022%9 Aug →
inferno3–13-0.0423%9 Aug →
nuke13–8-0.028%9 Aug →
anubis3–13-0.0418%26 Jul →
anubis9–13-0.046%26 Jul →
ancient8–13-0.0312%26 Jul →
ancient13–110.0615%26 Jul →
anubis16–140.018%26 Jul →
ancient6–13-0.0718%26 Jul →
ancient13–40.0512%26 Jul →
nuke7–13-0.1010%26 Jul →
cache13–10-0.018%26 Jul →
ancient9–13-0.0410%25 Jul →
inferno1–13-0.0829%25 Jul →
nuke13–9-0.0119%24 Jul →

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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