bigmxc

bigmxc — CS2 Stats

76561199104368675[U:1:1144102947]Steam profile ↗✓ No bans

117Tracked matches41%Win rate2023Tracked since
CSDB Rating1.9 LearningPositional Player
Ladder ranks via Leetify

Performance scores

Aim26
Positioning33
Utility4

0–100 skill scores via Leetify.

Recent form

STEADY44–49–7Last 10044%Win rateLTWWLLWWLL

Last 10 vs previous 10: +20pp win rate · −0.02 avg rating · −3.7pp headshot accuracy · −69ms reaction

Win rate up 20pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp). Average match rating moved the other way (−0.02), so results shifted more than performance did.

Last 5 · 10 · 20 matches

Last 5

  • 2–2–1 · 40% win rate
  • Avg rating -0.06
  • Avg headshot accuracy 11%
  • Avg reaction 675ms

Last 10

  • 4–5–1 · 40% win rate
  • Avg rating -0.06
  • Avg headshot accuracy 12%
  • Avg reaction 642ms

Last 20

  • 6–11–3 · 30% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 14%
  • Avg reaction 677ms

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 (+2.1 against its own average).

Aim2.6
Utility0.4
Positioning3.3
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.

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

jL

Plays most like jL 83% playstyle similarity

Most alike: positioning profile, aim profile.

Where you differ: lower utility contribution; lower opening-duel success.

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

T-side openings. Opening success drops from 33% on CT to 15% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 653ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 55% 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
Positioning3.3
Utility0.4
Mechanics1.1
Opening Duels0.4
Win Impact1.9

Composite 1.9/10 (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-0.03+0.01
first ⅓ avg -0.04 → last ⅓ avg -0.03
Reaction time651ms+8ms
first ⅓ avg 643ms → last ⅓ avg 651ms
Headshot accuracy12.7%+3.6%
first ⅓ avg 9.1% → last ⅓ avg 12.7%

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

0.07Best match rating · 13–3 · dust2, 11 Jun →
24%Best headshot accuracy · 1–13 · dust2, 21 Jun →
406msFastest reaction time · 12–12 · office, 24 May →
13–1Biggest win · cache, 29 Apr →

Across the last 100 tracked matches.

Highlights

11Longest win streak
2Overtime games

Map breakdown

mirageBest map · 63% over 19infernoWeakest map · 14% over 7
MapGradePlayedRecordWin rateAvg rating
dust2C2310–1343%-0.03
mirageA1912–763%-0.02
officeC167–944%-0.04
infernoD71–614%-0.04
cacheA74–357%-0.04
nukeD62–433%-0.02
ancient—40–40%-0.05
vertigo—42–250%-0.07
train—30–30%-0.03
overpass—31–233%-0.01
warden—33–0100%-0.02
boulder—11–0100%-0.01
shelter—10–10%-0.04
stronghold—10–10%-0.07
anubis—11–0100%-0.04
alpine—10–10%0.00

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (14% over 7). Start with the 6 essential Inferno 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.

12.9%Headshot accuracy
29.2%Accuracy (enemy spotted)
33.3%Spray accuracy
54.9%Counter-strafing
10.2°Preaim
653msReaction time
15.2%T opening success
32.8%CT opening success
0.05Enemies flashed / flash
0.0%Flash assists
5.03HE damage / grenade
0.31Flashes / 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. 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 12.9178% — below the 15% mark we flag

    Aim Training →
  2. 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 0.0485 — below the 0.5 mark we flag

    Grenade Lineups →
  3. 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 15.1911% — below the 40% mark we flag

Spend your practice time on Inferno

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.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
ancient1–6-0.090%25 Oct →
inferno12–12-0.0812%23 Sept →
dust213–10-0.059%19 Aug →
mirage13–10-0.0717%19 Aug →
nuke11–13-0.0217%12 Aug →
cache3–8-0.0420%3 Aug →
dust213–30.0613%11 Jul →
boulder13–6-0.0111%9 Jul →
shelter10–13-0.049%9 Jul →
vertigo4–9-0.2215%4 Jul →
mirage9–13-0.0223%27 Jun →
nuke11–7-0.0312%27 Jun →
ancient6–130.0015%27 Jun →
dust28–13-0.1010%27 Jun →
inferno5–13-0.0616%25 Jun →
dust21–13-0.0216%25 Jun →
dust213–90.0113%24 Jun →
dust215–15-0.0013%23 Jun →
dust212–12-0.0418%23 Jun →
dust21–13-0.0924%21 Jun →
mirage2–13-0.0113%21 Jun →
inferno8–13-0.048%21 Jun →
mirage13–16-0.0312%21 Jun →
dust213–50.0411%21 Jun →
nuke13–40.0110%20 Jun →
dust213–4-0.0313%20 Jun →
dust27–13-0.0513%19 Jun →
mirage13–10-0.0312%19 Jun →
dust29–13-0.0813%19 Jun →
inferno1–13-0.100%17 Jun →
inferno8–10.059%17 Jun →
train5–130.0515%17 Jun →
mirage5–130.0211%17 Jun →
nuke8–100.073%16 Jun →
mirage13–40.079%14 Jun →
mirage13–4-0.0710%14 Jun →
dust213–7-0.046%14 Jun →
cache13–3-0.060%13 Jun →
mirage13–70.037%13 Jun →
dust213–20.0219%13 Jun →
cache13–20.0718%12 Jun →
mirage13–40.0114%12 Jun →
dust213–10-0.0416%12 Jun →
mirage13–11-0.0013%11 Jun →
dust213–30.077%11 Jun →
dust28–13-0.109%11 Jun →
mirage13–7-0.0615%9 Jun →
mirage13–6-0.0311%9 Jun →
dust210–6-0.0611%9 Jun →
vertigo3–13-0.053%7 Jun →
vertigo13–100.0113%7 Jun →
office12–120.006%7 Jun →
vertigo13–5-0.0111%3 Jun →
dust27–13-0.057%3 Jun →
ancient5–13-0.037%3 Jun →
mirage4–13-0.0518%3 Jun →
mirage8–13-0.0612%3 Jun →
overpass10–13-0.0110%1 Jun →
dust26–13-0.069%1 Jun →
mirage3–13-0.039%1 Jun →
ancient4–13-0.096%31 May →
dust212–12-0.0416%31 May →
stronghold3–13-0.0714%25 May →
mirage13–3-0.010%25 May →
mirage13–11-0.033%24 May →
overpass3–13-0.022%24 May →
office12–12-0.018%24 May →
dust28–13-0.0413%24 May →
office13–5-0.043%23 May →
office7–13-0.0413%23 May →
mirage13–5-0.007%23 May →
anubis13–8-0.0410%23 May →
nuke6–13-0.0710%23 May →
office6–13-0.037%21 May →
inferno10–13-0.0312%20 May →
office13–9-0.035%20 May →
warden13–10-0.078%20 May →
warden13–20.0514%19 May →
office13–10-0.056%19 May →
nuke5–13-0.076%17 May →
office10–13-0.079%17 May →
office13–8-0.068%17 May →
office9–13-0.057%14 May →
office13–7-0.082%14 May →
office13–60.026%13 May →
office10–13-0.067%13 May →
office6–13-0.0421%13 May →
cache7–13-0.1114%12 May →
office13–10-0.037%11 May →
train4–13-0.1112%11 May →
office10–13-0.068%10 May →
warden13–3-0.033%9 May →
overpass13–10-0.0111%9 May →
train5–13-0.049%8 May →
inferno12–12-0.0412%8 May →
dust26–13-0.027%29 Apr →
cache13–7-0.0818%29 Apr →
cache10–13-0.0412%29 Apr →
cache13–10.004%29 Apr →
alpine10–130.0011%24 Apr →

Match data via Leetify.

Recent teammates

Milestones

100 Matches11-Win Streak
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

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