Mad Mango

Mad Mango — CS2 Stats

SE76561198068869352[U:1:108603624]Steam profile ↗✓ No bans

676Tracked matches31%Win rate2025Tracked since
CSDB Rating2.4 LearningPositional Player
Ladder ranks via Leetify

Performance scores

Aim36
Positioning29
Utility18

0–100 skill scores via Leetify.

Recent form

COLD31–59–10Last 10031%Win rateLLWWTLLLTL

Last 10 vs previous 10: 0pp win rate · +0.01 avg rating · +3.8pp headshot accuracy · −10ms reaction

Win rate 0pp 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

  • 2–2–1 · 40% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 12%
  • Avg reaction 608ms

Last 10

  • 2–6–2 · 20% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 13%
  • Avg reaction 551ms

Last 20

  • 4–14–2 · 20% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 12%
  • Avg reaction 556ms

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 (+1.3 against its own average).

Aim3.6
Utility1.8
Positioning2.9
Opening Duels0.0

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

NiKo

Plays most like NiKo 89% 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

Counter-strafing. Only 64% 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
Positioning2.9
Utility1.8
Mechanics3.1
Opening Duels0.0
Win Impact0.0

Composite 2.4/10 (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-0.05+0.02
first ⅓ avg -0.07 → last ⅓ avg -0.05
Reaction time549ms−71ms
first ⅓ avg 621ms → last ⅓ avg 549ms
Headshot accuracy9.9%+1.2%
first ⅓ avg 8.8% → last ⅓ avg 9.9%

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

0.04Best match rating · 7–13 · vertigo, 25 Sept →
33%Best headshot accuracy · 3–13 · office, 19 Sept →
344msFastest reaction time · 0–13 · nuke, 29 Aug →
13–1Biggest win · train, 15 Aug →

Across the last 100 tracked matches.

Highlights

8Longest win streak
L2Current streak
8–3In matches decided by ≤2 rounds
4Overtime games

Map breakdown

vertigoBest map · 40% over 10mirageWeakest map · 20% over 10
MapGradePlayedRecordWin rateAvg rating
officeD299–2031%-0.05
cacheC145–936%-0.06
infernoD133–1023%-0.07
nukeC114–736%-0.07
vertigoC104–640%-0.04
mirageD102–820%-0.05
dust2—41–325%-0.10
train—33–0100%-0.01
ancient—30–30%-0.10
anubis—10–10%-0.11
fachwerk—10–10%-0.02
overpass—10–10%-0.10

Across the last 100 tracked matches.

Mirage is currently your weakest sufficiently-sampled map (20% over 10). Start with the 5 essential Mirage 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.

10.0%Headshot accuracy
30.2%Accuracy (enemy spotted)
31.5%Spray accuracy
63.9%Counter-strafing
11.1°Preaim
542msReaction time
23.2%T opening success
23.2%CT opening success
0.18Enemies flashed / flash
0.0%Flash assists
10.71HE damage / grenade
0.76Flashes / 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 10.0065% — 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 0.1842 — 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 23.1751% — below the 40% mark we flag

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.

20% win rate across 10 tracked games — your weakest map with enough games to be worth reading into.

Mirage callouts & strategy →Mirage grenade lineups →

Recent matches

MapScoreRatingHS%Date
office5–13-0.0519%30 Sept →
vertigo7–130.0426%25 Sept →
vertigo12–2-0.046%25 Sept →
nuke13–80.0110%23 Sept →
anubis12–12-0.110%20 Sept →
office9–13-0.086%20 Sept →
office2–13-0.040%19 Sept →
office3–13-0.0833%19 Sept →
office12–12-0.0118%19 Sept →
mirage6–13-0.0317%16 Sept →
mirage3–13-0.0512%14 Sept →
inferno5–13-0.097%14 Sept →
office6–13-0.0423%12 Sept →
cache4–13-0.058%12 Sept →
inferno9–13-0.048%10 Sept →
office13–80.048%10 Sept →
vertigo9–13-0.087%9 Sept →
cache13–11-0.0511%9 Sept →
nuke13–16-0.109%9 Sept →
office9–13-0.074%7 Sept →
office12–12-0.059%7 Sept →
vertigo10–13-0.024%3 Sept →
office12–12-0.066%2 Sept →
office13–4-0.0711%2 Sept →
office10–13-0.097%30 Aug →
mirage5–13-0.098%29 Aug →
nuke0–13-0.0615%29 Aug →
cache13–11-0.036%29 Aug →
office13–8-0.020%29 Aug →
mirage13–9-0.0914%27 Aug →
office9–13-0.0211%27 Aug →
cache13–5-0.043%26 Aug →
cache6–13-0.074%26 Aug →
nuke2–13-0.045%26 Aug →
dust213–11-0.0923%25 Aug →
office9–2-0.0020%25 Aug →
nuke5–1-0.110%25 Aug →
mirage11–13-0.0213%25 Aug →
inferno2–13-0.070%25 Aug →
office12–12-0.016%22 Aug →
cache4–13-0.0410%21 Aug →
office13–9-0.083%16 Aug →
nuke4–13-0.109%16 Aug →
nuke4–0-0.110%16 Aug →
vertigo13–30.016%16 Aug →
inferno13–8-0.0112%15 Aug →
train13–110.0314%15 Aug →
train2–00.000%15 Aug →
train13–1-0.054%15 Aug →
office13–5-0.0115%15 Aug →
office13–8-0.0411%14 Aug →
office10–13-0.0712%14 Aug →
inferno3–13-0.0710%12 Aug →
ancient5–13-0.115%12 Aug →
office7–13-0.030%11 Aug →
vertigo13–11-0.074%11 Aug →
inferno0–13-0.107%11 Aug →
office12–12-0.016%9 Aug →
mirage13–6-0.0114%9 Aug →
vertigo3–13-0.0913%7 Aug →
mirage7–130.019%7 Aug →
nuke12–12-0.028%7 Aug →
cache7–13-0.120%6 Aug →
mirage10–13-0.0517%6 Aug →
dust27–13-0.085%6 Aug →
mirage12–12-0.110%5 Aug →
office13–10-0.038%5 Aug →
mirage10–13-0.078%5 Aug →
inferno11–13-0.1110%5 Aug →
dust25–13-0.149%5 Aug →
vertigo3–13-0.076%4 Aug →
cache6–13-0.1411%4 Aug →
office13–11-0.0615%4 Aug →
cache16–13-0.016%26 Jul →
ancient4–13-0.100%25 Jul →
vertigo11–2-0.010%25 Jul →
cache9–13-0.0214%25 Jul →
cache4–13-0.0621%24 Jul →
nuke3–13-0.1225%24 Jul →
inferno12–12-0.069%24 Jul →
dust21–13-0.1013%24 Jul →
ancient0–13-0.108%24 Jul →
cache13–4-0.033%24 Jul →
office6–13-0.067%23 Jul →
cache1–13-0.1213%22 Jul →
nuke16–12-0.0413%22 Jul →
inferno13–4-0.0911%22 Jul →
cache7–13-0.1110%20 Jul →
inferno2–13-0.110%20 Jul →
office3–13-0.0711%20 Jul →
vertigo3–13-0.095%19 Jul →
office5–13-0.0214%19 Jul →
office7–13-0.120%18 Jul →
nuke12–12-0.040%18 Jul →
fachwerk2–13-0.0211%18 Jul →
inferno11–13-0.038%18 Jul →
office2–13-0.085%17 Jul →
inferno3–13-0.0119%17 Jul →
overpass0–13-0.100%17 Jul →
inferno16–14-0.108%16 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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