Crypticity

Crypticity — CS2 Stats

76561197979325941[U:1:19060213]Steam profile ↗✓ No bans

116Tracked matches59%Win rate2025Tracked since
CSDB Rating3.7 LearningPositional Player
Ladder ranks via Leetify

Performance scores

Aim31
Positioning50
Utility10

0–100 skill scores via Leetify.

Recent form

HOT58–37–5Last 10058%Win rateWWWLLWWLWW

Last 10 vs previous 10: +10pp win rate · +0.02 avg rating · +1.9pp headshot accuracy · +29ms 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 0.05
  • Avg headshot accuracy 11%
  • Avg reaction 647ms

Last 10

  • 7–3 · 70% win rate
  • Avg rating 0.07
  • Avg headshot accuracy 14%
  • Avg reaction 709ms

Last 20

  • 13–7 · 65% win rate
  • Avg rating 0.06
  • Avg headshot accuracy 13%
  • Avg reaction 694ms

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 (+1.6 against its own average).

Aim3.1
Utility1.0
Positioning5.0
Opening Duels4.3

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

s1mple

Plays most like s1mple 70% playstyle similarity

Most alike: positioning profile, opening-duel success.

Where you differ: lower utility contribution; 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. 711ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

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

Aim3.1
Positioning5.0
Utility1.0
Mechanics3.1
Opening Duels3.7
Win Impact7.9

Composite 3.7/10 (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 rating0.05+0.02
first ⅓ avg 0.03 → last ⅓ avg 0.05
Reaction time717ms−20ms
first ⅓ avg 737ms → last ⅓ avg 717ms
Headshot accuracy13.8%−1.2%
first ⅓ avg 15.0% → last ⅓ avg 13.8%

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

0.19Best match rating · 13–8 · cache, 6 May →
31%Best headshot accuracy · 13–1 · office, 10 Apr →
484msFastest reaction time · 5–13 · vertigo, 9 Apr →
13–0Biggest win · dust2, 8 May →

Across the last 100 tracked matches.

Highlights

8Longest win streak
W3Current streak
3–6In matches decided by ≤2 rounds

Map breakdown

anubisBest map · 73% over 15mirageWeakest map · 38% over 13
MapGradePlayedRecordWin rateAvg rating
infernoB179–853%0.04
anubisS1511–473%0.04
dust2S1410–471%0.03
mirageC135–838%0.04
overpassA127–558%0.04
cacheS64–267%0.07
trainB63–350%0.04
nuke—43–175%0.06
vertigo—41–325%0.00
office—32–167%0.08
italy—31–233%0.05
ancient—21–150%-0.01
alpine—11–0100%0.08

Across the last 100 tracked matches.

Mirage is currently your weakest sufficiently-sampled map (38% over 13). 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.

13.6%Headshot accuracy
34.7%Accuracy (enemy spotted)
33.0%Spray accuracy
63.8%Counter-strafing
11.7°Preaim
711msReaction time
47.7%T opening success
42.1%CT opening success
0.13Enemies flashed / flash
2.6%Flash assists
7.43HE damage / grenade
1.54Flashes / 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 13.6071% — below the 15% mark we flag

    Aim Training →
  2. Best CS2 Settings

    Slow first shots are as often a setup problem as a reflex one — framerate, sensitivity and crosshair visibility all move this number.

    Reaction time 711.056ms — above the 700ms mark we flag

    Aim Training →
  3. 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 0.13 — below the 0.5 mark we flag

    Grenade Lineups →
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.

38% win rate across 13 tracked games — your weakest map with enough games to be worth reading into.

Mirage callouts & strategy →Mirage grenade lineups →

Recent matches

MapScoreRatingHS%Date
inferno13–70.1112%15 May →
anubis13–10-0.0011%9 May →
inferno13–70.1517%9 May →
ancient10–13-0.0110%9 May →
mirage1–130.008%9 May →
nuke13–40.0717%9 May →
dust213–00.1715%8 May →
cache4–130.0420%8 May →
inferno13–60.1014%8 May →
overpass13–60.1119%8 May →
mirage5–130.0218%8 May →
cache13–40.0216%8 May →
anubis13–60.099%6 May →
anubis6–13-0.0113%6 May →
cache13–80.197%6 May →
cache13–90.0411%30 Apr →
cache13–00.0715%30 Apr →
inferno2–13-0.035%30 Apr →
train13–80.0811%29 Apr →
cache8–130.0519%29 Apr →
overpass8–130.0115%24 Apr →
dust213–00.0517%24 Apr →
train6–13-0.0015%22 Apr →
train13–70.0410%22 Apr →
train6–130.0522%22 Apr →
office7–130.0816%16 Apr →
nuke13–50.0522%11 Apr →
anubis13–9-0.0310%11 Apr →
mirage12–120.0711%11 Apr →
italy11–130.0311%10 Apr →
inferno13–80.0313%10 Apr →
vertigo13–5-0.027%10 Apr →
vertigo9–130.0120%10 Apr →
anubis13–20.1413%10 Apr →
inferno9–130.0517%10 Apr →
inferno7–13-0.038%10 Apr →
nuke7–130.0622%10 Apr →
office13–10.1431%10 Apr →
italy6–10.1714%10 Apr →
anubis12–120.0612%9 Apr →
vertigo5–130.0320%9 Apr →
mirage13–90.1815%9 Apr →
mirage13–110.0417%8 Apr →
dust22–13-0.0825%6 Apr →
dust213–90.1421%5 Apr →
anubis13–100.106%5 Apr →
overpass3–13-0.016%4 Apr →
inferno13–110.0110%4 Apr →
inferno13–10-0.0311%4 Apr →
overpass10–13-0.0320%4 Apr →
ancient13–2-0.0017%3 Apr →
alpine5–00.0827%3 Apr →
anubis5–00.0017%3 Apr →
anubis13–40.0514%3 Apr →
office13–20.0115%3 Apr →
inferno13–90.0214%3 Apr →
dust213–60.019%3 Apr →
anubis13–70.023%26 Mar →
italy5–13-0.0410%25 Mar →
anubis10–13-0.027%25 Mar →
dust213–90.0213%25 Mar →
inferno11–130.016%25 Mar →
dust24–13-0.029%25 Mar →
overpass11–130.0413%22 Mar →
nuke13–100.069%22 Mar →
dust213–80.0310%21 Mar →
anubis13–70.048%21 Mar →
mirage5–13-0.075%15 Mar →
dust213–50.0016%15 Mar →
inferno6–130.1014%13 Mar →
dust22–130.0119%13 Mar →
mirage10–13-0.0417%13 Mar →
overpass13–100.0411%11 Mar →
overpass6–10.1216%11 Mar →
anubis12–120.0115%11 Mar →
overpass13–100.029%11 Mar →
overpass10–10.1219%11 Mar →
overpass13–70.0324%9 Mar →
mirage13–10.0221%9 Mar →
inferno11–130.0020%8 Mar →
dust213–70.0211%8 Mar →
overpass8–130.0117%8 Mar →
inferno13–70.0515%8 Mar →
mirage4–130.0719%8 Mar →
mirage10–130.0210%6 Mar →
dust213–60.0212%6 Mar →
inferno3–13-0.085%6 Mar →
anubis13–80.0414%5 Mar →
mirage13–110.0616%5 Mar →
inferno12–120.0817%4 Mar →
mirage12–120.0921%4 Mar →
mirage13–90.1115%4 Mar →
vertigo11–13-0.0114%4 Mar →
anubis13–80.0413%1 Mar →
dust213–10.0112%1 Mar →
train13–70.0219%27 Feb →
dust211–130.019%27 Feb →
overpass13–20.0215%27 Feb →
inferno13–30.1123%25 Feb →
train10–130.0611%25 Feb →

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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