Tera Ashiq darling — CS2 Stats

76561198709543681[U:1:749277953]

95Tracked matches59%Win rate2026Tracked since
CSDB Rating3.7 Learning
Premier CS Rating5,834Light Blue band · top ~74.8% of ranked players (population est.)
CSDB Leaderboard#42058 of 55389 tracked
Ladder ranks via Leetify

Share this profile

CSDB.GGTera Ashiq darlingPREMIER5,834 · Light Blue 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

Aim37
Positioning36
Utility49

0–100 skill scores via Leetify.

Recent form

HOT404410Last 9443%Win rateLWWWWLTWWW

Last 10 vs previous 10: +20pp win rate · +0.03 avg rating

Player DNA

Aim3.7
Aggression2.8
Utility4.9
Positioning3.6
Opening Duels0.0

Style profile from tracked-match aggregates — how this player plays, not how good they are. Classification rules are deterministic and documented in code.

Your pro match

NiKo

Plays most like NiKo 69% 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. 682ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

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

Aim3.7
Positioning3.6
Utility4.9
Mechanics5.2
Opening Duels0.0
Win Impact7.9

Composite 3.7/10 (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-0.02+0.05
first ⅓ avg -0.07 → last ⅓ avg -0.02
Reaction time674ms−89ms
first ⅓ avg 763ms → last ⅓ avg 674ms
Headshot accuracy20.5%+9.8%
first ⅓ avg 10.7% → last ⅓ avg 20.5%

Rolling 5-match average across the last 94 tracked matches, oldest to newest. The delta compares the first third of the window with the last.

Highlights

0.13Best rating — mirage 13–2
131Biggest win — anubis
5Longest win streak
47In matches decided by ≤2 rounds
3Overtime games

Map breakdown

anubisBest map · 67% over 12ancientWeakest map · 20% over 5
MapGradePlayedRecordWin rateAvg rating
dust2C28101836%-0.05
mirageC2181338%-0.03
anubisS128467%-0.05
infernoC104640%-0.06
nukeS64267%-0.04
cacheC52340%-0.05
ancientD51420%-0.05
vertigo42250%-0.03
train21150%-0.07
overpass1010%-0.09

Across the last 94 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (20% over 5). Start with the 6 essential Ancient 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.

21.1%Headshot accuracy
27.6%Accuracy (enemy spotted)
27.9%Spray accuracy
73.5%Counter-strafing
11.4°Preaim
682msReaction time
25.3%T opening success
28.6%CT opening success
0.66Enemies flashed / flash
8.5%Flash assists
6.30HE damage / grenade
6.44Flashes / 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

    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 25.3326% — below the 40% mark we flag

Spend your practice time on Ancient

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 5 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategyAncient grenade lineups

Recent matches

MapScoreRatingHS%Date
dust20–60.0918%30 Aug
mirage13–20.1330%30 Aug
anubis13–10.0354%30 Aug
anubis13–10-0.0636%26 Aug
cache13–10-0.0214%25 Aug
mirage7–130.0021%25 Aug
dust212–12-0.0313%25 Aug
inferno13–3-0.0027%24 Aug
dust213–110.0736%24 Aug
inferno13–7-0.1124%24 Aug
nuke13–11-0.0339%24 Aug
mirage13–8-0.0639%24 Aug
train10–13-0.0521%13 Aug
dust213–8-0.0614%13 Aug
vertigo11–13-0.0212%3 Aug
mirage6–13-0.0112%31 Jul
vertigo13–50.0213%31 Jul
ancient10–130.0019%31 Jul
ancient14–16-0.049%31 Jul
mirage13–90.0714%31 Jul
mirage13–5-0.0229%31 Jul
anubis13–7-0.0326%28 Jul
mirage13–11-0.0421%28 Jul
dust210–13-0.0616%27 Jul
dust23–13-0.0719%27 Jul
nuke13–6-0.0718%26 Jul
inferno4–8-0.0315%26 Jul
mirage1–13-0.058%26 Jul
nuke5–20.020%26 Jul
dust20–13-0.060%26 Jul
anubis13–10-0.0517%26 Jul
mirage6–13-0.0530%26 Jul
dust27–13-0.079%25 Jul
cache2–13-0.0328%25 Jul
dust23–13-0.047%25 Jul
inferno2–13-0.0710%25 Jul
mirage15–15-0.0131%25 Jul
nuke10–13-0.049%24 Jul
mirage13–6-0.0819%24 Jul
ancient7–13-0.0817%23 Jul
anubis13–5-0.110%22 Jul
anubis12–12-0.0915%22 Jul
dust25–13-0.067%22 Jul
mirage9–13-0.0133%21 Jul
dust213–5-0.0124%21 Jul
dust213–7-0.0223%20 Jul
ancient9–13-0.086%20 Jul
dust28–13-0.0612%20 Jul
dust21–8-0.0833%20 Jul
mirage5–13-0.0113%19 Jul
dust28–13-0.0813%19 Jul
cache16–14-0.0726%19 Jul
ancient13–6-0.070%19 Jul
dust213–10-0.066%19 Jul
cache9–13-0.0417%19 Jul
dust211–13-0.0512%19 Jul
dust22–11-0.0727%18 Jul
cache5–13-0.0718%18 Jul
mirage13–50.047%17 Jul
inferno13–9-0.0819%24 Jun
anubis12–12-0.0311%24 Jun
train13–5-0.0813%19 Jun
mirage12–12-0.0313%19 Jun
dust213–5-0.066%19 Jun
inferno2–13-0.0513%19 Jun
dust213–20.0419%18 Jun
mirage12–12-0.105%18 Jun
nuke13–5-0.083%18 Jun
dust211–13-0.0916%10 Jun
dust213–8-0.0713%9 Jun
overpass10–13-0.0911%8 Jun
vertigo3–13-0.063%3 Jun
vertigo13–7-0.0815%3 Jun
anubis7–13-0.1018%31 May
dust211–13-0.0921%31 May
nuke4–13-0.076%30 May
dust213–1-0.0218%30 May
inferno11–13-0.079%28 May
anubis13–50.0415%28 May
mirage7–1-0.060%27 May
anubis13–9-0.056%27 May
anubis3–6-0.0620%27 May
dust212–12-0.077%27 May
dust213–10-0.025%26 May
mirage12–12-0.0911%26 May
anubis13–7-0.0914%26 May
inferno3–13-0.083%25 May
mirage4–13-0.117%25 May
dust23–13-0.0923%25 May
dust211–13-0.099%22 May
mirage12–12-0.117%22 May
inferno4–7-0.070%22 May
mirage10–13-0.134%21 May
inferno12–12-0.0325%21 May

Match data via Leetify.

Recent teammates

Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →