FRIDA-

FRIDA- — CS2 Stats

76561199847964310[U:1:1887698582]Steam profile ↗✓ No bans

1,589Tracked matches53%Win rate2025Tracked since
CSDB Rating4.5 DevelopingSupport
FaceitLevel 4Top 81.0% of ranked FACEIT players
Ladder ranks via Leetify

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CSDB.GGFRIDA-FACEITLevel 4STANDINGTop 81.0% of rankedcsdb.gg/stats

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Performance scores

Aim45
Positioning42
Utility54

0–100 skill scores via Leetify.

Recent form

HOT46–54Last 10046%Win rateWWWWLWLWWW

Last 10 vs previous 10: +40pp win rate · +0.03 avg rating · +3.4pp headshot accuracy · +19ms reaction

Win rate up 40pp 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

  • 4–1 · 80% win rate
  • Avg rating 0.03
  • Avg headshot accuracy 14%
  • Avg reaction 630ms

Last 10

  • 8–2 · 80% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 13%
  • Avg reaction 614ms

Last 20

  • 12–8 · 60% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 11%
  • Avg reaction 605ms

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: Support — Utility contribution stands above the rest of this profile (+2.2 against its own average).

Aim4.5
Utility5.4
Positioning4.2
Opening Duels0.0
Clutch3.4

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 71% 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. 594ms 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

Aim4.5
Positioning4.2
Utility5.4
Mechanics5.2
Opening Duels1.4
Win Impact6.1

Composite 4.5/10 (Developing), 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-0.01+0.01
first ⅓ avg -0.02 → last ⅓ avg -0.01
Reaction time590ms−41ms
first ⅓ avg 631ms → last ⅓ avg 590ms
Headshot accuracy9.9%−1.6%
first ⅓ avg 11.5% → 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

0.17Best match rating · 13–1 · anubis, 29 Oct →
25%Best headshot accuracy · 10–4 · inferno, 17 Sept →
406msFastest reaction time · 10–4 · inferno, 17 Sept →
13–1Biggest win · anubis, 29 Oct →

Across the last 100 tracked matches.

Highlights

4Longest win streak
W4Current streak
7–11In matches decided by ≤2 rounds
11Overtime games

Map breakdown

ancientBest map · 70% over 20nukeWeakest map · 0% over 5
MapGradePlayedRecordWin rateAvg rating
anubisC239–1439%-0.02
ancientS2014–670%-0.01
infernoC188–1044%-0.01
mirageB147–750%-0.01
dust2B105–550%-0.05
nukeD50–50%-0.07
vertigo—41–325%0.01
cache—31–233%-0.04
overpass—21–150%-0.05
debris—10–10%0.03

Across the last 100 tracked matches.

Nuke is currently your weakest sufficiently-sampled map (0% over 5). Start with the 6 essential Nuke lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

503Matches
50%Win rate
0.89Avg K/D
76.9ADR
36%Headshot %

Clutches & streaks

37%1v1 clutch win
22%1v2 clutch win
9Longest win streak

Recent Faceit resultsWWWWL

MapMatchesWin rateAvg K/DAvg kills
Anubis11349%0.9014.8
Ancient9554%0.9014.8
Inferno8144%0.9515.1
Dust27949%0.8712.9
Mirage6348%0.8214.0
Nuke4951%0.8713.8
Cache1182%0.8011.9
Vertigo850%0.8212.1

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.

10.0%Headshot accuracy
30.8%Accuracy (enemy spotted)
29.9%Spray accuracy
73.4%Counter-strafing
8.8°Preaim
594msReaction time
41.1%T opening success
29.2%CT opening success
0.63Enemies flashed / flash
3.6%Flash assists
9.47HE damage / grenade
5.19Flashes / 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 10.0083% — below the 15% mark we flag

    Aim Training →
  2. Advanced Mechanics

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

    CT opening duels 29.2278% — below the 40% mark we flag

Spend your practice time on Nuke

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

Nuke callouts & strategy →Nuke grenade lineups →

Recent matches

MapScoreRatingHS%Date
ancient13–90.1121%4 Nov →
anubis16–120.0211%3 Nov →
mirage13–30.068%3 Nov →
ancient13–100.0413%3 Nov →
mirage8–13-0.0714%3 Nov →
dust213–10-0.037%3 Nov →
anubis5–13-0.028%2 Nov →
ancient13–9-0.038%2 Nov →
mirage16–14-0.0212%2 Nov →
anubis13–60.0122%2 Nov →
inferno5–13-0.086%31 Oct →
mirage6–13-0.1013%31 Oct →
anubis13–10.178%29 Oct →
anubis5–13-0.075%29 Oct →
inferno10–13-0.0011%29 Oct →
mirage8–13-0.0024%28 Oct →
dust213–6-0.009%28 Oct →
ancient7–13-0.054%24 Oct →
inferno13–6-0.083%24 Oct →
anubis13–8-0.009%22 Oct →
inferno13–5-0.017%22 Oct →
mirage9–130.0113%22 Oct →
cache12–16-0.016%22 Oct →
dust211–13-0.044%14 Oct →
ancient7–13-0.0112%14 Oct →
nuke6–13-0.123%8 Oct →
inferno13–50.0515%6 Oct →
ancient13–90.027%4 Oct →
vertigo6–13-0.007%4 Oct →
inferno13–90.026%4 Oct →
inferno11–130.0312%2 Oct →
anubis13–2-0.043%1 Oct →
anubis11–13-0.0713%1 Oct →
ancient14–16-0.0414%28 Sept →
mirage13–10-0.073%27 Sept →
nuke4–13-0.076%27 Sept →
anubis14–160.0113%25 Sept →
dust211–13-0.0917%25 Sept →
overpass19–17-0.0314%24 Sept →
ancient13–11-0.0112%23 Sept →
anubis1–13-0.0711%23 Sept →
ancient14–160.0116%23 Sept →
cache8–13-0.0610%23 Sept →
anubis13–9-0.018%19 Sept →
ancient13–3-0.0511%19 Sept →
anubis4–13-0.0812%19 Sept →
ancient13–80.019%19 Sept →
mirage5–13-0.0219%18 Sept →
anubis13–70.0213%18 Sept →
overpass10–13-0.0612%18 Sept →
inferno13–40.054%18 Sept →
mirage13–8-0.0210%18 Sept →
inferno10–4-0.0225%17 Sept →
dust23–13-0.100%17 Sept →
anubis11–13-0.076%17 Sept →
inferno8–13-0.0610%15 Sept →
ancient13–10-0.0011%15 Sept →
anubis4–13-0.114%13 Sept →
ancient13–10-0.0110%13 Sept →
ancient2–13-0.0618%12 Sept →
inferno13–110.0111%12 Sept →
dust213–11-0.0911%12 Sept →
anubis8–13-0.0321%10 Sept →
ancient19–16-0.028%10 Sept →
inferno7–13-0.039%8 Sept →
ancient13–10-0.0311%8 Sept →
anubis7–13-0.049%8 Sept →
nuke6–13-0.0511%7 Sept →
ancient13–6-0.0317%28 Aug →
inferno11–13-0.0611%28 Aug →
ancient13–110.0415%21 Aug →
inferno13–20.106%19 Aug →
dust213–6-0.0214%18 Aug →
vertigo2–13-0.069%18 Aug →
mirage13–11-0.008%18 Aug →
ancient13–70.0115%15 Aug →
mirage13–50.0611%15 Aug →
inferno8–130.0011%13 Aug →
vertigo13–90.109%13 Aug →
anubis8–13-0.0614%9 Aug →
dust213–16-0.0316%9 Aug →
vertigo9–13-0.0111%6 Aug →
mirage17–19-0.017%29 Jul →
dust216–13-0.0221%29 Jul →
anubis10–13-0.017%28 Jul →
dust28–13-0.0613%28 Jul →
cache13–5-0.0417%28 Jul →
inferno8–13-0.0510%28 Jul →
mirage13–80.0216%27 Jul →
inferno9–13-0.0315%27 Jul →
anubis13–9-0.0314%27 Jul →
nuke8–13-0.0410%26 Jul →
inferno8–13-0.0311%17 Jul →
anubis6–13-0.0212%17 Jul →
nuke6–13-0.055%17 Jul →
mirage4–13-0.042%16 Jul →
anubis10–13-0.0113%16 Jul →
ancient11–13-0.0912%16 Jul →
anubis13–10-0.037%16 Jul →
debris5–90.0311%16 Jul →

Match data via Leetify.

Recent teammates

Milestones

1,000 Matches
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

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