Big Ft. — CS2 Stats

76561199221283399[U:1:1261017671]

3,130Tracked matches59%Win rate2023Tracked since
CSDB Rating6.4 SolidAggressive Rifler
FaceitLevel 5Top 67.6% of ranked FACEIT players
WingmanGold Nova III
Ladder ranks via Leetify

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CSDB.GGBig Ft.FACEITLevel 5STANDINGTop 67.6% of rankedcsdb.gg/stats

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Performance scores

Aim78
Positioning49
Utility58

0–100 skill scores via Leetify.

Recent form

STEADY53407Last 10053%Win rateLWWLWLWWTW

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

Player DNA

Primary style: Aggressive RiflerTakes opening fights often, backed by a strong aim profile.

Aim7.8
Aggression6.8
Utility5.8
Positioning4.9
Opening Duels2.9
Clutch3.8

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

b1t

Plays most like b1t 91% playstyle similarity

Most alike: utility contribution, opening-duel success.

Where you differ: lower positioning profile; 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. 576ms 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

Aim7.8
Positioning4.9
Utility5.8
Mechanics7.4
Opening Duels2.9
Win Impact7.9

Composite 6.4/10 (Solid), 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 rating0.00+0.00
first ⅓ avg 0.00 → last ⅓ avg 0.00
Reaction time576ms−16ms
first ⅓ avg 592ms → last ⅓ avg 576ms
Headshot accuracy14.9%+1.1%
first ⅓ avg 13.7% → last ⅓ avg 14.9%

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.

Highlights

0.18Best rating — ancient 13–2
130Biggest win — nuke
5Longest win streak
24In matches decided by ≤2 rounds

Map breakdown

anubisBest map · 83% over 6infernoWeakest map · 27% over 11
MapGradePlayedRecordWin rateAvg rating
cacheB21111052%-0.00
nukeB179853%0.02
dust2C135838%-0.01
infernoD113827%-0.03
trainB105550%0.02
vertigoA85363%0.04
ancientS75271%0.04
mirageS75271%0.03
anubisS65183%0.04

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (27% over 11). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

59Matches
49%Win rate
1.09Avg K/D
81.1ADR
42%Headshot %

Clutches & streaks

39%1v1 clutch win
38%1v2 clutch win
3Longest win streak

Recent Faceit resultsLLWLL

MapMatchesWin rateAvg K/DAvg kills
Ancient1638%0.9414.9
Anubis1567%1.1616.4
Vertigo1258%1.3317.3
Mirage922%1.0212.0
Nuke425%1.0013.0
Inferno2100%0.7420.5
Dust21100%1.0814.0

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.

15.2%Headshot accuracy
40.0%Accuracy (enemy spotted)
42.2%Spray accuracy
83.3%Counter-strafing
10.8°Preaim
576msReaction time
34.9%T opening success
48.3%CT opening success
0.46Enemies flashed / flash
6.3%Flash assists
9.39HE damage / grenade
11.23Flashes / 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. 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 0.4587 — below the 0.5 mark we flag

    Grenade Lineups
  2. 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 34.8572% — 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.

27% win rate across 11 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategyInferno grenade lineups

Recent matches

MapScoreRatingHS%Date
dust28–130.0015%14 Aug
cache7–20.0410%14 Aug
nuke11–8-0.0219%13 Aug
nuke8–130.0517%13 Aug
ancient13–10-0.019%13 Aug
train4–13-0.0418%13 Aug
vertigo13–80.0311%12 Aug
cache13–100.0523%12 Aug
train12–12-0.0113%12 Aug
mirage13–7-0.057%12 Aug
ancient13–80.0815%11 Aug
inferno2–13-0.064%10 Aug
nuke13–90.0414%10 Aug
train13–90.0416%10 Aug
inferno6–13-0.0021%10 Aug
mirage13–40.1138%10 Aug
ancient13–50.0911%10 Aug
cache13–50.0721%10 Aug
cache4–13-0.0120%10 Aug
train13–9-0.0116%8 Aug
cache13–60.0614%8 Aug
nuke13–00.0513%7 Aug
nuke4–13-0.0213%5 Aug
inferno6–13-0.0917%5 Aug
dust21–13-0.0514%5 Aug
cache8–13-0.0212%3 Aug
nuke1–8-0.1113%3 Aug
train13–80.0714%3 Aug
inferno13–30.0110%3 Aug
vertigo11–13-0.0814%2 Aug
cache8–13-0.083%2 Aug
nuke11–13-0.028%1 Aug
cache13–100.0626%1 Aug
inferno12–120.0213%1 Aug
vertigo13–90.0412%1 Aug
mirage13–40.0524%1 Aug
dust26–130.0416%1 Aug
dust211–130.0016%31 Jul
vertigo6–130.0710%31 Jul
dust27–13-0.0210%31 Jul
cache13–10-0.0715%29 Jul
inferno13–8-0.0232%29 Jul
nuke13–90.0625%29 Jul
vertigo13–60.0713%29 Jul
cache12–120.0625%29 Jul
nuke12–120.0215%29 Jul
inferno3–8-0.108%29 Jul
cache6–13-0.0213%28 Jul
mirage13–9-0.0111%28 Jul
nuke5–130.1119%28 Jul
train12–120.0217%28 Jul
mirage4–13-0.0423%28 Jul
dust213–9-0.0317%28 Jul
ancient13–2-0.014%25 Jul
dust23–130.0234%25 Jul
dust213–40.0513%24 Jul
dust213–40.0120%24 Jul
cache13–5-0.0718%24 Jul
anubis13–80.0715%24 Jul
ancient13–20.1812%24 Jul
train12–120.0818%24 Jul
mirage13–100.1413%23 Jul
train8–13-0.0527%23 Jul
cache13–70.0343%23 Jul
ancient8–130.0615%23 Jul
nuke3–7-0.0113%23 Jul
anubis13–6-0.0226%23 Jul
anubis13–40.0314%23 Jul
cache4–13-0.097%23 Jul
cache13–7-0.0027%23 Jul
vertigo13–30.1014%23 Jul
cache10–13-0.0216%23 Jul
anubis13–110.0411%23 Jul
dust213–6-0.048%23 Jul
anubis13–50.065%23 Jul
train13–80.077%22 Jul
cache3–13-0.0016%22 Jul
dust211–00.0114%22 Jul
nuke13–30.0410%22 Jul
inferno9–13-0.037%22 Jul
mirage10–130.0219%21 Jul
inferno12–120.0013%21 Jul
nuke13–40.0624%21 Jul
dust23–13-0.109%20 Jul
nuke9–10.0821%20 Jul
ancient5–13-0.078%20 Jul
nuke9–130.038%20 Jul
anubis11–130.0518%18 Jul
vertigo13–90.1116%18 Jul
cache13–20.0718%18 Jul
train13–80.0219%18 Jul
cache13–11-0.0210%18 Jul
cache10–130.0414%17 Jul
inferno0–13-0.0723%17 Jul
inferno13–10-0.0422%17 Jul
dust24–13-0.0713%17 Jul
vertigo5–13-0.016%17 Jul
nuke7–130.0516%16 Jul
cache5–13-0.087%16 Jul
nuke13–8-0.0714%15 Jul

Match data via Leetify.

Recent teammates

Milestones

1,000 Matches
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