Basil

Basil — CS2 Stats

US76561198232700296[U:1:272434568]Steam profile ↗✓ No bans

160Tracked matches57%Win rate2020Tracked since
380Hours in CS
CSDB Rating5.1 DevelopingSupport
Premier CS Rating14,000Blue band · top ~33.7% of ranked players (population est.)
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 27 Mar 2026 (202 days ago). Ranks are recorded once per day this page is viewed.

No change since then. Play, then come back: the next observation lands here.

Rating over time

Premier CS Rating: 14,000 -2,609 29 Oct – 27 Mar · 14 days played
12,58216,609peak 16,60929 Oct27 Mar
16,609Peak Premier in tracked matches
40Days played since 2025-01-29

Premier CS Rating

  • 2,609 below peak (16,609)
  • +414 over 30 days · improving
  • Next: Purple band at 15,000 — 1,000 to go
  • Reached: Purple band · Blue band · Light Blue band

Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do.

How this compares with the same rank

Median values for Blue band among CSDB-tracked players (n=21,222), from Valve's own lifetime stats. Aim, positioning and utility scores are deliberately not benchmarked here — those are a third-party provider's derived metrics, which CSDB does not store.

MetricThis playerBlue band medianPurple band medianvs Purple band
Headshot rate47.4%42.0%44.3%above
Shot accuracy15.4%9.6%11.9%above
Kill/death ratio1.130.981.03above
Match win rate52.5%43.8%45.2%above

Across every metric we can compare, this profile already matches the typical Purple band player. Rank still comes from winning matches — this is a performance comparison, not a prediction.

Share this profile

CSDB.GGBasilPREMIER14,000 · Blue bandcsdb.gg/stats

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Performance scores

Aim58
Positioning50
Utility59

0–100 skill scores via Leetify.

Recent form

HOT51–45–4Last 10051%Win rateLWWWLWWWLW

Last 10 vs previous 10: +20pp win rate · −0.04 avg rating · −12.9pp headshot accuracy · −50ms reaction

Win rate up 20pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp). Average match rating moved the other way (−0.04), so results shifted more than performance did.

Last 5 · 10 · 20 matches

Last 5

  • 3–2 · 60% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 12%
  • Avg reaction 639ms

Last 10

  • 7–3 · 70% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 17%
  • Avg reaction 616ms

Last 20

  • 12–8 · 60% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 23%
  • Avg reaction 641ms

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 (+1.2 against its own average).

Aim5.8
Utility5.9
Positioning5.0
Opening Duels3.2

Strong CT-side opener

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 81% 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

T-side openings. Opening success drops from 53% on CT to 24% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 631ms 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

Aim5.8
Positioning5.0
Utility5.9
Mechanics5.2
Opening Duels2.9
Win Impact7.2

Composite 5.1/10 (Developing), a weighted mean of the bars with a small opposition adjustment (×0.95 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.01+0.00
first ⅓ avg -0.01 → last ⅓ avg -0.01
Reaction time633ms+11ms
first ⅓ avg 622ms → last ⅓ avg 633ms
Headshot accuracy20.8%−1.3%
first ⅓ avg 22.0% → last ⅓ avg 20.8%

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

0.07Best match rating · 16–12 · nuke, 29 Jan →
100%Best headshot accuracy · 2–10 · ancient, 27 Mar →
438msFastest reaction time · 8–3 · mirage, 24 Oct →
13–0Biggest win · inferno, 27 Mar →

Across the last 100 tracked matches.

Highlights

5Longest win streak
7–7In matches decided by ≤2 rounds
7Overtime games

Map breakdown

nukeBest map · 63% over 16ancientWeakest map · 36% over 11
MapGradePlayedRecordWin rateAvg rating
mirageA2212–1055%-0.01
infernoB199–1047%0.00
nukeA1610–663%0.00
ancientC114–736%-0.00
anubisA95–456%-0.01
overpassA95–456%-0.01
dust2A85–363%-0.02
train—41–325%-0.01
cache—10–10%-0.10
vertigo—10–10%-0.04

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (36% over 11). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Lifetime stats

19,837Lifetime kills
1.13K/D · Top 25% of Blue band
947Matches
52.5%Match win rate · Top 5% of Blue band
47.4%Headshot % · Top 25% of Blue band
15.4%Shot accuracy · Top 25% of Blue band
2,697MVPs
380Hours (in match)
768Bombs planted
178Bombs defused

Most-used weapons

Lifetime map wins

2,237inferno
1,285dust2
597nuke
246vertigo
225train
94cbble
68office
29italy

Lifetime totals via Steam — visible because this profile's game details are public. Spans CS:GO and CS2.

Faceit stats

Combat

2Matches
100%Win rate
1.66Avg K/D
—ADR
60%Headshot %

Clutches & streaks

—1v1 clutch win
—1v2 clutch win
2Longest win streak

Recent Faceit resultsLLLWW

MapMatchesWin rateAvg K/DAvg kills
Vertigo1100%1.5323.0
Ancient1100%1.7816.0

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.

19.8%Headshot accuracy
31.0%Accuracy (enemy spotted)
38.2%Spray accuracy
73.6%Counter-strafing
10.0°Preaim
631msReaction time
23.5%T opening success
53.2%CT opening success
0.61Enemies flashed / flash
6.6%Flash assists
6.54HE damage / grenade
9.18Flashes / 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 23.5066% — 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.

36% win rate across 11 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

MapScoreRatingHS%Date
cache3–13-0.105%1 May →
inferno13–9-0.0226%20 Apr →
nuke13–40.008%20 Apr →
anubis13–8-0.0013%20 Apr →
overpass11–13-0.0410%18 Apr →
inferno13–8-0.0817%10 Apr →
nuke13–70.0137%10 Apr →
mirage13–6-0.0515%10 Apr →
dust211–13-0.0523%10 Apr →
anubis13–50.0313%4 Apr →
mirage13–30.0519%4 Apr →
nuke13–60.0634%4 Apr →
ancient2–10-0.08100%27 Mar →
anubis10–130.0325%27 Mar →
mirage13–100.0218%27 Mar →
inferno13–00.0113%27 Mar →
train3–13-0.0017%25 Mar →
anubis3–13-0.0619%25 Mar →
train13–40.0419%23 Mar →
inferno3–13-0.0030%23 Mar →
mirage13–3-0.0226%23 Mar →
mirage9–13-0.0711%19 Mar →
ancient13–10-0.0026%19 Mar →
inferno9–130.0625%15 Mar →
inferno11–10.008%15 Mar →
ancient14–16-0.036%13 Mar →
inferno13–30.0724%11 Mar →
mirage14–16-0.0516%11 Mar →
anubis6–13-0.0612%6 Mar →
mirage13–110.0018%6 Mar →
overpass13–70.0621%6 Mar →
anubis13–70.0015%5 Mar →
mirage13–11-0.0219%5 Mar →
ancient8–130.0314%4 Mar →
inferno13–100.0522%4 Mar →
mirage8–13-0.0315%4 Mar →
dust216–13-0.0315%4 Mar →
ancient12–120.018%24 Feb →
nuke5–13-0.0518%24 Feb →
mirage1–8-0.0014%24 Feb →
vertigo2–13-0.0420%24 Feb →
anubis13–8-0.0412%24 Feb →
dust24–13-0.0217%24 Feb →
nuke13–40.0322%16 Feb →
overpass13–40.0120%16 Feb →
ancient13–20.0115%13 Feb →
anubis3–130.0213%13 Feb →
inferno6–13-0.014%13 Feb →
ancient13–30.068%13 Feb →
dust213–8-0.0426%13 Feb →
nuke13–10-0.019%13 Feb →
train3–13-0.0130%12 Feb →
nuke13–20.0723%11 Feb →
nuke10–13-0.0518%11 Feb →
mirage4–13-0.0124%30 Jan →
dust212–12-0.0234%30 Jan →
mirage9–13-0.0013%30 Jan →
inferno6–13-0.019%23 Jan →
nuke13–11-0.0433%23 Jan →
inferno2–0-0.0150%23 Jan →
dust213–7-0.027%23 Jan →
mirage13–2-0.0115%23 Jan →
ancient13–60.0516%17 Jan →
inferno9–130.0216%17 Jan →
nuke11–130.0522%17 Jan →
overpass7–13-0.0124%14 Jan →
nuke11–130.0224%13 Dec →
anubis13–8-0.0116%10 Dec →
mirage13–4-0.0319%14 Nov →
nuke10–13-0.1323%14 Nov →
mirage3–13-0.0215%14 Nov →
overpass8–130.0134%9 Nov →
inferno4–130.007%9 Nov →
dust213–60.0729%9 Nov →
inferno12–16-0.0112%5 Nov →
ancient7–13-0.0314%5 Nov →
overpass16–140.0413%5 Nov →
inferno5–13-0.0511%30 Oct →
inferno13–60.0218%29 Oct →
mirage9–13-0.0426%29 Oct →
inferno8–13-0.0412%29 Oct →
nuke13–20.0269%29 Oct →
mirage8–3-0.0429%24 Oct →
mirage6–13-0.0023%23 Oct →
inferno6–13-0.0146%23 Oct →
mirage13–80.0513%9 Oct →
overpass13–5-0.0611%27 Sept →
overpass13–11-0.0713%25 Sept →
overpass15–15-0.0214%6 Sept →
nuke13–70.0128%6 Sept →
train12–12-0.0420%1 Apr →
mirage11–130.0423%22 Feb →
mirage13–70.000%29 Jan →
nuke0–4-0.04100%29 Jan →
dust28–2-0.0313%29 Jan →
ancient6–130.0019%29 Jan →
inferno13–20.0418%29 Jan →
ancient2–13-0.064%29 Jan →
nuke16–120.0714%29 Jan →
mirage13–11-0.0421%29 Jan →

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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