BooBee

BooBee — CS2 Stats

DK76561199689421187[U:1:1729155459]Steam profile ↗✓ No bans

333Tracked matches68%Win rate2024Tracked since
CSDB Rating7.5 StrongPositional Player
CSDB Leaderboard#25672 of 259093 tracked
Ladder ranks via Leetify

Rating over time

Premier CS Rating: 24,736 +420 28 Feb – 12 Jun · 3 days played
23,61224,736peak 24,73628 Feb12 Jun
24,736Peak Premier in tracked matches
43Days played since 2026-02-26

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

Performance scores

Aim88
Positioning76
Utility47

0–100 skill scores via Leetify.

Recent form

STEADY61–32–7Last 10061%Win rateLWWWTLWTWW

Last 10 vs previous 10: +10pp win rate · +0.03 avg rating · +7.5pp headshot accuracy · −23ms 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

  • 3–1–1 · 60% win rate
  • Avg rating 0.05
  • Avg headshot accuracy 21%
  • Avg reaction 491ms

Last 10

  • 6–2–2 · 60% win rate
  • Avg rating 0.06
  • Avg headshot accuracy 22%
  • Avg reaction 504ms

Last 20

  • 11–7–2 · 55% win rate
  • Avg rating 0.04
  • Avg headshot accuracy 18%
  • Avg reaction 515ms

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 (+1.7 against its own average).

Aim8.8
Utility4.7
Positioning7.6
Opening Duels5.3

Sharp aimer

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

donk

Plays most like donk 89% playstyle similarity

Most alike: positioning profile, utility contribution.

Where you differ: lower opening-duel success.

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

Strengths

Aim. Aim score of 88 — the mechanical foundation is a clear strength.

T openings. 61% T opening-duel success — entries that actually open the round.

Areas to improve

Utility. Utility contribution runs far behind the mechanical game — the cheapest rating gain on this profile is thrown, not aimed.

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

Aim8.8
Positioning7.6
Utility4.7
Mechanics7.1
Opening Duels6.1
Win Impact10.0

Composite 7.5/10 (Strong), 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 rating0.05−0.00
first ⅓ avg 0.06 → last ⅓ avg 0.05
Reaction time513ms−33ms
first ⅓ avg 546ms → last ⅓ avg 513ms
Headshot accuracy19.9%+2.0%
first ⅓ avg 17.9% → last ⅓ avg 19.9%

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

0.31Best match rating · 13–7 · mirage, 17 May →
50%Best headshot accuracy · 1–8 · cache, 15 May →
344msFastest reaction time · 0–9 · vertigo, 25 Aug →
13–0Biggest win · vertigo, 22 Oct →

Across the last 100 tracked matches.

Highlights

10Longest win streak
3–3In matches decided by ≤2 rounds
1Overtime games

Map breakdown

infernoBest map · 82% over 11trainWeakest map · 43% over 7
MapGradePlayedRecordWin rateAvg rating
dust2A2011–955%0.03
nukeA138–562%0.07
anubisS1310–377%0.03
infernoS119–282%0.08
ancientS96–367%0.09
vertigoB84–450%0.01
trainC73–443%0.08
mirageA74–357%0.10
officeA74–357%0.06
cache—41–325%0.06
overpass—11–0100%0.12

Across the last 100 tracked matches.

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
38.8%Accuracy (enemy spotted)
40.8%Spray accuracy
82.2%Counter-strafing
8.4°Preaim
519msReaction time
61.0%T opening success
47.6%CT opening success
0.46Enemies flashed / flash
5.9%Flash assists
6.94HE damage / grenade
7.83Flashes / 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 0.4605 — below the 0.5 mark we flag

    Grenade Lineups →
Spend your practice time on Train

Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.

43% win rate across 7 tracked games — your weakest map with enough games to be worth reading into.

Train callouts & strategy →

Recent matches

MapScoreRatingHS%Date
dust210–13-0.0420%31 Oct →
nuke13–110.0517%31 Oct →
nuke13–90.0820%30 Oct →
dust213–70.1430%30 Oct →
ancient12–120.0317%30 Oct →
train11–130.1023%30 Oct →
dust213–30.1124%30 Oct →
nuke12–120.0122%28 Oct →
anubis13–60.0819%28 Oct →
dust213–90.0329%28 Oct →
nuke11–50.0717%27 Oct →
anubis10–13-0.0011%27 Oct →
nuke13–80.1016%27 Oct →
mirage9–130.097%27 Oct →
vertigo3–13-0.0416%27 Oct →
train10–13-0.0411%27 Oct →
anubis13–11-0.0712%27 Oct →
dust216–130.1126%25 Oct →
cache2–13-0.0122%25 Oct →
vertigo13–00.086%22 Oct →
anubis13–20.1317%22 Oct →
ancient13–90.1917%22 Oct →
inferno13–20.1335%22 Oct →
mirage13–40.2117%22 Oct →
mirage11–130.0222%17 Sept →
office13–40.0227%17 Sept →
anubis13–3-0.0220%17 Sept →
vertigo0–9-0.227%25 Aug →
ancient13–90.0819%19 Aug →
mirage3–13-0.0133%8 Aug →
train13–10.1328%5 Aug →
nuke13–80.1530%5 Aug →
dust212–120.0519%5 Aug →
inferno9–00.073%15 Jul →
office13–30.1323%10 Jul →
dust213–80.0228%9 Jul →
nuke8–8-0.0619%6 Jul →
dust29–13-0.0116%26 Jun →
train13–100.2033%26 Jun →
dust213–90.1526%26 Jun →
dust20–5-0.0020%26 Jun →
ancient13–90.0925%26 Jun →
ancient13–20.2216%12 Jun →
mirage13–6-0.0312%12 Jun →
dust22–130.0030%8 Jun →
nuke13–100.1213%8 Jun →
vertigo7–130.0012%8 Jun →
dust213–100.0523%3 Jun →
vertigo13–60.0219%3 Jun →
ancient13–90.1219%1 Jun →
dust213–30.0414%31 May →
nuke6–9-0.0122%29 May →
anubis13–1-0.0417%29 May →
anubis5–130.0319%29 May →
inferno9–3-0.0215%23 May →
dust21–70.0130%22 May →
vertigo0–9-0.0325%22 May →
inferno4–9-0.0215%22 May →
inferno9–00.2723%22 May →
inferno13–80.1416%17 May →
mirage13–70.3122%17 May →
anubis13–60.1112%15 May →
office10–130.2126%15 May →
cache1–80.1350%15 May →
office12–120.0724%15 May →
office7–13-0.0331%14 May →
dust24–13-0.0619%6 May →
cache11–130.0617%6 May →
cache13–40.0623%6 May →
inferno9–60.0919%2 May →
nuke12–120.0717%2 May →
dust212–00.030%2 May →
inferno9–50.1522%27 Apr →
inferno6–9-0.0226%27 Apr →
train13–60.1323%27 Apr →
train2–13-0.0531%27 Apr →
train7–130.0428%22 Apr →
ancient1–13-0.0116%13 Apr →
ancient5–130.0314%12 Apr →
vertigo13–50.1319%10 Apr →
vertigo13–70.1322%8 Apr →
anubis13–40.1223%2 Apr →
anubis4–13-0.0113%2 Apr →
anubis13–20.0717%2 Apr →
nuke10–130.0511%1 Apr →
dust213–20.1621%1 Apr →
nuke13–20.2126%27 Mar →
dust212–120.078%27 Mar →
ancient13–80.0810%27 Mar →
inferno13–80.0813%27 Mar →
office13–80.0123%22 Mar →
office13–20.0524%18 Mar →
overpass13–100.1222%13 Mar →
anubis13–11-0.0012%13 Mar →
nuke13–10.0410%28 Feb →
dust213–70.0316%28 Feb →
anubis13–70.0117%28 Feb →
mirage13–40.1223%28 Feb →
dust22–5-0.180%26 Feb →
inferno13–90.0227%26 Feb →

Match data via Leetify.

Recent teammates

Milestones

100 Matches10-Win Streak
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

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