Bike Man

Bike Man — CS2 Stats

76561198220031111[U:1:259765383]Steam profile ↗✓ No bans

621Tracked matches52%Win rate2020Tracked since
CSDB Rating5.3 DevelopingSupport
FaceitLevel 3Top 90.6% of ranked FACEIT players
WingmanGold Nova II
Ladder ranks via Leetify

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CSDB.GGBike ManFACEITLevel 3STANDINGTop 90.6% of rankedcsdb.gg/stats

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Performance scores

Aim71
Positioning37
Utility57

0–100 skill scores via Leetify.

Recent form

COLD37–51–12Last 10037%Win rateLWWLLWLLLL

Last 10 vs previous 10: −30pp win rate · −0.01 avg rating · +3.0pp headshot accuracy · +40ms reaction

Win rate down 30pp 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

  • 2–3 · 40% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 13%
  • Avg reaction 603ms

Last 10

  • 3–7 · 30% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 19%
  • Avg reaction 591ms

Last 20

  • 9–9–2 · 45% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 17%
  • Avg reaction 571ms

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 (+3.3 against its own average).

Aim7.1
Utility5.7
Positioning3.7
Opening Duels0.2
Clutch0.0

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

Twistzz

Plays most like Twistzz 88% playstyle similarity

Most alike: utility contribution, aim profile.

Where you differ: lower positioning profile; 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

Areas to improve

Positioning. Positioning trails aim by 33 points — deaths here waste a strong aim profile.

Reaction time. 607ms 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

Aim7.1
Positioning3.7
Utility5.7
Mechanics7.0
Opening Duels0.1
Win Impact5.6

Composite 5.3/10 (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-0.02−0.01
first ⅓ avg -0.01 → last ⅓ avg -0.02
Reaction time612ms−105ms
first ⅓ avg 717ms → last ⅓ avg 612ms
Headshot accuracy17.0%−6.2%
first ⅓ avg 23.3% → last ⅓ avg 17.0%

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

0.09Best match rating · 3–13 · nuke, 17 Feb →
46%Best headshot accuracy · 10–13 · train, 24 Aug →
461msFastest reaction time · 7–13 · dust2, 19 Aug →
13–1Biggest win · nuke, 5 May →

Across the last 100 tracked matches.

Highlights

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

Map breakdown

anubisBest map · 50% over 6infernoWeakest map · 18% over 11
MapGradePlayedRecordWin rateAvg rating
dust2C269–1735%-0.01
mirageC198–1142%-0.02
nukeC198–1142%-0.02
infernoD112–918%-0.03
anubisB63–350%-0.03
ancientD62–433%-0.03
cache—42–250%-0.03
train—42–250%0.05
vertigo—30–30%-0.02
poseidon—11–0100%-0.07
overpass—10–10%-0.01

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (18% over 11). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

396Matches
48%Win rate
0.99Avg K/D
67.0ADR
42%Headshot %

Clutches & streaks

12%1v1 clutch win
20%1v2 clutch win
7Longest win streak

Recent Faceit resultsLWWWL

MapMatchesWin rateAvg K/DAvg kills
Mirage2962%1.0413.8
Nuke862%1.1212.9
Anubis838%0.8912.8
Ancient633%0.8714.0
Inferno540%1.0413.4
Vertigo540%1.1213.4
Overpass450%0.8817.0
Dust2250%0.437.5

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.

16.4%Headshot accuracy
34.0%Accuracy (enemy spotted)
41.0%Spray accuracy
81.7%Counter-strafing
9.2°Preaim
607msReaction time
30.8%T opening success
26.1%CT opening success
0.69Enemies flashed / flash
5.1%Flash assists
14.37HE damage / grenade
8.99Flashes / 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 30.8307% — 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.

18% win rate across 11 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
inferno1–13-0.106%13 Sept →
mirage13–40.0618%13 Sept →
dust213–90.0212%11 Sept →
mirage9–13-0.0814%10 Sept →
anubis7–13-0.0717%10 Sept →
mirage13–4-0.0544%9 Sept →
dust210–13-0.0220%9 Sept →
dust22–13-0.0917%7 Sept →
vertigo7–130.0427%24 Aug →
dust27–13-0.0313%19 Aug →
inferno12–12-0.0312%19 Aug →
nuke13–110.0117%17 Aug →
mirage11–13-0.0924%16 Aug →
nuke13–50.0215%16 Aug →
anubis13–110.0311%8 Aug →
vertigo12–12-0.0411%6 Aug →
mirage10–13-0.0420%6 Aug →
cache13–4-0.0220%6 Aug →
anubis13–80.019%3 Aug →
nuke13–9-0.0219%3 Aug →
mirage13–7-0.019%3 Aug →
dust213–60.0718%3 Aug →
inferno11–130.0317%2 Aug →
mirage6–13-0.0311%2 Aug →
nuke5–13-0.0927%2 Aug →
inferno13–6-0.0611%2 Aug →
mirage13–8-0.0319%30 Jul →
dust213–60.026%30 Jul →
mirage11–130.0121%24 Jul →
dust212–12-0.0712%24 Jul →
dust27–13-0.0216%22 Jul →
nuke9–13-0.1025%21 Jul →
inferno10–13-0.0028%19 Jul →
nuke7–13-0.0232%17 Jul →
dust213–80.0620%15 Jul →
nuke3–3-0.0310%15 Jul →
poseidon9–7-0.0714%15 Jul →
cache13–11-0.0217%14 Jul →
dust27–130.0623%14 Jul →
mirage2–13-0.0320%12 Jul →
dust212–12-0.0115%12 Jul →
overpass8–13-0.0112%12 Jul →
nuke5–13-0.0911%10 Jul →
inferno10–13-0.0218%8 Jul →
anubis7–13-0.048%8 Jul →
cache4–13-0.0422%7 Jul →
inferno5–13-0.0718%7 Jul →
mirage5–13-0.0728%5 Jul →
nuke12–120.0432%4 Jul →
mirage3–13-0.0314%4 Jul →
mirage13–8-0.0422%3 Jul →
dust212–12-0.0612%2 Jul →
dust212–120.0016%30 Jun →
mirage13–100.0414%30 Jun →
mirage13–110.0017%29 Jun →
nuke12–12-0.0617%28 Jun →
dust27–13-0.0514%28 Jun →
cache2–13-0.0612%23 May →
ancient12–120.0113%23 May →
nuke9–13-0.1110%4 Feb →
dust213–90.0429%4 Feb →
mirage13–40.0421%31 Jan →
dust210–13-0.0127%31 Jan →
nuke7–2-0.0329%31 Jan →
train13–60.0633%9 Jan →
mirage7–130.0217%9 Jan →
ancient13–10-0.0416%9 Jan →
dust210–13-0.0717%16 Nov →
dust213–20.0131%7 Nov →
ancient6–13-0.0326%8 Sept →
ancient4–13-0.0712%1 Sept →
dust21–13-0.029%30 Aug →
mirage12–12-0.0023%29 Aug →
dust213–90.0126%29 Aug →
anubis6–13-0.0529%29 Aug →
dust212–120.0320%25 Aug →
dust29–130.0719%25 Aug →
nuke11–13-0.0142%25 Aug →
nuke13–60.0118%24 Aug →
inferno6–13-0.0729%24 Aug →
anubis13–11-0.0726%24 Aug →
train10–130.0546%24 Aug →
dust213–3-0.0125%24 Aug →
ancient10–13-0.0327%24 Aug →
nuke13–10-0.0117%24 Aug →
dust22–13-0.0435%18 Aug →
dust213–9-0.0211%13 Jul →
inferno2–13-0.0123%13 Jul →
vertigo8–13-0.0610%12 May →
train5–130.0341%5 May →
nuke13–10.0226%5 May →
nuke8–13-0.0417%4 May →
mirage9–130.0424%4 May →
inferno8–13-0.0425%4 Mar →
ancient13–5-0.0314%4 Mar →
dust211–13-0.0611%18 Feb →
nuke3–130.0919%17 Feb →
train13–10.0732%17 Feb →
inferno13–50.0321%31 Jan →
nuke12–12-0.0516%19 Jan →

Match data via Leetify.

Recent teammates

Milestones

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

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