󠁳⁧󠁳⁧⁧™The Magician

󠁳⁧󠁳⁧⁧™The Magician — CS2 Stats

PS76561198134679396[U:1:174413668]Steam profile ↗1 game ban

237Tracked matches46%Win rate2020Tracked since
662Hours in CS68Hrs last 2 wks
CSDB Rating5.3 DevelopingPositional Player
Ladder ranks via Leetify

Performance scores

Aim72
Positioning55
Utility26

0–100 skill scores via Leetify.

Recent form

STEADY45–47–8Last 10045%Win rateWWTWWWLLWL

Last 10 vs previous 10: +10pp win rate · −0.01 avg rating · −4.8pp headshot accuracy · −62ms 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

  • 4–0–1 · 80% win rate
  • Avg rating 0.02
  • Avg headshot accuracy 23%
  • Avg reaction 531ms

Last 10

  • 6–3–1 · 60% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 20%
  • Avg reaction 512ms

Last 20

  • 11–8–1 · 55% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 22%
  • Avg reaction 543ms

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 (+1.6 against its own average).

Aim7.2
Utility2.6
Positioning5.5
Opening Duels3.5

Limited utility dependence

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 96% playstyle similarity

Most alike: positioning profile, opening-duel success.

Where you differ: lower aim profile; lower utility contribution.

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

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

Aim7.2
Positioning5.5
Utility2.6
Mechanics5.1
Opening Duels5.2
Win Impact3.8

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 rating0.01+0.02
first ⅓ avg -0.00 → last ⅓ avg 0.01
Reaction time542ms+41ms
first ⅓ avg 502ms → last ⅓ avg 542ms
Headshot accuracy21.5%+1.0%
first ⅓ avg 20.5% → last ⅓ avg 21.5%

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

0.18Best match rating · 9–1 · nuke, 2 Nov →
55%Best headshot accuracy · 13–8 · office, 23 Aug →
344msFastest reaction time · 12–12 · office, 29 Oct →
13–0Biggest win · train, 10 Oct →

Across the last 100 tracked matches.

Highlights

4Longest win streak
W2Current streak
1Overtime games

Map breakdown

officeBest map · 71% over 7infernoWeakest map · 11% over 9
MapGradePlayedRecordWin rateAvg rating
nukeB157–847%0.01
dust2D124–833%-0.01
anubisA127–558%-0.01
mirageA106–460%0.01
infernoD91–811%-0.02
cacheA95–456%0.00
vertigoA85–363%0.01
officeS75–271%0.03
trainB63–350%0.00
ancientD61–517%-0.03
overpass—30–30%0.00
poseidon—21–150%0.06
debris—10–10%-0.07

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (11% over 9). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

11,037Lifetime kills
1.15K/D · Top 25% of tracked players
931Matches
30.1%Match win rate
44.8%Headshot % · Top 50% of tracked players
3.5%Shot accuracy
1,359MVPs
339Hours (in match)
451Bombs planted
134Bombs defused

Most-used weapons

Lifetime map wins

816nuke
628vertigo
431dust2
380inferno
370office
226train
18ar_shoots
15cbble

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

Faceit stats

Combat

1Matches
0%Win rate
0.50Avg K/D
—ADR
46%Headshot %

Clutches & streaks

—1v1 clutch win
—1v2 clutch win
0Longest win streak

Recent Faceit resultsLLLLL

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.

21.0%Headshot accuracy
32.5%Accuracy (enemy spotted)
34.9%Spray accuracy
73.1%Counter-strafing
9.0°Preaim
517msReaction time
51.1%T opening success
50.5%CT opening success
0.33Enemies flashed / flash
8.1%Flash assists
7.42HE damage / grenade
3.24Flashes / 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 0.333 — below the 0.5 mark we flag

    Grenade Lineups →
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.

11% win rate across 9 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
train13–90.0342%5 Nov →
mirage13–30.0115%4 Nov →
dust212–120.0224%4 Nov →
anubis13–20.0220%4 Nov →
mirage13–30.0216%4 Nov →
nuke13–50.0522%4 Nov →
nuke4–13-0.0315%4 Nov →
train8–13-0.0220%3 Nov →
anubis13–80.0314%3 Nov →
inferno4–13-0.0410%3 Nov →
mirage13–5-0.0018%2 Nov →
nuke6–130.0222%2 Nov →
nuke13–30.0131%2 Nov →
anubis3–13-0.0223%2 Nov →
mirage7–13-0.0238%2 Nov →
nuke6–9-0.1124%2 Nov →
nuke9–40.0323%2 Nov →
nuke9–30.0920%2 Nov →
nuke9–10.1824%2 Nov →
inferno7–9-0.0224%2 Nov →
dust212–12-0.0117%1 Nov →
vertigo13–60.0920%1 Nov →
office9–130.0129%1 Nov →
cache7–130.0220%1 Nov →
dust21–13-0.0712%31 Oct →
cache7–13-0.0211%31 Oct →
mirage13–100.0021%30 Oct →
nuke13–60.1127%30 Oct →
office13–70.0526%30 Oct →
anubis6–130.0023%29 Oct →
train13–100.0227%29 Oct →
nuke3–13-0.0217%29 Oct →
nuke7–90.0116%29 Oct →
vertigo8–13-0.109%29 Oct →
vertigo6–13-0.0121%29 Oct →
ancient1–130.0313%29 Oct →
inferno4–130.1222%29 Oct →
office12–120.0325%29 Oct →
poseidon5–9-0.0523%29 Oct →
debris1–9-0.0713%29 Oct →
poseidon9–40.1623%29 Oct →
overpass4–13-0.034%28 Oct →
anubis13–70.0120%28 Oct →
anubis13–4-0.0913%28 Oct →
vertigo6–4-0.000%28 Oct →
ancient8–13-0.0520%28 Oct →
ancient6–13-0.1022%28 Oct →
inferno0–13-0.1224%28 Oct →
mirage6–13-0.0023%27 Oct →
dust22–13-0.0829%27 Oct →
cache13–10.0618%27 Oct →
anubis7–13-0.0731%27 Oct →
dust212–12-0.0216%27 Oct →
nuke8–13-0.059%27 Oct →
dust213–20.0914%27 Oct →
inferno13–70.116%27 Oct →
dust213–9-0.0210%26 Oct →
mirage15–150.0914%26 Oct →
nuke2–9-0.0117%26 Oct →
inferno6–13-0.0111%26 Oct →
dust26–13-0.0223%26 Oct →
cache11–13-0.0620%25 Oct →
anubis13–70.0933%25 Oct →
vertigo2–130.0128%25 Oct →
cache13–7-0.0118%25 Oct →
ancient13–8-0.0014%25 Oct →
mirage13–10.0115%25 Oct →
vertigo11–70.0620%25 Oct →
mirage2–13-0.0215%25 Oct →
vertigo13–8-0.0610%25 Oct →
ancient7–130.0313%25 Oct →
mirage13–90.0122%24 Oct →
office13–20.0315%24 Oct →
cache13–6-0.0330%24 Oct →
cache4–13-0.0520%24 Oct →
cache13–30.0318%24 Oct →
inferno7–13-0.0429%23 Oct →
dust213–90.0817%23 Oct →
dust27–13-0.0411%23 Oct →
inferno10–13-0.1115%10 Oct →
train6–13-0.1113%10 Oct →
anubis13–90.0819%10 Oct →
train13–00.1611%10 Oct →
train6–13-0.0717%9 Oct →
vertigo13–20.0613%24 Sept →
cache13–80.1014%15 Sept →
anubis6–13-0.1322%15 Sept →
nuke2–13-0.0117%15 Sept →
dust212–12-0.024%10 Sept →
anubis4–13-0.0013%10 Sept →
office13–70.0554%3 Sept →
overpass12–12-0.0313%23 Aug →
office13–50.0150%23 Aug →
office13–8-0.0055%23 Aug →
ancient0–13-0.0733%22 Aug →
overpass12–120.0713%22 Aug →
anubis13–7-0.0147%22 Aug →
dust213–50.016%22 Aug →
nuke13–9-0.0513%21 Aug →
inferno1–13-0.0115%21 Aug →

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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