Toyota'n til Magdi

Toyota'n til Magdi — CS2 Stats

NO76561199570738985[U:1:1610473257]Steam profile ↗✓ No bans

302Tracked matches57%Win rate2023Tracked since
CSDB Rating5.4 DevelopingPositional Player
CSDB Leaderboard#89927 of 242733 tracked
Ladder ranks via Leetify
Track this profile

CSDB reads this profile's Premier rating from its tracked match history, and records Faceit ELO once on every day the page is viewed. 50 days played since 6 May 2024. Come back after the next session and the change shows above.

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Rating over time

Premier CS Rating: 19,255 +8,626 6 May – 23 Apr · 38 days played
9,59219,663peak 19,6636 May23 Apr
19,663Peak Premier in tracked matches
50Days played since 2024-05-06

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

Performance scores

Aim70
Positioning48
Utility21

0–100 skill scores via Leetify.

Recent form

STEADY54–42–4Last 10054%Win rateLLWLWWWWLL

Last 10 vs previous 10: −20pp win rate · −0.04 avg rating · +4.5pp headshot accuracy · −50ms reaction

Win rate down 20pp 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 -0.01
  • Avg headshot accuracy 18%
  • Avg reaction 647ms

Last 10

  • 5–5 · 50% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 22%
  • Avg reaction 631ms

Last 20

  • 12–8 · 60% win rate
  • Avg rating 0.00
  • Avg headshot accuracy 20%
  • Avg reaction 656ms

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 (+1.2 against its own average).

Aim7.0
Utility2.1
Positioning4.8
Opening Duels3.8

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

s1mple

Plays most like s1mple 98% playstyle similarity

Most alike: aim profile, positioning profile.

Where you differ: lower opening-duel success; 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.

Reaction time. 627ms 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

Aim7.0
Positioning4.8
Utility2.1
Mechanics6.6
Opening Duels3.3
Win Impact7.2

Composite 5.4/10 (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 rating0.00−0.04
first ⅓ avg 0.04 → last ⅓ avg 0.00
Reaction time631ms+1ms
first ⅓ avg 630ms → last ⅓ avg 631ms
Headshot accuracy23.3%+4.0%
first ⅓ avg 19.2% → last ⅓ avg 23.3%

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

0.39Best match rating · 9–0 · inferno, 26 Oct →
100%Best headshot accuracy · 0–7 · inferno, 16 Feb →
328msFastest reaction time · 0–7 · inferno, 16 Feb →
13–2Biggest win · italy, 1 Dec →

Across the last 100 tracked matches.

Highlights

5Longest win streak
L2Current streak
6–6In matches decided by ≤2 rounds
10Overtime games

Map breakdown

anubisBest map · 77% over 13ancientWeakest map · 44% over 16
MapGradePlayedRecordWin rateAvg rating
dust2B2210–1245%0.01
mirageA169–756%0.04
ancientC167–944%0.01
anubisS1310–377%0.02
trainA85–363%0.06
vertigoB84–450%0.04
infernoS64–267%0.07
nukeA53–260%0.04
overpass—21–150%0.13
cache—10–10%0.07
basalt—10–10%0.01
italy—11–0100%0.07
office—10–10%0.09

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (44% over 16). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Skill profile

Aggregate performance across tracked matches — stats via Leetify. Percentile context against other CSDB-tracked players arrives as our own benchmark data accumulates.

21.5%Headshot accuracy
33.0%Accuracy (enemy spotted)
37.7%Spray accuracy
79.6%Counter-strafing
9.3°Preaim
627msReaction time
37.4%T opening success
49.3%CT opening success
0.59Enemies flashed / flash
10.0%Flash assists
7.78HE damage / grenade
11.73Flashes / 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 37.4269% — 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.

44% win rate across 16 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

MapScoreRatingHS%Date
cache11–130.0711%3 May →
dust25–13-0.0420%15 Sept →
inferno16–14-0.0519%6 Aug →
dust24–13-0.0517%31 Jul →
inferno13–80.0322%31 Jul →
anubis13–8-0.029%23 Apr →
mirage13–50.0428%23 Apr →
ancient16–14-0.0222%23 Apr →
dust26–13-0.0643%23 Apr →
dust24–13-0.1027%14 Mar →
anubis13–11-0.0220%11 Mar →
ancient13–30.0119%3 Mar →
anubis13–90.0314%3 Mar →
train13–40.0619%3 Mar →
dust213–100.1019%2 Mar →
train11–130.0018%2 Mar →
anubis13–100.0522%2 Mar →
dust211–130.0014%26 Feb →
nuke13–90.0424%24 Feb →
ancient9–13-0.057%24 Feb →
train11–13-0.0422%23 Feb →
dust213–5-0.0331%23 Feb →
train2–13-0.0521%19 Feb →
inferno0–7-0.11100%16 Feb →
mirage4–13-0.0129%10 Feb →
anubis13–60.1026%10 Feb →
ancient16–130.1017%9 Feb →
dust29–130.0329%9 Feb →
nuke13–70.0427%9 Feb →
ancient13–100.0617%9 Feb →
ancient13–9-0.0123%9 Feb →
anubis13–70.0611%8 Feb →
ancient3–13-0.0620%8 Feb →
mirage13–110.1119%6 Feb →
dust24–13-0.065%4 Feb →
anubis7–13-0.0215%4 Feb →
mirage13–6-0.0319%3 Feb →
train16–140.0223%3 Feb →
train13–70.0426%3 Feb →
anubis13–100.1428%3 Feb →
inferno8–130.0323%3 Feb →
train13–60.3625%1 Feb →
mirage13–40.0914%1 Feb →
vertigo13–60.047%17 Dec →
overpass8–13-0.0220%12 Dec →
nuke10–13-0.0435%12 Dec →
basalt12–120.0132%1 Dec →
italy13–20.0733%1 Dec →
train13–90.0626%19 Nov →
anubis13–50.1526%13 Nov →
ancient8–130.0710%13 Nov →
mirage8–13-0.0544%13 Nov →
anubis13–9-0.0124%7 Nov →
mirage9–13-0.0331%7 Nov →
dust29–130.0622%6 Nov →
ancient9–130.1016%6 Nov →
vertigo11–130.0537%2 Nov →
dust22–13-0.0745%29 Oct →
ancient3–13-0.0721%29 Oct →
anubis3–13-0.0650%29 Oct →
anubis8–13-0.0729%29 Oct →
dust213–90.0240%28 Oct →
mirage13–160.0553%28 Oct →
mirage13–80.0520%26 Oct →
dust213–70.1126%26 Oct →
nuke9–30.1926%26 Oct →
overpass9–20.2815%26 Oct →
inferno9–00.3929%26 Oct →
dust215–150.0624%22 Oct →
vertigo9–13-0.0124%22 Oct →
dust29–130.0136%15 Oct →
vertigo13–50.0510%29 Sept →
mirage13–100.0716%20 Sept →
inferno13–50.1021%18 Sept →
mirage13–30.039%18 Sept →
ancient8–130.0410%9 Sept →
ancient13–60.087%7 Sept →
office12–120.0914%7 Sept →
dust213–90.0236%7 Sept →
ancient10–13-0.007%7 Sept →
nuke7–13-0.0516%6 Sept →
dust213–90.0721%6 Sept →
vertigo13–90.0811%4 Sept →
ancient8–130.018%4 Sept →
mirage16–120.0413%2 Sept →
mirage4–130.0326%2 Sept →
vertigo13–90.0220%2 Sept →
anubis13–11-0.0311%26 Aug →
vertigo15–15-0.0114%26 Aug →
mirage13–60.1418%19 Aug →
dust213–50.0723%19 Aug →
vertigo11–130.0816%19 Aug →
mirage13–160.0814%3 Aug →
ancient13–9-0.0320%3 Aug →
mirage9–130.0232%5 Jun →
ancient7–13-0.0521%24 May →
dust213–7-0.0322%21 May →
dust213–30.0834%7 May →
dust24–130.0128%7 May →
dust216–130.0123%6 May →

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 →