Professional Potato

Professional Potato — CS2 Stats

SE76561197998927486[U:1:38661758]Steam profile ↗✓ No bans

649Tracked matches31%Win rate2025Tracked since
CSDB Rating2.6 LearningPositional Player
Ladder ranks via Leetify

Performance scores

Aim18
Positioning56
Utility33

0–100 skill scores via Leetify.

Recent form

COLD31–58–11Last 10031%Win rateWLLLWWTLLL

Last 10 vs previous 10: +10pp win rate · −0.01 avg rating · +0.2pp headshot accuracy · −42ms 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

  • 2–3 · 40% win rate
  • Avg rating 0.02
  • Avg headshot accuracy 9%
  • Avg reaction 635ms

Last 10

  • 3–6–1 · 30% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 7%
  • Avg reaction 592ms

Last 20

  • 5–13–2 · 25% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 7%
  • Avg reaction 613ms

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 (+1.4 against its own average).

Aim1.8
Utility3.3
Positioning5.6
Opening Duels3.6

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

m0NESY

Plays most like m0NESY 57% playstyle similarity

Most alike: positioning profile, utility contribution.

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

Reaction time. 634ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 51% of shots are taken properly stopped — moving-shot inaccuracy is quietly taxing every duel.

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

Aim1.8
Positioning5.6
Utility3.3
Mechanics0.1
Opening Duels3.9
Win Impact0.0

Composite 2.6/10 (Learning), 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+0.02
first ⅓ avg -0.04 → last ⅓ avg -0.02
Reaction time632ms−53ms
first ⅓ avg 685ms → last ⅓ avg 632ms
Headshot accuracy7.2%−0.4%
first ⅓ avg 7.7% → last ⅓ avg 7.2%

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

0.24Best match rating · 13–1 · train, 15 Aug →
25%Best headshot accuracy · 2–0 · train, 15 Aug →
453msFastest reaction time · 2–13 · office, 19 Sept →
13–1Biggest win · train, 15 Aug →

Across the last 100 tracked matches.

Highlights

7Longest win streak
7–4In matches decided by ≤2 rounds
4Overtime games

Map breakdown

vertigoBest map · 40% over 10ancientWeakest map · 0% over 5
MapGradePlayedRecordWin rateAvg rating
officeD277–2026%-0.02
cacheD154–1127%-0.02
nukeC145–936%-0.03
vertigoC104–640%-0.02
infernoD103–730%-0.05
mirageD93–633%-0.00
ancientD50–50%-0.06
train—44–0100%0.05
anubis—20–20%-0.08
dust2—21–150%-0.08
fachwerk—10–10%-0.07
overpass—10–10%-0.04

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (0% over 5). 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.

7.1%Headshot accuracy
31.2%Accuracy (enemy spotted)
36.3%Spray accuracy
50.5%Counter-strafing
12.3°Preaim
634msReaction time
45.4%T opening success
46.1%CT opening success
0.37Enemies flashed / flash
1.5%Flash assists
15.70HE damage / grenade
1.91Flashes / 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

    Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.

    Preaim 12.2644° — above the 12° mark we flag

    Aim Training →
  2. Best CS2 Crosshair

    Your shots are landing on bodies more often than heads — usually a crosshair-height and placement habit rather than raw aim.

    Headshot accuracy 7.0801% — below the 15% mark we flag

    Aim Training →
  3. 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 0.37 — below the 0.5 mark we flag

    Grenade Lineups →
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.

0% win rate across 5 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

MapScoreRatingHS%Date
office13–11-0.028%1 Oct →
office10–13-0.019%1 Oct →
office5–13-0.0410%30 Sept →
vertigo7–130.098%25 Sept →
vertigo12–20.0810%25 Sept →
nuke13–8-0.068%23 Sept →
anubis12–12-0.052%20 Sept →
office9–13-0.058%20 Sept →
office2–13-0.060%19 Sept →
office3–13-0.095%19 Sept →
cache3–130.0511%19 Sept →
office12–120.0011%19 Sept →
mirage6–130.044%16 Sept →
mirage3–13-0.0110%14 Sept →
inferno5–130.003%14 Sept →
ancient10–13-0.018%12 Sept →
inferno13–8-0.072%12 Sept →
nuke13–50.0011%12 Sept →
office6–13-0.023%12 Sept →
cache4–13-0.063%12 Sept →
vertigo9–13-0.014%9 Sept →
cache13–11-0.0217%9 Sept →
nuke13–16-0.067%9 Sept →
office9–130.1211%7 Sept →
office12–12-0.018%7 Sept →
vertigo10–13-0.102%3 Sept →
office12–12-0.0410%2 Sept →
office13–4-0.036%2 Sept →
office10–13-0.013%30 Aug →
mirage13–9-0.027%27 Aug →
office9–13-0.0614%27 Aug →
cache13–5-0.022%26 Aug →
cache6–13-0.0013%26 Aug →
nuke2–13-0.0310%26 Aug →
dust213–11-0.060%25 Aug →
office9–2-0.043%25 Aug →
nuke5–10.009%25 Aug →
mirage11–130.017%25 Aug →
inferno2–130.023%25 Aug →
office12–12-0.038%22 Aug →
office13–9-0.043%16 Aug →
nuke4–13-0.1111%16 Aug →
nuke4–0-0.020%16 Aug →
vertigo13–30.086%16 Aug →
inferno13–80.0013%15 Aug →
train13–110.036%15 Aug →
train2–00.0025%15 Aug →
train13–10.2410%15 Aug →
office13–8-0.0610%14 Aug →
office10–13-0.017%14 Aug →
inferno3–13-0.093%12 Aug →
ancient5–13-0.105%12 Aug →
office7–13-0.047%11 Aug →
vertigo13–11-0.064%11 Aug →
inferno0–13-0.105%11 Aug →
office12–12-0.100%9 Aug →
mirage13–6-0.047%9 Aug →
vertigo3–13-0.130%7 Aug →
mirage7–13-0.0014%7 Aug →
nuke12–120.129%7 Aug →
cache7–13-0.0418%6 Aug →
mirage10–13-0.0811%6 Aug →
dust27–13-0.0914%6 Aug →
mirage12–120.067%5 Aug →
office13–100.0415%5 Aug →
vertigo3–13-0.1116%4 Aug →
cache6–13-0.0512%4 Aug →
office13–11-0.055%4 Aug →
cache11–130.088%1 Aug →
cache16–130.028%26 Jul →
ancient4–13-0.058%25 Jul →
vertigo11–20.015%25 Jul →
cache9–13-0.089%25 Jul →
cache4–13-0.084%24 Jul →
nuke3–13-0.048%24 Jul →
ancient0–13-0.0716%24 Jul →
cache13–4-0.064%24 Jul →
office6–13-0.049%23 Jul →
cache1–13-0.160%22 Jul →
nuke16–12-0.0811%22 Jul →
inferno13–4-0.0317%22 Jul →
cache9–130.0311%21 Jul →
vertigo3–13-0.0710%19 Jul →
office5–130.047%19 Jul →
office7–13-0.029%18 Jul →
nuke12–120.1013%18 Jul →
fachwerk2–13-0.075%18 Jul →
inferno11–13-0.079%18 Jul →
office2–130.028%17 Jul →
inferno3–13-0.060%17 Jul →
overpass0–13-0.049%17 Jul →
ancient4–13-0.088%16 Jul →
nuke4–13-0.063%16 Jul →
train13–10-0.063%16 Jul →
nuke12–12-0.047%16 Jul →
anubis1–13-0.110%16 Jul →
mirage16–120.013%15 Jul →
nuke11–13-0.0713%15 Jul →
cache12–120.057%15 Jul →
inferno8–13-0.1118%15 Jul →

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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