Potao

Potao — CS2 Stats

AU76561197997238890[U:1:36973162]Steam profile ↗✓ No bans

362Tracked matches46%Win rate2023Tracked since
CSDB Rating5.7 SolidSupport
FaceitLevel 4Top 81.0% of ranked FACEIT players
Ladder ranks via Leetify

Rating over time

Premier CS Rating: 13,281 -830 1 Apr – 22 Dec · 30 days played
11,94114,292peak 14,2921 Apr22 Dec
14,519Peak Premier in tracked matches
57Days played since 2024-04-01

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

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CSDB.GGPotaoFACEITLevel 4STANDINGTop 81.0% of rankedcsdb.gg/stats

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Performance scores

Aim64
Positioning54
Utility60

0–100 skill scores via Leetify.

Recent form

STEADY43–54–3Last 10043%Win rateWLWWLLWLWW

Last 10 vs previous 10: +40pp win rate · +0.01 avg rating · −0.3pp headshot accuracy · −45ms reaction

Win rate up 40pp 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

  • 3–2 · 60% win rate
  • Avg rating 0.02
  • Avg headshot accuracy 26%
  • Avg reaction 689ms

Last 10

  • 6–4 · 60% win rate
  • Avg rating 0.00
  • Avg headshot accuracy 23%
  • Avg reaction 638ms

Last 20

  • 8–10–2 · 40% win rate
  • Avg rating -0.00
  • Avg headshot accuracy 23%
  • Avg reaction 660ms

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 (+0.9 against its own average).

Aim6.4
Utility6.0
Positioning5.4
Opening Duels3.0
Clutch6.0

Effective flashes

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

NiKo

Plays most like NiKo 83% playstyle similarity

Most alike: utility contribution, positioning profile.

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

Strengths

Flashes. 0.77 enemies blinded per flash — utility that consistently lands.

Areas to improve

Reaction time. 669ms 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

Aim6.4
Positioning5.4
Utility6.0
Mechanics7.6
Opening Duels2.7
Win Impact3.8

Composite 5.7/10 (Solid), 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+0.01
first ⅓ avg -0.01 → last ⅓ avg 0.00
Reaction time663ms+10ms
first ⅓ avg 653ms → last ⅓ avg 663ms
Headshot accuracy21.8%−1.7%
first ⅓ avg 23.5% → last ⅓ avg 21.8%

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

0.10Best match rating · 13–2 · inferno, 31 Oct →
44%Best headshot accuracy · 13–6 · inferno, 13 Oct →
406msFastest reaction time · 13–6 · ancient, 12 Dec →
13–2Biggest win · inferno, 31 Oct →

Across the last 100 tracked matches.

Highlights

4Longest win streak
10–9In matches decided by ≤2 rounds
7Overtime games

Map breakdown

infernoBest map · 58% over 12nukeWeakest map · 25% over 8
MapGradePlayedRecordWin rateAvg rating
dust2B3114–1745%-0.00
mirageC228–1436%-0.01
infernoA127–558%0.01
ancientC104–640%-0.01
nukeD82–625%-0.01
trainD72–529%-0.01
anubis—44–0100%-0.00
overpass—41–325%-0.02
vertigo—10–10%-0.06
office—11–0100%0.00

Across the last 100 tracked matches.

Nuke is currently your weakest sufficiently-sampled map (25% over 8). Start with the 6 essential Nuke lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

14Matches
36%Win rate
0.95Avg K/D
75.5ADR
51%Headshot %

Clutches & streaks

50%1v1 clutch win
0%1v2 clutch win
2Longest win streak

Recent Faceit resultsWLWLL

MapMatchesWin rateAvg K/DAvg kills
Nuke650%1.0816.3
Mirage540%0.8611.6
Inferno10%0.7112.0
Vertigo10%1.1515.0
Ancient10%0.7311.0

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.

21.5%Headshot accuracy
32.9%Accuracy (enemy spotted)
34.6%Spray accuracy
84.3%Counter-strafing
10.3°Preaim
669msReaction time
42.1%T opening success
39.2%CT opening success
0.77Enemies flashed / flash
5.5%Flash assists
14.71HE damage / grenade
7.57Flashes / 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

    Losing the first CT duel repeatedly usually means holding angles that favour the peeker.

    CT opening duels 39.247% — below the 40% mark we flag

Spend your practice time on Nuke

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

25% win rate across 8 tracked games — your weakest map with enough games to be worth reading into.

Nuke callouts & strategy →Nuke grenade lineups →

Recent matches

MapScoreRatingHS%Date
anubis13–11-0.0517%18 Oct →
mirage4–130.0334%11 Jun →
dust213–10-0.0117%22 Dec →
dust213–70.0331%18 Dec →
dust22–80.1031%18 Dec →
inferno11–13-0.0516%17 Dec →
nuke13–4-0.0428%15 Dec →
overpass10–13-0.0223%15 Dec →
ancient13–6-0.0116%12 Dec →
overpass16–120.0116%12 Dec →
nuke12–12-0.0125%11 Dec →
mirage3–13-0.0424%8 Dec →
ancient13–100.0819%8 Dec →
dust23–13-0.0518%27 Nov →
ancient5–13-0.0024%26 Nov →
dust211–130.0319%26 Nov →
dust212–12-0.0217%24 Nov →
mirage13–10-0.0337%20 Nov →
overpass2–130.0122%17 Nov →
inferno3–13-0.0127%17 Nov →
dust211–130.0120%12 Nov →
ancient5–13-0.0321%11 Nov →
dust29–130.0212%11 Nov →
anubis13–60.0021%7 Nov →
mirage13–50.0512%7 Nov →
train13–30.0330%7 Nov →
dust213–110.0121%6 Nov →
train10–13-0.0028%5 Nov →
mirage8–13-0.0619%4 Nov →
dust213–90.0414%4 Nov →
dust213–110.0124%3 Nov →
ancient16–13-0.0321%3 Nov →
inferno13–20.1018%31 Oct →
dust28–130.0328%31 Oct →
nuke13–30.0228%30 Oct →
train10–13-0.0417%30 Oct →
dust28–13-0.1325%29 Oct →
mirage12–12-0.0123%29 Oct →
dust29–13-0.0426%28 Oct →
ancient16–120.0017%28 Oct →
vertigo5–13-0.0625%27 Oct →
dust20–4-0.080%27 Oct →
mirage4–13-0.0518%27 Oct →
dust213–70.0628%23 Oct →
mirage13–10-0.0014%22 Oct →
dust213–90.0318%21 Oct →
inferno5–13-0.0716%20 Oct →
inferno13–80.0821%20 Oct →
nuke9–13-0.0728%20 Oct →
dust24–13-0.0913%17 Oct →
dust28–3-0.0338%17 Oct →
mirage13–7-0.0522%15 Oct →
dust213–16-0.0517%14 Oct →
inferno5–13-0.0239%13 Oct →
inferno13–60.0244%13 Oct →
dust211–130.0420%13 Oct →
dust27–13-0.0132%10 Oct →
mirage6–13-0.0414%10 Oct →
dust216–13-0.0126%9 Oct →
dust213–90.0816%8 Oct →
nuke2–13-0.0527%8 Oct →
inferno5–13-0.0226%7 Oct →
mirage9–130.0027%7 Oct →
mirage13–70.0622%6 Oct →
inferno13–110.0820%6 Oct →
nuke6–13-0.0119%6 Oct →
train8–13-0.0516%3 Oct →
mirage6–130.0731%2 Oct →
inferno13–11-0.0215%1 Oct →
train13–90.0242%1 Oct →
nuke13–160.0222%30 Sept →
dust210–13-0.0922%29 Sept →
dust213–110.0325%29 Sept →
ancient10–13-0.0424%25 Sept →
mirage11–13-0.0726%23 Sept →
inferno13–110.0318%23 Sept →
overpass13–16-0.0815%22 Sept →
dust210–13-0.0222%22 Sept →
ancient11–13-0.0817%17 Sept →
train0–20.000%17 Sept →
mirage13–8-0.0815%17 Sept →
mirage4–13-0.0123%16 Sept →
ancient10–13-0.0525%15 Sept →
dust213–100.0434%15 Sept →
nuke6–130.0240%15 Sept →
dust211–13-0.0320%15 Sept →
ancient8–130.0113%15 Sept →
inferno13–6-0.0227%15 Sept →
mirage10–13-0.0316%11 Sept →
mirage13–110.0035%10 Sept →
mirage7–13-0.0128%7 Sept →
dust213–7-0.0129%7 Sept →
train6–13-0.0525%7 Sept →
dust213–10-0.0121%7 Sept →
anubis13–30.0123%7 Sept →
office13–40.0020%4 Aug →
mirage11–130.0226%3 Apr →
anubis13–110.0321%3 Apr →
mirage13–110.0526%2 Apr →
mirage6–130.0129%1 Apr →

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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