I love Peaches

I love Peaches — CS2 Stats

76561198008639175[U:1:48373447]Steam profile ↗✓ No bans

595Tracked matches50%Win rate2021Tracked since
CSDB Rating6.4 SolidAll-Rounder
FaceitLevel 3Top 90.6% of ranked FACEIT players
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 7 Sep 2026 (54 days ago). Ranks are recorded once per day this page is viewed.

+1Tracked matches · now 595
0.0ppWin rate · 50.0% → 50.0%

Rating over time

Premier CS Rating: 16,443 +1,592 29 Apr – 27 Dec · 37 days played
14,78717,952peak 17,95229 Apr27 Dec
17,952Peak Premier in tracked matches
64Days played since 2025-04-29

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

Share this profile

CSDB.GGI love PeachesFACEITLevel 3STANDINGTop 90.6% of rankedcsdb.gg/stats

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Performance scores

Aim69
Positioning65
Utility62

0–100 skill scores via Leetify.

Recent form

COLD48–48–4Last 10048%Win rateWLLLWLLLWL

Last 10 vs previous 10: −20pp win rate · −0.01 avg rating · +1.7pp headshot accuracy · +46ms 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 0.00
  • Avg headshot accuracy 20%
  • Avg reaction 622ms

Last 10

  • 3–7 · 30% win rate
  • Avg rating 0.00
  • Avg headshot accuracy 21%
  • Avg reaction 634ms

Last 20

  • 8–12 · 40% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 20%
  • Avg reaction 611ms

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: All-Rounder — No style dimension stands clear of the others in this profile.

Aim6.9
Utility6.2
Positioning6.5
Opening Duels5.3

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

Twistzz

Plays most like Twistzz 84% playstyle similarity

Most alike: utility contribution, opening-duel success.

Where you differ: 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

T-side openings. Opening success drops from 58% on CT to 41% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 613ms 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

Aim6.9
Positioning6.5
Utility6.2
Mechanics7.0
Opening Duels5.0
Win Impact5.0

Composite 6.4/10 (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 rating0.01−0.01
first ⅓ avg 0.02 → last ⅓ avg 0.01
Reaction time631ms−42ms
first ⅓ avg 673ms → last ⅓ avg 631ms
Headshot accuracy19.7%+0.9%
first ⅓ avg 18.8% → last ⅓ avg 19.7%

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

0.21Best match rating · 13–8 · train, 13 May →
60%Best headshot accuracy · 2–3 · inferno, 26 Dec →
484msFastest reaction time · 7–13 · dust2, 10 Oct →
13–3Biggest win · dust2, 30 Aug →

Across the last 100 tracked matches.

Highlights

4Longest win streak
7–11In matches decided by ≤2 rounds
14Overtime games

Map breakdown

infernoBest map · 77% over 13mirageWeakest map · 32% over 19
MapGradePlayedRecordWin rateAvg rating
dust2C3214–1844%0.00
mirageD196–1332%0.00
infernoS1310–377%0.01
ancientB126–650%0.00
trainA117–464%0.02
nukeC52–340%-0.02
grail—42–250%0.07
overpass—20–20%-0.07
anubis—10–10%-0.00
agency—11–0100%0.16

Across the last 100 tracked matches.

Mirage is currently your weakest sufficiently-sampled map (32% over 19). Start with the 5 essential Mirage lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

8Matches
38%Win rate
1.29Avg K/D
—ADR
36%Headshot %

Clutches & streaks

—1v1 clutch win
—1v2 clutch win
2Longest win streak

Recent Faceit resultsLLLWW

MapMatchesWin rateAvg K/DAvg kills
Nuke250%1.2618.0
Dust21100%0.699.0
Mirage10%1.5420.0

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.

18.9%Headshot accuracy
33.4%Accuracy (enemy spotted)
33.6%Spray accuracy
81.7%Counter-strafing
8.5°Preaim
613msReaction time
41.4%T opening success
58.4%CT opening success
0.73Enemies flashed / flash
5.7%Flash assists
10.42HE damage / grenade
15.46Flashes / match

Recommended for you

Spend your practice time on Mirage

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

32% win rate across 19 tracked games — your weakest map with enough games to be worth reading into.

Mirage callouts & strategy →Mirage grenade lineups →

Recent matches

MapScoreRatingHS%Date
dust216–140.0419%6 Oct →
mirage7–13-0.0217%8 Aug →
nuke5–13-0.0219%7 Aug →
dust23–13-0.0122%7 Aug →
inferno16–140.0324%19 Jun →
dust28–130.0413%19 Jun →
ancient9–13-0.0311%27 Dec →
dust214–160.0113%26 Dec →
inferno2–3-0.0160%26 Dec →
mirage3–13-0.0214%8 Dec →
ancient5–130.0525%26 Nov →
mirage13–100.0322%20 Nov →
ancient16–120.0416%28 Oct →
overpass9–13-0.0817%26 Oct →
dust213–70.0223%23 Oct →
mirage13–70.1325%15 Oct →
dust27–13-0.0516%10 Oct →
mirage6–130.0417%10 Oct →
dust213–9-0.0613%8 Oct →
nuke2–13-0.0419%8 Oct →
mirage13–7-0.0224%6 Oct →
dust216–120.0127%5 Oct →
inferno13–110.0420%1 Oct →
train13–90.0514%1 Oct →
mirage8–13-0.0123%28 Sept →
mirage7–130.0020%30 Aug →
dust213–3-0.019%30 Aug →
mirage7–13-0.0419%27 Aug →
dust213–60.0411%27 Aug →
mirage13–40.0826%26 Aug →
mirage13–10-0.0022%10 Aug →
mirage12–160.0115%3 Aug →
mirage7–130.0416%2 Aug →
dust211–13-0.0230%1 Aug →
inferno13–80.0816%25 Jul →
dust211–13-0.0321%25 Jul →
inferno13–100.0115%21 Jul →
train13–40.0120%21 Jul →
dust213–5-0.0117%20 Jul →
mirage13–30.0118%20 Jul →
dust213–160.0028%19 Jul →
mirage12–16-0.0413%16 Jul →
overpass3–13-0.0614%16 Jul →
inferno12–12-0.0216%15 Jul →
inferno6–13-0.0915%14 Jul →
dust213–11-0.0512%14 Jul →
train5–130.0015%14 Jul →
ancient10–3-0.0238%10 Jul →
inferno13–80.0415%10 Jul →
dust211–13-0.0421%2 Jul →
mirage7–13-0.0620%2 Jul →
dust28–13-0.0619%1 Jul →
dust22–13-0.039%30 Jun →
nuke13–6-0.0229%30 Jun →
dust29–130.0224%30 Jun →
mirage11–13-0.0012%24 Jun →
ancient15–150.0311%16 Jun →
train16–12-0.024%12 Jun →
inferno13–3-0.0021%12 Jun →
dust213–160.0615%9 Jun →
grail13–100.1312%8 Jun →
dust25–130.019%8 Jun →
ancient2–13-0.0425%5 Jun →
nuke13–11-0.0211%5 Jun →
dust213–100.0423%5 Jun →
mirage9–13-0.0933%3 Jun →
anubis14–16-0.006%2 Jun →
ancient2–13-0.0221%2 Jun →
dust210–13-0.0318%2 Jun →
inferno16–130.0510%1 Jun →
inferno13–100.0414%31 May →
dust213–50.0319%31 May →
dust213–60.0615%30 May →
inferno11–13-0.0215%29 May →
dust213–10-0.0324%29 May →
ancient13–50.0410%28 May →
dust25–130.0319%28 May →
train13–60.0720%25 May →
dust211–13-0.0131%25 May →
ancient13–3-0.025%21 May →
train11–130.0123%21 May →
ancient10–13-0.045%20 May →
ancient13–50.0636%20 May →
dust212–50.019%20 May →
train6–130.0329%15 May →
inferno13–11-0.0414%14 May →
train13–80.2113%13 May →
dust29–20.0915%13 May →
agency13–50.1627%10 May →
grail7–13-0.0231%10 May →
dust212–120.0415%9 May →
grail10–130.089%8 May →
grail13–40.1025%8 May →
dust211–130.0120%7 May →
mirage15–150.0113%6 May →
ancient13–80.0021%1 May →
train13–7-0.0543%1 May →
nuke11–13-0.0116%30 Apr →
train13–8-0.0628%30 Apr →
train7–13-0.048%29 Apr →

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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