Sembrina Carpenter

Sembrina Carpenter — CS2 Stats

US76561197998528510[U:1:38262782]Steam profile ↗✓ No bans

1,461Tracked matches26%Win rate2020Tracked since
5,555Hours in CS2Hrs last 2 wks
CSDB Rating3.5 LearningPositional Player
FaceitLevel 2Top 97.9% of ranked FACEIT players
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 29 Aug 2026 (70 days ago). Ranks are recorded once per day this page is viewed.

+18Tracked matches · now 1,461
+8.1ppWin rate · 17.9% → 25.9%

Rating over time

37Days played since 2026-03-07

Premier is CSDB’s own observation — it builds from the day a profile is first viewed and cannot be backfilled.

How this compares with the same rank

Median values for Level 2 among CSDB-tracked players (n=6,128), from Valve's own lifetime stats. Aim, positioning and utility scores are deliberately not benchmarked here — those are a third-party provider's derived metrics, which CSDB does not store.

MetricThis playerLevel 2 medianLevel 3 medianvs Level 3
Headshot rate36.2%41.3%41.8%5.6% short
Shot accuracy20.6%12.7%12.2%above
Kill/death ratio0.930.950.980.05 short
Match win rate40.0%42.6%43.3%3.3% short

This profile matches the typical Level 3 player on 1 of 4 comparable metrics.

Widest gap: Headshot rate. That is the metric furthest from the Level 3 median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.

Share this profile

CSDB.GGSembrina CarpenterFACEITLevel 2STANDINGTop 97.9% of rankedcsdb.gg/stats

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Performance scores

Aim49
Positioning31
Utility10

0–100 skill scores via Leetify.

Recent form

STEADY34–57–9Last 10034%Win rateWLWWWLLLLL

Last 10 vs previous 10: +20pp win rate · −0.01 avg rating · −0.5pp headshot accuracy · +16ms reaction

Win rate up 20pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp). Average match rating moved the other way (−0.01), so results shifted more than performance did.

Last 5 · 10 · 20 matches

Last 5

  • 4–1 · 80% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 14%
  • Avg reaction 727ms

Last 10

  • 4–6 · 40% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 19%
  • Avg reaction 695ms

Last 20

  • 6–12–2 · 30% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 19%
  • Avg reaction 688ms

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 (+1.7 against its own average).

Aim4.9
Utility1.0
Positioning3.1
Opening Duels0.0

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

sh1ro

Plays most like sh1ro 90% playstyle similarity

Most alike: positioning profile, opening-duel success.

Where you differ: lower utility contribution; 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

Utility. Utility contribution runs far behind the mechanical game — the cheapest rating gain on this profile is thrown, not aimed.

Reaction time. 711ms 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

Aim4.9
Positioning3.1
Utility1.0
Mechanics7.9
Opening Duels0.3
Win Impact0.0

Composite 3.5/10 (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-0.04−0.01
first ⅓ avg -0.03 → last ⅓ avg -0.04
Reaction time718ms−75ms
first ⅓ avg 792ms → last ⅓ avg 718ms
Headshot accuracy19.5%+3.1%
first ⅓ avg 16.3% → last ⅓ avg 19.5%

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

0.09Best match rating · 4–0 · mirage, 4 Nov →
50%Best headshot accuracy · 1–1 · mirage, 23 Aug →
484msFastest reaction time · 13–0 · inferno, 7 Mar →
13–0Biggest win · inferno, 7 Mar →

Across the last 100 tracked matches.

Highlights

4Longest win streak
2–5In matches decided by ≤2 rounds

Map breakdown

cacheBest map · 54% over 13ancientWeakest map · 11% over 9
MapGradePlayedRecordWin rateAvg rating
dust2D194–1521%-0.05
mirageB178–947%-0.03
nukeD164–1225%-0.04
cacheB137–654%-0.01
infernoC125–742%-0.02
ancientD91–811%-0.05
anubisD62–433%-0.02
trainD62–433%-0.02
overpass—21–150%0.02

Across the last 100 tracked matches.

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

Lifetime stats

176,866Lifetime kills
0.93K/D
11,453Matches
40.0%Match win rate
36.2%Headshot %
20.6%Shot accuracy · Top 5% of Level 2 players
21,602MVPs
3,632Hours (in match)
4,702Bombs planted
1,516Bombs defused

Most-used weapons

AK-4756,752
AWP11,922
MP76,326
SSG 085,689
P2505,111

Lifetime map wins

11,216inferno
8,037dust2
5,312nuke
3,675train
3,073vertigo
1,763cbble
1,532office
682italy

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

Faceit stats

Combat

155Matches
46%Win rate
0.70Avg K/D
—ADR
37%Headshot %

Clutches & streaks

—1v1 clutch win
—1v2 clutch win
4Longest win streak

Recent Faceit resultsWWLWL

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.

18.7%Headshot accuracy
28.9%Accuracy (enemy spotted)
29.6%Spray accuracy
85.4%Counter-strafing
9.8°Preaim
711msReaction time
32.4%T opening success
21.3%CT opening success
0.08Enemies flashed / flash
0.0%Flash assists
6.99HE damage / grenade
0.21Flashes / 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. Best CS2 Settings

    Slow first shots are as often a setup problem as a reflex one — framerate, sensitivity and crosshair visibility all move this number.

    Reaction time 710.6117ms — above the 700ms mark we flag

    Aim Training →
  2. 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 0.0788 — below the 0.5 mark we flag

    Grenade Lineups →
  3. 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 32.382% — 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.

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

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

MapScoreRatingHS%Date
nuke13–7-0.0315%4 Nov →
anubis9–13-0.0321%4 Nov →
mirage4–00.0922%4 Nov →
dust25–0-0.110%3 Nov →
cache13–100.0313%3 Nov →
nuke6–13-0.1423%13 Oct →
mirage6–13-0.0827%13 Oct →
train8–130.0118%13 Oct →
inferno11–13-0.0916%8 Sept →
dust26–13-0.0533%8 Sept →
inferno12–12-0.0213%7 Sept →
cache6–13-0.0813%7 Sept →
nuke7–130.0121%4 Sept →
inferno13–40.0433%3 Sept →
dust212–12-0.0611%3 Sept →
mirage9–13-0.0421%1 Sept →
dust25–13-0.0323%31 Aug →
nuke10–13-0.0134%31 Aug →
mirage13–5-0.0216%26 Aug →
mirage0–13-0.087%23 Aug →
mirage1–10.0350%23 Aug →
dust24–13-0.0215%23 Aug →
inferno11–13-0.0318%23 Aug →
dust24–13-0.0619%23 Aug →
cache1–13-0.0228%23 Aug →
mirage12–12-0.0311%8 Aug →
ancient2–13-0.0221%7 Aug →
cache8–13-0.0415%1 Aug →
mirage13–9-0.0411%1 Aug →
ancient3–13-0.1015%1 Aug →
ancient5–13-0.1110%28 Jul →
dust27–13-0.0216%28 Jul →
train2–13-0.0733%28 Jul →
inferno7–13-0.0210%28 Jul →
mirage9–13-0.0513%27 Jul →
ancient13–8-0.0118%25 Jul →
mirage12–12-0.0617%25 Jul →
dust29–13-0.0516%25 Jul →
anubis13–9-0.0512%24 Jul →
cache5–13-0.0415%24 Jul →
nuke4–13-0.0515%23 Jul →
inferno7–13-0.0910%23 Jul →
train13–4-0.0225%23 Jul →
dust29–13-0.0620%23 Jul →
ancient6–13-0.1218%15 Jul →
inferno7–130.0515%5 Jul →
ancient11–130.0117%5 Jul →
dust210–130.0625%5 Jul →
mirage13–20.0729%30 Jun →
nuke13–80.0119%30 Jun →
cache8–13-0.0117%30 Jun →
train13–6-0.0110%29 Jun →
dust26–13-0.0221%29 Jun →
dust210–13-0.0214%29 Jun →
ancient6–13-0.0512%29 Jun →
inferno13–30.0515%28 Jun →
cache13–10-0.0510%2 Jun →
train7–13-0.0418%2 Jun →
dust213–9-0.0215%1 Jun →
nuke5–13-0.0313%1 Jun →
ancient6–13-0.060%12 May →
cache11–20.0530%12 May →
dust29–5-0.089%12 May →
nuke13–6-0.0615%12 May →
mirage5–3-0.1220%11 May →
nuke6–13-0.006%2 May →
cache13–30.0814%2 May →
cache13–100.0112%2 May →
cache13–4-0.0226%2 May →
cache3–13-0.0610%2 May →
cache13–20.0416%1 May →
mirage4–13-0.0610%1 May →
nuke8–13-0.0517%16 Apr →
anubis13–5-0.0212%16 Apr →
nuke5–13-0.065%7 Apr →
inferno12–12-0.0224%7 Apr →
anubis11–13-0.029%6 Apr →
mirage13–5-0.0536%6 Apr →
inferno13–11-0.0623%15 Mar →
nuke12–12-0.028%15 Mar →
dust26–13-0.0322%15 Mar →
nuke5–13-0.0418%9 Mar →
overpass6–13-0.0113%9 Mar →
ancient12–120.0116%8 Mar →
dust29–20.0333%8 Mar →
inferno13–8-0.0023%8 Mar →
mirage13–70.0118%8 Mar →
nuke9–13-0.0812%7 Mar →
overpass6–00.0525%7 Mar →
anubis10–13-0.0116%7 Mar →
inferno13–0-0.0222%7 Mar →
mirage13–10-0.029%7 Mar →
dust29–13-0.0817%7 Mar →
nuke13–6-0.069%7 Mar →
anubis12–12-0.0211%7 Mar →
dust27–13-0.1015%7 Mar →
train12–12-0.0214%7 Mar →
mirage6–13-0.0915%7 Mar →
nuke7–13-0.0715%7 Mar →
dust27–13-0.135%7 Mar →

Match data via Leetify.

Recent teammates

Milestones

1,000 Matches
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

This profile is built from public Steam data. If it is yours, you can remove it, or delete your CSDB account.