Ankroton

Ankroton — CS2 Stats

DE76561198855417239[U:1:895151511]Steam profile ↗✓ No bans

355Tracked matches53%Win rate2021Tracked since
512Hours in CS9Hrs last 2 wks
CSDB Rating6.7 SolidSupport
Premier CS Rating24,999Top 25% of 211,827 CSDB-tracked playersPink band · top ~3% of ranked players (population est.)
CSDB Leaderboard#22884 of 258923 tracked
WingmanMaster Guardian Elite
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 29 Oct 2026 (2 days ago). Ranks are recorded once per day this page is viewed.

No change since then. Play, then come back: the next observation lands here.

Rating over time

Premier CS Rating: 24,999 +4,642 15 Mar – 29 Oct · 23 days played
19,26124,999peak 24,99915 Mar29 Oct
24,999Peak Premier in tracked matches
32Days played since 2026-03-15

Premier CS Rating

  • At peak — 24,999
  • Next: Red band at 25,000 — 1 to go
  • Reached: Pink band · Purple band · Blue band · Light Blue band
  • Pink band first seen 2026-08-31
  • Purple band first seen 2026-08-31

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

How this compares with the same rank

Median values for Pink band among CSDB-tracked players (n=38,294), 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 playerPink band medianRed band medianvs Red band
Headshot rate48.6%46.9%49.3%meets
Shot accuracy10.4%13.0%13.4%2.9% short
Kill/death ratio1.041.081.120.08 short
Match win rate45.1%46.7%48.5%3.4% short

This profile matches the typical Red band player on 1 of 4 comparable metrics.

Widest gap: Shot accuracy. That is the metric furthest from the Red band median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.

Share this profile

CSDB.GGAnkrotonPREMIER24,999 · Pink bandcsdb.gg/stats

The image is a snapshot; the link keeps updating. Nothing here is published anywhere — it is generated in your browser when you click.

Performance scores

Aim81
Positioning60
Utility66

0–100 skill scores via Leetify.

Recent form

HOT53–45–2Last 10053%Win rateWLWLWWWLWW

Last 10 vs previous 10: 0pp win rate · −0.04 avg rating · +1.4pp headshot accuracy · +3ms reaction

Win rate 0pp 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

  • 3–2 · 60% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 21%
  • Avg reaction 547ms

Last 10

  • 7–3 · 70% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 22%
  • Avg reaction 533ms

Last 20

  • 14–6 · 70% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 22%
  • Avg reaction 531ms

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 (+2.1 against its own average).

Aim8.1
Utility6.6
Positioning6.0
Opening Duels0.8

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

iM

Plays most like iM 82% playstyle similarity

Most alike: positioning profile, utility contribution.

Where you differ: lower opening-duel success.

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. 559ms 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

Aim8.1
Positioning6.0
Utility6.6
Mechanics7.5
Opening Duels1.9
Win Impact6.1

Composite 6.7/10 (Solid), a weighted mean of the bars with a small opposition adjustment (×1.02 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating0.00−0.00
first ⅓ avg 0.00 → last ⅓ avg 0.00
Reaction time549ms−87ms
first ⅓ avg 636ms → last ⅓ avg 549ms
Headshot accuracy21.7%−0.6%
first ⅓ avg 22.3% → last ⅓ avg 21.7%

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

0.14Best match rating · 13–3 · dust2, 8 Oct →
43%Best headshot accuracy · 2–13 · inferno, 13 Apr →
391msFastest reaction time · 5–13 · ancient, 26 Oct →
13–1Biggest win · anubis, 14 Oct →

Across the last 100 tracked matches.

Highlights

5Longest win streak
8–6In matches decided by ≤2 rounds
8Overtime games

Map breakdown

infernoBest map · 65% over 20dust2Weakest map · 40% over 15
MapGradePlayedRecordWin rateAvg rating
infernoS2013–765%-0.01
mirageB1910–953%0.00
nukeA1710–759%0.00
dust2C156–940%0.01
ancientC125–742%0.01
anubisA74–357%0.00
cacheC52–340%-0.01
overpass—42–250%0.01
poseidon—11–0100%0.06

Across the last 100 tracked matches.

Dust 2 is currently your weakest sufficiently-sampled map (40% over 15). Start with the 6 essential Dust 2 lineups, review the callouts, then spin up a practice server.

Lifetime stats

15,870Lifetime kills
1.04K/D
843Matches
45.1%Match win rate
48.6%Headshot % · Top 50% of Pink band
10.4%Shot accuracy
1,882MVPs
305Hours (in match)
794Bombs planted
203Bombs defused

Most-used weapons

Lifetime map wins

1,575inferno
1,157dust2
967nuke
283vertigo
189train
56lake
16cbble
12safehouse

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

Skill profile

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

21.4%Headshot accuracy
33.9%Accuracy (enemy spotted)
37.0%Spray accuracy
83.7%Counter-strafing
8.3°Preaim
559msReaction time
41.9%T opening success
33.1%CT opening success
0.70Enemies flashed / flash
2.6%Flash assists
12.93HE damage / grenade
9.84Flashes / 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 33.1456% — below the 40% mark we flag

Spend your practice time on Dust 2

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

40% win rate across 15 tracked games — your weakest map with enough games to be worth reading into.

Dust 2 callouts & strategy →Dust 2 grenade lineups →

Recent matches

MapScoreRatingHS%Date
anubis13–80.0225%29 Oct →
cache5–13-0.0012%28 Oct →
nuke13–11-0.0320%28 Oct →
inferno5–13-0.0221%28 Oct →
inferno13–11-0.0529%28 Oct →
inferno13–110.0218%26 Oct →
nuke13–100.0118%26 Oct →
ancient5–13-0.0524%26 Oct →
inferno13–8-0.0419%26 Oct →
inferno13–20.0239%26 Oct →
anubis13–10.0317%14 Oct →
dust28–130.0218%8 Oct →
dust213–30.1417%8 Oct →
mirage13–80.0518%8 Oct →
mirage9–13-0.0213%4 Oct →
ancient16–120.0223%12 Sept →
anubis13–60.0531%12 Sept →
nuke13–60.0034%19 Aug →
anubis11–130.0323%19 Aug →
nuke13–6-0.0216%22 Jul →
inferno9–130.0620%22 Jul →
inferno4–13-0.0523%20 Jul →
cache10–13-0.0113%20 Jul →
dust210–13-0.0515%15 Jul →
cache11–13-0.0714%14 Jul →
inferno13–9-0.0417%14 Jul →
nuke4–13-0.0518%14 Jul →
dust29–130.0740%14 Jul →
nuke13–110.0324%14 Jul →
ancient4–13-0.0433%14 Jul →
dust29–13-0.0621%14 Jul →
mirage16–12-0.0022%13 Jul →
inferno13–100.0523%12 Jul →
cache16–140.0129%12 Jul →
ancient8–130.0019%12 Jul →
anubis16–120.0012%12 Jul →
nuke13–60.0424%12 Jul →
cache13–70.0229%12 Jul →
ancient13–160.0116%6 Jul →
ancient10–130.0927%26 Jun →
nuke7–13-0.0518%3 Jun →
mirage13–50.0229%8 May →
inferno3–13-0.0226%18 Apr →
mirage13–90.0511%18 Apr →
mirage13–20.0131%16 Apr →
overpass13–90.0326%16 Apr →
mirage13–30.0120%15 Apr →
ancient13–110.0129%15 Apr →
mirage8–13-0.0129%15 Apr →
dust213–60.0216%15 Apr →
ancient13–60.0218%15 Apr →
mirage13–100.0424%15 Apr →
mirage11–130.0330%15 Apr →
nuke15–15-0.0225%13 Apr →
inferno13–6-0.0619%13 Apr →
mirage13–11-0.0524%13 Apr →
inferno2–13-0.0143%13 Apr →
overpass4–13-0.0319%13 Apr →
mirage10–13-0.0223%12 Apr →
mirage13–70.0131%12 Apr →
mirage7–130.0327%12 Apr →
anubis10–13-0.0322%12 Apr →
overpass13–70.0515%12 Apr →
mirage5–13-0.0129%12 Apr →
overpass10–130.0029%12 Apr →
anubis6–13-0.0716%12 Apr →
nuke13–30.0815%12 Apr →
ancient15–15-0.0023%12 Apr →
dust26–13-0.0138%12 Apr →
inferno13–7-0.0123%11 Apr →
dust23–13-0.0322%11 Apr →
nuke14–16-0.0022%11 Apr →
dust213–8-0.0112%11 Apr →
nuke9–130.0530%6 Apr →
nuke13–4-0.0114%5 Apr →
mirage11–13-0.0527%5 Apr →
inferno13–90.0023%4 Apr →
inferno13–7-0.0024%30 Mar →
nuke11–13-0.0223%30 Mar →
inferno13–40.048%30 Mar →
dust25–130.0311%30 Mar →
nuke13–50.1124%29 Mar →
dust24–13-0.0026%29 Mar →
dust213–100.0331%29 Mar →
mirage10–13-0.0531%29 Mar →
ancient7–13-0.0328%29 Mar →
inferno13–10.0122%29 Mar →
dust213–1-0.0524%26 Mar →
mirage9–13-0.0011%26 Mar →
ancient13–90.0728%26 Mar →
dust24–130.0314%26 Mar →
inferno6–130.0321%26 Mar →
nuke13–40.0010%26 Mar →
poseidon9–30.0624%16 Mar →
inferno13–4-0.0323%16 Mar →
nuke7–13-0.0432%16 Mar →
dust213–100.0617%16 Mar →
ancient13–11-0.0027%16 Mar →
mirage13–9-0.0127%15 Mar →
inferno4–13-0.1016%15 Mar →

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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