Giant-toe-Moss

Giant-toe-Moss — CS2 Stats

76561198053509955[U:1:93244227]Steam profile ↗✓ No bans

759Tracked matches57%Win rate2021Tracked since
1,232Hours in CS1Hrs last 2 wks
CSDB Rating3.2 LearningSupport
Ladder ranks via Leetify

Performance scores

Aim26
Positioning27
Utility32

0–100 skill scores via Leetify.

Recent form

STEADY44–50–6Last 10044%Win rateWLWWLLWTWW

Last 10 vs previous 10: +20pp win rate · +0.01 avg rating · −1.7pp headshot accuracy · +19ms reaction

Win rate up 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

  • 3–2 · 60% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 10%
  • Avg reaction 775ms

Last 10

  • 6–3–1 · 60% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 10%
  • Avg reaction 746ms

Last 20

  • 10–9–1 · 50% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 11%
  • Avg reaction 736ms

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 (+1.2 against its own average).

Aim2.6
Utility3.2
Positioning2.7
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

ZywOo

Plays most like ZywOo 75% 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

Areas to improve

Reaction time. 727ms 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

Aim2.6
Positioning2.7
Utility3.2
Mechanics4.5
Opening Duels0.0
Win Impact7.4

Composite 3.2/10 (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+0.00
first ⅓ avg -0.04 → last ⅓ avg -0.04
Reaction time740ms−6ms
first ⅓ avg 745ms → last ⅓ avg 740ms
Headshot accuracy12.6%+1.0%
first ⅓ avg 11.6% → last ⅓ avg 12.6%

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

0.07Best match rating · 13–1 · nuke, 29 Aug →
33%Best headshot accuracy · 11–13 · dust2, 19 Jul →
430msFastest reaction time · 6–1 · inferno, 22 Jul →
13–1Biggest win · nuke, 29 Aug →

Across the last 100 tracked matches.

Highlights

4Longest win streak
5–11In matches decided by ≤2 rounds
2Overtime games

Map breakdown

trainBest map · 67% over 12grailWeakest map · 20% over 5
MapGradePlayedRecordWin rateAvg rating
infernoB199–1047%-0.02
dust2D186–1233%-0.04
mirageC166–1038%-0.02
trainS128–467%-0.02
nukeC73–443%-0.04
overpassC73–443%-0.02
grailD51–420%-0.05
ancientA53–260%-0.02
office—42–250%-0.08
vertigo—31–233%-0.04
anubis—21–150%-0.03
cache—10–10%-0.03
alpine—11–0100%-0.02

Across the last 100 tracked matches.

Lifetime stats

29,383Lifetime kills
0.81K/D
1,870Matches
36.6%Match win rate
37.2%Headshot %
16.0%Shot accuracy · Top 25% of tracked players
2,714MVPs
663Hours (in match)
1,286Bombs planted
460Bombs defused

Most-used weapons

Lifetime map wins

3,124dust2
1,787inferno
1,470nuke
912vertigo
707train
522office
132cbble
25lake

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.

12.0%Headshot accuracy
29.8%Accuracy (enemy spotted)
28.0%Spray accuracy
70.2%Counter-strafing
10.2°Preaim
727msReaction time
17.4%T opening success
24.6%CT opening success
0.58Enemies flashed / flash
0.4%Flash assists
11.89HE damage / grenade
2.68Flashes / 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 Crosshair

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

    Headshot accuracy 12.0179% — below the 15% mark we flag

    Aim Training →
  2. 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 727.069ms — above the 700ms mark we flag

    Aim Training →
  3. Grenades & Utility

    You are buying and holding utility rather than using it. Unthrown flashes are wasted money every round.

    Flashes per match 2.6847 — below the 4 mark we flag

    Grenade Lineups →
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.

33% win rate across 18 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
dust213–4-0.030%31 Jul →
cache3–13-0.0312%3 May →
inferno13–10-0.018%3 May →
dust213–7-0.0215%24 Apr →
dust28–13-0.0514%24 Apr →
nuke11–13-0.0618%19 Apr →
alpine13–5-0.0211%19 Apr →
dust212–12-0.086%19 Apr →
overpass13–8-0.087%19 Apr →
inferno13–10-0.0011%16 Apr →
overpass13–11-0.0013%16 Apr →
vertigo11–13-0.0814%15 Mar →
office13–4-0.087%15 Mar →
anubis13–10-0.0212%14 Mar →
inferno11–130.029%14 Mar →
nuke10–13-0.057%6 Feb →
dust21–13-0.0817%6 Feb →
mirage11–13-0.119%6 Feb →
inferno13–90.0115%6 Feb →
nuke0–13-0.1014%19 Dec →
inferno13–110.037%8 Dec →
train13–8-0.0319%8 Dec →
office3–13-0.0516%1 Dec →
mirage7–13-0.0311%3 Oct →
inferno4–13-0.089%2 Sept →
overpass13–7-0.0510%2 Sept →
nuke13–3-0.0514%2 Sept →
train13–9-0.0211%2 Sept →
mirage12–120.0123%29 Aug →
nuke13–10.0727%29 Aug →
train7–13-0.0415%29 Aug →
office7–13-0.056%29 Aug →
grail7–13-0.0128%28 Aug →
grail13–5-0.044%28 Aug →
nuke4–13-0.0710%18 Aug →
mirage13–40.0516%18 Aug →
dust213–10-0.0312%18 Aug →
train13–50.0112%17 Aug →
inferno13–6-0.0223%14 Aug →
ancient7–130.0210%14 Aug →
grail4–13-0.043%14 Aug →
vertigo13–90.0016%12 Aug →
vertigo10–13-0.0412%11 Aug →
inferno13–10-0.0210%11 Aug →
train13–1-0.0417%2 Aug →
grail12–12-0.035%2 Aug →
mirage16–130.0617%2 Aug →
inferno6–13-0.0813%30 Jul →
dust213–110.0715%26 Jul →
overpass7–13-0.0410%25 Jul →
inferno4–13-0.0516%23 Jul →
dust210–13-0.0215%23 Jul →
anubis11–13-0.042%22 Jul →
inferno6–10.0122%22 Jul →
train11–13-0.0316%21 Jul →
nuke13–4-0.0614%21 Jul →
inferno13–30.017%21 Jul →
overpass8–130.048%21 Jul →
mirage13–40.0514%20 Jul →
dust22–13-0.0912%20 Jul →
mirage13–9-0.0214%20 Jul →
ancient5–13-0.0614%20 Jul →
train13–10-0.024%20 Jul →
dust211–13-0.0233%19 Jul →
train13–50.0517%18 Jul →
ancient13–7-0.0215%18 Jul →
overpass8–130.0414%17 Jul →
mirage10–13-0.069%17 Jul →
train13–7-0.0514%17 Jul →
mirage5–13-0.046%17 Jul →
dust213–10-0.0414%13 Jul →
mirage13–50.0213%13 Jul →
mirage10–130.0115%12 Jul →
dust211–13-0.0712%11 Jul →
ancient13–5-0.0115%11 Jul →
dust213–11-0.087%11 Jul →
dust21–7-0.029%11 Jul →
dust28–13-0.048%11 Jul →
ancient13–7-0.0312%11 Jul →
inferno11–130.0421%10 Jul →
train15–150.068%9 Jul →
inferno0–2-0.140%8 Jul →
mirage4–13-0.0711%8 Jul →
overpass7–13-0.0323%8 Jul →
grail9–13-0.114%8 Jul →
inferno13–110.0214%5 Jul →
dust211–13-0.0221%5 Jul →
train13–8-0.0213%5 Jul →
inferno6–13-0.0411%5 Jul →
mirage2–13-0.028%4 Jul →
inferno8–130.0212%4 Jul →
mirage13–8-0.147%2 Jul →
mirage3–13-0.0515%2 Jul →
mirage10–13-0.0413%22 Jun →
inferno4–13-0.0816%22 Jun →
train12–12-0.074%22 Jun →
office3–0-0.130%22 Jun →
dust28–13-0.0415%22 Jun →
inferno5–9-0.027%22 Jun →
dust212–12-0.0125%7 Jun →

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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