mom

mom — CS2 Stats

NO76561198166352280[U:1:206086552]Steam profile ↗✓ No bans

401Tracked matches59%Win rate2025Tracked since
CSDB Rating3.7 LearningPositional Player
Premier CS Rating4,160Grey band · top ~82.4% of ranked players (population est.)
WingmanSilver II
Ladder ranks via Leetify

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CSDB.GGmomPREMIER4,160 · Grey bandcsdb.gg/stats

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Performance scores

Aim27
Positioning66
Utility4

0–100 skill scores via Leetify.

Recent form

STEADY41–52–7Last 10041%Win rateLLWWWWTLTL

Last 10 vs previous 10: −20pp win rate · −0.02 avg rating · +2.1pp headshot accuracy · −9ms 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

  • 3–2 · 60% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 8%
  • Avg reaction 603ms

Last 10

  • 4–4–2 · 40% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 9%
  • Avg reaction 634ms

Last 20

  • 10–8–2 · 50% win rate
  • Avg rating -0.00
  • Avg headshot accuracy 8%
  • Avg reaction 638ms

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 (+3.3 against its own average).

Aim2.7
Utility0.4
Positioning6.6
Opening Duels2.9

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

s1mple

Plays most like s1mple 56% 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

CT openings. Opening success drops from 55% on T to 38% on CT — first contacts on the defending side are being lost.

Reaction time. 640ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 69% of shots are taken properly stopped — moving-shot inaccuracy is quietly taxing every duel.

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

Aim2.7
Positioning6.6
Utility0.4
Mechanics4.3
Opening Duels4.2
Win Impact8.1

Composite 3.7/10 (Learning), a weighted mean of the bars with a small opposition adjustment (×0.90 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.01+0.04
first ⅓ avg -0.05 → last ⅓ avg -0.01
Reaction time638ms−139ms
first ⅓ avg 777ms → last ⅓ avg 638ms
Headshot accuracy8.4%−3.8%
first ⅓ avg 12.3% → last ⅓ avg 8.4%

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

0.16Best match rating · 13–3 · nuke, 28 Sept →
33%Best headshot accuracy · 13–3 · anubis, 8 Aug →
492msFastest reaction time · 4–13 · train, 1 Oct →
13–2Biggest win · train, 11 Oct →

Across the last 100 tracked matches.

Highlights

4Longest win streak
L2Current streak
6–5In matches decided by ≤2 rounds

Map breakdown

vertigoBest map · 64% over 11infernoWeakest map · 20% over 5
MapGradePlayedRecordWin rateAvg rating
nukeC3513–2237%-0.03
trainD174–1324%-0.04
vertigoA117–464%-0.03
anubisB63–350%-0.05
infernoD51–420%-0.04
mirage—43–175%0.01
dust2—43–175%-0.01
cache—42–250%-0.05
overpass—32–167%-0.04
ancient—21–150%-0.05
alpine—20–20%-0.06
office—20–20%-0.07
agency—21–150%-0.03
shelter—10–10%-0.06
italy—11–0100%-0.10
warden—10–10%-0.04

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (20% over 5). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Skill profile

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

8.7%Headshot accuracy
34.9%Accuracy (enemy spotted)
35.3%Spray accuracy
69.3%Counter-strafing
14.9°Preaim
640msReaction time
55.1%T opening success
38.1%CT opening success
0.04Enemies flashed / flash
0.0%Flash assists
5.01HE damage / grenade
0.04Flashes / 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

    Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.

    Preaim 14.928° — above the 12° mark we flag

    Aim Training →
  2. 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 8.6995% — below the 15% mark we flag

    Aim Training →
  3. 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 0.0403 — below the 0.5 mark we flag

    Grenade Lineups →
Spend your practice time on Inferno

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

20% win rate across 5 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
vertigo4–13-0.077%25 Oct →
train11–130.016%22 Oct →
overpass13–10-0.040%20 Oct →
inferno13–6-0.018%20 Oct →
nuke13–9-0.0820%19 Oct →
nuke13–5-0.028%16 Oct →
nuke12–12-0.0211%14 Oct →
nuke11–130.0010%14 Oct →
nuke12–120.0915%13 Oct →
train2–130.015%12 Oct →
nuke13–11-0.016%11 Oct →
train13–2-0.033%11 Oct →
nuke6–13-0.076%10 Oct →
train13–4-0.044%9 Oct →
nuke10–13-0.014%7 Oct →
nuke13–110.014%4 Oct →
train4–13-0.0710%1 Oct →
nuke7–130.0511%1 Oct →
train13–90.0414%29 Sept →
nuke13–30.166%28 Sept →
vertigo12–12-0.0514%27 Sept →
train13–110.1417%27 Sept →
vertigo13–11-0.0310%25 Sept →
train9–13-0.066%22 Sept →
vertigo4–13-0.0113%17 Sept →
vertigo13–2-0.010%13 Sept →
nuke3–13-0.054%13 Sept →
nuke13–10-0.073%7 Sept →
vertigo13–7-0.0314%23 Aug →
mirage13–11-0.016%23 Aug →
anubis7–130.0110%16 Aug →
dust23–130.0118%10 Aug →
nuke3–13-0.016%9 Aug →
anubis13–3-0.1133%8 Aug →
vertigo13–4-0.084%8 Aug →
vertigo13–5-0.036%31 Jul →
anubis7–13-0.054%31 Jul →
train6–13-0.089%30 Jul →
nuke3–13-0.0613%29 Jul →
vertigo7–13-0.024%29 Jul →
anubis13–8-0.0812%23 Jul →
inferno1–13-0.038%23 Jul →
anubis6–13-0.080%21 Jul →
nuke13–8-0.0711%21 Jul →
nuke7–13-0.046%21 Jul →
vertigo13–6-0.0414%20 Jul →
nuke13–11-0.048%20 Jul →
ancient7–13-0.080%20 Jul →
cache2–13-0.0511%20 Jul →
dust213–70.0211%17 Jul →
shelter4–13-0.065%16 Jul →
overpass13–3-0.034%16 Jul →
cache13–5-0.036%16 Jul →
nuke13–20.017%16 Jul →
nuke10–13-0.0711%7 Jul →
nuke12–12-0.0118%7 Jul →
vertigo13–50.059%27 Jun →
cache10–13-0.0825%26 Jun →
cache13–4-0.0412%10 May →
nuke10–13-0.028%25 Apr →
nuke13–50.009%18 Apr →
alpine8–13-0.0318%2 Apr →
nuke13–5-0.086%14 Mar →
office3–13-0.0910%10 Mar →
nuke10–13-0.089%10 Mar →
nuke9–13-0.069%7 Mar →
office11–13-0.0517%7 Mar →
nuke2–13-0.049%6 Mar →
italy13–5-0.1017%3 Mar →
nuke7–13-0.0817%1 Mar →
train9–13-0.1221%27 Feb →
train9–13-0.0110%26 Feb →
train11–13-0.083%26 Feb →
nuke12–12-0.036%26 Feb →
nuke12–12-0.088%21 Feb →
warden0–3-0.040%21 Feb →
inferno6–13-0.058%20 Feb →
train5–13-0.124%14 Feb →
nuke2–13-0.076%13 Feb →
ancient13–3-0.0313%13 Feb →
alpine6–13-0.0920%12 Feb →
agency13–100.0021%13 Jan →
agency5–13-0.066%13 Jan →
train10–13-0.0415%13 Jan →
nuke13–4-0.0518%8 Jan →
nuke11–3-0.1121%26 Dec →
overpass10–13-0.0617%26 Dec →
train2–13-0.0521%25 Dec →
mirage9–13-0.0511%25 Dec →
inferno3–13-0.0621%25 Dec →
dust213–7-0.0717%25 Dec →
inferno12–12-0.0414%16 Dec →
nuke1–13-0.063%15 Dec →
anubis13–80.0115%15 Dec →
train2–13-0.0613%15 Dec →
nuke10–13-0.059%14 Dec →
dust213–10-0.0114%14 Dec →
mirage13–100.015%14 Dec →
train11–13-0.0710%11 Dec →
mirage13–20.1113%11 Dec →

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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