HandsomeTom

HandsomeTom — CS2 Stats

76561197997606229[U:1:37340501]Steam profile ↗✓ No bans

1,395Tracked matches50%Win rate2020Tracked since
CSDB Rating5.4 DevelopingPositional Player
Premier CS Rating14,894Blue band · top ~29% of ranked players (population est.)
FaceitLevel 6 · 1,547 ELOTop 54.0% of ranked FACEIT players
WingmanGold Nova Master
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 10 Oct 2026 (yesterday). 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: 14,894 -1,085 3 May – 10 Oct · 25 days played
10,00016,223peak 16,2233 May10 Oct
Faceit ELO: 1,547 +0 1 Sept – 24 Sept · 3 observations
1,5471,547peak 1,5471 Sept24 Sept
16,223Peak Premier in tracked matches
1,547Highest Faceit ELO seen on CSDB
46Days played since 2026-05-02

Faceit ELO

  • At peak — 1,547
  • 0 over 30 days · steady
  • Next: Level 9 at 1,751 — 204 to go
  • Reached: Level 8 · Level 7 · Level 6 · Level 5
  • Level 8 first seen 2026-09-01
  • Level 7 first seen 2026-09-01

Premier CS Rating

  • 1,329 below peak (16,223)
  • -53 over 30 days · steady
  • +4,894 over 90 days
  • Next: Purple band at 15,000 — 106 to go
  • Reached: Purple band · Blue band · Light Blue band
  • Purple band first seen 2026-09-24
  • Blue band first seen 2026-09-18

Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do. Faceit ELO is CSDB’s own observation — no feed exposes ELO per match, so that line only has the days the profile was viewed and cannot be backfilled.

Share this profile

CSDB.GGHandsomeTomFACEITLevel 6 · 1,547 ELOSTANDINGTop 54.0% of rankedPREMIER14,894 · Blue band30 DAYS0 ELOPEAK ELO1,547csdb.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

Aim60
Positioning61
Utility57

0–100 skill scores via Leetify.

Recent form

HOT38–59–3Last 10038%Win rateWWWLLWWLWW

Last 10 vs previous 10: +20pp win rate · +0.02 avg rating · +2.1pp headshot accuracy · −78ms 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 0.00
  • Avg headshot accuracy 16%
  • Avg reaction 534ms

Last 10

  • 7–3 · 70% win rate
  • Avg rating -0.00
  • Avg headshot accuracy 14%
  • Avg reaction 565ms

Last 20

  • 12–8 · 60% win rate
  • Avg rating -0.01
  • Avg headshot accuracy 13%
  • Avg reaction 604ms

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 (+1.8 against its own average).

Aim6.0
Utility5.7
Positioning6.1
Opening Duels2.7
Clutch2.8

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 82% playstyle similarity

Most alike: positioning profile, utility contribution.

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. 602ms 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

Aim6.0
Positioning6.1
Utility5.7
Mechanics6.9
Opening Duels3.1
Win Impact5.0

Composite 5.4/10 (Developing), a weighted mean of the bars with a small opposition adjustment (×0.95 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.01+0.01
first ⅓ avg -0.03 → last ⅓ avg -0.01
Reaction time593ms−72ms
first ⅓ avg 666ms → last ⅓ avg 593ms
Headshot accuracy14.7%−2.2%
first ⅓ avg 16.9% → last ⅓ avg 14.7%

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

0.18Best match rating · 9–4 · nuke, 15 Jun →
50%Best headshot accuracy · 13–10 · cache, 1 Sept →
406msFastest reaction time · 13–6 · dust2, 8 Sept →
13–2Biggest win · inferno, 22 Aug →

Across the last 100 tracked matches.

Highlights

4Longest win streak
W3Current streak
11–15In matches decided by ≤2 rounds
8Overtime games

Map breakdown

ancientBest map · 52% over 23dust2Weakest map · 15% over 13
MapGradePlayedRecordWin rateAvg rating
ancientB2312–1152%-0.02
infernoB189–950%0.01
anubisD153–1220%-0.02
nukeC145–936%-0.02
dust2D132–1115%-0.02
cacheB63–350%-0.03
overpass—41–325%-0.04
poseidon—21–150%0.03
vertigo—20–20%-0.07
mirage—22–0100%-0.02
sanctum—10–10%0.15

Across the last 100 tracked matches.

Dust 2 is currently your weakest sufficiently-sampled map (15% over 13). Start with the 6 essential Dust 2 lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

278Matches
48%Win rate
0.93Avg K/D
72.9ADR
36%Headshot %

Clutches & streaks

34%1v1 clutch win
22%1v2 clutch win
11Longest win streak

Recent Faceit resultsLWWWW

MapMatchesWin rateAvg K/DAvg kills
Ancient4961%1.0414.6
Nuke4146%0.9213.7
Dust23850%1.0815.2
Inferno2839%0.7812.2
Anubis2650%0.9213.5
Overpass2045%0.8513.1
Mirage1443%0.7812.2
Train1362%0.9814.2

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.

15.0%Headshot accuracy
33.0%Accuracy (enemy spotted)
36.1%Spray accuracy
81.2%Counter-strafing
10.5°Preaim
602msReaction time
41.8%T opening success
42.9%CT opening success
0.61Enemies flashed / flash
5.8%Flash assists
9.04HE damage / grenade
6.46Flashes / 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 14.9765% — below the 15% mark we flag

    Aim Training →
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.

15% win rate across 13 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
ancient16–140.0023%11 Oct →
inferno13–60.059%11 Oct →
ancient13–10-0.0311%11 Oct →
dust211–130.0523%10 Oct →
cache4–13-0.0613%4 Oct →
ancient13–10-0.038%26 Sept →
inferno13–5-0.0210%25 Sept →
inferno10–130.0314%23 Sept →
inferno13–3-0.0215%21 Sept →
inferno13–8-0.0111%21 Sept →
nuke10–13-0.066%12 Sept →
dust24–13-0.0514%12 Sept →
ancient13–6-0.060%11 Sept →
anubis8–13-0.043%8 Sept →
inferno13–11-0.058%8 Sept →
dust29–13-0.0214%8 Sept →
ancient9–13-0.0113%8 Sept →
anubis13–80.0419%8 Sept →
cache13–80.0215%8 Sept →
dust213–60.0023%8 Sept →
inferno9–13-0.0221%8 Sept →
anubis8–13-0.0118%8 Sept →
nuke7–130.0223%6 Sept →
nuke7–13-0.068%3 Sept →
cache13–100.0350%1 Sept →
inferno6–13-0.0510%1 Sept →
nuke10–13-0.076%30 Aug →
anubis4–13-0.0116%30 Aug →
inferno13–8-0.0025%29 Aug →
ancient13–100.0613%29 Aug →
cache7–13-0.0317%29 Aug →
inferno13–20.0110%22 Aug →
ancient13–11-0.0517%22 Aug →
inferno13–100.0219%13 Aug →
nuke13–11-0.0733%10 Aug →
dust29–130.0220%29 Jul →
inferno13–40.1111%23 Jul →
ancient13–80.0425%23 Jul →
nuke8–13-0.060%23 Jul →
ancient6–13-0.0713%13 Jul →
dust23–13-0.0817%5 Jul →
dust214–16-0.0522%5 Jul →
ancient6–13-0.110%4 Jul →
anubis10–13-0.0614%4 Jul →
overpass7–13-0.079%24 Jun →
ancient7–130.0213%24 Jun →
overpass6–9-0.1417%22 Jun →
inferno8–80.0017%22 Jun →
poseidon9–7-0.014%21 Jun →
vertigo7–90.0226%21 Jun →
ancient7–13-0.0413%20 Jun →
nuke9–40.1820%15 Jun →
anubis9–13-0.0423%14 Jun →
nuke13–90.0111%14 Jun →
sanctum7–90.1523%14 Jun →
ancient7–13-0.090%13 Jun →
nuke11–13-0.0119%10 Jun →
inferno8–13-0.047%10 Jun →
anubis12–160.0117%10 Jun →
inferno6–130.0544%7 Jun →
dust26–130.0416%7 Jun →
inferno15–15-0.026%7 Jun →
overpass3–130.0119%7 Jun →
vertigo3–9-0.1612%5 Jun →
poseidon7–90.0831%5 Jun →
inferno8–80.1330%5 Jun →
nuke11–13-0.0332%4 Jun →
anubis13–11-0.009%4 Jun →
ancient13–90.0610%4 Jun →
nuke11–13-0.0318%4 Jun →
nuke13–11-0.0412%31 May →
anubis11–13-0.069%31 May →
mirage13–10-0.069%31 May →
ancient8–13-0.0617%30 May →
mirage13–60.0139%30 May →
dust24–13-0.0821%30 May →
anubis0–13-0.0913%28 May →
inferno11–130.0116%28 May →
anubis13–8-0.0316%28 May →
ancient13–100.0423%28 May →
dust213–16-0.1019%24 May →
ancient14–16-0.0930%23 May →
dust29–13-0.0322%23 May →
ancient8–13-0.0610%10 May →
anubis13–16-0.035%7 May →
overpass13–40.0514%6 May →
anubis11–130.0113%6 May →
ancient13–11-0.0213%6 May →
anubis5–13-0.0317%6 May →
nuke13–110.0336%6 May →
nuke9–13-0.0314%5 May →
ancient16–13-0.0414%5 May →
ancient11–13-0.0411%4 May →
ancient13–110.039%4 May →
anubis11–13-0.0512%4 May →
ancient2–13-0.0316%4 May →
dust211–13-0.0119%3 May →
cache3–13-0.077%2 May →
dust213–11-0.0117%2 May →
cache9–0-0.0550%2 May →

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 →

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