TrisbEZ

TrisbEZ — CS2 Stats

76561198932355048[U:1:972089320]Steam profile ↗✓ No bans

431Tracked matches62%Win rate2020Tracked since
1,302Hours in CS11Hrs last 2 wks
CSDB Rating6.3 SolidPositional Player
Premier CS Rating12,457Blue band · top ~41.9% of ranked players (population est.)
WingmanGold Nova II
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 1 Nov 2026 (4 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: 12,457 +2,337 4 Aug – 1 Nov · 23 days played
4,52012,457peak 12,4574 Aug1 Nov
12,577Peak Premier in tracked matches
39Days played since 2025-08-04

Premier CS Rating

  • At peak — 12,457
  • Next: Purple band at 15,000 — 2,543 to go
  • Reached: Blue band · Light Blue band

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 Blue band among CSDB-tracked players (n=21,222), 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 playerBlue band medianPurple band medianvs Purple band
Headshot rate47.9%42.0%44.4%above
Shot accuracy16.0%9.6%11.9%above
Kill/death ratio0.810.981.030.22 short
Match win rate44.0%43.8%45.2%1.3% short

This profile matches the typical Purple band player on 2 of 4 comparable metrics.

Widest gap: Kill/death ratio. That is the metric furthest from the Purple band median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.

Share this profile

CSDB.GGTrisbEZPREMIER12,457 · Blue bandcsdb.gg/stats

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Performance scores

Aim66
Positioning71
Utility44

0–100 skill scores via Leetify.

Recent form

STEADY51–46–3Last 10051%Win rateLWWWWTWLWL

Last 10 vs previous 10: 0pp win rate · +0.02 avg rating · +1.6pp headshot accuracy · +23ms 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

  • 4–1 · 80% win rate
  • Avg rating 0.05
  • Avg headshot accuracy 18%
  • Avg reaction 555ms

Last 10

  • 6–3–1 · 60% win rate
  • Avg rating 0.06
  • Avg headshot accuracy 19%
  • Avg reaction 566ms

Last 20

  • 12–7–1 · 60% win rate
  • Avg rating 0.05
  • Avg headshot accuracy 18%
  • Avg reaction 554ms

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 (+0.9 against its own average).

Aim6.6
Utility4.4
Positioning7.1
Opening Duels7.0

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

s1mple

Plays most like s1mple 83% 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

Strengths

CT openings. 61% CT opening-duel success — winning the first fight on the defending side is rare and valuable.

Areas to improve

Reaction time. 583ms 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

Aim6.6
Positioning7.1
Utility4.4
Mechanics6.2
Opening Duels7.6
Win Impact9.0

Composite 6.3/10 (Solid), 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 rating0.05+0.05
first ⅓ avg 0.00 → last ⅓ avg 0.05
Reaction time584ms−80ms
first ⅓ avg 664ms → last ⅓ avg 584ms
Headshot accuracy17.3%−1.2%
first ⅓ avg 18.4% → last ⅓ avg 17.3%

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

0.25Best match rating · 9–2 · poseidon, 2 Apr →
33%Best headshot accuracy · 11–13 · mirage, 6 Dec →
406msFastest reaction time · 5–13 · inferno, 17 Mar →
13–1Biggest win · nuke, 29 Oct →

Across the last 100 tracked matches.

Highlights

5Longest win streak
12–8In matches decided by ≤2 rounds
11Overtime games

Map breakdown

infernoBest map · 65% over 23ancientWeakest map · 33% over 12
MapGradePlayedRecordWin rateAvg rating
infernoS2315–865%0.01
mirageC219–1243%0.02
nukeB189–950%0.03
ancientD124–833%0.01
overpassC114–736%0.00
dust2—43–175%0.04
anubis—42–250%0.00
cache—22–0100%0.05
train—21–150%0.02
debris—11–0100%0.20
poseidon—11–0100%0.25
vertigo—10–10%0.06

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (33% over 12). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Lifetime stats

36,785Lifetime kills
0.81K/D
1,720Matches
44.0%Match win rate · Top 50% of Blue band
47.9%Headshot % · Top 25% of Blue band
16.0%Shot accuracy · Top 25% of Blue band
5,457MVPs
875Hours (in match)
1,539Bombs planted
407Bombs defused

Most-used weapons

Lifetime map wins

4,022inferno
1,649nuke
1,512train
1,105lake
988vertigo
793dust2
105cbble
33office

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.

18.5%Headshot accuracy
35.3%Accuracy (enemy spotted)
36.0%Spray accuracy
77.9%Counter-strafing
10.9°Preaim
583msReaction time
59.3%T opening success
61.4%CT opening success
0.67Enemies flashed / flash
1.6%Flash assists
11.89HE damage / grenade
2.35Flashes / 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. Grenades & Utility

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

    Flashes per match 2.347 — below the 4 mark we flag

    Grenade Lineups →
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.

33% win rate across 12 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

MapScoreRatingHS%Date
nuke9–130.0520%1 Nov →
mirage13–110.0513%1 Nov →
dust216–130.0316%29 Oct →
anubis13–11-0.0119%29 Oct →
nuke13–10.1323%29 Oct →
dust215–150.0726%29 Oct →
nuke13–50.1316%29 Oct →
nuke7–120.1117%27 Oct →
inferno16–140.0519%23 Oct →
nuke14–16-0.0025%23 Oct →
inferno13–20.0717%23 Oct →
anubis13–50.0424%22 Oct →
inferno11–13-0.0119%22 Oct →
ancient13–80.0221%22 Oct →
inferno13–20.0811%22 Oct →
mirage5–130.0417%18 Oct →
anubis3–13-0.0222%18 Oct →
debris9–50.2018%16 Oct →
inferno13–90.0115%16 Oct →
ancient11–130.0212%10 May →
cache13–3-0.0117%5 May →
cache13–50.1027%5 May →
inferno9–70.0912%3 Apr →
inferno2–1-0.090%3 Apr →
poseidon9–20.2519%2 Apr →
inferno6–90.0410%2 Apr →
overpass13–90.0515%17 Mar →
anubis7–130.0119%17 Mar →
mirage13–50.0616%17 Mar →
dust216–140.0217%17 Mar →
inferno5–13-0.0314%17 Mar →
mirage7–130.0423%13 Mar →
inferno13–90.0613%13 Mar →
mirage13–100.0323%24 Feb →
ancient6–80.0219%23 Feb →
vertigo8–80.0622%23 Feb →
nuke5–90.0121%23 Feb →
nuke2–13-0.0310%16 Feb →
nuke13–70.0119%19 Jan →
ancient13–11-0.0116%19 Jan →
overpass13–160.0320%18 Jan →
inferno13–110.0623%18 Jan →
nuke16–140.0211%18 Jan →
mirage4–13-0.0620%16 Jan →
nuke9–130.0223%16 Jan →
mirage13–50.1130%16 Jan →
nuke4–13-0.0713%16 Jan →
inferno13–10-0.0129%15 Jan →
nuke13–10-0.0126%15 Jan →
mirage7–13-0.0315%15 Jan →
ancient13–40.0316%14 Jan →
ancient3–130.0331%13 Jan →
inferno2–13-0.0113%7 Dec →
mirage5–13-0.0017%7 Dec →
mirage11–130.0433%6 Dec →
nuke6–130.0229%6 Dec →
inferno13–10-0.0011%5 Dec →
mirage13–70.1120%5 Dec →
mirage9–130.0120%4 Dec →
overpass13–110.0716%17 Sept →
inferno13–50.0711%17 Sept →
mirage13–7-0.0513%12 Sept →
ancient16–140.0120%12 Sept →
mirage8–13-0.0318%20 Aug →
inferno13–90.0722%19 Aug →
overpass13–80.0323%19 Aug →
mirage8–130.0124%18 Aug →
nuke13–70.1222%18 Aug →
overpass11–13-0.0523%17 Aug →
nuke13–9-0.0127%17 Aug →
inferno4–13-0.035%17 Aug →
train13–80.0122%13 Aug →
inferno16–130.0422%13 Aug →
mirage13–7-0.0312%11 Aug →
overpass4–13-0.0518%11 Aug →
train9–130.0426%11 Aug →
inferno13–10-0.0119%11 Aug →
mirage5–130.0210%10 Aug →
overpass11–13-0.0310%10 Aug →
ancient8–130.017%7 Aug →
overpass4–13-0.0619%7 Aug →
mirage8–130.0325%6 Aug →
inferno9–13-0.0423%6 Aug →
nuke13–4-0.0228%6 Aug →
dust213–60.0619%6 Aug →
mirage13–60.0414%6 Aug →
mirage13–110.0823%6 Aug →
ancient11–130.0313%6 Aug →
inferno13–90.0314%6 Aug →
nuke13–100.0325%6 Aug →
overpass12–16-0.0019%5 Aug →
overpass6–13-0.0218%5 Aug →
inferno5–13-0.0510%5 Aug →
ancient10–130.0119%5 Aug →
nuke15–15-0.0215%5 Aug →
mirage7–13-0.0219%5 Aug →
inferno8–13-0.0513%5 Aug →
ancient7–13-0.0424%5 Aug →
ancient2–13-0.0028%5 Aug →
overpass13–20.0519%4 Aug →

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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