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Bryan Nolasco — CS2 Stats

76561198854611874[U:1:894346146]

520Tracked matches28%Win rate2021Tracked since
227Hours in CS
CSDB Rating2.4 Learning
FaceitLevel 3Top 90.6% of ranked FACEIT players
Ladder ranks via Leetify

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CSDB.GGBryan NolascoFACEITLevel 3STANDINGTop 90.6% of rankedcsdb.gg/stats

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Performance scores

Aim27
Positioning20
Utility17

0–100 skill scores via Leetify.

Recent form

COLD36–60–4Last 10036%Win rateLLLLLLLLWL

Last 10 vs previous 10: -10pp win rate · -0.00 avg rating

Player DNA

Aim2.7
Aggression0.0
Utility1.7
Positioning2.0
Opening Duels0.0
Clutch1.0

Style profile from tracked-match aggregates — how this player plays, not how good they are. Classification rules are deterministic and documented in code.

Your pro match

NiKo

Plays most like NiKo 56% playstyle similarity

Most alike: opening-duel success, positioning profile.

Where you differ: lower opening-fight frequency; 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. 683ms 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

Aim2.7
Positioning2.0
Utility1.7
Mechanics6.2
Opening Duels0.0
Win Impact0.0

Composite 2.4/10 (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-0.06−0.03
first ⅓ avg -0.02 → last ⅓ avg -0.06
Reaction time687ms+23ms
first ⅓ avg 664ms → last ⅓ avg 687ms
Headshot accuracy13.9%−0.2%
first ⅓ avg 14.2% → last ⅓ avg 13.9%

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.

Highlights

0.12Best rating — mirage 13–2
13–2Biggest win — anubis
5Longest win streak
L8Current streak
3–9In matches decided by ≤2 rounds
4Overtime games

Map breakdown

vertigoBest map · 54% over 13infernoWeakest map · 0% over 6
MapGradePlayedRecordWin rateAvg rating
mirageC166–1038%-0.03
dust2D133–1023%-0.05
vertigoB137–654%-0.03
nukeB115–645%-0.03
ancientD113–827%-0.06
anubisC94–544%-0.04
cacheB84–450%-0.03
infernoD60–60%-0.03
officeD62–433%-0.04
agency—21–150%0.03
overpass—10–10%-0.09
warden—10–10%-0.08
train—11–0100%-0.03
jura—10–10%-0.04
grail—10–10%-0.08

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (0% over 6). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

7,256Lifetime kills
0.74K/D
582Matches
42.6%Match win rate
38.5%Headshot %
7.0%Shot accuracy
807MVPs
227Hours (in match)
673Bombs planted
111Bombs defused

Most-used weapons

AK-472,163
M4A4 / M4A1-S1,684
AWP681
P2000 / USP-S360
MP9306
Glock-18286
Galil AR260
SSG 08212
Desert Eagle177
SCAR-20176

Lifetime map wins

1,788vertigo
750dust2
636nuke
225inferno
82office
32train
10lake
2sugarcane

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

Faceit stats

Combat

28Matches
39%Win rate
0.50Avg K/D
48.7ADR
50%Headshot %

Clutches & streaks

25%1v1 clutch win
0%1v2 clutch win
5Longest win streak

Recent Faceit resultsWLLLL

MapMatchesWin rateAvg K/DAvg kills
Mirage714%0.417.0
Anubis520%0.447.6
Ancient540%0.557.6
Nuke475%0.5411.0
Inferno475%0.568.5
Dust2333%0.629.7

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.

13.3%Headshot accuracy
25.2%Accuracy (enemy spotted)
30.1%Spray accuracy
78.0%Counter-strafing
11.9°Preaim
683msReaction time
9.8%T opening success
21.3%CT opening success
0.46Enemies flashed / flash
2.2%Flash assists
2.61HE damage / grenade
2.72Flashes / 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 13.3193% — below the 15% mark we flag

    Aim Training →
  2. 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 0.4595 — below the 0.5 mark we flag

    Grenade Lineups →
  3. Advanced Mechanics

    You are losing most of the first duels you take on T side, which is usually a peeking and spacing problem, not aim.

    T opening duels 9.8357% — below the 40% mark we flag

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.

0% win rate across 6 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
anubis8–13-0.090%28 Aug →
nuke6–13-0.080%27 Aug →
inferno4–4-0.108%20 Aug →
mirage6–13-0.078%20 Aug →
dust25–13-0.0725%20 Aug →
ancient6–13-0.050%1 Aug →
nuke11–13-0.0413%1 Aug →
cache6–13-0.0617%1 Aug →
nuke16–14-0.0316%1 Aug →
ancient4–13-0.1120%1 Aug →
ancient13–7-0.0115%31 Jul →
dust21–13-0.1011%28 Jul →
nuke6–13-0.0828%27 Jul →
inferno11–13-0.104%26 Jul →
anubis15–15-0.0515%26 Jul →
mirage3–13-0.079%25 Jul →
ancient13–6-0.097%13 Jul →
anubis11–13-0.0513%13 Jul →
mirage3–13-0.110%12 Jul →
cache10–13-0.0020%11 Jul →
anubis6–13-0.0719%11 Jul →
anubis6–13-0.0522%20 Jun →
vertigo11–13-0.077%13 Jun →
vertigo13–9-0.0613%17 May →
cache13–30.0123%17 May →
vertigo13–100.0316%17 May →
vertigo13–5-0.069%17 May →
vertigo13–40.0325%17 May →
vertigo5–13-0.0326%17 May →
cache6–13-0.0413%4 May →
vertigo4–13-0.0522%4 May →
cache1–13-0.090%3 May →
cache13–3-0.0238%3 May →
cache13–100.0023%2 May →
cache13–10-0.035%1 May →
vertigo13–10-0.0619%13 Apr →
overpass3–13-0.090%22 Mar →
anubis13–2-0.0511%22 Mar →
vertigo13–9-0.0320%22 Mar →
warden5–13-0.0815%2 Feb →
vertigo12–12-0.0811%2 Feb →
ancient10–13-0.104%19 Jan →
dust213–6-0.0311%11 Jan →
ancient6–13-0.0414%11 Nov →
inferno8–130.0016%10 Nov →
dust210–13-0.0219%2 Nov →
office9–13-0.0128%21 Jul →
vertigo12–12-0.0118%15 Jul →
office13–10-0.0112%15 Jul →
mirage13–50.0516%14 Jul →
mirage7–13-0.0517%12 Jul →
anubis13–40.0230%12 Jul →
mirage3–13-0.0624%11 Jul →
nuke8–13-0.064%11 Jul →
dust210–13-0.0313%11 Jul →
office2–13-0.116%10 Jul →
office12–12-0.0928%10 Jul →
dust21–13-0.0211%10 Jul →
office10–13-0.0423%10 Jul →
office13–70.018%7 Jul →
vertigo5–12-0.0615%30 Jun →
vertigo13–60.0013%30 Jun →
agency10–10.0620%30 Jun →
nuke13–11-0.0216%28 Jun →
dust26–13-0.1017%28 Jun →
train13–7-0.0311%27 Jun →
dust213–10-0.0223%27 Jun →
mirage3–13-0.0724%27 Jun →
agency9–13-0.0110%26 Jun →
dust213–90.0113%23 Jun →
jura4–11-0.048%16 Jun →
ancient13–5-0.028%10 Jun →
mirage11–130.0419%8 Jun →
mirage13–50.017%8 Jun →
nuke5–130.0130%8 Jun →
grail1–13-0.0818%7 Jun →
inferno8–130.0016%6 Jun →
nuke4–13-0.0624%6 Jun →
ancient5–13-0.093%6 Jun →
mirage13–6-0.0212%5 Jun →
mirage13–20.1221%4 Jun →
dust24–13-0.095%4 Jun →
nuke16–13-0.0217%1 Jun →
ancient11–13-0.0910%29 May →
anubis13–90.0115%17 May →
dust24–13-0.1015%17 May →
nuke13–7-0.0317%17 May →
mirage13–10-0.0612%12 May →
mirage13–10-0.079%11 May →
nuke13–60.048%1 May →
dust28–130.0318%1 May →
inferno3–13-0.0214%1 May →
anubis13–11-0.0112%1 May →
inferno6–130.0520%1 May →
mirage11–13-0.029%1 May →
ancient12–16-0.046%29 Apr →
mirage11–13-0.0111%29 Apr →
dust28–13-0.0620%29 Apr →
ancient3–10-0.0725%29 Apr →
mirage9–13-0.0511%29 Apr →

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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