london_PLAYA — CS2 Stats

76561198197468469[U:1:237202741]

4,167Tracked matches50%Win rate2023Tracked since
CSDB Rating6.2 SolidAggressive Rifler
FaceitLevel 10 · 2,547 ELOTop 12.4% of ranked FACEIT players
WingmanSupreme Master First Class
Ladder ranks via Leetify

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CSDB.GGlondon_PLAYAFACEITLevel 10 · 2,547 ELOSTANDINGTop 12.4% of rankedcsdb.gg/stats

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Performance scores

Aim81
Positioning58
Utility46

0–100 skill scores via Leetify.

Recent form

STEADY4852Last 10048%Win rateLWWLLWWLLL

Last 10 vs previous 10: -20pp win rate · -0.02 avg rating

Player DNA

Primary style: Aggressive RiflerTakes opening fights often, backed by a strong aim profile.

Aim8.1
Aggression9.5
Utility4.6
Positioning5.8
Opening Duels4.3
Clutch3.2

Strong CT-side opener

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

device

Plays most like device 91% playstyle similarity

Most alike: opening-duel success, opening-fight frequency.

Where you differ: lower aim profile; lower utility contribution.

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

Utility. Utility contribution runs far behind the mechanical game — the cheapest rating gain on this profile is thrown, not aimed.

T-side openings. Opening success drops from 56% on CT to 38% on T — the same duels are being taken with worse setups on the attacking side.

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

Aim8.1
Positioning5.8
Utility4.6
Mechanics5.6
Opening Duels4.3
Win Impact5.0

Composite 6.2/10 (Solid), a weighted mean of the bars with a small opposition adjustment (×1.02 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating0.01+0.02
first ⅓ avg -0.01 → last ⅓ avg 0.01
Reaction time555ms−13ms
first ⅓ avg 568ms → last ⅓ avg 555ms
Headshot accuracy24.7%+5.8%
first ⅓ avg 18.9% → last ⅓ avg 24.7%

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.

Highlights

0.13Best rating — anubis 13–4
132Biggest win — ancient
6Longest win streak
106In matches decided by ≤2 rounds
13Overtime games

Map breakdown

trainBest map · 60% over 5infernoWeakest map · 43% over 7
MapGradePlayedRecordWin rateAvg rating
mirageB21111052%0.00
anubisB1910953%0.03
ancientB168850%0.03
nukeB157847%-0.01
dust2B84450%0.00
infernoC73443%0.02
trainA53260%-0.00
overpass42250%0.01
vertigo3030%0.01
cache1010%0.03
office1010%0.05

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (43% over 7). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

4,851Matches
51%Win rate
1.16Avg K/D
85.1ADR
47%Headshot %

Clutches & streaks

36%1v1 clutch win
21%1v2 clutch win
11Longest win streak

Recent Faceit resultsWWLLL

MapMatchesWin rateAvg K/DAvg kills
Nuke77758%1.2317.7
Ancient70749%1.1617.3
Mirage66246%1.1216.6
Anubis51347%1.1716.9
Vertigo45853%1.1817.6
Inferno45353%1.2217.6
Dust228247%1.1316.4
Overpass17252%1.1416.6

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.

24.4%Headshot accuracy
35.0%Accuracy (enemy spotted)
37.9%Spray accuracy
75.1%Counter-strafing
8.3°Preaim
549msReaction time
38.4%T opening success
56.1%CT opening success
0.52Enemies flashed / flash
8.8%Flash assists
5.97HE damage / grenade
5.83Flashes / 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

    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 38.4366% — 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.

43% win rate across 7 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategyInferno grenade lineups

Recent matches

MapScoreRatingHS%Date
cache6–130.0317%27 Aug
mirage16–120.0117%24 Aug
nuke13–70.0715%24 Aug
anubis14–16-0.0217%24 Aug
mirage11–13-0.0317%23 Aug
nuke16–130.0126%7 Aug
mirage13–110.0322%7 Aug
anubis11–130.0614%1 Aug
mirage10–13-0.0520%31 Jul
nuke5–130.0250%26 Jul
dust216–140.0522%25 Jul
anubis13–40.1342%25 Jul
nuke13–60.0224%25 Jul
inferno13–100.1032%5 Jul
mirage13–10-0.0113%3 Jul
mirage6–13-0.0027%27 Jun
anubis9–13-0.0325%3 Jun
ancient10–130.1011%2 Jun
nuke6–13-0.0522%1 Jun
dust213–8-0.0232%30 May
ancient13–60.0830%30 May
mirage10–130.0433%29 May
dust213–40.0236%29 May
ancient8–13-0.0241%28 May
ancient13–60.0839%26 May
nuke3–13-0.0822%10 May
dust213–7-0.0024%10 May
anubis12–16-0.0614%9 May
mirage7–130.0229%6 May
anubis13–6-0.0121%26 Apr
mirage13–100.0417%26 Apr
inferno7–13-0.0623%20 Apr
anubis13–100.0223%17 Apr
inferno10–130.0423%12 Apr
nuke9–130.0620%7 Apr
ancient13–20.0615%6 Apr
ancient13–100.049%2 Apr
ancient13–40.0818%26 Mar
inferno4–13-0.0716%22 Mar
ancient13–100.0223%22 Mar
inferno13–50.0416%22 Mar
ancient13–90.0625%14 Mar
mirage13–110.0820%14 Mar
ancient6–130.0218%13 Mar
ancient11–13-0.0116%10 Mar
anubis13–60.1119%26 Feb
anubis4–13-0.0019%25 Feb
anubis13–110.0112%24 Feb
anubis13–110.0112%24 Feb
inferno13–50.0916%23 Feb
ancient6–13-0.0521%19 Feb
anubis7–130.0114%18 Feb
mirage13–11-0.0428%17 Feb
anubis10–130.0419%16 Feb
anubis13–16-0.0315%14 Feb
dust26–130.0528%13 Feb
anubis9–130.0633%13 Feb
ancient13–60.0420%13 Feb
nuke13–11-0.0025%8 Feb
mirage13–40.0726%1 Feb
anubis10–00.0910%31 Jan
anubis13–90.0217%30 Jan
overpass13–8-0.0013%29 Jan
dust210–13-0.0326%26 Jan
mirage8–130.0521%23 Jan
ancient16–190.0221%22 Jan
mirage9–13-0.0011%21 Jan
dust29–130.0127%21 Jan
anubis16–13-0.0026%20 Jan
dust26–13-0.0525%14 Jan
nuke13–10-0.0022%14 Jan
vertigo6–13-0.0117%14 Jan
mirage13–9-0.0218%14 Jan
mirage4–13-0.0326%13 Jan
nuke13–9-0.0319%6 Jan
vertigo4–8-0.0216%31 Dec
inferno7–130.0123%30 Dec
ancient9–13-0.074%25 Dec
train16–190.0021%21 Dec
mirage13–110.0232%21 Dec
vertigo9–130.0413%18 Dec
overpass10–13-0.0413%14 Dec
nuke14–160.0426%13 Dec
anubis13–70.1323%11 Dec
office2–60.0514%27 Nov
overpass13–70.0420%19 Nov
nuke20–22-0.0318%24 Oct
mirage7–13-0.0413%21 Oct
train13–40.026%20 Oct
nuke4–13-0.085%16 Oct
nuke4–13-0.0825%13 Oct
train13–8-0.0017%13 Oct
nuke13–90.0116%30 Sept
mirage6–130.0011%29 Sept
mirage13–11-0.0626%28 Sept
train12–16-0.0211%25 Sept
train13–11-0.0212%25 Sept
ancient13–16-0.0025%24 Sept
overpass9–130.0424%18 Sept
mirage13–8-0.0630%17 Sept

Match data via Leetify.

Recent teammates

Milestones

Faceit Level 102.5K ELO1,000 Matches
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →