ChillAIM — CS2 Stats

CN76561198190849718[U:1:230583990]Steam profile ↗✓ No bans

872Tracked matches61%Win rate2023Tracked since

CSDB Rating6.5 SolidAggressive Rifler

Premier CS Rating17,710Purple band · top ~17.6% of ranked players (population est.)

CSDB Leaderboard#27004 of 54351 tracked

FaceitLevel 7Top 42.3% of ranked FACEIT players

Ladder ranks via Leetify

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Performance scores

Aim80

Positioning52

Utility66

0–100 skill scores via Leetify.

Recent form

HOT51–44–5Last 10051%Win rateWWWWWLWWWW

Last 10 vs previous 10: +60pp win rate · -0.03 avg rating

Player DNA

Primary style: Aggressive Rifler — Takes opening fights often, backed by a strong aim profile.

Aim8.0

Aggression7.2

Utility6.6

Positioning5.2

Opening Duels2.0

Clutch4.2

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

Plays most like Twistzz 92% playstyle similarity

Most alike: opening-duel success, positioning profile.

Where you differ: 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. 551ms 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

Aim8.0

Positioning5.2

Utility6.6

Mechanics6.9

Opening Duels2.0

Win Impact8.6

Composite 6.5/10 (Solid), 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 rating0.04+0.00

first ⅓ avg 0.03 → last ⅓ avg 0.04

Reaction time556ms−67ms

first ⅓ avg 623ms → last ⅓ avg 556ms

Headshot accuracy14.9%+2.6%

first ⅓ avg 12.3% → last ⅓ avg 14.9%

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

0.20Best rating — nuke 7–1

13–0Biggest win — mirage

7Longest win streak

W5Current streak

6–1In matches decided by ≤2 rounds

9Overtime games

Map breakdown

nukeBest map · 80% over 5infernoWeakest map · 35% over 17

Map	Grade	Played	Record	Win rate	Avg rating
mirage	C	21	9–12	43%	0.02
inferno	C	17	6–11	35%	0.04
dust2	A	16	9–7	56%	0.03
ancient	S	16	11–5	69%	0.03
overpass	B	14	7–7	50%	0.02
anubis	C	9	4–5	44%	0.05
nuke	S	5	4–1	80%	0.07
vertigo	—	1	0–1	0%	-0.06
train	—	1	1–0	100%	0.05

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (35% over 17). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

246Matches

53%Win rate

1.23Avg K/D

82.8ADR

32%Headshot %

Clutches & streaks

41%1v1 clutch win

25%1v2 clutch win

7Longest win streak

Recent Faceit resultsWWWLL

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	77	49%	1.19	16.7
Ancient	40	48%	1.15	16.7
Anubis	33	64%	1.42	17.0
Vertigo	32	66%	1.41	17.0
Dust2	24	42%	1.14	14.5
Inferno	21	52%	1.15	16.3
Nuke	19	58%	1.16	18.4

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.

14.6%Headshot accuracy

40.9%Accuracy (enemy spotted)

42.4%Spray accuracy

80.9%Counter-strafing

10.3°Preaim

551msReaction time

36.9%T opening success

39.4%CT opening success

0.52Enemies flashed / flash

10.5%Flash assists

8.70HE damage / grenade

11.71Flashes / 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.

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 14.6242% — below the 15% mark we flag
Aim Training →
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 36.9249% — 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.

35% win rate across 17 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
dust2	13–5	-0.00	7%	29 Jun →
mirage	13–7	0.01	8%	29 Jun →
ancient	13–9	0.07	9%	28 Jun →
dust2	13–10	0.07	20%	28 Jun →
inferno	13–6	-0.01	16%	27 Jun →
inferno	5–13	-0.02	8%	24 Jun →
anubis	13–5	0.02	20%	24 Jun →
ancient	13–4	0.01	17%	24 Jun →
overpass	13–10	-0.03	19%	24 Jun →
nuke	13–8	-0.01	19%	24 Jun →
inferno	6–13	0.13	27%	23 Jun →
anubis	10–13	0.10	11%	23 Jun →
inferno	13–11	0.04	35%	23 Jun →
overpass	13–10	0.03	18%	23 Jun →
mirage	7–13	-0.02	17%	22 Jun →
dust2	6–13	0.05	9%	22 Jun →
inferno	7–13	0.01	17%	22 Jun →
ancient	13–16	0.04	8%	20 Jun →
ancient	13–9	0.03	17%	15 Jun →
inferno	8–13	-0.01	6%	15 Jun →
nuke	7–1	0.20	16%	10 Jun →
overpass	15–15	0.05	8%	9 Jun →
mirage	13–16	0.03	10%	9 Jun →
dust2	6–13	0.01	17%	9 Jun →
inferno	15–15	0.02	8%	9 Jun →
mirage	4–13	-0.03	18%	9 Jun →
mirage	13–9	0.05	13%	7 Jun →
mirage	13–10	-0.02	14%	7 Jun →
ancient	13–9	0.05	15%	7 Jun →
mirage	13–0	0.05	22%	6 Jun →
inferno	13–1	0.19	16%	6 Jun →
ancient	6–13	0.02	19%	6 Jun →
dust2	13–8	0.07	7%	5 Jun →
overpass	9–13	-0.01	7%	5 Jun →
inferno	13–11	0.01	19%	5 Jun →
dust2	10–13	-0.02	10%	5 Jun →
dust2	13–10	0.03	17%	4 Jun →
nuke	13–9	0.10	17%	4 Jun →
overpass	7–13	0.13	10%	4 Jun →
mirage	4–13	-0.07	23%	4 Jun →
vertigo	9–13	-0.06	13%	10 Apr →
ancient	13–6	0.01	17%	17 Mar →
overpass	13–11	-0.04	2%	17 Mar →
mirage	10–13	-0.01	8%	17 Mar →
anubis	7–13	0.05	8%	17 Mar →
mirage	4–13	0.00	13%	17 Mar →
overpass	13–2	0.07	12%	17 Mar →
inferno	12–0	0.05	7%	16 Mar →
overpass	13–4	0.07	21%	16 Mar →
ancient	13–8	0.01	15%	16 Mar →
mirage	13–5	0.11	8%	16 Mar →
dust2	13–11	-0.01	17%	15 Mar →
ancient	13–8	0.00	6%	15 Mar →
mirage	9–13	-0.01	16%	15 Mar →
mirage	13–4	0.11	22%	15 Mar →
dust2	15–15	-0.00	12%	15 Mar →
mirage	13–7	0.01	10%	15 Mar →
ancient	16–13	0.02	5%	14 Mar →
ancient	9–13	0.04	9%	14 Mar →
anubis	13–16	0.04	9%	13 Mar →
inferno	4–13	0.03	35%	13 Mar →
ancient	8–13	-0.02	19%	12 Mar →
mirage	2–13	-0.02	10%	12 Mar →
inferno	10–13	0.01	10%	12 Mar →
overpass	6–13	0.02	14%	11 Mar →
train	13–11	0.05	16%	10 Mar →
inferno	8–13	0.03	2%	10 Mar →
ancient	13–7	0.07	17%	10 Mar →
anubis	13–6	0.05	14%	10 Mar →
dust2	9–13	0.04	16%	10 Mar →
inferno	13–7	0.06	10%	9 Mar →
overpass	13–9	-0.05	12%	9 Mar →
mirage	13–8	0.03	10%	9 Mar →
ancient	13–5	0.05	7%	9 Mar →
overpass	8–13	0.04	9%	8 Mar →
mirage	15–15	0.04	9%	8 Mar →
dust2	9–13	0.02	23%	8 Mar →
anubis	13–6	0.12	10%	8 Mar →
dust2	13–8	-0.01	17%	8 Mar →
inferno	6–13	-0.03	25%	8 Mar →
mirage	8–13	0.02	12%	5 Mar →
ancient	13–6	0.07	14%	4 Mar →
overpass	1–13	-0.07	5%	2 Mar →
dust2	13–10	0.07	5%	2 Mar →
overpass	13–11	0.05	10%	1 Mar →
nuke	14–16	0.01	10%	25 Feb →
mirage	13–3	0.12	13%	25 Feb →
dust2	13–3	0.05	9%	22 Feb →
nuke	13–9	0.06	9%	22 Feb →
ancient	6–13	0.05	8%	20 Feb →
overpass	5–13	-0.02	22%	8 Feb →
anubis	7–13	-0.01	16%	7 Feb →
inferno	7–13	0.02	13%	25 Jan →
anubis	9–13	0.00	6%	23 Jan →
inferno	12–12	0.09	9%	23 Jan →
dust2	13–6	-0.03	12%	23 Jan →
mirage	4–13	-0.00	7%	21 Jan →
dust2	8–13	0.06	17%	21 Jan →
anubis	13–3	0.05	8%	21 Jan →
mirage	5–13	0.01	26%	21 Jan →