橘小橙 — CS2 Stats

76561199198556113[U:1:1238290385]Steam profile ↗✓ No bans

1,371Tracked matches50%Win rate2022Tracked since

CSDB Rating4.0 Developing

FaceitLevel 5 · 1,179 ELOTop 67.6% of ranked FACEIT players

WingmanGold Nova III

Ladder ranks via Leetify

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Performance scores

Aim58

Positioning28

Utility33

0–100 skill scores via Leetify.

Recent form

STEADY43–51–6Last 10043%Win rateWLWLLWLLLW

Last 10 vs previous 10: -20pp win rate · +0.01 avg rating

Player DNA

Aim5.8

Aggression0.7

Utility3.3

Positioning2.8

Opening Duels0.0

Clutch3.6

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 NiKo 68% playstyle similarity

Most alike: opening-duel success, utility contribution.

Where you differ: lower aim profile; lower opening-fight frequency.

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. 633ms 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

Aim5.8

Positioning2.8

Utility3.3

Mechanics8.0

Opening Duels0.0

Win Impact5.0

Composite 4.0/10 (Developing), a weighted mean of the bars with a small opposition adjustment (×0.90 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.04−0.01

first ⅓ avg -0.04 → last ⅓ avg -0.04

Reaction time637ms−12ms

first ⅓ avg 649ms → last ⅓ avg 637ms

Headshot accuracy21.3%−0.2%

first ⅓ avg 21.5% → last ⅓ avg 21.3%

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.

Highlights

0.06Best rating — nuke 13–7

13–1Biggest win — ancient

4Longest win streak

8–11In matches decided by ≤2 rounds

10Overtime games

Map breakdown

anubisBest map · 53% over 15infernoWeakest map · 35% over 20

Map	Grade	Played	Record	Win rate	Avg rating
dust2	C	33	13–20	39%	-0.04
ancient	B	25	12–13	48%	-0.04
inferno	C	20	7–13	35%	-0.05
anubis	B	15	8–7	53%	-0.03
mirage	—	3	1–2	33%	-0.02
nuke	—	2	2–0	100%	0.02
cache	—	2	0–2	0%	-0.04

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (35% over 20). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

481Matches

51%Win rate

0.80Avg K/D

70.9ADR

58%Headshot %

Clutches & streaks

38%1v1 clutch win

18%1v2 clutch win

12Longest win streak

Recent Faceit resultsWLWWW

Map	Matches	Win rate	Avg K/D	Avg kills
Ancient	120	58%	0.82	12.9
Inferno	112	53%	0.84	13.0
Anubis	79	46%	0.78	12.1
Mirage	70	50%	0.74	12.2
Dust2	46	48%	0.81	13.0
Vertigo	43	49%	0.70	12.3
Nuke	7	29%	0.71	12.1
Train	3	67%	1.00	13.3

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.

20.6%Headshot accuracy

26.7%Accuracy (enemy spotted)

26.6%Spray accuracy

86.0%Counter-strafing

8.5°Preaim

633msReaction time

17.8%T opening success

23.3%CT opening success

0.51Enemies flashed / flash

6.1%Flash assists

4.66HE damage / grenade

2.59Flashes / 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.

Grenades & Utility
You are buying and holding utility rather than using it. Unthrown flashes are wasted money every round.
Flashes per match 2.5927 — below the 4 mark we flag
Grenade Lineups →
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 17.752% — 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 20 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
inferno	13–3	-0.05	20%	12 Aug →
inferno	8–13	-0.03	10%	12 Aug →
dust2	13–5	-0.04	11%	12 Aug →
inferno	1–8	-0.06	43%	12 Aug →
ancient	9–13	-0.07	15%	26 Jul →
ancient	13–11	-0.03	37%	26 Jul →
dust2	11–13	-0.07	30%	22 Jul →
dust2	10–13	-0.03	31%	22 Jul →
dust2	11–13	-0.06	25%	22 Jul →
dust2	13–6	-0.01	24%	16 Jul →
ancient	8–13	-0.09	33%	13 Jul →
inferno	4–13	-0.11	12%	30 Jun →
nuke	13–10	-0.02	31%	22 Jun →
mirage	7–13	-0.05	13%	22 Jun →
dust2	13–8	-0.02	23%	18 Jun →
inferno	5–2	-0.07	17%	17 Jun →
anubis	15–15	-0.04	24%	16 Jun →
anubis	13–3	0.00	32%	12 Jun →
dust2	13–10	-0.08	10%	4 Jun →
ancient	13–3	-0.03	21%	4 Jun →
ancient	10–13	-0.06	19%	23 May →
dust2	13–5	-0.02	21%	23 May →
ancient	13–7	0.05	17%	20 May →
dust2	1–13	-0.11	21%	18 May →
ancient	9–13	-0.08	14%	18 May →
dust2	0–13	-0.09	19%	16 May →
anubis	13–7	-0.00	14%	16 May →
cache	12–12	-0.06	24%	14 May →
dust2	13–16	-0.07	8%	8 May →
inferno	13–9	-0.05	9%	7 May →
inferno	9–13	-0.02	21%	7 May →
ancient	12–12	-0.01	29%	6 May →
dust2	5–13	-0.00	27%	6 May →
anubis	13–10	-0.02	16%	5 May →
anubis	7–13	-0.08	11%	4 May →
ancient	13–7	-0.06	17%	4 May →
cache	11–13	-0.03	29%	4 May →
dust2	6–13	-0.03	30%	4 May →
ancient	11–13	-0.05	28%	3 May →
anubis	11–13	-0.01	26%	3 May →
ancient	13–6	-0.02	8%	28 Apr →
dust2	1–13	-0.09	14%	28 Apr →
ancient	1–13	-0.07	4%	28 Apr →
dust2	13–6	0.03	36%	28 Apr →
dust2	15–15	-0.01	27%	27 Apr →
dust2	16–14	-0.06	23%	25 Apr →
anubis	13–6	-0.02	13%	24 Apr →
ancient	13–10	-0.03	17%	24 Apr →
ancient	9–13	-0.08	17%	23 Apr →
nuke	13–7	0.06	24%	23 Apr →
anubis	13–10	-0.07	21%	21 Apr →
dust2	13–11	-0.01	26%	21 Apr →
ancient	11–13	-0.08	16%	21 Apr →
ancient	9–13	-0.06	33%	21 Apr →
ancient	8–13	-0.01	17%	21 Apr →
ancient	13–6	-0.03	50%	19 Apr →
inferno	16–14	-0.07	31%	18 Apr →
ancient	15–15	-0.06	19%	17 Apr →
anubis	13–6	-0.03	26%	17 Apr →
inferno	7–13	-0.08	22%	16 Apr →
inferno	2–13	-0.05	36%	14 Apr →
dust2	1–13	-0.03	29%	14 Apr →
dust2	13–11	-0.04	18%	13 Apr →
inferno	13–11	-0.05	29%	13 Apr →
anubis	13–16	-0.05	20%	12 Apr →
inferno	7–13	-0.01	24%	8 Apr →
dust2	11–13	-0.08	33%	7 Apr →
dust2	11–13	-0.05	19%	4 Apr →
dust2	11–13	-0.05	30%	2 Apr →
dust2	11–13	-0.09	33%	31 Mar →
inferno	6–13	-0.05	24%	30 Mar →
anubis	8–13	-0.07	14%	30 Mar →
inferno	15–15	-0.08	17%	30 Mar →
ancient	13–1	0.05	23%	30 Mar →
dust2	8–13	-0.06	19%	30 Mar →
anubis	16–14	-0.05	14%	29 Mar →
anubis	2–13	-0.02	23%	28 Mar →
dust2	13–2	-0.02	21%	28 Mar →
dust2	13–6	-0.08	12%	28 Mar →
dust2	2–13	-0.07	33%	27 Mar →
dust2	7–13	-0.04	29%	27 Mar →
inferno	13–9	-0.08	8%	27 Mar →
anubis	13–9	-0.03	39%	25 Mar →
dust2	8–13	0.01	23%	25 Mar →
ancient	13–9	0.02	25%	24 Mar →
inferno	6–13	0.01	27%	24 Mar →
dust2	13–8	0.02	28%	24 Mar →
inferno	4–13	-0.10	35%	24 Mar →
ancient	13–16	-0.02	21%	24 Mar →
inferno	3–13	-0.04	16%	24 Mar →
ancient	4–0	-0.07	0%	23 Mar →
dust2	13–10	-0.05	13%	23 Mar →
ancient	13–11	-0.05	20%	23 Mar →
inferno	13–6	-0.06	19%	22 Mar →
mirage	5–13	-0.04	28%	22 Mar →
dust2	4–13	-0.08	21%	22 Mar →
anubis	10–13	-0.02	24%	21 Mar →
inferno	11–13	0.01	21%	21 Mar →
mirage	13–6	0.02	13%	20 Mar →
ancient	13–7	-0.04	22%	20 Mar →