Kojo — CS2 Stats

76561198170080317[U:1:209814589]

156Tracked matches62%Win rate2021Tracked since

CSDB Rating6.4 SolidAggressive Rifler

Premier CS Rating10,727Blue band · top ~51% of ranked players (population est.)

CSDB Leaderboard#21813 of 30426 tracked

FaceitLevel 6Top 54.0% of ranked FACEIT players

WingmanLegendary Eagle Master

Ladder ranks via Leetify

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Performance scores

Aim82

Positioning63

Utility37

0–100 skill scores via Leetify.

Recent form

HOT59–38–3Last 10059%Win rateLWWLLWWWWW

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

Player DNA

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

Aim8.2

Aggression8.5

Utility3.7

Positioning6.3

Opening Duels4.1

Clutch7.6

Limited utility dependenceReliable in 1v1s

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 ropz 90% playstyle similarity

Most alike: positioning profile, opening-duel success.

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

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

Reaction time. 577ms 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

Aim8.2

Positioning6.3

Utility3.7

Mechanics7.8

Opening Duels4.1

Win Impact9.0

Composite 6.4/10 (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 rating0.03+0.01

first ⅓ avg 0.02 → last ⅓ avg 0.03

Reaction time583ms−5ms

first ⅓ avg 587ms → last ⅓ avg 583ms

Headshot accuracy17.9%−0.2%

first ⅓ avg 18.0% → last ⅓ avg 17.9%

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

0.27Best rating — vertigo 9–3

13–0Biggest win — nuke

6Longest win streak

6–7In matches decided by ≤2 rounds

3Overtime games

Map breakdown

dust2Best map · 80% over 10infernoWeakest map · 40% over 5

Map	Grade	Played	Record	Win rate	Avg rating
nuke	A	40	23–17	57%	0.04
dust2	S	10	8–2	80%	0.02
mirage	A	9	5–4	56%	0.00
ancient	A	7	4–3	57%	-0.00
train	S	7	5–2	71%	0.01
cache	S	6	4–2	67%	0.04
anubis	A	5	3–2	60%	0.02
inferno	C	5	2–3	40%	0.02
vertigo	—	4	2–2	50%	0.08
overpass	—	3	1–2	33%	-0.07
office	—	2	1–1	50%	0.07
palais	—	1	1–0	100%	0.03
whistle	—	1	0–1	0%	-0.02

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (40% over 5). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

108Matches

66%Win rate

1.44Avg K/D

83.3ADR

32%Headshot %

Clutches & streaks

58%1v1 clutch win

14%1v2 clutch win

7Longest win streak

Recent Faceit resultsWLLWW

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	10	60%	1.41	17.7
Inferno	4	100%	1.33	15.5
Ancient	2	100%	0.86	12.0
Dust2	2	100%	1.78	18.5
Anubis	2	100%	0.82	12.0
Train	2	50%	0.73	11.5

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.

18.4%Headshot accuracy

38.6%Accuracy (enemy spotted)

39.3%Spray accuracy

85.3%Counter-strafing

9.5°Preaim

577msReaction time

39.5%T opening success

53.3%CT opening success

0.40Enemies flashed / flash

11.3%Flash assists

9.13HE damage / grenade

4.38Flashes / 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
Most of your flashes are blinding nobody. A handful of reliable pop-flash lineups fixes this faster than anything else.
Enemies flashed per flash 0.3994 — below the 0.5 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 39.5152% — 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.

40% win rate across 5 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
nuke	9–13	-0.04	9%	20 Aug →
cache	13–3	0.13	17%	23 Jul →
nuke	13–11	-0.05	20%	23 Jul →
nuke	2–13	-0.01	12%	23 Jul →
office	11–13	0.04	22%	4 Jul →
office	13–7	0.11	24%	4 Jul →
dust2	13–3	0.10	23%	4 Jul →
anubis	13–6	0.02	27%	4 Jul →
cache	13–10	-0.00	9%	4 Jul →
cache	13–11	0.07	13%	4 Jul →
cache	8–13	-0.03	10%	4 Jul →
nuke	12–12	0.04	31%	4 Jul →
nuke	13–0	0.10	31%	4 Jul →
nuke	9–13	-0.03	10%	4 Jul →
nuke	13–10	0.16	35%	28 Jun →
nuke	13–8	0.10	28%	28 Jun →
ancient	1–5	0.01	10%	5 Jun →
cache	6–13	-0.05	11%	5 Jun →
nuke	13–7	0.02	28%	3 Jun →
cache	13–5	0.14	28%	27 May →
nuke	13–5	0.10	19%	26 May →
nuke	13–9	0.03	19%	24 May →
nuke	13–10	-0.00	18%	24 May →
nuke	4–13	0.01	4%	22 May →
nuke	6–13	0.08	19%	22 May →
nuke	13–3	0.02	13%	22 May →
train	13–4	0.07	15%	4 Jul →
ancient	10–13	-0.01	20%	4 Jul →
dust2	7–13	-0.05	8%	4 Jul →
anubis	13–10	-0.04	15%	17 May →
train	8–13	-0.00	17%	17 May →
nuke	11–13	0.01	6%	14 Mar →
mirage	13–8	0.02	20%	14 Mar →
nuke	1–13	0.01	17%	10 Mar →
mirage	7–13	0.04	17%	5 Mar →
nuke	6–13	0.09	14%	2 Mar →
anubis	13–16	-0.02	4%	22 Feb →
nuke	10–0	0.17	23%	21 Feb →
ancient	13–8	-0.08	8%	19 Feb →
mirage	10–13	-0.02	7%	16 Feb →
mirage	13–8	-0.02	16%	16 Feb →
nuke	13–2	0.12	15%	16 Feb →
nuke	13–10	0.05	14%	15 Feb →
nuke	13–7	0.05	16%	15 Feb →
nuke	13–16	-0.05	17%	15 Feb →
dust2	13–7	0.03	27%	15 Feb →
vertigo	9–3	0.27	24%	15 Feb →
nuke	9–4	0.22	33%	15 Feb →
ancient	13–7	0.03	13%	13 Feb →
nuke	13–8	0.18	11%	11 Feb →
inferno	7–9	-0.04	14%	9 Feb →
inferno	9–6	-0.00	9%	9 Feb →
overpass	1–9	-0.18	15%	9 Feb →
palais	9–6	0.03	10%	9 Feb →
dust2	13–7	0.04	24%	9 Feb →
ancient	13–9	-0.04	12%	9 Feb →
mirage	13–7	0.01	9%	9 Feb →
anubis	13–4	0.09	13%	8 Feb →
train	13–8	-0.01	9%	8 Feb →
train	11–13	-0.04	22%	8 Feb →
dust2	13–11	-0.03	17%	8 Feb →
nuke	9–4	0.08	15%	8 Feb →
vertigo	3–9	-0.04	24%	8 Feb →
inferno	9–4	0.04	14%	8 Feb →
train	13–7	0.08	21%	4 Feb →
nuke	10–13	-0.03	16%	2 Feb →
nuke	13–10	0.00	7%	2 Feb →
nuke	0–6	-0.02	100%	2 Feb →
nuke	5–13	0.07	17%	2 Feb →
ancient	16–12	0.06	11%	2 Feb →
train	13–5	-0.02	21%	30 Jan →
dust2	13–3	-0.00	26%	29 Jan →
train	13–11	-0.04	13%	29 Jan →
nuke	3–13	-0.04	21%	29 Jan →
anubis	11–13	0.05	13%	29 Jan →
dust2	13–5	-0.02	9%	29 Jan →
ancient	8–13	0.01	10%	29 Jan →
vertigo	13–5	0.09	13%	26 Jan →
nuke	5–13	-0.01	12%	26 Jan →
overpass	11–13	-0.06	13%	26 Jan →
mirage	5–13	-0.01	29%	26 Jan →
mirage	7–13	0.00	12%	26 Jan →
dust2	13–9	-0.02	14%	25 Jan →
mirage	13–7	-0.03	17%	23 Jan →
nuke	13–10	-0.00	11%	23 Jan →
nuke	13–11	0.03	12%	23 Jan →
nuke	12–12	0.06	11%	23 Jan →
nuke	13–1	0.03	13%	22 Jan →
dust2	13–6	0.16	26%	21 Jan →
dust2	6–13	0.01	14%	21 Jan →
mirage	13–7	0.04	25%	19 Jan →
nuke	8–13	0.03	13%	19 Jan →
nuke	13–3	0.14	23%	19 Jan →
inferno	7–13	0.16	26%	19 Jan →
vertigo	8–8	0.02	12%	18 Jan →
overpass	9–1	0.02	25%	18 Jan →
nuke	9–7	0.01	5%	18 Jan →
nuke	9–6	0.06	14%	18 Jan →
inferno	7–9	-0.05	11%	18 Jan →
whistle	3–9	-0.02	4%	18 Jan →