jOHN — CS2 Stats

US76561198100368626[U:1:140102898]Steam profile ↗

755Tracked matches62%Win rate2020Tracked since

CSDB Rating7.8 StrongAggressive Rifler

Premier CS Rating22,511Top 50% of 17,162 CSDB-tracked playersPink band · top ~5.7% of ranked players (population est.)

CSDB Leaderboard#9300 of 35968 tracked

WingmanLegendary Eagle Master

Ladder ranks via Leetify

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Performance scores

Aim91

Positioning64

Utility54

0–100 skill scores via Leetify.

Recent form

HOT59–40–1Last 10059%Win rateLWWWWWLWLW

Last 10 vs previous 10: +30pp win rate · -0.01 avg rating

Player DNA

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

Aim9.1

Aggression8.9

Utility5.4

Positioning6.4

Opening Duels5.8

Clutch3.0

Sharp aimerExcellent counter-strafingStrong 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

Plays most like sh1ro 92% playstyle similarity

Most alike: opening-fight frequency, positioning 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

Strengths

Aim. Aim score of 91 — the mechanical foundation is a clear strength.

Counter-strafing. 90% of shots taken properly stopped — movement discipline most players never reach.

CT openings. 61% CT opening-duel success — winning the first fight on the defending side is rare and valuable.

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 61% on CT to 45% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 591ms 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

Aim9.1

Positioning6.4

Utility5.4

Mechanics8.9

Opening Duels5.8

Win Impact9.0

Composite 7.8/10 (Strong), 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 rating0.02+0.01

first ⅓ avg 0.01 → last ⅓ avg 0.02

Reaction time587ms+4ms

first ⅓ avg 583ms → last ⅓ avg 587ms

Headshot accuracy29.6%+7.2%

first ⅓ avg 22.5% → last ⅓ avg 29.6%

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

0.16Best rating — dust2 13–3

13–1Biggest win — mirage

10Longest win streak

9–9In matches decided by ≤2 rounds

11Overtime games

Map breakdown

ancientBest map · 86% over 7infernoWeakest map · 25% over 8

Map	Grade	Played	Record	Win rate	Avg rating
mirage	A	35	20–15	57%	0.00
dust2	B	24	13–11	54%	0.02
anubis	A	15	9–6	60%	0.03
inferno	D	8	2–6	25%	-0.01
ancient	S	7	6–1	86%	0.02
cache	—	4	3–1	75%	-0.03
vertigo	—	3	3–0	100%	0.05
nuke	—	2	1–1	50%	0.05
overpass	—	2	2–0	100%	0.07

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (25% over 8). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

454Matches

56%Win rate

1.22Avg K/D

82.8ADR

53%Headshot %

Clutches & streaks

35%1v1 clutch win

30%1v2 clutch win

8Longest win streak

Recent Faceit resultsWLLWL

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	118	53%	1.14	16.2
Dust2	91	67%	1.21	17.6
Ancient	45	58%	1.32	18.0
Anubis	43	30%	1.06	16.8
Inferno	36	47%	1.18	16.9
Nuke	17	71%	1.49	17.8
Overpass	14	79%	1.48	17.9
Cache	12	50%	1.00	16.0

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.

28.0%Headshot accuracy

38.8%Accuracy (enemy spotted)

37.4%Spray accuracy

90.2%Counter-strafing

8.3°Preaim

591msReaction time

44.9%T opening success

61.1%CT opening success

0.61Enemies flashed / flash

5.7%Flash assists

11.76HE damage / grenade

13.85Flashes / match

Recommended for you

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.

25% win rate across 8 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
mirage	10–13	0.05	30%	29 Aug →
dust2	13–3	0.16	18%	29 Aug →
anubis	13–4	0.04	34%	12 Aug →
dust2	13–8	-0.01	29%	12 Aug →
inferno	16–12	-0.03	17%	10 Aug →
anubis	13–11	0.04	26%	9 Aug →
inferno	9–13	-0.01	25%	9 Aug →
mirage	13–7	0.01	20%	9 Aug →
inferno	2–6	-0.10	100%	8 Aug →
mirage	13–11	0.04	17%	8 Aug →
mirage	13–6	0.06	33%	7 Aug →
dust2	10–13	-0.03	17%	7 Aug →
mirage	13–6	0.11	31%	7 Aug →
anubis	5–13	0.01	29%	6 Aug →
mirage	10–13	-0.02	25%	5 Aug →
anubis	15–15	0.02	17%	5 Aug →
vertigo	13–11	0.10	32%	4 Aug →
mirage	10–13	0.02	24%	3 Aug →
inferno	13–5	0.06	26%	2 Aug →
mirage	3–13	0.00	26%	1 Aug →
dust2	11–13	0.04	56%	1 Aug →
vertigo	13–9	-0.00	30%	1 Aug →
mirage	13–9	-0.03	21%	29 Jul →
mirage	16–19	0.01	24%	29 Jul →
anubis	13–10	0.03	25%	27 Jul →
cache	13–7	-0.05	23%	26 Jul →
dust2	13–5	0.03	32%	25 Jul →
dust2	14–16	0.01	33%	25 Jul →
mirage	13–10	-0.02	38%	19 Jul →
anubis	13–3	0.03	35%	19 Jul →
mirage	13–10	-0.02	27%	17 Jul →
dust2	13–8	0.00	40%	16 Jul →
ancient	13–10	0.09	17%	15 Jul →
mirage	13–10	-0.04	25%	15 Jul →
anubis	13–8	0.12	24%	14 Jul →
mirage	13–1	0.08	28%	14 Jul →
mirage	13–6	0.00	25%	13 Jul →
nuke	13–6	0.13	21%	12 Jul →
inferno	6–13	-0.03	17%	11 Jul →
cache	8–13	-0.07	16%	11 Jul →
dust2	5–13	-0.06	28%	9 Jul →
mirage	17–19	0.03	29%	8 Jul →
vertigo	13–11	0.05	34%	8 Jul →
cache	16–14	-0.04	32%	6 Jul →
mirage	13–2	0.11	31%	6 Jul →
mirage	13–9	0.02	41%	5 Jul →
dust2	13–5	0.07	37%	5 Jul →
mirage	5–13	-0.00	37%	5 Jul →
ancient	9–13	-0.00	30%	5 Jul →
dust2	13–11	-0.00	32%	5 Jul →
mirage	13–7	-0.01	18%	4 Jul →
mirage	7–13	-0.04	42%	4 Jul →
inferno	5–13	-0.04	13%	4 Jul →
anubis	13–11	0.01	17%	3 Jul →
nuke	13–16	-0.03	22%	2 Jul →
dust2	7–13	-0.03	19%	2 Jul →
inferno	11–13	0.00	21%	2 Jul →
anubis	21–25	-0.00	22%	1 Jul →
dust2	13–5	0.10	26%	29 Jun →
dust2	11–13	-0.04	22%	27 Jun →
dust2	13–1	0.10	31%	25 Jun →
mirage	3–13	-0.02	26%	24 Jun →
anubis	6–13	-0.01	23%	24 Jun →
mirage	13–9	-0.04	31%	22 Jun →
anubis	7–13	0.04	33%	18 Jun →
anubis	10–13	-0.02	28%	17 Jun →
ancient	13–5	0.04	19%	16 Jun →
ancient	13–7	0.04	10%	14 Jun →
cache	13–2	0.03	17%	14 Jun →
anubis	13–10	-0.00	28%	13 Jun →
dust2	6–13	-0.00	48%	13 Jun →
dust2	9–13	-0.05	24%	12 Jun →
inferno	8–13	0.04	22%	12 Jun →
mirage	11–13	0.06	24%	11 Jun →
ancient	13–11	-0.01	29%	11 Jun →
ancient	16–14	-0.03	30%	8 Jun →
mirage	13–1	-0.02	21%	5 Jun →
dust2	13–7	0.02	23%	5 Jun →
ancient	13–10	0.05	20%	4 Jun →
dust2	8–13	-0.04	19%	3 Jun →
overpass	13–10	0.12	24%	2 Jun →
mirage	10–13	-0.05	35%	27 May →
mirage	13–2	-0.05	22%	26 May →
overpass	16–13	0.02	16%	20 May →
dust2	13–16	-0.02	23%	19 May →
mirage	11–13	-0.05	23%	18 May →
mirage	13–10	0.01	16%	15 May →
mirage	11–13	-0.05	21%	14 May →
mirage	10–13	-0.03	27%	13 May →
dust2	11–13	-0.02	12%	13 May →
anubis	13–10	0.04	17%	13 May →
dust2	13–7	0.10	18%	12 May →
mirage	13–7	-0.09	8%	12 May →
mirage	7–3	0.09	16%	12 May →
anubis	13–2	0.04	22%	11 May →
dust2	9–6	0.02	19%	11 May →
mirage	13–8	0.04	29%	10 May →
mirage	8–13	-0.00	24%	9 May →
dust2	13–10	0.04	35%	9 May →
dust2	13–8	0.05	20%	9 May →