hateful — CS2 Stats

76561199411332465[U:1:1451066737]Steam profile ↗✓ No bans

547Tracked matches41%Win rate2023Tracked since

CSDB Rating4.9 DevelopingHybrid Rifler

WingmanGold Nova III

Ladder ranks via Leetify

Performance scores

Aim69

Positioning47

Utility43

0–100 skill scores via Leetify.

Recent form

STEADY46–49–5Last 10046%Win rateWLLLTWLLWW

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

Player DNA

Primary style: Hybrid Rifler — Aim-led profile without a single dominant tendency.

Aim6.9

Aggression6.5

Utility4.3

Positioning4.7

Opening Duels3.3

Clutch3.2

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 ropz 87% playstyle similarity

Most alike: opening-fight frequency, utility contribution.

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. 626ms 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

Aim6.9

Positioning4.7

Utility4.3

Mechanics8.1

Opening Duels3.3

Win Impact2.1

Composite 4.9/10 (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-0.01−0.01

first ⅓ avg 0.01 → last ⅓ avg -0.01

Reaction time633ms−33ms

first ⅓ avg 666ms → last ⅓ avg 633ms

Headshot accuracy15.8%−5.0%

first ⅓ avg 20.9% → last ⅓ avg 15.8%

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 — inferno 13–9

13–2Biggest win — mirage

6Longest win streak

3–9In matches decided by ≤2 rounds

4Overtime games

Map breakdown

trainBest map · 60% over 5overpassWeakest map · 29% over 7

Map	Grade	Played	Record	Win rate	Avg rating
dust2	A	22	13–9	59%	-0.01
mirage	D	21	7–14	33%	-0.01
ancient	B	11	5–6	45%	-0.01
inferno	A	9	5–4	56%	-0.00
nuke	B	8	4–4	50%	-0.01
anubis	C	7	3–4	43%	0.02
overpass	D	7	2–5	29%	0.00
train	A	5	3–2	60%	-0.02
vertigo	—	4	2–2	50%	0.01
office	—	2	0–2	0%	-0.04
alpine	—	1	0–1	0%	0.02
palacio	—	1	1–0	100%	-0.02
golden	—	1	1–0	100%	0.03
agency	—	1	0–1	0%	-0.07

Across the last 100 tracked matches.

Overpass is currently your weakest sufficiently-sampled map (29% over 7). Start with the 6 essential Overpass lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

494Matches

54%Win rate

1.13Avg K/D

83.0ADR

36%Headshot %

Clutches & streaks

36%1v1 clutch win

22%1v2 clutch win

8Longest win streak

Recent Faceit resultsWLWLW

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	71	46%	1.03	16.9
Dust2	70	61%	1.07	15.4
Ancient	46	74%	1.22	18.3
Anubis	37	43%	1.08	16.6
Inferno	27	26%	1.03	17.4
Nuke	17	41%	1.04	17.5
Vertigo	12	67%	1.02	14.9
Train	2	0%	0.54	10.0

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.

16.4%Headshot accuracy

36.3%Accuracy (enemy spotted)

38.6%Spray accuracy

86.6%Counter-strafing

11.0°Preaim

626msReaction time

36.6%T opening success

49.9%CT opening success

0.47Enemies flashed / flash

6.6%Flash assists

6.86HE damage / grenade

5.13Flashes / 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 0.4685 — 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 36.5982% — below the 40% mark we flag

Spend your practice time on Overpass

Map knowledge compounds faster than mechanics — lineups and callouts you learn once keep paying out every time the map comes up.

29% win rate across 7 tracked games — your weakest map with enough games to be worth reading into.

Overpass callouts & strategy →Overpass grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
inferno	13–9	0.20	16%	23 Jul →
mirage	7–13	-0.02	18%	17 May →
ancient	3–13	-0.08	7%	24 Apr →
alpine	6–13	0.02	13%	4 Apr →
ancient	12–12	0.00	11%	20 Mar →
mirage	13–10	-0.04	17%	19 Mar →
mirage	2–13	-0.06	25%	18 Mar →
mirage	6–13	0.02	24%	16 Mar →
ancient	13–3	0.04	16%	15 Mar →
mirage	13–6	0.05	23%	14 Mar →
mirage	5–13	0.02	16%	13 Mar →
mirage	3–13	-0.04	0%	13 Mar →
nuke	4–13	0.06	25%	13 Mar →
vertigo	8–13	0.06	16%	11 Mar →
dust2	8–13	-0.04	27%	10 Mar →
nuke	11–13	0.03	19%	9 Mar →
vertigo	9–4	-0.04	8%	8 Mar →
dust2	13–10	0.03	16%	8 Mar →
anubis	13–8	-0.01	7%	7 Mar →
ancient	10–13	-0.01	15%	6 Mar →
overpass	6–13	-0.01	29%	6 Mar →
inferno	13–3	0.02	13%	28 Feb →
inferno	13–6	-0.06	13%	26 Feb →
mirage	13–11	0.02	17%	24 Jan →
ancient	13–9	0.02	12%	23 Oct →
nuke	13–10	0.01	20%	23 Oct →
vertigo	7–13	0.01	13%	20 Oct →
office	9–13	-0.06	21%	20 Oct →
mirage	13–10	-0.09	11%	20 Oct →
dust2	11–13	-0.06	21%	20 Oct →
nuke	3–13	-0.09	9%	8 Oct →
mirage	14–16	-0.04	3%	7 Oct →
inferno	6–13	-0.04	24%	5 Oct →
dust2	13–7	-0.00	20%	5 Oct →
ancient	10–13	-0.02	13%	5 Oct →
mirage	8–13	-0.06	13%	5 Oct →
train	13–9	-0.00	15%	4 Oct →
nuke	13–6	-0.02	7%	4 Oct →
palacio	13–4	-0.02	19%	4 Oct →
vertigo	13–10	0.01	8%	4 Oct →
golden	13–7	0.03	6%	4 Oct →
agency	2–13	-0.07	31%	3 Oct →
office	8–13	-0.03	14%	3 Oct →
train	8–13	-0.06	14%	29 Sept →
mirage	6–13	-0.01	12%	29 Sept →
anubis	11–13	0.02	20%	23 Aug →
dust2	13–10	-0.09	11%	19 Aug →
ancient	13–9	-0.03	6%	17 Aug →
dust2	7–13	-0.08	25%	16 Aug →
dust2	1–13	-0.08	22%	9 Aug →
ancient	7–13	-0.05	18%	3 Aug →
ancient	13–5	0.00	10%	2 Aug →
overpass	15–15	-0.03	16%	1 Aug →
dust2	11–13	0.00	11%	1 Aug →
overpass	5–13	-0.08	15%	1 Aug →
overpass	12–12	0.04	21%	1 Aug →
inferno	13–9	0.01	9%	31 Jul →
mirage	8–13	-0.00	20%	31 Jul →
overpass	2–7	-0.03	33%	30 Jul →
mirage	13–7	-0.04	14%	28 Jul →
dust2	13–6	-0.02	11%	28 Jul →
overpass	13–5	0.12	17%	28 Jul →
mirage	2–13	-0.04	14%	27 Jul →
train	13–9	0.04	10%	27 Jul →
dust2	13–10	-0.07	13%	27 Jul →
mirage	7–13	-0.05	12%	26 Jul →
inferno	8–13	-0.07	26%	25 Jul →
dust2	13–9	-0.03	23%	25 Jul →
nuke	1–10	-0.01	15%	25 Jul →
mirage	14–16	0.05	15%	24 Jul →
overpass	13–10	0.02	19%	24 Jul →
dust2	9–13	0.02	17%	22 Jul →
dust2	13–11	0.02	24%	21 Jul →
inferno	9–13	-0.06	20%	13 Jul →
ancient	8–13	-0.03	30%	6 Jul →
mirage	13–5	0.08	40%	9 Jun →
train	13–7	-0.06	22%	25 May →
mirage	13–2	0.03	28%	19 May →
dust2	13–4	0.06	33%	19 May →
inferno	13–4	0.03	21%	5 May →
dust2	6–13	0.05	37%	5 May →
mirage	11–13	0.10	39%	27 Apr →
anubis	12–12	-0.02	22%	27 Apr →
inferno	10–13	-0.04	19%	23 Apr →
dust2	13–6	0.05	29%	22 Apr →
dust2	3–13	0.08	16%	21 Apr →
mirage	11–13	-0.02	13%	21 Apr →
train	2–13	-0.02	16%	21 Apr →
anubis	3–13	0.05	24%	21 Apr →
dust2	13–7	0.07	21%	13 Apr →
dust2	15–15	-0.06	12%	12 Apr →
anubis	13–10	-0.03	10%	12 Apr →
anubis	13–10	0.08	11%	12 Apr →
nuke	13–5	-0.02	7%	12 Apr →
ancient	13–5	0.06	12%	12 Apr →
dust2	13–10	-0.06	8%	11 Apr →
nuke	13–5	-0.06	22%	11 Apr →
anubis	11–13	0.04	29%	10 Apr →
dust2	13–7	-0.05	15%	9 Apr →
dust2	13–11	-0.01	23%	7 Apr →