Hammurapi — CS2 Stats

EE76561198181678111[U:1:221412383]Steam profile ↗✓ No bans

2,365Tracked matches69%Win rate2020Tracked since

CSDB Rating7.2 StrongPositional Player

CSDB Leaderboard#231302 of 257011 tracked

FaceitLevel 10 · 1,955 ELOTop 12.4% of ranked FACEIT players

WingmanLegendary Eagle Master

Ladder ranks via Leetify

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Performance scores

Aim83

Positioning75

Utility63

0–100 skill scores via Leetify.

Recent form

HOT69–25–6Last 10069%Win rateWWWTLWWWWL

Last 10 vs previous 10: +10pp win rate · +0.04 avg rating · +2.8pp headshot accuracy · +10ms reaction

Win rate +10pp across the last 10 against the 10 before — within the normal variation of a 10-match window (±20pp), so no real shift yet.

Last 5 · 10 · 20 matches

Last 5

3–1–1 · 60% win rate
Avg rating 0.11
Avg headshot accuracy 18%
Avg reaction 530ms

Last 10

7–2–1 · 70% win rate
Avg rating 0.08
Avg headshot accuracy 18%
Avg reaction 488ms

Last 20

13–6–1 · 65% win rate
Avg rating 0.06
Avg headshot accuracy 17%
Avg reaction 484ms

Newest first, from the last 100 tracked matches. Each block is its own sample — one result moves a 5-match win rate by 20 points.

Player DNA

Primary style: Positional Player — Positioning stands above the rest of this profile (+1.7 against its own average).

Aim8.3

Utility6.3

Positioning7.5

Opening Duels5.9

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.

What this cannot see yet: which weapons you use — so CSDB cannot identify an AWPer, and no style here implies a rifle or a sniper. It also cannot see how often you take opening duels, only how often you win them, nor where you hold, so roles that depend on those (entry, lurk, anchor) are deliberately absent rather than guessed. All of it needs round-by-round demo data, which is the next thing being built.

Your pro match

Plays most like donk 93% playstyle similarity

Most alike: utility contribution, 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

Strengths

T openings. 63% T opening-duel success — entries that actually open the round.

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

Aim8.3

Positioning7.5

Utility6.3

Mechanics6.2

Opening Duels6.7

Win Impact10.0

Composite 7.2/10 (Strong), 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 rating0.06+0.00

first ⅓ avg 0.06 → last ⅓ avg 0.06

Reaction time480ms−23ms

first ⅓ avg 503ms → last ⅓ avg 480ms

Headshot accuracy16.9%−2.3%

first ⅓ avg 19.2% → last ⅓ avg 16.9%

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.

Personal bests

0.27Best match rating · 5–0 · ancient, 5 Oct →

50%Best headshot accuracy · 4–2 · cache, 30 Sept →

359msFastest reaction time · 8–1 · overpass, 11 Oct →

13–0Biggest win · dust2, 15 Oct →

Across the last 100 tracked matches.

Highlights

9Longest win streak

W3Current streak

4–1In matches decided by ≤2 rounds

Map breakdown

trainBest map · 86% over 7infernoWeakest map · 58% over 12

Map	Grade	Played	Record	Win rate	Avg rating
dust2	A	15	9–6	60%	0.04
mirage	S	14	10–4	71%	0.05
vertigo	A	13	8–5	62%	0.04
ancient	S	12	9–3	75%	0.07
inferno	A	12	7–5	58%	0.08
train	S	7	6–1	86%	0.07
anubis	S	7	5–2	71%	0.06
cache	S	6	4–2	67%	0.09
nuke	S	6	4–2	67%	0.06
overpass	—	4	4–0	100%	0.10
office	—	4	3–1	75%	0.10

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (58% over 12). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

3,145Matches

50%Win rate

1.12Avg K/D

79.3ADR

38%Headshot %

Clutches & streaks

38%1v1 clutch win

21%1v2 clutch win

10Longest win streak

Recent Faceit resultsLWLLL

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	240	50%	1.07	15.4
Ancient	143	48%	1.11	15.5
Dust2	137	46%	0.99	14.5
Inferno	122	61%	1.32	17.3
Nuke	62	50%	1.22	17.6
Anubis	58	48%	1.04	14.9
Overpass	43	56%	1.21	17.9
Train	21	38%	1.15	16.6

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.

16.6%Headshot accuracy

38.2%Accuracy (enemy spotted)

39.3%Spray accuracy

77.9%Counter-strafing

9.0°Preaim

479msReaction time

63.3%T opening success

50.3%CT opening success

0.66Enemies flashed / flash

11.9%Flash assists

12.16HE damage / grenade

5.07Flashes / match

Recent matches

Map	Score	Rating	HS%	Date
ancient	13–5	0.07	14%	23 Oct →
overpass	13–11	0.18	21%	23 Oct →
train	13–6	0.03	16%	23 Oct →
inferno	12–12	0.09	16%	23 Oct →
cache	6–13	0.17	24%	23 Oct →
inferno	13–6	0.03	10%	23 Oct →
vertigo	13–2	0.12	18%	23 Oct →
mirage	13–2	0.02	23%	23 Oct →
vertigo	13–8	0.04	19%	22 Oct →
vertigo	7–13	0.06	21%	22 Oct →
vertigo	8–13	0.00	15%	22 Oct →
dust2	10–13	-0.01	20%	22 Oct →
train	13–1	0.16	15%	22 Oct →
anubis	13–8	0.02	16%	22 Oct →
vertigo	8–13	0.01	11%	22 Oct →
nuke	13–7	0.18	17%	22 Oct →
overpass	13–1	0.06	12%	22 Oct →
ancient	13–11	0.02	15%	22 Oct →
anubis	8–13	-0.03	15%	21 Oct →
cache	13–6	0.04	18%	21 Oct →
train	13–7	0.07	18%	21 Oct →
vertigo	13–9	0.14	11%	21 Oct →
inferno	13–6	0.01	9%	21 Oct →
ancient	13–9	0.07	20%	21 Oct →
dust2	13–1	0.08	21%	21 Oct →
overpass	13–7	0.10	15%	21 Oct →
nuke	9–13	0.15	16%	21 Oct →
ancient	8–13	0.02	18%	20 Oct →
train	7–13	0.03	17%	19 Oct →
dust2	13–4	0.06	20%	19 Oct →
ancient	13–6	0.13	15%	18 Oct →
dust2	5–13	-0.05	26%	18 Oct →
anubis	13–10	0.03	17%	18 Oct →
mirage	13–6	0.05	14%	16 Oct →
mirage	13–4	0.11	25%	16 Oct →
vertigo	5–13	-0.04	29%	16 Oct →
nuke	12–12	0.04	30%	16 Oct →
inferno	11–13	-0.00	14%	16 Oct →
dust2	13–0	0.03	29%	15 Oct →
train	13–6	0.08	36%	15 Oct →
mirage	10–13	0.12	19%	15 Oct →
office	13–8	0.10	26%	14 Oct →
ancient	13–7	0.01	23%	14 Oct →
vertigo	13–3	0.05	33%	14 Oct →
dust2	13–4	0.14	18%	13 Oct →
anubis	13–8	0.25	25%	12 Oct →
vertigo	13–8	0.03	20%	12 Oct →
mirage	13–4	0.01	12%	12 Oct →
nuke	13–6	-0.05	9%	12 Oct →
ancient	13–6	0.04	21%	12 Oct →
inferno	7–13	0.05	18%	12 Oct →
cache	13–1	0.03	22%	12 Oct →
train	13–7	0.06	20%	11 Oct →
overpass	8–1	0.06	33%	11 Oct →
vertigo	13–7	0.03	28%	11 Oct →
anubis	13–5	0.02	12%	11 Oct →
dust2	13–8	0.03	18%	11 Oct →
dust2	12–12	-0.00	21%	8 Oct →
inferno	13–2	0.05	21%	8 Oct →
office	4–13	-0.02	8%	8 Oct →
nuke	13–8	0.02	5%	8 Oct →
cache	13–1	0.16	15%	8 Oct →
dust2	13–8	0.13	21%	7 Oct →
dust2	13–4	0.09	26%	7 Oct →
mirage	12–12	0.06	11%	7 Oct →
mirage	8–13	0.04	20%	6 Oct →
inferno	2–8	0.15	19%	6 Oct →
mirage	5–13	-0.02	6%	6 Oct →
dust2	12–12	0.04	9%	5 Oct →
mirage	13–5	0.09	17%	5 Oct →
inferno	8–13	0.01	17%	5 Oct →
nuke	8–1	-0.01	28%	5 Oct →
dust2	13–10	-0.01	11%	5 Oct →
inferno	13–9	0.03	15%	5 Oct →
anubis	12–12	0.07	15%	5 Oct →
mirage	13–5	0.05	16%	5 Oct →
ancient	10–13	0.09	14%	5 Oct →
vertigo	13–10	0.01	18%	5 Oct →
ancient	5–0	0.27	21%	5 Oct →
cache	7–13	0.07	18%	2 Oct →
ancient	13–8	0.08	16%	2 Oct →
dust2	7–13	-0.04	16%	2 Oct →
mirage	13–9	0.03	15%	2 Oct →
ancient	13–6	0.03	20%	1 Oct →
train	13–8	0.05	18%	1 Oct →
inferno	13–5	0.25	17%	1 Oct →
ancient	6–13	-0.05	11%	1 Oct →
office	13–7	0.12	27%	1 Oct →
inferno	13–2	0.05	30%	1 Oct →
mirage	13–9	0.06	20%	1 Oct →
inferno	13–3	0.20	20%	30 Sept →
cache	4–2	0.05	50%	30 Sept →
anubis	13–5	0.07	34%	29 Sept →
vertigo	13–5	0.11	19%	28 Sept →
dust2	8–13	-0.01	11%	28 Sept →
office	13–11	0.18	7%	28 Sept →
vertigo	7–13	0.00	9%	27 Sept →
mirage	13–3	0.01	19%	23 Sept →
dust2	13–2	0.04	22%	22 Sept →
mirage	13–3	0.08	47%	22 Sept →