heLL — CS2 Stats

76561197981609574[U:1:21343846]Steam profile ↗✓ No bans

619Tracked matches47%Win rate2020Tracked since

CSDB Rating6.8 SolidSupport

Ladder ranks via Leetify

Performance scores

Aim84

Positioning59

Utility63

0–100 skill scores via Leetify.

Recent form

COLD55–45Last 10055%Win rateWLWLLLLLLW

Last 10 vs previous 10: −20pp win rate · +0.00 avg rating · +2.0pp headshot accuracy · −49ms reaction

Win rate down 20pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp). Average match rating moved the other way (+0.00), so results shifted more than performance did.

Last 5 · 10 · 20 matches

Last 5

2–3 · 40% win rate
Avg rating 0.01
Avg headshot accuracy 22%
Avg reaction 489ms

Last 10

3–7 · 30% win rate
Avg rating 0.01
Avg headshot accuracy 21%
Avg reaction 466ms

Last 20

8–12 · 40% win rate
Avg rating 0.01
Avg headshot accuracy 20%
Avg reaction 491ms

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: Support — Utility contribution stands above the rest of this profile (+1.4 against its own average).

Aim8.4

Utility6.3

Positioning5.9

Opening Duels3.5

Clutch4.0

Strong 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.

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 Twistzz 95% playstyle similarity

Most alike: utility contribution, 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. 62% 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

Aim8.4

Positioning5.9

Utility6.3

Mechanics7.2

Opening Duels6.7

Win Impact3.9

Composite 6.8/10 (Solid), a weighted mean of the bars with a small opposition adjustment (×1.00 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating0.01+0.01

first ⅓ avg 0.00 → last ⅓ avg 0.01

Reaction time497ms−4ms

first ⅓ avg 501ms → last ⅓ avg 497ms

Headshot accuracy21.3%−4.5%

first ⅓ avg 25.9% → 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.

Personal bests

0.16Best match rating · 13–3 · ancient, 25 Sept →

41%Best headshot accuracy · 5–13 · anubis, 14 Sept →

344msFastest reaction time · 13–9 · ancient, 30 Aug →

13–1Biggest win · anubis, 16 Oct →

Across the last 100 tracked matches.

Highlights

6Longest win streak

5–7In matches decided by ≤2 rounds

12Overtime games

Map breakdown

infernoBest map · 65% over 26cacheWeakest map · 45% over 11

Map	Grade	Played	Record	Win rate	Avg rating
inferno	S	26	17–9	65%	0.02
ancient	B	23	11–12	48%	0.01
anubis	B	16	8–8	50%	0.00
cache	B	11	5–6	45%	-0.01
nuke	A	8	5–3	63%	0.00
dust2	A	7	4–3	57%	0.02
overpass	B	6	3–3	50%	-0.02
mirage	—	3	2–1	67%	0.01

Across the last 100 tracked matches.

Faceit stats

Combat

346Matches

55%Win rate

1.25Avg K/D

83.4ADR

50%Headshot %

Clutches & streaks

40%1v1 clutch win

39%1v2 clutch win

12Longest win streak

Recent Faceit resultsWWWWL

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	8	38%	0.99	16.6
Ancient	4	75%	1.11	18.8
Dust2	4	75%	1.20	16.8
Anubis	3	100%	0.99	18.0
Inferno	2	50%	2.05	17.0
Nuke	1	100%	1.44	23.0

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.

20.8%Headshot accuracy

35.0%Accuracy (enemy spotted)

36.5%Spray accuracy

82.4%Counter-strafing

8.5°Preaim

493msReaction time

62.0%T opening success

51.8%CT opening success

0.57Enemies flashed / flash

5.7%Flash assists

9.97HE damage / grenade

11.36Flashes / match

Recommended for you

Spend your practice time on Cache

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

45% win rate across 11 tracked games — your weakest map with enough games to be worth reading into.

Cache callouts & strategy →

Recent matches

Map	Score	Rating	HS%	Date
anubis	13–10	0.05	30%	29 Oct →
anubis	10–13	-0.01	19%	27 Oct →
inferno	13–9	0.02	20%	27 Oct →
cache	10–13	-0.01	19%	26 Oct →
inferno	9–13	0.03	24%	22 Oct →
cache	9–13	0.01	24%	22 Oct →
overpass	14–16	-0.02	15%	21 Oct →
ancient	10–13	0.00	18%	21 Oct →
ancient	10–13	0.01	19%	19 Oct →
nuke	16–13	0.02	22%	19 Oct →
inferno	11–13	0.01	33%	19 Oct →
mirage	13–5	0.02	13%	18 Oct →
ancient	3–13	-0.07	19%	18 Oct →
cache	13–10	0.03	12%	18 Oct →
ancient	9–13	-0.03	11%	18 Oct →
anubis	13–1	-0.01	23%	16 Oct →
anubis	13–8	0.01	22%	16 Oct →
inferno	11–13	-0.01	19%	16 Oct →
cache	6–13	0.02	16%	14 Oct →
ancient	13–7	0.07	19%	14 Oct →
anubis	13–5	-0.01	30%	13 Oct →
dust2	13–6	0.00	18%	13 Oct →
dust2	13–6	0.03	24%	13 Oct →
ancient	7–13	-0.04	31%	12 Oct →
anubis	4–13	0.01	20%	12 Oct →
anubis	13–3	0.01	24%	9 Oct →
anubis	13–11	-0.01	19%	8 Oct →
inferno	4–13	-0.02	22%	8 Oct →
ancient	10–13	-0.02	25%	8 Oct →
anubis	13–4	0.03	17%	8 Oct →
inferno	13–10	0.12	32%	5 Oct →
nuke	16–12	0.06	25%	5 Oct →
cache	13–16	-0.02	21%	2 Oct →
inferno	13–8	0.01	10%	2 Oct →
inferno	13–10	-0.05	26%	2 Oct →
overpass	13–5	-0.04	18%	30 Sept →
inferno	19–17	0.02	25%	30 Sept →
ancient	13–3	0.08	24%	28 Sept →
cache	5–13	-0.02	17%	25 Sept →
anubis	2–13	-0.06	23%	25 Sept →
overpass	16–13	-0.04	19%	25 Sept →
inferno	13–6	0.07	15%	25 Sept →
ancient	13–3	0.16	19%	25 Sept →
ancient	7–13	-0.03	37%	22 Sept →
dust2	3–13	0.01	18%	22 Sept →
inferno	10–13	0.01	27%	22 Sept →
cache	13–4	-0.06	23%	21 Sept →
ancient	13–5	0.09	25%	21 Sept →
inferno	13–8	0.07	21%	21 Sept →
ancient	16–14	0.01	18%	21 Sept →
anubis	7–13	0.01	20%	20 Sept →
ancient	5–13	0.04	29%	15 Sept →
inferno	13–8	0.14	29%	15 Sept →
anubis	5–13	-0.03	41%	14 Sept →
ancient	13–6	0.03	26%	14 Sept →
ancient	10–13	-0.00	29%	14 Sept →
inferno	13–10	-0.02	31%	14 Sept →
inferno	9–13	-0.03	37%	14 Sept →
inferno	19–15	0.02	23%	11 Sept →
anubis	4–13	-0.04	21%	11 Sept →
mirage	5–13	-0.02	17%	11 Sept →
ancient	13–9	0.01	30%	11 Sept →
inferno	13–9	0.04	14%	11 Sept →
nuke	13–8	0.01	22%	8 Sept →
cache	13–8	-0.00	31%	7 Sept →
inferno	13–7	0.03	29%	7 Sept →
dust2	13–9	0.10	24%	4 Sept →
overpass	9–13	0.03	24%	3 Sept →
nuke	6–13	-0.04	28%	3 Sept →
inferno	13–2	0.09	20%	3 Sept →
cache	6–13	-0.01	33%	3 Sept →
nuke	6–13	-0.00	37%	2 Sept →
cache	13–10	-0.02	14%	1 Sept →
overpass	13–9	0.01	25%	1 Sept →
anubis	7–13	-0.02	32%	1 Sept →
inferno	13–10	0.00	13%	1 Sept →
dust2	12–16	-0.04	26%	31 Aug →
nuke	13–6	0.01	18%	31 Aug →
overpass	6–13	-0.03	28%	31 Aug →
ancient	13–9	0.00	24%	30 Aug →
ancient	13–11	-0.00	16%	26 Aug →
cache	13–8	0.01	24%	26 Aug →
nuke	8–13	0.00	33%	24 Aug →
dust2	13–7	0.04	30%	24 Aug →
mirage	13–9	0.04	20%	23 Aug →
inferno	16–12	0.00	25%	23 Aug →
anubis	6–13	0.04	37%	21 Aug →
ancient	10–13	-0.04	11%	20 Aug →
inferno	11–13	0.02	39%	18 Aug →
inferno	25–23	0.01	32%	15 Aug →
inferno	16–12	-0.04	24%	15 Aug →
ancient	13–8	-0.05	15%	15 Aug →
nuke	13–6	-0.02	38%	15 Aug →
ancient	11–13	-0.06	21%	15 Aug →
ancient	13–4	0.13	23%	11 Aug →
inferno	6–13	-0.04	25%	11 Aug →
anubis	13–5	0.08	33%	11 Aug →
dust2	11–13	-0.01	31%	10 Aug →
ancient	0–13	-0.01	20%	10 Aug →
inferno	11–13	-0.02	34%	10 Aug →