Porya — CS2 Stats

PROPera EsportsRussiaRiflerSettings & gear profile →

76561198033533067[U:1:73267339]

2,300Tracked matches57%Win rate2020Tracked since

CSDB Rating7.0 StrongHybrid Rifler

FaceitLevel 10 · 3,888 ELOTop 12.4% of ranked FACEIT players

Ladder ranks via Leetify

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Performance scores

Aim86

Positioning61

Utility54

0–100 skill scores via Leetify.

Recent form

STEADY62–38Last 10062%Win rateLWWLWLLLWW

Last 10 vs previous 10: +0pp win rate · -0.01 avg rating

Player DNA

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

Aim8.6

Aggression5.9

Utility5.4

Positioning6.1

Opening Duels3.4

Clutch4.4

Sharp aimerStrong 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 b1t 93% playstyle similarity

Most alike: opening-duel success, positioning profile.

Where you differ: lower opening-fight frequency.

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 86 — the mechanical foundation is a clear strength.

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 55% on CT to 32% on T — the same duels are being taken with worse setups on the attacking side.

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

Aim8.6

Positioning6.1

Utility5.4

Mechanics6.8

Opening Duels3.4

Win Impact7.2

Composite 7.0/10 (Strong), a weighted mean of the bars with a small opposition adjustment (×1.05 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating0.00−0.04

first ⅓ avg 0.04 → last ⅓ avg 0.00

Reaction time520ms−74ms

first ⅓ avg 594ms → last ⅓ avg 520ms

Headshot accuracy25.9%−0.1%

first ⅓ avg 25.9% → last ⅓ avg 25.9%

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

0.21Best rating — vertigo 13–2

13–1Biggest win — nuke

12Longest win streak

9–3In matches decided by ≤2 rounds

5Overtime games

Map breakdown

infernoBest map · 75% over 8ancientWeakest map · 29% over 7

Map	Grade	Played	Record	Win rate	Avg rating
nuke	A	18	11–7	61%	0.04
mirage	A	14	9–5	64%	0.02
anubis	A	13	8–5	62%	0.02
dust2	S	12	8–4	67%	0.03
inferno	S	8	6–2	75%	0.01
cache	A	7	4–3	57%	0.03
ancient	D	7	2–5	29%	0.01
overpass	S	7	5–2	71%	0.02
train	A	7	4–3	57%	0.08
vertigo	—	3	2–1	67%	0.11
office	—	2	1–1	50%	0.01
alpine	—	2	2–0	100%	0.10

Across the last 100 tracked matches.

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

Faceit stats

Combat

11,229Matches

62%Win rate

1.34Avg K/D

85.0ADR

51%Headshot %

Clutches & streaks

42%1v1 clutch win

25%1v2 clutch win

43Longest win streak

Recent Faceit resultsWWLLL

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	471	58%	1.27	17.6
Ancient	224	61%	1.29	16.5
Dust2	201	60%	1.29	16.6
Nuke	194	63%	1.33	17.9
Anubis	193	64%	1.49	17.5
Train	80	69%	1.37	15.8
Overpass	64	58%	1.40	17.9
Vertigo	28	54%	1.18	15.7

Faceit-match stats via the FACEIT Data API — a separate match pool from the sections above.

Inventory

$184Inventory value24ItemsFull inventory breakdown →

$122

$23

$13

Skill profile

Aggregate performance across tracked matches — stats via Leetify. Percentile context against other CSDB-tracked players arrives as our own benchmark data accumulates.

24.5%Headshot accuracy

36.0%Accuracy (enemy spotted)

41.5%Spray accuracy

80.6%Counter-strafing

9.8°Preaim

518msReaction time

32.0%T opening success

55.1%CT opening success

0.60Enemies flashed / flash

6.2%Flash assists

7.89HE damage / grenade

6.92Flashes / 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.

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 32.0338% — below the 40% mark we flag

Spend your practice time on Ancient

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.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
dust2	13–16	-0.00	32%	27 Aug →
nuke	13–10	-0.04	23%	27 Aug →
mirage	13–9	-0.01	16%	27 Aug →
nuke	11–13	0.01	25%	24 Aug →
nuke	13–1	0.20	37%	20 Aug →
mirage	10–13	0.04	29%	13 Aug →
inferno	9–13	-0.04	13%	26 Jul →
mirage	4–13	-0.03	22%	26 Jul →
anubis	13–10	0.01	23%	22 Jul →
inferno	19–17	-0.03	15%	22 Jul →
nuke	9–13	-0.01	24%	22 Jul →
cache	13–11	0.08	24%	21 Jul →
cache	10–13	0.05	32%	21 Jul →
dust2	13–8	0.04	27%	21 Jul →
mirage	13–7	-0.01	15%	21 Jul →
anubis	8–13	-0.03	12%	17 Jul →
dust2	2–13	-0.02	20%	17 Jul →
cache	13–1	0.10	32%	14 Jul →
dust2	13–5	0.01	15%	14 Jul →
cache	8–13	-0.01	35%	14 Jul →
anubis	9–13	-0.08	36%	14 Jul →
inferno	13–6	0.00	18%	13 Jul →
nuke	13–7	-0.00	21%	13 Jul →
nuke	5–13	-0.07	28%	13 Jul →
cache	13–4	-0.04	33%	13 Jul →
anubis	13–10	0.04	22%	13 Jul →
nuke	13–8	0.05	26%	12 Jul →
mirage	13–7	0.03	15%	12 Jul →
anubis	13–16	-0.06	30%	12 Jul →
cache	13–11	0.01	37%	12 Jul →
cache	0–2	-0.02	50%	12 Jul →
nuke	6–13	-0.03	42%	12 Jul →
mirage	1–13	-0.02	23%	4 Jul →
dust2	4–13	-0.05	23%	21 Jun →
mirage	13–3	0.09	18%	21 Jun →
anubis	13–11	0.00	14%	21 Jun →
anubis	13–10	-0.02	23%	20 Jun →
dust2	13–11	-0.02	19%	20 Jun →
anubis	13–10	0.03	28%	13 Jun →
dust2	13–6	0.13	20%	5 Jun →
mirage	13–9	-0.05	14%	5 Jun →
inferno	16–14	0.03	19%	3 Jun →
nuke	13–8	0.12	16%	3 Jun →
nuke	16–12	-0.03	35%	1 Jun →
anubis	13–9	0.00	21%	1 Jun →
inferno	13–10	0.08	30%	18 May →
inferno	4–13	-0.02	25%	17 May →
ancient	10–13	0.02	34%	9 May →
mirage	3–13	-0.07	41%	26 Apr →
vertigo	13–6	-0.00	25%	11 Apr →
anubis	13–5	0.07	40%	7 Apr →
anubis	13–4	0.10	31%	7 Apr →
ancient	5–13	0.02	29%	25 Mar →
dust2	13–4	0.02	32%	25 Mar →
mirage	13–1	0.12	42%	18 Mar →
office	7–13	0.02	24%	18 Mar →
office	13–9	-0.00	41%	18 Mar →
inferno	13–3	0.06	33%	12 Mar →
alpine	13–1	0.13	24%	18 Feb →
alpine	13–2	0.07	41%	18 Feb →
overpass	13–3	0.09	28%	11 Feb →
overpass	2–6	0.09	32%	10 Feb →
anubis	11–13	0.16	22%	5 Feb →
nuke	13–1	0.16	29%	29 Jan →
mirage	13–10	0.08	27%	28 Jan →
mirage	13–2	0.02	16%	27 Jan →
dust2	13–4	0.18	20%	24 Jan →
overpass	13–11	0.03	25%	20 Jan →
dust2	13–3	0.03	24%	7 Jan →
nuke	10–13	0.16	34%	7 Jan →
train	13–8	0.17	34%	1 Jan →
ancient	13–11	0.03	29%	28 Dec →
nuke	13–3	0.03	39%	28 Dec →
ancient	3–13	-0.02	33%	29 Nov →
nuke	9–13	0.01	18%	29 Nov →
dust2	13–6	0.05	38%	29 Nov →
mirage	13–6	0.02	21%	28 Nov →
nuke	13–3	-0.02	15%	28 Nov →
ancient	6–13	0.01	33%	7 Nov →
nuke	7–13	0.01	16%	7 Nov →
overpass	13–11	-0.04	10%	6 Nov →
train	13–9	-0.00	16%	6 Nov →
ancient	4–13	-0.04	15%	6 Nov →
inferno	13–8	-0.03	22%	6 Nov →
train	7–13	0.07	31%	5 Nov →
train	0–7	0.05	41%	5 Nov →
train	13–3	0.20	35%	5 Nov →
train	3–13	0.06	35%	5 Nov →
overpass	4–13	-0.06	16%	3 Nov →
train	13–8	0.00	16%	3 Nov →
ancient	13–5	0.03	24%	3 Nov →
vertigo	7–13	0.12	31%	24 Oct →
vertigo	13–2	0.21	30%	24 Oct →
overpass	13–10	0.05	35%	22 Oct →
nuke	13–10	0.05	27%	22 Oct →
dust2	5–13	0.01	48%	16 Oct →
mirage	8–13	0.01	17%	16 Oct →
overpass	13–4	-0.00	12%	16 Oct →
nuke	13–10	0.04	14%	16 Oct →
anubis	10–13	0.05	21%	15 Oct →