pecori — CS2 Stats

JP76561198116292002[U:1:156026274]Steam profile ↗✓ No bans

427Tracked matches65%Win rate2021Tracked since

CSDB Rating5.3 Developing

FaceitLevel 5Top 67.6% of ranked FACEIT players

Ladder ranks via Leetify

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Performance scores

Aim52

Positioning46

Utility49

0–100 skill scores via Leetify.

Recent form

STEADY60–32–8Last 10060%Win rateLLLLWLLWWW

Last 10 vs previous 10: -10pp win rate · -0.03 avg rating

Player DNA

Aim5.2

Aggression5.0

Utility4.9

Positioning4.6

Opening Duels0.8

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.

Your pro match

Plays most like NiKo 80% playstyle similarity

Most alike: opening-fight frequency, utility contribution.

Where you differ: lower opening-duel success; 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. 664ms 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

Aim5.2

Positioning4.6

Utility4.9

Mechanics6.7

Opening Duels0.8

Win Impact10.0

Composite 5.3/10 (Developing), 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 rating-0.01−0.01

first ⅓ avg 0.01 → last ⅓ avg -0.01

Reaction time665ms+30ms

first ⅓ avg 635ms → last ⅓ avg 665ms

Headshot accuracy14.6%+5.6%

first ⅓ avg 9.1% → last ⅓ avg 14.6%

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

0.15Best rating — anubis 13–10

13–0Biggest win — vertigo

7Longest win streak

L4Current streak

8–4In matches decided by ≤2 rounds

4Overtime games

Map breakdown

anubisBest map · 83% over 18nukeWeakest map · 0% over 6

Map	Grade	Played	Record	Win rate	Avg rating
dust2	S	22	15–7	68%	0.01
anubis	S	18	15–3	83%	0.02
inferno	S	17	12–5	71%	0.01
ancient	C	14	5–9	36%	-0.02
mirage	A	10	6–4	60%	-0.01
overpass	D	6	2–4	33%	-0.01
vertigo	S	6	5–1	83%	0.01
nuke	D	6	0–6	0%	-0.02
train	—	1	0–1	0%	-0.01

Across the last 100 tracked matches.

Nuke is currently your weakest sufficiently-sampled map (0% over 6). Start with the 6 essential Nuke lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

86Matches

52%Win rate

0.82Avg K/D

65.4ADR

32%Headshot %

Clutches & streaks

38%1v1 clutch win

4%1v2 clutch win

7Longest win streak

Recent Faceit resultsWWWLL

Map	Matches	Win rate	Avg K/D	Avg kills
Dust2	26	46%	0.76	13.0
Ancient	25	48%	0.84	12.4
Anubis	17	71%	0.92	15.1
Inferno	8	75%	0.77	12.1
Mirage	4	50%	0.58	9.3
Nuke	4	0%	0.76	12.0
Train	2	50%	1.09	18.0

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.

14.7%Headshot accuracy

32.9%Accuracy (enemy spotted)

38.4%Spray accuracy

80.2%Counter-strafing

11.2°Preaim

664msReaction time

32.3%T opening success

34.4%CT opening success

0.59Enemies flashed / flash

10.0%Flash assists

6.45HE damage / grenade

4.60Flashes / 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.

Best CS2 Crosshair
Your shots are landing on bodies more often than heads — usually a crosshair-height and placement habit rather than raw aim.
Headshot accuracy 14.7489% — below the 15% mark we flag
Aim Training →
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 32.2777% — below the 40% mark we flag

Spend your practice time on Nuke

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

0% win rate across 6 tracked games — your weakest map with enough games to be worth reading into.

Nuke callouts & strategy →Nuke grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
inferno	7–13	-0.06	17%	24 Aug →
ancient	4–13	-0.09	25%	29 Dec →
ancient	1–13	-0.12	14%	29 Dec →
ancient	8–13	0.02	22%	11 Dec →
dust2	13–5	0.01	18%	11 Dec →
mirage	2–11	-0.08	25%	11 Dec →
overpass	8–13	0.00	10%	10 Dec →
dust2	13–11	-0.07	14%	10 Dec →
mirage	13–2	-0.00	16%	10 Dec →
inferno	13–8	-0.00	19%	4 Dec →
ancient	12–12	-0.01	10%	4 Dec →
dust2	12–12	-0.02	18%	4 Dec →
mirage	13–11	-0.06	20%	10 Nov →
inferno	13–2	0.00	8%	10 Nov →
dust2	7–13	0.02	9%	10 Nov →
inferno	12–12	0.00	13%	4 Nov →
ancient	13–6	-0.03	11%	4 Nov →
mirage	9–13	-0.02	13%	4 Nov →
dust2	13–8	0.02	22%	4 Nov →
overpass	13–4	0.04	16%	30 Oct →
dust2	13–2	0.00	20%	30 Oct →
ancient	12–12	-0.04	8%	30 Oct →
dust2	13–9	0.05	13%	22 Oct →
inferno	13–11	0.02	13%	22 Oct →
overpass	13–2	0.05	13%	20 Oct →
dust2	13–5	-0.07	15%	20 Oct →
dust2	13–9	0.04	12%	18 Oct →
dust2	13–8	0.07	10%	18 Oct →
anubis	9–13	0.03	11%	16 Oct →
dust2	13–10	0.03	16%	28 Sept →
inferno	13–10	0.05	15%	28 Sept →
mirage	13–10	0.02	8%	28 Sept →
mirage	13–10	-0.00	12%	27 Sept →
dust2	8–0	0.04	27%	27 Sept →
overpass	9–13	-0.04	24%	27 Sept →
overpass	11–13	-0.04	12%	21 Sept →
inferno	13–8	0.04	15%	21 Sept →
dust2	12–12	-0.05	11%	18 Sept →
inferno	13–9	-0.01	18%	18 Sept →
inferno	12–12	-0.02	22%	15 Sept →
mirage	13–6	0.03	13%	15 Sept →
dust2	9–13	-0.04	16%	15 Sept →
inferno	10–13	0.05	20%	26 May →
vertigo	6–11	-0.04	13%	18 Apr →
vertigo	13–7	0.11	20%	18 Apr →
dust2	13–4	0.05	10%	18 Apr →
anubis	6–1	0.05	0%	18 Apr →
vertigo	13–7	-0.03	18%	30 Mar →
anubis	16–14	-0.01	12%	30 Mar →
anubis	13–6	-0.00	18%	30 Mar →
ancient	13–8	0.01	15%	29 Mar →
dust2	5–13	0.03	13%	29 Mar →
anubis	13–10	0.02	9%	28 Mar →
inferno	13–11	-0.04	2%	26 Mar →
ancient	19–17	-0.03	15%	25 Mar →
vertigo	13–1	0.02	4%	22 Mar →
dust2	13–10	-0.06	16%	22 Mar →
inferno	11–13	0.03	13%	22 Mar →
anubis	13–9	-0.01	7%	22 Mar →
ancient	6–13	0.04	10%	22 Mar →
nuke	9–13	0.00	21%	22 Mar →
anubis	13–5	0.01	15%	22 Mar →
anubis	13–11	0.01	4%	21 Mar →
anubis	13–8	0.03	10%	21 Mar →
nuke	12–16	-0.04	11%	18 Mar →
dust2	12–12	0.08	23%	18 Mar →
dust2	13–11	-0.03	15%	17 Mar →
nuke	10–13	0.03	12%	16 Mar →
ancient	11–13	-0.02	7%	16 Mar →
inferno	13–8	-0.09	4%	16 Mar →
anubis	13–6	0.03	10%	16 Mar →
vertigo	13–0	0.02	0%	16 Mar →
mirage	4–13	-0.01	6%	16 Mar →
train	6–13	-0.01	14%	14 Mar →
ancient	5–13	-0.05	11%	14 Mar →
anubis	13–10	0.15	9%	13 Mar →
ancient	13–2	-0.03	5%	13 Mar →
dust2	8–13	-0.01	18%	13 Mar →
anubis	13–3	0.02	9%	13 Mar →
nuke	3–13	-0.10	7%	13 Mar →
nuke	11–13	-0.04	9%	13 Mar →
ancient	13–7	0.01	10%	12 Mar →
anubis	6–3	0.05	0%	12 Mar →
inferno	13–9	0.06	3%	12 Mar →
nuke	10–13	0.04	19%	12 Mar →
anubis	13–7	-0.02	10%	12 Mar →
anubis	9–13	-0.03	7%	12 Mar →
ancient	15–15	0.01	8%	12 Mar →
inferno	13–2	0.03	19%	11 Mar →
mirage	8–13	-0.02	2%	11 Mar →
anubis	5–13	-0.00	14%	11 Mar →
mirage	13–10	0.02	15%	11 Mar →
dust2	13–8	0.11	14%	11 Mar →
overpass	9–13	-0.05	4%	10 Mar →
dust2	13–5	-0.01	11%	9 Mar →
inferno	13–3	0.06	14%	9 Mar →
inferno	13–8	0.04	8%	9 Mar →
anubis	13–4	-0.03	3%	9 Mar →
vertigo	13–10	-0.03	6%	9 Mar →
anubis	13–1	0.14	14%	9 Mar →