Pappatonio 🥺👶❤ — CS2 Stats

GS76561199062944328[U:1:1102678600]Steam profile ↗✓ No bans

1,513Tracked matches50%Win rate2020Tracked since

4,486Hours in CS27Hrs last 2 wks

CSDB Rating6.4 SolidOpening Duellist

CSDB Leaderboard#34115 of 223705 tracked

FaceitLevel 8Top 31.2% of ranked FACEIT players

Ladder ranks via Leetify

Track this profile

CSDB reads this profile's Premier rating from its tracked match history, and records Faceit ELO once on every day the page is viewed. 51 days played since 15 Apr 2025. Come back after the next session and the change shows above.

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Rating over time

Premier CS Rating: 23,456 +4,767 15 Apr – 7 Jun · 19 days played

23,765Peak Premier in tracked matches

51Days played since 2025-04-15

Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do. Faceit ELO is CSDB’s own observation — no feed exposes ELO per match, so that line only has the days the profile was viewed and cannot be backfilled.

How this compares with the same rank

Median values for Level 8 among CSDB-tracked players (n=12,738), from Valve's own lifetime stats. Aim, positioning and utility scores are deliberately not benchmarked here — those are a third-party provider's derived metrics, which CSDB does not store.

Metric	This player	Level 8 median	Level 9 median	vs Level 9
Headshot rate	47.2%	46.8%	48.3%	1.1% short
Shot accuracy	16.4%	13.0%	13.2%	above
Kill/death ratio	1.39	1.06	1.07	above
Match win rate	56.3%	46.3%	46.9%	above

This profile matches the typical Level 9 player on 3 of 4 comparable metrics.

Widest gap: Headshot rate. That is the metric furthest from the Level 9 median in relative terms — not necessarily the one holding a rank back, which no statistic here can establish.

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Performance scores

Aim74

Positioning63

Utility57

0–100 skill scores via Leetify.

Recent form

COLD54–42–4Last 10054%Win rateLLLLLWWWLL

Last 10 vs previous 10: −20pp win rate · +0.02 avg rating · −5.1pp headshot accuracy · −88ms 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 (+0.02), so results shifted more than performance did.

Last 5 · 10 · 20 matches

Last 5

0–5 · 0% win rate
Avg rating 0.00
Avg headshot accuracy 33%
Avg reaction 541ms

Last 10

3–7 · 30% win rate
Avg rating 0.05
Avg headshot accuracy 28%
Avg reaction 555ms

Last 20

8–12 · 40% win rate
Avg rating 0.04
Avg headshot accuracy 31%
Avg reaction 598ms

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: Opening Duellist — Winning opening duels is what stands out in this profile (+1.8 against its own average). How often you WIN the first duel — CSDB cannot yet see how often you take it.

Aim7.4

Utility5.7

Positioning6.3

Opening Duels7.5

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.

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 m0NESY 89% playstyle similarity

Most alike: opening-duel success, utility contribution.

Where you differ: lower positioning profile; 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. 600ms 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

Aim7.4

Positioning6.3

Utility5.7

Mechanics5.7

Opening Duels7.0

Win Impact5.0

Composite 6.4/10 (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 rating0.05+0.01

first ⅓ avg 0.04 → last ⅓ avg 0.05

Reaction time613ms+12ms

first ⅓ avg 600ms → last ⅓ avg 613ms

Headshot accuracy28.7%+3.2%

first ⅓ avg 25.5% → last ⅓ avg 28.7%

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

0.23Best match rating · 13–3 · dust2, 19 May →

50%Best headshot accuracy · 13–10 · dust2, 20 Aug →

406msFastest reaction time · 12–4 · inferno, 29 Aug →

13–2Biggest win · dust2, 2 Jun →

Across the last 100 tracked matches.

Highlights

4Longest win streak

L5Current streak

11–6In matches decided by ≤2 rounds

11Overtime games

Map breakdown

mirageBest map · 80% over 15ancientWeakest map · 31% over 13

Map	Grade	Played	Record	Win rate	Avg rating
inferno	B	19	9–10	47%	0.02
dust2	S	19	13–6	68%	0.06
mirage	S	15	12–3	80%	0.05
ancient	D	13	4–9	31%	0.05
nuke	B	13	7–6	54%	0.07
anubis	C	11	4–7	36%	0.03
train	A	5	3–2	60%	0.05
cache	—	1	1–0	100%	0.03
overpass	—	1	0–1	0%	0.01
brewery	—	1	1–0	100%	0.10
palais	—	1	0–1	0%	-0.07
whistle	—	1	0–1	0%	-0.05

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (31% over 13). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Lifetime stats

68,017Lifetime kills

1.39K/D · Top 10% of Level 8 players

2,499Matches

56.3%Match win rate · Top 5% of Level 8 players

47.2%Headshot % · Top 50% of Level 8 players

16.4%Shot accuracy · Top 25% of Level 8 players

10,353MVPs

1,304Hours (in match)

3,338Bombs planted

1,103Bombs defused

Most-used weapons

AK-4725,130

AWP11,349

M4A4 / M4A1-S8,681

Desert Eagle4,633

P2000 / USP-S3,287

Glock-182,003

Knife1,690

FAMAS1,414

Galil AR1,310

MAC-101,213

Lifetime map wins

6,054inferno

5,213nuke

3,807dust2

2,564vertigo

956train

225cbble

28bank

27lake

Lifetime totals via Steam — visible because this profile's game details are public. Spans CS:GO and CS2.

Faceit stats

Combat

180Matches

54%Win rate

1.76Avg K/D

106.5ADR

54%Headshot %

Clutches & streaks

36%1v1 clutch win

35%1v2 clutch win

7Longest win streak

Recent Faceit resultsWLLLL

Map	Matches	Win rate	Avg K/D	Avg kills
Dust2	35	34%	1.69	23.7
Nuke	28	71%	1.80	23.8
Ancient	28	57%	1.71	24.5
Anubis	22	55%	1.65	23.9
Inferno	22	50%	1.44	23.3
Mirage	18	61%	2.62	22.1
Train	14	57%	1.78	23.6
Cache	6	50%	1.29	20.3

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.

28.7%Headshot accuracy

34.7%Accuracy (enemy spotted)

36.1%Spray accuracy

75.6%Counter-strafing

11.3°Preaim

600msReaction time

58.1%T opening success

58.1%CT opening success

0.66Enemies flashed / flash

17.9%Flash assists

8.01HE damage / grenade

11.71Flashes / match

Recommended for you

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.

31% win rate across 13 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
ancient	9–13	0.05	26%	11 Sept →
inferno	16–19	0.02	33%	11 Sept →
nuke	14–16	-0.01	37%	10 Sept →
anubis	8–13	-0.04	24%	2 Sept →
nuke	6–13	0.00	45%	30 Aug →
nuke	13–10	0.08	21%	29 Aug →
ancient	13–8	0.15	30%	29 Aug →
inferno	12–4	0.06	22%	29 Aug →
nuke	10–13	0.13	24%	29 Aug →
ancient	12–16	0.02	18%	28 Aug →
dust2	13–6	-0.01	33%	28 Aug →
dust2	8–13	0.12	25%	23 Aug →
dust2	13–3	-0.08	27%	21 Aug →
nuke	4–13	0.08	21%	21 Aug →
dust2	13–11	0.07	26%	21 Aug →
dust2	13–10	0.15	50%	20 Aug →
inferno	2–9	-0.07	46%	9 Aug →
cache	13–10	0.03	38%	8 Aug →
dust2	5–13	-0.03	31%	5 Aug →
overpass	7–13	0.01	35%	5 Aug →
inferno	16–19	-0.01	30%	20 Jul →
inferno	13–11	-0.03	26%	18 Jul →
anubis	6–13	0.02	26%	9 Jul →
nuke	13–6	0.19	42%	9 Jul →
inferno	8–8	0.10	28%	9 Jul →
mirage	13–10	0.08	35%	6 Jul →
train	13–11	0.04	14%	6 Jul →
nuke	9–13	0.14	21%	6 Jul →
dust2	13–16	0.04	40%	2 Jul →
anubis	13–5	0.12	20%	29 Jun →
anubis	13–9	0.10	21%	26 Jun →
ancient	13–6	-0.01	16%	26 Jun →
nuke	9–5	0.10	16%	25 Jun →
inferno	3–9	-0.04	29%	25 Jun →
dust2	13–9	0.08	19%	15 Jun →
brewery	9–7	0.10	15%	15 Jun →
inferno	13–11	0.08	23%	12 Jun →
ancient	15–15	0.00	30%	7 Jun →
train	13–11	0.04	22%	5 Jun →
ancient	10–13	0.11	18%	3 Jun →
inferno	13–11	-0.02	17%	3 Jun →
mirage	9–13	0.06	33%	2 Jun →
dust2	13–9	0.03	18%	2 Jun →
dust2	13–2	0.00	21%	2 Jun →
anubis	11–13	0.02	15%	2 Jun →
ancient	13–6	0.03	22%	1 Jun →
mirage	16–12	0.05	14%	1 Jun →
mirage	10–13	0.01	22%	28 May →
dust2	13–3	0.08	26%	28 May →
mirage	13–10	0.01	31%	28 May →
train	13–5	0.07	19%	26 May →
dust2	13–6	0.05	29%	26 May →
inferno	8–8	0.03	13%	26 May →
mirage	13–10	0.04	42%	24 May →
nuke	13–6	0.06	16%	23 May →
anubis	5–13	-0.06	24%	23 May →
mirage	2–13	-0.09	45%	23 May →
dust2	11–13	-0.00	19%	22 May →
ancient	13–6	0.02	34%	21 May →
mirage	13–7	0.07	33%	21 May →
dust2	9–13	-0.00	28%	20 May →
mirage	16–14	0.09	20%	19 May →
dust2	13–3	0.23	33%	19 May →
nuke	13–7	0.05	17%	19 May →
mirage	13–7	0.07	28%	19 May →
ancient	5–13	0.04	19%	18 May →
inferno	10–13	0.05	22%	16 May →
nuke	13–9	0.05	29%	16 May →
mirage	13–5	0.10	48%	16 May →
ancient	8–13	0.11	16%	16 May →
mirage	13–11	0.07	28%	15 May →
inferno	13–11	0.13	31%	15 May →
dust2	13–8	0.07	21%	11 May →
train	11–13	0.03	16%	6 May →
train	10–13	0.09	31%	4 May →
nuke	9–13	0.01	20%	2 May →
inferno	13–8	-0.00	20%	2 May →
ancient	2–6	0.03	31%	1 May →
anubis	1–13	-0.05	25%	1 May →
ancient	10–13	-0.04	6%	1 May →
nuke	13–7	0.07	20%	1 May →
anubis	13–8	0.12	27%	1 May →
dust2	13–5	0.07	33%	30 Apr →
anubis	15–15	0.06	16%	30 Apr →
inferno	10–13	0.02	27%	26 Apr →
ancient	7–13	0.09	21%	24 Apr →
palais	3–9	-0.07	18%	24 Apr →
whistle	3–9	-0.05	16%	20 Apr →
dust2	11–13	0.10	31%	18 Apr →
mirage	13–8	-0.02	41%	18 Apr →
inferno	10–13	0.08	22%	17 Apr →
inferno	16–12	0.04	30%	17 Apr →
inferno	13–10	0.02	27%	17 Apr →
inferno	13–5	0.12	27%	17 Apr →
mirage	13–11	0.07	25%	17 Apr →
anubis	10–13	-0.04	23%	16 Apr →
inferno	0–2	-0.11	0%	16 Apr →
anubis	13–3	0.07	46%	16 Apr →
dust2	16–13	0.09	38%	16 Apr →
mirage	13–9	0.07	33%	15 Apr →