Kaalipappa — CS2 Stats

76561198003858377[U:1:43592649]Steam profile ↗✓ No bans

242Tracked matches45%Win rate2020Tracked since

CSDB Rating2.7 LearningSupport

CSDB Leaderboard#158457 of 260000 tracked

FaceitLevel 4Top 81.0% of ranked FACEIT players

Ladder ranks via Leetify

Rating over time

Premier CS Rating: 14,025 +1,871 27 Jan – 27 Jun · 6 days played

14,359Peak Premier in tracked matches

48Days played since 2024-01-27

Premier comes from this profile’s tracked match history, so it reaches back as far as those matches do.

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Performance scores

Aim17

Positioning38

Utility52

0–100 skill scores via Leetify.

Recent form

STEADY39–57–4Last 10039%Win rateLLLLLLWWWW

Last 10 vs previous 10: −20pp win rate · −0.01 avg rating · −1.0pp headshot accuracy · −6ms reaction

Win rate down 20pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp).

Last 5 · 10 · 20 matches

Last 5

0–5 · 0% win rate
Avg rating -0.06
Avg headshot accuracy 16%
Avg reaction 751ms

Last 10

4–6 · 40% win rate
Avg rating -0.05
Avg headshot accuracy 14%
Avg reaction 711ms

Last 20

10–9–1 · 50% win rate
Avg rating -0.04
Avg headshot accuracy 15%
Avg reaction 714ms

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 (+1.8 against its own average).

Aim1.7

Utility5.2

Positioning3.8

Opening Duels0.0

Clutch4.6

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 iM 57% playstyle similarity

Most alike: utility contribution, positioning profile.

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. 706ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 60% of shots are taken properly stopped — moving-shot inaccuracy is quietly taxing every duel.

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

Aim1.7

Positioning3.8

Utility5.2

Mechanics2.2

Opening Duels0.1

Win Impact3.3

Composite 2.7/10 (Learning), 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-0.04−0.02

first ⅓ avg -0.02 → last ⅓ avg -0.04

Reaction time706ms+5ms

first ⅓ avg 701ms → last ⅓ avg 706ms

Headshot accuracy14.1%−0.9%

first ⅓ avg 15.0% → last ⅓ avg 14.1%

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

0.14Best match rating · 13–3 · ancient, 17 Jun →

36%Best headshot accuracy · 3–13 · train, 28 Jun →

492msFastest reaction time · 5–13 · anubis, 3 May →

13–1Biggest win · cache, 13 Sept →

Across the last 100 tracked matches.

Highlights

5Longest win streak

L6Current streak

10–7In matches decided by ≤2 rounds

10Overtime games

Map breakdown

infernoBest map · 56% over 9trainWeakest map · 0% over 5

Map	Grade	Played	Record	Win rate	Avg rating
ancient	A	20	11–9	55%	-0.01
anubis	D	19	4–15	21%	-0.03
nuke	B	15	8–7	53%	-0.04
mirage	B	13	7–6	54%	-0.01
inferno	A	9	5–4	56%	-0.05
cache	D	7	2–5	29%	-0.05
vertigo	D	7	1–6	14%	-0.04
train	D	5	0–5	0%	-0.08
dust2	—	3	0–3	0%	-0.04
cache_b	—	1	0–1	0%	-0.02
overpass	—	1	1–0	100%	0.01

Across the last 100 tracked matches.

Faceit stats

Combat

101Matches

47%Win rate

0.97Avg K/D

72.8ADR

42%Headshot %

Clutches & streaks

43%1v1 clutch win

13%1v2 clutch win

10Longest win streak

Recent Faceit resultsLLLLL

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	20	45%	0.91	12.6
Ancient	19	58%	0.89	14.9
Nuke	17	53%	1.08	13.2
Cache	16	44%	0.99	11.0
Anubis	11	45%	0.95	13.4
Dust2	7	29%	0.91	15.9
Inferno	7	57%	1.31	12.6
Vertigo	4	0%	0.62	10.8

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.

14.3%Headshot accuracy

27.8%Accuracy (enemy spotted)

30.8%Spray accuracy

59.9%Counter-strafing

12.9°Preaim

706msReaction time

23.7%T opening success

30.9%CT opening success

0.50Enemies flashed / flash

2.7%Flash assists

7.89HE damage / grenade

12.24Flashes / 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
Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.
Preaim 12.8655° — above the 12° mark we flag
Aim Training →
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 14.295% — below the 15% mark we flag
Aim Training →
Best CS2 Settings
Slow first shots are as often a setup problem as a reflex one — framerate, sensitivity and crosshair visibility all move this number.
Reaction time 705.6882ms — above the 700ms mark we flag
Aim Training →

Spend your practice time on Train

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 5 tracked games — your weakest map with enough games to be worth reading into.

Train callouts & strategy →

Recent matches

Map	Score	Rating	HS%	Date
nuke	9–13	0.02	15%	1 Nov →
cache	14–16	-0.06	17%	18 Oct →
anubis	7–13	-0.04	21%	18 Oct →
anubis	4–13	-0.10	7%	18 Oct →
nuke	11–13	-0.11	21%	18 Oct →
train	7–13	-0.06	8%	9 Oct →
nuke	13–9	-0.02	16%	20 Sept →
cache	13–1	-0.02	11%	13 Sept →
ancient	13–8	-0.06	19%	13 Sept →
anubis	13–6	-0.01	9%	13 Sept →
inferno	10–13	-0.04	15%	12 Sept →
anubis	15–15	-0.03	15%	12 Sept →
nuke	13–8	0.00	15%	12 Sept →
nuke	13–5	-0.04	28%	12 Sept →
inferno	13–6	-0.04	14%	12 Sept →
ancient	16–14	-0.04	11%	12 Sept →
inferno	13–4	-0.07	13%	11 Sept →
cache	13–16	-0.06	12%	11 Sept →
cache	13–7	-0.03	5%	11 Sept →
cache	4–13	-0.02	25%	1 Sept →
ancient	10–13	-0.06	11%	29 Aug →
anubis	6–13	-0.09	12%	29 Aug →
cache	8–13	-0.05	12%	29 Aug →
vertigo	5–13	-0.10	7%	28 Aug →
ancient	16–14	-0.03	18%	16 Aug →
cache	3–13	-0.07	8%	16 Aug →
mirage	13–5	0.03	10%	23 Jun →
anubis	10–13	-0.00	17%	4 Mar →
inferno	4–13	-0.06	19%	26 Feb →
ancient	13–11	-0.04	16%	26 Feb →
anubis	11–13	-0.00	10%	6 Feb →
anubis	10–13	-0.04	15%	6 Feb →
ancient	15–15	-0.04	16%	30 Jan →
ancient	13–3	0.05	12%	30 Jan →
mirage	16–14	0.01	11%	15 Jan →
mirage	9–13	-0.00	32%	13 Dec →
mirage	7–13	-0.07	15%	10 Dec →
vertigo	11–13	0.00	14%	5 Dec →
inferno	13–5	0.01	13%	5 Dec →
cache_b	10–13	-0.02	13%	4 Dec →
mirage	13–11	-0.05	17%	4 Dec →
anubis	9–13	-0.06	6%	3 Dec →
anubis	5–13	-0.07	7%	3 Dec →
mirage	13–11	-0.02	19%	3 Dec →
mirage	13–9	-0.07	15%	3 Dec →
nuke	13–6	0.03	15%	25 Nov →
ancient	13–11	-0.03	23%	25 Nov →
nuke	13–9	-0.04	24%	11 Oct →
ancient	10–13	-0.07	14%	11 Oct →
anubis	5–13	-0.04	13%	23 Aug →
inferno	12–12	-0.05	5%	23 Aug →
ancient	8–13	-0.00	15%	22 Aug →
ancient	13–5	-0.00	12%	22 Aug →
nuke	5–13	-0.04	30%	22 Aug →
mirage	6–13	-0.01	23%	11 Aug →
train	3–13	-0.13	36%	28 Jun →
anubis	13–9	-0.02	26%	28 Jun →
inferno	13–11	-0.04	22%	28 Jun →
overpass	13–4	0.01	21%	28 Jun →
nuke	13–10	-0.06	27%	28 Jun →
ancient	12–12	0.02	13%	28 Jun →
vertigo	3–13	-0.04	29%	28 Jun →
mirage	5–13	-0.02	18%	27 Jun →
dust2	9–13	0.03	24%	27 Jun →
nuke	6–13	-0.07	8%	13 Jun →
anubis	3–13	-0.10	14%	3 May →
ancient	6–13	-0.08	18%	3 May →
ancient	13–16	-0.03	19%	3 May →
anubis	5–13	-0.04	7%	3 May →
dust2	5–13	-0.07	13%	16 Apr →
train	8–13	-0.09	14%	16 Apr →
nuke	4–13	-0.06	14%	15 Apr →
anubis	16–14	-0.01	24%	15 Apr →
nuke	13–11	-0.03	19%	15 Apr →
nuke	9–13	-0.03	17%	13 Apr →
anubis	5–13	0.05	19%	13 Apr →
dust2	8–13	-0.08	10%	13 Mar →
train	6–13	-0.05	8%	12 Mar →
mirage	13–5	0.05	32%	11 Mar →
mirage	13–7	-0.01	19%	25 Feb →
train	7–13	-0.09	19%	7 Feb →
ancient	13–6	-0.01	16%	7 Feb →
vertigo	9–13	-0.06	13%	16 Aug →
anubis	13–5	0.06	14%	14 Aug →
mirage	8–13	0.01	11%	27 Jun →
ancient	13–1	0.12	25%	18 Jun →
vertigo	9–13	-0.08	19%	18 Jun →
inferno	13–6	-0.03	6%	17 Jun →
ancient	13–3	0.14	27%	17 Jun →
inferno	6–13	-0.08	11%	8 May →
vertigo	11–13	0.07	11%	8 May →
nuke	13–6	-0.05	3%	8 May →
anubis	8–13	-0.08	9%	8 May →
ancient	13–10	0.01	26%	8 May →
mirage	10–13	0.01	13%	7 May →
ancient	2–12	-0.08	7%	5 Apr →
nuke	14–16	-0.08	14%	27 Jan →
vertigo	13–8	-0.07	16%	27 Jan →
anubis	11–13	-0.04	15%	27 Jan →
ancient	9–13	-0.01	9%	27 Jan →