Fodz — CS2 Stats

AU76561197960418395[U:1:152667]Steam profile ↗✓ No bans

805Tracked matches46%Win rate2020Tracked since

CSDB Rating5.8 SolidClutch Specialist

FaceitLevel 6Top 54.0% of ranked FACEIT players

WingmanMaster Guardian Elite

Ladder ranks via Leetify

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Performance scores

Aim63

Positioning58

Utility61

0–100 skill scores via Leetify.

Recent form

STEADY31–62–7Last 10031%Win rateWWTLLLLWWL

Last 10 vs previous 10: +10pp win rate · +0.02 avg rating · −2.0pp headshot accuracy · −110ms reaction

Win rate +10pp across the last 10 against the 10 before — within the normal variation of a 10-match window (±20pp), so no real shift yet.

Last 5 · 10 · 20 matches

Last 5

2–2–1 · 40% win rate
Avg rating 0.01
Avg headshot accuracy 11%
Avg reaction 548ms

Last 10

4–5–1 · 40% win rate
Avg rating 0.00
Avg headshot accuracy 13%
Avg reaction 540ms

Last 20

7–12–1 · 35% win rate
Avg rating -0.01
Avg headshot accuracy 14%
Avg reaction 595ms

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: Clutch Specialist — Late-round 1vX conversion stands above the rest of this profile (+3.5 against its own average).

Aim6.3

Utility6.1

Positioning5.8

Opening Duels4.0

Clutch10.0

Reliable in 1v1s

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 ZywOo 81% 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. 603ms 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

Aim6.3

Positioning5.8

Utility6.1

Mechanics6.7

Opening Duels4.5

Win Impact3.8

Composite 5.8/10 (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 rating-0.00−0.02

first ⅓ avg 0.02 → last ⅓ avg -0.00

Reaction time599ms−33ms

first ⅓ avg 632ms → last ⅓ avg 599ms

Headshot accuracy13.9%−0.6%

first ⅓ avg 14.5% → last ⅓ avg 13.9%

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.

Personal bests

0.12Best match rating · 13–6 · dust2, 3 Sept →

38%Best headshot accuracy · 9–13 · cache, 2 Sept →

422msFastest reaction time · 13–5 · office, 6 Apr →

13–0Biggest win · train, 7 Sept →

Across the last 100 tracked matches.

Highlights

6Longest win streak

W2Current streak

5–11In matches decided by ≤2 rounds

1Overtime games

Map breakdown

infernoBest map · 61% over 18ancientWeakest map · 0% over 12

Map	Grade	Played	Record	Win rate	Avg rating
inferno	A	18	11–7	61%	0.02
nuke	D	14	3–11	21%	-0.01
dust2	D	13	2–11	15%	0.00
ancient	D	12	0–12	0%	-0.01
train	C	9	4–5	44%	0.02
cache	D	7	2–5	29%	0.00
mirage	D	6	1–5	17%	-0.02
vertigo	A	5	3–2	60%	0.00
overpass	C	5	2–3	40%	0.03
anubis	—	4	1–3	25%	-0.05
warden	—	3	0–3	0%	-0.00
office	—	2	2–0	100%	-0.01
rush_001	—	1	0–1	0%	-0.02
italy	—	1	0–1	0%	-0.01

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (0% over 12). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

123Matches

54%Win rate

1.33Avg K/D

71.3ADR

37%Headshot %

Clutches & streaks

100%1v1 clutch win

100%1v2 clutch win

10Longest win streak

Recent Faceit resultsLLLLL

Map	Matches	Win rate	Avg K/D	Avg kills
Inferno	1	0%	0.67	12.0
Ancient	1	0%	0.93	14.0

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.

13.5%Headshot accuracy

34.5%Accuracy (enemy spotted)

36.4%Spray accuracy

80.2%Counter-strafing

9.6°Preaim

603msReaction time

46.9%T opening success

49.2%CT opening success

0.55Enemies flashed / flash

6.0%Flash assists

7.03HE damage / grenade

12.49Flashes / 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 13.5071% — below the 15% mark we flag
Aim Training →

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.

0% win rate across 12 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
inferno	13–5	0.01	9%	11 Oct →
train	13–10	0.04	8%	10 Oct →
ancient	12–12	-0.03	10%	2 Oct →
dust2	6–13	0.01	18%	2 Oct →
ancient	1–13	0.02	12%	2 Oct →
rush_001	6–8	-0.02	13%	1 Oct →
nuke	6–13	-0.05	19%	1 Oct →
inferno	13–9	0.03	16%	1 Oct →
inferno	13–1	0.06	15%	28 Sept →
nuke	11–13	-0.03	7%	28 Sept →
train	4–13	-0.08	17%	28 Sept →
inferno	13–10	0.04	8%	28 Sept →
inferno	13–2	0.04	28%	28 Sept →
anubis	2–13	-0.05	18%	22 Sept →
vertigo	7–13	-0.06	6%	22 Sept →
nuke	13–4	-0.02	17%	16 Sept →
ancient	7–13	-0.04	12%	16 Sept →
inferno	11–13	0.09	13%	16 Sept →
dust2	5–13	-0.05	11%	15 Sept →
anubis	7–13	-0.05	16%	15 Sept →
ancient	10–13	0.01	16%	15 Sept →
cache	8–13	0.00	15%	15 Sept →
nuke	7–13	-0.03	26%	15 Sept →
inferno	13–11	-0.00	14%	8 Sept →
cache	13–5	0.10	16%	8 Sept →
nuke	13–10	-0.06	9%	7 Sept →
cache	13–7	0.01	10%	7 Sept →
nuke	13–11	0.04	9%	7 Sept →
train	13–0	0.08	21%	7 Sept →
dust2	12–12	-0.03	14%	7 Sept →
dust2	9–13	-0.04	11%	4 Sept →
ancient	4–13	-0.03	15%	4 Sept →
mirage	9–13	-0.07	15%	3 Sept →
dust2	13–6	0.12	18%	3 Sept →
inferno	13–6	0.05	17%	3 Sept →
ancient	3–13	-0.09	16%	3 Sept →
nuke	11–13	0.04	14%	2 Sept →
inferno	13–11	0.10	11%	2 Sept →
cache	9–13	-0.03	38%	2 Sept →
dust2	11–13	0.02	25%	27 Aug →
inferno	8–13	-0.03	15%	27 Aug →
inferno	12–12	-0.02	11%	26 Aug →
mirage	13–10	0.02	9%	26 Aug →
nuke	6–13	0.07	31%	26 Aug →
mirage	11–13	-0.04	4%	24 Aug →
nuke	8–13	-0.02	17%	24 Aug →
inferno	0–13	-0.11	21%	24 Aug →
dust2	13–7	0.00	11%	24 Aug →
ancient	13–16	-0.04	20%	17 Aug →
dust2	5–13	0.03	9%	7 Aug →
dust2	10–13	-0.02	8%	3 Aug →
vertigo	12–12	-0.06	11%	28 Jul →
overpass	9–1	0.02	4%	28 Jul →
train	7–13	0.03	14%	28 Jul →
cache	5–13	-0.08	5%	28 Jul →
anubis	0–13	-0.06	17%	29 May →
inferno	13–3	0.00	12%	22 May →
cache	9–13	-0.05	14%	22 May →
cache	6–13	0.05	21%	15 May →
mirage	1–13	-0.03	21%	15 May →
overpass	12–12	-0.04	10%	27 Apr →
nuke	10–13	-0.05	12%	27 Apr →
ancient	3–13	0.04	16%	27 Apr →
train	2–13	-0.00	21%	22 Apr →
dust2	5–13	-0.02	21%	22 Apr →
nuke	5–13	0.01	10%	22 Apr →
office	7–2	-0.05	0%	17 Apr →
mirage	6–13	0.01	16%	16 Apr →
train	7–13	-0.02	5%	6 Apr →
warden	11–13	-0.03	13%	6 Apr →
office	13–5	0.03	12%	6 Apr →
warden	9–13	0.01	10%	4 Apr →
anubis	13–11	-0.03	10%	4 Apr →
vertigo	13–4	0.03	15%	4 Apr →
inferno	11–13	0.01	24%	4 Apr →
nuke	11–13	0.03	16%	4 Apr →
inferno	2–13	0.02	13%	2 Apr →
mirage	1–13	-0.02	19%	1 Apr →
ancient	12–12	0.02	16%	31 Mar →
italy	5–13	-0.01	15%	31 Mar →
inferno	4–13	-0.05	19%	31 Mar →
overpass	13–6	0.03	8%	31 Mar →
train	13–9	0.02	16%	31 Mar →
ancient	11–13	0.03	15%	27 Mar →
vertigo	13–11	0.06	12%	26 Mar →
ancient	8–13	-0.05	11%	26 Mar →
train	13–4	0.07	14%	26 Mar →
ancient	12–12	0.01	13%	23 Mar →
train	10–13	-0.01	6%	23 Mar →
inferno	13–8	0.04	14%	23 Mar →
dust2	5–13	0.02	20%	18 Mar →
nuke	10–13	0.04	8%	18 Mar →
overpass	11–13	0.11	15%	18 Mar →
dust2	2–13	-0.03	26%	18 Mar →
inferno	13–7	0.10	16%	18 Mar →
vertigo	13–3	0.05	12%	17 Mar →
nuke	4–13	-0.04	23%	17 Mar →
warden	6–13	0.01	13%	17 Mar →
overpass	6–13	0.02	18%	16 Mar →
dust2	8–13	0.06	16%	16 Mar →