mongol — CS2 Stats

US76561198078065340[U:1:117799612]Steam profile ↗✓ No bans

1,752Tracked matches61%Win rate2020Tracked since

CSDB Rating7.4 StrongAggressive Rifler

Premier CS Rating25,241Top 25% of 29,473 CSDB-tracked playersRed band · top ~2.9% of ranked players (population est.)

CSDB Leaderboard#4913 of 45773 tracked

WingmanLegendary Eagle Master

Ladder ranks via Leetify

Rating over time

25,241Peak Premier on CSDB

1Days observed since 2026-08-28

Premier CS Rating

At peak — 25,241
Next: Gold band at 30,000 — 4,759 to go
Reached: Red band · Pink band · Purple band · Blue band
Red band first seen 2026-08-28
Pink band first seen 2026-08-28

CSDB's own observations — this history builds from the day a profile is first viewed and cannot be backfilled.

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Performance scores

Aim82

Positioning65

Utility55

0–100 skill scores via Leetify.

Recent form

STEADY56–37–7Last 10056%Win rateWLWWWLLLWW

Last 10 vs previous 10: +0pp win rate · +0.03 avg rating

Player DNA

Primary style: Aggressive Rifler — Takes opening fights often, backed by a strong aim profile.

Aim8.2

Aggression8.3

Utility5.5

Positioning6.5

Opening Duels3.4

Clutch2.4

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: positioning profile, opening-duel success.

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. 601ms 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

Aim8.2

Positioning6.5

Utility5.5

Mechanics8.1

Opening Duels3.4

Win Impact8.6

Composite 7.4/10 (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 rating-0.00−0.01

first ⅓ avg 0.01 → last ⅓ avg -0.00

Reaction time598ms+8ms

first ⅓ avg 590ms → last ⅓ avg 598ms

Headshot accuracy23.2%−0.7%

first ⅓ avg 23.9% → last ⅓ avg 23.2%

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

0.10Best rating — dust2 13–3

13–1Biggest win — nuke

6Longest win streak

3–4In matches decided by ≤2 rounds

4Overtime games

Map breakdown

cacheBest map · 81% over 16dust2Weakest map · 42% over 12

Map	Grade	Played	Record	Win rate	Avg rating
nuke	A	23	13–10	57%	0.01
inferno	B	21	10–11	48%	0.01
cache	S	16	13–3	81%	0.01
dust2	C	12	5–7	42%	-0.01
mirage	S	10	7–3	70%	0.01
ancient	B	8	4–4	50%	-0.01
train	—	4	2–2	50%	0.01
anubis	—	4	0–4	0%	-0.05
overpass	—	2	2–0	100%	-0.03

Across the last 100 tracked matches.

Dust 2 is currently your weakest sufficiently-sampled map (42% over 12). Start with the 6 essential Dust 2 lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

844Matches

58%Win rate

1.31Avg K/D

81.7ADR

41%Headshot %

Clutches & streaks

32%1v1 clutch win

33%1v2 clutch win

11Longest win streak

Recent Faceit resultsLLLLW

Map	Matches	Win rate	Avg K/D	Avg kills
Inferno	46	57%	1.22	15.5
Nuke	35	69%	1.48	15.8
Mirage	28	57%	1.13	16.0
Anubis	26	54%	1.21	15.5
Ancient	19	58%	1.30	16.5
Overpass	13	54%	1.26	15.8
Dust2	10	70%	1.16	15.3
Cache	8	62%	1.00	13.4

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.

22.2%Headshot accuracy

36.3%Accuracy (enemy spotted)

40.0%Spray accuracy

86.6%Counter-strafing

9.7°Preaim

601msReaction time

39.0%T opening success

48.6%CT opening success

0.51Enemies flashed / flash

7.6%Flash assists

9.13HE damage / grenade

12.36Flashes / 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 39.0104% — below the 40% mark we flag

Spend your practice time on Dust 2

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

42% win rate across 12 tracked games — your weakest map with enough games to be worth reading into.

Dust 2 callouts & strategy →Dust 2 grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
nuke	13–7	0.05	29%	28 Aug →
inferno	5–13	-0.06	13%	20 Aug →
nuke	13–8	0.04	20%	18 Aug →
cache	13–4	0.05	15%	16 Aug →
mirage	13–10	0.01	28%	16 Aug →
inferno	7–13	-0.04	21%	16 Aug →
inferno	10–13	-0.02	9%	16 Aug →
dust2	3–13	0.01	19%	16 Aug →
dust2	13–10	-0.02	31%	16 Aug →
cache	13–10	0.08	25%	16 Aug →
dust2	13–10	-0.03	14%	11 Aug →
nuke	15–15	0.08	20%	10 Aug →
nuke	13–9	-0.03	26%	8 Aug →
ancient	13–4	0.04	37%	8 Aug →
cache	13–9	-0.07	21%	5 Aug →
nuke	13–9	-0.06	16%	3 Aug →
nuke	5–13	-0.08	20%	2 Aug →
inferno	6–13	0.01	17%	2 Aug →
inferno	1–13	-0.04	42%	2 Aug →
mirage	13–11	-0.01	16%	2 Aug →
nuke	13–8	0.06	19%	28 Jul →
ancient	1–13	-0.01	16%	28 Jul →
inferno	13–7	0.04	25%	28 Jul →
cache	13–4	0.00	48%	28 Jul →
mirage	13–10	-0.04	26%	28 Jul →
dust2	10–13	0.03	18%	27 Jul →
inferno	11–13	-0.00	27%	27 Jul →
cache	4–13	-0.01	28%	26 Jul →
nuke	12–12	0.03	13%	25 Jul →
ancient	13–7	-0.01	20%	25 Jul →
ancient	3–3	-0.14	36%	23 Jul →
cache	13–7	-0.05	21%	22 Jul →
nuke	13–1	0.06	30%	22 Jul →
inferno	5–13	-0.12	38%	19 Jul →
nuke	0–13	0.00	0%	18 Jul →
inferno	4–13	-0.08	30%	18 Jul →
dust2	13–9	-0.02	10%	18 Jul →
mirage	11–13	-0.02	26%	17 Jul →
nuke	13–10	-0.02	8%	17 Jul →
inferno	5–13	0.02	36%	17 Jul →
train	12–12	-0.02	26%	17 Jul →
cache	13–3	0.08	22%	17 Jul →
nuke	0–13	-0.02	0%	17 Jul →
cache	8–13	-0.03	12%	16 Jul →
inferno	16–12	0.05	17%	16 Jul →
cache	16–13	0.01	28%	15 Jul →
inferno	13–5	0.08	13%	14 Jul →
dust2	10–13	-0.03	22%	13 Jul →
inferno	13–4	0.00	10%	13 Jul →
nuke	13–5	0.04	30%	13 Jul →
nuke	0–13	-0.05	11%	13 Jul →
nuke	13–7	0.02	22%	12 Jul →
dust2	15–15	-0.00	13%	12 Jul →
ancient	8–13	-0.02	17%	12 Jul →
cache	13–10	-0.02	25%	12 Jul →
inferno	13–5	-0.01	18%	12 Jul →
nuke	8–13	-0.00	25%	12 Jul →
dust2	13–6	0.05	33%	12 Jul →
mirage	13–7	0.06	25%	12 Jul →
nuke	13–8	-0.01	17%	12 Jul →
dust2	7–13	-0.03	26%	11 Jul →
cache	13–7	0.08	29%	11 Jul →
ancient	13–7	0.06	17%	11 Jul →
dust2	13–3	0.10	30%	10 Jul →
mirage	10–3	0.08	23%	10 Jul →
anubis	0–7	-0.06	25%	8 Jul →
train	7–13	-0.03	16%	7 Jul →
inferno	13–5	0.04	19%	7 Jul →
cache	13–7	0.08	27%	7 Jul →
dust2	7–13	-0.06	18%	7 Jul →
nuke	13–3	0.01	24%	7 Jul →
inferno	13–8	0.04	23%	6 Jul →
mirage	13–8	-0.03	19%	6 Jul →
cache	13–10	-0.00	26%	6 Jul →
nuke	13–6	0.06	26%	6 Jul →
inferno	13–2	0.07	33%	6 Jul →
anubis	6–13	0.01	12%	5 Jul →
inferno	13–11	0.10	15%	5 Jul →
dust2	3–13	-0.05	15%	5 Jul →
overpass	13–10	-0.04	20%	5 Jul →
train	13–9	0.07	22%	5 Jul →
nuke	13–9	0.06	13%	5 Jul →
cache	13–8	0.01	34%	3 Jul →
inferno	13–10	0.02	30%	3 Jul →
nuke	6–13	-0.07	21%	3 Jul →
mirage	12–12	0.03	22%	1 Jul →
anubis	7–13	-0.04	20%	1 Jul →
overpass	3–0	-0.02	38%	1 Jul →
inferno	12–12	0.04	23%	1 Jul →
mirage	9–13	-0.05	20%	29 Jun →
cache	11–13	-0.04	41%	29 Jun →
train	13–1	0.01	14%	28 Jun →
nuke	9–13	0.04	30%	28 Jun →
ancient	13–10	0.04	43%	28 Jun →
inferno	4–13	0.03	25%	27 Jun →
anubis	9–13	-0.10	16%	27 Jun →
ancient	12–12	-0.02	20%	26 Jun →
cache	13–10	0.02	21%	26 Jun →
mirage	13–11	0.04	19%	24 Jun →
nuke	3–13	-0.04	40%	24 Jun →