montana 🧃 — CS2 Stats

MF76561198169533711[U:1:209267983]Steam profile ↗2 game bans

983Tracked matches47%Win rate2020Tracked since

1,670Hours in CS

CSDB Rating4.9 DevelopingSupport

CSDB Leaderboard#207300 of 227434 tracked

FaceitLevel 8 · 1,826 ELOTop 31.2% of ranked FACEIT players

Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 6 Sep 2026 (13 days ago). Ranks are recorded once per day this page is viewed.

No change since then — still 983 tracked matches. Play, then come back: the next observation lands here.

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. 44 days played since 1 Nov 2025. Come back after the next session and the change shows above.

Is this you? Sign in with Steam to claim it and connect match tracking →

Rating over time

Premier CS Rating: 29,396 +11,943 1 Nov – 13 Nov · 12 days played

29,396Peak Premier in tracked matches

1,826Highest Faceit ELO seen on CSDB

44Days played since 2025-11-01

Faceit ELO

At peak — 1,826
Next: Level 10 at 2,001 — 175 to go
Reached: Level 9 · Level 8 · Level 7 · Level 6
Level 9 first seen 2026-09-06
Level 8 first seen 2026-09-06

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.

Compare periods

Last 7 days vs previous 7

No matches recorded between 10 Sep 2026 and 19 Sep 2026.

Measured 10 Sep 2026 → 19 Sep 2026; previous 6 Sep 2026 → 10 Sep 2026.

Differences between Valve's lifetime totals on the days CSDB observed this profile — every mode Valve counts, not only ranked. A window appears only when an observation sits within a few days of each end.

How this compares with the same rank

Median values for Level 8 among CSDB-tracked players (n=13,115), 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	51.3%	46.8%	48.3%	above
Shot accuracy	15.8%	13.0%	13.2%	above
Kill/death ratio	1.11	1.06	1.07	above
Match win rate	48.9%	46.3%	46.9%	above

Across every metric we can compare, this profile already matches the typical Level 9 player. Rank still comes from winning matches — this is a performance comparison, not a prediction.

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Performance scores

Aim65

Positioning41

Utility57

0–100 skill scores via Leetify.

Recent form

STEADY65–34–1Last 10065%Win rateLLWWWLLWWL

Last 10 vs previous 10: −20pp win rate · −0.02 avg rating · −2.7pp headshot accuracy · −57ms 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

3–2 · 60% win rate
Avg rating 0.01
Avg headshot accuracy 21%
Avg reaction 600ms

Last 10

5–5 · 50% win rate
Avg rating 0.01
Avg headshot accuracy 20%
Avg reaction 606ms

Last 20

12–8 · 60% win rate
Avg rating 0.02
Avg headshot accuracy 21%
Avg reaction 635ms

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).

Aim6.5

Utility5.7

Positioning4.1

Opening Duels2.7

Clutch3.0

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 NiKo 84% playstyle similarity

Most alike: utility contribution, opening-duel success.

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

T-side openings. Opening success drops from 45% on CT to 24% on T — the same duels are being taken with worse setups on the attacking side.

Reaction time. 626ms 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

Aim6.5

Positioning4.1

Utility5.7

Mechanics6.3

Opening Duels1.9

Win Impact3.9

Composite 4.9/10 (Developing), a weighted mean of the bars with a small opposition adjustment (×0.95 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating0.02−0.07

first ⅓ avg 0.09 → last ⅓ avg 0.02

Reaction time632ms+156ms

first ⅓ avg 477ms → last ⅓ avg 632ms

Headshot accuracy20.2%−4.7%

first ⅓ avg 24.9% → last ⅓ avg 20.2%

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.

Personal bests

0.24Best match rating · 13–4 · overpass, 5 Nov →

74%Best headshot accuracy · 6–13 · ancient, 11 Nov →

195msFastest reaction time · 5–13 · mirage, 9 Nov →

13–1Biggest win · dust2, 4 Nov →

Across the last 100 tracked matches.

Highlights

15Longest win streak

L2Current streak

11–5In matches decided by ≤2 rounds

9Overtime games

Map breakdown

overpassBest map · 82% over 11infernoWeakest map · 47% over 15

Map	Grade	Played	Record	Win rate	Avg rating
mirage	A	21	12–9	57%	0.04
nuke	S	18	12–6	67%	0.04
inferno	B	15	7–8	47%	0.01
overpass	S	11	9–2	82%	0.11
dust2	A	11	7–4	64%	0.12
ancient	S	9	7–2	78%	0.04
train	S	8	6–2	75%	0.04
cache	—	3	1–2	33%	0.01
anubis	—	2	2–0	100%	0.02
italy	—	1	1–0	100%	0.17
palacio	—	1	1–0	100%	0.15

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (47% over 15). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Lifetime stats

70,582Lifetime kills

1.11K/D · Top 50% of Level 8 players

2,718Matches

48.9%Match win rate · Top 50% of Level 8 players

51.3%Headshot % · Top 50% of Level 8 players

15.8%Shot accuracy · Top 50% of Level 8 players

8,534MVPs

1,670Hours (in match)

4,157Bombs planted

883Bombs defused

Most-used weapons

AK-4726,693

M4A4 / M4A1-S12,689

AWP7,927

Desert Eagle5,680

P2000 / USP-S4,175

Glock-182,920

FAMAS1,786

Galil AR1,786

Knife1,483

MP9998

Lifetime map wins

5,831inferno

3,588dust2

2,618nuke

2,108vertigo

1,373train

127office

84cbble

46lake

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

Faceit stats

Combat

1,820Matches

50%Win rate

1.16Avg K/D

87.3ADR

48%Headshot %

Clutches & streaks

35%1v1 clutch win

29%1v2 clutch win

14Longest win streak

Recent Faceit resultsWLLWL

Map	Matches	Win rate	Avg K/D	Avg kills
Mirage	155	49%	1.20	17.9
Dust2	58	57%	1.19	17.4
Nuke	57	51%	1.32	17.8
Inferno	55	36%	1.09	17.7
Ancient	49	49%	1.04	15.9
Overpass	36	53%	1.27	19.8
Anubis	33	33%	0.94	14.8
Vertigo	17	59%	1.00	16.5

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.

19.4%Headshot accuracy

34.5%Accuracy (enemy spotted)

38.1%Spray accuracy

78.4%Counter-strafing

10.7°Preaim

626msReaction time

23.8%T opening success

45.4%CT opening success

0.58Enemies flashed / flash

6.9%Flash assists

8.66HE damage / grenade

12.33Flashes / 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 23.7997% — below the 40% mark we flag

Spend your practice time on Inferno

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

47% win rate across 15 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

Map	Score	Rating	HS%	Date
inferno	8–13	0.00	24%	23 Aug →
cache	8–13	-0.08	16%	20 Aug →
nuke	13–11	-0.03	15%	16 Aug →
overpass	13–7	0.13	26%	3 Jul →
nuke	13–8	-0.00	24%	16 May →
mirage	1–13	-0.05	21%	14 May →
nuke	8–13	-0.04	12%	6 May →
cache	13–7	0.12	29%	3 May →
ancient	13–4	0.06	21%	25 Apr →
cache	9–13	-0.02	9%	23 Apr →
mirage	13–7	0.07	13%	22 Apr →
anubis	16–12	0.02	18%	21 Apr →
mirage	7–13	0.02	18%	21 Apr →
mirage	13–9	0.03	18%	21 Apr →
nuke	13–7	-0.03	30%	19 Apr →
nuke	13–6	0.15	35%	1 Apr →
inferno	12–16	0.03	29%	27 Mar →
ancient	13–4	-0.03	15%	26 Mar →
anubis	13–9	0.01	26%	24 Mar →
dust2	14–16	0.05	23%	24 Mar →
inferno	5–11	-0.12	50%	15 Feb →
inferno	5–13	0.01	20%	15 Feb →
inferno	13–11	0.09	18%	9 Feb →
dust2	8–13	0.07	12%	25 Jan →
mirage	16–19	0.00	15%	8 Jan →
inferno	7–13	-0.01	18%	2 Jan →
mirage	7–13	0.07	18%	30 Dec →
mirage	13–7	0.03	21%	18 Dec →
nuke	4–13	-0.06	10%	18 Dec →
overpass	8–13	-0.01	8%	18 Dec →
mirage	13–4	0.08	18%	18 Dec →
nuke	7–13	0.01	25%	17 Dec →
mirage	16–14	0.03	11%	17 Dec →
inferno	9–13	-0.01	24%	16 Dec →
dust2	13–6	0.10	24%	15 Dec →
overpass	28–31	0.02	10%	9 Dec →
train	11–13	0.05	23%	9 Dec →
mirage	5–13	-0.08	27%	8 Dec →
mirage	13–11	0.02	25%	8 Dec →
mirage	11–13	0.06	21%	7 Dec →
inferno	11–13	-0.04	27%	7 Dec →
train	16–14	0.02	13%	6 Dec →
overpass	13–11	0.04	14%	6 Dec →
nuke	13–11	-0.02	24%	6 Dec →
nuke	7–13	-0.00	19%	4 Dec →
inferno	13–7	0.05	27%	13 Nov →
overpass	13–9	0.11	22%	13 Nov →
italy	13–10	0.17	18%	13 Nov →
palacio	13–9	0.15	27%	13 Nov →
train	13–8	0.04	30%	12 Nov →
mirage	15–15	0.09	22%	12 Nov →
dust2	13–6	0.05	21%	12 Nov →
train	2–13	0.04	29%	12 Nov →
overpass	2–0	0.13	0%	12 Nov →
inferno	9–5	-0.05	44%	11 Nov →
nuke	9–13	0.17	15%	11 Nov →
dust2	13–5	0.20	15%	11 Nov →
nuke	8–2	0.04	29%	11 Nov →
ancient	6–13	0.03	74%	11 Nov →
overpass	13–9	0.09	36%	11 Nov →
inferno	13–11	0.07	23%	11 Nov →
ancient	0–13	-0.09	20%	11 Nov →
dust2	3–13	0.01	41%	10 Nov →
nuke	13–9	0.08	26%	10 Nov →
ancient	13–5	0.09	20%	10 Nov →
mirage	4–13	0.01	27%	10 Nov →
overpass	13–10	0.12	13%	9 Nov →
inferno	13–5	0.11	23%	9 Nov →
mirage	5–13	0.03	40%	9 Nov →
nuke	2–13	-0.01	27%	9 Nov →
train	13–8	0.08	29%	9 Nov →
overpass	13–7	0.10	13%	8 Nov →
inferno	7–13	-0.09	21%	8 Nov →
ancient	13–10	0.13	15%	8 Nov →
ancient	13–3	0.05	22%	7 Nov →
inferno	13–8	0.06	24%	7 Nov →
mirage	13–8	0.02	18%	7 Nov →
train	9–1	-0.02	17%	7 Nov →
nuke	13–4	0.09	20%	7 Nov →
dust2	13–10	0.13	26%	5 Nov →
overpass	13–4	0.24	34%	5 Nov →
train	13–7	0.14	29%	5 Nov →
mirage	13–2	0.13	28%	5 Nov →
ancient	13–5	0.03	35%	5 Nov →
mirage	13–3	0.05	21%	5 Nov →
overpass	13–4	0.23	36%	4 Nov →
nuke	13–9	0.08	41%	4 Nov →
dust2	13–1	0.22	37%	4 Nov →
dust2	14–16	0.12	20%	3 Nov →
nuke	13–11	0.09	33%	3 Nov →
mirage	13–2	0.10	15%	3 Nov →
train	13–4	0.00	53%	3 Nov →
inferno	13–9	0.02	8%	3 Nov →
ancient	13–9	0.05	18%	2 Nov →
mirage	13–3	0.02	10%	2 Nov →
dust2	13–11	0.15	20%	2 Nov →
nuke	13–7	0.04	23%	2 Nov →
mirage	13–8	0.16	12%	2 Nov →
dust2	13–7	0.23	27%	2 Nov →
nuke	13–7	0.22	27%	1 Nov →