NorFaiR

NorFaiR — CS2 Stats

76561197966282833[U:1:6017105]Steam profile ↗✓ No bans

511Tracked matches55%Win rate2025Tracked since
CSDB Rating5.2 DevelopingSupport
FaceitLevel 4Top 81.0% of ranked FACEIT players
WingmanGold Nova III
Ladder ranks via Leetify

Rating over time

Premier CS Rating: 14,684 -316 16 May – 6 Jul · 11 days played
12,90015,000peak 15,00016 May6 Jul
15,000Peak Premier in tracked matches
43Days played since 2025-05-15

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

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CSDB.GGNorFaiRFACEITLevel 4STANDINGTop 81.0% of rankedcsdb.gg/stats

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Performance scores

Aim50
Positioning45
Utility62

0–100 skill scores via Leetify.

Recent form

COLD55–36–9Last 10055%Win rateWLLLWLLLLL

Last 10 vs previous 10: −40pp win rate · +0.01 avg rating · −6.6pp headshot accuracy · −20ms reaction

Win rate down 40pp 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 (+0.01), so results shifted more than performance did.

Last 5 · 10 · 20 matches

Last 5

  • 2–3 · 40% win rate
  • Avg rating 0.06
  • Avg headshot accuracy 21%
  • Avg reaction 613ms

Last 10

  • 2–8 · 20% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 21%
  • Avg reaction 674ms

Last 20

  • 8–12 · 40% win rate
  • Avg rating -0.00
  • Avg headshot accuracy 24%
  • Avg reaction 683ms

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 (+2.7 against its own average).

Aim5.0
Utility6.2
Positioning4.5
Opening Duels3.5
Clutch0.0

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

device

Plays most like device 72% 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

Reaction time. 646ms 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

Aim5.0
Positioning4.5
Utility6.2
Mechanics6.3
Opening Duels3.0
Win Impact6.7

Composite 5.2/10 (Developing), 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 rating0.01−0.02
first ⅓ avg 0.02 → last ⅓ avg 0.01
Reaction time646ms+65ms
first ⅓ avg 581ms → last ⅓ avg 646ms
Headshot accuracy21.4%+2.8%
first ⅓ avg 18.6% → last ⅓ avg 21.4%

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

0.17Best match rating · 9–13 · ancient, 30 May →
36%Best headshot accuracy · 10–13 · train, 23 Jul →
383msFastest reaction time · 13–6 · ancient, 18 May →
13–0Biggest win · anubis, 7 Jul →

Across the last 100 tracked matches.

Highlights

7Longest win streak
6–2In matches decided by ≤2 rounds
3Overtime games

Map breakdown

anubisBest map · 75% over 8trainWeakest map · 30% over 10
MapGradePlayedRecordWin rateAvg rating
ancientA148–657%0.02
infernoB136–746%-0.00
dust2S139–469%0.02
nukeB115–645%0.02
trainD103–730%-0.00
anubisS86–275%0.03
mirageS75–271%0.01
agencyC52–340%0.05
overpass—41–325%-0.01
vertigo—44–0100%0.04
jura—44–0100%0.08
grail—41–325%0.04
italy—21–150%0.01
office—10–10%0.05

Across the last 100 tracked matches.

Faceit stats

Combat

8Matches
38%Win rate
1.08Avg K/D
60.6ADR
45%Headshot %

Clutches & streaks

0%1v1 clutch win
33%1v2 clutch win
2Longest win streak

Recent Faceit resultsLWLLL

MapMatchesWin rateAvg K/DAvg kills
Dust2250%1.2613.0
Anubis20%0.9215.5
Nuke10%0.6213.0

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.

21.9%Headshot accuracy
27.8%Accuracy (enemy spotted)
30.6%Spray accuracy
78.5%Counter-strafing
10.7°Preaim
646msReaction time
38.2%T opening success
46.2%CT opening success
0.64Enemies flashed / flash
6.8%Flash assists
12.12HE damage / grenade
7.12Flashes / 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.

  1. 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 38.1546% — below the 40% mark we flag

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.

30% win rate across 10 tracked games — your weakest map with enough games to be worth reading into.

Train callouts & strategy →

Recent matches

MapScoreRatingHS%Date
nuke13–100.1024%7 Aug →
agency6–130.0428%7 Aug →
agency4–130.1218%7 Aug →
ancient9–13-0.0122%6 Aug →
nuke13–50.0212%5 Aug →
ancient6–13-0.0722%5 Aug →
train7–13-0.0524%4 Aug →
mirage8–13-0.129%4 Aug →
overpass10–13-0.0023%1 Aug →
nuke9–130.0128%31 Jul →
ancient13–4-0.0229%29 Jul →
inferno8–13-0.0332%28 Jul →
ancient13–7-0.0529%25 Jul →
inferno13–110.0228%24 Jul →
nuke7–13-0.0426%24 Jul →
dust213–9-0.0125%24 Jul →
train10–130.0436%23 Jul →
ancient13–70.0128%22 Jul →
inferno13–60.0316%22 Jul →
dust25–13-0.0327%22 Jul →
train8–130.0028%11 Jul →
train13–110.0119%10 Jul →
overpass12–12-0.0216%10 Jul →
ancient13–90.0116%9 Jul →
anubis13–70.0414%9 Jul →
ancient13–100.0119%7 Jul →
train13–60.0015%7 Jul →
anubis13–00.108%7 Jul →
dust216–14-0.0215%6 Jul →
dust213–70.0016%5 Jul →
mirage12–12-0.0419%5 Jul →
nuke13–30.1123%2 Jul →
train8–13-0.039%2 Jul →
mirage13–10-0.0826%2 Jul →
ancient13–20.0319%1 Jul →
overpass5–130.0318%1 Jul →
dust213–00.0512%24 Jun →
vertigo13–80.0226%23 Jun →
anubis13–11-0.0319%23 Jun →
train4–13-0.0929%21 Jun →
dust28–13-0.0521%21 Jun →
anubis13–100.0310%19 Jun →
dust213–40.1027%19 Jun →
mirage13–90.038%19 Jun →
jura13–70.1135%16 Jun →
vertigo13–60.0421%16 Jun →
anubis13–20.0521%15 Jun →
dust22–130.0313%15 Jun →
ancient10–130.1624%15 Jun →
agency13–30.1122%15 Jun →
nuke4–130.0320%15 Jun →
grail3–130.0016%10 Jun →
inferno0–13-0.1125%10 Jun →
grail12–120.1116%10 Jun →
nuke13–110.0716%10 Jun →
inferno15–15-0.0318%9 Jun →
nuke15–150.0215%9 Jun →
ancient13–6-0.058%9 Jun →
train12–120.0819%8 Jun →
jura13–100.1322%8 Jun →
nuke13–70.0419%7 Jun →
inferno13–20.1226%7 Jun →
dust211–13-0.1117%7 Jun →
mirage12–10.0329%7 Jun →
inferno13–30.0113%6 Jun →
dust213–110.1029%6 Jun →
mirage13–30.1723%5 Jun →
dust213–10.0110%5 Jun →
inferno5–13-0.0610%1 Jun →
train13–10-0.0127%1 Jun →
nuke9–13-0.052%31 May →
dust213–30.1125%31 May →
inferno13–10-0.023%31 May →
ancient9–130.1721%30 May →
vertigo13–30.0630%30 May →
anubis7–130.0722%30 May →
italy13–90.0327%28 May →
vertigo13–50.0418%28 May →
agency13–7-0.0216%28 May →
mirage13–50.1025%28 May →
jura13–60.0923%28 May →
agency5–130.0228%28 May →
overpass13–8-0.0425%28 May →
ancient8–130.059%28 May →
dust213–50.1219%28 May →
grail13–100.0521%28 May →
anubis6–13-0.0319%27 May →
inferno9–13-0.0522%25 May →
ancient7–13-0.0227%25 May →
anubis13–8-0.014%25 May →
inferno13–50.0420%25 May →
train7–130.0222%25 May →
nuke5–13-0.0610%20 May →
ancient13–60.0418%18 May →
jura13–80.0016%18 May →
office12–120.0517%17 May →
italy12–12-0.0125%17 May →
inferno11–13-0.0115%17 May →
inferno8–130.0227%16 May →
grail12–120.0110%15 May →

Match data via Leetify.

Recent teammates

Milestones

500 Matches
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

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