salamanteri

salamanteri — CS2 Stats

FI76561198284108013[U:1:323842285]Steam profile ↗✓ No bans

1,167Tracked matches46%Win rate2020Tracked since
CSDB Rating4.1 DevelopingPositional Player
Premier CS Rating11,229Blue band · top ~48.4% of ranked players (population est.)
WingmanGold Nova II
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 4 Nov 2026 (yesterday). Ranks are recorded once per day this page is viewed.

No change since then. Play, then come back: the next observation lands here.

Rating over time

Premier CS Rating: 11,229 -1,420 13 Sept – 4 Nov · 15 days played
11,22914,237peak 14,23713 Sept4 Nov
14,934Peak Premier in tracked matches
30Days played since 2026-09-01

Premier CS Rating

  • 3,008 below peak (14,237)
  • -2,530 over 30 days · declining
  • Next: Purple band at 15,000 — 3,771 to go
  • Reached: Blue band · Light Blue band

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

Share this profile

CSDB.GGsalamanteriPREMIER11,229 · Blue bandcsdb.gg/stats

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Performance scores

Aim50
Positioning53
Utility45

0–100 skill scores via Leetify.

Recent form

STEADY47–45–8Last 10047%Win rateLWLWWLWLLW

Last 10 vs previous 10: +10pp win rate · −0.01 avg rating · −1.1pp headshot accuracy · +16ms 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

  • 3–2 · 60% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 15%
  • Avg reaction 498ms

Last 10

  • 5–5 · 50% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 14%
  • Avg reaction 509ms

Last 20

  • 9–9–2 · 45% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 15%
  • Avg reaction 501ms

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: Positional Player — Positioning stands above the rest of this profile (+1.3 against its own average).

Aim5.0
Utility4.5
Positioning5.3
Opening Duels2.2

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

Twistzz

Plays most like Twistzz 78% playstyle similarity

Most alike: positioning profile, utility contribution.

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

Counter-strafing. Only 56% 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

Aim5.0
Positioning5.3
Utility4.5
Mechanics1.4
Opening Duels4.4
Win Impact3.8

Composite 4.1/10 (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 rating-0.03−0.02
first ⅓ avg -0.01 → last ⅓ avg -0.03
Reaction time508ms−41ms
first ⅓ avg 548ms → last ⅓ avg 508ms
Headshot accuracy12.7%−8.4%
first ⅓ avg 21.0% → last ⅓ avg 12.7%

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

0.07Best match rating · 6–13 · dust2, 5 Oct →
44%Best headshot accuracy · 6–13 · inferno, 21 Sept →
328msFastest reaction time · 1–6 · inferno, 29 Sept →
13–1Biggest win · train, 5 Oct →

Across the last 100 tracked matches.

Highlights

5Longest win streak
6–4In matches decided by ≤2 rounds
1Overtime games

Map breakdown

mirageBest map · 63% over 8infernoWeakest map · 33% over 9
MapGradePlayedRecordWin rateAvg rating
dust2C3213–1941%-0.01
ancientA1911–858%-0.01
anubisB168–850%-0.03
infernoD93–633%-0.03
trainB84–450%0.00
mirageA85–363%-0.00
cache—42–250%-0.02
nuke—31–233%-0.02
vertigo—10–10%-0.05

Across the last 100 tracked matches.

Inferno is currently your weakest sufficiently-sampled map (33% over 9). Start with the 6 essential Inferno lineups, review the callouts, then spin up a practice server.

Skill profile

Aggregate performance across tracked matches — stats via Leetify. Percentile context against other CSDB-tracked players arrives as our own benchmark data accumulates.

13.2%Headshot accuracy
33.2%Accuracy (enemy spotted)
38.6%Spray accuracy
56.2%Counter-strafing
10.2°Preaim
510msReaction time
53.8%T opening success
41.3%CT opening success
0.51Enemies flashed / flash
4.1%Flash assists
7.41HE damage / grenade
6.16Flashes / 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. 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 13.2424% — below the 15% mark we flag

    Aim Training →
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.

33% win rate across 9 tracked games — your weakest map with enough games to be worth reading into.

Inferno callouts & strategy →Inferno grenade lineups →

Recent matches

MapScoreRatingHS%Date
cache8–13-0.046%4 Nov →
ancient13–11-0.0311%4 Nov →
train10–130.0118%4 Nov →
dust213–9-0.0120%4 Nov →
anubis13–7-0.0122%4 Nov →
ancient7–13-0.0611%4 Nov →
anubis13–6-0.0112%4 Nov →
dust25–13-0.0715%4 Nov →
ancient7–13-0.0411%4 Nov →
dust213–9-0.0417%2 Nov →
ancient12–12-0.0010%2 Nov →
dust213–60.0224%2 Nov →
inferno8–13-0.0915%29 Oct →
dust212–12-0.0120%28 Oct →
anubis13–7-0.0429%28 Oct →
dust24–130.0613%28 Oct →
ancient13–70.0412%25 Oct →
dust24–13-0.0411%21 Oct →
dust29–13-0.0811%18 Oct →
anubis13–6-0.0811%13 Oct →
cache13–7-0.029%13 Oct →
dust213–40.0412%13 Oct →
ancient7–13-0.0113%12 Oct →
anubis13–10-0.0313%12 Oct →
train13–7-0.0110%12 Oct →
dust211–13-0.073%12 Oct →
dust28–13-0.088%11 Oct →
anubis7–13-0.016%11 Oct →
nuke11–13-0.0122%11 Oct →
anubis11–13-0.083%11 Oct →
mirage6–13-0.038%11 Oct →
ancient13–20.059%10 Oct →
anubis13–4-0.053%10 Oct →
dust210–13-0.0638%10 Oct →
dust213–70.0318%9 Oct →
inferno7–13-0.076%9 Oct →
ancient2–12-0.0615%9 Oct →
dust22–13-0.0917%9 Oct →
anubis5–13-0.0424%8 Oct →
dust28–130.0417%8 Oct →
anubis8–13-0.0412%7 Oct →
train13–50.038%7 Oct →
dust213–11-0.019%7 Oct →
dust212–12-0.038%7 Oct →
ancient6–130.0211%7 Oct →
anubis6–13-0.0322%7 Oct →
inferno7–13-0.010%7 Oct →
ancient16–14-0.0121%7 Oct →
ancient13–30.0518%7 Oct →
dust213–110.0221%5 Oct →
train13–10.0510%5 Oct →
train0–4-0.050%5 Oct →
vertigo0–13-0.050%5 Oct →
mirage13–110.0221%5 Oct →
anubis12–12-0.0115%5 Oct →
dust26–130.0727%5 Oct →
train12–12-0.0311%5 Oct →
ancient13–100.0214%5 Oct →
anubis13–30.0012%5 Oct →
dust29–130.0430%5 Oct →
anubis5–13-0.0915%4 Oct →
dust212–120.0115%4 Oct →
nuke10–13-0.0913%3 Oct →
dust25–130.0635%1 Oct →
inferno1–6-0.0920%29 Sept →
cache9–13-0.0316%27 Sept →
mirage6–13-0.0021%27 Sept →
ancient13–100.0220%27 Sept →
mirage13–9-0.0630%27 Sept →
inferno13–100.0126%26 Sept →
ancient10–13-0.0517%26 Sept →
ancient13–9-0.053%26 Sept →
mirage13–70.0421%26 Sept →
dust24–13-0.0526%24 Sept →
inferno6–13-0.0344%21 Sept →
mirage13–11-0.0118%20 Sept →
cache13–80.0226%20 Sept →
anubis11–130.0321%19 Sept →
dust212–120.0339%19 Sept →
inferno3–130.0218%17 Sept →
dust213–6-0.0419%17 Sept →
ancient13–3-0.0118%16 Sept →
dust25–13-0.0219%16 Sept →
inferno13–30.0515%16 Sept →
dust213–8-0.0219%16 Sept →
dust213–20.0315%16 Sept →
dust213–6-0.0436%16 Sept →
mirage5–130.0425%16 Sept →
dust213–2-0.0120%15 Sept →
nuke13–90.0210%15 Sept →
ancient13–60.0613%15 Sept →
train12–30.0525%15 Sept →
anubis13–9-0.019%15 Sept →
dust26–13-0.107%15 Sept →
inferno13–4-0.0540%15 Sept →
ancient3–13-0.0229%15 Sept →
dust213–7-0.0211%15 Sept →
train12–12-0.0119%14 Sept →
ancient13–8-0.0213%14 Sept →
mirage13–4-0.0223%13 Sept →

Match data via Leetify.

Recent teammates

Milestones

1,000 Matches
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

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