THE P90 SLAMMER

THE P90 SLAMMER — CS2 Stats

76561198019079762[U:1:58814034]Steam profile ↗✓ No bans

397Tracked matches43%Win rate2023Tracked since
CSDB Rating5.9 SolidAll-Rounder
Premier CS Rating18,836Top 50% of 209,427 CSDB-tracked playersPurple band · top ~13.1% of ranked players (population est.)
CSDB Leaderboard#99962 of 256932 tracked
Ladder ranks via Leetify

What changed since last observed

CSDB last observed this profile on 22 Oct 2026 (yesterday). Ranks are recorded once per day this page is viewed.

+354Premier CS Rating · 18,482 → 18,836

Rating over time

Premier CS Rating: 18,836 +4,690 3 Jan – 23 Oct · 19 days played
14,04621,002peak 21,0023 Jan23 Oct
21,002Peak Premier in tracked matches
48Days played since 2025-11-01

Premier CS Rating

  • 2,166 below peak (21,002)
  • Next: Pink band at 20,000 — 1,164 to go
  • Reached: Pink band · Purple band · 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.GGTHE P90 SLAMMERPREMIER18,836 · Purple bandcsdb.gg/stats

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Performance scores

Aim63
Positioning66
Utility52

0–100 skill scores via Leetify.

Recent form

HOT49–49–2Last 10049%Win rateWWLWWWLWWL

Last 10 vs previous 10: +50pp win rate · +0.01 avg rating · +0.8pp headshot accuracy · −59ms reaction

Win rate up 50pp 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

  • 4–1 · 80% win rate
  • Avg rating 0.03
  • Avg headshot accuracy 17%
  • Avg reaction 541ms

Last 10

  • 7–3 · 70% win rate
  • Avg rating 0.03
  • Avg headshot accuracy 16%
  • Avg reaction 520ms

Last 20

  • 9–11 · 45% win rate
  • Avg rating 0.03
  • Avg headshot accuracy 16%
  • Avg reaction 550ms

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: All-Rounder — No style dimension stands clear of the others in this profile.

Aim6.3
Utility5.2
Positioning6.6
Opening Duels6.3

Strong CT-side openerLimited utility dependence

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

donk

Plays most like donk 79% 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

Strengths

CT openings. 66% CT opening-duel success — winning the first fight on the defending side is rare and valuable.

Areas to improve

T-side openings. Opening success drops from 66% on CT to 45% on T — the same duels are being taken with worse setups on the attacking side.

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
Positioning6.6
Utility5.2
Mechanics6.6
Opening Duels6.4
Win Impact2.8

Composite 5.9/10 (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 rating0.03−0.01
first ⅓ avg 0.04 → last ⅓ avg 0.03
Reaction time538ms−113ms
first ⅓ avg 651ms → last ⅓ avg 538ms
Headshot accuracy15.3%+1.1%
first ⅓ avg 14.2% → last ⅓ avg 15.3%

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

0.18Best match rating · 13–1 · anubis, 23 Jan →
36%Best headshot accuracy · 6–13 · overpass, 6 Nov →
391msFastest reaction time · 13–11 · anubis, 4 Apr →
13–1Biggest win · inferno, 19 Oct →

Across the last 100 tracked matches.

Highlights

5Longest win streak
W2Current streak
5–7In matches decided by ≤2 rounds
12Overtime games

Map breakdown

anubisBest map · 71% over 14mirageWeakest map · 33% over 9
MapGradePlayedRecordWin rateAvg rating
dust2B2513–1252%0.04
infernoB2110–1148%0.02
ancientA1810–856%0.03
anubisS1410–471%0.04
mirageD93–633%0.02
nuke—40–40%0.04
cache—31–233%0.10
overpass—30–30%-0.04
train—32–167%0.07

Across the last 100 tracked matches.

Mirage is currently your weakest sufficiently-sampled map (33% over 9). Start with the 5 essential Mirage 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.

15.5%Headshot accuracy
30.6%Accuracy (enemy spotted)
41.6%Spray accuracy
79.6%Counter-strafing
11.4°Preaim
548msReaction time
45.1%T opening success
65.8%CT opening success
0.65Enemies flashed / flash
10.5%Flash assists
8.93HE damage / grenade
1.49Flashes / 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.

  1. Grenades & Utility

    You are buying and holding utility rather than using it. Unthrown flashes are wasted money every round.

    Flashes per match 1.4875 — below the 4 mark we flag

    Grenade Lineups →
Spend your practice time on Mirage

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.

Mirage callouts & strategy →Mirage grenade lineups →

Recent matches

MapScoreRatingHS%Date
ancient13–30.0416%23 Oct →
inferno13–5-0.0219%22 Oct →
mirage10–130.0124%22 Oct →
ancient13–50.0117%21 Oct →
dust213–80.1212%21 Oct →
ancient13–60.0110%20 Oct →
dust25–13-0.0724%20 Oct →
ancient13–70.0820%19 Oct →
inferno13–10.0913%19 Oct →
anubis9–130.088%19 Oct →
mirage13–3-0.056%17 Oct →
mirage9–130.1020%17 Oct →
dust25–13-0.0310%17 Oct →
dust210–130.1022%16 Oct →
nuke6–130.0316%16 Oct →
dust27–130.0420%16 Oct →
dust213–40.0215%16 Oct →
cache4–70.0815%8 Oct →
dust24–130.0020%8 Oct →
mirage9–13-0.0310%5 Oct →
nuke11–130.0624%5 Oct →
ancient13–50.0518%2 Oct →
inferno13–110.0113%2 Oct →
dust24–130.0111%1 Oct →
dust211–13-0.0516%11 Aug →
inferno11–130.0512%11 Aug →
anubis13–160.0713%10 Aug →
anubis13–60.0213%3 Aug →
inferno13–9-0.0211%3 Aug →
ancient11–130.0612%2 Aug →
anubis13–70.0411%2 Aug →
ancient8–130.0214%2 Aug →
anubis2–130.0116%1 Aug →
cache13–40.1618%1 Aug →
mirage13–60.056%29 Jul →
ancient14–16-0.0511%29 Jul →
anubis13–8-0.048%29 Jul →
cache6–130.059%23 Jul →
inferno9–130.028%23 Jul →
anubis13–80.028%18 Apr →
mirage8–130.017%18 Apr →
ancient13–70.0310%18 Apr →
anubis6–13-0.037%10 Apr →
dust213–9-0.0214%10 Apr →
dust213–110.0417%4 Apr →
anubis13–11-0.0216%4 Apr →
inferno16–130.1713%4 Apr →
nuke7–130.0414%3 Apr →
inferno0–13-0.0814%3 Apr →
ancient7–130.017%3 Apr →
inferno13–40.0510%2 Apr →
mirage13–80.0818%28 Mar →
inferno11–130.0412%28 Mar →
anubis13–60.0811%28 Mar →
ancient11–130.0315%26 Mar →
dust23–130.0224%26 Mar →
dust216–120.0611%21 Mar →
inferno13–110.0222%21 Mar →
anubis13–90.029%21 Mar →
dust216–130.049%20 Mar →
dust213–100.0617%13 Mar →
inferno9–13-0.0218%13 Mar →
inferno16–130.018%6 Mar →
ancient13–70.0116%27 Feb →
anubis16–130.0716%27 Feb →
ancient5–13-0.0410%14 Feb →
dust213–80.0613%14 Feb →
dust26–130.0117%14 Feb →
anubis13–30.0516%14 Feb →
ancient13–100.0111%13 Feb →
overpass1–13-0.0412%6 Feb →
inferno16–140.0610%6 Feb →
ancient8–130.0218%6 Feb →
dust28–130.1011%23 Jan →
anubis13–10.1810%23 Jan →
dust213–100.0822%17 Jan →
train13–50.0618%17 Jan →
inferno8–130.049%10 Jan →
dust213–7-0.027%3 Jan →
ancient13–10.1711%3 Jan →
dust213–100.018%1 Jan →
inferno13–50.1314%1 Jan →
inferno6–130.0112%20 Dec →
inferno2–13-0.059%13 Dec →
train9–130.0316%13 Dec →
overpass3–13-0.037%12 Dec →
nuke10–130.0313%12 Dec →
inferno5–130.0216%12 Dec →
mirage15–150.0617%6 Dec →
inferno3–13-0.0412%6 Dec →
dust215–150.1610%22 Nov →
inferno12–16-0.0216%19 Nov →
dust25–13-0.0321%19 Nov →
train13–70.1222%18 Nov →
ancient13–40.1015%18 Nov →
ancient8–130.0112%8 Nov →
dust216–120.0514%6 Nov →
overpass6–13-0.0336%6 Nov →
mirage1–13-0.0713%1 Nov →
dust213–50.1613%1 Nov →

Match data via Leetify.

Recent teammates

Milestones

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

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