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jhnnytsnm

jhnnytsnm — CS2 Stats

US76561198375027470[U:1:414761742]Steam profile ↗✓ No bans

343Tracked matches50%Win rate2020Tracked since
CSDB Rating2.5 Learning
Ladder ranks via Leetify

Performance scores

Aim19
Positioning18
Utility21

0–100 skill scores via Leetify.

Recent form

STEADY36–62–2Last 10036%Win rateWLWWWLLLWW

Last 10 vs previous 10: +30pp win rate · +0.01 avg rating

Player DNA

Aim1.9
Aggression0.4
Utility2.1
Positioning1.8
Opening Duels0.0

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

NiKo

Plays most like NiKo 52% playstyle similarity

Most alike: opening-duel success, utility contribution.

Where you differ: lower opening-fight frequency; 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. 733ms 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

Aim1.9
Positioning1.8
Utility2.1
Mechanics5.2
Opening Duels0.0
Win Impact5.0

Composite 2.5/10 (Learning), 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 rating-0.07−0.00
first ⅓ avg -0.07 → last ⅓ avg -0.07
Reaction time758ms+9ms
first ⅓ avg 749ms → last ⅓ avg 758ms
Headshot accuracy12.5%−2.7%
first ⅓ avg 15.2% → last ⅓ avg 12.5%

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 — inferno 13–8
13–3Biggest win — inferno
4Longest win streak
7–8In matches decided by ≤2 rounds
6Overtime games

Map breakdown

dust2Best map · 71% over 7trainWeakest map · 13% over 8
MapGradePlayedRecordWin rateAvg rating
infernoB3516–1946%-0.06
ancientD174–1324%-0.06
anubisB157–847%-0.06
vertigoD102–820%-0.08
trainD81–713%-0.08
dust2S75–271%-0.02
overpass—41–325%-0.11
nuke—20–20%-0.11
mirage—20–20%-0.07

Across the last 100 tracked matches.

Skill profile

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

12.8%Headshot accuracy
23.3%Accuracy (enemy spotted)
25.2%Spray accuracy
73.4%Counter-strafing
9.9°Preaim
733msReaction time
23.6%T opening success
12.0%CT opening success
0.31Enemies flashed / flash
2.1%Flash assists
7.41HE damage / grenade
3.59Flashes / 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 12.7969% — below the 15% mark we flag

    Aim Training →
  2. Best CS2 Settings

    Slow first shots are as often a setup problem as a reflex one — framerate, sensitivity and crosshair visibility all move this number.

    Reaction time 733.398ms — above the 700ms mark we flag

    Aim Training →
  3. Grenades & Utility

    Most of your flashes are blinding nobody. A handful of reliable pop-flash lineups fixes this faster than anything else.

    Enemies flashed per flash 0.3065 — below the 0.5 mark we flag

    Grenade Lineups →
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.

13% win rate across 8 tracked games — your weakest map with enough games to be worth reading into.

Train callouts & strategy →

Recent matches

MapScoreRatingHS%Date
dust213–6-0.0013%30 Aug →
anubis9–13-0.0718%30 Aug →
anubis13–6-0.085%16 Aug →
dust216–13-0.0616%16 Aug →
ancient13–9-0.079%12 Jul →
ancient10–13-0.0814%12 Jul →
ancient5–13-0.058%9 May →
ancient5–13-0.0421%9 May →
inferno13–5-0.0913%3 May →
inferno13–4-0.075%26 Apr →
inferno8–130.0219%25 Apr →
inferno5–13-0.077%19 Apr →
inferno13–4-0.077%11 Apr →
inferno5–13-0.0215%5 Apr →
overpass6–13-0.0819%26 Oct →
overpass0–9-0.1420%25 Oct →
dust213–5-0.0225%23 Aug →
overpass13–10-0.120%23 Aug →
nuke0–9-0.110%27 Jul →
train4–13-0.0814%26 Jul →
dust213–5-0.0512%26 Jul →
inferno10–13-0.149%20 Jul →
inferno13–7-0.0825%20 Jul →
overpass9–13-0.1038%20 Jul →
mirage11–13-0.095%19 Jul →
ancient13–5-0.0211%19 Jul →
train5–13-0.0613%6 Jul →
ancient8–13-0.0910%6 Jul →
inferno11–5-0.094%4 Jul →
train16–14-0.0813%4 Jul →
anubis13–5-0.047%4 Jul →
anubis5–13-0.087%4 Jul →
ancient15–15-0.0911%4 Jul →
inferno2–13-0.0817%20 May →
inferno3–13-0.074%4 May →
vertigo3–13-0.108%4 May →
ancient8–13-0.059%27 Apr →
mirage9–13-0.0414%27 Apr →
inferno13–3-0.0010%26 Apr →
vertigo13–9-0.039%26 Apr →
vertigo13–11-0.0812%26 Apr →
dust28–13-0.0512%26 Apr →
inferno13–80.1015%26 Apr →
anubis13–8-0.0214%20 Apr →
anubis11–13-0.0521%20 Apr →
train9–13-0.0915%20 Apr →
anubis6–13-0.078%20 Apr →
train10–13-0.084%6 Apr →
inferno13–3-0.099%6 Apr →
ancient13–9-0.0413%30 Mar →
ancient11–13-0.0529%30 Mar →
anubis4–13-0.100%29 Mar →
train9–13-0.0616%29 Mar →
dust213–50.0427%29 Mar →
nuke6–13-0.119%23 Mar →
train10–13-0.089%23 Mar →
inferno13–6-0.0322%23 Mar →
ancient10–13-0.0611%23 Feb →
ancient11–13-0.1016%23 Feb →
train4–13-0.0935%23 Feb →
anubis16–14-0.0413%23 Feb →
ancient8–13-0.113%16 Feb →
anubis8–13-0.117%16 Feb →
ancient11–13-0.117%9 Feb →
ancient16–14-0.059%9 Feb →
inferno13–4-0.038%8 Feb →
anubis13–9-0.0721%8 Feb →
inferno13–110.0018%1 Feb →
dust211–13-0.0321%25 Jan →
inferno11–13-0.1014%12 Jan →
vertigo9–13-0.043%12 Jan →
vertigo7–13-0.1024%12 Jan →
ancient7–13-0.0321%11 Jan →
inferno13–10-0.072%11 Jan →
ancient10–13-0.0611%11 Jan →
anubis13–30.0422%11 Jan →
vertigo11–13-0.0411%11 Jan →
vertigo4–13-0.069%11 Jan →
anubis4–13-0.1024%11 Jan →
anubis13–11-0.0522%5 Jan →
anubis10–13-0.0822%5 Jan →
inferno7–13-0.0810%5 Jan →
inferno12–12-0.0517%4 Jan →
inferno8–13-0.0311%22 Dec →
vertigo6–13-0.1016%21 Dec →
inferno9–130.0114%21 Dec →
vertigo10–13-0.0911%10 Nov →
vertigo13–16-0.1320%10 Nov →
inferno7–1-0.0421%10 Nov →
inferno10–13-0.1014%10 Nov →
inferno7–13-0.068%9 Nov →
inferno13–10-0.0413%2 Nov →
inferno13–11-0.0616%27 Oct →
inferno12–7-0.1018%27 Oct →
inferno4–13-0.1116%13 Oct →
inferno2–13-0.1117%13 Oct →
inferno5–13-0.0614%12 Oct →
inferno9–13-0.1124%6 Oct →
inferno4–13-0.140%6 Oct →
inferno6–13-0.1119%26 Sept →

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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