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Dankpancake — CS2 Stats

76561198222542920[U:1:262277192]✓ No bans

680Tracked matches54%Win rate2024Tracked since
CSDB Rating3.5 Learning
Ladder ranks via Leetify

Performance scores

Aim25
Positioning39
Utility47

0–100 skill scores via Leetify.

Recent form

STEADY46–50–4Last 10046%Win rateWLLWWWWLLW

Last 10 vs previous 10: +40pp win rate · +0.01 avg rating

Player DNA

Aim2.5
Aggression2.6
Utility4.7
Positioning3.9
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 67% playstyle similarity

Most alike: utility contribution, positioning profile.

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. 623ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

Counter-strafing. Only 69% 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

Aim2.5
Positioning3.9
Utility4.7
Mechanics4.3
Opening Duels0.4
Win Impact6.2

Composite 3.5/10 (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-0.04−0.01
first ⅓ avg -0.02 → last ⅓ avg -0.04
Reaction time618ms−10ms
first ⅓ avg 628ms → last ⅓ avg 618ms
Headshot accuracy8.9%−1.8%
first ⅓ avg 10.7% → last ⅓ avg 8.9%

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

0.13Best rating — cache 13–7
13–1Biggest win — cache
4Longest win streak
6–5In matches decided by ≤2 rounds
7Overtime games

Map breakdown

infernoBest map · 71% over 21anubisWeakest map · 25% over 8
MapGradePlayedRecordWin rateAvg rating
ancientD248–1633%-0.03
infernoS2115–671%-0.02
nukeD186–1233%-0.02
cacheB158–753%-0.02
anubisD82–625%-0.04
overpassA74–357%-0.04
mirageC52–340%-0.03
dust2—10–10%-0.04
train—11–0100%-0.03

Across the last 100 tracked matches.

Anubis is currently your weakest sufficiently-sampled map (25% over 8). Start with the 6 essential Anubis 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.

8.8%Headshot accuracy
27.7%Accuracy (enemy spotted)
31.7%Spray accuracy
69.4%Counter-strafing
12.1°Preaim
623msReaction time
24.7%T opening success
33.3%CT opening success
0.52Enemies flashed / flash
3.8%Flash assists
9.69HE damage / grenade
4.65Flashes / 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

    Your crosshair sits further from where enemies appear than it needs to. Crosshair placement is the cheapest accuracy you can buy.

    Preaim 12.1037° — above the 12° mark we flag

    Aim Training →
  2. 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 8.7903% — below the 15% mark we flag

    Aim Training →
Spend your practice time on Anubis

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

25% win rate across 8 tracked games — your weakest map with enough games to be worth reading into.

Anubis callouts & strategy →Anubis grenade lineups →

Recent matches

MapScoreRatingHS%Date
ancient13–9-0.024%11 Aug →
inferno5–13-0.109%26 Jul →
ancient6–13-0.088%19 Jul →
cache13–7-0.0110%19 Jul →
cache13–10.0216%19 Jul →
mirage13–9-0.028%17 Jul →
inferno13–30.0115%17 Jul →
cache5–13-0.0612%16 Jul →
ancient10–13-0.0711%15 Jul →
inferno13–9-0.037%13 Jul →
cache13–6-0.044%13 Jul →
inferno8–13-0.037%3 Jul →
cache12–12-0.0316%2 Jul →
mirage2–7-0.045%29 Jun →
inferno7–9-0.128%24 Jun →
inferno4–13-0.0718%24 Jun →
ancient9–13-0.045%23 Jun →
nuke4–13-0.058%22 Jun →
ancient13–8-0.052%20 Jun →
cache9–13-0.0415%20 Jun →
cache13–11-0.058%19 Jun →
ancient13–70.017%17 Jun →
cache6–13-0.080%17 Jun →
cache13–8-0.0210%13 Jun →
cache13–6-0.069%13 Jun →
inferno13–90.0018%12 Jun →
cache13–50.034%11 Jun →
cache12–12-0.0715%10 Jun →
ancient9–13-0.083%7 Jun →
overpass13–10-0.055%2 Jun →
inferno9–13-0.045%2 Jun →
cache8–13-0.0311%13 May →
cache13–70.1310%11 May →
cache2–13-0.0612%11 May →
ancient6–13-0.0313%11 Apr →
nuke10–13-0.0710%11 Apr →
inferno13–60.0213%2 Apr →
nuke13–7-0.009%31 Mar →
nuke13–8-0.067%19 Mar →
ancient12–16-0.0511%19 Mar →
nuke13–40.0113%18 Mar →
nuke9–13-0.0613%13 Mar →
ancient11–13-0.005%13 Mar →
inferno13–10-0.0614%13 Mar →
ancient13–7-0.0011%12 Mar →
nuke13–90.1012%12 Mar →
overpass16–14-0.0214%10 Mar →
nuke7–13-0.0311%10 Mar →
ancient13–9-0.0315%9 Mar →
overpass13–11-0.0514%8 Mar →
nuke5–13-0.068%8 Mar →
ancient6–13-0.086%8 Mar →
inferno13–40.005%8 Mar →
anubis9–13-0.068%7 Mar →
overpass9–13-0.0313%7 Mar →
nuke13–60.0413%7 Mar →
inferno13–110.0313%3 Mar →
mirage6–13-0.0217%1 Mar →
inferno13–80.0119%1 Mar →
nuke4–13-0.013%1 Mar →
nuke1–130.0014%1 Mar →
dust24–13-0.0411%25 Feb →
mirage12–4-0.020%25 Feb →
inferno13–9-0.0020%21 Feb →
nuke10–13-0.0510%21 Feb →
ancient9–13-0.0214%20 Feb →
inferno13–60.0210%20 Feb →
inferno13–3-0.017%19 Feb →
ancient13–10.0311%19 Feb →
nuke13–80.018%18 Feb →
mirage9–13-0.0613%13 Feb →
overpass10–13-0.0821%12 Feb →
ancient7–13-0.046%12 Feb →
overpass11–13-0.078%12 Feb →
overpass13–40.016%11 Feb →
ancient9–13-0.068%11 Feb →
ancient7–13-0.0614%11 Feb →
ancient5–13-0.090%11 Feb →
ancient14–16-0.028%7 Feb →
nuke10–13-0.038%7 Feb →
ancient16–140.007%7 Feb →
nuke4–13-0.0517%7 Feb →
inferno13–11-0.0124%3 Feb →
anubis13–80.0213%3 Feb →
ancient2–13-0.079%28 Jan →
anubis5–13-0.056%27 Jan →
anubis15–15-0.016%26 Jan →
inferno16–130.039%25 Jan →
inferno13–4-0.0222%24 Jan →
anubis13–3-0.0613%24 Jan →
anubis6–13-0.0511%19 Jan →
anubis1–5-0.0513%19 Jan →
ancient15–150.0412%14 Jan →
inferno13–60.0111%14 Jan →
nuke8–13-0.0310%16 Dec →
anubis0–10-0.0412%16 Dec →
nuke3–13-0.0917%16 Dec →
train13–6-0.035%15 Dec →
inferno11–130.0013%14 Dec →
ancient13–30.107%14 Dec →

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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