rokkikitara

rokkikitara — CS2 Stats

FI76561198119409554[U:1:159143826]Steam profile ↗✓ No bans

917Tracked matches40%Win rate2021Tracked since
CSDB Rating2.4 LearningSupport
Ladder ranks via Leetify

Rating over time

Premier CS Rating: 18,556 +2,683 28 Nov – 14 Mar · 22 days played
15,87320,858peak 20,85828 Nov14 Mar
21,130Peak Premier in tracked matches
1,309Highest Faceit ELO seen on CSDB
42Days played since 2025-11-23

Faceit ELO

  • At peak — 1,309
  • Next: Level 7 at 1,351 — 42 to go
  • Reached: Level 6 · Level 5 · Level 4 · Level 3

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

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CSDB.GGrokkikitaraPEAK ELO1,309csdb.gg/stats

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Performance scores

Aim14
Positioning44
Utility60

0–100 skill scores via Leetify.

Recent form

COLD48–48–4Last 10048%Win rateLLLLLWLLWL

Last 10 vs previous 10: −40pp win rate · −0.03 avg rating · +2.3pp headshot accuracy · −59ms reaction

Win rate down 40pp 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

  • 0–5 · 0% win rate
  • Avg rating -0.04
  • Avg headshot accuracy 13%
  • Avg reaction 683ms

Last 10

  • 2–8 · 20% win rate
  • Avg rating -0.03
  • Avg headshot accuracy 9%
  • Avg reaction 615ms

Last 20

  • 8–12 · 40% win rate
  • Avg rating -0.02
  • Avg headshot accuracy 8%
  • Avg reaction 644ms

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 (+2.5 against its own average).

Aim1.4
Utility6.0
Positioning4.4
Opening Duels1.6
Clutch2.2

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.

Strengths & areas to improve

Areas to improve

Reaction time. 656ms from enemy-visible to first shot leaves fights decided before they start — warmup routines move this number more than anything else.

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

Aim1.4
Positioning4.4
Utility6.0
Mechanics1.4
Opening Duels1.0
Win Impact1.7

Composite 2.4/10 (Learning), a weighted mean of the bars with a small opposition adjustment (×0.90 for this rank band). Formula versioned (v1) and documented in code.

Trends

Match rating-0.03−0.03
first ⅓ avg -0.00 → last ⅓ avg -0.03
Reaction time627ms−37ms
first ⅓ avg 665ms → last ⅓ avg 627ms
Headshot accuracy7.8%−2.7%
first ⅓ avg 10.5% → last ⅓ avg 7.8%

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

0.09Best match rating · 13–5 · dust2, 10 Mar →
26%Best headshot accuracy · 7–13 · inferno, 31 Mar →
438msFastest reaction time · 11–13 · inferno, 17 Feb →
13–0Biggest win · train, 7 Jan →

Across the last 100 tracked matches.

Highlights

5Longest win streak
L5Current streak
15–9In matches decided by ≤2 rounds
11Overtime games

Map breakdown

mirageBest map · 59% over 17ancientWeakest map · 27% over 26
MapGradePlayedRecordWin rateAvg rating
ancientD267–1927%-0.03
infernoB2111–1052%-0.01
mirageA1710–759%-0.02
overpassA127–558%0.01
anubisA116–555%-0.03
nukeB84–450%-0.03
train—32–167%0.00
dust2—21–150%0.04

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (27% over 26). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

240Matches
50%Win rate
0.89Avg K/D
68.1ADR
30%Headshot %

Clutches & streaks

31%1v1 clutch win
18%1v2 clutch win
5Longest win streak

Recent Faceit resultsLLLWW

MapMatchesWin rateAvg K/DAvg kills
Ancient4947%0.9212.9
Inferno4757%0.8912.8
Anubis4749%0.9313.0
Mirage3661%0.8613.1
Nuke2060%0.9113.4
Dust21828%0.9413.0
Vertigo743%0.8611.9
Train560%0.7011.0

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.

7.9%Headshot accuracy
30.2%Accuracy (enemy spotted)
34.1%Spray accuracy
56.3%Counter-strafing
13.9°Preaim
656msReaction time
34.7%T opening success
33.6%CT opening success
0.56Enemies flashed / flash
4.2%Flash assists
9.79HE damage / grenade
11.51Flashes / 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 13.8968° — 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 7.8964% — below the 15% mark we flag

    Aim Training →
Spend your practice time on Ancient

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

27% win rate across 26 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

MapScoreRatingHS%Date
ancient11–13-0.045%3 Jul →
mirage8–13-0.0510%3 Jul →
anubis9–13-0.027%28 Jun →
ancient7–13-0.0919%11 Apr →
inferno7–130.0126%31 Mar →
overpass13–9-0.025%15 Mar →
ancient0–50.000%14 Mar →
inferno2–13-0.090%13 Mar →
inferno13–11-0.015%13 Mar →
ancient10–13-0.0314%13 Mar →
dust213–50.095%10 Mar →
ancient10–13-0.0413%10 Mar →
anubis8–13-0.033%10 Mar →
mirage16–140.002%9 Mar →
ancient13–110.039%9 Mar →
inferno12–16-0.058%7 Mar →
ancient14–16-0.021%7 Mar →
nuke13–11-0.024%1 Mar →
mirage13–60.028%1 Mar →
inferno13–70.0015%1 Mar →
mirage12–16-0.077%26 Feb →
ancient11–13-0.044%26 Feb →
anubis8–13-0.0811%26 Feb →
ancient9–13-0.0917%25 Feb →
nuke11–13-0.044%25 Feb →
inferno13–11-0.084%25 Feb →
nuke13–6-0.017%20 Feb →
mirage6–13-0.054%20 Feb →
overpass13–70.006%19 Feb →
anubis13–8-0.0514%19 Feb →
mirage9–13-0.077%18 Feb →
inferno13–7-0.029%18 Feb →
ancient15–15-0.036%18 Feb →
overpass7–130.036%17 Feb →
nuke15–15-0.034%17 Feb →
inferno11–13-0.0514%17 Feb →
mirage13–11-0.029%15 Feb →
inferno13–6-0.046%15 Feb →
nuke6–13-0.069%15 Feb →
ancient13–110.019%15 Feb →
anubis13–4-0.045%14 Feb →
mirage13–11-0.039%14 Feb →
dust27–13-0.019%10 Feb →
mirage1–13-0.0917%10 Feb →
ancient5–13-0.052%10 Feb →
overpass13–10-0.073%10 Feb →
anubis4–13-0.104%9 Feb →
ancient5–130.0112%9 Feb →
inferno15–150.059%8 Feb →
anubis13–30.0615%8 Feb →
ancient15–15-0.022%8 Feb →
inferno11–130.018%8 Feb →
mirage8–13-0.074%8 Feb →
nuke6–13-0.0112%8 Feb →
nuke16–12-0.064%8 Feb →
mirage9–130.0310%1 Feb →
inferno13–50.0017%31 Jan →
anubis13–7-0.040%31 Jan →
overpass10–13-0.039%25 Jan →
inferno8–13-0.0517%25 Jan →
ancient13–10-0.0213%24 Jan →
inferno13–11-0.0111%24 Jan →
anubis13–6-0.0317%23 Jan →
ancient13–5-0.046%23 Jan →
anubis8–13-0.0013%21 Jan →
overpass13–50.039%21 Jan →
inferno11–130.0414%21 Jan →
mirage13–80.016%21 Jan →
ancient8–13-0.0618%20 Jan →
ancient3–00.030%20 Jan →
anubis13–110.027%20 Jan →
overpass13–10.0624%18 Jan →
mirage16–140.049%18 Jan →
overpass13–7-0.002%17 Jan →
ancient8–13-0.029%17 Jan →
inferno13–40.078%15 Jan →
overpass11–130.048%11 Jan →
mirage13–6-0.0012%9 Jan →
ancient3–13-0.066%9 Jan →
ancient10–13-0.0010%9 Jan →
inferno5–13-0.0510%9 Jan →
overpass16–14-0.0011%9 Jan →
ancient13–8-0.056%7 Jan →
inferno5–13-0.0723%7 Jan →
train13–00.0413%7 Jan →
nuke13–5-0.0114%7 Jan →
ancient7–13-0.0411%4 Jan →
inferno13–9-0.0219%16 Dec →
ancient13–50.0714%16 Dec →
overpass10–130.0210%6 Dec →
inferno13–80.0613%6 Dec →
overpass10–130.0218%4 Dec →
mirage13–11-0.016%4 Dec →
train8–13-0.035%28 Nov →
inferno13–110.0115%28 Nov →
mirage13–11-0.0510%28 Nov →
mirage13–10-0.0012%28 Nov →
train13–7-0.0110%25 Nov →
ancient3–13-0.063%23 Nov →
ancient11–13-0.044%23 Nov →

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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