Dog n' Croc

Dog n' Croc — CS2 Stats

76561198069366959[U:1:109101231]Steam profile ↗✓ No bans

615Tracked matches32%Win rate2023Tracked since
625Hours in CS
CSDB Rating3.0 LearningSupport
FaceitLevel 4Top 81.0% of ranked FACEIT players
WingmanGold Nova Master
Ladder ranks via Leetify

How this compares with the same rank

Median values for Level 4 among CSDB-tracked players (n=14,630), from Valve's own lifetime stats. Aim, positioning and utility scores are deliberately not benchmarked here — those are a third-party provider's derived metrics, which CSDB does not store.

MetricThis playerLevel 4 medianLevel 5 medianvs Level 5
Headshot rate48.1%42.7%43.8%above
Shot accuracy15.1%11.2%12.0%above
Kill/death ratio1.040.991.02above
Match win rate44.3%43.9%44.7%meets

Across every metric we can compare, this profile already matches the typical Level 5 player. Rank still comes from winning matches — this is a performance comparison, not a prediction.

Share this profile

CSDB.GGDog n' CrocFACEITLevel 4STANDINGTop 81.0% of rankedcsdb.gg/stats

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Performance scores

Aim33
Positioning29
Utility33

0–100 skill scores via Leetify.

Recent form

COLD42–47–11Last 10042%Win rateLTLTTLLLWL

Last 10 vs previous 10: −10pp win rate · −0.01 avg rating · +3.3pp headshot accuracy · −30ms 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

  • 0–2–3 · 0% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 24%
  • Avg reaction 659ms

Last 10

  • 1–6–3 · 10% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 21%
  • Avg reaction 665ms

Last 20

  • 3–13–4 · 15% win rate
  • Avg rating -0.05
  • Avg headshot accuracy 20%
  • Avg reaction 680ms

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 (+1.2 against its own average).

Aim3.3
Utility3.3
Positioning2.9
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.

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 80% playstyle similarity

Most alike: utility contribution, positioning profile.

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

Reaction time. 687ms 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

Aim3.3
Positioning2.9
Utility3.3
Mechanics6.1
Opening Duels0.0
Win Impact0.0

Composite 3.0/10 (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-0.05−0.02
first ⅓ avg -0.03 → last ⅓ avg -0.05
Reaction time701ms+122ms
first ⅓ avg 579ms → last ⅓ avg 701ms
Headshot accuracy19.4%+2.0%
first ⅓ avg 17.4% → last ⅓ avg 19.4%

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

0.11Best match rating · 13–3 · italy, 6 Oct →
50%Best headshot accuracy · 1–13 · train, 21 Jun →
344msFastest reaction time · 0–4 · overpass, 3 Oct →
13–2Biggest win · basalt, 14 Mar →

Across the last 100 tracked matches.

Highlights

4Longest win streak
8–3In matches decided by ≤2 rounds
2Overtime games

Map breakdown

infernoBest map · 67% over 6dust2Weakest map · 0% over 5
MapGradePlayedRecordWin rateAvg rating
officeC156–940%-0.04
overpassC94–544%-0.02
anubisC83–538%-0.05
trainC83–538%-0.03
vertigoB84–450%-0.04
theraB84–450%-0.01
mirageD71–614%-0.04
italyA74–357%-0.00
millsA74–357%-0.03
infernoS64–267%-0.02
dust2D50–50%-0.06
nuke—33–0100%-0.01
edin—30–30%-0.04
cache—10–10%-0.06
grail—10–10%-0.06
jura—11–0100%-0.03
agency—10–10%-0.06
basalt—11–0100%-0.07
ancient—10–10%-0.06

Across the last 100 tracked matches.

Dust 2 is currently your weakest sufficiently-sampled map (0% over 5). Start with the 6 essential Dust 2 lineups, review the callouts, then spin up a practice server.

Lifetime stats

29,219Lifetime kills
1.04K/D · Top 50% of Level 4 players
1,801Matches
44.3%Match win rate · Top 50% of Level 4 players
48.1%Headshot % · Top 50% of Level 4 players
15.1%Shot accuracy · Top 50% of Level 4 players
3,294MVPs
625Hours (in match)
1,866Bombs planted
248Bombs defused

Most-used weapons

Lifetime map wins

3,514dust2
1,911inferno
640vertigo
431nuke
397office
159cbble
142train
118italy

Lifetime totals via Steam — visible because this profile's game details are public. Spans CS:GO and CS2.

Faceit stats

Combat

9Matches
56%Win rate
0.83Avg K/D
—ADR
51%Headshot %

Clutches & streaks

—1v1 clutch win
—1v2 clutch win
2Longest win streak

Recent Faceit resultsWLLLW

MapMatchesWin rateAvg K/DAvg kills
Ancient3100%0.9813.7
Mirage250%0.719.5
Inferno20%0.7812.0
Vertigo10%0.569.0
Anubis1100%1.0015.0

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.

19.1%Headshot accuracy
25.2%Accuracy (enemy spotted)
29.6%Spray accuracy
77.4%Counter-strafing
11.8°Preaim
687msReaction time
20.7%T opening success
25.8%CT opening success
0.50Enemies flashed / flash
1.5%Flash assists
9.41HE damage / grenade
11.30Flashes / 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

    You are losing most of the first duels you take on T side, which is usually a peeking and spacing problem, not aim.

    T opening duels 20.6509% — below the 40% mark we flag

Spend your practice time on Dust 2

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

0% win rate across 5 tracked games — your weakest map with enough games to be worth reading into.

Dust 2 callouts & strategy →Dust 2 grenade lineups →

Recent matches

MapScoreRatingHS%Date
anubis10–13-0.080%21 Jun →
mirage12–12-0.0433%21 Jun →
train1–13-0.0850%21 Jun →
overpass12–12-0.0220%7 Jun →
inferno12–12-0.0316%7 Jun →
cache6–13-0.0614%7 Jun →
vertigo1–13-0.099%7 Jun →
mirage10–13-0.0122%29 Aug →
vertigo13–6-0.0830%29 Aug →
grail7–13-0.0620%29 Aug →
vertigo3–13-0.0422%9 Aug →
jura13–9-0.0317%9 Aug →
agency3–13-0.0624%9 Aug →
office10–130.0023%6 Apr →
train8–13-0.0613%6 Apr →
mirage4–13-0.0633%6 Apr →
overpass13–8-0.0520%6 Apr →
mirage5–13-0.0512%16 Mar →
vertigo1–13-0.0613%16 Mar →
italy12–12-0.064%16 Mar →
anubis5–13-0.0715%16 Mar →
overpass6–13-0.0923%16 Mar →
mirage13–8-0.0621%15 Mar →
train13–8-0.0016%15 Mar →
nuke13–100.0117%15 Mar →
basalt13–2-0.078%14 Mar →
dust212–12-0.0823%14 Mar →
vertigo4–13-0.0327%9 Mar →
office13–8-0.0120%9 Mar →
anubis6–13-0.0514%9 Mar →
dust27–13-0.0821%8 Feb →
dust215–15-0.0420%31 Jan →
anubis10–13-0.0916%31 Jan →
anubis13–90.0015%31 Jan →
nuke13–8-0.0718%31 Jan →
inferno16–140.0115%31 Jan →
train10–13-0.0221%31 Jan →
edin12–12-0.0120%25 Jan →
office12–12-0.0133%25 Jan →
dust23–13-0.0716%24 Jan →
anubis12–120.0113%24 Jan →
vertigo13–60.0123%24 Jan →
overpass9–13-0.0924%24 Jan →
train13–100.0241%24 Jan →
edin2–13-0.0919%24 Jan →
anubis13–11-0.0417%24 Jan →
edin6–13-0.0323%18 Jan →
italy5–12-0.0211%18 Jan →
train13–110.0314%18 Jan →
train0–5-0.080%18 Jan →
anubis13–7-0.0918%18 Jan →
vertigo13–10-0.0226%18 Jan →
dust24–13-0.059%12 Jan →
inferno13–7-0.0621%12 Jan →
office7–13-0.0120%12 Jan →
mirage12–12-0.0241%12 Jan →
overpass13–50.0918%11 Jan →
vertigo13–11-0.0215%11 Jan →
train11–13-0.0517%11 Jan →
office8–13-0.0215%11 Jan →
mills13–30.0222%8 Oct →
office8–13-0.0224%8 Oct →
italy13–11-0.0725%6 Oct →
mills13–30.0519%6 Oct →
italy13–30.1123%6 Oct →
thera13–80.0232%6 Oct →
ancient5–13-0.0625%6 Oct →
overpass13–60.0122%5 Oct →
italy13–70.0415%5 Oct →
nuke13–80.0416%5 Oct →
thera8–13-0.068%5 Oct →
office13–11-0.088%5 Oct →
overpass13–50.0216%5 Oct →
thera7–13-0.0112%3 Oct →
office13–9-0.030%3 Oct →
mills13–11-0.0521%3 Oct →
office5–13-0.1111%3 Oct →
thera12–12-0.0321%3 Oct →
mills11–130.0423%3 Oct →
italy7–13-0.1020%3 Oct →
overpass0–4-0.020%3 Oct →
mills2–13-0.1113%3 Oct →
thera5–13-0.0314%3 Oct →
office6–13-0.0623%3 Oct →
italy13–60.0924%3 Oct →
mills10–13-0.0919%3 Oct →
overpass2–13-0.0318%3 Oct →
inferno13–11-0.0817%3 Oct →
mirage9–13-0.0412%2 Oct →
inferno13–8-0.0118%2 Oct →
thera13–50.0218%28 Sept →
office6–2-0.0025%28 Sept →
office5–13-0.1012%28 Sept →
mills13–9-0.0517%28 Sept →
inferno11–130.0229%27 Sept →
thera13–6-0.0530%27 Sept →
office7–13-0.0831%27 Sept →
office13–90.0116%26 Sept →
thera13–80.0113%25 Sept →
office13–10-0.0233%25 Sept →

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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