Anthony Zimmer :--D!!

Anthony Zimmer :--D!! — CS2 Stats

76561198119174476[U:1:158908748]Steam profile ↗✓ No bans

2,057Tracked matches43%Win rate2020Tracked since
CSDB Rating6.0 SolidPositional Player
WingmanSupreme Master First Class
Ladder ranks via Leetify

Performance scores

Aim64
Positioning67
Utility66

0–100 skill scores via Leetify.

Recent form

STEADY40–56–4Last 10040%Win rateWLLLWWLLWW

Last 10 vs previous 10: +20pp win rate · −0.04 avg rating · +0.7pp headshot accuracy · +12ms reaction

Win rate up 20pp across the last 10 against the 10 before — more than a 10-match window's normal noise (±20pp). Average match rating moved the other way (−0.04), so results shifted more than performance did.

Last 5 · 10 · 20 matches

Last 5

  • 2–3 · 40% win rate
  • Avg rating 0.02
  • Avg headshot accuracy 24%
  • Avg reaction 619ms

Last 10

  • 5–5 · 50% win rate
  • Avg rating 0.02
  • Avg headshot accuracy 22%
  • Avg reaction 603ms

Last 20

  • 8–10–2 · 40% win rate
  • Avg rating 0.04
  • Avg headshot accuracy 21%
  • Avg reaction 597ms

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: Positional Player — Positioning stands above the rest of this profile (+0.9 against its own average).

Aim6.4
Utility6.6
Positioning6.7
Opening Duels6.0
Clutch4.0

Effective flashes

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

device

Plays most like device 79% playstyle similarity

Most alike: positioning profile, utility contribution.

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. 593ms 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

Aim6.4
Positioning6.7
Utility6.6
Mechanics6.6
Opening Duels5.3
Win Impact2.6

Composite 6.0/10 (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 rating0.02−0.01
first ⅓ avg 0.03 → last ⅓ avg 0.02
Reaction time592ms−19ms
first ⅓ avg 611ms → last ⅓ avg 592ms
Headshot accuracy20.4%−4.2%
first ⅓ avg 24.6% → last ⅓ avg 20.4%

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

0.36Best match rating · 9–1 · inferno, 7 Sept →
42%Best headshot accuracy · 13–2 · inferno, 8 Apr →
359msFastest reaction time · 2–0 · dust2, 2 Jan →
13–2Biggest win · inferno, 8 Apr →

Across the last 100 tracked matches.

Highlights

4Longest win streak
13–8In matches decided by ≤2 rounds
13Overtime games

Map breakdown

dust2Best map · 60% over 25ancientWeakest map · 13% over 15
MapGradePlayedRecordWin rateAvg rating
dust2A2515–1060%0.01
nukeA2313–1057%0.05
infernoD165–1131%0.03
ancientD152–1313%-0.01
trainD103–730%0.01
anubisD62–433%0.04
mirage—30–30%0.04
cache—10–10%0.02
overpass—10–10%0.01

Across the last 100 tracked matches.

Ancient is currently your weakest sufficiently-sampled map (13% over 15). Start with the 6 essential Ancient lineups, review the callouts, then spin up a practice server.

Faceit stats

Combat

1,971Matches
50%Win rate
1.20Avg K/D
90.4ADR
48%Headshot %

Clutches & streaks

40%1v1 clutch win
25%1v2 clutch win
12Longest win streak

Recent Faceit resultsLWLLL

MapMatchesWin rateAvg K/DAvg kills
Dust231555%1.2418.7
Nuke22157%1.3420.3
Ancient15448%1.2718.3
Anubis9938%1.1918.7
Inferno9533%1.1718.3
Train6258%1.3419.1
Mirage3435%1.1418.7
Vertigo1030%1.1016.4

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.

20.6%Headshot accuracy
31.2%Accuracy (enemy spotted)
30.0%Spray accuracy
79.8%Counter-strafing
9.2°Preaim
593msReaction time
48.3%T opening success
54.0%CT opening success
0.72Enemies flashed / flash
9.2%Flash assists
9.56HE damage / grenade
14.17Flashes / match

Recommended for you

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.

13% win rate across 15 tracked games — your weakest map with enough games to be worth reading into.

Ancient callouts & strategy →Ancient grenade lineups →

Recent matches

MapScoreRatingHS%Date
dust216–140.0529%21 Aug →
anubis10–130.0119%8 May →
cache5–130.0229%30 Apr →
anubis11–130.0424%26 Apr →
inferno13–11-0.0218%3 Apr →
dust213–70.0112%9 Mar →
ancient4–13-0.0326%8 Mar →
anubis22–250.0415%4 Mar →
nuke16–130.0920%2 Mar →
nuke13–110.0025%9 Feb →
inferno19–16-0.0215%1 Feb →
train4–130.0026%24 Jan →
nuke12–120.1926%24 Jan →
mirage3–130.0820%24 Jan →
dust21–13-0.0316%24 Jan →
inferno12–120.0821%24 Jan →
anubis13–30.1231%18 Jan →
dust213–60.0312%18 Jan →
nuke10–130.0822%11 Jan →
inferno6–130.0520%6 Jan →
nuke8–130.0022%6 Jan →
nuke16–13-0.0121%5 Jan →
dust213–10-0.0217%4 Jan →
dust22–0-0.060%2 Jan →
train9–130.0324%2 Jan →
nuke13–90.0517%29 Dec →
nuke11–13-0.0317%14 Dec →
train8–130.0326%14 Dec →
ancient13–16-0.0223%13 Dec →
train1–13-0.0812%13 Dec →
overpass3–40.0118%1 Dec →
nuke13–100.0029%1 Dec →
ancient8–13-0.0520%25 Nov →
dust213–60.0211%18 Nov →
dust213–70.0221%16 Nov →
inferno4–13-0.0716%15 Nov →
dust27–13-0.0328%15 Nov →
inferno3–9-0.0024%30 Oct →
inferno9–60.1711%30 Oct →
nuke13–11-0.0213%20 Oct →
train7–13-0.0211%8 Oct →
dust213–100.0224%7 Oct →
ancient5–130.0613%7 Oct →
inferno5–12-0.0332%5 Oct →
inferno14–160.0129%4 Oct →
dust213–110.1027%4 Oct →
nuke8–130.0519%24 Sept →
inferno17–190.0524%12 Sept →
nuke13–60.0014%12 Sept →
inferno6–9-0.1031%7 Sept →
nuke9–60.2731%7 Sept →
inferno9–10.3622%7 Sept →
nuke13–70.0827%31 Aug →
ancient4–130.0126%30 Aug →
dust27–20.0228%30 Aug →
dust27–13-0.0313%30 Aug →
train8–13-0.0222%20 Aug →
ancient2–13-0.0419%19 Aug →
ancient9–13-0.0115%12 Aug →
dust219–220.0523%10 Aug →
nuke16–19-0.0118%5 Aug →
inferno1–13-0.0414%3 Aug →
dust23–13-0.0425%3 Aug →
dust212–120.0129%11 Jun →
dust24–130.0618%29 May →
dust25–00.0225%29 May →
inferno7–130.0017%8 May →
train13–110.0528%22 Apr →
nuke9–13-0.0124%17 Apr →
ancient7–13-0.0016%9 Apr →
anubis13–90.0428%8 Apr →
inferno13–20.0842%8 Apr →
ancient2–13-0.0918%8 Apr →
anubis8–130.0216%30 Mar →
nuke13–110.0921%27 Mar →
train13–50.0822%23 Mar →
dust213–110.0642%23 Mar →
dust213–100.0316%22 Mar →
ancient7–13-0.0423%16 Mar →
dust213–9-0.0114%16 Mar →
mirage5–130.0122%13 Mar →
ancient14–16-0.0321%9 Mar →
nuke4–130.0424%9 Mar →
nuke13–110.0826%5 Mar →
ancient13–80.0530%4 Mar →
mirage12–120.0425%27 Feb →
nuke16–140.0531%25 Feb →
ancient9–130.1039%18 Feb →
inferno8–130.0131%16 Feb →
dust213–150.0140%1 Feb →
train10–130.0423%28 Jan →
train13–10-0.0319%25 Jan →
ancient13–70.0314%22 Jan →
ancient10–13-0.0115%19 Jan →
nuke11–130.0618%17 Jan →
dust213–11-0.0118%17 Jan →
dust28–130.0924%30 Dec →
dust27–13-0.0221%18 Dec →
nuke8–130.0731%13 Dec →
nuke13–110.0829%12 Dec →

Match data via Leetify.

Recent teammates

Milestones

2K ELO1,000 Matches
Compare this player with someone →Inventory value for this account →Where does this rating sit? Premier rank tiers →

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