Tappo

Tappo — CS2 Stats

GB76561198038969470[U:1:78703742]Steam profile ↗✓ No bans

313Tracked matches45%Win rate2021Tracked since
CSDB Rating5.4 DevelopingPositional Player
CSDB Leaderboard#88662 of 259202 tracked
FaceitLevel 7Top 42.3% of ranked FACEIT players
WingmanMaster Guardian Elite
Ladder ranks via Leetify

Rating over time

Premier CS Rating: 19,831 +2,344 13 Oct – 23 Jan · 38 days played
17,25120,506peak 20,50613 Oct23 Jan
20,738Peak Premier in tracked matches
57Days played since 2024-10-13

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

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CSDB.GGTappoFACEITLevel 7STANDINGTop 42.3% of rankedcsdb.gg/stats

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Performance scores

Aim71
Positioning56
Utility20

0–100 skill scores via Leetify.

Recent form

STEADY51–45–4Last 10051%Win rateLLLWLWWLLW

Last 10 vs previous 10: 0pp win rate · +0.04 avg rating · +6.1pp headshot accuracy · +34ms reaction

Win rate 0pp 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

  • 1–4 · 20% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 22%
  • Avg reaction 602ms

Last 10

  • 4–6 · 40% win rate
  • Avg rating 0.02
  • Avg headshot accuracy 24%
  • Avg reaction 595ms

Last 20

  • 8–11–1 · 40% win rate
  • Avg rating 0.01
  • Avg headshot accuracy 21%
  • Avg reaction 578ms

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 (+2.9 against its own average).

Aim7.1
Utility2.0
Positioning5.6
Opening Duels3.0
Clutch0.0

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

donk

Plays most like donk 87% playstyle similarity

Most alike: positioning profile, aim profile.

Where you differ: lower utility contribution; lower opening-duel success.

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

Utility. Utility contribution runs far behind the mechanical game — the cheapest rating gain on this profile is thrown, not aimed.

Reaction time. 571ms 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

Aim7.1
Positioning5.6
Utility2.0
Mechanics8.2
Opening Duels2.6
Win Impact3.3

Composite 5.4/10 (Developing), 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-0.00+0.00
first ⅓ avg -0.00 → last ⅓ avg -0.00
Reaction time574ms−1ms
first ⅓ avg 575ms → last ⅓ avg 574ms
Headshot accuracy20.7%−1.7%
first ⅓ avg 22.4% → last ⅓ avg 20.7%

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

0.23Best match rating · 13–1 · inferno, 18 Jan →
48%Best headshot accuracy · 13–5 · vertigo, 8 Dec →
438msFastest reaction time · 6–13 · inferno, 22 Jan →
13–1Biggest win · inferno, 18 Jan →

Across the last 100 tracked matches.

Highlights

7Longest win streak
L3Current streak
11–12In matches decided by ≤2 rounds
13Overtime games

Map breakdown

vertigoBest map · 86% over 7trainWeakest map · 20% over 5
MapGradePlayedRecordWin rateAvg rating
anubisB2713–1448%0.01
ancientB1910–953%0.00
nukeS1712–571%0.03
dust2C135–838%-0.01
infernoD82–625%0.03
vertigoS76–186%-0.01
trainD51–420%-0.02
mirage—31–233%-0.01
overpass—11–0100%0.06

Across the last 100 tracked matches.

Faceit stats

Combat

58Matches
60%Win rate
1.50Avg K/D
83.7ADR
46%Headshot %

Clutches & streaks

17%1v1 clutch win
17%1v2 clutch win
5Longest win streak

Recent Faceit resultsWWWLW

MapMatchesWin rateAvg K/DAvg kills
Anubis2100%2.7423.0
Dust2250%0.7610.5
Nuke2100%1.3417.5
Ancient10%0.539.0

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.

20.4%Headshot accuracy
31.6%Accuracy (enemy spotted)
35.8%Spray accuracy
86.9%Counter-strafing
10.9°Preaim
571msReaction time
34.7%T opening success
46.5%CT opening success
0.66Enemies flashed / flash
6.2%Flash assists
8.57HE damage / grenade
9.74Flashes / 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 34.6586% — below the 40% mark we flag

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.

20% win rate across 5 tracked games — your weakest map with enough games to be worth reading into.

Train callouts & strategy →

Recent matches

MapScoreRatingHS%Date
anubis8–130.0326%26 Jul →
nuke8–13-0.026%17 Sept →
anubis9–100.0126%11 Sept →
nuke13–90.0530%11 Sept →
inferno4–13-0.0323%27 Mar →
overpass9–20.0619%25 Mar →
inferno9–40.1844%25 Mar →
dust214–16-0.0120%23 Mar →
train3–13-0.0620%12 Mar →
dust216–130.0329%24 Feb →
anubis14–160.0216%20 Feb →
nuke16–120.0422%15 Feb →
nuke8–80.0217%15 Feb →
inferno2–9-0.0819%15 Feb →
anubis6–13-0.0522%11 Feb →
anubis13–11-0.0324%9 Feb →
ancient16–14-0.0114%9 Feb →
anubis13–50.0012%9 Feb →
train2–13-0.0520%8 Feb →
train9–130.0016%4 Feb →
dust211–13-0.0621%3 Feb →
anubis13–6-0.0217%2 Feb →
ancient15–15-0.0225%23 Jan →
inferno6–13-0.1116%22 Jan →
vertigo13–11-0.0414%22 Jan →
dust216–140.0123%21 Jan →
dust211–130.0422%20 Jan →
inferno10–13-0.0322%20 Jan →
ancient13–50.0718%19 Jan →
inferno12–160.0415%19 Jan →
vertigo13–90.0419%19 Jan →
ancient13–11-0.0122%18 Jan →
anubis7–13-0.0325%18 Jan →
inferno13–10.2326%18 Jan →
anubis9–13-0.0014%14 Jan →
dust213–8-0.0816%12 Jan →
ancient8–13-0.0119%12 Jan →
nuke13–110.0825%12 Jan →
anubis9–130.0015%5 Jan →
anubis12–16-0.0515%3 Jan →
anubis14–16-0.0119%3 Jan →
anubis13–60.0417%2 Jan →
nuke13–50.0612%1 Jan →
ancient13–60.028%1 Jan →
anubis15–150.0221%31 Dec →
nuke13–10-0.0410%29 Dec →
ancient13–50.1331%29 Dec →
anubis13–5-0.0114%29 Dec →
nuke13–40.0622%29 Dec →
anubis8–13-0.0512%29 Dec →
nuke13–60.1518%29 Dec →
nuke11–13-0.0415%29 Dec →
vertigo13–90.0711%29 Dec →
ancient11–13-0.0115%29 Dec →
nuke13–8-0.0222%28 Dec →
dust23–130.0220%28 Dec →
anubis13–80.0717%28 Dec →
anubis13–70.0529%24 Dec →
vertigo13–80.0120%24 Dec →
anubis8–130.0220%22 Dec →
ancient13–70.0322%20 Dec →
nuke13–4-0.0126%20 Dec →
mirage10–130.0318%19 Dec →
dust29–130.0027%19 Dec →
inferno15–150.0120%17 Dec →
ancient3–13-0.0026%16 Dec →
ancient5–13-0.0516%16 Dec →
nuke11–13-0.0124%14 Dec →
mirage13–11-0.0022%14 Dec →
ancient4–13-0.0717%10 Dec →
dust22–12-0.0624%9 Dec →
ancient14–160.0412%8 Dec →
vertigo13–50.0248%8 Dec →
ancient5–13-0.0424%7 Dec →
anubis13–70.0416%7 Dec →
vertigo13–80.0521%7 Dec →
anubis13–80.0534%7 Dec →
ancient13–11-0.039%7 Dec →
nuke13–50.1039%6 Dec →
anubis13–110.0420%4 Dec →
nuke13–50.0022%4 Dec →
ancient5–13-0.0110%2 Dec →
anubis13–70.0224%2 Dec →
dust213–110.0221%1 Dec →
anubis13–10-0.0225%30 Nov →
anubis10–13-0.0322%30 Nov →
anubis11–130.0231%28 Nov →
ancient13–80.0614%28 Nov →
dust213–11-0.0316%17 Nov →
train13–90.0220%17 Nov →
train5–13-0.0329%14 Nov →
nuke13–80.0220%10 Nov →
ancient13–6-0.0425%9 Nov →
dust26–130.0236%29 Oct →
mirage1–13-0.0522%29 Oct →
ancient13–8-0.0421%20 Oct →
dust210–130.0019%16 Oct →
vertigo5–9-0.2015%14 Oct →
anubis13–50.0219%14 Oct →
nuke11–130.0819%13 Oct →

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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