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Webman Simulator

Webman Simulator

A Roblox experience owned by 123 Games Studio.

SimulatorENsimulatormultiversemuscle training

Webman Simulator is a simulator experience created on 2021-11-27. It has recorded 2,967,080 lifetime visits and 15,062 favorites. The game holds a 67% like percentage. Currently, zero users are playing. The description highlights swinging objects, training, and discovering areas.

Description (written by the developer; not a statement by this register)

Webman Simulator and become the best Webman in the multiverse! 🕷️Swing objects across the finish line! ⚡Train to become stronger! 🏔️Discover new areas! Tags: Muscle Legends of Speed Pet Admin Obby Tycoon

Last updated by the developer 16 Aug 2024, 16:30 UTC.

0
playing (measured 10 Aug 2026, 16:08 UTC)
2,967,080
lifetime visits
67%
liked (2,890 👍 / 1,450 👎)
15,062
favourites (5.1 per 1k visits)
27 November 2021
created (per Roblox)

Play on Roblox →

Live players over time

Growth

Growth history is still building. We hold one measurement for this entry. A trend needs at least two, taken days apart — so this will fill in on its own rather than being estimated. We do not publish numbers we have not measured.

Measurement log — every players count we have recorded
Measured (UTC)PlayersChange
10 Aug 2026, 16:080first reading

Concurrent players at each of our measurements (insert-on-change).

Monetization — game passes

x2 Energy
57,143 R$
VIP
17,858 R$
x2 Strength
299 R$
+150 Storage
299 R$
x2 Luck
249 R$
+4 Pets Equipped
229 R$
x3 Egg Opener
199 R$
+2 Pets Equipped
149 R$
x3 Speed
149 R$
+50 Storage
129 R$
x2 Speed
49 R$

Game passes for sale and their prices, measured from the Roblox passes API. Developer products bought inside the game aren’t publicly listable.

Badge funnel

Welcome to Webman Simulator! · welcome1,467,938 · 0.00% win

How many players have ever earned each badge — a proxy for how far players get before they stop. Steep drop-offs mark where a game loses people. Win rate is the past-day share of players who earned it; categories are assigned by a language model from each badge’s own name and description.