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💪Strong Legends!

💪Strong Legends!

A Roblox experience owned by QuickSim Games.

SimulatorENclickerstrength trainingsimulator

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

Welcome to 💪Strong Legends 🎮 Trains Offline 🏋️ Train To Gain Strength 💪 Buy New Weights In The Tool shop! 🏆 Unlock New Stages And Reach The Top! PREMIUM PLAYERS GET: - 💰100% More Coins - ⚔️100% More Damage ❤️ Like the game and join the group for auto-clicker! ✨ Code: release! Next code at 1,000 likes! Tags: Simulator, Lifting Simulator, Strength, Strong, Muscle, Clicking, Clicker, Strong Legends X Simulator, Zones, Rebirth, Weights, Weight,

Last updated by the developer 21 May 2026, 12:35 UTC.

0
playing (measured 10 Aug 2026, 19:07 UTC)
122,232
lifetime visits
83%
liked (44 👍 / 9 👎)
84
favourites (0.7 per 1k visits)
27 December 2023
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 We do not publish numbers we have not measured.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, 19:070first reading

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

Monetization — game passes

Infinite Size
349 R$
Fast Lifter
329 R$
Double Coins
149 R$
Double Strength
149 R$
Double Kills
29 R$
VIP
29 R$
Fast Runner
9 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! · welcome95,100 · 0.00% win
Stage 2 · progression351 · 0.00% win
Stage 5 · progression43 · 0.00% win
Stage 10 · progression2 · 0.00% win
Stage 15 · progression0 · 0.00% win
Stage 20 · progression0 · 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.