[SHADOW DRAGON SLAYER] Magic Revelations

A Roblox experience owned by Revelation Games.

[SHADOW DRAGON SLAYER] Magic Revelations is a fighting-pvp experience created on 2017-07-16. The game has accumulated 1,387,836 lifetime visits and 30,023 favorites. Currently, zero users are playing. The title holds an 87% like percentage. The description notes the game is in beta with planned features.

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

GAME IS IN BETA EXPECT TONS OF GLITCHES/BUGS! READ THIS https://devforum.roblox.com/t/magic-revelations/71676 CONTROLS: Left ctrl - Sprint Double tap WSAD - Dodge Q - Combat Code: UjPM37b Lots of things planned for this game, if you enjoy the game so far leave a thumbs/favorite!

Last updated by the developer 7 Aug 2020, 19:45 UTC.

playing (measured 10 Aug 2026, 23:35 UTC)

1,387,836

lifetime visits

87%

liked (6,639 👍 / 986 👎)

30,022

favourites (21.6 per 1k visits)

16 July 2017

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 We do not publish numbers we have not measured.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)	Players	Change
10 Aug 2026, 23:35	0	first reading

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

Monetization — game passes

Third Magic

700 R$

Second Magic

300 R$

2x Daily Reward

150 R$

Ultimate Spells

100 R$

Aura

25 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

Beta Tester · other28,246 · 0.00% win

Alpha Tester · other3,258 · 0.00% win

Pre-Alpha Tester · other1,366 · 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.