Drone Racing League Zooms Into Metaverse, Bringing ‘Play to Earn’ to Algorand
Competitive drone racing is joining the play-to-earn party.
Drone Racing League (DRL) and Web 3 game developer Playground Games have tapped Algorand’s blockchain to create the first play-to-earn drone game in the metaverse, announced by the league on Wednesday.
The details for the game itself have yet to be determined – a representative from DRL told CoinDesk it will look to its community to determine what style of gaming they’d most enjoy. Play-to-earn, or GameFi, refers to an emerging subsector of the crypto world where gamers are rewarded with digital assets that carry value beyond the confines of a particular title, as is often the case in mainstream gaming.
While the details are still to be worked out, the league said “players will race DRL drones” (presumably digital ones) for cryptocurrency and NFTs on Algorand.
DRL’s involvement in blockchain is on par with its already hybrid and experimental slate of competition, which includes real-life races with physical drones as well as virtual esports matches. The GameFi tie-up follows a reported five-year, $100 million deal with Algorand that was announced last September.
The league has also introduced sports betting to the platform in an effort to stay ahead of the times, being “the first sport in the air” that fans can bet on, according to a press release.
“We’re building a roadmap of so many different things in the next several years,” DRL President Rachel Jacobson told CoinDesk in an interview. “We have to know blockchain because we always want to be 10 steps ahead and first to market. We’re going to be Playground Games’ crown jewel.”
DRL has also tapped Algorand as the title sponsor for its world championship event held at T-Mobile Arena in Las Vegas, which will include a performance from American rock staple Weezer, among other festivities.
Playground Labs is an affiliate of Hivemind Capital Partners, which announced in November a $1.5 billion fund to invest in projects building on Algorand.Source