WebApr 7, 2024 · Find many great new & used options and get the best deals for Green Day - Greatest Hits: God's Favorite Band - CD - NEW at the best online prices at eBay! Free shipping for many products! http://www.yes24.com/Product/Goods/118366378
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WebGreen Day is an American punk rock band formed in 1987. The band consists of lead vocalist and guitarist Billie Joe Armstrong, bassist and backing vocalist Mike Dirnt, drummer Tré Cool and guitarist and backing vocalist Jason White, who became a full member after playing in the band as a session and touring guitarist for 13 years. WebOct 24, 2016 · Greatest Hits: God's Favorite Band chronicles the sonic journey of one of the most essential and dynamic rock bands in recent history. The playlist includes 20 hits spanning Green Day's prolific 31 years as a band, plus a brand new song called "Back In The USA" and a new duet with award-winning Country Music superstar Miranda … bit rate chart
Greatest Hits: God
God's Favorite Band features 20 of Green Day's previous hits, as well as 2 new songs: a new version of the Revolution Radio track "Ordinary World", featuring country singer Miranda Lambert, and a previously unreleased song entitled "Back in the USA". The album includes songs from all of Green Day's studio albums, … See more Greatest Hits: God's Favorite Band is the second greatest hits album by American rock band Green Day, released on November 17, 2024. See more Green Day • Billie Joe Armstrong – electric and acoustic guitars, lead vocals, harmonica, piano See more WebJump 12 years later, Green Day preformed Still Breathing on The Late Show with Stephen Colbert and God introduced the them as his favorite band, referencing BiaB. Green Day had absolutely no part in that, and were surprised and erratic when God said it. The name also just fits a Greatest Hits album. 45 CityOfTheDamned • 5 yr. ago WebShop Greatest Hits: God's Favorite Band [LP] VINYL at Best Buy. Find low everyday prices and buy online for delivery or in-store pick-up. Price Match Guarantee. data in machine learning