John Emms, John Nunn, Graham Burgess, Michael Adams, Nunn og Wesley So The Mammoth Book of the World's Greatest Chess Games .

Improve your chess by studying the greatest games of all time, from Adolf Anderssen's 'Immortal Game' to Magnus Carlsen's world championship victories , and featuring a foreword by five-times World Champion Vishy Anand. This book is written by an all-star team of authors. Wesley So is the reigning Fischer Random World Champion, the 2017 US Champion and the winner of the 2016 Grand Chess Tour. Michael Adams has been the top British player for the last quarter of a century and was a finalist in the 2004 FIDE World Championship. Graham Burgess is the author of thirty books, a former champion of the Danish region of Funen, and holds the world record for marathon blitz chess playing. John Nunn is a three-time winner of both the World Solving Championship and the British Chess Federation Book of the Year Award. John Emms is an experienced chess coach and writer, who finished equal first in the 1997 British Championship and was chess columnist of the Young Telegraph . The 145 greatest chess games of all time, selected, analysed, re-evaluated and explained by a team of British and American experts and illustrated with over 1,100 chess diagrams. Join the authors in studying these games, the cream of two centuries of international chess, and develop your own chess-playing skills - whatever your current standard. Instructive points at the end of each game highlight the lessons to be learned. First published in 1998, a second edition of The Mammoth Book of the World's Greatest Chess Games in 2004 included an additional twelve games. Another new edition in 2010 included a further thirteen games as well as some significant revisions to the analysis and information regarding other games in earlier editions of the book, facilitated by the use of a variety of chess software. This 2021 edition, further updated and expanded, now includes 145 games. The authors have made full use of the new generation of chess analysis engines that apply neural-network based AI.