Majora's Mask's blue dog finally wins the doggy race after 25 years and 30,000 attempts
25 years on from the release of The Legend of Zelda: Majora's Mask, two players have finally proven that the doggy race minigame's most unfortunate contestant is capable of winning, though it might take them a while to do it.
At the Romani Ranch in Majora's Mask, players can take part in a minigame where they can bet on a race between a group of dogs, each with different hidden stats. The golden dog, having the best stats, is typically the most likely to win. On the flip side, the blue dog has such low stats that for the 25 years since the game's release, it was largely accepted that it was impossible for the blue dog to win.
That was until now, with a recent video from YouTube channel Vidya James detailing how he and another YouTuber, falkush, managed to achieve it. Initially, the two looked to abuse a rare glitch in the game that causes the race to end early, selecting a random dog to win the race. After creating a script to automate repeatedly running the race, it took falkush three days and more than 8,000 attempts for the blue dog to trigger the glitch, winning the race seemingly for the first time ever.
Not content with that, however, falkush wanted to record a legitimate first-place victory for the blue dog, winning without the help of the glitch. Before each race, each dog is randomly assigned a condition that will increase or decrease its stats. Knowing this, falkush altered his script to check for instances where the blue dog had the best condition, while the faster dogs like the golden dog had the worst conditions, maximizing the blue dog's chances.
Between the dogs' base stats, the varying conditions of the dogs, and good old-fashioned RNG, Vidya James calculated that there were more than 1 quadrillion possible races that could be run. With a number that big, falkush understandably believed that the blue dog must be able to win a race in at least some of those races, whether that was one win or one million, believing that by optimising the conditions of
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