Moneyball-Uncovering_the_process

My first project on sports analytics ⛹️‍♂️

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This project draws inspiration from the book 'Moneyball: The Art of Winning an Unfair Game', written by Michael Lewis, based on the story of the Major League Baseball team ⚾, The Oakland Athletics, which consistently performed well in the tournament despite having one of the lowest payrolls among its competitors. The Major League Baseball is an American professional baseball league. A total of 30 teams participate in this tournament, one of which is the Oakland Athletics, also known as the Oakland A's.
At the time of the 2001 season, Billy Beane was the manager of the Oakland A's. He was a baseball player who was tagged as an up-and-coming player by the recruitment scouts at his debut. Although he could not become a superstar as a player, having experienced the recruitment and judging criteria in the MLB teams, he came to believe that these methods were not efficient. After his retirement, he became the manager of the Oakland A's. The Oakland A's invariably had the lowest to second lowest payrolls among all its competitors. All the players who suited the traditional judging criteria were signed by the teams willing to pay more, leaving the groups like Oakland A's to compete with whatever was left. But when Billy became the manager, he was ready to use the scientific approach of Data Science for creating the team, and to help him with that he hired, Harvard graduate Paul DePodesta. The rest of the story is how Oakland A's succeeded by using their efficient methods.
In the story, there are mentions of a few strategies by Paul, like winning 95 games to qualify for the playoffs and the importance of On-base percentage, but the process behind arriving at that conclusion is not there in the book. In this project, I applied the data analysis aspect to its dataset from Kaggle and worked out these strategies using ML.