Futbol Club Barcelona, more popularly known as FC Barcelona is one of the most successful clubs in the history of football winning the UEFA Champions' league, Europe's premier club competition as well as the biggest club competition in world football, five times and having one of the highest tallies of trophies in the world. The club has also had the highest revenue of all in the world for a few years and is as of now, the most valuable club in the world. But the club is in a debt of $ \$ $1.6 billion as of 2021. The question naturally arises, what did a club like Barcelona do wrong to meet such fate despite being so commercially successful? Though the pandemic has had a sizable impact on the club's financial wellbeing, the bulk of the blame must go to the horrible transfer record that Barcelona has had since their last UEFA Champions' league triumph in 2015. FC Barcelona has spent a whopping $ \$ $1.3 billion on transfers alone since June 2015. The signings were supposed to prolong the club's stay at the summit of club football, ironically these have brought the club down significantly. Players like Phillipe Coutinho, Ousmane Dembele and Antoine Griezmann who were bought in for a combined fee of $ \$ $500 million, were supposed to be some of the best in the world, but didn't have remotely good spells at Barcelona. They certainly were world class talents, so why did they barely get into the starting 11 when they should have been the next generation of superstars? Was it pressure, injuries or were they just not the right fit for Barcelona? Though Barcelona is an extreme example, every year dozens of big money transfers fail despite some of the most experienced and smartest brains working on them. Though player potential is immensely important for a player to achieve success, in the end it invariably depends on the playing style of the player and the team. The primary objective of this paper is to leverage analytics to identify players who are the best fit for the teams. The idea is to analyze, and decipher underlying quantitative patterns and make an association that could help identify which players a team should pursue in the transfer market. This can be done by identifying the glaring weaknesses in a team and finding a suitable player which can fill the gaping hole and fit the style of play of said team using real-life statistics of players and teams.