Influence of crowdsourcing, popularity and previous year statistics in market value estimation of football players

Abstract

Every week, millions of dollars are pumped into the sport of football (Association Football). The transfer value of football players proliferates each year and transfer records get shattered every other transfer window. Players are signed with a certain transfer fee which is determined during the transfer of that player. However, it is noteworthy to have a metric or economic valuation signifying the price tag on a player throughout the season rather than only during the transfer. This paper introduces the contribution of consistency, popularity, crowd estimation and performance parameters on top of the factors used in previous studies in predicting market value of the player using machine learning algorithms. The results show that the predicting accuracy is enhanced when these parameters are considered for evaluating market value of the football player.

Keywords:

• Machine Learning
• Association Football
• transfer fee
• transfer window