A study of Prediction models for football player valuations by quantifying statistical and economic attributes for the global transfer market

The global transfer market in the field of football has long been devoid of technology. Over the last two decades, the economic dynamics have completely been changed, and the amount of money which has been pumped has increased exponentially. Valuations for any physical entity with a well-defined set of parameters is a challenge in itself and depends on numerous other things. And doing the same for an individual with varying parameters, based on the source of data, is even more challenging. With this paper, we attempt to find the most optimum way of fetching data of football players and applying the right model on it so as to extract meaningful information, thus reducing the gap between the estimated prices and the Final price. We do so by crowdsourcing the data and applying regression techniques along with Opta index. We also try to find the results using Neural Networks and conclude with a comparison between our models