Google Trends is a valuable tool for measuring popularity since it collects a large amount of information related to Google searches. However, Google Trends has been underused by sports analysts. This research proposes a novel method to calculate several popularity indicators for predicting players’ market value. Google Trends was used to calculate six popularity indicators by requesting information about two football players simultaneously and creating popularity layers to compare players of unequal popularity. In addition, as the main idea is to obtain the popularity indicators of all players on the same scale, a cumulative conversion factor was used to rescale these indicators. The results show that the proposed popularity indicators are essential to predicting a player’s market value. In addition, using the proposed popularity indicators decreases the transfer fee prediction error for three different models that are fitted to the data using the multiple linear regression, random forest, and gradient boosting machine methods. The popularity indicator Min, which is a robust reflection of the popularity that represents a player’s popularity during the periods when they are less popular, is the most important popularity indicator, with a significant effect on the market value. This research provides practical guidance for developing and incorporating the proposed indicators, which could be applied in sports analytics and in any study in which popularity is relevant.

Search popularity, as reported by Google Trends, has previously been demonstrated to be useful when studying many time series. However, its use in cross-section studies is not straightforward because search popularity is not provided in absolute terms but as a normalized index that impedes comparisons. This paper proposes a novel methodology for calculating popularity indicators obtained from Google Trends to improve the prediction of football players' transfer fees. The database is formed by 1428 players who competed in LaLiga, Premier League, Bundesliga, Serie A, and Ligue 1 on the 2018-2019 season. Random forest algorithm and multiple linear regression are used to study the popularity indicators' importance and significativity, respectively. Results showed that the proposed popularity indicators provide significant information to predict players’ transfer fees, as models including such popularity indicators had lower prediction error than those without them.  This study's developed method could be used not only for analysts specialized in sports data analysis but for researchers of other fields.