Revolutionizing the Football Players’ Market Value
The Comparisonator family is proud to announce its brand-new feature, the result of rigorous and lengthy research by its data science team. Accurately determining the market value of football players is of utmost importance, especially in today’s increasingly speculative and high-priced market.
When it comes to determining a professional football player’s monetary value, we often rely on well-known reference websites or reports published by consultancy companies. Unfortunately, these figures are often determined arbitrarily by the overall assessment of a group of experts.
Even though valuations from these references are considered acceptable, there are two major problems with them. Firstly, they might not be as accurate as we imagine, as there are significant discrepancies between the actual transfer fees or real valuations by the intended market and the figures provided by reference entities. Additionally, human bias can exist when determining these valuations, which are often not aligned with the players’ actual sportive performance. Secondly, the human assessment process is complicated and conflicted, leading to infrequent updates in the valuations. This means that it can take months, sometimes even tens of months, to learn the latest market value of a player. However, these outdated reference valuations may be extremely different from the player’s actual performance.
Having a tool that provides instant value estimates of players aligned with their recent performance on the field with pinpoint accuracy has been a long-sought goal for researchers and the market. Although various attempts have been made to derive econometric models for this purpose using players’ age, team and league monetary value, performance indicators, transfer history, and even online popularity, it is currently difficult to find a tool like this available to users as a service.
The Science Behind Compa Value – A Mixture of Machine Learning and Traditional Statistical Techniques
To address this need, our team, in collaboration with top-level academic experts and football specialists, worked tirelessly for months to develop the predictive model called “Compa Value”. With just one click on the Comparisonator platform, clients can now access an accurate and updated market value estimate in euros for any player.
The advanced statistical model behind “Compa Value” takes into account the player’s position, age, performance, league, team information, and history, as well as the valuation models from reference entities. This enables us to moderately and intelligently incorporate external factors such as popularity or rumors affecting the player’s price tag, while still providing a justifiable and performance-based estimate that is up-to-date.
Without delving into complex technical details, we can simplify the model behind ‘Compa Value’ as a mixture of advanced machine learning models and traditional statistical techniques, where specific hyperparameters are optimized through ongoing discussions with experts. One unique aspect of the algorithm is its reliance on our in-house AI-based key performance indicator, which our clients have been using for years.
The ‘Comparisonator Index’ forms the foundation of ‘Compa Value’. This AI-powered index is a result of years of collaboration by our research team and has proven to be accurate and impartial through a constant stream of positive feedback from clients and the media. To leverage its accuracy, we placed it at the center of the ‘Compa Value’ algorithm. During the research process, our team of data scientists also discovered that the index provides reliable, understandable, and consistent statistical properties that align well with monetary values. This further highlights the superiority of the ‘Comparisonator Index’ compared to other competing KPIs.
With ‘Compa Value’, we are pleased to announce that our clients can now access accurate, unbiased, and instant market valuations of players in hundreds of football leagues worldwide, track the changes in values based on performance, and make better financial decisions.