Look Up Say More Versus Creator Hub Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Trending Now Safety Net In My Bag VidCon with Mashable Back to School Furtastic All Series Live stream select Ligue 1 fixtures without spending anything. Joseph Green is the Global Shopping Editor for Mashable. He covers VPNs, headphones, fitness gear, dating sites, streaming, and shopping events like Black Friday and Prime Day. All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission.

One of the most important and challenging problems in football is predicting future player performance when transferred to another club within and between different leagues. In addition to being the most valuable prediction a team makes, it is also the most complex analytics task to perform as it needs to take into consideration: a) differences in playing style between the player's current team and target team, b) differences in style and ability of other players on each team, c) differences in league quality and style, and d) the role the player is desired to play. In this paper, we present a method which addresses these issues and enables us to make accurate predictions of future performance. Our Transfer Portal model utilizes a personalized neural network accounting for both stylistic and ability level input representations for players, teams, and leagues to simulate future player performance at any chosen club. Furthermore, we use a Bayesian updating framework to dynamically modify player and team representations over time which enables us to generate predictions for rising stars with small amounts of data.

We use European football performance data to select teams to form the proposed European football Super League, using only unsupervised techniques. We first used random forest regression to select important variables predicting goal difference, which we used to calculate the Euclidian distances between teams. Creating a Laplacian eigenmap, we bisected the Fielder vector to identify the five major European football leagues' natural clusters. Our results showed how an unsupervised approach could successfully identify four clusters based on five basic performance metrics: shots, shots on target, shots conceded, possession, and pass success. The top two clusters identify those teams who dominate their respective leagues and are the best candidates to create the most competitive elite super league. Keywords: OR in sports; Selection; Unsupervised; Spectral clustering; Laplacian Eigenmap; Machine Learning 1. Introduction Operational research (OR) has a long history of using sport to explore operational insights and methodologies (see Wright, 2009 for a review).

The trio is considered the best players in the world right now with Messi and Neymar playing alongside each other for Barcelona from 2013 to 2017 in a spell that saw the club win two La Liga titles, three Copa del Reys, one Champions League crown and the FIFA Club World Cup. However, Neymar shockingly made the move from Barcelona to Paris Saint-Germain last summer after the oil-rich Ligue 1 side activated his €222 million ($273.7 million) release clause in what is still the world record transfer deal. It is highly believed that the Brazilian did not move for money but rather, the chance to play for a side built around him rather than Messi as with his current injury aside, he has flourished this season with 29 goals and 19 assists in 30 games in all competitions with PSG requiring just four points from their last six games to seal a fifth Ligue 1 title in six years. But, there are rumors that the 25-year-old is unhappy in Paris with speculation rising that he could return to La Liga for Barcelona or even rivals Real Madrid where he would be teammates with Ronaldo. There were also reports that Zahavi, who acted as an intermediary for Neymar's move last summer, accompanied a PSG delegation to Brazil last month to check on his injury and determine his value for another potential transfer.