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The market report titled “Soccer Gloves Market by Global Industry Perspective, Comprehensive Analysis and Forecast, 2016 – 2022” and published by Beathen Reports will put forth a systematized evaluation of the vital facets of the global Soccer Gloves Market. The report will function as a medium for the better assessment of the existing and future situations of the global market. It will be offering a 360-degree framework of the competitive landscape and dynamics of the market and related industries. Further, it contains major competitors and emerging companies in the market as well as their comprehensive detailed information, such as market share based on revenue, demand, high-quality product manufacturers, sales and service providers. The report will also shed light on the numerous growth prospects dedicated to diverse industries, organizations, suppliers, and associations providing several services and products. The report will provide buyers with detailed directions on market growth, thereby further providing them with a competitive advantage during the forecast period.
Soccer is one of the most popular sports in the world, especially in Europe and South America. Numerous people are enthusiastic about this sport. More than a billion people watched the final match of World Cup in 2018. Another fact of soccer is that it is quite excellent for betting at different levels. The outcome of a soccer game depends on many factors, like the home-away ground effect, the physical and psychological conditions of key players. On the other hand, for each team, a good understanding and analysis of their past games is effective to help improve their skills, strategies and training methodologies.
The study of soccer game data has many applications for both fans and teams. The effective analytical work can not only help the teams to improve their offensive and defensive skills and strategies, but also could assist the fans in making a bet.
In the study published in the journal of Insight - Statistics, the authors studied the European League Dataset with statistical methods to analyze the game data. Moreover, machine learning techniques are designed to predict the game results based on in-game performance and pre-game odds provided by bookmakers. Through reasonable feature engineering and model selection, their model can achieve an overall accuracy of 95%.
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