“Smart Football Player Performance Analysis using Streamlit”, aims to harness the power of digital identity and learning analytics to revolutionize football performance and engagement. It is themed on “Digital Identity and Learning Analytics” which belongs to the domain of “Quality Education, Livelihood and Creative Opportunities. A platform that focuses on the fan base of football, to give a personal and interactive experience of football world. Users can follow a team, which will give them the liberty of choosing a favorite club/team. This profile completion will give the required analytics of that particular team on few aspects such as ‘Player analyses, ‘Team Analysis’. This will allow any user to dive deep into the favorite team by knowing all ins and outs. This will involve creating dataset of players of different teams, which includes player and team stats, including relevant attributes. This will lead to a Python powered application, which can be used to provide interactive analysis as well as comparison between players of the same team. The goal is to empower fans with data-driven insights and interactive tools to deepen their connection with the sport and enhance their overall experience.

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Smart Football Player Performance Analysis Using Streamlit

  • R. J. Anandhi,
  • S. Sivaramakrishnan,
  • Krishna Kumar,
  • Ashlesh Ujjwal Tandulkar,
  • Faizan Abdul Azeez,
  • V. Chirag,
  • Athul Satheesh,
  • K. Thamarai Selvi

摘要

“Smart Football Player Performance Analysis using Streamlit”, aims to harness the power of digital identity and learning analytics to revolutionize football performance and engagement. It is themed on “Digital Identity and Learning Analytics” which belongs to the domain of “Quality Education, Livelihood and Creative Opportunities. A platform that focuses on the fan base of football, to give a personal and interactive experience of football world. Users can follow a team, which will give them the liberty of choosing a favorite club/team. This profile completion will give the required analytics of that particular team on few aspects such as ‘Player analyses, ‘Team Analysis’. This will allow any user to dive deep into the favorite team by knowing all ins and outs. This will involve creating dataset of players of different teams, which includes player and team stats, including relevant attributes. This will lead to a Python powered application, which can be used to provide interactive analysis as well as comparison between players of the same team. The goal is to empower fans with data-driven insights and interactive tools to deepen their connection with the sport and enhance their overall experience.