Projects are Statathlon’s R&D and they run in parallel with our core business activities extracting valuable insights for each topic. Through them our researcher team develop its scientific skills by increasing the accuracy of our statistical algorithms and models that we use later for client engagements.
The main categories our projects cover are: Performance Analysis, Advanced Scouting, and Fan Engagement.
Green Grove Project:
The project monitors the In-Game performance of a basketball team. Using Statistical Analysis our team has generated more than 40 advanced individual and team indexes. The main goal is to provide useful insights regarding the technical and tactical performance of a basketball team.
Prototype Data: Zalgiris Kaunas
The project monitors the In-Game performance of football team.. Using Statistical Analysis our team has generated more than 35 Statathlon-unique individual and team indexes. The main goal is to provide useful insights regarding the technical and tactical performance of the team.
Prototype Data: Bayer 04 Leverkusen
UEFA Champions League Project:
This is our main Fan Engagement Project that analyzes team and individual data of teams playing against each other in Champions League 2018/19. The reports include the analysis of the statistical profiles of the clubs and the probabilistic prediction regarding the game result. The reports are fully interactive and can extract valuable insights regarding the football teams.
The Projects is focused on how important are the individual Football players to their teams. Using Statistical Analysis and Visualization techniques the team answers one simple question: “Does a player really contributes to his team”?
FIFA World Cup 2018 Project
Our team prepared 64 Data Science daily reports regarding each game of the FIFA World Cup 2018. Our team, gathered all the required in-game data for each one of the 32 different teams. The next step, was the data analysis and the construction of the probabilistic algorithm regarding the prediction of the result in each game. We used data mining techniques, by formulating a simulation model.
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