Projects are Statathlon’s R&D. These projects run continuously using the same methods as in our core business activities extracting valuable insights for each topic. Through these projects our researchers develop their scientific skills by discovering accurate statistical rates and models that we use later for client engagements. There are some projects that are getting prepared exclusively by our team and others that are prepared in collaboration with some partners.
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 a Soccer team. Using Statistical Analysis our team has generated more than 35 advanced individual and team indexes. The main goal is to provide useful insights regarding the technical and tactical performance of a Football team.
Prototype Data: Bayer Leverkusen
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”?
UEFA Champions League Project:
We prepare two weekly reports regarding the 4 most debatable matches in the Champions League 2018/19. The reports include the analysis of the statistical profiles of the clubs and the probabilistic prediction regarding the upcoming game result. The reports are fully interactive and can extract valuable insights regarding the football teams.
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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