Introducing: Clairvoyance

Do you enjoy to appear an expert predicting the results of say professional Dota, LoL or CS Go matches? Do you sometimes feel the need to appear prescient in front of friends, colleagues or bookmakers? We have to admit, we do love to look smart. And we can make you look smart, too maybe, occasionally.

We cannot know the future, but we and you can learn from the past. Brighter people than us have thankfully developed ways to predict the strength of a player or team in a competitive sport. Implementing these rankings requires four distinct steps (utilizing Spring):

  • Programs to scrape data of teams and results from websites
  • PostgreSQL database to persist the data
  • Code to generate our predictions
  • A modern vue.js website to smartly present the likely possiblefuture.

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  1. Hello SaSEp-Team,
    We like your introduction in your blog but i can’t really understand what’s your goal. Maybe you can add a more accurate description of your project so everyone could understand better what you want to reach.
    Moreover, I like the links you used in your bullet list because it helped us to understand better what you want to use.
    We would also like to know what you think will be your biggest challenge in your project. Maybe you could also add this to your blog.
    Kind Regards,

    1. Hello Pascal,

      thank you for your comment.
      We want to create a website to predict the trends/evolution of sport teams and players depending on the results of matches and transfers of players and coaches. Our goal is to query all this information from public APIs and use statistic analysis to predict how specific transfers of players could affect the performance of teams.

      Our biggest challenge could be to use all the information in the right way, moreover to provide useful predictions from that.

      Jan from SaSEp/Clairvoyance

  2. Dear Clairvoyance-Team,
    we really like your first article and your idea! We see the vision you have and think it has great potential.
    The big problem we see, is that you do not focus on the part to generate the predictions at all in this article? Do you want to use Machine Learning, Deep Learning, do you want to hard-code predictions, do simple statistical analysis or do you just want to use an API?
    The technology-stack looks good for the actual user-interface, but we see the big problem with the analysis of the data. It would be great, if you could give us some details on how you want to generate the predictions, which is the main part of the application.
    Also, you should specify in which language you want to implement a backend for the app. You probably won’t use the database directly from your app. You should then also specify, how these components will interact with each other (HTTP, WebSockets, …, ?).

    1. Hello Hopper Team,

      as I said in the previous comment, we want to use basic statistics analysis for the predictions. We may use machine learning later on, but we first have to develop all the other stuff (back/frontend, database…).

      The backend will be written in Java, as we use SpringMVC for that.

      The interaction of the components will be handled by HTTP API requests.

      Jan from SaSEp/Clairvoyance

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