Developing Rebound Probability Predicting App using ML and Flask

Developing Rebound Probability Predicting App using ML and Flask

Developing Rebound Probability Predicting App - in this article, I am going to introduce how I develop an ML-based web application that can predict the odds of individual players and sides to secure a rebound on the court.

In this article, I am going to introduce how I develop an ML-based web application that can predict the odds of individual players and sides to secure a rebound on the court.

First of all, let me show what the app eventually looks like:

As is indicated from the image above, users will be able to get the probability for each player/side to get the rebound, which is useful to basketball operators when they need to arrange tactics about rebounding. And some of the takeaways from the GIF above are:

  1. We need an interface in the frontend that allows users to place the players on the court by dragging the items;
  2. we need a kernel in the backend that can predict any individual probability for a player to grab the rebound (and the summed probability for a team) based on their location on the court;
  3. we need to have a server that hosts the website 24/7.

In the rest of the article, I’m going to describe my ideas in that order.

Frontend design

There are two main difficulties we need to tackle during the development:

  1. how can we enable users to place the players by dragging the elements on the panel?
  2. how should I transport the data from the frontend to the backend so that our model uses it for prediction?

data-visualization machine-learning basketball web-app-development flask

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