Machine Learning over historical baseball data using latest Microsoft AI & Development technology stack (.Net Core & Blazor)

Realtime MLB Insights using Streamlit

Repository where you can find my recent work in Sports Data Analysis. In this case, we are inspiring on Moneyball's Bill James calculations adapted to football. More info: https://www.linkedin.com/feed/update/urn:li:activity:6810238034794090497/ Certificate obtained in Sports Perf Analytics Spec: https://bit.ly/3XGbvOL

Data Science & Machine Learning Data Capstone based on Moneyball dataset

AI-Driven Insights into Baseball Performance, Business, and Analytics

A short notebook analysing batting statistics from the 1983 baseball season. The broad aim of the project is to design models that can predict a players salary from some of their offensive performance statistics.

This is a program which uses data exported using the view and google sheets shown in this video. This data is then imported as a csv file into this program which can then be used to visualise the data and chose players to scout and sign based on statistics.

Use R to do data analysis on the data set of baseball players in MLB and try to select desired players according to qualifications.

My first project on sports analytics. Used ML techniques to arrive at the same conclusions as mentioned in the book 'Moneyball' by Michael Lewis.

Inspired by the film moneyball, this repo contains the files to replicate a way of doing real football scouting: collecting all the transfermarkt player economic values and the player statistics of APIfootball.com of the current season.

Apply Moneyball approach to Serie A Fantacalcio data.