moneyball

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

🌩️ "Moneyball with a Heart" for Valorant. An AI-powered tactical engine combining real-time GRID analytics, Gemini AI coaching, and psychological support in a sleek Flask dashboard.

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

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.

Data Science & Machine Learning Data Capstone based on Moneyball dataset

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.

A small Moneyball analysis application using data from Sean Lahman's MLB dataset to evaluate a baseball player's salary based on their performance in previous/recent seasons.

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.

Moneyball-style analysis of Premier League 2023/24 attacking performance, focusing on value, efficiency, and young breakout talents using Power BI.

⚽ End-to-end data analytics and ML project analyzing the impact of transfer spending on Premier League success among the Big Six (2011-2026).

Automated pipeline to identify undervalued football players in American leagues using web scraping, LLM curation (Moneyball), and Monte Carlo financial risk simulation.

We're losing X. Who replaces him? Ranked, priced replacement shortlists for Big-5 clubs, built on twelve seasons of data and validated by replaying ten years of real transfers against the clubs' actual signings.

Applied case study: multivariable regression and graph centrality pipeline for sports sponsorship valuation and offline inventory auditing — educational/research demo.

⚽ Talent AI Scouting. Pipeline de Machine Learning (Similitud del Coseno) y web app en Streamlit para identificar "clones" estadísticos de futbolistas y detectar ineficiencias de mercado salarial (Moneyball) en las 5 Grandes Ligas.

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

Professional multi-agent sports betting intelligence platform built with Clojure.

If you've seen Moneyball or like sports then you know we can't measure everything with just data or just our eyes.