ANALYTICS IN ACTION
Sports Betting Analytics
DIY Sports Analytics: Predicting Player Stats with Data.
Developed a data-driven tool using Python and Google Sheets to assess individual player performance and predict the likelihood of going over or under specific stats. This project expanded into a YouTube series where I guide viewers through the process, teaching them how to build and use analytics tools for sports predictions themselves.
Predicting Baseball Games
In this video, I build a model in Google Sheets to predict the winning team for upcoming baseball games. Learn how to create your own model for making smarter predictions.
Statistical Modeling in Google Sheets
Leveraging Google Sheets, I created formulas to analyze player stats and determine the likelihood of players going over or under specific thresholds. For baseball games, I extended this to predict exact scores, experimenting with statistical models to increase prediction accuracy.
Advanced Team Analysis with ELO Ratings
To add a layer of depth to game predictions, I developed a Python script to scrape current baseball team records and calculate an ELO rating for each team. This rating system helped assess team strength and provided additional context for player and game performance.
Data Collection & Scraping
I used Python to scrape websites for up-to-date betting lines and player statistics, automating data collection from various sources. This approach allowed me to pull real-time data on player performance and betting odds, making the tool both dynamic and relevant.
YouTube Series for DIY Enthusiasts
This project became a DIY series on YouTube, where I guide viewers through creating similar tools themselves. The series breaks down each step, from scraping data to building analytical models, making it accessible for sports enthusiasts interested in predictive analytics.
Let’s Connect
Interested in working together? I’d love to explore new opportunities—drop me a message, and let’s chat about how I can bring value to your team!