Data Scientist · Sports Analytics · Valencia

Turning data into clear decisions

I'm the bridge between data analysis and elite sport: I turn performance metrics into strategies that make the difference on the ground.

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Python
R
SQL
Power BI
Machine Learning

About me

Data with the context of the track, the road, and competition

I'm Mario Álvarez, a Data Science student at UPV with real experience inside professional cycling. I combine data analysis with elite sports performance to turn every metric into a decision that improves results.

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UPV

Education

Data Science degree, Universitat Politècnica de València.

Elite

Experience

Real work inside a professional cycling team.

Data + Sport

Focus

Analytics applied to improving sports performance.

Experience

Two worlds, one language: performance

I've competed at the highest level and now apply that same mindset to data: I understand what a number means to an athlete, because I've been that athlete.

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Work

Projects

An automatic selection of my public GitHub repositories.

Specializations

HTML
Power BI
Python
R
SQL

VIS

HTML

This repository contains coursework, practical exercises, and study materials for a university data visualization course. It includes solved programming tasks using R and Python libraries like ggplot2 and Plotly, alongside practice dashboards built in Tableau.

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EVALUACION-DE-MODELOS-

HTML

This repository contains practical assignments for a university model evaluation course, covering topics such as cost-sensitive classification, fairness metrics on the COMPAS dataset, and model interpretability using partial dependence plots. The exercises and analyses are implemented using Python and R across Jupyter Notebooks and R Markdown reports.

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TRABAJO_MDP

An academic data analysis project in R that explores and characterizes a physicochemical crystal dataset. It covers data cleaning, preprocessing, and dimensionality reduction through Principal Component Analysis (PCA) using packages such as FactoMineR, factoextra, and ggplot2.

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TRABAJO_CORREDORES

Python

A Streamlit web application that helps manage cycling teams by scraping race and rider data from ProCyclingStats and FirstCycling to analyze upcoming events and cyclist specialties. It is built with Python using BeautifulSoup, Selenium, Pandas, and the Gemini API to process data and extract UCI points regulations from PDFs.

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COFIDIS

Python

Developed as an academic project, this tool scrapes UCI rankings and race data from ProCyclingStats to calculate race difficulty scores tailored to the Cofidis cycling team's calendar. It is built with Python, using Selenium and BeautifulSoup for web scraping, Pandas for data processing, and Streamlit for the interactive interface.

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