This project aimed to analyze the 2025 NFL Big Data Bowl tracking data to predict whether a play is a run-pass option (RPO) before the ball is snapped, and to identify the pre-snap “tells” that give RPOs away. An XGBoost model was trained on every frame from lineset to snap to produce a live RPO probability, and was tested with leave-one-week-out cross-validation against a logistic regression baseline and a SumerSports-style transformer. SHAP values were used to interpret the model, and the most influential features were combined into a league-wide tell sheet and team-by-team tell rankings that show which looks each offense uses most when running RPOs. Interactive play examples show the RPO probability changing live with motions and shifts. This document outlines all methods, data, and conclusions.

Integração e análise de dados cambiais (2019–2026)

Uma análise de características voltada à crianças com TDAH.

Uma análise de como um benchmark funciona rodando no Linux com Proton e sua diferença de utilização no Windows.

Análisis experimental de las calificaciones sensoriales de Café Tostao, Café Viejo Molino y Café Colcafé, utilizando un Diseño Completamente Aleatorizado. Se aplican técnicas estadísticas como ANOVA, pruebas de comparación múltiple (LSD, Tukey y Duncan) y pruebas de verificación de supuestos para identificar diferencias entre los tratamientos y evaluar la confiabilidad de los resultados.