Project #1

Tennis match prediction & betting analytics

A full-stack prediction workflow for tennis markets: collect match history, engineer features, train calibrated models, and surface the strongest opportunities in a usable interface instead of a notebook.

Project #1

Tennis match prediction Python · XGBoost · Flask · SQLite

Overview

This project is built around a practical question: can model-based estimates turn raw tennis data into a cleaner view of probability than the market is currently pricing? The system combines match and player context, trains on historical results, and presents the output in a way that helps with decision-making rather than just experimentation.

What the system does

The pipeline ingests structured match data, builds player and surface-aware features, runs an XGBoost model, and stores outputs in SQLite so the web layer can serve predictions quickly. A Flask UI then turns that backend into a readable tool for comparing model probability against live market pricing.

Modeling Gradient-boosted classification with calibration and validation windows

Data focus Match history, player form, surface splits, and context features

Product surface Flask dashboard for reviewing picks, prices, and confidence

Primary stack Python, XGBoost, Flask, SQLite, pandas

Why it matters

The interesting part is not just whether a model can predict match outcomes. It is whether the whole system can stay disciplined: feature engineering that respects time, evaluation that avoids leakage, and an interface that keeps the result interpretable. That combination is what makes it closer to a real product than a class project.