Tennis match prediction & betting analytics

Machine learning May 27, 2026

Project Summary

End-to-end ML tooling for tennis prediction with a lightweight web UI built around readability, calibration, and careful evaluation.

Status: Independent
Role: ML engineer + backend + UI
Stack: Python, pandas, XGBoost, Flask
Code: Private repo

I. Overview

An end-to-end machine learning project for tennis match prediction: collect historical results, engineer player-context features, train calibrated models, and present the output in a lightweight interface built for fast interpretation.

This project started from a practical modeling question: can a disciplined feature pipeline produce match probabilities that are more useful than a quick intuition or a raw ranking comparison? To answer that, I built a system that turns messy tennis history into structured inputs, trains on time-aware splits, and exposes the results through a simple interface rather than a notebook or one-off script.

II. What I Built

The pipeline cleans per-player match histories, normalizes naming and tournament context, builds surface-aware and form-sensitive features, and feeds them into an XGBoost classifier with calibration. On top of that, I built a Flask-based interface so I could inspect predictions, compare player profiles, and review model output in a product-like workflow instead of bouncing between scripts and CSV files.

Modeling Gradient-boosted classification with calibration, feature guards, and time-aware validation

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

Product surface Flask dashboard for reviewing probabilities, feature context, and prediction confidence

Primary stack Python, pandas, XGBoost, Flask, pickle-based model artifacts

Focus areas Data cleaning, feature engineering, calibration, evaluation discipline, and web presentation

III. Interactive Analytics Visualization

This dashboard-style visual walks through the prediction workflow: engineered feature groups, additive XGBoost scoring, Kalshi market-implied probability blending, and the final betting edge signal.

IV. System Architecture

Data ingestion

Historical match records are collected and standardized into per-player histories with consistent naming and tournament metadata.

Feature engineering

Surface splits, recent form, opponent context, and fallback profile logic are assembled into a structured feature vector.

Modeling

An XGBoost classifier is trained with time-aware evaluation and calibrated so predicted probabilities behave more reliably.

Review interface

A lightweight Flask UI makes predictions readable, traceable, and fast to inspect without dropping back into scripts.

V. Sample Prediction Snapshot

Illustrative example

Reviewing a single match prediction

Calibrated output

Player A Carlos Alcaraz

63.4%

Player B Jannik Sinner

Surface Hard

Model view Moderate edge

Driver Recent-form + surface profile

The real app supports deeper inspection, but the point of the interface is the same: turn a model output into something a person can review quickly and understand with context.

VI. Technical Challenges

The hardest part was not training a classifier. It was keeping the entire workflow honest. Tennis data is noisy: players appear under inconsistent names, surface context matters, recent form matters, and evaluation can become misleading if future information leaks into training features. A big part of the work was building safeguards around those issues so that model quality meant something outside the notebook.

VII. What This Project Demonstrates

More than a single model, this project shows how I approach end-to-end engineering work: define the problem, clean and shape the data, design features around the domain, validate carefully, and build a usable interface around the result. That combination of ML, backend logic, and product thinking is what makes this a strong representation of how I like to build systems.