01 , Case Study
AI & DATA ENGINEERING · FULL STACK · ALGORITHM DESIGN · 2025
Sports bettors lose because they rely on gut feeling and basic stats. The edge exists — but extracting it requires models most punters don't have access to.
TIMELINE
14 weeks
ROLE
AI + Data Engineering + Full Stack
STACK
Python, Next.js, Postgres, custom ML models
OUTCOME
Live platform with paying users
02 , The Problem
課題Sports betting is a market where information asymmetry is everything. Most punters lose because they rely on gut feeling, televised pundits, or basic statistical lines that the bookmakers have already priced in. The edge exists — but finding it requires models the average bettor doesn't have access to, and couldn't build themselves.
We set out to build a platform that gave serious punters access to the kind of signal extraction normally reserved for professional syndicates.
03 , Approach
手法We started with the data. What signals actually predict outcomes better than the bookmaker's line, and how often? We built ingestion pipelines for every match-level statistic we could legally source, then iterated on models that could identify undervalued lines in real time.
The platform layer wraps the ML output in a legible UI — match cards with confidence scores, reasoning traces, and historical performance of the model's past calls. The user isn't asked to trust a black box. They can see why the system thinks a line is mispriced, and how often similar calls have been right.
“The goal was never to predict winners. It was to find the gap between the real probability and the bookmaker's published odds.”
Gallery
04 , Outcome
成果TopBet is a live platform serving paying users. The models continue to improve as more match data flows in, and the platform's calls are tracked publicly — wins and losses both.
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