From Crypto Bots to Football: I applied my 400+ model "Quant" system to sports betting
(This is a repost, because for some reason Reddit didn't like the language I used on my previous post)(This is a repost, because for some reason Reddit didn't like the language I used on my previous post)
Hey everyone,
I’m an IT student currently refining a quantitative framework I’ve been developing over the last two years. Initially, my focus was on high-volatility assets like crypto, where I spent time building models to account for market sentiment and volume. Recently, I decided to take on a much more complex challenge: the football (soccer) market.
For the last 6 months, I’ve been migrating my "Quant" architecture to handle sports metrics. My goal shifted from managing capital to building a purely statistical "second brain"—a research tool designed to strip away the "gut feeling" and narratives that usually cloud this space.
- The Architecture: A pipeline of 400+ ensemble models processing 60+ variables per fixture.
- Data Inputs: I focus strictly on market movement (odds) and team-level performance metrics (xG, possession, etc.). I’ve deliberately excluded individual player data to reduce noise and maintain model scalability.
- Markets: Currently modelling HDA, Over/Under, Goals and BTTS. I’ve recently added Corners, and Double Chance is currently in testing.
- Coverage: Wide-scale coverage including the big 5 European leagues, Championship, MLS, and several Scandinavian and South American divisions.
The approach is 100% mathematical. I’m looking at this as a probability problem rather than a sports problem. In early testing with a small group of users, the model has shown a consistent ability to identify value in high-variance markets (specifically the MLS and lower European divisions).
I’ve reached a point where my own backtesting and limited forward-testing show a steady statistical edge (maintaining a 60%+ hit rate on primary markets during the last cycle), but I need more "stress testing" from people who understand algorithmic modelling.
I’ve built a dashboard to host these daily statistical projections to keep the project organised. It’s a completely free research tool—I’m not looking to sell anything.
I’m looking for "power users" and fellow quants/developers to help me refine the logic. I want to confirm these data points are useful to other researchers before I look into scaling the infrastructure or approaching potential investors.
I’m happy to share the data or the link for the dashboard in the comments if anyone wants to look at the projections for this weekend’s fixtures.
I'm keen to hear your critiques on the methodology:
- Are there specific high-signal variables I might be missing?
- Should I expand into player-specific props, or does that introduce too much variance for a quant model?
- What other niche leagues tend to follow statistical trends better than the "top-heavy" ones like the Premier League?
Looking forward to some technical feedback! I have also shared some pictures of my system, how the process is going and also my own picks for the day based on what the system suggested
