Everyone has an opinion on football. "City will crush them," "United is in a slump," "This has 0-0 written all over it." But how often is our gut feeling actually right? And more importantly: is it profitable?
A few weeks ago, I decided to stop guessing and start coding. My goal was simple yet ambitious: build a Python model that strips away emotion and predicts the English Premier League purely based on data, math, and market inefficiencies.
It wasn't easy. It took 32 iterations, hundreds of lines of code, and a lot of trial and error. But today, I am proud to introduce Market Master V32.0.
Here is how I built it, how it works, and why I think we might have an edge over the bookies.
🤖 Almost Fully Automated: The "One-Click" Workflow
One of the coolest features of Market Master is that it isn't a manual spreadsheet. It is a fully automated Python pipeline.
When I launch the script, it performs a complex sequence of actions in seconds:
- Data Fetching: It automatically connects to external servers to download the latest match results and the specific fixture list for the upcoming weekend.
- Training: It retrains the entire Machine Learning model on the fly, using data up until the very last match played.
- Calculating: It runs thousands of Poisson simulations and Dixon-Coles adjustments for every upcoming match.
The Human Touch The only time the script "pauses" is to ask me for input. Once it has selected the best potential tickets, it asks: "Do you want to update the odds?" This allows me to enter the exact real-time odds from my specific bookmaker to ensure the Payout calculations are 100% accurate.
🧪 The 'Sandbox' Protocol: Testing without Risk
Predictive modeling is never finished; it requires constant fine-tuning.
The script is designed with a safety valve. After generating all predictions and tickets, it ends with a critical prompt: 💾 Save to Betting Log? (y/n):
If I answer 'n' (No), I can analyze the model's "thoughts" without corrupting my official betting record or risking money. This allows me to perform Test Runs. I can simulate different strategies week-by-week and only commit real money when the math proves itself.
⚙️ Under the Hood: The Engine
The current version (V32) is a beast. It combines three advanced concepts to generate a prediction.
The Oracle: Market Implied Goals
Instead of guessing how many goals Liverpool will score based on history, the model now reverse-engineers the live betting odds. We look at the Asian Handicap and the Over/Under 2.5 lines set by the market. By running these through a mathematical formula, we extract exactly what the "market expectation" (xG) is for today's match. This serves as our dynamic baseline.The Dixon-Coles Matrix
Standard statistics assume that goals are independent events. But football is psychological. If a team is winning 1-0, they defend. If it's 1-1, both teams might settle for a point. We implemented the Dixon-Coles model, which adjusts the probability matrix to increase the chances of low-scoring draws (0-0 and 1-1). This makes our Correct Score predictions much more realistic.The Hybrid Ensemble (60/40)
This is the secret sauce. The model calculates a final probability based on a weighted mix:
60% Market Wisdom: We trust the "wisdom of the crowd" (the odds) for the baseline reality.
40% Our Edge: We apply our own Poisson & Machine Learning calculations to find where the specific match might deviate from the odds.
🎫 The Strategy: Building the Tickets
The model doesn't just spit out random games. It constructs three specific "Tickets" every week:
The 1X2 "Value Builder": A balanced portfolio of 6 matches. It mixes "Anchors" (Safe bets) with "Value Builders" (Higher odds) to create a stable accumulator.
The O/U Ticket: A selection of 6 matches where both our statistical model and the market agree on the direction (Over or Under 2.5 goals), but where we still find a mathematical edge.
The Correct Score "Yankee": The Moonshot. We take the 4 most probable correct scores (calculated via Dixon-Coles) and put them in a Yankee system. High risk, high reward.
🚧 Work in Progress: Dashboards & Hitrate
While the math is solid, the presentation is an ongoing project.
I am currently building a Custom HTML Dashboard to visualize the output in "Tiles" or Cards, making it easier to read at a glance.
Tracking the Hitrate Most importantly, I am not just tracking the winning money. I am logging the Hitrate of ALL predictions. Even if a match didn't make it into a betting ticket, the model still predicted an outcome. By analyzing the success rate of every single prediction (10 games a week), we can fine-tune the engine.
🚀 What to Expect on this Blog?
Starting now, I will let Market Master run every week for the Premier League.
Fridays: I will post the model's predictions and the selected tickets/bets.
Mondays: I will post the results. Full transparency. If the model fails, I will show it. If we win, we celebrate.
I am not selling anything. I am documenting my journey of trying to beat the market using Python and Data Science.
Follow me to track the project! 📈