🏁 Market Master Results: Round 15

I promised transparency when I started this journey, and today, I am delivering on that promise. Matchweek 15 dealt the model a significant blow, with red results across the board.

The weekend got off to a difficult start immediately. The model was highly confident that Arsenal would secure 3 points, but Aston Villa had other plans, handing us our first loss of the round. The bad luck continued with Crystal Palace; I had high hopes for a draw, but a late-minute goal swung the result, killing the prediction.

It was a harsh reality check. While previous weeks showed promise, the variance caught up to us this week.

🔍 Matchweek 15 Details

Here is a focused "Post-Mortem Analysis" section that you can copy and paste directly into your blog post, right after the tables.

I have structured it to highlight the "Top 6" bias and the O/U regression as you requested.

📉 Post-Mortem: What Went Wrong?

Looking at the data, two specific trends caused the model to derail this week.

1. The "Big 6" Blind Spot The model displayed too much confidence in the traditional "Top 6" teams, particularly when playing away from home. It relied heavily on superior squad metrics, predicting comfortable wins for Arsenal, Chelsea, and Liverpool. However, the reality of the Premier League is different. Aston Villa, Bournemouth, and Leeds all proved that home advantage and tactical discipline can neutralize superior squads. By overestimating the favorites, the model was left chasing results that never materialized.

2. The O/U Profit Wipeout Week 14 gave us false hope with a massive 70% hit rate in the Over/Under market. Week 15 brought a harsh correction. The loss of -3.59u in this sector didn't just hurt the weekly total; it completely wiped out the hard-earned profits from the previous round. The model misread the "game script" on key matches—expecting tight affairs in what turned out to be goal-fests (like Leeds v Liverpool) and open games in what turned out to be stalemates (like Bournemouth v Chelsea).

📊 Model: Week 15 (1u Flat)

📈 History per Week

🎫 Ticket Overview (Week 15)

Bad Predictions = Dead Tickets Ultimately, a betting ticket is only as strong as the individual predictions behind it. Because the fundamental "base" picks were off (50% on 1X2 and 40% on O/U), the combined tickets never stood a chance. The "Ticket" strategy relies on consistency, and when the model is flipping a coin on match winners, the accumulators are destined to fail.

❌ 1X2 Ticket | Profit: €-2.20

❌ O/U Ticket | Profit: €-2.20

❌ CS Ticket | Profit: €-1.10

💰 Financial Summary per Week

💰 Total Weekly Net: €-5.50

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🛡️ Trusting the Process: The 4-Round Rule

In data science and betting, the enemy of success is over-reaction. It is tempting to tear the model apart after a sea of red, but I set a strict rule at the start of this experiment: I will run the model for 4 full rounds before making any structural changes.

Why? Because sample size matters.

Weeks 13 & 14: The predictions were actually solid. The underlying logic was there, and we were often just one goal or one bounce away from knocking it out of the park. The tickets had potential, even if they didn't land.

Week 15: This was different. The predictions were fundamentally off, meaning the tickets never really stood a chance.

However, one bad week does not invalidate the previous two. We have one more round in this testing block. Week 16 will be the final piece of the puzzle. Once that data is in, we will have a full month of performance to review. That is when we will decide if the model needs a minor tune-up or a major overhaul.

Next Stop: Matchweek 16. We stick to the plan. 👊

Cheers,
Peter

Disclaimer: Educational experiment. 18+ Only.

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