Just like every season, I am excited to share my experiences with using different betting models. This year, I have been particularly focused on my ensemble model, which incorporates a diverse range of data and algorithms. Over the first three months of the season, I placed numerous bets, and now I want to take a closer look at the outcomes. I aim to share my insights and analyses regarding these bets. Let’s delve into the realm of sports betting together and explore what we can learn from these initial months.
Current betting results
Despite an auspicious beginning, my betting endeavors have taken a downturn since mid-September, resulting in a continuous loss. At present, my overall profit margin has plummeted to -3.88%. A comprehensive analysis of my betting strategies is imperative to pinpoint the underlying patterns contributing to these losses. Developing a fresh approach is essential to reverse this trend. Identifying the precise causes behind the decline in profitability is paramount for making well-informed decisions in the future.
Esemble model simulation
One might assume that the poor results are just a phase, but here’s the catch: the model simulation of my ensemble model doesn’t exhibit such a sharp decline; it is still in the positive territory. The question that arises is: why do the actual betting outcomes significantly differ from the simulated ones?
Mollybet data
I utilize MollyBet as my betting broker, which allows me to export data related to the placed bets. I have integrated this data into my system to compare it with the betting simulation. This integration enables a detailed analysis of the variance between the actual bets placed through MollyBet and the predictions made by my betting simulation model. By leveraging this comparison, I aim to identify discrepancies and refine my strategies for more accurate future predictions.
Betting Analysis
In dissecting the disparities between my real bets and the simulation, it becomes evident that while the simulation adheres to a conservative approach with a 1-unit flat stake strategy, my real betting strategy introduces tactical variations. The simulation strategically focuses on singular bets, emphasizing home wins, draws, or away wins based on calculated value. In contrast, my approach incorporates Asian Handicap bets selectively, particularly when e.g. both home win and draw present value. This tactical inclusion aims to lower average odds, thereby reducing variance and enhancing overall betting portfolio stability.
The bets can be categorized into three groups for analysis:
Same Bet: Instances where my bet perfectly aligns with the simulation, showcasing precise agreement between real-time intuition and the model’s simulation.
Same Side: Bets that, while different in specifics, share the same fundamental prediction. For example, if the simulation predicts a home win, my bets might include both a home win and a draw.
Wrong Side: Instances where my bets significantly diverge from the simulation. For instance, if the simulation selects a home win, my bet might be on an away win, highlighting discrepancies between the model’s predictions and my betting choices.
This categorization provides nuanced insights into the dynamics of real-time betting choices. By analyzing these categories, a deeper understanding of the factors impacting betting outcomes emerges, fostering continual refinement of strategies for more precise future predictions.
A quantitative analysis of my betting patterns against the simulation data yields intriguing insights. Out of all instances, my bets aligned precisely with the simulation in 44.6% of cases. Furthermore, in 28.94% of scenarios, my bets mirrored the simulation’s side through Asian Handicap selections, indicating a degree of strategic harmony. The remaining 26.46% marked a significant deviation, where my bets and the simulation were entirely disparate, showcasing diverse perspectives on the match outcomes.
At the outset, I had reservations about the effectiveness of Asian Handicap bets. However, a meticulous analysis of average profits paints a nuanced picture, challenging my initial assumptions.
Same Bets:
The simulation yielded a 5.6% profit, mirroring my real profit at 4.5%. This slight variance aligns precisely with expectations, corresponding to the bookmaker’s margin. Notably, the simulation operates on fair odds without margins, making this alignment a positive outcome.
Bets on the Same Side:
My real placed bets in this category faced substantial losses. However, this outcome is acceptable, considering that the simulation also displayed losses here.
Different Bets:
The crux of the issue lies here. The simulation demonstrated a robust 14.6% profit, whereas my real-world results were negative. This significant disparity begs the question: what accounts for this difference?
“Wrong side” matches
In light of these findings, I delved into the specifics by closely examining individual matches. What became evident was that many of the selections were made based on rather small value differentials. The choice of bets often hinged on minimal variations in calculated values.
Match: Roma – Monza (22.10.2023)
Placed Bet: Draw / Away (2.301)
Betting simulation: Home
Final Result: Home
| Home | Draw | Away | |
|---|---|---|---|
| Odds | 1.76 | 3.86 | 5.82 |
| Value | 0.027 | -0.068 | 0.019 |
Match: Lens- Lille (08.10.2023)
Placed Bet: Home (2.301)
Betting simulation: Draw
Final Result: Draw
| Home | Draw | Away | |
|---|---|---|---|
| Odds | 2.35 | 3.86 | 3.18 |
| Value | -0.009 | 0.035 | -0.013 |
Building upon the insights gained, I conducted a detailed analysis of my betting profits categorized by different value groups. This meticulous examination served to reinforce my observations from scrutinizing individual matches.
It became clear that within the narrow value range of < 0.05, the majority of my “wrong side” bets were clustered, which deviated significantly from the simulation’s predictions, contributed significantly to the stark contrast between the simulation and my real-world betting results. The concentration of discrepancies in this small value bracket underscored the challenges faced in accurately predicting outcomes within this nuanced range.
Filtering matches with low value
In response to these revelations, I applied the insights gained to the analysis of the comparison groups. Specifically, I filtered all bets with a value of less than 0.05.
This strategic filtering led to a notable improvement. The substantial losses incurred in the “Wrong-Side” bets were effectively mitigated; this specific group no longer exhibited significant disparities. Interestingly, both the “Same Bet” and “Same Side” groups maintained their similar patterns even after this refinement.
Conclusions
Reflecting on the insights gathered from this comprehensive analysis, several key conclusions emerge, shaping the way forward in my betting endeavors.
Utilizing Asian Handicap Bets with Dual-Value Markets:
The strategic incorporation of Asian Handicap bets in scenarios where value exists across two markets proves to be a viable approach. This tactic offers flexibility and capitalizes on nuanced opportunities, aligning with both the simulation’s predictions and real-world outcomes.
Avoiding Bets with Low Value (<0.05):
Bets presenting minimal value differentials (<0.05) should be steered clear of. This range proved to be particularly challenging, leading to notable disparities between the simulation and actual results. By avoiding bets within this bracket, I mitigate the risk of inaccurate predictions and potential losses.
Diversification through League Expansion:
To counterbalance the reduction in the number of potential bets due to the value filter, diversification becomes key. Incorporating a broader range of leagues into my betting portfolio introduces varied dynamics and opportunities. Exploring additional leagues enhances the pool of potential high-value bets, providing a strategic countermeasure to the narrowed selection caused by the value filter.
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