Horse Racing 2.0: The Impact of Machine Learning on Betting Strategies

In the world of technological progression, a singular phenomenon prominently emerges due to its metamorphic influence on various sectors: the swift ascendancy of Artificial Intelligence (AI). 

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This trailblazing technology has reshaped conventional methodologies and sparked new utilizations across a multitude of industries

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A specific industry that has warmly welcomed the influence of AI in unexpected dimensions is horse racing. 

Previously a field exclusively dependent on human acumen and intuition, horse racing presently situates itself at the crossroads of heritage and modernity, with AI’s predictive mastery and analytical potency being utilized to decipher the complexities of this venerable sport.

Everyone has unique strategies when it comes to wagering on a horse race. Some individuals choose contenders based on their preceding performances. Others make selections guided by instinct or emotion, while some may consistently opt for the crowd’s favorite

Machine Learning

There are no right or wrong strategies in betting. However, an aspect that frequently goes unnoticed is lineage. Does the chosen horse inherit exceptional genetic attributes? This consideration could play a pivotal role in influencing racetrack performance

Consider the integration of AI and machine learning into this decision-making process. Can these sophisticated technologies aid in foretelling the results of a race?

Would it be nice to deploy a machine that will accurately predict the Breeders’ Cup Classic results
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? Of course, we all want that!

A few years prior, posing this question would have elicited an unequivocal “NO.” However, with the recent strides made in AI and machine learning, our understanding of their potential has become somewhat clearer.

What is Machine Learning? 

Machine learning is prevalently employed in various chronological analyses and prognostications. Armed with an abundance of historical data and augmented computing capacities, ML models can occasionally furnish profoundly valuable perspectives and directions for sports wagering decisions.

Machine learning operates as a subset of artificial intelligence, broadly conceptualized as machines’ ability to mimic intelligent human actions. AI systems execute intricate tasks, aligning closely with human problem-solving approaches.

It is without a doubt one of the most anticipated technologies of all time, which is why many businesses invest in AI and machine learning nowadays.

AI aspires to formulate computer models capable of exhibiting “intelligent behaviors” akin to human actions, meaning machines capable of interpreting visual scenes, comprehending naturally phrased texts, or executing physical world actions.

The Importance of Data 

Machine learning is nothing without data. The idea is to have enough data on horse races that will allow the algorithm to calculate probability and to find a pattern that is most likely to happen.

Let’s get one thing straight from the start, machine learning cannot predict a horse race with 100% accuracy. Not in a million years.

Horse racing is a sport where the outcome depends on many factors both in and out of control of the horse owner. 

Machine learning has been present in the horse racing betting industry, and most of these models track a lot of data that is impossible to handle for humans, even not for experienced handicappers.

They collect:

Several parameters are meticulously analyzed, encompassing:

  • The age of the horse, its draw, and odds five minutes before the race show a minimal correlation with the likelihood of winning. 
  • Emerging attributes derived from historical performances (for instance, results from the last five matches, previous odds, total victories in the past 180 days, completion times, etc.) appear to be comparatively insightful. 
  • Incorporating external information such as weather conditions, temperature, the horse’s origin, and jockey details enhances the efficacy of tree-based models. 
  • Utilizing various binary classification models to estimate the probability of securing the first position or being among the top three yields improved outcomes. 
  • Employing model stacking approaches (such as NN, XGBT, GBRT, Linear, etc.) notably amplifies performance levels. 
  • Predictive outcomes (probabilities of winning) ought to be recalibrated and standardized in relation to other participants in the same race. 
  • Implementing target encoding for both the horse and jockey significantly augments the model’s effectiveness. 
  • In addressing this as a time series challenge, prioritizing time-based cross-validation becomes essential for optimizing performance and fine-tuning parameters.

However, going through all the factors even with big data, won’t be able to predict the winner of the race with 100% accuracy.

With that said, we can always use machine learning to narrow down the selection of horses. Most machine learning models that have already been used in the process of horse racing betting give you three horses that have the highest probability of winning a race.

This is where you have to step in as a bettor and create a strategy (approach) to the data given by the AI model. 

Crafting a Betting Strategy

Upon crafting a model that proficiently predicts the top one and top three winning probabilities of each race, substantial time is necessitated for experimentation and research to harness positive returns from these models. 

Given the prevailing uncertainties and rigorous human interventions to mitigate unfair advantages in horse racing, the formulation of a strategic betting approach becomes paramount. Through extensive empirical testing against actual races, I have delineated a strategy comprising three fundamental precepts:

  • Expectation Return Ratio: This strategy pragmatically modulates risk exposure, representing a mathematically optimized approach beyond merely amplifying expected returns. It meticulously balances the expected return against the associated risk, relying on probabilities discerned from one’s anticipatory judgments rather than definitive certainties.
  • Lowest Risk Betting: This tactic embodies a structured rule-set guiding bet selection, the determination of wager amounts, and subsequent bet placements. Envisaging a longer-term perspective, it advocates a conservative approach, fostering gradual profitability through minimized risks and moderated returns.
  • Kelly Criterion: Unlike Conventional betting paradigms, such as Martingale and D’Alembert primarily suited to even-money casino stakes, the Kelly Criterion introduces a nuanced methodology specific to sports betting. 

It eschews simplistic bet-doubling postulates, concentrating instead on optimizing wager sizes in alignment with the evaluated success probabilities of bets. Integral to this strategy is the art of handicapping, necessitating a precise estimation of actual winning odds, as opposed to mere reliance on externally stipulated odds. 

Carl Petoskey is an accomplished writer with years of experience covering the tech industry. When he's not writing you can find him covering companies like Comcast Business Class.