of horse races data. We used artificial neural network and logistic regression models to train then test to prediction without graph-based features and with. codebonus1xbet.website › R › Home › Forums › Statistical Software. Conditional logistic regression has remained a mainstay in predicting horse racing out- comes since the 's. In this paper, we propose. BEST ONLINE SPORTS BETTING SITES PAYPAL BILL

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In the same timeframe, both the Logistic Regression and Random Forest classifiers made selections that resulted in smaller losses than the benchmarks. Additionally, the win rate of each model had a significant increase. Single unit bets made only on the TIPS favorite accurately predicted the winner in Logistic Regression, however, predicted Additionally, the models were able to track the better performing index, rising as Tips returns diverged from Odds in November, and increasing again in February as Tips returns turned upwards.

While additional data will be needed for a higher level of confidence, it appears that the Paddock data is, in fact, a confounding variable which leads to enhanced prediction accuracy and reduces overall loss under a one unit betting strategy. If no horse in a race exceeded the threshold then no bets were placed. This highlights the value of correctly selecting horses in well-matched races as well as long-shots to drive ROI.

A major concern from this analysis was the distribution of the probability to win for the horses selected by the model to win a respective race. That is, how frequently a horse with probability to win of x had the highest percentage chance, as predicted by the model, to win its race.

The primary cause of this problem was the binomial nature of the modeling. When the win probabilities from the Logistic model are graphed for all horses, a large number of horses are given virtually no chance to win the race. As the project moves forward and a multinomial is exchanged, these probabilities will decline. Interestingly, the Random Forest classifier had more difficulty in identifying strictly losing horses.

Moving forward, the Kelly Criterion, also known as the scientific gambling method, will take these probabilities as inputs and allocate funds accordingly. Until these probabilities are brought in line, no asset allocation can be completed. Data Takeaways The initial challenge faced surrounded the data itself.

While the TIPS Index dataset spanned five years, the Paddock data only included observations for one season of racing. This choice was made to a large extent so that the results could be clear to the client, and the team would be able to present options of how to remedy issues that were encountered in the data. As this project would eventually be moved to an internal team, transparency around decisions was vital.

While the model did outperform both indexes, the sensitivity of the models required significant improvement before they could be implemented. The need for more observations was clear. Several techniques could have been used to increase observations of the minority class in this situation.

However, within the horse racing community there are domain-based practices of resampling with origins based in the Ranking Choice Theorem developed by Luce and Suppes This leads to an increase in the number of independent choice sets available for analysis and, ultimately, would provide more precise parameter estimates. Due to the limited amount of quantitative data, feature engineering appeared to be a prescient path to take in order to bolster prediction accuracy. Average lengths distance behind the winner appeared to be a good starting point.

For horses that won, this number would be the negative lengths between the horse and second place. However, the frequency distributions of races ran by a horse are heavily right-skewed, as many have run only one race. Additionally, this metric would be biased against horses that have never run before. In a random forest model, this issue could be slightly mitigated by using a dedicated large number to represent such horses but for the logistic model, a solution could not be so easily implemented.

However, the implementation of such a technique over all the variables in the dataset might not actually be necessary in this situation. It has already been discussed that bodyweight, trainer, and jockey are important variables from the Paddock dataset. Testing this theory using AIC or BIC over the aforementioned variables and additional variables from the Paddock dataset would be a very informative next step. After analyzing the average lengths behind the winner data the team saw a fairly frequent trend of high variance in horses that finished below fourth place.

Even winning horses would have runs that had a drastically negative impact on their average distance to the winner. Because of this, the team was back to not only the first question of how to manage horses that have not run but also how to manage horses that have not placed in the top four.

Several approaches were presented on how to manage this issue. The first was to One Hot Encode various distance gaps and add an additional label to signify a placing below fourth. To accomplish this task would require a SME who could validate the methodology and additional statistical analysis. Before delving into such an undertaking, the team decided to track whether or not wins-to-races ran as well as places-to-races ran was a strong indicator of future performance.

The team did this by separating the training and testing dataset by race date and then calculating total races won to total races for all horses in the training set. These numbers were then applied to each horse in the testing set with unseen horses receiving a value of zero. We were provided with race data for more than races from the and seasons for races held by Hong Kong Jockey Club.

To be profitable we had to first clear the hurdle of a We began with a deep-dive into the Kelly Criterion and an exploration of the data made available to us. Our original inclination was to develop linear models that could predict horse running times, build probability distribution functions around those predicted times, simulate races, and apply a betting algorithm to them. Instead, we opted to proceed with logistic and classification based modeling, as this process relaxed some of the prerequisites, and would more easily output to us winning probabilities that we could use to feed our betting model.

We engineered several features and imputed missing values on a feature by feature basis. We created several new features to try and better estimate the probability of a horse winning a race. Based on the assumption that horses that weigh in at close to their average winning body weight have a higher likelihood of winning, we created a binary flag to signal that.

We created a composite weighted winning percentage that also considered the recent number of wins. If a horse was new, it did not have an average horse body weight, so we imputed this feature with its previous weight. Using correlation matrices, random forest classification, and coefficient analysis on normalized variables, we evaluated the relative predictive power and importance of each feature.

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