-Item Introduction-

Methods to Earn from Japan’s Public Gambling, Horse Racing!

-Item Introduction-
The Soul World
The Soul World
-Item Introduction-

Methods to Earn from Japan’s Public Gambling, Horse Racing!
Japan is a country with a strong presence in public gambling. In Japan, government agencies monopolize gambling to create their own hidden funds. However, Japan’s public gambling industry has relatively low levels of fraud and enjoys a significant fan base. Especially in the case of horse racing, under the Ministry of Agriculture, Forestry and Fisheries, it has become a major source of revenue, with substantial amounts involved. How about using AI to conquer such Japanese horse racing?

become successful

You could establish a fund in your own country and make a killing from Japanese horse racing! With this in mind, we’ve contemplated AI development methods that could be applied to horse racing predictions. Theoretically, it’s possible to develop AI models that predict the future based on past data. However, due to the multitude of factors influencing horse racing outcomes, achieving 100% accurate predictions is difficult. Nevertheless, attempts to predict horse racing results using statistical models and machine learning algorithms have been made. Here is an overview of the common approach:

1.Data Collection

Collect historical horse racing result data, including past race performance data, rankings, track conditions, and jockey information for each horse.

2.Data Preprocessing

Clean the collected data and select/encode features. Proper feature selection is crucial.

3.Model Selection

Choose a machine learning algorithm. For time-series data, recurrent neural networks (RNNs) or long short-term memory (LSTM) networks might be used. Other options such as random forests and gradient boosting can also be considered.

4.Model Training

Train the chosen model with historical data. During training, use the features and corresponding outputs (e.g., rankings).

5.Model Evaluation

Evaluate the predictive accuracy of the model on unknown data. Assess the model’s performance using techniques like cross-validation or test datasets.


Use the trained model to predict future horse racing outcomes. However, note that predictions are probabilistic, and perfect predictions are challenging.

7.Real-time Updates

As horse racing information updates over time, the model needs periodic retraining to adapt to the latest data.
It’s important to remember that horse racing involves many variables, limiting the accuracy of models. Additionally, unexpected events can occur despite past data. When using AI for predictions, consider the results as reference and make decisions based on comprehensive consideration of other information.”