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To effectively analyse the data, we applied feature encoding and scaling techniques. Categorical features were encoded using the LabelEncoder, assigning unique numerical labels to each category. Numerical features were standardised using the StandardScaler, ensuring all features were on a similar scale for accurate comparison.
To establish a baseline profile for the typical Galway winner, we calculated the median values of the historical winners' encoded and scaled feature set. This allowed us to identify the most representative combination of attributes that characterised the typical winner.
To determine the similarity between each historical winner and the most typical winner, we employed cosine similarity. This metric enabled us to measure the similarity in feature space, providing a quantifiable measure of how closely each historical winner matched the most typical winner.
Recognising that certain features carry more significance in determining the winner, we assigned weights to the important elements within the data. These weights were based on domain knowledge and expert insights. The distance of previous performances, most recent race performance, breeding, key Trial races, betting positions were given higher weights to reflect their greater impact on the prediction.
Next, we compared the current contenders for the 2024 Galway Plate against the most typical winner using the calculated similarity scores. Each contender's feature set was encoded and scaled following the same methodology used for historical winners. The similarity scores were adjusted by multiplying them with the respective weights of the important features.
To identify the most likely winner from the current contenders, we aggregated the adjusted similarity scores for each contender. The median value was chosen as the summary statistic for the overall similarity scores. By considering both the feature similarity and the weighted importance of features, we obtained a comprehensive measure of each contender's likelihood of winning the Galway Plate.
This prediction suggests that Life In The Park would most likely win the Galway Plate according to our ML model. A cosine similarity score of 0.994 suggests a relatively high degree of similarity between the most similar contender and the average historical winner. The closer the score is to 1, the more similar the contender is to the historical winners in terms of their features used in the analysis.
