Determinants of Scoring in the Alfred Dunhill Links Championship

This analysis is based on scores and stats from individual rounds in the last ten events in the Alfred Dunhill Links Championship: 5,229 rounds in total.

Section 1: Absolute Correlation Coefficients with Score

Absolute Correlation between Score and SG Metrics

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Absolute Correlation between Score and Traditional Metrics

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Absolute Correlation between Score and Par Metrics

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Section 2: Importance of Each Metric in Determining Score

Random Forest Regressor and Feature Importance

Random Forest Regressor is an ensemble learning method that constructs multiple decision trees during training and outputs the average prediction. It combines the predictions of several models to improve accuracy and robustness.

Feature importance is a technique used to interpret a machine learning model. It refers to the score that quantifies the contribution of each feature to the prediction made by the model.

In a Random Forest, the importance of a feature is computed by looking at how much the feature decreases the impurity (e.g., variance for regression tasks) across all the trees in the forest. The more a feature decreases the impurity, the more important it is considered.

The calculated importance scores for all features are then normalized to give relative importance as a percentage. This shows the relative contribution of each feature to the prediction task.

Interpreting Feature Importance

Features with high relative importance percentages have a strong impact on the model's predictions. They are crucial for accurate predictions and indicate key areas where performance matters most.

Features with low relative importance have a minimal impact on the model's predictions. While they can still contribute, they are less critical.

Relative Importance of SG Metrics on Score

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Relative Importance of Traditional Metrics on Score

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Relative Importance of Par Metrics on Score

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Top 5 Ranked Players - 2024 Alfred Dunhill Links Championship

The table below shows the top-5 ranked players across the three different Random Forest models above.

Rank Surname Firstname Avg Predicted Score
1 Rahm Jon 69.15
2 Koepka Brooks 69.47
3 Hatton Tyrrell 69.48
4 Mckibbin Tom 69.51
5 Mcilroy Rory 69.56

Rankings and estimated scores for all players can be found here.