Determinants of Scoring at Narashino Country Club

This analysis is based on scores and stats from individual rounds in the four Zozo Championships at Narashino Country Club: 1,237 rounds in total.

Section 1: Absolute Correlation Coefficients with Score

Key Points:

• DrivingAccuracy and Scrambling show higher correlations, highlighting the importance of accuracy and recovery.
• DrivingDistance has a minimal impact on Score, reflecting the precision required at Narashino Country Club.
• GreensInRegulation and PPGIR demonstrate moderate impact, showcasing the importance of approach play and putting.

Key Points:

• Par3 holes have consistent correlation with Score, indicating their difficulty and importance at the venue.
• Par4 holes contribute more to scoring performance in many years, typical of most courses.
• Par5 holes show lower correlations, suggesting less impact on overall score performance.

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.

Key Points:

• GreensInRegulation and PPGIR are the most important metrics, emphasising precision and putting.
• Scrambling is crucial, reflecting the importance of recovery shots.
• DrivingDistance and DrivingAccuracy have lower importance compared to most PGA Tour courses.

Key Points:

• Par4 holes are the most critical, reflecting the demanding layout of Narashino's par-4s.
• Par5 holes are less significant, suggesting fewer scoring opportunities on these holes.
• Par3 holes are moderately important, indicating their role as challenging but not score-defining.

Top 5 Ranked Players - 2024 Zozo Championship

The table below shows the top-5 ranked players and their average estimated scores from the different Random Forest models above.

Estimated scores for all players can be found here.