Determinants of Scoring at TPC Summerlin

This analysis is based on scores and stats from individual rounds in the ten Tour events at TPC Summerlin: 4,280 rounds in total.

Section 1: Absolute Correlation Coefficients with Score

Key Points:

• SGApp consistently shows a high correlation with score, crucial for approach shots at TPC Summerlin.
• SGTee and SGP vary in their importance, affected by conditions like wind or pin placements.
• SGATG has the weakest correlation with score, suggesting recovery play is less influential.

Key Points:

• Greens in Regulation (GIR) shows a high correlation, critical at TPC Summerlin.
• Driving Distance and Driving Accuracy have fluctuating importance year by year.
• Scrambling is less critical compared to GIR and PPGIR.

Key Points:

• Par4 performance consistently correlates with score, important at TPC Summerlin.
• Par5 scoring shows a weaker correlation, possibly due to easier birdie opportunities.
• Par3 has a moderate impact on overall score, less critical than par-4s.

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:

• SGApp is more important at TPC Summerlin compared to the PGA Tour average.
• SGTee plays a less crucial role, possibly due to the shorter course layout.
• SGP is slightly more influential, reflecting the importance of putting at this course.

Key Points:

• Greens in Regulation (GIR) is the dominant factor, more important than the PGA average.
• Driving Accuracy has a higher relative importance than the PGA average.
• Scrambling is less crucial at TPC Summerlin, as players avoid trouble better.

Key Points:

• Par4 performance remains the most important factor, consistent with the course layout.
• Par3s play a larger role than the PGA average, indicating a challenge at TPC Summerlin.
• Par5 performance has a lower relative impact, suggesting fewer scoring opportunities on these holes.

Top 5 Ranked Players - 2024 Shriners Childrens Open

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.