Determinants of Scoring at Silverado Resort
This analysis is based on scores and stats from individual rounds in the ten Tour events at Silverado Resort: 4,424 rounds in total.
Section 1: Absolute Correlation Coefficients with Score
Key Points:
- SGApp (Strokes Gained Approach) is the most critical metric for scoring well at Silverado Resort.
- SGP (Strokes Gained Putting) shows moderate importance, reflecting the role of putting in overall performance.
- SGTee (Strokes Gained Off the Tee) has a weaker impact, suggesting driving is less influential at Silverado.
Key Points:
- Greens in Regulation (GIR) is the strongest traditional metric correlated with scoring at Silverado.
- Driving Accuracy is more important than Driving Distance, emphasising precision off the tee.
- Scrambling shows variability but is important for recovery when greens are missed.
Key Points:
- Performance on Par 4 holes is crucial for achieving a good score at Silverado Resort.
- Par 5 holes provide moderate scoring opportunities, though less critical than Par 4s.
- Par 3 performance shows a weaker correlation, suggesting it is less decisive for overall scoring.
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:
- SGP (35.83%) and SGApp (34.70%) are the most important factors for scoring at Silverado Resort.
- SGTee and SGATG have less influence, contributing around 14.5% each to the score.
- Compared to the PGA Tour averages, putting and approach play are more critical at Silverado.
Key Points:
- PPGIR (43.10%) and Scrambling (25.91%) are the most critical traditional metrics at Silverado Resort.
- Greens in Regulation plays a significant but lesser role (22.82%).
- Tee shots, both distance and accuracy, have a minimal impact compared to recovery and putting.
Key Points:
- Par 4 performance (58.13%) is the most crucial factor for scoring at Silverado Resort.
- Par 5 performance (25.18%) is more important than Par 3, suggesting scoring on long holes is critical.
- Par 3 performance is the least influential, similar to PGA averages.
Top 5 Ranked Players - 2024 Procore Championship
The table below shows the top-5 ranked players and their average estimated scores from the different Random Forest models above.
| Rank | Surname | Firstname | Average Predicted Score |
|---|---|---|---|
| 1 | Clanton | Luke | 69.55 |
| 2 | Kim | Chan | 70.16 |
| 3 | Theegala | Sahith | 70.16 |
| 4 | Bridgeman | Jacob | 70.27 |
| 5 | Meissner | Mac | 70.32 |
Estimated scores for all players can be found here.