Determinants of Scoring at The Old White

This analysis is based on scores and stats from individual rounds in the last 10 Tour events at The Old White: 4,201 rounds in total.

Section 1: Absolute Correlation Coefficients with Score

Section 1 - Correlation Coefficients

Absolute Correlation between Score and Traditional Metrics

Driving Distance: This metric often shows an increasing correlation with the score, particularly in recent years where The Old White course favours long hitters.

Driving Accuracy: The correlation with the score can fluctuate significantly year by year. On The Old White course, accuracy off the tee can be critical, but appears to be less so in recent years.

Greens in Regulation (GIR): This metric typically has a consistently strong and very stable correlation with the score.

Scrambling: The correlation of scrambling with score is generally moderate, though higher than for the driving metrics.

PPGIR (Putts per GIR): This metric usually has a strong correlation with score, with the highest absolute correlation coefficient across all ten yers.

Absolute Correlation between Score and Par Metrics

Par 3: The correlation between score and Par 3 performance is generally very consistent at The Old White.

Par 4: As with other courses, the Par 4 holes have a strong correlation with the overall score on this course.

Par 5: The correlation with Par 5 performance is the lowest of the three metrics across nine of the ten years.

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 Traditional Metrics on Score

Results:

Driving Distance: Although important, Driving Distance shows a lower impact compared to other metrics, accounting for just over 5% of the relative importance. This suggests that while distance is beneficial, it's not as crucial for scoring well at The Old White, likely due to the course's design which doesn't overly penalize shorter drives if accuracy and approach play are solid.

Driving Accuracy: The relative importance of Driving Accuracy is surprisingly low at 2.60%. This could indicate that the course's rough or hazards are not overly punishing, allowing players to recover even after missing the fairway.

Greens in Regulation (GIR): With a relative importance of 23.28%, GIR is a critical factor. Successfully hitting greens in regulation significantly contributes to lower scores.

Scrambling: Scrambling is almost as important as GIR, with a relative importance of 23.27%. This emphasizes the importance of recovery skills on this course.

PPGIR: The most significant factor is PPGIR at 45.62%. This highlights the importance of putting efficiency; converting birdie chances is a key determinant of success at The Old White.

Summary: The analysis indicates that putting (PPGIR) and approach play (GIR and Scrambling) are the most critical factors in achieving a low score at The Old White. This is consistent with the course's design, where precise iron play and solid putting on its challenging greens are essential for scoring well. Compared to the LIV Golf averages (Driving Distance: 9.94%; Driving Accuracy: 5.09%; GIR: 30.87%; Scrambling: 27.46%; PPGIR: 26.64%), The Old White places even more emphasis on PPGIR, reflecting the specific challenges of its greens.

Relative Importance of Par Metrics on Score

Results:

Par 3: The Par 3 holes have a moderate impact on the overall score.

Par 4: The overwhelming importance of Par 4 performance (72.01%) suggests that these holes are the true differentiators on the course. Success on Par 4s, which make up the bulk of the course, is crucial for a low score.

Par 5: Par 5s contribute the least to the overall score, with a relative importance of 11.85%. This suggests that these holes are more forgiving, offering birdie opportunities that do not significantly differentiate the field.

Summary: Performance on Par 4s is the most critical factor at The Old White, far surpassing the importance of Par 3s and Par 5s. This suggests that the ability to navigate these holes is key to success. When compared to LIV Golf averages (Par 3: 18.47%; Par 4: 64.76%; Par 5: 16.77%), The Old White places an even greater emphasis on Par 4 performance, reinforcing the idea that consistent play on these holes is vital.

Top 5 Ranked Players - 2024 LIV Golf Greenbrier

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

Player Score
Joaquin Niemann 68.58
Jon Rahm 68.58
Tyrrell Hatton 68.65
Louis Oostzuizen 68.75
Talor Gooch 69.03

Estimated scores for all players can be found here.