Determinants of Scoring at The Renaissance Club
This analysis is based on scores and stats from individual rounds in the last 5 Tour events at The Renaissance Club: 2,245 rounds in total.
Section 1: Absolute Correlation Coefficients with Score
The plot shows how the absolute value of the correlation coefficients between Score and the four factors (SGTee, SGApp, SGATG, and SGP) varies by year.
SGTee (Strokes Gained Tee-to-Green): The correlation between Score and SGTee varies but remains relatively lower compared to other factors. The highest correlation is observed in 2020, and it decreases in subsequent years. SGTee is consistently less correlated with the overall score. This suggests that while driving accuracy and distance are important, they are not the most critical factors influencing scores at the Renaissance Club.
SGApp (Strokes Gained Approach): SGApp shows a consistently high correlation with Score across all years, with some variation. The highest correlations are observed in 2020 and 2021. This indicates that approach shots are crucial at the Renaissance Club. Players who perform well in their approach shots tend to have better scores, highlighting the importance of precision in hitting the greens.
SGATG (Strokes Gained Around-the-Green): The correlation between Score and SGATG is moderate and shows some fluctuation across the years. The correlation tends to be lower than SGApp and SGP but higher than SGTee. This suggests that while short game skills around the green are important, they are less critical than approach shots and putting in determining the overall score at this course.
SGP (Strokes Gained Putting): SGP shows a high correlation with Score, especially notable in 2021 and 2022. This factor remains significant across all years. Putting is one of the most significant factors influencing scores at the Renaissance Club. Good putting can greatly improve a player's score, indicating the importance of proficiency on the greens.
The analysis shows that at the Renaissance Club, approach shots (SGApp) and putting (SGP) are consistently the most important factors influencing players' scores. Driving accuracy and distance (SGTee) and short game around the green (SGATG) are also relevant but less critical.
The plot shows how the absolute value of the correlation coefficients between Score and five factors (DrivingDistance, DrivingAccuracy, GreensInRegulation, Scrambling, and PPGIR) varies by year.
Driving Distance: The correlation between Score and Driving Distance is relatively low across all years, with some fluctuations. The highest correlation is seen in 2020. This suggests that while driving distance is a factor, it is not the most critical in determining scores at the Renaissance Club. Other aspects of the game play a more significant role.
Driving Accuracy: Driving Accuracy shows a moderate correlation with Score, with noticeable increases in 2022 and 2023. Accuracy off the tee is more important than distance, especially in recent years. This indicates that keeping the ball in play and avoiding penalties is crucial at the Renaissance Club.
Greens in Regulation (GIR): GIR consistently shows a high correlation with Score across all years, with peaks in 2020 and 2022. Hitting greens in regulation is vital for scoring well at the Renaissance Club. This factor highlights the importance of accurate approach shots and the ability to hit the greens consistently.
Scrambling: Scrambling shows a moderate to high correlation with Score, with the highest values observed in 2022. The ability to recover and save par when missing the green is crucial. Scrambling skills are essential, especially on a challenging course like the Renaissance Club.
Putting per GIR (PPGIR): PPGIR exhibits a high correlation with Score, especially notable in 2021 and 2023. Putting is consistently one of the most important factors. This emphasizes the importance of putting proficiency at the Renaissance Club.
At the Renaissance Club, hitting greens in regulation (GIR) and putting (PPGIR) are consistently the most critical factors influencing players' scores. Scrambling also plays a significant role, particularly in years when conditions may make hitting greens more challenging. Driving Accuracy is more important than Driving Distance, indicating that precision off the tee is more valuable than power.
The plot shows how the absolute value of the correlation coefficients between Score and three factors (Par3, Par4, and Par5 scores) varies by year.
Par3 Scores: The correlation between Score and Par3 scores is moderate to high, with noticeable increases in 2022 and 2023. Performance on Par3 holes has a significant impact on overall scores at the Renaissance Club, especially in the recent years.
Par4 Scores: Par4 scores show the highest correlation with overall scores across all years, consistently remaining the most significant factor. Par4 performance is the most critical factor influencing scores at the Renaissance Club.
Par5 Scores: The correlation between Score and Par5 scores is moderate, with higher values in 2020 and 2021, and a decline in 2023. While Par5 performance is important, it is less critical than Par4 and Par3 scores.
At the Renaissance Club, performance on Par4 holes is consistently the most critical factor influencing players' scores. Par3 holes have become increasingly important in recent years, highlighting the need for precision and accuracy on these shorter holes. While Par5 performance is also relevant, it is less critical than Par4 and Par3 scores.
Section 2: Partial Dependence Plots against Score
Partial dependence plots (PDPs) are a tool used in machine learning and statistical modeling to illustrate the relationship between a target variable and one or more feature (e.g. SGApp, SGATG, DrivingDistance, GreensInRegulation). They show the marginal effect of a feature on the predicted outcome of a model. PDPs are particularly useful for understanding how individual features impact the target variable, allowing for better interpretation and insights from the model.
In determining the value of Score, PDPs can help visualize how changes in each feature impact the predicted score, holding other features constant. This can provide insights into which features are most influential and how they affect the score.
The partial dependence plots (PDPs) for SGTee, SGApp, SGATG, and SGP against Score provide insights into the influence of these factors on the overall score at the Renaissance Club.
SGTee (Strokes Gained Tee-to-Green)
The PDP shows a moderate effect on the score as SGTee increases. This indicates that better performance off the tee improves scores but is not the dominant factor.
SGApp (Strokes Gained Approach)
The PDP for SGApp indicates a strong effect on the score, suggesting that better approach shots significantly lower scores, highlighting the importance of precision in approach shots at the Renaissance Club.
SGATG (Strokes Gained Around-the-Green)
The PDP shows a moderate effect on the score as SGATG increases, indicating that a better short game around the greens helps in reducing the score.
SGP (Strokes Gained Putting)
The PDP shows a strong effect on the score with increasing SGP, emphasizing the critical role of putting in achieving better scores.
The partial dependence plots for DrivingDistance, DrivingAccuracy, GreensInRegulation, Scrambling, and PPGIR against Score reveal the relative importance of these factors at the Renaissance Club.
Driving Distance
The PDP shows a weak effect on the score, suggesting that while driving distance matters, it is not a primary determinant of lower scores.
Driving Accuracy
The PDP shows a moderate effect on the score, indicating that better driving accuracy helps in achieving lower scores.
Greens in Regulation (GIR)
The PDP shows a strong effect on the score with increasing GIR, underscoring the importance of hitting greens in regulation to achieve lower scores.
Scrambling
The PDP indicates a moderate effect on the score, suggesting that better scrambling ability helps in reducing the score.
Putting per GIR (PPGIR)
The PDP shows a strong effect on the score with better PPGIR, emphasizing the importance of putting after hitting greens in regulation.
The partial dependence plots for Par3, Par4, and Par5 against Score highlight the impact of performance on different hole types at the Renaissance Club.
Par3 Scores
The PDP shows a moderate effect on the score, indicating that better performance on Par3 holes is important for achieving lower scores.
Par4 Scores
The PDP shows a strong effect on the score, suggesting that excellent performance on Par4 holes is crucial for lower scores.
Par5 Scores
The PDP shows a moderate effect on the score, indicating that while good performance on Par5 holes helps, it is less critical than Par3 and Par4 holes.
Section 3: 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.
The bar chart shows the relative importance of SGTee, SGApp, SGATG, and SGP on the score. The percentages indicate how much each factor contributes to the prediction of the score.
SGTee (Strokes Gained Tee-to-Green): 14.48%
The chart shows that SGTee has a moderate impact on the score. Improving performance off the tee can help improve overall scores, but it is not the most critical factor.
SGApp (Strokes Gained Approach): 38.58%
SGApp is shown to be the most important factors. Better approach shots significantly contribute to lower scores, highlighting the importance of precision in hitting the greens.
SGATG (Strokes Gained Around-the-Green): 13.29%
SGATG has a moderate impact on the score. A good short game around the greens is beneficial but less critical than approach shots and putting.
SGP (Strokes Gained Putting): 33.66%
SGP is the second most critical factor. Excellent putting greatly improves a player's score, emphasizing the importance of proficiency on the greens.
At the Renaissance Club, putting (SGP) and approach shots (SGApp) are the most significant factors influencing players' scores. Performance off the tee (SGTee) and around the green (SGATG) are also important but to a lesser extent.
The bar chart shows the relative importance of DrivingDistance, DrivingAccuracy, GIR, Scrambling, and PPGIR on the score. The percentages indicate how much each factor contributes to the prediction of the score.
Driving Distance: 5.74%
The chart shows that Driving Distance has a lower impact on the score. While it contributes to the overall performance, it is not the primary determinant of lower scores.
Driving Accuracy: 3.68%
Driving Accuracy has a moderate impact on the score, similar to Driving Distance.
Greens in Regulation (GIR): 24.41%
GIR is one of the most critical factors. Hitting greens in regulation significantly contributes to lower scores, highlighting the importance of accurate approach shots.
Scrambling: 22.22%
Scrambling has a moderate impact on the score. The ability to recover and save par when missing the green is essential for good performance.
Putting per GIR (PPGIR): 43.95%
PPGIR is the most critical factor. Excellent putting after hitting the greens greatly improves a player's score.
At the Renaissance Club, hitting greens in regulation (GIR) and putting (PPGIR) are the most significant factors influencing players' scores. Scrambling is also important but to a lesser extent. Driving distance and accuracy have the least impact on scores.
The bar chart shows the relative importance of Par3, Par4, and Par5 scores on the overall score. The percentages indicate how much each factor contributes to the prediction of the score.
Par3 Scores: 18.28%
The chart shows that Par3 scores have a moderate impact on the overall score. Good performance on Par3 holes is important for achieving lower scores.
Par4 Scores: 62.53%
Par4 scores are the most critical factor. Excellent performance on Par4 holes significantly contributes to lower scores, highlighting the importance of consistency on these holes.
Par5 Scores: 19.19%
Par5 scores have a moderate impact on the overall score. While good performance on Par5 holes helps, it is less critical than Par4 holes.
Summary
At the Renaissance Club, performance on Par4 holes is the most critical factor influencing players' scores, while Par3 and Par5 holes have a moderate impact.
Top 5 Ranked Players - 2024 Scottish Open
The table below shows the top-5 ranked players and their average estimated scores from the three different Random Forest models above.
| Player |
Score |
| Ludvig Aberg |
68.02 |
| Xander Schauffele |
68.36 |
| Rory McIlroy |
68.41 |
| Erik Van Rooyen |
68.49 |
| Davis Thompson |
68.70 |
Estimated scores for all players can be found here.