Determinants of Scoring at Le Golf National
This analysis is based on scores and stats from individual rounds in the last 10 DP World Tour events at Le Golf National: 6,113 rounds in total.
Section 1: Absolute Correlation Coefficients with Score
The graph "Absolute Correlation between Score and SG Metrics" shows the variation in the absolute value of the correlation coefficients between the Score and various SG (Strokes Gained) metrics (SGTee, SGApp, SGATG, and SGP) by year.
- SGTee: The correlation between Score and SGTee shows a consistent upward pattern, indicating a significant influence of tee performance on the overall score. This highlights the importance of the initial shot in determining the score, aligning with the strategic importance of precision and power in tee shots, especially on a challenging course like Le Golf National.
- SGApp: The correlation between Score and SGApp (approach shots) demonstrates a notable influence across the years, signifying the critical role of approach shots. Consistently high correlation values suggest that players who excel in their approach shots tend to score better, which is crucial for navigating the undulating fairways and precise greens at Le Golf National.
- SGATG: The correlation with SGATG (around the green) fluctuates more compared to SGTee and SGApp, indicating variability in how short game skills impact the score year by year. This could be due to the varying course conditions and pin placements at Le Golf National, emphasizing the need for adaptability in the short game.
- SGP: The correlation between Score and SGP (putting) varies significantly, reflecting the challenges players face on the greens. The variability in correlation suggests that while putting is crucial, its impact on scoring can vary depending on the green conditions and player proficiency, particularly on the tricky greens of Le Golf National.
The graph "Absolute Correlation between Score and Traditional Metrics" illustrates how the absolute value of the correlation coefficients between the Score and traditional metrics (DrivingDistance, DrivingAccuracy, GreensInRegulation, Scrambling, and PPGIR) varies by year.
- DrivingDistance: The correlation between Score and DrivingDistance shows moderate influence, suggesting that while driving distance is important, it is not the sole determinant of success at Le Golf National. This aligns with the course's design, which places a premium on accuracy and strategic shot placement over sheer distance.
- DrivingAccuracy: The correlation with DrivingAccuracy is consistently significant, highlighting the importance of accuracy off the tee. Given the penal rough and strategic hazards at Le Golf National, maintaining accuracy is crucial for achieving low scores.
- GreensInRegulation: The strong correlation with GreensInRegulation underscores the importance of reaching greens in regulation. This metric is consistently one of the most influential, reflecting the necessity of precision and control in approach shots to avoid the numerous hazards and difficult bunkers.
- Scrambling: The correlation between Score and Scrambling varies, indicating that the ability to recover from missed greens can be vital. This variability suggests that while scrambling is important, its impact can change depending on the course setup and conditions.
- PPGIR (Putts Per Greens In Regulation): The correlation with PPGIR is moderately high, emphasizing the importance of putting efficiency once on the green. This is particularly relevant at Le Golf National, where the greens can be challenging, and putting prowess can significantly influence the overall score.
The graph "Absolute Correlation between Score and Par Metrics" shows the variation in the absolute value of the correlation coefficients between the Score and Par metrics (Par3, Par4, and Par5) by year.
- Par3: The correlation with Par3 performance shows notable influence, indicating that scoring well on par 3s is important. These holes often require precision and can be challenging, making them significant in the overall scoring at Le Golf National.
- Par4: The correlation between Score and Par4 performance is consistently high, highlighting the importance of excelling on par 4s. Given that par 4s make up a substantial portion of the course, strong performance on these holes is critical for a good overall score.
- Par5: The correlation with Par5 performance varies more, suggesting that while par 5s offer scoring opportunities, their impact on the overall score can fluctuate. This variability might be due to the risk-reward nature of par 5s at Le Golf National, where strategic decision-making is key.
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 for SG metrics (SGTee, SGApp, SGATG, and SGP) against Score provide insights into how changes in these metrics impact the overall score.
- SGTee: The partial dependence plot for SGTee shows a negative slope, indicating that as the strokes gained from tee shots increase, the score tends to decrease. This reaffirms the importance of strong tee performance, as better tee shots contribute to lower scores, particularly at Le Golf National where precise tee shots are essential.
- SGApp: The plot for SGApp also exhibits a negative relationship with Score. Improved strokes gained on approach shots are associated with lower scores, emphasizing the critical role of approach accuracy and precision in achieving better scores on the challenging fairways and greens of Le Golf National.
- SGATG: The partial dependence plot for SGATG demonstrates variability, suggesting that while better performance around the green can lower scores, its impact may vary depending on specific conditions and player skills. This highlights the need for a solid short game to navigate the diverse challenges presented by the course.
- SGP: The plot for SGP indicates a negative correlation, where better putting performance (higher strokes gained) leads to lower scores. Efficient putting is crucial for scoring well, especially on the tricky greens of Le Golf National.
The partial dependence plots for traditional metrics (DrivingDistance, DrivingAccuracy, GreensInRegulation, Scrambling, and PPGIR) against Score provide valuable insights into their impact on overall performance.
- DrivingDistance: The plot for DrivingDistance shows a moderate negative correlation with Score. Increased driving distance generally leads to lower scores, but its impact is not as pronounced, suggesting that while distance is beneficial, it must be complemented by accuracy and strategy at Le Golf National.
- DrivingAccuracy: The partial dependence plot for DrivingAccuracy shows a significant negative correlation with Score. Higher driving accuracy consistently leads to lower scores, emphasizing the importance of precision off the tee in avoiding penalties and difficult lies on this demanding course.
- GreensInRegulation: The plot for GreensInRegulation indicates a strong negative relationship with Score. Consistently reaching greens in regulation is crucial for lower scores, as it allows more birdie opportunities and reduces the risk of bogeys.
- Scrambling: The partial dependence plot for Scrambling suggests that better scrambling skills (recovering from missed greens) can significantly lower scores. This underscores the importance of a solid recovery game in maintaining low scores despite challenging conditions.
- PPGIR (Putts Per Greens In Regulation): The plot for PPGIR shows a negative correlation with Score. Efficient putting once on the green is critical for converting greens in regulation into low scores, highlighting the need for strong putting skills on the complex greens of Le Golf National.
The partial dependence plots for par metrics (Par3, Par4, and Par5) against Score illustrate their influence on overall scoring.
- Par3: The plot for Par3 shows a significant negative correlation with Score. Better performance on par 3s leads to lower scores, indicating that precision and skill on these shorter but often challenging holes are important for overall success at Le Golf National.
- Par4: The partial dependence plot for Par4 reveals a strong negative relationship with Score. Excelling on par 4s, which make up a large portion of the course, is essential for achieving low scores. This underscores the importance of consistent performance across these holes.
- Par5: The plot for Par5 demonstrates variability but generally indicates that better performance on par 5s can contribute to lower scores. The strategic nature of par 5s at Le Golf National means that players who can effectively manage risk and reward on these holes can gain a significant advantage.
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.
Section 3(a): Relative Importance of SG Metrics on Score
Relative Importance of SG Metrics on Score
The bar chart titled "Relative Importance of SG Metrics on Score" shows the following relative importances:
- SGTee: 13.32%
- SGApp: 44.36%
- SGATG: 10.46%
- SGP: 31.87%
- SGTee (13.32%): Moderate impact on score. Tee shots are important but other aspects of the game play a larger role in determining the score.
- SGApp (44.36%): Highest impact on score. Precision in approach shots significantly influences scoring, suggesting proficiency in approach shots is crucial for success at Le Golf National.
- SGATG (10.46%): Least impact among the four metrics. Recovery shots contribute to the score but not as significantly as approach shots or putting.
- SGP (31.87%): Substantial impact on score, underscoring the importance of the short game in achieving low scores at Le Golf National.
The analysis shows that approach shots (SGApp) are the most critical factor in determining the score at Le Golf National, followed by putting (SGP). Tee shots (SGTee) and around-the-green play (SGATG) are less influential but still important.
When comparing these findings with the PGA Tour averages:
- SGTee: The result for Le Golf National (13.32%) is significantly lower than the PGA Tour average (24.82%).
- SGApp: The result for Le Golf National (44.36%) is significantly higher than the PGA Tour average (26.77%).
- SGATG: The result for Le Golf National (10.46%) is lower than the PGA Tour average (24.44%).
- SGP: The result for Le Golf National (31.87%) is higher than the PGA Tour average (23.98%).
Relative Importance of Traditional Metrics on Score
The bar chart titled "Relative Importance of Traditional Metrics on Score" shows the following relative importances:
- Driving Distance: 6.66%
- Driving Accuracy: 3.93%
- Greens In Regulation (GIR): 33.11%
- Scrambling: 29.52%
- Putting Per GIR (PPGIR): 26.78%
- Driving Distance (6.66%): Least impact on score. While longer drives can be advantageous, they do not significantly affect the score at Le Golf National.
- Driving Accuracy (3.93%): Minimal impact, indicating that hitting fairways is less critical than other aspects of the game.
- Greens In Regulation (GIR) (33.11%): Highest impact on score. Consistently reaching greens in regulation is crucial for scoring well here.
- Scrambling (29.52%): Very important, highlighting the significance of recovery skills.
- Putting Per GIR (PPGIR) (26.78%): Substantial impact, emphasizing the importance of converting opportunities after reaching the green in regulation.
The analysis reveals that reaching greens in regulation (GIR) is the most critical factor in determining the score at Le Golf National, followed closely by scrambling and putting per GIR. Driving distance and accuracy are less influential.
When comparing these findings with the PGA Tour averages:
- Driving Distance: The result for Le Golf National (6.66%) is lower than the PGA Tour average (9.31%).
- Driving Accuracy: The result for Le Golf National (3.93%) is similar to the PGA Tour average (3.77%).
- Greens In Regulation: The result for Le Golf National (33.11%) is higher than the PGA Tour average (29.77%).
- Scrambling: The result for Le Golf National (29.52%) is slightly higher than the PGA Tour average (27.02%).
- PPGIR: The result for Le Golf National (26.78%) is lower than the PGA Tour average (30.13%).
Relative Importance of Par Metrics on Score
The bar chart titled "Relative Importance of Par Metrics on Score" shows the following relative importances:
- Par 3: 16.03%
- Par 4: 67.15%
- Par 5: 16.82%
- Par 3 (16.03%): Moderate impact on score. Success on par 3 holes is important but not as critical as performance on par 4 holes.
- Par 4 (67.15%): Highest impact on score. Performance on par 4 holes is the most significant factor in determining the overall score, underscoring the importance of managing these holes effectively.
- Par 5 (16.82%): Moderate impact, slightly more than par 3 holes. Scoring well on these holes can provide a significant advantage.
The analysis reveals that performance on par 4 holes is the most critical factor in determining the score at Le Golf National, followed by par 5 and par 3 holes.
When comparing these findings with the PGA Tour averages:
- Par 3: The result for Le Golf National (16.03%) is similar to the PGA Tour average (17.32%).
- Par 4: The result for Le Golf National (67.15%) is almost identical to the PGA Tour average (67.12%).
- Par 5: The result for Le Golf National (16.82%) is similar to the PGA Tour average (15.56%).
These results indicate that the relative importance of performance on par 3, par 4, and par 5 holes at Le Golf National aligns closely with the averages observed on the PGA Tour.
Top 5 Ranked Players - 2024 Olympic Men's Golf Championship
The table below shows the top-5 ranked players and their average estimated scores from the three different Random Forest models above.
| Player | Score |
|---|---|
| Xander Schauffele | 68.66 |
| Scottie Scheffler | 68.68 |
| Ludvig Aberg | 68.90 |
| Rory McIlroy | 69.44 |
| Jon Rahm | 69.55 |
Estimated scores for all players can be found here.