Determinants of Scoring at Le Golf National

This analysis is based on scores and stats from individual rounds in the last ten events at Le Golf National: 4,183 rounds in total.

Section 1: Absolute Correlation Coefficients with Score

Absolute Correlation between Score and SG Metrics

Key Points:

  • SGP (Strokes Gained Putting) consistently shows higher correlations with the Score, highlighting its strong impact on performance.
  • SGTee (Strokes Gained Tee) has fluctuating correlations, suggesting its importance varies by course setup and conditions.
  • At Le Golf National, SGApp (Strokes Gained Approach) is notably important due to the precision required for approach shots.
Absolute Correlation between Score and Traditional Metrics

Key Points:

  • Driving Accuracy has a strong correlation with Score, emphasising its importance on courses like Le Golf National.
  • Greens in Regulation (GIR) is highly correlated with Score, particularly at courses requiring precise approaches.
  • Driving Distance shows lower correlation, reflecting the greater need for accuracy over power at strategic courses.
Absolute Correlation between Score and Par Metrics

Key Points:

  • Par 4 performance has the strongest correlation with Score, indicating its key role in overall scoring.
  • Par 3 metrics show moderate correlation, suggesting they are important but not as critical as Par 4s or Par 5s.
  • At Le Golf National, Par 4 performance is particularly emphasised due to the course's challenging Par 4 layout.

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 SG Metrics on Score

Key Points:

  • SGApp (43.05%) shows significantly higher importance compared to the DP World Tour average (28.85%), highlighting the importance of approach shots at Le Golf National.
  • SGTee (13.73%) is much lower than the average (25.36%), indicating less emphasis on driving off the tee at Le Golf National.
  • SGP (33.03%) shows higher importance than average (23.33%), suggesting that putting is critical at Le Golf National.
Relative Importance of Traditional Metrics on Score

Key Points:

  • Greens in Regulation (GIR) shows higher importance (33.03%) than the DP World Tour average (28.39%), reflecting the need for precise approach shots at Le Golf National.
  • Driving metrics (Distance and Accuracy) show lower importance than average, indicating the course favours accuracy over distance off the tee.
  • Scrambling shows slightly higher importance (29.97%), suggesting recovery play is vital when players miss the greens at Le Golf National.
Relative Importance of Par Metrics on Score

Key Points:

  • Par 4 performance has the highest importance (67.44%), even higher than the DP World Tour average (64.77%), showing its critical role in scoring well at Le Golf National.
  • Par 3 and Par 5 metrics show similar importance to their DP World Tour averages, indicating a balanced challenge across these hole types.
  • The dominance of Par 4 metrics suggests that players need to excel on these holes to perform well at this course.

Top 5 Ranked Players - 2024 Open de France

The table below shows the top-5 ranked players across the three different Random Forest models above.

Rank Surname Firstname Average Predicted Score
1 Wallace Matt 69.31
2 Soderberg Sebastian 69.42
3 Olesen Thorbjorn 69.51
4 Smith Jordan 69.85
5 Svensson Jesper 69.96

Rankings and estimated scores for all players can be found here.