Determinants of Scoring at Valderrama
This analysis is based on scores and stats from individual rounds in the last 10 Tour events at Valderrama: 3,496 rounds in total.
Section 1: Absolute Correlation Coefficients with Score
The first graph illustrates the absolute value of the correlation coefficient between the score and various factors (Driving Distance, Driving Accuracy, Greens In Regulation, Scrambling, and PPGIR) by year.
- Driving Distance: The correlation between Driving Distance and performance is relatively low across all years, suggesting that the distance a player drives the ball has minimal impact on their score at Valderrama. This course may favor precision over distance.
- Driving Accuracy: The correlation between Driving Accuracy and performance shows moderate variation, with higher values observed in certain years (e.g., 2011, 2017). This indicates that hitting the fairways accurately can be beneficial for scoring at Valderrama, particularly in years when the course setup emphasizes penalizing errant drives.
- Greens In Regulation (GIR): GIR consistently shows a strong correlation with performance, underscoring its importance. Players who hit a higher percentage of greens in regulation tend to score better, highlighting the significance of approach shots at Valderrama.
- Scrambling: The correlation between Scrambling and performance is relatively strong, suggesting that the ability to recover from missed greens and save par is crucial for good performance at Valderrama. This reflects the challenging nature of the course's greens and surrounding areas.
- Putts Per GIR (PPGIR): PPGIR also shows a significant correlation with performance, indicating that putting performance is a key determinant of scoring at Valderrama. Players who can convert their GIR opportunities into low scores have an advantage.
The second graph illustrates the absolute value of the correlation coefficient between the score and Par 3, Par 4, and Par 5 performance by year.
- Par 3 Performance: The correlation between Par 3 performance and overall performance varies moderately but remains significant. This suggests that performance on Par 3 holes can influence overall scoring, likely due to the precision required on these shorter holes at Valderrama.
- Par 4 Performance: Par 4 performance consistently shows a very strong correlation with overall performance. This is expected, as Par 4s make up the majority of holes on most courses, including Valderrama. Success on these holes is crucial for a good overall score.
- Par 5 Performance: The correlation between Par 5 performance and overall performance is moderate to strong, highlighting the importance of taking advantage of scoring opportunities on these longer holes. Efficient play on Par 5s can significantly impact the overall score, particularly in years where the Par 5s are set up to be more challenging.
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 first set of partial dependence plots illustrates the relationship between the score and various factors (Driving Distance, Driving Accuracy, Greens In Regulation, Scrambling, and PPGIR).
- Driving Distance: The plot shows that changes in Driving Distance have a relatively minor effect on the score. This reinforces the observation that Valderrama rewards precision over driving distance.
- Driving Accuracy: There is a noticeable impact of Driving Accuracy on the score. Better accuracy correlates with lower scores, indicating the importance of hitting the fairways accurately at Valderrama.
- Greens In Regulation (GIR): GIR shows a strong impact on the score. Higher GIR percentages significantly lower the score, highlighting the critical nature of approach shots and the importance of reaching the green in regulation.
- Scrambling: Scrambling ability also affects the score considerably. Players who can effectively recover and save par from difficult positions tend to score better, underscoring the challenging nature of Valderrama's greens.
- Putts Per GIR (PPGIR): PPGIR demonstrates a clear relationship with the score. Fewer putts per GIR lead to better scores, emphasizing the importance of putting performance at Valderrama.
The second set of partial dependence plots illustrates the relationship between the score and Par 3, Par 4, and Par 5 performance.
- Par 3 Performance: The plot indicates that performance on Par 3 holes has a significant impact on the overall score. Good performance on these holes, which often require precise shot-making, leads to lower scores.
- Par 4 Performance: Par 4 performance has a pronounced effect on the score. Given that Par 4s make up a substantial portion of the course, excelling on these holes is crucial for a good overall score.
- Par 5 Performance: The plot shows that Par 5 performance also influences the score, though to a slightly lesser extent than Par 4s. Efficient play and capitalizing on scoring opportunities on Par 5s are important for overall performance.
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.
Using Random Forest Regressor, the relative importance of each traditional stats factor on Score is quantified as follows:
- DrivingDistance: 7.08%
- DrivingAccuracy: 4.72%
- GreensInRegulation: 28.46%
- Scrambling: 35.72%
- PPGIR (Putts per GIR): 24.02%
The first bar chart illustrates the relative importance of various factors (Driving Distance, Driving Accuracy, Greens In Regulation, Scrambling, and PPGIR) on the score, quantified using a Random Forest Regressor.
- Driving Distance: Driving Distance has the second lowest relative importance among the factors. This suggests that, at Valderrama, the distance a player drives the ball is less critical compared to other aspects of the game.
- Driving Accuracy: Driving Accuracy shows low importance. This indicates that being able to hit the fairways accurately can have an impact on the score, but other features of the game are more important at Valderrama.
- Greens In Regulation (GIR): GIR has a significant relative importance, underscoring its critical role. Players who can consistently reach the greens in regulation tend to have better scores, highlighting the importance of approach shots.
- Scrambling: Scrambling has the highest relative importance. The ability to recover and save par from difficult positions is essential for good performance at Valderrama, given the course's challenging greens and surrounding areas.
- Putts Per GIR (PPGIR): PPGIR is also a key factor. This demonstrates that putting performance is crucial at Valderrama. Efficiently converting GIR opportunities into low scores can significantly affect a player's performance.
Using Random Forest Regressor, the relative importance of each par stats factor on Score is quantified as follows:
- Par3: 15.58%
- Par4: 62.78%
- Par5: 21.64%
The second bar chart illustrates the relative importance of Par 3, Par 4, and Par 5 performance on the score, quantified using a Random Forest Regressor
- Par 3 Performance: Par 3 performance has a moderate relative importance. This indicates that while performance on these holes is important, it is less influential than performance on Par 4s.
- Par 4 Performance: Par 4 performance has the highest relative importance. Given that Par 4s constitute a significant portion of the course, excelling on these holes is crucial for achieving a good overall score at Valderrama.
- Par 5 Performance: Par 5 performance shows lower relative importance compared to Par 4s but is still significant. Efficient play on Par 5s, which are often seen as scoring opportunities, can positively impact the overall score.
Top 5 Ranked Players - 2024 LIV Golf Valderrama
The table below shows the top-5 ranked players and their average estimated scores from the two different Random Forest models above.
| Player |
Score |
| Louis Oostzuizen |
68.30 |
| Marc Leishman |
68.43 |
| Carlos Ortiz |
68.47 |
| Richard Bland |
68.52 |
| Bryson DeChambeau |
68.53 |
Estimated scores for all players can be found here.
