Determinants of Scoring at the Belfry

This analysis is based on scores and stats from individual rounds in the four DP World Tour events at the Belfry in the last four years Czech Masters: 1,762 rounds in total.

Section 1: Absolute Correlation Coefficients with Score

Absolute Correlation between Score and SG Metrics

The graph illustrates the absolute value of the correlation coefficient between the score and SG Metrics (SGTee, SGApp, SGATG, SGP) from 2020 to 2023.

SGTee: The correlation between SGTee and the score has shown a consistent increase from 2020 to 2023, indicating a stronger relationship between a player's performance off the tee and their overall score over time. This suggests that at venues like the Belfry, where driving accuracy and distance are crucial, players who excel off the tee are increasingly likely to perform well.

SGApp: SGApp (Strokes Gained Approach) consistently exhibits the strongest correlation with the score, hovering around 0.59-0.61 over the years. This metric's strong and stable correlation highlights the importance of iron play and approach shots in determining overall performance, particularly in courses with challenging greens like the Belfry.

SGATG: The correlation for SGATG (Strokes Gained Around the Green) fluctuates, with a notable dip in 2023. This variability suggests that the importance of short game skills may vary depending on conditions at the Belfry in different years.

SGP: The correlation for SGP (Strokes Gained Putting) is relatively stable but shows some variability. While putting is crucial, its impact on the score appears to be less predictable year-on-year, possibly due to changes in green conditions at the Belfry.

Overall, the increasing importance of metrics like SGTee and the stable dominance of SGApp suggest that strong ball striking is increasingly vital at the Belfry.

Absolute Correlation between Score and Traditional Metrics

This graph shows the absolute value of the correlation coefficient between the score and traditional metrics (Driving Distance, Driving Accuracy, GIR, Scrambling, PPGIR).

Driving Distance: The correlation between Driving Distance and the score remains relatively low, though it shows a slight increase from 2020 to 2023. This suggests that while distance is beneficial, it is not as critical as other factors in determining success at the Belfry.

Driving Accuracy: Driving Accuracy shows a moderately increasing correlation with the score, indicating that hitting fairways is becoming more important. This aligns with the tight, tree-lined nature of the Belfry, where accuracy off the tee can set up easier approach shots.

Greens in Regulation (GIR): GIR consistently shows a strong correlation with the score, reinforcing the importance of consistently hitting greens in regulation at the Belfry. This metric's strong and stable correlation across years suggests that successful players at the Belfry are those who give themselves the most birdie opportunities.

Scrambling: Scrambling also maintains a significant correlation, particularly in 2020 and 2021, indicating the importance of recovering from missed greens. However, its decreasing trend suggests that as GIR increases in importance, scrambling becomes slightly less critical.

PPGIR: The correlation for PPGIR is strong, especially in 2021, indicating that putting efficiency after hitting the green in regulation is a critical determinant of scoring. This highlights the importance of putting on the often tricky greens of the Belfry.

These trends suggest that traditional metrics like GIR and PPGIR are key to success at the Belfry, with accuracy off the tee becoming increasingly important.

Absolute Correlation between Score and Par Metrics

The graph depicts the absolute value of the correlation coefficient between the score and Par Metrics (Par 3, Par 4, Par 5).

Par 3: The correlation between Par 3 performance and the score is moderate and shows some variability. This suggests that while Par 3s are important, their influence on the overall score can vary year by year depending on course setup.

Par 4: Par 4s consistently show the highest correlation with the score, underscoring their importance. Given that the Belfry is known for its challenging Par 4s, success on these holes is crucial for a good overall score.

Par 5: The correlation for Par 5s is lower but still significant, indicating that while Par 5s offer scoring opportunities, they are not as decisive as Par 4s at the Belfry.

The strong correlation with Par 4s highlights their critical role in determining scoring at the Belfry, where strategic play and precision are required to navigate these challenging holes successfully.

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.

Section 2 - Importance of Each Metric

(a) SG Metrics

Relative Importance of SG Metrics on Score

The analysis using a Random Forest Regressor revealed the following relative importance of SG Metrics on the score:

SGApp (Strokes Gained Approach) is the most crucial factor, highlighting the importance of precision in approach shots at the Belfry. SGP (Strokes Gained Putting) is also significant, reflecting the challenging greens at the Belfry.

Compared to DP World Tour averages, the Belfry places more emphasis on approach shots and putting, with slightly less importance on tee shots and around the green performance.

(b) Traditional Metrics

Relative Importance of Traditional Metrics on Score

The Random Forest Regressor analysis produced the following relative importance of traditional metrics on the score:

Scrambling and PPGIR are the most important, emphasizing the importance of recovery and efficient putting. Driving metrics are less critical at the Belfry.

Compared to DP World Tour averages, the Belfry emphasizes scrambling and putting, with less focus on driving metrics.

(c) Par Metrics

Relative Importance of Par Metrics on Score

The analysis showed the following relative importance of Par Metrics on the score:

Par 4 performance is overwhelmingly the most critical at the Belfry, aligning with its reputation for tough Par 4s.

Compared to DP World Tour averages, Par 4s are even more dominant at the Belfry.

Top 5 Ranked Players - 2024 British Masters

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

Player Score
Tom McKibben 70.55
Thorbjorn Olesen 70.60
Matt Wallace 70.62
Rasmus Hojgaard 70.82
Jesper Svensson 70.96

Estimated scores for all players can be found here.