Determinants of Scoring in the DP World Tour Championship

This analysis is based on scores and stats from individual rounds in the last ten DP World Tour Championships: 2,264 rounds in total.

Section 1: Absolute Correlation Coefficients with Score

SG Metrics Correlation
  • SGApp showed the strongest and most consistent correlation with scores.
  • SGP correlations were lower and more variable year-to-year.
  • Approach shots were critical during the DP World Tour Championship.


Traditional Metrics Correlation
  • DrivingDistance had stronger correlations than DrivingAccuracy.
  • GreensInRegulation consistently showed the highest correlation in this event.
  • Scrambling highlighted the importance of recovery shots.


Par Metrics Correlation
  • Par4 performance showed the strongest correlations, crucial for success in this event.
  • Par5 performance correlations were notable but variable.
  • Par3 performance remained consistent but less impactful.

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.

SG Metrics Importance
  • SGApp was the most critical factor, dominating in its influence on scores in the DP World Tour Championship.
  • SGTee showed a lower relative importance compared to DP World Tour averages.
  • SGP had a significant impact, closely aligning with the DP World Tour averages.


Traditional Metrics Importance
  • GreensInRegulation emerged as the most impactful metric, underscoring its relevance in this event.
  • Scrambling and PPGIR were critical, although slightly variable compared to the Tour averages.
  • DrivingAccuracy had a limited impact relative to DrivingDistance.


Par Metrics Importance
  • Par4 holes were the most decisive, mirroring their importance in the DP World Tour Championship.
  • Par5 performance showed variability, but is more important in this event than on the DP World Tour on average.
  • Par3 importance was limited but consistent with DP World Tour averages.

Top 5 Ranked Players - 2024 DP World Tour Championship

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

Surname Firstname Average Score
Hatton Tyrrell 67.92
Niemann Joaquin 68.87
Mcilroy Rory 69.49
Fleetwood Tommy 69.66
Hojgaard Rasmus 69.68

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