Determinants of Scoring in the Abu Dhabi Championship
This analysis is based on scores and stats from individual rounds in the last ten Abu Dhabi Championships: 3,987 rounds in total. Note: this event was played at Abu Dhabi Golf Club until 2021.
Section 1: Absolute Correlation Coefficients with Score
Key Points:
- SGP (Strokes Gained Putting) has shown a strong influence on score, peaking at 0.62 in 2023, highlighting putting’s role in the Abu Dhabi Championship.
- SGApp (Strokes Gained Approach) demonstrates significant impact, especially in 2021 and 2022, aligning with the course demands.
- SGTee (Strokes Gained Tee) showed moderate influence, with a notable rise in 2023, suggesting growing importance.
2022 and 2023 Yas Links vs. Abu Dhabi Golf Club: Elevated correlations for SGP and SGApp in recent years indicate that Yas Links prioritises putting and approach accuracy.
Key Points:
- Greens in Regulation (GIR) shows a consistently strong correlation with score, peaking in 2022 (0.71), crucial at both Yas Links and Abu Dhabi Golf Club.
- PPGIR (Putts Per Green in Regulation) demonstrates significant influence, especially in 2023, with a correlation of 0.68.
- Scrambling shows moderate correlation, indicating its value in maintaining score stability across challenging courses.
2022 and 2023 Yas Links vs. Abu Dhabi Golf Club: The recent focus on GIR and PPGIR highlights Yas Links’ greater emphasis on green approach and putting accuracy compared to Abu Dhabi Golf Club.
Key Points:
- Par-4 Holes consistently show high correlation with score, often exceeding 0.75, underscoring their impact at the Abu Dhabi Championship.
- Par-5 Holes display moderate correlation, especially in 2022, indicating scoring potential on these longer holes.
- Par-3 Holes contribute to score consistency, with lower correlation but still relevant across years.
2022 and 2023 Yas Links vs. Abu Dhabi Golf Club: The higher Par-5 correlation at Yas Links suggests a greater emphasis on scoring opportunities on longer holes.
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.
Key Points:
- SGP (35.32%) shows the highest importance, highlighting the influence of putting on score.
- SGApp (33.78%) is nearly as important as putting, aligning with the approach accuracy demands of the Abu Dhabi Championship.
- SGTee (18.26%) and SGATG (12.63%) have comparatively lower impacts, indicating less influence from tee shots and around-the-green play.
Comparison with DP World Tour Averages: The Abu Dhabi Championship places a greater emphasis on putting and approach than the DP World Tour averages, suggesting that these factors are particularly impactful in this setting.
Key Points:
- PPGIR (37.50%) is the most influential, underscoring the role of putting accuracy.
- Greens in Regulation (GIR) (25.79%) and Scrambling (25.14%) play significant roles, especially in difficult course conditions.
- Driving Distance (7.54%) and Driving Accuracy (4.03%) show limited influence, as tee shot distance and accuracy are less critical in scoring at this event.
Comparison with DP World Tour Averages: The Abu Dhabi Championship sees a heightened importance for putting and greens in regulation, suggesting a need for accuracy and consistency on the greens.
Key Points:
- Par-4 (60.31%) shows the highest importance, aligning with the strategic impact of Par-4 performance at the Abu Dhabi Championship.
- Par-5 (23.24%) has a moderate influence, highlighting the scoring opportunities on longer holes.
- Par-3 (16.45%) has the least impact, contributing to consistency without substantial scoring influence.
Comparison with DP World Tour Averages: The data indicates a slightly higher emphasis on Par-4 performance at this event compared to the DP World Tour, reflecting the layout and strategy requirements of the Abu Dhabi Championship.
Top 5 Ranked Players - 2024 Abu Dhabi Championship
The table below shows the top-5 ranked players across the three different Random Forest models above.
| Rank | Surname | Firstname | Average Predicted Score |
|---|---|---|---|
| 1 | Niemann | Joaquin | 69.41 |
| 2 | Hatton | Tyrrell | 69.98 |
| 3 | Fleetwood | Tommy | 70.06 |
| 4 | Hojgaard | Rasmus | 70.20 |
| 5 | Smith | Jordan | 70.36 |
Rankings and estimated scores for all players can be found here.