Clippd has further upgraded its two powerful, proprietary performance metrics, Shot Quality and Player Quality, by factoring in course conditions. This enables us to present an even more refined understanding and measurement of golfing skill — both overall and in each area of the game.
Shot Quality and Player Quality build on the advances made by Strokes Gained, but factor in additional layers of all-important context to give golfers an even more precise picture of how good a particular shot was or how good a player is at a given point in time.
The additional layers of context Clippd factors in are course difficulty, weather conditions and, now, course conditions.
The functionality for players to add information on rough length, fairway firmness, green speed and green firmness has been available in Clippd for some time. We extensively tested this new model with tour data for some time. Now, the algorithms process this information, providing higher resolution insights into your game.
All rounds previously added to Clippd with course condition information included will be recalculated. This will deliver a more accurate picture of your play on a given day.
If course condition information has been added to previous rounds, you might see small changes in your Player Quality scores and in your Shot Quality and Average Shot Quality scores. You might also see slight changes to your What To Work On priority list.
“The interactions between course difficulty, course conditions and weather conditions are highly complex, making it incredibly difficult to decipher the impact any one element has on performance,” says Dr Chris Robertson, Clippd’s Chief Data Scientist. “Thankfully machine learning excels in such a use case and the data science team here at Clippd has trained our Shot Quality machine learning models on years worth of tour data, across a range of different weather and course conditions.”
By doing so, the models are able to develop a detailed understanding of the complex interplay between each factors - for example. strong winds, coupled with firm greens and deep rough. “We will continue to develop our models to take in more and more granular information such as wind direction,” says Dr Robertson, “but this represents a significant step forward in our journey to bring context to each and every shot.”
To get a more accurate picture of your own performance, be sure to add the all-important context. In Clippd, it’s very easy to go back into previous rounds and add this information, where you know it.
Here’s how: Select a round in Clippd > tap the three dots (top right) > Select Edit Round > add information in requested fields about weather and course conditions > Save Details.
Here’s a quick guide.
Shot Quality and Player Quality build on the advances made by Strokes Gained, but factor in additional layers of all-important context to give golfers an even more precise picture of how good a particular shot was or how good a player is at a given point in time.
The additional layers of context Clippd factors in are course difficulty, weather conditions and, now, course conditions.
The functionality for players to add information on rough length, fairway firmness, green speed and green firmness has been available in Clippd for some time. We extensively tested this new model with tour data for some time. Now, the algorithms process this information, providing higher resolution insights into your game.
All rounds previously added to Clippd with course condition information included will be recalculated. This will deliver a more accurate picture of your play on a given day.
If course condition information has been added to previous rounds, you might see small changes in your Player Quality scores and in your Shot Quality and Average Shot Quality scores. You might also see slight changes to your What To Work On priority list.
“The interactions between course difficulty, course conditions and weather conditions are highly complex, making it incredibly difficult to decipher the impact any one element has on performance,” says Dr Chris Robertson, Clippd’s Chief Data Scientist. “Thankfully machine learning excels in such a use case and the data science team here at Clippd has trained our Shot Quality machine learning models on years worth of tour data, across a range of different weather and course conditions.”
By doing so, the models are able to develop a detailed understanding of the complex interplay between each factors - for example. strong winds, coupled with firm greens and deep rough. “We will continue to develop our models to take in more and more granular information such as wind direction,” says Dr Robertson, “but this represents a significant step forward in our journey to bring context to each and every shot.”
To get a more accurate picture of your own performance, be sure to add the all-important context. In Clippd, it’s very easy to go back into previous rounds and add this information, where you know it.
Here’s how: Select a round in Clippd > tap the three dots (top right) > Select Edit Round > add information in requested fields about weather and course conditions > Save Details.
Here’s a quick guide.
Last edited by Josh; 09-02-23 at 10:53.
Clippd's Community Manager
