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  • Writer's pictureHarvey Hillary

A data-led approach: where golf can learn from other sports

Taken from an article in the Clippd Golf Platform

Harvey Hillary is a UK Sport 'Elite Coach' who served as Head of High Performance & Innovation through multiple Olympic cycles for the hugely successful British Sailing Team. He advises individuals, teams and organisations on delivering optimal performance and maximising competitive advantage. In 2020, Harvey joined Trinifold Sports Management as Performance Coach, tasked with applying Olympic development methodology to his work with a group of leading British amateurs and tournament professionals. Here, Harvey looks at what golf can learn from the data-led approach taken by other sports.

Harvey Hillary at a recent Trinifold Sports training camp in Wales. Photo: Angus Murray ​​​​​​ ​​​​​​​Background: the early adopters Over the last decade, we’ve seen phenomenal growth in sports analytics, with advanced data capture and AI analysis now accessible to all consumers. As with all new technologies, there have been trailblazers who have shown the way for others to follow. Motorsport was the exemplar, and one from which all Olympic sports have looked to learn. The engineering-led approach and development of data capture hardware have now filtered down into other sports like cycling, rowing, sailing and skeleton. Previously, most sports were looking to use Descriptive Analysis to objectively rate and compare each component part of performance, be that the human, the equipment, or the results or outcome measures (in other words, what a player actually did). This ‘objectivity’ helped to evidence ‘best practice’ or demonstrate a performance delta between, say, changes in equipment or technique. In the last 10 years, cycling has led the world in terms of social engagement and virtual competition data, which drives participation and adds value to training. First Training Peaks, then Strava, Garmin and Peloton have engaged, motivated and, in the case of Zwift, provided in-the-moment input to enhance training. The world has changed for cycling and running, so why not golf?

Where motorsports led other sports have followed. Photo by Glen Wheeler/Unsplash Where can golf learn from other sports? From the perspective of this ‘naive expert’, golfers have always had access to a robust data set; they just haven’t seen the value in that data. Launch monitors, video analysis and scoring statistical analysis are all commonplace but don’t seem to engage the club golfer in a way I would expect. The aim should be to use an integrated approach to allow professionals, competitive amateurs and avid club golfers to identify their training priorities and direct their practice to increase their enjoyment and realise their potential. In my experience working with elite golfers, however, a reticence persists over fully committing to data. ​​​​​​​As a first step, it's helpful to look at the barriers to adoption experienced by other sports, and what we can do to overcome these barriers as program directors, coaches, teachers and players. 1. Failure to recognise the value of the data and the opportunity for a performance gain Player education needs to be built around telling the stories of how others have used data to impact their game, make better decisions, work better with their coaches, build confidence and make better use of their available time. 2. Time taken to capture data and analyse Logging scores and reviewing data needs to be part of the daily routine. The critical issue I see is that players don’t add 30 minutes to the end of their training day, practice round or tournament daily routine. If they value data, then time management is the next skill to develop! 3. Costs or resources required to deploy data capture and analysis tools In golf, the commercial forces mean exceptional data capture and analysis are available for every budget. Much of the unwillingness to invest will surround perceived value. An 18-year-old aspiring pro might use cost as a reason not to buy a launch monitor and then make the same investment in a new iPhone11. Similarly, they might spend happily £20-30 a month on iTunes and Netflix, yet spending the same on an online analysis platform would be seen as high cost. It all comes down to perceived value and their motivation to really make a difference to their game. 4. Insufficient, inconsistent or unrepresentative data available for robust analysis Take the example of a club runner or cyclist, they will collect data from every training session and from all competitions. Formula 1 learns equally from integrating simulator, testing and race data. In sailing, Americas Cups are won from developing ‘polars’ in training that define the target numbers they trim for in racing. So why don’t golfers record all practice rounds, all scored drills and all accelerometer sessions? The opportunity here is to build a deeper picture of a player’s current level of performance. To use an example from Tim Gallwey’s ‘Inner Game’ series, your practice rounds will help show your potential – the gap between your scores in training and competition is the ‘interference’. 5. Unable to interpret the data to direct positive change Data science and data visualisation are essential partners, and this is one of the significant opportunities for statistical analysis in golf. Let's put the VR used in simulation to one side and focus on statistical analysis. The nature of this quantitative analysis means that it can be very numeric and require interpreting trends. Add into this dyslexia and dyscalculia, and you can see why a significant percentage of players struggle to realise the benefit of tools like Golf Stats Lab and Golf Data Lab. Strava and Garmin have gamified this data, while tools like Tableau focus on the visualisation and accessibility of data to interpret the story. Simplified reports with great visuals are key. ​​​​​

Former England rugby international Lewis Moody addresses Harvey and the Trinifold Sports players. Photo: Angus Murray Intelligent effort through AI Recently, we have seen machine learning and Artificial Intelligence used to help us identify patterns and, in some cases, ‘predictive’ performance data to direct the development of new techniques, tactics and training programmes. This opens up opportunities for science to lead the art of coaching: What it takes to win – A descriptive analysis that shows how to execute a winning outcome or winning score. Training priorities – The areas that will provide the greatest performance return and increase the likelihood of winning performances. Training progress – Evidencing your actual progress against the plan. Great for building a player’s confidence or in helping to continually refine the design of the training program based on the rate of progress. ​​​​​​​What is my current potential – The "super lap" in F1 is a composite of the best sector times to provide a theoretical best based on what a driver is capable of today. The challenge here from a coaching perspective, is to demonstrate potential – ‘what you are capable of’ – based on shots recorded in practice. The art then is to balance potential versus managing expectations in competition. ​​​​​ ​​​​​​​In summary ​​​​​​The real opportunity for golf is to harness data to increase players’ enjoyment of the game and add value to practice, in the same way that engagement with data has driven a huge growth in cycling over the last 10 years. How will we know when we’ve made a gain? When data starts to become commonplace within the language of golf. @harvey.hillary ​​​​​​​

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