One of the oldest sports in the world, golf provides incredible data science opportunities for both professionals and casual fans, particularly since the sport involves so much deliberation before swings on the field than other sports. Golf courses, as well, are not uniform and therefore provide a unique dimension to the category of golf data.
The PGA Tour, sports media, and other leagues provide most golf data, at the most granular level. Yet, while much player-generated data may be proprietary, there is no shortage of information that fans and sports commentators can use to make predictions.
Further, in addition to the cameras that cover player performance live, wearable technologies have made their way into the sport. For individual athletes, whether amateur or professional, these can become invaluable sources of data, offering immediate feedback.
Golf data covers player performance, first. The level of analysis can range from player scoring average over the course of a year to the point of impact at which a ball was hit in a given swing. Common performance data are variations on strokes gained, or SG. These include SG/APP, SG/BS, SG/T2G and so on. In these statistics, a player’s performance is measured against the statistical average to determine whether their off-the-tee, tee-to-green, or approach-the-green strokes were over or under the average.
This data also records golf course information. This includes full GPS mapping, average course slope, obstacles, and so on. Golf is unique in that players do not have standardized fields or tracks to contend with; every course requires different skill sets.
Finally, this data includes viewership, fan demographic, and equipment data. Companies use viewer and demographic data to make advertising or sponsorship decisions whereas equipment companies may use equipment data to design or market new golf balls, clubs, or carts.
Coaches and players primarily use this data to improve performance or prepare for tournaments. However, fans also use a lot of this data to discuss player performance, calculate the chances of a dangerous animal encounter, place bets, and more. Amateur golfers can also use training data and wearable technology to improve their own performance.
Finally, of course, companies use viewership and fan demographic data to vie for sponsorships and advertising space during tournaments.
In addition to relevancy and accuracy, a quality dataset should use a wide number of data sources. However, researchers should take into account that the most frequently used statistics measure player performance in relation to the average of all layers in a single game.
The evolution of stats and data to better predict golf events has grown rapidly in a short amount of time. And that makes it easier than ever to make what a “sharp” bettor would call a “good bet.” The trick here is that with so many “new” stats at your fingertips, picking and choosing which ones to focus on can be a challenge.