One of the most “annoying” things about the Strokes Gained variable is that 0.000 is an average male tour player. This, of course, is by design, since the tour that came up with this statistical variable is the one in charge of the data collection for its players, and naturally, they are interested in how their own players perform.
Data collection is a cumbersome process and very expensive, which is a big reason why other tours have been slow to copy the methodology. Figures have been thrown around indicating that the laser-system used to measure each players’ shot distance runs upwards of $20 million, and for the data collection to work properly, 250 volunteers are needed each week. In short, this is a massive process.
Unless you are a male pro with your short term performance goal of making it to the PGA Tour, then there is another, more relevant comparison that you can do in the short term.
But what if you’re not a male tour player? We would actually argue that the direct comparison to a male tour player is irrelevant for almost everyone, including high performing male players. Unless you are a male pro with your short term performance goal of making it to the PGA Tour, then there is another, more relevant comparison that you can do in the short term.
As we wrote about in our blog post about Strokes Gained, we really like the methodology of calculating our performances using the Strokes Gained statistic, since it catches the nuances of our performances that other variables don’t catch. We have also written about the need and importance of finding a relevant comparison for each player which transforms the stats from just numbers to highly motivating short-term performance targets.
we are absolutely convinced that the more relevant the comparison that you can make, the more motivating the statistics become.
We are absolutely convinced that the more relevant the comparison that you can make, the more motivating the statistics become. Therefore, one of our goals with Anova is for it to be a research project: we want to find out exactly how good players at various skill levels are so that we then can create statistical variables and comparisons that are extremely relevant, and as far as we have been able to tell, no one has seriously attempted to collect information about users in such a way before.
Anova is also a research project – we want to find out exactly how good players are at various skill levels in order to be able to create the most relevant comparisons possible.
In 2017-2018, around a dozen of women’s teams got together and shared information with each other, enabling us to calculate a Strokes Gained value specifically for this group called ‘Anova College (W) Strokes Gained’. 0.000 is the average performance of a member of this group and a positive number means that you are performing better than the average player in the group.
The very nature of golf statistics is that they become more interesting if you can compare them to your peers
The more information we are able to collect about the performance levels of college golfers, the better the feedback is that becomes available for college players. As we talked about in our blog post about supercharging performances, you have to start by optimizing for on-course performance and then work backwards, using measured data from that on-course performance to perform a statistical analysis of a player’s game to figure out the most efficient way to design a practice regimen.
The more teams and players that contribute to building these college player benchmarks, the more specific we can be in comparing ourselves to those benchmarks. Since most of the available research is using the numbers from male players, it would be absolutely amazing to have the opportunity to analyze where a typical women’s player’s strengths and weaknesses lie, and research how we can improve practice plans to make them hyper efficient specifically for women golfers.
The more teams and players that contribute to building these college player benchmarks, the more specific we can be in comparing ourselves to those benchmarks.
This also gives us an opportunity to be even more specific: with enough information, we can further break these benchmarks down into even more detailed categories such as a women’s NCAA division 1 freshman benchmark, or a conference junior benchmark. With enough information in our database, it is only our imagination that is the limitation in the research we can make about how good players really are at various performance levels.
This is an amazing opportunity. We are super excited to be a part of it and enabling players and coaches to have access to the performance statistics they need to make important decisions about their improvement.