PGA DFS Ownership Report: 2024 Pebble Beach Pro-Am

In PGA DFS, the landscape is a bit different from its NFL counterpart. Unlike the gridiron battles where individual player performances can significantly impact each other, golf is a solitary pursuit. What one player does cannot help, nor prevent, another from doing the same on the course. In other words, the correlation that plays an enormous role in other DFS sports like NFL and MLB is almost entirely absent from PGA. This fundamental difference magnifies the importance of game theory – it’s arguably all we have. Well, if game theory is all we have, then we’d better be using the best ownership projections.

This weekly article will seek to answer two questions:

1. How accurate are our PGA ownership projections at FTN, and what are the effects when we outproject (or underperform) the competition?
2. Does chalk all hit at a similar rate, or are we better off using chalk in certain price ranges while differentiating elsewhere?

This week’s article will once again just focus on the first. Next week, we will begin to dive into the second question with a heavily data-driven approach, so stay tuned!

Key Differences from the Industry

How much does ownership really matter? This is a question often discussed in our industry, so allow me to take a stab at answering it. I believe ownership matters only a little bit, until it matters a ton. More specifically, a small difference in ownership from one source to another is close to meaningless (like 12% vs. 13%). Large differences, however, are enormously important, in large part because they affect the decisions we actually make on who to play or fade.

It therefore makes sense to look at the instances where our ownership projection deviated most from the industry average and compare those projections to the actual ownerships observed in contests.

All actual ownership percentages for this section will come from the large $12 single-entry contest. This tournament is large enough to represent the entire field of DFS players, yet the single-entry aspect makes it more about the psychology of clicking on each individual player as opposed to what an optimizer spits out.

Our three largest differences were:

Max Homa

• Industry: 21.2%
• FTN: 29.3%
• Actual: 36.0%

Viktor Hovland

• Industry: 19.0%
• FTN: 28.7%
• Actual: 25.4%

Sam Burns

• Industry: 12.8%
• FTN: 8.0%
• Actual: 4.1%

Fading Max Homa turned out to be one of the most important decisions of the week, and aside from the fact that he often plays well in California (is that even a thing? I doubt it), I’m not sure why he was so popular. That said, the signs were all there that he would be, so it was nice to see that we nailed that one and put our users in position to make the fade.

We overshot Viktor Hovland’s ownership a bit, but we were right that people would prefer the two “cheap” $10k options over the two expensive $11k options, providing an edge in expected roster construction, not just individual player ownership.

For the second straight week our key differences were more about fading the mega chalk than who to play (that would be more contrarian than expected), but Sam Burns did deliver with a top 10.

We also introduced new GPP Scores this past week, leading me to build a core of Sahith Theegala, Keegan Bradley, and Wyndham Clark. As you might imagine, that core produced solid results (boy do I wish they played Round 4!):

Importantly, these GPP Scores are extremely sensitive to projected ownership, so it’s crucial that we continue to produce accurate ownership projections if they’re to remain as useful as they were this week.

Accuracy Analysis

Along those lines, we can compare accuracy around the industry by looking at r-squared and RMSE to make sure we’re continuing to meet our high standard. For those unfamiliar with these metrics, you can focus on just r-squared. If we were exactly right about every single player’s ownership, to the exact decimal place, our r-squared would be 100%. The closer to 100%, the better. The lower the RMSE, the better.

We have included the INDUSTRY descriptor to show how our ownership compares to aggregations of other sites, a common practice among DFS players. In future weeks, we will only include the two best-measured competitors in the aggregation to avoid skewing the data too much. I will once again point out the fact that we saw our best results (most separation) in the smallest contest where a higher proportion of people hand-build because this is where I believe our GPP Scores are best utilized.

P.S. We would love to add any other sites to the analysis! If you’re interested in adding yours to the mix, DM me on Twitter @alexblickle1.