Advice for Average Joes
British Open 2021 picks: 5 secrets from a data scientist to win your Open Championship pool
It’s time to fill out the team for your Open Championship pool—where do you start? Using data as a strategic tool to help contextualize your picks and optimize your golf lineups is only helpful if you organize, value and use the results. And don’t worry: You don’t have to be a data scientist like me to use stats effectively to build an effective team.
My strategy is to provide a framework of insights that adds computer vision-derived stats onto the advanced data that is publicly available, and then vet the correlations my models find with experts (caddies, coaches and players) to provide additional perspective.
(Computer vision is a type of code that allows me to ‘measure’ almost anything using the television broadcast video. You know those launch monitors and the shot tracer on broadcast? It’s similar to that, and it gives me measurements of things my expert helpers say could be important like hips/balance, club movement, hand placement at time of strike, and much more. Then I use a lot of statistical models to verify or disprove the relationships between them mathematically.)
I also really enjoy the competitive elements surrounding choosing which golfers the public believes in, and who might be flying under the radar. And that’s what I want to help you do: Use my strategy to apply your own approach to the stats available and beat your friends and coworkers—or place high in a DFS contest. Here are five ways to do it for Royal St. George’s.
How to add value to quick recoveries
Royal St. George’s can be seen as a bit of a data nightmare: I’ve heard a lot of people describe it as “quirky” with a lot of dependency on weather (wind) conditions and design elements like blind shots, sloped fairways and dramatic dune-like bunkers really affecting play with an element of randomness.
Instead, I’m choosing to see all of that as an opportunity to be as creative with my model as the players will have to be with their game. Because the weather could change quickly and the bounce of the fairways could turn seemingly accurate shots turn into nightmares, I tried to come up with a way to capture who is best on their next shot after one that isn’t what they were expecting.
So, I tracked which clubs a player used, where it landed, how these factors changed round-over-round and tied it to their score. Where available I added weather information. There isn’t enough data on links style courses only (most PGA Tour players face a true links course maybe twice; European Tour players a few more times), but I did take a look at links “recovery” compared to their average “recovery” as the cherry on top.
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Jordan Spieth tops this metric in this elite field—adding to the long list of reasons of why he forecasts to be the most-owned DFS play this week (24.5 percent, per FanShare Sports), and also why he’s my model’s choice for who wins.
Collin Morikawa doesn’t have anywhere near the amount of data that I would need to feel mathematically comfortable, but it does show some high probability signals of positive things to come. When Morikawa’s ball-striking metric is high (and it has been) his recovery metric is also high. Here’s how I would apply this: If you believe Morikawa will be able to stay efficient in ball-striking while debuting on this course, he’s a great add to your DFS lineup (Morikawa’s projected to be 9.3 percent owned).
Other high scorers in this metric that aren’t Jon Rahm (because he’s too obvious as the consensus favorite); Louis Oosthuizen (16.9 percent) and Shane Lowry (3.3 percent).
Assessing Par-4 scoring differently—by factoring in apex height
Here’s my logic: eight of the 12 par 4s at Royal St. George’s measure between 400 and 450 yards. With wind as a probable and influential element, I factored in apex height (favoring lower but accurate shots) in this range as a separating characteristic. Then I added in wind and looked at performance in this blended metric over time.
Daniel Berger (9.8 percent owned) becomes a must-play for me after modeling this out and combining it with his wind results (below). No other player I tracked in this metric kept the ball within the same two-yard apex—adjusted for club—more than Berger. Berger finishes in the top 10 in 22 percent of my simulations.
Scottie Scheffler (12 percent) pops, too. I could have written him in the first section, too, as his recovery ability is trending way up. But he was in the top seven in this metric, which distinguishes him from a strong crowd in the same price range (he’s $8,200 on DraftKings; $9,700 on FanDuel).
Finding success around the greens and out of the sand ... consistently
Scrambling and strokes gained around the green, along with sand saves, project to be a big source of scoring variance at this Open, and it would seem the winner and/or overperformers relative to their DFS salaries will have to start and finish strong in this metric, from Thursday through Sunday. So I modeled out performance using daily hole-by-hole data as opposed to averages to capture more information on consistency (to account for a player holing out from off the disproportionately influencing their average for the week).
It’s probably worth noting that my models don’t disregard other crucial factors (like putting, strokes gained off the tee, on approach, recent form, etc.), but what I am doing is adding in logical data points to customize the projections for each player. I looked at Thursday through Sunday performance to reward players who were more consistent in scrambling, strokes gained/around the green and sand saves.
Harry How
Patrick Cantlay (13.8 percent owned) is trending up in this metric as of his past four finishes. His strokes gained/tee to green rank stands out as a source of strength, and his recent form will make him a popular cash play—but perhaps a contrarian play in pools. Patrick Reed (16.7 percent) is another highly owned selection, but for good reason. To me, both have safe floors and are high upside foundational pieces to my GPP strategy (aka I will use one of these to off-set some of my riskier picks).
Two more players who flag in this metric: Justin Thomas (17.2 percent) and Webb Simpson (5.4 percent).
Playing well in a tough field—and in the wind— and who forecasts to do both well
Strokes gained/tee to green in the wind was where I started but then I wanted to also highlight players who could win against a tough field. The point of all of my models is to use past trends to indicate who will pop against this crowd. Jon Rahm is strongest again, but some others who popped here: Harris English (16.9), Lucas Herbert (5.2) and Corey Conners (6.6). Interestingly, Viktor Hovland (8.2), who also has a small sample size has indicators of success in this metric based on his last four events played, he also has improved in his around the green consistency (my metric above) in that same timeframe.
Three contrarian picks and one more sleeper play
Some of the players who I mentioned above are chalkier, popular plays. Especially in big pools, the way to differentiate yourself is to use a contrarian pick that few others would consider. Here are three of those who currently project as having low ownership, but rate out well in my modeling:
Rickie Fowler (6.7 percent projected ownership) — My model rates Fowler as the highest risk-reward pick on the board related to price. All of the metrics I created for this assessment are areas Fowler has improved in as of late, giving him lots of forecasted upside.
Chris Trotman
Will Zalatoris (6.7 percent) — No experience? No problem. Just kidding, I would so much rather have a dearth of comparable data, but Zalatoris’ wind, par-4 metric (above) and his potential to return to his top ball-striking form drives this pick.
Christiaan Bezuidenhout (6.1 percent) — SG/around the green, par-4 scoring and my associated metrics all pop Bezuidenhout as an overperform relative to his cost.
And my sleeper: Kevin Kisner (5.3 percent) — Recent trends for the metrics I described above all show that as of lately, Kisner is trending in the right direction. Of all the lowest-tier priced players, he has the best chances of ending up in the top 10 per my model, which he does in 7.3 percent of simulations.
Cynthia Frelund is an analytics expert for the NFL Network who has applied her game-theory analysis to building models for golf.