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Plus4 recap
We’re back after a week off. Full transparency, I needed a reset and was churning through significant model updates. All of my updates are not fully in, but I LOVE what the model is now spitting out to me. Being able to get a much deeper attribution of skills – and to use that to project who will play well this week – is awesome and should pay dividends.
Given my favorite plays would have been Jake Knapp and Rory McIlroy, who both withdrew from the golf tournament, we’re all lucky for that. Bay Hill has NEVER been a sweet spot for me and struggles to get modelled out through my process – Sawgrass on the other hand is way more attainable.
MODEL UPDATES:
With that, our model has a completely new backbone. Built on Microsoft Fabric, we now have 2 key metrics created and enforced with my data science sidekick, Claude. These 2 metrics will serve as endpoints to our analysis.
Skill-driven Corollary Course Metrics
Rather than treating courses as static, interchangeable entities, this model identifies corollary courses at the individual skill level. In this, we find those courses that share playing characteristics with our target course and use a player’s performance history at those analogues to infer likely performance at the target. The “skill-driven” layer means the correlation isn’t just course-to-course similarity; it’s filtered through which skills each course rewards.
It goes without saying that some players love Bermuda grass. Some players love hitting the ball left-to-right. Some players are better than others from the deep rough, and some players are great from tight greenside lies. Each course rewards these traits independently – now, we capture and score these individually.
Machine Learning SG: Total
A ML model for performance prediction models on features derived from two key analytical layers: exponential decay-weighted player history (emphasizing recent form) and a course correlation model that matches course stats at the target with historical event-level course difficulty across a number of dimensions (difficulty of strokes-gained putting, GIR %, number of water balls). This is still WIP but coming along as a feature.
Lineup Optimizer
As a bonus to me, I have built a lineup optimizer that takes my projections and scores lineups based on player’s projected performances and historical player-player correlations (who plays better, together?). I have manually hand-built these lineups for years, and the ultimate goal was to codify it in a way that the CPU could do it for me – we are 90% of the way there.
In future months, I’d love to deploy this to the masses. That’s a heavier engineering exercise than you’d expect.
Anybody can be a scientist – have an idea, realize it.
Plus4: TPC Sawgrass course notes
- Toyota Prius: Not a sponsored ad here. Jack Nicklaus compared TPC Sawgrass to landing balls on the hood of a car. For difficulty of approach within 150 yards nowhere is more difficult than TPC Sawgrass… perfecting short irons and wedges is what gets it done here. TPC Sawgrass has the most approaches from 50-150 yards of any course the great players play.
- OTT difficulty: Per datagolf, Sawgrass OTT difficulty ranks in the last 4 years (out of ~41) go… 4th, 4th, 1st, 4th. More pronounced since the change to March, driving the ball is difficult here. You need to be in the right spot, with the right angle, in the fairway – or go into attack mode for the shorter holes and separate that way. Water will be left, bunkers and OB will be right, and you need to split it. Additionally, get-able par 5s, and playing a drivable par 4 have put more emphasis on the big stick. It’s not a long course, but driver typically needs to be a weapon, not an inhibitor.
- ARG correlations: Augusta National, St. Andrews Links, Pinehurst, TPC Sawgrass. These are courses where showmanship around the green is damn near required – the golfing skate parks. Everything at Sawgrass is shaved, a bunker, or water – you best be able to spin your 60 degree with some ARG creativity.
Plus4: TPC Sawgrass corollary courses
You can tell this is WIP – but someday soon you won’t need this section because my Course breakdown page will be live.
New here? Visit our Plus4 approach page to learn about our process
Plus4 Picks: THE PLAYERS
Reminder: the player pool below is focused on DraftKings ownership, inclusive of leverage considerations and player upside.
Favorite Play:
Min Woo Lee
Kurt Kitayama
Star Anchor:
Scottie Scheffler
Collin Morikawa
Si Woo Kim
Gut Check:
Harris English
Favorite Sub-$7,000:
Ryan Fox
Pierceson Coody
Plus4 Bets: THE PLAYERS
Disclaimer
Transparency to the 4heads: I will not be betting Outright golfers at the rate I did last year (aggressively) given the current legislation related to non-fully-deductible gambling loses. I need to see full clarity from DraftKings in addressing this preposterous attempt by Congress to destroy the gambling ecosystem of which lobbyist have poured billions into and the public at-large has been referendummed to death on.
The classic counter to this for any educated gambler at large? Underground and illegal sports betting. Venmo accounts, paper money bags, a college junior named Kyle you meet behind Butch’s Delicatessen… whatever gets the job done. More info: Gambling Tax Alert: New Law Cuts Loss Deductions, Bettors Face Big Hit
How I fight back? I will play more Daily Fantasy, as income through DFS is miscellaneous through a 1099-MISC, not a W2-G that will be subject the 90% deduction of losses.
My odds shown below via the datagolf.com Custom Model tool
Plus4 Player Pool: THE PLAYERS
The strategies will be very simple this week, with Rory very low-owned following his WD last week and lack of information this week.
Scottie + Min Woo + the low 6s/7s
OR
Drop to two or three of Collin, Si Woo, Aberg, and Hideki
Overweight
Scottie Scheffler
Collin Morikawa
Min Woo Lee
Si Woo Kim
Kurt Kitayama
Small bites
Ludvig Aberg
Matsuyama
Ryan Gerard
Harris English
Jake Knapp
Alex Noren
Sepp Straka
Bezuidenhout
Sub-7s
Pierceson Coody
Ryan Fox
Matt McCarty
Ricky Castillo
As always, GL GL GL.
All content on this website is for informational and entertainment purposes only. We do not guarantee the accuracy, completeness, or timeliness of any information provided. Betting involves risk, and you may lose money.
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