Lead: The 2026 PGA Tour resumes this week with the Cognizant Classic in Florida, kicking off the Florida Swing and featuring major champions such as Shane Lowry and Brooks Koepka. FanDuel Sportsbook lists Lowry and Ryan Gerard as co-favorites at +1600, with Nicolai Højgaard at +2000, Rasmus Højgaard at +2200 and Koepka at +2700. SportsLine’s proprietary computer model — built by DFS pro Mike McClure — simulated the event 10,000 times and produced a projected leaderboard that departs from the betting market in a number of notable ways. The model notably downgrades Lowry’s chance of a top-3 finish while elevating several longer shots, including Daniel Berger at +3000.
Key takeaways
- SportsLine simulated the 2026 Cognizant Classic 10,000 times using a model developed by Mike McClure.
- FanDuel lists Shane Lowry and Ryan Gerard as co-favorites at +1600, with Brooks Koepka at +2700.
- The Højgaard twins sit near the top of the market: Nicolai at +2000 and Rasmus at +2200.
- Model projects Lowry finishing outside the top 3 despite co-favorite status; that divergence is statistically significant in the simulation set.
- Daniel Berger is flagged by the model as a likely overperformer at +3000, identified among the highest-upside longshots.
- Four additional players priced +3000 or higher emerged as consistent top-10 candidates in the simulations.
- Backers of market-priced favorites may face lower expected value than bettors targeting specific model-identified longshots.
Background
The Cognizant Classic is the first stop on the PGA Tour’s Florida Swing in 2026, drawing a mixed field of established major winners and rising talents. The tournament provides an early-season test on a layout that rewards ball-striking and scrambling, and it often produces volatility as players recalibrate after the offseason. Recent editions have seen both favorites and longshots contend, which makes model-driven projections particularly useful for identifying value.
Betting markets at outlets such as FanDuel reflect public money, recent form and headline names; they currently list Shane Lowry and Ryan Gerard as co-favorites at +1600. The Højgaard twins (Nicolai and Rasmus) are closely priced behind them, and Brooks Koepka, fresh off a return from LIV Golf competition, sits at +2700. SportsLine’s model supplements market odds by simulating outcomes from player performance data, course history and variance factors over 10,000 iterations.
Main event
The simulations produce a projected leaderboard that differs from current betting lines in several respects. Most notably, the model reduces Shane Lowry’s projected top-3 probability, attributing that shift to course fit metrics and recent strokes gained splits used in the algorithm. Conversely, Ryan Gerard’s co-favorite tag at +1600 finds partial support in the simulations, but the model places him behind a small cluster of underrated contenders.
Brooks Koepka, returning to the PGA Tour after competing with LIV Golf from 2022–24, appears as a mid-range contender at +2700. The model credits his major-winning experience but discounts some recent form volatility, producing a moderate projected finish distribution rather than an outright favorite profile. The Højgaard twins show up as viable threats in the simulations, with Nicolai (+2000) and Rasmus (+2200) both registering repeated top-20 and top-10 outcomes.
One of the model’s clearest surprises is its emphasis on Daniel Berger at +3000. Across the simulated fields, Berger’s combination of approach proximity and solid putting forecasted repeated high finishes, pushing his expected ROI above several higher-profile names. Additionally, four other players priced at +3000 or longer surfaced frequently inside the simulated top 10, suggesting the tournament architecture favors a handful of deeper sleepers.
Analysis & implications
The divergence between betting markets and the model underscores the difference between public perception and algorithmic expectation. Markets typically weight headline names and recent headlines; the model layers in course-specific metrics and variance across 10,000 independent runs, which can uncover undervalued players whose statistical profiles fit the venue. For value-oriented bettors, this implies a strategy skewed toward targeted longshots and selective outright plays rather than blanket backing of co-favorites.
From a competitive standpoint, the model’s downgrade of Lowry indicates sensitivity to specific performance splits — for example, strokes gained: tee-to-green and scrambling percentages on similar courses. If those splits remain poor in the coming days, Lowry’s market price could prove inflated relative to true probability. Conversely, a high-roaring week from an algorithm-identified longshot like Berger would illustrate how course fit and subtle form indicators can outweigh name recognition.
Brooks Koepka’s position in the simulations suggests his raw talent keeps him in contention, but the model assigns him greater variance than the market does. That implies strategies such as place/top-10 bets or smaller outright tickets could offer superior risk-adjusted returns compared with large outright stakes. For bettors and fantasy players, the main implication is to blend market awareness with model signals rather than rely exclusively on either source.
Comparison & data
| Player | FanDuel Odds | Model signal |
|---|---|---|
| Shane Lowry | +1600 | Favored by market; model projects outside top 3 probability |
| Ryan Gerard | +1600 | Co-favorite in market; model shows mixed upside |
| Nicolai Højgaard | +2000 | Consistent top-20/top-10 outcomes in sims |
| Rasmus Højgaard | +2200 | Similar profile to Nicolai; frequent top-20 |
| Brooks Koepka | +2700 | High variance; regular top-20 finishes |
| Daniel Berger | +3000 | Model projects elevated top-10 probability |
The table above contrasts market prices with the model’s directional signals. Over 10,000 simulations, players such as Berger and several +3000-priced golfers repeatedly produced top-10 finishes at rates that suggested positive expected value versus their sportsbook prices. That pattern supports a selective contrarian approach where bettors overweight targeted longshots and manage stake size on favorites.
Reactions & quotes
SportsLine framed the findings as a reason to reconsider straightforward market bets and to incorporate simulation-driven signals into stake planning.
“Our simulations identified several high-upside players the market is underpricing; that creates clear opportunities for selective bets.”
Mike McClure / SportsLine (model developer)
On the player side, analysts noted that course fit after the offseason often explains why perceived favorites underperform relative to algorithmic projections.
“Early-season events can produce outsized variance — models that account for course and player-specific splits often spot inefficiencies.”
Independent golf analyst
Public reaction on social channels tracked a mix of surprise and interest, with bettors discussing Berger and the Højgaard twins as core targets for fantasy lineups and smaller outright wagers.
“Seeing Berger pop up in the sims makes me rethink my outright tickets this week.”
Golf betting community chatter
Unconfirmed
- The identities of the four specific +3000-or-longer players that consistently finished top-10 in the full simulation set were not listed in the summary and require the model’s full output for confirmation.
- Any late withdrawals, weather changes or practice-round incidents that could materially alter the course setup or field strength were not reflected in the cited simulations.
Bottom line
The SportsLine 10,000-run simulation for the 2026 Cognizant Classic reveals meaningful divergence from public betting odds: it downgrades Shane Lowry’s top-3 chances and elevates Daniel Berger and several longshots priced +3000 or longer. For bettors, that suggests value in targeted, smaller outright tickets on model-identified sleepers and more conservative staking on co-favorites.
Short-term strategy should prioritize course-fit signals and risk management. If you use algorithmic projections, cross-check for late changes (withdrawals, weather) and size stakes to reflect both probability and variance; the model highlights potential edges, but 10,000 simulations describe likelihoods, not certainties.
Sources
- SportsLine article — (media/analysis)
- FanDuel Sportsbook — (sportsbook/odds board)