When the Orioles drafted high-school flamethrower Grayson Rodriguez with the 11th pick in 2018, they were buying a future they could only imagine. Last night, that distant bet finally came to a head. With injuries having clouded Rodriguez’s trajectory, Baltimore traded the still–cost-controlled right-hander to the Angels for a single season of Taylor Ward, converting tomorrow’s hope into today’s utility and crystallizing the draft’s enduring tension: how much faith to place in what a teenager might become.
By flipping a once-prized but uncertain future asset for an immediate, short-lived upgrade, the Orioles offered a real-time snapshot of a long-standing friction in draft rooms: how to weigh projected upside against usable, bankable production right now.
Baseball’s amateur draft is nominally designed to resolve that tension. Every summer, 30 clubs take turns selecting the best teenage arms and college bats the country can produce. The worst teams get the best picks, bonuses are governed by slot values, and everyone walks away with a prospect who, at least in the press release, lays out a route to Cooperstown. But beneath the slot-value spreadsheets and bonus-tracker pages lies a less flattering reality – one shaped by runaway optimism, herd behavior, and a phenomenon economists call the winner’s curse.
What makes this dynamic interesting is not that the draft’s rules are irrational. The slot system and bonus pools were introduced to curb overspending. But people aren’t purely rational. Even with guardrails, front offices keep falling for the same behavioral traps auction theorists warn about. Understanding why sets the stage for the economic and behavioral dynamics that follow.
The auction that isn’t an auction
The winner’s curse originated in the 1970s when oil companies noticed that Gulf of Mexico drilling leases tended to deliver poor returns. Petroleum engineers Edward Capen, Robert Clapp and William Campbell found that companies were systematically overpaying because they lacked good information about the leases’ true value. When bidders compete under uncertainty, the company that guesses highest (i.e., most optimistically) wins, and is therefore most likely to have overshot the mark. Behavioral scientists later generalized the idea: in auctions or competitions where all participants value the same asset, the winner is often the one who overestimates its worth the most.
The MLB draft isn’t literally an auction, but it functions like one in several important ways. Teams “bid” on players by spending draft capital (picks) and bonus‑pool money. The pick order is fixed rather than chosen. Slot values serve as bidding chips: clubs can go under slot to save pool money or over slot to woo a player. For example, a team might take a player underslot at No. 5, then use the savings to sign a coveted prep arm overslot in round two. Everyone values a prospect’s future wins above replacement (WAR) differently based on scouting looks, performance data and medical reports. And the uncertainty is extreme: 18‑year‑old arms may need Tommy John surgery tomorrow, while a college hitter’s breakout may vanish under better pitching.
In this world of uncertain talent, the winner’s curse works its mischief. The team that is most bullish on a prospect’s upside will expend the highest pick and largest bonus. If its evaluation is merely average rather than exceptional, it has overpaid relative to the field. That’s the curse: Winning the rights to a player simply because you were the most optimistic. As Capen, Clapp and Campbell observed, the winning bid often exceeds the item’s intrinsic value because the winner overestimates what it is worth. In baseball, overestimation manifests as inflated signing bonuses, lost pool flexibility and, years later, painful retrospective pieces about draft busts.
How MLB’s slot system masks the curse
On paper, MLB’s draft should reduce the risk of overbidding. Since 2012, every pick comes with an assigned slot value and each team receives a fixed bonus pool, turning the draft into something close to a regulated marketplace. In theory, this should limit the runaway optimism that produces the winner’s curse: if the price is pre-set, no one can pay too much.
In practice, slot values function less like fixed prices and more like currency. Teams routinely underslot early picks to bank pool space, then push that saved money toward later targets they believe are undervalued. The results can be dramatic. In 2024, for example, the Reds signed second-rounder Tyson Lewis for $3.05 million—well above his $1.80 million slot—an aggressive overslot bet enabled by savings elsewhere. Moves like this show how fluid the system really is: clubs aren’t bound to slot values so much as they’re choosing where to concentrate their optimism.
This is where intent and bias blur together. Underslot strategies are often framed as rational portfolio management. Trade a bit of surplus on a predictable early pick to take a bigger swing later. But reallocating money doesn’t eliminate the risk of overpaying; it merely shifts that risk deeper into the draft, where uncertainty is even greater. Any overslot deal, whether calculated or impulsive, still hinges on a team’s internal conviction that its evaluation is sharper than the industry’s. And that belief (our read is right, and everyone else is low) is exactly the mindset that gives the winner’s curse room to work.
The slot system, then, doesn’t prevent overbidding. It obscures it. By packaging optimism as strategy and allowing teams to move money around the board, the system encourages clubs to express their highest-variance bets not at the top of the draft, but in the murkier middle rounds, where the gap between a confident projection and a faulty one is widest.
High‑school tools and projectability
Prep players tantalize scouts with raw tools and long developmental runways. Showcase circuits amplify scarcity, persuading teams that a 17‑year‑old’s bat speed or arm strength is rare. When multiple clubs covet the same player, he becomes a classic common‑value asset. Without reliable performance data against top competition, evaluations diverge wildly. The team that dreams the brightest dreams writes the biggest check. The Nationals, for instance, saved money on their first‑round selection and then poured $2.81 million (about $1.7 million over slot) into second‑rounder Luke Dickerson. In winner’s‑curse terms: Differing valuations of a prep prospect mean the highest optimist pays a price that exceeds the player’s expected value.
College arms and the illusion of safety
Conventional wisdom holds that college pitchers are safer than high‑schoolers. Yet pitching is hazardous regardless of age. The epidemic of UCL tears and velocity‑driven development undermines the “floor” teams think they’re buying. The Mets learned this in 2021 when they drafted Kumar Rocker 10th overall. Rocker agreed to a $6 million bonus ($1.26 million over slot) but the club balked at his medicals and declined to sign him. Similarly, in 2014 the Astros selected high‑school phenom Brady Aiken first overall. Team doctors flagged his UCL, and Houston tried to reduce his $6.5 million agreed bonus to $5 million. Negotiations collapsed; the Astros forfeited their top pick and jeopardized an overslot deal with fifth‑rounder Jacob Nix. Aiken later needed Tommy John surgery, leaving Houston without a top pick and with wasted pool money. Here the curse is clear: Believing that college arms are “safe” leads teams to pay a premium, yet the riskiest outcomes often still materialize, leaving the winning bidder holding the bill.
The consensus top‑three problem
Public prospect rankings create a powerful form of social gravity. When every industry board agrees that a handful of players sit atop a class, no general manager wants to be the one who strays. The incentives tilt toward conformity: if the consensus is wrong, failure is shared; if you deviate and miss, the failure is yours alone. That professional asymmetry nudges teams to align their decisions with the crowd rather than with their own models, even when internal data suggests a different course.
Herd behavior thrives in this environment. Instead of treating rankings as noisy signals about uncertain future value, teams sometimes interpret them as confirmation of what “everyone knows.” The draft becomes a sequence of teams reacting to one another’s expectations. And in a market where prospects function as common-value assets – players whose true value is the same for everyone but imperfectly known – this convergence has a predictable side effect. The club sitting at the very top of the board effectively becomes the most optimistic bidder, locked into paying full slot for the industry’s consensus pick whether its internal valuation supports that enthusiasm or not.
Recent drafts illustrate how fragile consensus can be once a single team breaks formation. In 2020, Vanderbilt shortstop Austin Martin was widely projected to go second, yet the Orioles passed on him at No. 2 in favor of Heston Kjerstad in an underslot move. Martin tumbled to fifth. Hours earlier it seemed unthinkable he’d fall out of the top three, a reminder that consensus often reflects shared assumptions more than shared certainty. In 2016 the Phillies took Mickey Moniak first overall partly because he would sign cheaply, not because he was universally graded as the top talent. That move, driven by portfolio calculus rather than pure ranking, exposed how thin the foundation of industry agreement can be.
Case studies: cautionary tales and near misses
To see these dynamics in action, it’s helpful to look at individual drafts. Every year offers its own tragedies and triumphs, but a few names loom large as cautionary tales.
Tyler Kolek, the Marlins’ second overall pick in 2014, embodied the high‑school tools archetype. Miami signed the 102 mph Texan for $6 million, luring him away from a Texas Christian University commitment. Kolek’s elite velocity seduced the Marlins into passing on polished college hitters like Kyle Schwarber and Aaron Nola. Injuries (including Tommy John surgery) and poor command derailed his career; he never advanced beyond Low‑A. The Marlins spent over slot on a lottery ticket and drew a losing number.
In contrast, sometimes players flagged by consensus boards outperform expectations. During the 2024 draft the Angels gave 11th‑round infielder Trey Gregory‑Alford $1.96 million, an overslot bet emblematic of the strategy. Occasionally an undervalued prospect blossoms into a star, but those hits are rare. Survivorship bias tempts teams to overweight the few successes and forget the many misses; the curse raises the cost of failure and makes the rare jackpot seem more alluring than it truly is.
Quantifying the curse: expected value curves
Anecdotes reveal how individual drafts can go wrong, but expected value curves show the structural math behind those failures. Analysts have long tried to quantify draft picks by converting future WAR into present dollars and then subtracting the expected signing bonus to estimate surplus value. A FanGraphs study, for example, smoothed values across picks and found that the first overall selection in 2012 carried roughly $45.5 million in present value, while pick 38 was worth about $8.1 million. The shape of the curve is steep at the top and then levels into a gradual decline, with the sharpest drop occurring within the first half-dozen picks.
These curves matter because they reveal how fast the margin for error disappears. Early selections come with enough expected surplus to absorb some overenthusiasm. But as the draft progresses and intrinsic pick value shrinks, any overslot deal—especially one driven by optimism about projectability—can wipe out what little surplus remains. In other words, the further a team moves down the board, the less room it has to survive being the most optimistic evaluator in the room. The winner’s curse becomes more punitive precisely where teams often feel emboldened to “let it eat” with saved bonus pool money.
Expected value curves don’t tell teams whom to draft, but they do expose the economic terrain: a landscape where optimism grows costlier with every pick, and where the gap between disciplined valuation and wishful thinking may be measured in millions.
Why smart people still fall for it
Two behavioral forces help explain why front offices repeatedly succumb to the winner’s curse: optimism bias and herd behavior.
Optimism bias leads scouts and executives to overestimate upside while downplaying risks. Prospects’ ceilings loom larger than their floors, and the allure of potential stardom encourages overslot offers that seem rational in the moment. Layered atop the draft’s inherent uncertainty, this bias inflates valuations and makes risky bets appear like bargains.
Herd behavior compounds the effect. When draft boards, public rankings, and rival teams coalesce around the same players, deviating from consensus can feel professionally costly. Executives often align with the crowd because failing conventionally is safer than failing unconventionally. In a market of common-value assets, where all teams ultimately value the same underlying talent, this conformity can transform cautious optimism into overpayment.
Together, optimism and herd mentality create a self-reinforcing loop: scouts imagine best-case scenarios, the market validates them, and teams overcommit. The result is a repeated pattern of overslot deals and missed surplus, classic symptoms of the winner’s curse.
How teams can break the curse
Economists Capen, Clapp and Campbell recommended that bidders counteract the winner’s curse by adjusting for uncertainty and the number of competitors. Baseball has analogous tools:
- Bayesian updating and analytics: Teams can build probabilistic models that integrate scouting, performance data and injury risk, then adjust projections downward to account for optimism bias. FanGraphs’ draft‑value curves show that adjusting for uncertainty reduces expected surplus; teams should bid (i.e., draft) accordingly.
- Independent draft boards: Successful organizations construct their own rankings rather than parroting industry consensus. The Orioles and Dodgers are reputed for blending scouting with proprietary analytics and sticking to their boards. The Guardians’ “pitching factory,” with nine of their top 30 prospects being pitchers, exemplifies disciplined development. By weighting internal information more heavily than rumor velocity, such teams reduce the risk of overpaying for consensus darlings.
- Portfolio approach: Rather than allocating most of the bonus pool to a single high‑risk pick, some teams diversify by distributing money across multiple selections. Under MLB’s bonus‑pool rules, undersigning early picks and reinvesting savings into later rounds is common. Used judiciously, this can balance risk by spreading bets across several players. But diversification only works if each pick’s expected value exceeds its price—something optimism bias routinely obscures. Without proper valuation, a club simply trades one overslot mistake for several smaller ones. The portfolio approach should therefore be paired with rigorous models and sober assessments.
- Better medical forecasting: Many curse‑driven catastrophes stem from hidden injuries. Investing in biomechanics research and injury‑prevention analytics can reduce uncertainty. When the Astros declined to sign Aiken because of UCL concerns, they absorbed short‑term pain but may have avoided a larger long‑term cost.
Conclusion: Accepting uncertainty, embracing humility
The winner’s curse teaches that drafting is less about predicting the future than respecting its unknowability. Clubs fall for upside because they must, and they chase consensus because it feels safer than standing alone. Even so, the teams that navigate the draft best are the ones willing to temper hope with skepticism, to adjust their valuations downward, and to acknowledge just how fragile any projection really is.
Last night, Baltimore provided a case study. By flipping Rodriguez for a single season of Ward, the Orioles traded upside for certainty and long-term hope for short-term clarity. It was a small transaction with a big message: on draft day, as in roster-building more broadly, the smartest teams aren’t the ones that dream the biggest, they’re the ones that know the limits of their dreams.
Big West Dugout 