Zack Scott, former Mets Acting GM and four-time champion with the Red Sox, empowers sports operations and individuals to win through Four Rings Sports Solutions. He specializes in data-driven strategies and leadership development. His Sports Ops Launchpad helps aspiring sports ops pros break into the industry. Connect with him on LinkedIn here. Zack will be contributing periodically to MLB Trade Rumors.
As Florida’s Grapefruit League approaches its halfway point, Yankees and Mets fans are already venting their fury. Prized offseason pitching acquisitions Sean Manaea and Frankie Montas are injured before even throwing a regular-season pitch for the Mets. Yankees ace Gerrit Cole is also hurt and facing the prospect of possibly missing the entire season.
Having given Montas and Manaea a combined $109MM, the Mets faithful want to know how the team doctors green-lit those deals. Likewise, Yankees supporters question what Cole’s physical exams missed after the Bombers convinced him not to opt out and walk away from the $144MM left on his deal.
As a former baseball executive, I’ve fielded those same frustrated queries. Forecasting player injury risk involves far more art than science, often leaving teams and fans dissatisfied. I hear these complaints frequently since I live in the NY metro area and contribute to SNY’s weeknight show, Baseball Night in New York.
There’s rarely a satisfying answer because the assessment process is highly imperfect. Every veteran pitcher has wear and tear if you look hard enough. Acute injuries occur after the fact. Let’s examine how it typically works, its key flaws, and some ways it could be improved.
The Current (Flawed) Process
When a free agent agrees to terms, the deal is almost always contingent on a physical. The team’s medical staff examines the player, including clinical evaluations, strength and flexibility tests, and MRI imaging of joints like shoulders and elbows for pitchers.
Experts from across the organization weigh in with opinions that a head athletic trainer or performance director synthesizes into an overall risk rating for the GM. For trades, it’s a similar review of medical records, but there’s no in-person exam.
There are several issues with this approach:
• Doctors and trainers interpret MRI findings differently
• Individual expert biases color the assessments
• Lack of standardized, objective metrics
• Siloed information without enough collaboration
• Over-reliance on a single organizational voice
• Underutilization of advanced data analytics
In this high-stakes environment, a process reliant on human judgment is open to significant error.
A System Ripe for Abuse
Valid, complete information is critical for proper risk assessments. However, in this ultra-competitive industry, teams are motivated to gain edges wherever possible, sometimes unethically.
When I was with the Red Sox in 2016, we traded top pitching prospect Anderson Espinoza to the Padres for Drew Pomeranz. Our medical staff reviewed the records San Diego shared and signed off on the deal.
After Pomeranz reported, we discovered he was managing multiple health issues that were not disclosed to us. ESPN reported that the Padres instructed their athletic trainers to maintain two sets of files—one for internal use and a sanitized one for trade purposes. While MLB never divulged details, they investigated and concluded there was wrongdoing. GM A.J. Preller was suspended for 30 days (Take that, wrist!).
The incident eroded trust so much that any subsequent transactions with the Padres were thought to need additional vetting by a third party. It exemplified the system’s vulnerability to exploitation and dependence on clubs exchanging information in good faith.
From Biased Experts to Big Mistakes
Even when injury records are complete, human bias and error can still lead teams astray. As the Mets’ Acting GM before the 2021 season, I explored signing veteran starter Rich Hill. Our medical team reviewed his records and strongly recommended against the move, given his age and injury history. While I had reservations about the assessment, I ultimately decided to heed their advice and pass on Hill.
In retrospect, that was a mistake. Hill signed with the Rays for a reasonable $2.5MM and gave them nearly 100 solid innings. When we traded for him that July, I learned Rich was understandably frustrated that our medical assessment was pessimistic months earlier.
I called Rich to clarify the situation and take responsibility for the decision. While our assessor likely took a conservative approach, as the GM, I had to own the final call. This experience reinforced how these assessments can vary based on the individuals and organizational histories involved. Years prior, former Mets performance staff members took bullets, rightly or wrongly, for player injuries, influencing the current staff to take a more risk-averse approach.
Moving forward, I pushed our group to focus more on objective data and collaborate across silos to mitigate individual biases. We had to balance risks with potential rewards and understand that perfect prediction is impossible. Judgment calls wouldn’t always work out, but we needed to approach them with discipline, openness, and the bigger picture in mind.
That same month, we selected Vanderbilt pitcher Kumar Rocker 10th overall in the draft. After the pick, we did a deep dive into his medicals, which included multiple expert opinions. Despite Rocker’s talent, we ultimately decided not to offer him a contract due to the high perceived risk. A year later, the Rangers drafted Rocker third overall and signed him for $5.2MM. Two teams evaluating similar information came to opposite conclusions. Rocker is now a top-50 prospect, excelling in the minors. Our assessment was clearly wrong, and it cost us at least a valuable trade chip and potentially a frontline starter. That’s how impactful these judgments can be.
Finding a Better Way Forward
To reduce costly human bias and error, MLB and individual clubs must evolve to a more data-driven, objective methodology. Some suggested improvements:
MLB should:
• Standardize protocols for medicals, physicals, and imaging
• Mandate sharing of training and biomechanical data
• Use validated tools to assess psychological factors
Teams should:
• Leverage AI and machine learning to analyze images (e.g., MRI) and predict injury risk
• Develop personalized biomechanical and kinetic player models
• Improve collaboration between medical, performance, and analytics staff
• Have subjective evaluators predict outcomes (e.g., innings pitched) and assign confidence scores
By taking these steps and focusing on hard data while still valuing expert insights, teams can optimize the art and science of this process. It won’t be perfect but will be significantly better than current practices.
Progressive teams are already moving in this direction, and others are sure to follow as they recognize the competitive advantages it brings. Smarter, more precise health forecasting is the future of player acquisitions. Hopefully, fans will soon have more confidence in the medical evaluations that drive roster decisions.
