📚 Upside Analysis: When New Practitioners Push for New Tech: The Hidden Cost of Constant Change
Technology has become inseparable from modern professional sports. From GPS wearables and heart-rate sensors to video analytics and recovery platforms, tech tools now inform nearly every decision—from load management and injury prevention to recruitment and rehab. But while innovation has accelerated, the stability of technology ecosystems inside teams has not.
Each time a new head of performance, athletic trainer, or sports scientist joins an organization, they often bring their own set of preferred tools and platforms. This is understandable—practitioners naturally lean toward technologies they trust and have used successfully before. Yet for organizations, this frequent rotation of technology vendors can cause hidden disruption, financial strain, and operational fatigue.
This analysis explores the growing issue of high tech turnover in professional sports—why it happens, what challenges it creates, and how teams can better manage technology transitions to maintain continuity, data integrity, and staff efficiency.
Reality: High Tech Turnover in Pro Sports
In elite sport, change is constant. Coaches, GMs, and performance directors move frequently—sometimes every season or two. With every leadership shift, comes a wave of new methods, new philosophies, and, often, new technology.
Consider a typical scenario:
A new performance director joins a club and prefers Vendor A for athlete monitoring instead of the Vendor B system the team has used for three years. Vendor A’s system might indeed offer strong capabilities, but the switch requires purchasing new hardware, new software subscriptions, and retraining the staff.
A few years later, when that director leaves, their replacement might repeat the process—introducing Vendor C.
The result is an endless loop of technological turnover. While this cycle might appear harmless or even progressive on the surface (“we’re always upgrading”), in reality, it erodes consistency, creates friction between departments, and hinders the team’s ability to build long-term performance datasets—arguably one of the most valuable assets in modern sports science.
In the last decade, as leagues like the NBA, NFL, MLS, NHL, and European football have ramped up investment in performance technology, many organizations have quietly acknowledged the same problem: too much change, too often.
The Shiny Object Syndrome: When Change Lacks Purpose
Beyond staff turnover, another key driver of tech instability is the industry’s obsession with the “latest shiny object.”
Some teams and practitioners chase novelty for its own sake—purchasing the newest wearable, app, or AI dashboard not because it solves a specific problem, but because it’s new, trendy, or backed by a big name.
This behavior stems from a mix of curiosity, pressure, and fear of being left behind. With innovation moving fast, no one wants to appear outdated. But this “tech FOMO” often leads to reactive decisions, where tools are adopted without a clear implementation plan, defined KPIs, or integration pathway into existing workflows.
The result is predictable:
Tools are bought but rarely used beyond the pilot stage.
Staff engagement drops because no one fully understands why the system was introduced.
Data collection becomes fragmented, inconsistent, and disconnected from performance outcomes.
Chasing innovation without a defined goal leads to technology fatigue, where staff feel overwhelmed by too many tools offering overlapping features. In some clubs, five or six platforms may collect similar metrics with no unified strategy for how those insights are used.
True innovation should solve a specific operational challenge, enhance decision-making, or simplify staff workflows—not just tick a box that the team is “using AI” or “doing something new.”
When tech adoption becomes a race for appearances rather than outcomes, it undermines the credibility of performance science and distracts from what matters most: player health, availability, and long-term team development.
Issues Arising from Frequent Tech Changes
1. New Hardware – The Hidden Logistical Burden
Switching vendors often means completely replacing wearable units, sensors, charging stations, tablets, and even storage cases. This might seem minor, but it’s costly and time-consuming.
It can also create logistical headaches—decommissioning old units, recycling hardware, and ensuring new equipment arrives and is calibrated before the season starts.
2. New Software Ecosystems
Every software platform structures and processes data differently. Transitioning to a new one means rebuilding databases, adjusting data pipelines, and sometimes losing years of historical data in the process.
IT teams and data scientists must reconfigure integrations (e.g., with AMS systems or data warehouses), leading to weeks or months of lost efficiency.
3. Staff Training and Knowledge Gaps
Staff members—athletic trainers, S&C coaches, data analysts—must be retrained to use the new system. While some tools are intuitive, others require hours of onboarding and calibration.
This learning curve often coincides with busy periods in the competition calendar, pulling practitioners away from athlete-facing work.
4. Budget Strain and Contract Overlap
Technology contracts are typically annual or multi-year. When a team adopts a new system prematurely, they may still be financially tied to the previous vendor, effectively paying for two systems simultaneously.
Even if the new system offers marginal benefits, the budget impact can be significant—especially for smaller-market teams or collegiate programs.
5. Data Continuity and Performance Analysis
Perhaps the most overlooked issue is the loss of longitudinal data. When systems change, it becomes difficult to compare performance trends over multiple seasons.
For example, if Player A’s workload or HRV data were tracked in one system for three years, switching platforms may disrupt that dataset’s integrity or even make it incompatible with previous records.
This loss of consistency reduces the value of internal analytics and makes it harder to measure progress accurately.
6. Internal Instability and Staff Frustration
Constant change can also create frustration and fatigue within departments. Staff who invested time mastering one system may feel their efforts are wasted. It can undermine morale and create resistance toward future innovation—ironically achieving the opposite of what tech adoption aims for.
When It Is Time to Switch: The Case for Strategic Change
While stability is critical, there are legitimate reasons—and even necessities—for switching technology providers. The goal should not be to avoid change entirely, but to change intelligently and purposefully.
Here are key justifications for moving to a new provider:
1. Poor Customer Service or Lack of Support
If a vendor fails to provide responsive technical support, training, or account management, operational efficiency and user confidence suffer.
A system is only as good as the service behind it. If downtime, unanswered tickets, or lack of updates become chronic, switching becomes a sound strategic move.
2. Emergence of Superior Technologies
The sports tech landscape evolves rapidly. Sometimes, a competing platform offers meaningful advantages—better accuracy, more advanced analytics, greater interoperability, or stronger AI insights.
If these improvements directly align with the organization’s strategic goals (e.g., injury reduction, time savings, or data integration), a change may deliver long-term value that outweighs short-term disruption.
3. Contractual or Financial Model Constraints
Many teams find themselves locked into expensive, inflexible multi-year contracts that don’t scale or evolve with their needs. Transitioning to a provider offering a CAPEX (Capital Expenditure) model—where hardware is purchased outright and only software is renewed—can provide more flexibility and cost control over time.
4. Vendor Instability
In cases where a vendor faces financial uncertainty, acquisition, or declining market presence, it may be wise to pivot early to a more stable long-term partner.
5. Lack of Data Ownership or Accessibility
If a vendor restricts data exports or uses proprietary formats, teams risk losing access to their own performance history. This is a strong signal to reconsider partnerships in favor of open, interoperable solutions.
In short: change should not be avoided—but earned. Teams should move when clear, data-driven reasons support the switch, not when new leadership simply prefers something different.
Ways to Mitigate Tech Turnover
1. Develop a Centralized Technology Strategy
Teams should establish a long-term technology roadmap governed by a cross-departmental committee—spanning performance, medical, data, and IT.
This body can evaluate proposed vendor changes through standardized criteria such as performance metrics, interoperability, and cost-effectiveness.
This ensures continuity even when individual staff members move on.
2. Implement Data Portability Standards
When change is necessary, teams must ensure that data migration and compatibility are non-negotiable conditions in vendor contracts.
Vendors that can’t or won’t provide data export capabilities create future risks for continuity. Teams should prioritize those who adhere to open data standards or offer API access.
3. Cross-Training and Knowledge Sharing
No single staff member should be the sole expert on a technology platform. By cross-training multiple users and documenting workflows, teams ensure resilience if key personnel leave.
Establishing internal “tech champions” across roles (ATCs, performance analysts, etc.) can spread knowledge and reduce dependency on one individual or department.
4. Independent Product Evaluation
Before switching vendors, teams should conduct structured evaluations—ideally using independent, third-party assessments (like Upside’s scouting reports or comparative studies).
This approach helps ensure changes are data-driven and not solely based on personal preference or past familiarity.
5. Manage Transition Periods Strategically
If a new practitioner insists on a new system, encourage a gradual transition rather than an abrupt one. Running both systems in parallel for one season allows comparison of data quality and functionality before fully committing.
6. Integrate Organizational Buy-In
Major technology changes should require input from all key departments, not just one individual. This collective decision-making ensures that new tools address broader organizational goals rather than personal comfort zones.
Pros vs. Cons of Switching Technology Providers
Source: Upside Global, November 2025
Pros of Switching Technology Providers (When Done Strategically)
Access to superior technologies: Upgrading systems can provide better accuracy, improved analytics, or enhanced AI-driven insights.
Improved vendor support: Moving to a provider with stronger customer service ensures better training, faster troubleshooting, and ongoing technical updates.
Cost flexibility: Transitioning to vendors with CAPEX or scalable models can reduce long-term costs and increase financial flexibility.
Better data integration: Modern platforms often feature open APIs and improved interoperability across AMS, GPS, and wellness systems.
Alignment with new goals: Switching can help teams realign their tech stack with evolving performance, medical, or organizational objectives.
Enhanced long-term outcomes: When the new technology fits team needs, it can lead to more efficient workflows, better decision-making, and improved player health insights.
Future-proofing: Ensures the organization stays ahead of technological obsolescence and leverages innovation at the right time.
Cons of Switching Technology Providers (When Done Reactively)
High upfront costs: Hardware purchases, setup fees, and integration expenses can strain annual budgets.
Data continuity loss: Historical data often becomes fragmented or incompatible when migrating between systems.
Training burden: Staff must learn new interfaces, workflows, and reporting structures, reducing time available for athlete support.
Operational downtime: Implementation periods can delay day-to-day operations and disrupt in-season routines.
Overlapping contracts: Teams may face termination fees or pay for multiple systems simultaneously during transition.
Change fatigue: Constant system switches can reduce staff engagement, trust, and long-term adoption.
Short-term instability: New systems often require months before they deliver consistent, reliable results.
Loss of internal credibility: Frequent changes can signal a lack of strategic direction to both staff and players.
Recommendations for Teams
Create a Tech Continuity Policy:
Require that all major tech decisions align with a long-term organizational roadmap, reviewed annually by leadership.Include Data Transfer Clauses in Contracts:
Ensure every vendor agreement allows full data export in standard formats upon termination.Adopt a 12-Month Evaluation Rule:
Avoid replacing any major system before a full season of evaluation—allowing time to gather user feedback and performance results.Prioritize Vendor Partnerships Over Purchases:
Build multi-year relationships with vendors focused on collaboration and customization rather than one-off transactions.Audit Tech Stack Annually:
Conduct internal reviews to assess which tools deliver real ROI and where redundancies or inefficiencies exist.Educate Incoming Staff During Onboarding:
New hires should receive a tech orientation—explaining current systems, rationale behind their selection, and performance metrics achieved to date. This fosters understanding and continuity before changes are proposed.
Conclusion
Technology is a cornerstone of modern performance operations—but it’s also a potential source of disruption when change happens without structure or purpose.
Constant switching of systems—whether due to new staff preferences or the allure of “latest and greatest” tech—can undermine long-term success, drain budgets, and erode data consistency. Yet, there are moments when change is justified: when a vendor underperforms, when better technologies clearly advance performance, or when financial models improve flexibility.
Ultimately, stability and adaptability must coexist.
Teams that establish clear governance, prioritize interoperability, and anchor every tech decision in purpose—not preference—will maximize the return on their technology investments.
In elite sport, success isn’t about who has the newest tools—it’s about who uses their tools most effectively, consistently, and intelligently.
You may also like:
Fascinating. You've really hit on a crucial point about the invisible friction in tech adoption. I wonder, what are the biggest technical challenges when migrating historical athlete data between completly different vendor ecosystems?