How to Evaluate Sports Performance Technology Before You Adopt It

Evaluate sports performance technology against a specific decision and a relevant alternative. Define the benefit you want, check whether the research addresses that benefit, and run a small pilot that measures both usefulness and staff workload. Decide in advance what would justify keeping, changing, or stopping the tool.

A velocity tracker, an athlete-monitoring platform, and an AI video-analysis system solve different problems. “Better performance” is too broad an objective to evaluate any of them well.

My starting question is simple: What will this tool help our staff do that we cannot do well enough today? The framework below is my practical interpretation of research on training methods and high-performance work. It has not been tested as a combined intervention.

What problem should the technology solve?

Write the proposed benefit in one sentence before reviewing the product features. For example:

• Give athletes clearer feedback about the intended speed of a lift.
• Reduce the time required to find relevant training clips.
• Make unresolved monitoring concerns visible before a staff meeting.

These are different outcomes. A tool that makes feedback easier has not automatically demonstrated larger strength gains. A faster report has not automatically improved athlete health.

That distinction matters in the research. Orange and colleagues’ meta-analysis compared velocity-based with traditional resistance training across four trials involving 88 participants. Differences in strength, power, and sprint adaptations were small and imprecise, with low or very low certainty. The review does not establish a broad performance advantage for velocity-based training. It also does not settle every possible communication or workflow benefit. Read the review.

Match the evidence to the claim you intend to make. If your reason for adoption is clearer feedback, evaluate whether the feedback is clearer and useful to its recipients.

What is the relevant alternative?

Compare the proposed tool with the actual process it would replace or supplement. That might be a spreadsheet, an existing platform, a coach’s current method, or a simpler piece of equipment.

Describe both options fairly. Include the current process’s strengths as well as its limitations. A polished demonstration compared with an undefined “usual practice” tells you very little.

Ward and colleagues’ resisted-sprint review illustrates why the comparison matters. Across 21 studies, its primary between-group analysis found no clear differences between resisted and unresisted sprinting across sprint phases. Within the resisted-training groups, acceleration improved. Improvement over time and superiority to another method are different claims. Read the review.

The same reasoning applies to a local pilot. If athletes improve after a new system arrives, record what else changed: programming, coaching, attendance, competition exposure, or testing conditions. A before-and-after result alone cannot isolate the contribution of the technology.

How should you test an AI performance tool?

Ask the provider or analyst to explain the output in ordinary language. What does it estimate? For whom? Over what time period? Against which comparison? What happened when it was evaluated on athletes or settings outside its development data?

Then test the proposed use. Consider a hypothetical AI system that helps analysts locate specific passages of play. A useful local evaluation could record:

• Time spent finding and checking the clips.
• Relevant clips missed by the system.
• Incorrect clips that staff had to remove.
• Whether coaches could use the final selection for the intended discussion.

Those observations address the analysis workflow. They would not demonstrate that the system improved match outcomes.

For athlete-monitoring models, keep the output’s meaning equally specific. A predicted wellness response should retain that label as it moves into the staff report. My article on giving athlete-monitoring data a clear job explains how to connect a result to an accountable next step.

What does the tool cost in staff time?

The subscription price is only one part of the operating cost. Someone must set up the system, check data, resolve missing entries, interpret outputs, explain them, and follow up.

Mercer and colleagues’ scoping review maps work demands, responses, and coping strategies among high-performance staff. It supports considering the people operating a system alongside the system itself; it does not validate a particular adoption checklist. Read the review.

Before starting a pilot, assign each recurring task to a named role. Ask those staff members what would need to move off their workload. During the pilot, record duplicate entry, interruptions, corrections, and unclear handoffs. A quick software output can still create substantial work around it.

A one-page checklist for an adoption decision

I would bring these six items to the decision meeting:

1. Problem: the specific task or decision that needs improvement.
2. Alternative: the current process and the simplest credible substitute.
3. Evidence: the relevant findings, population, comparison, and limitations.
4. Pilot: intended users, observations to collect, and a review date.
5. Operating demands: ownership, staff time, corrections, and follow-up.
6. Decision criteria: what would support keeping, modifying, or stopping the tool.

Invite someone who did not select the product to examine the results. Give staff a way to raise concerns and record how those concerns were addressed. These are proposed review practices, not proven effects of the cited studies.

When should you keep, change, or stop a tool?

Keep it for a defined use when the evidence from your evaluation supports that use and the operating demands are acceptable. Modify it when the benefit is plausible but the workflow or evaluation needs work. Stop when it does not address the intended problem or consumes more capacity than the department can justify.

An inconclusive pilot can still improve the next decision. Record what remains unknown and what would trigger another review. The goal is a recommendation that someone else can examine, understand, and revise.

For a related application, read what should happen after an athlete completes a wellness questionnaire.

Evidence note: This article adapts my September 16, 2026 research commentary. It uses selected reviews to inform an editorial decision framework; it is not a systematic review or a validated technology-assessment instrument.