updated: The (elevated) Danger of 'DIY' Data Management in Sport
- Matt Hauck
- Feb 10
- 11 min read
"Do-it-yourself" bootstrapping is a badge of honor- as well as means of proving value in an organization- amongst staff who manage athlete support and the data that comes from it. A deeper look reveals hidden threats of stifling organizational success and operational sustainability while creating significant liabilities for universities and professional organizations.
Update February 2026: I first published this article in February 2025 after seeing a number of former colleagues and associates showcase the data workflow projects they were building. It was never intended to bash anyone, nor was that the outcome. I had a number of inbound messages after the article from AMS companies, old co-workers, and human performance practitioners from all over the globe.
The AMS companies were mostly complementary as their marketing and sales teams had been working to drive the concept home. A few articles would come out in the weeks and months also covering the topic, and I was happy to see dialogue continue not just from AMS companies, but by practitioners in the field.
What I have observed in the last 12 months has not been a radical shift towards investing in AMS platforms, but rather a renewed call from consumers on key items such as:
Ability to quickly customize platforms to accommodate a stream of new visuals from data analysis tools like Power BI, R, Python, and more.
Accommodate enterprise-level support beyond sports science, medical and performance staff needs for data storage and workflows for scouting, NIL data, contracts, historical documents, to name a few.
Lastly, and most importantly: exploring how to incorporate AI and ML into massive datasets that are well trained and clean.
While these aren't the only topics in the AMS-sphere in collegiate, professional and elite sport, the topics are areas that have been communicated as dealbreakers for staffs and organizations when evaluating AMS and data management needs.
Given the developments over the last year, my article below from last year highlights the same question but with an elevated sense of urgency.
Just who is going to build and manage these interfaces for multi-million and billion dollar entities over the long haul?
Original article from my February 2025 LinkedIn post below:
I've seen a lot of discussion in the past few years all the way up into the last several weeks on the state of the athlete management software (AMS) market. A basic tenet of this dialogue has focused in on whether or not sports organization should invest in a suitable AMS solution, or simply build their own with existing sport and data science staff. After all, the thought of building an in-house AMS is a means of proving positional value for sport science staff who want to make an impression on organizational leadership. I've been in those shoes myself at the collegiate and professional level.
Thanks to having worked in that world, I've also collected some hard-earned lessons in this area of our field and these items must be openly discussed. I've spent 20+ years in human performance and witnessed the explosion of data and technology from consumer-based goods as well as B2B enterprise solutions in sports.
The explosive sports tech industry is on track to topple the $80 Billion mark within the next decade. That growth coexisted with practices of data management in sports that pre-dated the innovation boom: toggling through multiple spreadsheets, piecing together elementary visuals, basic data storage and analysis...
.. and lets be honest about ourselves in the field and not forget the common practice of "we're going to use these tools to show how smart we are and tell them what the data means".
Software Solution Surge
Enter the AMS solutions, which met the market demand of needing to streamline athlete data collection, organization, visualization and analysis. Companies from around the globe crafted solutions that, early on, supported primarily health and performance data of athletes.
Back in 2013 during grad school at Oregon State University, I was given an incredible opportunity by the Director of Sports Performance, Bryan Miller, to help take on the task of implementing sport science initiatives with the football team. As it would turn out, that opportunity turned into my grad school thesis project, a 90+ page piece entitled "Practical Implementation and Application of Integrated Technologies in an NCAA Football Team Setting". As it turns out, this piece would evolve and be revisited often in the coming years as the topic of establishing a framework for sport science methodology, i.e. how should we go about using athlete data to help drive performance, continues to be hotly debated.
The management process of all the data we collected from our Zephyr GPS units, heart rate monitors, customized recovery surveys, hydration and body weight charts, weightlifting data, combine stats and more made me realize I was spending entirely too much time in the weeds with spreadsheets and data at the expense of time with athletes, staff and actually coaching. I became a real-life embodiment of the early-days sales pitch of an AMS: collecting lots of data but with inefficient management and producing laborious, limited insights.
So in 2014, I decided to try and build my own AMS...
...but it was not really an AMS, rather it was a limited GPS & wellness data visualizer. I was the only one with access, my skills were limited, and the visuals lived on a spreadsheet on my personal laptop. Once I moved on from Oregon State, the solution would leave with me.
My first 'AMS' attempt circa 2013-2014, which contained an athlete avatar and visualization that will look familiar to those who spent hours on the web learning how to build the speedometer visual from custom data summaries.
I identified my knowledge gap and needed to elevate those skills, or at a minimum expand my understanding of how a real software system would automate collection, organization, visualization and analysis of athlete data. And needless to say, "data" being an item that would continue to balloon along with the expansion of tech and services in the sports ecosystem.
Just as I finished grad school I was granted a unique opportunity to work-shadow an applied sport science team supporting pro-soccer clients around the world. I spent a short time at Exos in 2015 working on their internal data collection platform. It was eye opening learning how to piece together data connectivity tools, but in reality I was simply working on an enhanced data visualizer tool that could reference more historical data from broader data streams.
Feeling emboldened by this experience, I co-founded a company alongside a team of software developers in 2016 that delivered an AMS offering focused on customized data collection. We built an MVP and went to market leveraging a value-proposition of an AMS that was actually built by coaches (working with actual software developers), for coaches. While the marketing angle was clever, the process forced me to consume a giant slice of humble pie learning what goes into software development, security, accessibility, scalability and more. That project would run out of funds well en-route to our vision, but well shy of where the market evolved over time.
A screenshot from the demo environment of our Voyager AMS platform in 2016, where our metric creation management page would enable customized data tracking from manual input prior to creation of our first API connections with companies like Omegawave.
From 2018-2020 during my time in Major League Soccer, club leadership tasked me with completing 4 comprehensive proof of concept projects evaluating field-leading AMS solutions to onboard in a pro-sport environment across multiple team levels & inter-department collaboration with unique demands well beyond the traditional medical-and- performance-only outlets.
(Side note: I should also add that prior to my time at the club, the management had proposed the idea of building an in-house solution to custom software developers at very a large company who passed on the idea, citing it as too big and too expensive of a project to take on to do correctly, and not within the wheelhouse they were focused on).
At that time our staff collectively adopted a mantra of “every AMS is a 7 out of 10, we just need to find the right 7 for our needs.”
A lot has changed since then, and a "7 out of 10" AMS in the current market won't be enough to keep up with modern demands of sports organizations who need multiple departments making use of their data under one organization-wide, secure roof.
Sports-Data: Solution Revelation
I am convinced of several truths of organizational needs within sports that the AMS market has been, and must continue to, reconcile with:
1) The AMS field as we know it has fundamentally changed. The need to use data from diverse ecosystems outside of performance alone have created a requirement for an end to end platform to unite health and performance, medical/EMR, nutrition, technical staff, video, scouting, recruitment, scheduling, messaging, storage, customization, research and analysis, social media, fan engagement, business operations, and more. This is the embodiment of the "breaking silos" cliche we often use in this field.
2A) The AMS field has increasingly required hefty investment for suitable, field-leading solutions to capture the spectrum of the above domains in supporting pro and collegiate sports organizations. The premise of this investment is sold in aims of achieving functionality that is impactful for all staff, yet not constrained by internal IT or gatekeepers, all while being able to meet security and multi-workflow accessibility requirements. Software development and support is not cheap- especially when done professionally by dependable entities- there is no escaping that fact.
2B) Simultaneously, the ever-present and ever-evolving "building your own AMS" concept is a sexy idea that unfortunately creates extreme liabilities, workflow and accessibility limitations, does not offer "end to end platform" functionality, and still incurs it's own costs for sports organizations. It is also a sentiment that fails to recognize an organization-centric focus and modern-market needs of diverse staffs and workflows in sports. In other words, sport and data science staff trying to enhance their own value to an organization may actually end up detracting from it in other areas.
3) Data-visualizations, while valuable, are a small portion of an end-to-end secure, scalable AMS platform, and should not be considered a "platform" or "AMS" on their own. Suitable player profiles, dashboards, and widgets perform best operating on top of robust database and inquiry infrastructure, and even then these items pieced together fail to meet platform level functionality and exist as standalone items. If you are the only person that can directly access athlete data and analysis- yet many other staff members depend on that information and also work with those athletes- is that useful for the entire organization?
This point of inflection requires a serious discussion on the state of the AMS field balanced against actions of sporting entities trying to solve their sporting data needs. To lay a foundation for discussion, below is a breakdown of organizational risks and downsides for professional sports teams & athletic departments to rely on existing sport and data science employees- without formal software development skills- to build custom data collection and visualization tools for sport organization data needs:
1. Data Privacy Risks: Health, Performance, and Propriety Knowledge
Insufficient Compliance: Custom-built tools may fail to comply with relevant data privacy regulations (e.g., GDPR, HIPAA, and more recently HECVAT), particularly when storing sensitive medical or biometric data.
Unauthorized Access: Without robust security measures, unauthorized individuals might access athlete performance or health data, leading to privacy violations and potential legal repercussions.
2. Data Security Vulnerabilities: Information Risk Points
Lack of Encryption: Employee-built tools may not incorporate strong encryption, leaving sensitive data vulnerable to breaches.
Inadequate Safeguards: Security features like multi-factor authentication, secure data transfer, and regular updates might be overlooked, increasing the risk of hacking or data loss.
No Incident Response Plan: Custom tools may not integrate with organizational security systems, making it harder to respond to data breaches effectively.
Unfortunately: high profile athletes have been made targets of cyber attacks on poorly guarded data infrastructure for many years.
3. Ease of Access to Information: Gatekeeping Against Progress
Fragmented Data Systems: Custom tools developed independently by different staff members may lack integration, creating silos and making it difficult for others to access or analyze data efficiently.
Poor User Interfaces: If non-technical staff build these tools, the design may not prioritize user-friendliness, limiting their utility for others.
Inconsistent Data Formats: Lack of standardization in data collection can result in inconsistent data formats, complicating analysis and decision-making.
4. Employee Turnover: Back to Square One
Knowledge Loss: When an employee who built a custom tool leaves, the organization may lose critical knowledge about the tool’s design, functionality, and maintenance.
High Maintenance Costs: Successors may struggle to maintain or upgrade the tools, potentially requiring costly redevelopment or external support.
Disruption of Operations: The departure of key staff can cause delays in data collection and analysis, hampering decision-making processes, such as game preparation or injury management.
5. Scalability and Reliability Issues: Failure to Launch
Limited Scalability: Employee-built tools may not be designed to handle large volumes of data or multiple users, which can hinder growth as the organization expands its data operations.
System Failures: Custom tools may lack rigorous testing, increasing the likelihood of errors, crashes, or data loss during critical periods (e.g., playoffs or injury assessments).
6. Legal and Reputational Risks: All Risk, Minimal Reward
Data Breaches: Any breach of athlete data could result in lawsuits, fines, or reputational damage, potentially impacting athlete trust and the team’s brand .
Improper Data Usage: If the tools do not restrict data access or usage adequately, there’s a risk of misuse, leading to ethical concerns and legal challenges.
Respecting the risks and downside to having existing sport or data science employees lacking formal experience in software development deploy an 'in-house' AMS solution may tempt organizational leadership to explore how to properly build and manage a secure platform for all organizational sport and performance data needs.
Establishing a dedicated and qualified team to oversee centralized IT functions, support an internal data framework, train staff on data privacy and security, conduct audits, document tool functionalities, and provide daily support for a custom data platform would be essential functions and responsibilities. If done to meet those requirements with adequate staff, a hefty investment and lengthy timeline must be committed to in order to attain the level of support required in team sport.
For entities who are serious of delivering an in-house data management solution, below is a breakdown of the annual costs based on median salaries in the United States to support a sufficient staff to do so:
1. IT Management and Oversight
Chief Information Officer (CIO) / IT Manager: Responsible for overall IT strategy and management.
Median Salary: $169,510 per year.
2. Data Management
Data Manager: Oversees data collection, storage, and maintenance.
Median Salary: $90,733 per year.
Data Management Specialists (2 positions): Assist in data-related tasks and ensure data integrity.
Median Salary: $80,781 per year, per specialist.
3. Information Security
Information Security Analyst: Ensures the security of data and IT systems.
Median Salary: $120,360 per year.
Cybersecurity Trainer: Provides training on data privacy and security protocols.
Average Salary: $58,171 per year.
4. Data Privacy
Data Privacy Analyst: Monitors compliance with data privacy regulations.
Median Salary: $99,684 per year.
5. System Support and Maintenance
Data Security Administrator: Manages data security measures and system maintenance.
Average Salary: $47,840 per year.
6. Additional Support Staff
IT Support Specialists (2 positions): Provide day-to-day technical support to users.
Estimated Median Salary: $60,000 per year, per specialist.
Total Estimated Annual Salary Costs : $867,860 per year.
This estimate covers salaries only and does not include additional costs such as benefits, training, equipment, software licenses, or other operational expenses. In the global industry of sport, these salaries may fluctuate in order to attract top talent to safeguard propriety knowledge and assets of multi-billion-dollar organizations.
Additionally, the complexity of the data platform and the specific needs of the organization may require adjustments to team size and composition.
The above proposal reflects a best-use case for construction and on-going support of a robust end-to-end data platform for use in team sport. Trying to achieve the same result with fewer staff may be plausible by consolidating roles and focus on hiring versatile individuals with a broad skill set. While this sounds attractive, there are inherent risks of reduced staffing with role overload, skill gaps, and scalability challenges.
To DIY or not to DIY?
Sports are past the point of no return when it comes to athlete data, as the broader landscape of athlete-centric data will continue expanding beyond even the scope of the booming sports technology marketplace. If universities or professional organizations have not migrated their athlete data into comprehensive and secure platforms, it is feasible that they may be required to in the near future.
For entities with serious commitment to long term visions of data use, development of AI tools, and collaboration between departments to achieve organizational success, the investment in staff and structures to own and develop these tools could have lasting impact. In the world of sports where turnover is inevitable in every department at every level, those realities present a consistent and direct challenge to realize the full potential of such an initiative.
Alternatively, evaluating the AMS marketplace for secure platforms offering timely, end to end support and customization for the evolving needs of sports entities is indeed a worthwhile - if not mandatory- task for department and organizational leadership. Having navigated that path in-depth, there are many good options to explore in the space. Ultimately, organizational leadership must know the true scope involved with "DIY" AMS and data systems while they balance strategic needs for sustainable operation from current staff as well as prospective vendors.