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The Challenge of Integrating New Data and Collection Tech with Historic Data

Updated: Oct 17, 2022

By: Raf Keustermans

Anytime new technologies or systems become too valuable or ubiquitous not to integrate for businesses across an industry, there are holdouts that cling to the old ways or prioritize the familiar over the innovative. Those organizations tend not to last long.

But even among adopters, there are those companies that try and fail to merge the old with the new, while others make it happen. We’re seeing this on full display in the areas of sports, where organizations are challenged to integrate legacy data with new collection technologies and data sets. What sets the success stories apart?

When faced with waves of new data due to advancements in automation and data collection methods, a sports organization should first acknowledge that it’s a good problem to have. With technology like LiDAR, a laser-based movement-tracking system, improving the accuracy, depth of information and seamlessness of data collection, performance evaluators now have access to an enormous, untapped trove of data that can be used to better inform their decisions. The question then becomes: How does a club manage that influx of new data?

First, preach patience. Consider that organizations and their data teams have been using the same methods and approaches, making the same assumptions and associations, for years. Old habits die hard. And because advanced analytics can be applied to everything from game strategy to the optimal types of soda served at the stadium concession stands, an organization adopting these technologies for the first time will need across-the-board buy-in. That takes time.

The biggest challenge, however, is integrating an organization’s historic data with modern information. Collection technologies and methods aren’t all that have changed in this area. Today’s data looks very different than that of the past, and in some cases, the types of measurements don’t align with previous data sets. How does an organization’s data teams solve this problem? Start here:

  • Run translation exercises. Set aside a transitional period during which a detailed analysis of all data and methods – both modern and historic – is conducted.

  • Amass a statistically significant amount of data. Avoid any statistical noise or false positives a too-small sample size could yield. You’ll want to get this right the first time.

  • Be aware of biases. Certain predilections could occur in the calibration of the system. Identifying and correcting them are important to avoid building bias into your baselines and future calculations.

  • Account for differences in data collection methods. Different sports venues use a variety of tracking technology, some of which have inherent limitations that influence the data collected.

  • Know that some translations can be probabilistic in nature. Measure to a constant: In other words, player X runs at a speed of Y, so the new measurement output should be equal to Y.

  • Integrating old and new data can be laborious. Making sure that old data sets aren’t lost while embracing the insights new data unlocks can be costly and time consuming. But it’s important to remember after the exercise that an organization will be better positioned to make personnel decisions.

The key for sports organizations integrating old and new technologies, methodologies and information is to take a deep, thorough dive into the data. Raw data doesn’t help most clubs. It needs to be easily understood by new user profiles down to make it viable, which takes valuable time and which may leech all the usefulness from it in the process.

A schism may exist between data sets tracking similar or identical movements using different technologies or approaches. When measuring the force of a kick on the pitch, for instance, data collected from wearables attached to a player’s boot may not easily integrate with data collected that measured that same kick using laser-based LiDAR.

And because wearable technologies are limiting in where and how often those measurements can be tracked, there may be gaps in the feedback from the tech due to missing data points. Data smoothing can’t stitch this information together.

Upgrading to new technologies is, of course, is often worth it. Take LiDAR, which is unmatched in its tracking accuracy while being more portable and unobtrusive from the player’s standpoint than past tech. The challenge of data integration is the only noteworthy downside to adopting LiDAR for a club’s player evaluation department. And with the right plan, even that challenge can be solved.

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