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Turning Advanced Analytics Into Digestible Data

The concept of advanced analytics is a relatively young one. Bill James, one of its earliest pioneers, began publishing his annual (and seminal) “Baseball Abstract” in 1977, describing his progressive approach to statistical analysis in baseball as sabermetrics. As James saw it, a sport that, since its earliest days, had relied almost exclusively on simple “counting stats” in its evaluation of player performance was sorely lacking in nuanced quantifying models that helped better describe true player value.



Specifically, bellwether baseball statistics such as RBIs for hitters and wins for pitchers could be easily understood by anyone remotely familiar with the game. But those figures measured outcomes, heavily influenced by contextual variables such as batting order spot and teammate performance. Having already learned this, James set out to find more reliable statistical methods for determining player success. His innovation of formulas such as runs created for hitters and win shares for pitchers offered a more accurate reading of player contributions to game outcomes.

James would go on to work for the Boston Red Sox as an advisor, and in 2006 was even named one of the 100 most influential people in the world by “Time” magazine. His work, it could be argued, was the muse for baseball’s Moneyball movement. Although initially met with skepticism and, in some cases, open resentment, James’ gift to baseball was a shared language that everyone – first fans, then front office executives, and eventually coaches and players – could understand and speak. That analytics shorthand, which has been replicated in other sports, has become a critical driver for clubs and entire leagues in player performance optimization and the continued improvement and evolution of our games.


How Technology Has Supercharged Advanced Analytics

While James and some of his contemporaries helped broaden perspectives across sports with advanced statistical models and new ways of thinking about player value, even their most descriptive analytical innovations had limitations. The rub: although more accurate and detailed in their measurement of player performance than statistics of the past, advanced analytics were only slightly more helpful (if at all) in predicting future performance. Additionally, the statistical inputs could be culled only from game scenarios, which made comparing a player’s value – or potential – to that of another all but impossible in many cases.


Enter technology. Whereas tech innovation in sports progressed at a crawl through much of last century, yielding the stopwatch and baseball’s pitching gun, and little else, the past decade has seen an explosion of new tech solutions enter the market. The vast majority of these tools are focused on improving player performance and, notably, measuring kinetic potential. Instead of relying solely on statistics analysis – which, although improved, remains outcome-based and limited by game participation – performance evaluators now have technology resources to track all player movements across games, practices and training sessions, a treasure trove of information that has more predictive value than any statistical model.


Making the Most of Advanced Analytics

Although in-game statistics and kinetic-movement measurements provide player evaluators with distinctly different inputs, both now tend to be tossed into the bucket of advanced analytics, at least in the broadest use of the term. At the end of the day, it’s all data – and data is the currency of the land when attempting to draw conclusions about player value in sports today.

What isn’t discussed nearly as widely as sports’ Big Data revolution is the importance of translating these discoveries – making data digestible, as Bill James once did in baseball, so that all necessary parties could speak a shared language. Sports scientists have the ability to interpret player data collected from performance statistics and technological means. But if they aren’t able to convey those findings organization- or league-wide, to a more diverse audience, the opportunity to share and discuss these insights through collective analysis and evaluation is lost.

To that end, the next frontier of advanced analytics may well be the “translation” of complex and often abstract player data into clear and relatable insights. The creation of any shared organizational language will require time and a purposeful plan, and it will likely be expressed differently from one club or league to the next. But bridging that gap may be the key to future success in sports, and pulling it off will likely require keeping a few important points in mind:

Stay updated for relevance. Use cases are the secret sauce in teasing out and codifying statistical insights, but an organization must be mindful of riding the gap between use cases and staying connected to the latest data. Although continual learning and adaptation should be strongly encouraged, any new inputs should be elegantly incorporated into the shared organizational language in order to expedite widespread understanding.


Link metrics to facility knowledge. Data should be connected to the specifics of an organizational facility, and a distinction should be made when an unfamiliar or unaligned facility context exists. Making assumptions without the proper information can significantly set back any advanced analytics endeavors.


Ensure clarity in communication. Achieving a balance between the innocence of digestible data and a nuanced understanding of the information is never easy, but it should be a top priority. Use grounded words. Make significant terms accessible to a wider audience. Address criticisms of oversimplification by also providing nuanced views.


Foster connection and avoid patronizing. Every sports organization is characterized by different departments with disparate cultures and agendas, which can make for a rocky political landscape. Focus on the needs and perspectives of performance practitioners while taking pains to involve and inform the wider organization as part of the process.

Identify proper use cases.


It’s crucial to recognize the limitations of legacy tech in accommodating high-intensity, non-linear metrics. Overselling the capabilities of certain technology solutions can ultimately undermine trust and credibility. Emphasize the importance of accurately assessing technology’s capabilities to ensure transparency and trust in its application.


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