As we wrap up week six of the DSSRF program, I finally feel I have made a unique contribution to the rapidly changing world of sports analytics. Though my model is far from Earth-shattering, I am confident such a model could be used as a jumping-off point to continue immersing oneself in sports analytics. Such a simple model created by a hockey enthusiast begs the question: Is sports analytics entirely beneficial?

Critics of sports analytics are quick to highlight a few examples of blatantly wrong analysis. For example, ESPN published a “state of the industry” site in 2015, examining every team in the major four sports leagues in North America: MLB, NFL, NBA, and NHL. Critics pointed out the “Bottom 10” and “Top 10.” These sections highlighted the franchises that embraced sports analytics the least (nonbelievers) and the most (all in) [1]. In the “Bottom 10,” ESPN shames teams for resisting the analytics movement. Though I am a supporter of the integration of analytics in sports, this language further widens the gap between “nonbelievers” and those who are “all-in.” If those who are against sports analytics justify their position with old-school methods, then shunning them for these beliefs is only going to drive them further away from number-based methods.

I think “The Great Analytics Rankings” is a great leaning opportunity for digital scholars such as myself. The material ESPN based their writing on was factual, included statistics (go figure) and incorporated direct quotes. However, the text between stats and quotes was dismissive and condescending. This language ought to be informative and possibly serve as an education platform rather than an opinionated jab. For example, ESPN objectively discussed the Colorado Avalanche and their lack of acceptance when it comes to advanced analytics: “Last season, the Avalanche were Central [division] champions, however their Corsi for percentage, which indicates productive puck possession, was 25th in the league, indicating their success would not last” [1]. However when discussing the NFL’s New York Jets, ESPN wrote: “New GM Mike Maccagnan and new coach Todd Bowles likewise sports old-school credentials and were not hired to spearhead a stats awakening” [1]. ESPN proceeds to mention the “good ol’ eye test,” as if there is no way to scout players outside of advanced biological and performance analysis. Putting down the Jets for neglecting advanced analytical methods must be justified, but hiring individuals with different opinions is not reason enough.

When conducting any sort of work in the Digital Humanities, communication is key. Though I spent the majority of my time this summer working with numbers, the language used to describe my final project must be informative and open-minded. Though analytics has barged its way into the sports world over the past 15 years, other outlooks regarding decision making still exist, and each has its own reason to be appreciated. The next two weeks will be spent fine-tuning my final site, and I must keep in mind the importance of providing information clearly, not forcing research findings on readers.


[1] “The Great Analytics Rankings.” Accessed July 6, 2017.