Baseball fans have seen a rise in the use of data analytics over the past fifteen years. A shift in the sport has seen teams now embracing analytics and implementing data-driven decision making. While this adoption has been a slow process over that period of time, all 30 teams now have at least one person in their operations department whose job involves using analytics.
What has made this growth in sports data analysis possible? In short, leagues began investing in technology to gather meaningful data. In 2007, MLB had pitchf/x, a pitch-by-pitch data accumulation system, installed in all ballparks. This technology has resulted in pitch trajectory graphics and velocity & spin rate information often seen on television broadcasts. More recently, technology has further expanded to include player tracking in all ballparks.
Much slower to embrace data analytics is hockey. Many teams still do not have a dedicated operations employee for analytics. This can be partially explained by the lack of “success” in organizations that have adopted analytics. In baseball, the best-known case of success for analytics is the early-2000’s Oakland Athletics, who were so successful that their story was retold by Hollywood a decade later. In hockey, however, no such success story is attributed to the front office’s embrace of data and data analysis.
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Hockey’s most popular advanced statistic is called Corsi. It is a measure of how many more (or fewer) shots a team had compared to how many shots their opponents had. In a sense, this mimics basketball’s plus/minus system for evaluating score differential when a specific player is on the court. The NHL does not have a player or puck tracking system to gather data, instead, data is gathered by different parties. This results in inconsistencies that further undermine the effectiveness of analysis.
For baseball, it took the prolonged success of some teams for the rest of the league to fully and embrace data analysis. What would it take for hockey to undergo the same process? Perhaps it would be the success of a team like the Arizona Coyotes or Florida Panthers, teams that have invested in analytics and hired executives with backgrounds in data analysis. Smaller hockey markets such as these experiencing prolonged success with data-driven insights would most definitely influence the league just as the Athletics did in the MLB a decade ago. As we have seen in baseball, player tracking is the next frontier in regards to gathering data to analyze. As proven, better data leads to better insights which lead to better gameplay.
Bio: Brian Salerno is a Junior at Southern Methodist University double majoring in Sport Management and Statistics. He is interested in working in football and baseball data and analytics. You can follow Brian on Linkedin here.