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The Intersection Between Analytics and Sports

Wondering how analytics intersects with sports and the ways that the two overlap? This article, written by former club advisor and chair of the Sport Management Department, Dr. Norm O'Reilly explores how analytics has emerged in sports.

Around the turn of the century, attention grew around the topic of ‘sport analytics’. A few factors led to such attention. First, “Moneyball,” the widely read book by Michael Lewis turned the story of Billy Beane, the General Manager of the Oakland Athletics, into a household tale. The book talked about a club with a lower salary budget out-performing all others using an analytic approach to player development, selection and transfers.

Second, although data has been available in sport for decades and decades, technological tools, software and data warehouses grew in availability and capacity to allow for efficient analysis of extraordinarily large data sets. This has allowed teams to run more advanced analysis and make complex decisions about issues involving both business issues and team personnel issues. This can be seen, for instance, with teams increasingly moving towards dynamic ticket pricing.

Third, the area began to formalize with conferences (e.g., the MIT/Sloan Sport Analytics Conference debuted in 2007 and today draws more than 2,500 attendees and dozens of case cup teams), academic programs/courses, and specific jobs at leagues, clubs, teams and agencies in the sport world. At Ohio, for instance, we have an analytics co-major in the College of Business. More information can be found on this at the "OHIO Analytics Offerings" page on this website.

There are four main areas of sport analytics where jobs are found, problems are solved and differences are made by analysts. 

  1. Player Selection and Contracting: whether via draft, transfer, free agency or other forms of selection, data (past performance, physical attributes, previous injuries, physical/mental tests, personality tests, etc.) can inform decisions on player selection and contracting (length, salary, incentives, options, guarantees, etc.) to improve on-field performance and efficiency of player salary spend.

  2. In-Game Decision Making: the analysis of data can allow teams to make better decisions (pass or shoot, kick or go for it, punt or long field goal, 3-pointer or lay-up, etc.) during the game to increase their chances to win.

  3. Injury Prevention and Rehab: databases of player injuries, games missed, training outputs, etc. can allow coaches and medical staff to set up programs to prevent injuries and to insure a player comes back at the right time, all in an effort to reduce missed games and maximize club performance.

  4. Training Programs: the modern evolution of equipment, such as watches, heart-rate monitors, and other wearable devices, allows athletes and coaches to track better every workout and gauge the effectiveness of their training.