Current Climate of Sports Analytics
Despite the increased availability to data and analysis techniques that can potentially aid sports management organizations of decisions, many teams do not use the tools available to them.
Interest in the sports analytics field has been rapidly growing as of late, highlighted by the book and film, Moneyball, which not only grossed over $75 million but also drew attention to the significant potential digital analytics holds within the sporting environment.
While the term ‘Moneyball’ was coined by author Michael Lewis in his 2003 book to describe a strategy that utilizes analytics, “Sports Analytics” involves the gathering and collection of data, data management, statistical analysis, data visualization and information systems to deliver better information more efficiently to decision makers within an organization. The technology behind these tools has advanced rapidly in the last decade, making access to immense amounts of data more readily available than ever before. 
Evolution of Analytics within Sports
The digital analytics field itself is still a developing industry therefore asking executives, managers, coaches and players to embrace analytical tools and techniques is a difficult task within the high pressure field of professional sports. Organizations are hesitant to expand their investment in a sports analytics program without an understanding of a clear proven way forward and sense of potential value. While sports analytics will continue to evolve as a field the following are challenges facing adoption of these tools and techniques: 
· Natural inclination to resist change - Individuals and organizations both internal and external to the sporting environment are naturally inclined to resist change, making the shift toward basing major decisions on data and analysis driven models a slow moving process.
· Distrust of the unfamiliar - Team executives are unfamiliar with statistical modeling, therefore have a predisposed distrust or discomfort with analytic tools and techniques.
· Old-school traditional decision making vs. progressive data driven decision making – Similar to the business world, decision making in the sports industry is intuitive and instinctual. Shot-calling executives may have started as scouts or coaches, which had to rely on personal experience to make decisions. These same decision makers would view the use of digital analytics and data models as non-credible resources. As this video clip from Moneyball shows, organizational friction can occur in regards to how analytics should be utilized and trusted when making player personnel decisions.
· Technical Barriers – General communication barrier between analysts and front office of a sports franchise, as typically those with decision making power in an organization are not familiar with the language of analytics.
· Limited financial resources to spend on analytics
· Implementation - Turning the data gathered from statistical tools into useful data is the main challenge teams now face as they implement new data collection and analysis methods. Then a team must determine what tools and techniques create the best value to help the team win. 
The pace of adoption and evolution in sports analytics will depend on how quickly leaders are aware that significant investments into analytics will deliver a true competitive advantage. Within the past few years increased knowledge sharing and development of these analytic programs have come to the forefront of the sporting environment. Global conferences and college courses aid in improving communication skills between sports, analytics and data sciences.
The ‘Who’, ‘What’, and ‘Why’ of Sports Analytics
The expansion into sports analytics territory also comes with many questions. Understanding the strengths and weaknesses in your organizations can help guide how sports analytics are best utilized. Being able to harness its analytic capabilities and work past obstacles can create a significant competitive advantage on the field
Who can use Analytics?
· General Manager and Executives – making player personnel decisions
· Coaches – reinforces gut instinct or uncovers something not previously seen, whether this be tactically or personnel related. As one NBA head coach noted “It’s a good backup for what your eyes see, but we can’t make all your decisions based on it; the tools can’t measure heart, chemistry and personality.”
· Players -
o Assists in injury analysis - understanding when to taper off individual training schedules to avoid overuse injuries and ensure players are kept fresh for game day
o Performance analysis – objectively know players are in optimal condition going into games by monitoring training load and determine who isn’t working hard enough
o Technical analysis – tracking individual movements to improve tactical analysis 
· Referees - Improve officiating of games by using analytics to determine if a refs positioning and site lines were appropriate based on the calls which were made
· Fans – Digital analytics can also help improve fan/consumer experience, by putting meaningful numbers in front of fans and GMs alike, rather than outdated meaning less statistics. 
What is used to gather sports analytic data?
The following companies are changing the way data is collected as well as the types of data available for consumption:
· STATS LLC – Utilizes cameras and optical tracing technology to capture the positioning of everything that moves on a court or field of play, from the players to the ball to the referees. This data can be captured at a rate of 25 times a second, critical in high movement sports. 
· Catapult Sports – Utilizes GPS, accelerometers and other wearable technology to track players movements and physical characteristics such as heart rates.Video: How does the Catapult system collect data from athletes?
Why are sports analytics effective?
Digital analytics are causing a major shift in the type of data available in sports from specific court/field actions (attempted shots, passes, rebounds etc.) to data drawn from continuous movements of every element within a play. Analytics also play a large role in front-office player personnel decisions.
· The Rockets signed Carlos Delfino last season in part because the camera data revealed he grabbed an unusually large percentage of rebounds that fell near him, very valuable for a player in his position. 
· Regarding the analysis of officiating, “We will use whatever data and means we can to improve our referees,” says Steve Hellmuth, the NBA’s executive vice president of operations and technology. “The refs haven’t been tracked before. Now for the first time, they will be.” 
· The Moneyball strategy is a prime example of how sports analytics can be utilized, implemented and developed into a value added investment. The Moneyball strategy is a concept used to identify undervalued players, so that teams with lower payrolls can still compete at a high level. Currently both the Oakland A’s and the Tampa Bay Rays have followed this strategy to success, making the 2013 playoffs despite being in the bottom 5 payrolls league-wide. 
· Data visualization showing advanced player statistics and movement tracking
Sports Analytics Overview
With today’s speed of computing, increase in processing power, as well as economies of scale sport organizations are more able than ever to move toward implementation of a data-driven sports analytics model, with the end goal being able to create a competitive advantage on the field of play.
Along with the significant leap forward in technology in terms of data gathering, through companies such as Stats LLC and Catapult Sports, more knowledge is readily available for those franchises willing to move forward into this field. Benjamin Alamar, an industry leading sports analytics consultant, speaker and author of “Sports Analytics”, has successfully has helped teams, businesses and individuals establish analytic systems – translating facts, figures and other data – into usable information.
“The franchises who are successful in truly leveraging analytics will be those that come to see data and model results as the mechanism through which information (unstructured text as well structured data) is transformed to deliver insight to decision makers in a well-contextualized format” - Ben Alamar, whose clients include San Francisco 49ers, ESPN, Portland Trailblazers, OKC Thunder, among many others. 
Building more effective and unified communication channels between digital analytics and the sporting environment is vital to the success of a sports analytic program. This will enable the analysts, coaches, players, scouts and general managers to work with one another to improve the team and the ultimate goal: Wins.
 Lowe, Zach. “Seven Ways the NBA’s Camera System Can Change the Future of Baseketball.” http://grantland.com/the-triangle/seven-ways-the-nbas-new-camera-system-can-change-the-future-of-basketball/. 4 Sept 2013. Web. Feb 2014.
 Alamar, Benjamin. “Sports Analytics”. http://www.alamarsportsanalytics.com. Aug 2013. Web. Feb 18th.