Big Data and Business Intelligence (BI) tools have flooded today’s market. What was once only accessible to large-scale organizations working on mission-critical decisions, has translated into tools integrated into daily work, and now into devices that are accessible to individual users.
As the Big Data market shifts from B2B to B2C, the products become increasingly user-friendly and integrated into the daily lives of just about everyone on the grid. What once required a statistical background to use, is now plug-and-play. With data visualization, infographics, and other outputs of large-scale data analysis popping up in ads, comics, movies, and more.
However, the challenge of big data in each sector remains the same: how to determine what type of data is meaningful, collect that data in an efficient and accurate manner, and analyze that data in a substantive way (producing actionable information).
So how has the rise and accessibility of analytical tools shifted our approach to sports?
Advanced Analysis for Player Management
Baseball is a game that favors the use of statistics, from the stats found on baseball cards all the way to Michael Lewis’ book and movie, Moneyball. The book tells the story of how the Oakland Athletics’ general manager Billy Beane used large-scale analysis of player data to improve the evaluation, selection, and placement of players. Datasets included on-base percentages, slugging percentages, and other measurements that enabled Oakland to leverage players which might otherwise have been overlooked. Today, practically all sports teams use some variation of advanced analytics for player evaluation. Essentially, sports teams have also learned to treat data as a substantive asset.
Beyond teams, franchises are using tools such as Radio Frequency Identification (RFID), (generally used in business for the tracking of inventory assets) to collect data on players such as distance traveled, speed, and other factors. Data obtained from RFID and other technology can then be stored in databases and used for historical analysis. Soon, sports teams will be facing the issues that businesses face when dealing with vast amounts of data. Such issues include searching for points of inflection and relationships among the data points.
Leveraging Fan Behavior
Currently, team franchises are using mass data from online user behavior to support dynamic ticket pricing. The San Francisco Giants were the first professional sports team to employ the concept, which is now being tested within the National Football League (NFL). The use and application of what is now almost limitless data, is limited only by imagination. For example, some professional sports teams use business analytics to improve advertising and fan engagement. In the current world of mass customization, sports teams are looking to create personalized experiences where fans are immersed in the action and specifically targeted. Statistical tools are required for this type of action, and these marketing analysis platforms are spawned from similar business tools.
Mass Simulation in Fantasy Sports
If professional sports teams like the Oakland Athletics can use player statistics and data to evaluate players, and team franchises can use analytical data tools to improve marketing campaigns, it’s no surprise that fantasy sports players are using similar techniques to draft players and build superior teams. Fantasy sports players can run a baseball simulation game using advanced data and metrics, to gain an edge on their less analytical counterparts. These simulations even include the use of historical rosters, weather conditions, and venues.
If anyone has ever wondered if a historic baseball team such as the 1939 New York Yankees could beat any of today’s current teams, with big data – the answer is out there, it may even be out there a few million times (via simulation) to make sure deviation errors were taken into account.
The world of business intelligence has revolutionized the way that we look at data, statistics, and analysis. Tools like IBM’s Artificial Intelligence (AI) engine, named Watson (after the company’s founder), demonstrate how the use of technology can be incorporated into virtually any industry and successfully used to make better decisions. In the world of sports and sports fantasy teams, the tools will evolve and improve as we continue to get savvier about how we process data into information and apply those insights to improve lives.