Summer intern perspective: Sports and data analytics
Being a summer intern here at OnApproach, I’ve had a different view on data analytics. It’s more than just looking at numbers and seeing trends. To be successful in data analytics, it is very important to have a clear vision and understanding of the industry that you are working in. From working in the industries of sports and credit unions I’ve found a commonality between them. The main goal between the two industries is to make the best decisions for their fans (sports), and members (credit unions). For both industries to achieve that, they need data analytics.
Money is not everything to most people, but it is possible that money is the most important asset that we need for our lives. The way you manage money will be a vital part of anyone’s life or business. I’ll take one of the biggest industries out there, sports, as an example.
In recent years, the world of sports has experienced an explosion in the use of analytics. Every major sports team has a salary cap, an agreement or rule that places a limit on the amount of money that a team can spend on players’ salaries. General managers play a major role in the overall success of professional sports. For example, general managers for professional baseball teams are tasked with finding the right level of talent to better field, pitch, catch, hit, etc. for the betterment of their organization’s success. In the movie Moneyball, the goal is to answer objective questions about baseball using data. This relates to the technique of Sabermetrics, which is the application of statistical analysis to baseball records, especially to evaluate and compare performance of individual players. This would help track which player contributes the most to the team’s offense, pitching rotation, and even which infielder has the most range for fielding. The Moneyball method that Billy Beane, the General Manager of the Oakland Athletics, used was called on-base plus slugging (OPS). Bean used OPS to increase his team’s chances of scoring the most runs by signing players who could get on base. From this method he thought he could maximize runs, which would lead to maximized wins within the budget. Using this unbiased approach to signing players resulted in one of the longest win streaks in MLB history of 20 games.
Credit unions should consider ways to utilize analytics to better serve their members even on budgets that are the lowest among financial institutions. The most important word I would use for sport teams and credit unions to look for in their fans/members is loyalty. The fans put a lot of time and money into their favorite sports teams by going to games, watching them on television, or by buying team merchandise and repping their brand out in the eyes of the public. Members at credit unions are very similar in two ways: 1) They put a lot of time into selecting the right credit union and 2) they trust that the credit union is a trustworthy source for handling all (or some) of their money (which typically involves a lot more money than sports for the average consumer). Analytics is very important to both industries. If everything runs smoothly, and it does make operations more efficient, then it benefits both parties involved. As time progresses, processes get more efficient and effective, and the same goes for the field of analytics. As you can see from these two very different industries, both see direct and indirect benefits from the use of data analytics. Active analytics allow credit unions to more effectively target customers, develop more personal relationships with members, and compete against larger banks. This trend will continue to grow and become more prominent as the business world continues to develop.
