Analytics, big data, sabermetrics–call it what you will, but the rise in popularity of using data in more sophisticated ways, not only in the world of sports but in the world of business in general, has brought about a fundamental shift not only in how data is analyzed and outcomes are predicted, but also in how success is measured.
Across industries, businesses look to compile and extract hidden value and information from the vast amounts of data already available in their own data hubs.
Two industries which from the surface seem to have little in common, have tremendous parallels in the way in which they use data analytics. In the sports industry, and in particular baseball, and in the investment industry, both rely on data to make better decisions about their business. Baseball analytics, known as sabermetrics, and investment portfolio analytics both strive to provide greater insight into their “games” by delving into higher math and analysis.
In its simplest form, portfolio analysis or analytics, according to the Wizzley.com Beginners’ Guide to Portfolio Analysis, “is a set of statistical techniques that are used to examine the performance of investment portfolios under different circumstances. It can help you to understand how you can make the most suitable trade-offs between risk and returns for your given circumstances.” Sabermetrics, is similarly defined in the Merriam-Webster dictionary as “the statistical analysis of baseball data”.
Finding the MVP
Portfolio managers use analytics to help manage their investment strategy and the potential risks to these investments. Portfolio analysis is all about finding ways to quantify the financial and operational impact of the investments. To truly evaluate the risk of an investment portfolio and the factors influencing potential returns, performing more detailed analysis and interpreting those results is critical. For instance, utilizing performance contribution can tell a portfolio manager which individual position in a portfolio had the biggest impact to that portfolio’s return and in much the same way, sabermetrics attempts to answer objective questions about player performance, such as “which player contributed the most to the team’s offense?”
In a simple example of the parallels between sabermetrics and portfolio analysis, individuals typically see the overall return of the fund as the ultimate measure of its viability as a potential investment vehicle. However, portfolio analysis (risk measures such as beta, standard deviation, etc. and both portfolio contribution and attribution) dives deeper into the overall contributors and detail behind the returns to identify the true performance and risk of the investment. Contribution looks to derive the contribution of individual sectors, positions or securities to the portfolio’s overall return, beta measures volatility, and standard deviation simply measures the volatility of returns over time – the larger the standard deviation, the greater the volatility. Each of these when compared to the overall strategy of the portfolio, can provide greater insight to the portfolio manager and other internal stakeholders.
Similarly, in baseball, statistical measures such as WAR (wins above replacement), which measures fielding and base running, along with hitting and power stats, provide more granular detail on the success of any individual player as opposed to more common measures such as batting average, home runs, etc. Analytics and the various measurements available to portfolio managers and provide increased visibility and strategic planning around investment strategy and philosophy, while in a similar way, sabermetrics allows scouts, coaches and front office executives increased visibility to a player’s historical and potential future performance.
Building the Best “Team”
Doug Hanchett’s Playing Hardball with Big Data provides great detail into the background of sabermetrics and its use of all of the available data that is collected from baseball statisticians. In the paper, John Dewan, a prominent sports statistician, is quoted as saying, “There became a realization that, wow, you can supplement your on-the-field visual scouting techniques with hardcore data. And if you combine the two, you’re going to make better decisions than if you just use your scouting information.” The parallels to portfolio analytics and the use of underlying data to provide additional insight into the investment process, risk potential, and potential future performance are endless.
Much like baseball executives use the information available to them to put together the most competitive team possible, showcased in Michael Lewis’ best-selling baseball book and subsequent movie, Moneyball, about manager Billy Beane and the Oakland As, portfolio managers utilize portfolio analytics to have a more accurate and precise understanding of the players, or positions, that affect their success in the game of investing.