Early March is one of my favorite times of year. Spring training is finally here, after a depressing and dark stretch of no baseball; March Madness is set to begin; the race for the playoffs in the NHL and NBA is becoming more exciting. I may be losing some of the non-sports fans, but if that’s the case, don’t stop reading – this really is a post about philanthropy. (I’m just going to start with sports.)
In 1996, Baseball Prospectus was launched, introducing a wealth of new statistical tools to fans of the sport. Many early adopters of these analytics saw impressive success, so much so that the most famous of them – Billy Beane – was even portrayed by Brad Pitt. Other sports followed, developing advanced analytics to better evaluate which athletes were, among other things, worth investment. This past weekend, MIT Sloan held their eighth annual Sports Analytics Conference, taking advantage of the fact that sports is moving ever closer toward being a data-driven industry.
Philanthropy has seen a similar awakening. As in sports, evaluation and measurement of outcomes in philanthropy can be traced back to 1910s and 20s. The grand old foundations were partially founded on the principle of achieving positive social outcomes. Many worked with academics in the social sciences to discover ways to be sure they were doing just that. As more advanced metrics have been introduced over the last two decades, a rising chorus has encouraged donors to move from “sentimental charity” to “results-oriented giving.”
Sports have made a similar move, largely due to the money at stake. The US sports industry is estimated to be worth about $422 billion. In the field of philanthropy, where contributions totaled approximately $316 billion in 2012, there is more at stake than just money. The end results are even more compelling – instead of victory on the field and at the bank, we’re talking about improving the lives of billions of people. There is more on the line, more disagreement about measurement and approaches, and greater costs to failure. Whether practicing philanthropy as a corporation, foundation, or individual, donors have the responsibility to at least attempt to verify that their money is making a difference.
That is easier said than done.
There are countless factors to evaluate, and the methods and reasons for doing so are as numerous. Advanced analytics are not the right fit for some funders, and despite “conventional wisdom,” I don’t believe that every impact can necessarily be measured in numbers. Evaluation can also be expensive, although it doesn’t have to be. If you’re giving away $200,000 a year, it likely doesn’t make sense to spend $50,000 conducting a quantitative analysis of your grantmaking. However, you can create an informal qualitative process to improve your future philanthropy. You just need to find an evaluation model that works in the context and practice of your giving.
There is no shortage of writing on this topic – in philanthropy or sports. Bill Barnwell’s article on grantland.com is easily transferable to the realm of philanthropy. Especially the notion that “analytics aren’t really all that advanced at all. It’s not about reducing sports to numbers; it’s about finding evidence.” Sometimes analytics are totally useless, but they can also disrupt conventional wisdom to surprising and positive effect. There are many tools,examples, suggestions, and resources out there to help and I hope you will take the time to think about new ways that you can measure your philanthropy.
As for statistics in the sports world, maybe they’ll help you win Warren Buffet’s challenge.Pick a perfect March Madness bracket and he’ll shell out a cool $1 billion. While my odds of winning are one in 9,223,372,036,854,775,808, if I do, I promise to devote a few million to evaluating what I give away.