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The Moneyball Effect: Using Your Data

Maddie and I were lucky enough to attend a cool event put on by Avectra a few weeks ago, that they called The Moneyball Effect. They invited a group of association execs and consultants to Nationals Park in DC to have a discussion about using data to make better decisions, and then watch the Nats play baseball (and they won!).

The “Moneyball” reference, of course, is to the book/movie by that name that tells the story of Oakland A’s manager Billy Beane. In the early 1990s he took a radically different approach to fielding a team, using statistics that were not the ones that baseball scouts and managers have traditionally used. This allowed him to field a team with players who were not being paid high salaries (because their performance–as measured by traditional statistics–didn’t warrant it), yet they won as many games as teams with payrolls that were three or four times higher.

So what does this mean for associations? At one level, I think it is a wakeup call for associations regarding how they use their data. That’s what it was for baseball–a wakeup call. They thought they knew how to evaluate players in order to field a team that had the best chance at winning it all. Beane showed them that their assumptions were not all correct. This is a very important use of data that I think associations often miss: it can be most valuable when it proves you wrong. As technology advances, associations are now becoming flooded with data. “Data” used to mean survey results that we got once a year. Now, if your systems are up to speed, you can get access to data on member and prospect transactions, interactions on the web site, even mentions on social media, all continuously in real time. As you analyze all this, remember to look for things that prove you wrong. Remember to look for correlations that surprise you, and then dig into those surprises, instead of writing them off as anomalies. The goal is not to use data to justify the conclusions you’ve already come to. The goal is to learn.

And the second lesson for associations is about experimenting. Experimentation is the vital link between data and learning, and sometimes I think we forget that. Experimentation was one of our 12 principles that make up a human organization, as it is undervalued in traditional management. But especially that we now have access to a lot of new data, experimentation will become even more important. I think it’s a part of the Moneyball lesson that gets overlooked. Billy Beane had to field a team based on different assumptions. He actually had to put players on the field that most people didn’t think made sense, and see if it won ballgames. It was an experiment. I don’t know how accurate the movie was compared to real life, but in the movie, Beane actually had to trade some emerging star players in order to force the manager to start playing the players that Beane thought would help the team win. Experimentation is generally frowned upon by people who feel they already have the answer. So the data is one side of this, but you’d better also start preparing all the people in your system for the experimentation part. Remember, you hired them (or recruited them to the Board) because they know what they’re doing. This could get in the way of experimentation. And that will hurt your learning. And that could make all the data analysis you’re doing a waste.




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