Data Story Series: Story 4 – Moneyball

Hello everyone, we are back with a new story! This time, let’s veer away from companies, and move towards another theme: Sports. Is there a link between data analysis and sport? Just carry along reading, it will all become clear in a second.

Moneyball: The Art of Winning an Unfair Game. Michael Lewis published a book under this title in 2003, which was adapted into a movie starring Brad Pitt and Jonah Hill in 2011. More than the title of a book or movie, Moneyball is now a term used for describing a player performance analysis strategy in baseball, and it has changed the way in which baseball is run.

Faced with a small budget, Oakland Athletics baseball team was unable to win the bidding-wars that exist in baseball to purchase the best players. In 1999, the team’s general manager, Billy Beane, hired Paul DePodesta. DePodesta was a Harvard economics graduate who thought of a new way to draft players, using sabermetrics. Sabermetrics is a method of empirical analysis of baseball in-game statistics. It summarises batting, pitching and fielding measurements to answer specific questions. Sabermetrics is not that new actually, first being mentioned in the 1964 book, Percentage Baseball, and later developed by Bill James in the early 1970s. DePodesta thought of focusing only on sabermetrics to gauge player performances and draft new players in. Typical scouts look at stolen bases (when a runner reached a base he is not entitled to due to his speed), runs batted in (a hit that allows a run) and batting average (the number of runs divided by the number of times the player is out) to gauge players, whilst often ignoring on-base percentage (how often a batter reaches base due to his own performance) and slugging percentage (the power of a hitter). DePodesta focused on these last statistics to identify undervalued players. This strategy worked, leading the team up the tables in an unprecedented way.

At the heart of the matter was getting rid of subjective bias. We always consciously or unconsciously try to find causal links between events, finding it hard to change our minds when we have reached a conclusion that is then proven false. For example, take these sentences “Sarah is waiting for her parents to take her to the movies. Her parents are late. Sarah is angry”. Most would think that Sarah is angry because her parents are late, thereby changing the sentence to “Sarah is waiting for her parents to take her to the movies, but her parents are late, so Sarah is angry”. However, the reason for Sarah’s anger is never stated, we make the assumption that it is because of her parents. The same applies to baseball, one may think a baseball player is great because of his stolen-base percentage, but in the big picture, this player may not be as good as other players in other areas. If all the members of the team have a specific purpose, a specific task in which they excel, the team will work like well-oiled gears and having players that have great stolen-base scores won’t be as important. DePodesta and Beane fought subjectivity, kept an open-mind and followed data. They were rewarded.

The same mentality can be applied to business strategies. Following instincts may have worked in the past, but data does not lie. If analysed correctly, better conclusions may be drawn. Think about it, you can also turn your business into a Moneyball.


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