Looking from afar, Moneyball looks like a male-centric sport movie that teaches us about how baseball works. But looking more closely, it is a film about business and negotiation, vital skills for someone who wants to be a good team manager. Starring Brad Pitt, the film sticks with tough, middle-aged Billy Beane, a team manager of Oakland A’s. He was recently defeated by the Yankees and found his team short-funded and losing star players to richer teams. Beane tried to find a solution for his team by turning to old, grizzled scouts who used intuition to pick good players, but ended up disappointed by the old-school system. Coincidentally, he met Peter Brand, a Yale graduated statistician who proposed a new way of organising team: to buy win and not players. Brand used statistic to find imperfect players who were underpaid, and by combining imperfections, both lead to team to break records of the decades with much less fund than other teams.
Data is the key factor in Beane and Brand’s success. It is used to predict players’ behaviour and create independent strategic moves that are combined to win the match. As Moneyball is mainly about the power of data in business success, this reminds me of one word that is frequently used these days: ‘big data’. Actually, what Brand used is not really called big data because there are three factors that must be concerned when using this word. The first one is ‘volume’, big data must consists of a large amount of data that makes it impossible for traditional methods to process it. The second is ‘variety’. There are many kinds of data, such as audio, video, text, Facebook posts, etc and this make the organisation of data more complex. The last one is ‘velocity’, means the speed of data generation. It refers to continuous and massive flow of data that happens simultaneously in a very short time. Social medias are one example of big data generation. When millions of people post on their wall all at once, the overflows of data begin and continue endlessly.
One thing about big data that captures my attention is that ‘the importance of big data doesn’t revolve around how much data you have, but what you do with it’ [1] Big data is usually used to spot defects in the process, calculate risks in business plans, and identify potential selling (or in baseball, scoring) points. Beane and Brand used their statistical data, which is administered in traditional ways, to accomplish modern tasks that all statisticians dreamt to succeed in. The predated methods of Beane and Brand paved the way for other major league teams in bringing statistic in use and changed the way baseball works forever. This makes me think of one scenario: ‘what if all the baseball records are fed to AI to create an absolutely winning team?’. The answer is more thrilling than I expected.
In the old days, scouts used intuition to pick good players. Their guts told them that some players were more talented than others and endless possibilities pop up in their imagination. ‘Possibility’ is a very powerful word because it comes with free will: the ability to choose between different possible courses of action unimpeded [2]. The scout knew they can choose players and design their game freely with their experienced minds and each player was free to act in the field to create a winning or losing game. Well, they could somehow predict the results but there was a significant space for unpredictable factors, and these unpredictable factors are vital for human conditions: we don’t want to be like robots of which all the moves can be predicted. We want to be more than gears in a close-system machine. We want to be able to ‘choose’.
It seems like the ability to choose is erased in the scenario I mentioned above. If one day AI learns how to predict absolute results of all games, that might be the end of baseball (and maybe all other sports). All the beautiful things about expectation will be gone. How can we be excited if there is nothing to expect? There will be no cheering and bets if we all know the prediction will be 100% correct. Gifts and hard works will only be reduced to numbers in sheets.
It’s true that the movie highly valued statistic and this robotic method but in the end, Beane found himself losing in major leagues. This means calculation is not always correct, but can we comfort ourselves that it was because human abilities are beyond calculations? Or do we must admit that it was because the tools he had were not advanced enough? Intuition might be an old-school tool, but isn’t it because of intuition that we have come this far? There are a lot more questions to be asked and these all will lead to the most important question: ‘what makes human condition meaningful?’
[1] https://www.sas.com/en_th/insights/big-data/what-is-big-data.html
[2] https://en.wikipedia.org/wiki/Free_will
