It is September 4th, 2002. Oakland Athletics, a Major League Baseball team, wins its 20th consecutive game – the highest score in history. All that, besides having one of the lowest budgets in the season with a team composed of unknown players. So how did they achieve such, as it would seem, an impossible task?
It all started when the General Manager of Oakland A’s, Billy Bean, hired an exceptional special assistant – Paul DePodesta. Building upon sabermetrics, they created a revolutionary system of evaluating players that would allow them to go on this spectacular streak. Sabermetrics is an analysis of baseball using statistics, a system which uses in-game data to track players’ performance in various areas. It was first defined by Bill James in 1980, who argued that the way the players are evaluated is inherently wrong. Instead of focusing on speed, power and hitting, coaches should look for players who can “run bases” – as this is what ultimately wins games.
Then, 22 years later, the Oakland Athletics team found itself in a difficult spot. They did not have the budget to compete with the top teams, and on top of that, they lacked star players that could win games. That is why Billy Bean, going against the judgement of his advisors, started using maths and statistics instead of their experience to search for players.
Basing on sabermetrics and James’s work, together with DePodesta, they have created a model which assessed athletes on their performance when scoring runs. Although they may have been missing attributes that were typically desirable, they excelled in the areas that mattered. Additionally, because of that, they were vastly undervalued, which meant that even with a small budget, Oakland could afford them.
This innovative approach allowed the team to win game after game and completely stun the competition. Not only did it beat the long-lasting record, but it did it at the lowest budget-to-wins ratio in the league. Other teams quickly adapted to the new trend and used sabermetrics during the subsequent drafts. Today, the number of data points rose from 10,000 to a staggering 10 billion! However, there still exist some teams that prefer to use experience and their own judgement when looking for new teammates.
Biases in play
Besides being a fascinating story, this tale is also an intriguing case study about the behaviour and biases of baseball scouts and coaches. Every time Bean tried to persuade his scouts and coach to use his new method, he was ridiculed. Nobody believed that statistics could do a better job than the knowledge they possessed. Even though many players they had drafted did not turn out to be as good as predicted, they were sure of their capabilities. They clearly fell for a self-service bias; every time their pick turned out to be good, they pat their backs. Meanwhile, if it was otherwise, they attributed it to externalities, such as contusions or bad luck. Baseball is littered with crushed dreams and hopes resulting from drafters’ bad decisions. Of course, their knowledge is not worthless, but they largely overestimated their capabilities. Meanwhile, the model makes more reliable predictions based on in-game data.
This behaviour also led scouts to be victims of outcome bias. Even though every pick was a gamble, when it turned out successful, they would always overvalue the quality of the athletes. That created the highly craved “stars” every team wanted just because they previously won some games. Not only did it make their prices skyrocket, up to the point where their average salary in 2002 was 2.3 million dollars, but it also created a huge gap between them and typical hitters, who would only earn a couple hundred thousand. This opportunity was what Oakland used to its advantage.
Scouts looked at their statistics and games, but the halo effect played a vital role too. Athletes’ appearance, stance, personality and looks persuaded many people to choose them instead of others. This flaw was another thing that A’s could use to their advantage. Just because a player weirdly threw a ball, no team wanted to associate with otherwise a phenomenal pitcher.
Even after the news about the Oakland success broke, some teams had problems transferring to the new mindset because of the sunk cost fallacy. At this point, they had spent millions on their players and had not wanted to cut them. They have already lost that money; nothing could bring it back. At a point like this, the best you can do is try to cut back losses and do the best with what you have. In the end, that is what they did, using money they got from selling their most expensive assets.
In conclusion, Billy Bean and Paul DePodesta not only changed the way baseball is managed but also revealed the biases and fallacies that the teams often fell for. Their system is now widely used, and other sports try to imitate it to work for their respectable fields. Still, it is clear that the management of teams needed to change too. Now, they can use this example to learn from, as being aware of these effects is transferable to every team in every team sport.
Bean, R. (2022, September 18). Moneyball 20 Years Later: A Progress Report On Data And Analytics In Professional Sports. Forbes. Retrieved from forbes.com
Blum, R. (2002). Average MLB Salary Up 7.3 Percent in 2002, the Intelligencer, Retrieved from theintelligencer.com
Grabiner, D. (1994), The Sabermetric Manifesto
Lewis, M. M. (2003). Moneyball: The Art of Winning an Unfair Game. W.W. Norton