Deciphering the Astros winning formula
Jim Crane inherited a depleted roster and what was widely considered the worst baseball farm system in the majors when he bought the Houston Astros in 2011. Analytics wasn’t much of a focus for the Astros when they transitioned into the new regime. That remained the case until Jeff Luhnow interviewed for the newly available GM position. Luhnow pitched to Crane that he wouldn’t make ‘cosmetic decisions’, such as acquiring expensive players that won’t yield success in the long term. Rather, he would use data analytics to make acquisitions that were fiscally responsible, and once they had a winning team that generated profits, they would bring in higher priced players. Jim Crane bit. Crane’s previous ventures in shipping and logistics often utilized data analysis to facilitate certain aspects of his businesses. “If you have better information, faster than your competitors, you can run ‘em ragged.”, Crane explains in Ben Reiter’s book Astroball: The New Way to Win It All.
Initially, it was extremely difficult for Luhnow to introduce analytics as a guide to decision making for scouts and coaches to use. Components such as behavioral changes from lineup decisions to defensive configurations were also necessary to use in conjunction with Astros’ analytics. “That was harder, and took three or four years to get to a point that we felt good about it,” said Luhnow when reflecting upon his early portion of his tenure as GM in the Quarterly.
The human side of the Astros’ decision making is still vital to this day. The idea of implementing traditional evaluations on players from ‘old school’ coaches and using them alongside analytics was something that had never been successfully accomplished before.
Data collected from qualitative observations, or soft data, like leadership, ambition and desire are converted into quantitative numerical values. These values are then also plugged into the mathematical regression models that the Astros utilize to evaluate a player’s potential. All while trying to identify and avoid decision making psychological defectors like cognitive bias which leads to irrational decisions and unreliable heuristics or mental shortcuts.
How do you quantify a player’s propensity to get injured? A player’s motivation or drive? In ‘Astroball’, Reiter delves into how the Astros applied these soft data to their regression models. Again, how do you quantify motivation, and what does it even mean or include?
Situational dilemmas such as these pose a major challenge for econometricians, like Luhnow, who try and predict behaviors in healthcare consumption, causes of poverty in third world countries, or identify production deficiencies in big businesses. The Houston Astros, however, have seemingly mastered soft data quantification.
A challenge so major that it led Chris Correa to federal prison for 46 months because he was continuously hacking into the Astros’ databases. Correa was the scouting director for the St. Louis Cardinals when Deadspin leaked rumors that ignited an FBI investigation to look further into the allegations. Later, he was found guilty of 5 counts of unauthorized access of a protected computer. The Cardinals organization was fined two million dollars and handed over two top draft picks for the 2017 draft. The extent of the breach is still difficult to assess.
Data based on verified measurables or hard data, however, is easy to come by and interpret. Many online sources contain databases with all the quantifiable statistics and metrics baseball players are judged and ranked by. More tangible statistics, such as On Base Percentage (OBP) or a pitcher’s spin rate are also used and plugged into the Astros’ regression models.
The historic 2002 Oakland A’s ‘Moneyball’ team heavily relied on hard data and its inefficiencies. The misconception of the Houston Astros is that they follow a similar method. They don’t. Every team in modern baseball uses data analytics. The difference from that Oakland A’s team and today’s Houston Astros team is that the Astros heavily rely on their scouts’ and coaches’ gut instincts and experiences, alongside sophisticated data analytics to make decisions on acquiring the right players for the right price, via draft, free agency, or trades.
The Astros new way of winning it all is an organic method. They are learning as they go. The 2015 release of now, 2018 MVP frontrunner JD Martinez, can induce endless speculation of what could’ve been. It may also be difficult to forget when Jeff Luhnow and his then fellow St. Louis Cardinals’ scouting department passed on a generational player like Mike Trout in the 2009 draft.
Luck just as it does with anything else also plays a factor in winning. Whether it’s acquiring Justin Verlander with merely two seconds to spare from the closing of the trade window last season, or having Brady Aiken leave the Astros after negotiations fell apart with a reduced offer due to ongoing health concerns, which in turn led to the opportunity for Alex Bregman to join the good guys via the 2nd overall pick in the 2015 draft as compensation. You can argue that without either of these two, the Astros wouldn’t have gone on to win the World Series.
The future holds an ever-growing investment in artificial intelligence to produce faster and more accurate results. That is the key to identifying new and innovative ways of concurrently capturing hard and soft data and implementing it faster than your competition. Combining analytics and technology with human experience and instinct, will give you winning results at the end of the day.
Special thanks to Mohammad-Saqib Aziz, Economics student specializing in Quantitative Analysis at the University of Houston, for your contributions to this piece. Also, I encourage all to read New York Times Best Seller, Astroball: A New Way to Win It All, by Ben Reiter. You can order a copy here.