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Book Review: Big Data Baseball

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Big Data Baseball by Travis Sawchik takes an in-depth look into the Pittsburgh Pirates 2013 season and an end to their postseason drought. Sawchik explores the tactics implemented by the Pirates organization and how it led to their success. The Pirate’s 2013 season ended their 21-year playoff drought as they finally got back to the postseason. As a small market team, it’s difficult for that kind of team to find success. They need to turn to competitive advantages and discovered over-looked players to make up their roster. By succeeding in both of those categories, the Pirates were able to record a winning season and return to the long sought after postseason.

As I mentioned, part of their success is credited with implementing new tactics. The tactic that put them over the edge was their use of the shift. In 2013, the shift was not as widespread as we see today. During that time, it was still in the beginning stages and there was a large variation as to how much each team used it.

Data via FanGraphs

In the 2013 season, the Pirates ranked fourth among the teams that shifted the most. It was a big competitive advantage that impacted their success and allowed them to finally end their drought and make the playoffs. In the book, Travis writes about exactly how they implemented this data and used it to succeed. He informs the reader about how the team put this detail into practice all throughout their organization until working it into the big league level. For the seasons leading up to the 2013 season, the Pirates organization used shifts often in the minor leagues. Not only did this allow the Pirates to see that this strategy was successful, but it also allowed their future talent to become adjusted to it before they even reached the bigs.

Shift usage is effective however it doesn’t directly correlate to success. You can see below, I correlated the number of shifts to team wins. The Y-axis represents the shifting usage and the X-axis represents teams total wins in the 2013 season. Upon calculation, r=0.105. This is an extremely weak correlation however I believe that it doesn’t give an accurate representation of its effectiveness.

Data via FanGraphs, Calculated on Social Science Statistics

When teams shift, it doesn’t directly increase their chances of winning. Instead, it puts them in a better position to win. In Big Data Baseball, Sawchik explains how it helped the Pirates to win in 2013.

One big factor impacting the success of a shift is team ability. Obviously, the teams with better players win in any sport. However, in the game of baseball finding that talent is difficult. This is especially difficult for low-payroll organizations. They basically have to look at a large pool of mediocre players and decide which ones will give them the best chance to win. In some cases, if the franchise chooses the right player, it can pay off in big ways. It did for the Pirates in 2013 and in Big Data Baseball, Travis Sawchik explores each individual player that went through that process and led to the team’s success. He explores the data that the Pirates used to choose the players that would fit best in their organization. They were able to maximize each of their players potential through strategy in addition to pitch usage adjustments. They tailored each players ability to give them the best possible opportunity to win. Truly, it’s a great book and it allowed readers to get a peek behind the curtain to see how the Pirates found success and ended their playoff drought.

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