In 2002 Oakland A’s General Manager Billy Beane brought the idea of analytics-driven decisions in major league baseball in a whole new way. Of course, analytics have been a part of baseball for as long as the sport has existed. Teams (and fans) have known about batting average and earned run average as statistics and used them to try to determine a player’s value for more than a century. But nobody called it analytics.
You’re almost certainly familiar with the story, now, of how Beane and his front office identified a market inefficiency when it came to on-base percentage. Specifically, they noticed that teams drastically undervalued the stat and preferred players who got more hits. They were able to leverage their advanced analysis to build playoff teams on shoestring budgets in 2002 and 2003.
The problem with Moneyball analytics
Small market teams wishing to rely on market inefficiencies have discovered a problem: once one is identified and exploited, other teams catch on and close the gap. Now that everyone more or less understands the true value of a walk, that market inefficiency no longer exists. When the inefficiency is eliminated, the higher-spending teams pull away again because once a skill is properly valued, they can still afford more of it.
On top of that, every team now knows to look for these inefficiencies, which makes them that much harder to find and to exploit once found before they vanish again. Funnily enough, the last team to exploit a market inefficiency may have been the Royals. It’s not clear how intentional it was, but their discovery that a lights-out bullpen could carry a team through the post-season in 2014 was revolutionary at the time. Teams had long known that having a great closer was a good thing, but the Royals squeaked into the post-season and then unleashed three closers on their opponents, to devastating effect. If the 2014 Athletics, 2015 Astros, or 2015 Mets had had a bullpen to equal the Royals of those same years, they might have much more easily handled KC. One could even argue that the 2014 Giants, by turning Madison Bumgarner into a multi-inning shutdown reliever in game 7, used a modified version of the Royals’ own strategy to claim that World Series - and the only post-season series victory against the Royals during those two seasons - for themselves.
Under Dayton Moore, the Royals weren’t completely adverse to using analytics. But they were woefully behind the times in how to use them. They continued searching for market inefficiencies. One way in which we saw this play out was the team’s strong affinity for athletic players who could play a variety of positions. They seemed to think that could be an inefficiency to exploit. If it was, however, they were never able to accomplish their goal.
Modern analytics have to be much more personal
You’re probably now wondering: if analytics are no longer surprising and provide relatively little value, then why are they still such a big deal to so many teams?
The answer is that while the term has stayed the same, the act has changed drastically over the last 20 years. Information that was once impossible to gather has now become common knowledge. Terms like “spin rate” and “exit velocity” are now a part of the common fan vernacular. But teams are using advanced technologies and analysis programs to gather data even more obscure and granular than those. That is where the heart of modern analytics work can begin.
Take the curious case of Brady Singer. Analytics showed that his fastball vertical drop had decreased since the college campaign that led to him being drafted in the first round. They further showed that the reason for this drop was because of a misalignment in his release point. Having identified both the effect and the cause, the Royals were able to communicate those things to Singer and help him remedy the situation. After a couple of months of working at it, he was dominating major league hitters once more.
This new kind of analytics can work incredibly fast when the right people are reading the right pieces of data and communicating effectively with players. During his introductory press conference, new manager Matt Quatraro indicated that he felt the Royals, and most teams, were working from the same data. If he can be believed, the Royals are not very far behind in data collection. They simply haven’t been implementing or communicating it effectively.
If that’s true, we could see small but effective improvements from several pitchers on the staff relatively quickly. Some things - such as pitch selection or pitch sequencing - can be relatively quick to change. The Royals pitchers - especially Daniel Lynch, Kris Bubic, and more - have a certain level of talent. These are not the days of the early ’00s when the staff was just filled with whichever pitchers the Royals could draft or sign for cheap.
To be clear, this is to take nothing away from RoyalTreatment’s excellent piece earlier this week. There are certainly some things that analytics cannot fix overnight. Some of these pitchers need more than a little bit of sequencing help. They’ll need help building better repertoires whether that means whole new pitches or reshaping their existing offerings.
Still, the most important part is that the Royals are now talking like a team that understands the way analytics have changed in the last 20 years. The understanding may lead to immediate results or it may take some time to really see how the Royals can benefit from it. In either case, Royals fans finally have a reason to trust the process.