Por Renan Filho · 2026-10-28**Lead**
In a pivotal Game 6 of the World Series, Milwaukee Brewers manager Pat Murphy made a decision that has since been scrutinized by analysts and fans alike: he opted to bypass his closer in the ninth inning, relying instead on a middle reliever whose advanced metrics suggested a favorable matchup. The move backfired, allowing the opposing team to tie the game and eventually win in extra innings. With the Brewers now facing a decisive Game 7, many are questioning whether Murphy’s data-driven approach—a hallmark of modern baseball—cost his team a chance to clinch the championship on home soil.
**Context**
The Brewers’ 2026 season has been defined by their embrace of technology and analytics. Under Murphy, the team has leaned heavily on Statcast data, pitch-tracking systems, and machine-learning models to optimize in-game decisions. This approach helped Milwaukee secure the National League pennant, but it also created a culture where traditional baseball instincts sometimes take a back seat to algorithmic recommendations. In Game 6, with a one-run lead and two outs in the top of the ninth, Murphy chose to bring in right-hander Jake Cousins instead of All-Star closer Devin Williams. According to post-game reports, the decision was based on a proprietary analytics model that showed Cousins had a higher probability of inducing a ground ball against the upcoming left-handed batter—a calculation that ignored Williams’s proven postseason track record.
The technology behind such decisions is not new. Teams across Major League Baseball use similar systems, often developed in partnership with data analytics firms. The Brewers, in particular, have invested heavily in a custom platform that integrates real-time pitch data, spray charts, and historical matchup statistics. Murphy has publicly praised the system, calling it “an essential tool for modern managing.” However, critics argue that over-reliance on these tools can lead to counterintuitive choices, especially in high-pressure moments where human judgment and experience matter most.
**Análise**
The fallout from Murphy’s decision highlights a broader tension in professional sports: the balance between data-driven strategy and traditional managerial intuition. In this case, the analytics model may have correctly identified a statistical advantage, but it failed to account for the psychological and situational factors that define postseason baseball. The reliever chosen, Cousins, had not pitched in a high-leverage situation in over a week, while Williams had been dominant in save opportunities throughout October. The technology, for all its sophistication, cannot measure adrenaline, crowd noise, or the weight of a World Series moment.
Moreover, the incident raises questions about the transparency and reliability of the analytics used. While teams rarely disclose their proprietary models, independent analysts have noted that many such systems suffer from small sample sizes and overfitting. In the Brewers’ case, the model’s recommendation may have been based on a limited set of data points—such as Cousins’s performance against a specific hitter in a handful of at-bats—rather than a robust, season-long evaluation. This is a known pitfall in sports analytics: the temptation to treat every decision as a binary optimization problem, ignoring the chaos and randomness inherent in the game.
**Fontes**
This article draws on post-game coverage from MLB.com, analysis from Baseball Prospectus, and commentary from ESPN’s baseball analytics desk. No direct quotes from Pat Murphy or Brewers personnel were used, as the team has not yet held a formal press conference regarding the decision. The description of the Brewers’ analytics system is based on publicly available reports from The Athletic and Sports Business Journal.
As the Brewers prepare for Game 7, the debate over Murphy’s choice will likely intensify. Whether the manager sticks with his data-driven philosophy or reverts to conventional wisdom could determine not only the outcome of the series but also the future of how technology is used in baseball’s biggest moments. For now, the lesson is clear: even the most advanced algorithms cannot replace the human element that makes sports unpredictable—and sometimes, costly.