Lt. Col. Jonathan D. Bate, 1st Lt. Ethan Barangan, 1st Lt. Nicholas Calhoon and Staff Sgt. Jacob Seitz
When Billy Beane, general manager of the Oakland Athletics from 1997-2015, started using data analytics to build a winning baseball team on a budget, many in the baseball community were skeptical. However, the team’s performance demonstrated that leveraging in-game data to identify undervalued players could provide an edge. During the 2002 season, the team won 20 games in a row on a budget less than a third of the league’s most expensive teams. He accomplished this by applying a “sabermetrics” approach of collecting and analyzing in-game activity to build a cost-effective team, as described in the 2003 book Moneyball: The Art of Winning an Unfair Game.1
Inspired by Beane’s approach, our data analytics team in the Ivy Raider Brigade (1st Stryker Brigade Combat Team, 4th Infantry Division) asked a similar question: Can data analytics help us improve crew performance during mounted machine gun (MMG) lethality? Similar to the Oakland A’s, combat units are constrained in terms of time and ammunition. Producing better Table VI results more efficiently builds lethality.
We found that similar to baseball, in-game statistics during gunnery can identify factors that correlate with better crew performance. Our results, which suggest that Table III is an undervalued player, stem from only a single brigade’s Stryker gunnery, but the project underscores the general approach’s potential. Of note, we do not argue that analytics should replace leader experience or “gut instinct;” rather, the insights data provides can elevate intuition while reducing cognitive bias.
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