ESGlogo “Cray spinout, YarcData…uses Linux-ized Cray processors to blow through graph analyses at speeds beyond classic RDBMS' imagination - jet plane versus burro.” - NoSQL is Here and Now (ESG Blog – Evan Quinn, August 24, 2012)

Innovating Baseball Analytics: YarcData's technology helps baseball teams project a batter's performance

YarcData Solutions

Every game, a baseball manager is faced with the decision of selecting the optimal lineup. It's all about creating matchups between batters and pitchers, which favor his team. For decades, managers use the literal data that measures the performance of each hitter vs. each pitcher. Sample sizes are often small and the data may span two decades of matchups--a timeframe over which the talent level of both the pitcher and batter may have changed.

YarcData's Urika™ graph analytics appliance allows teams to be more strategic about their choices. Graph analytics is the ideal technology to clustering groups of pitchers based on the similarity in their attributes and pitching approaches. By grouping pitchers into clusters, we can now measure how a batter performs against a like group of pitchers, giving us larger, more statistically significant sample size, as well as a more recent slice of a player's performance. Teams can now migrate from evaluating a matchup based on how a batter performed against one pitcher over the last decade, to how a batter performed against a "like" cluster of pitchers over the most recent season.

YarcData Support
Graph depicting how pitchers are clustered, based on similarities in their pitching attributes and pitching "style" (e.g., pitch velocity, type of pitches they throw, pitch movement, etc.). Each color represents a cluster of "like" pitchers, specifically left-handed pitchers when matched up against left-handed hitters.