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We are experiencing an analytics revolution in soccer, made possible by the collection of player- and spatially-tagged event sequences that occur during games. Hypergeometric Enrichment Analysis (HEA) was invented in the biomedical research realm to help researchers discover genes and patterns related to cancer biology. In partnership with the San Jose Earthquakes, we apply HEA to Major League Soccer event sequences. Through this approach, we identify patterns of players and player-interactions that significantly contribute to goals for or against. We identify strong players and weak players that might not be noticeable on a stat sheet via traditional metrics such as goals and assists. We present two new statistical metrics, Enriched in Goals For (EGF) and Enriched in Goals Against (EGA). We identify and discuss the contribution of MLS players with higher than expected EGF and EGA scores. Finally, we show how HEA can be used to educate coaches and players in advance of a match, highlighting an opponent’s recent strengths and weaknesses in front of goal.