Wilson Eisner

Data analytics and machine learning have made a huge impact on decision making around the world and are improving statistics-based decisions in all aspects of life. The influence of data in sports has recently become very evident, but it’s been affecting the sports world for years. It initially began impacting sports like baseball and basketball that can be more clearly defined by basic statistics. In baseball you have each pitcher’s ERA and each hitter’s batting average, and basketball has each player's PTS, REB, AST, etc. The use of big data in baseball made its way into popular culture when the movie Moneyball came out in 2010. Moneyball is a film about the Oakland A’s owner, Billy Beane, who used data analytics to lead the team to the playoffs for four straight years, despite operating with the lowest budget in the MLB. Beane looked at how players were valued around the league and found that the entire league was way too focused on batting average, and that on base percentage and the ability of a hitter to draw walks was more important in determining a hitter’s value. He used this finding to target undervalued players and sign them for contracts that offered good value (a high performing player for a reasonable cost) to the team. The use of data analytics has taken a bit longer to become popular in soccer as there is less scoring and one must use more advanced stats to really determine a player’s value. There has also been plenty of pushback from fans and managers that don’t believe in data and would rather just use the “eye test.” It is true that it would be incredibly silly to only rely on data to make decisions, but given the recent success of teams like Brighton and Liverpool it is clear that if used correctly data can be extremely fruitful in guiding decisions about player signings and match tactics.