How Data and Statistics Changed the World of Competitive Professional Sports

The world of baseball (a very popular American sport) and the world of competitive sports in general underwent a major transformation in the year 2002 when the Oakland A’s (a baseball team in the California city of Oakland) introduced data analysis in the process of player selection and team make up.  Prior to this revolutionary approach in player selection, the collective wisdom of baseball insiders or experts (including players, managers, coaches, scouts and the general management also known as the front office) in selecting players for a team was based on subjective assessments which were often flawed.  Players were drafted for their good looks or sexual appeal, popularity among fans, perceived physical strength and even the way they talked and dressed in public.

 In 2002, the Oakland A’s were a poorly performing team who also happened to be financially strapped. Hence, could not afford the strategy of buying the top-performing but very expensive players.  According to the book, Moneyball which was authored by Michael Lewis and published in 2003, the Oakland A’s office took advantages of the analytical gauges of a player’s performance.  Two of these key metrics were OBP (on base percentage) and slugging percentage.  In baseball, as illustrated in Figure 1, the opposing team’s player comes to the home plate where they have 3 attempts to hit the baseball into the playing field.  The baseball is thrown by the opposing team’s pitcher and the white lines define the boundary of the playing field.  When a ball is hit within the white lines, it is fair play.  When it is hit outside the white lines it is a foul play. After a player makes a fair play and is able to reach at least the first base it is counted as a hit.  So during a game if a player shows up at the home plate 9 times and is able to reach the first base 8 times out of the 9, their OBP is 8/9 = 88.89%.  So the OBP simply measures how often a batter/hitter can reach at least the first base.  Unlike the batting average, the slugging percentage gives more weight to extra base hits with doubles, triples and home runs relative to single hits.  In short, the slugging percentage is a measure of the batting productivity of a hitter.  To put in basic business terms the OBP measures how often a worker shows up at work and the slugging percentage measures the productivity of a worker when they show up for work.

Dr. Albert Essiam Dr. Albert Essiam is the Data & Analytics Lead at OZÉ, a Ghana-based business that helps businesses and banks use data to make more profitable decisions. Dr. Essiam holds a PhD from MIT. If you want to learn how OZÉ can help your bank better model risk, email