Using Analytics To Challenge Conventional Baseball Wisdom

Baseball, like most other sports, has a set of tenets that began early and have survived virtually unquestioned.  Modern analytics gives us an opportunity to examine some of these long-held tenets to see if they were founded on solid evidence.  This research examines some common baseball wisdom through an initial study utilizing simulation.  In particular, the profiles of several baseball teams are constructed and various factors are examined by simulating ten baseball seasons under various configurations with the different teams. Contrary to conventional wisdom, a batting order where high-average hitters bat third in a lineup and the team’s best power hitter bats cleanup (fourth), for example, does not necessarily generate the most runs per game over the long run.  Moreover, high-average hitters with less power can generate more runs per game than power hitters with lesser averages.  Finally, it appears that hitters who perform well with runners in scoring position are more influential in helping their team score more runs than even more powerful or higher average hitters who do not produce as frequently in such cases.  Players with lower star profiles, but who rise to the occasion with runners in scoring position, can often be purchased by baseball clubs that have a more constrained payroll; teams that are less well-off financially may thus purchase or trade for these hitters and still field a team with a competitive level of high run production.