In Major League Baseball, since the end of the Steroid Era around the mid-2000's, numerous teams realigned their offensive strategy, bypassing the home run heavy line-ups in favor or quicker, faster players with the ability to successfully steal bases. While the stolen base has long been an arrow in the quiver of MLB offenses, this shift in the game has brought the stolen base back into the discussion regarding ways for a team to score more runs. Around the league, front offices desiring speed rely more heavily on statistics to assist their player analysis and to approximate the expected value of stolen base attempts in certain situations. Additionally, with the introduction of the Oakland Athletics' 'Moneyball' style, general managers have started to push for improved quantitative input that will aid in decisions, both in game and with roster development.

The three components to the stolen base are:

  1. Runner
  2. Pitcher
  3. Catcher

Dee Gordon stolen base (Wikipedia)

By analyzing data from the 2015 MLB season, we can develop ratings for each component, and therefore generate a decision making tool that will offer perspective for the team's manager when deciding whether or not to attempt a stolen base.

Our objective for this project is to develop a tool that MLB managers can use when deciding whether or not to attempt a stolen base. Due to the rapid transformation of the game over the last decade, a quantitative tool will greatly enhance in-game ability to generate positive expected value plays. We use the data collected from ESPN to determine the three ratings: Runner, Pitcher, and Catcher. We will look at the data from both the offensive and defensive side in order to paint a more comprehensive picture, and we include a more detailed offensive statistic collection in order to capture a players true stolen base ability. It is our hope that this tool can assist managers with the difficult decisions regarding stolen bases. While this tool will not provide certainty whether or not a base runner will successfully steal a base, it will focus on comparative analysis between the base runner and the defensive pitcher/catcher tandem, drawing conclusions based on the average ability of both.

Building from a simple understanding of the stolen base, we generate three ratings that quantify the skill of MLB players. Further, with the ratings we can proceed to develop an equation that outputs a binary decision making tool, offering an enhanced understanding of the probability that a stolen base will be successful. Our results and comparisons will serve as an additional resource for managers interested in more specific player by player analysis, which often is desired in the front office of teams across the league.