Early in this century, the Oakland Athletics used readily available traditional player performance statistics in new ways to decide which players to put on the field, and this change led to better play and to several division-winning seasons. Their efforts were memorialized in Michael Lewis’s book Moneyball, and in the 2011 movie of the same name. 

Major league teams are now all using data analysis to improve player selection, player performance, in-game decision making, and player development. The techniques and tools now in use have moved way beyond what was described in Moneyball. Now, data on every pitch is captured by a doppler radar system that samples the ball position 2,000 times a second. At the same time, the batter’s swing is recorded, capturing data about the ball’s speed as it comes off the bat and the ball’s launch angle. Cameras behind third base record the position of players on the field 30 times a second. A terabyte of data is captured each game. This is now done at all major and minor league parks, in most Division 1 college parks, and even at some high schools. This wealth of performance data is used as input to analytical software for a variety of purposes.

Questions:

1. Baseball executives typically call their analysis programs “analytics”. Based on this chapter’s BI and Analytics definitions, would you say that their programs are more Business Intelligence or more Analytics? Or, some of both? 

2. Excel is a popular and powerful program with a good statistical package. Why do you think baseball teams use tailored software applications for their data analysis, instead of Excel?

3. Baseball teams have used “scouts” to watch young men play at the high school and college levels. Scouts would report the evaluations to the front office, and players were hired based on these reports. Teams still do employ scouts to do this, but increasingly player potential is based on an analysis of doppler and video data. Do you think there will come a day when scouts are no longer needed by major league teams? 

4. Most teams have at least a dozen data scientists and other analysts in their programs. Analysts earn high salaries and benefits. Office space, equipment, hardware and software are costly as well. What would you roughly think the data analysis program would cost a major league team each year?