The Impact of Analytics in Professional Baseball: How Long Before Performance Improves

This study extends previous analyses of winning percentage since 2014 for the 30 MLB teams and whether these performance data could have been predicted by the teams’ analytics adoption. The study also includes stadium attendance as a secondary indicator of performance. Based on MLB teams’ 2014 analytics adoption as reported by ESPN, there are statistically significant differences in teams’ winning percentage, attendance percentage, and cumulative (multiple seasons combined) winning percentage when looking at performance data in the three subsequent years (2015-2017). The differences in winning percentage remain significant for multiple seasons, though with decreasing statistical strength. MLB teams should be aware that an immediate benefit from analytics adoption may not occur, but subsequent years may see stronger results. This aspect of analytics adoption is a critical aspect to analytics usage, and this potential lag effect should be considered when adopting new methods and techniques in any part of the organization

The Effects of the Use of Cognitive Models on Shared Understanding During the Requirements Analysis Phase of Information Systems Development

During the requirements analysis phase of systems development, the user and analyst attempt to come to an agreement on the purpose of the system and the needs of the future users. When completed effectively (and efficiently), this process leads to the creation of an information system that fulfills the intended purpose and meets the needs of the users on time and within budget. Often times, information systems are designed and created that do not meet the needs of the users or fulfill their intended purpose (Jenkins et al., 1984; Guinan et al., 1998). These systems are often the result of problems that occur during the requirements analysis phase (Guinan et al., 1998; Holtzblatt and Beyer, 1995; Schenk et al., 1998). Of the many potential problems, miscommunication between the user and the analyst has a great potential for causing “bad” systems (Marakas and Elam, 1998; Tan, 1994)