scholarly journals Analysis of Decision Making Process in Moneyball: The Art of Winning an Unfair Game

The Winners ◽  
2015 ◽  
Vol 16 (1) ◽  
pp. 57
Author(s):  
Mochamad Sandy Triady ◽  
Ami Fitri Utami
Keyword(s):  
Decision Making ◽  
Excellent Result ◽  
Data Driven ◽  
Decision Making Process ◽  
Data Driven Decision Making ◽  
General Manager ◽  
Use Of Data ◽  
Bottom Position ◽  
Using Data ◽  
Baseball Team

Billy Beanes’s success in using data-driven decision making in baseball industry is wonderfully written by Michael Lewis in Moneyball. As a general manager in baseball team that were in the bottom position of the league from the financial side to acquire the players, Beane, along with his partner, explored the use of data in choosing the team’s player. They figured out how to determine the worth of every player.The process was not smooth, due to the condition of baseball industry that was not common with using advanced statistic in acquiring   players. Many teams still use the old paradigm that rely on experts’ judgments, intuition, or experience in decision making process. Moneyball approached that using data-driven decision making gave excellent result for Beane’s team. The team won 20 gamessequently in the 2002 season and also spent the lowest cost per win than other teams.This paper attempts to review the principles of Moneyball – The Art of Winning an Unfair Game as a process of decision making and gives what we can learn from the story in order to win the games, the unfair games.

scholarly journals Data-driven decision-making in creating class rosters

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rebecca Wolf ◽  
Joseph M. Reilly ◽  
Steven M. Ross
Keyword(s):  
Decision Making ◽  
Student Learning ◽  
Low Income ◽  
School Leaders ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Use Of Data ◽  
Using Data ◽  
Rich Data

PurposeThis article informs school leaders and staffs about existing research findings on the use of data-driven decision-making in creating class rosters. Given that teachers are the most important school-based educational resource, decisions regarding the assignment of students to particular classes and teachers are highly impactful for student learning. Classroom compositions of peers can also influence student learning.Design/methodology/approachA literature review was conducted on the use of data-driven decision-making in the rostering process. The review addressed the merits of using various quantitative metrics in the rostering process.FindingsFindings revealed that, despite often being purposeful about rostering, school leaders and staffs have generally not engaged in data-driven decision-making in creating class rosters. Using data-driven rostering may have benefits, such as limiting the questionable practice of assigning the least effective teachers in the school to the youngest or lowest performing students. School leaders and staffs may also work to minimize negative peer effects due to concentrating low-achieving, low-income, or disruptive students in any one class. Any data-driven system used in rostering, however, would need to be adequately complex to account for multiple influences on student learning. Based on the research reviewed, quantitative data alone may not be sufficient for effective rostering decisions.Practical implicationsGiven the rich data available to school leaders and staffs, data-driven decision-making could inform rostering and contribute to more efficacious and equitable classroom assignments.Originality/valueThis article is the first to summarize relevant research across multiple bodies of literature on the opportunities for and challenges of using data-driven decision-making in creating class rosters.

scholarly journals Data competence maturity: developing data-driven decision making

2018 ◽  
Vol 11 (2) ◽  
pp. 139-158 ◽  
Author(s):  
Thomas G. Cech ◽  
Trent J. Spaulding ◽  
Joseph A. Cazier
Keyword(s):  
Decision Making ◽  
Secondary Education ◽  
Data Science ◽  
Data Driven ◽  
Decision Making Process ◽  
Maturity Model ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Development Data ◽  
Education Performance

Purpose The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education. Design/methodology/approach Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education. Findings The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process. Practical implications Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes. Originality/value This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decision-making process and trying to help the organization mature within this framework.

scholarly journals The Rapid Adoption of Data-Driven Decision-Making

2016 ◽  
Vol 106 (5) ◽  
pp. 133-139 ◽  
Author(s):  
Erik Brynjolfsson ◽  
Kristina McElheran
Keyword(s):  
Decision Making ◽  
Companion Paper ◽  
Census Bureau ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Manufacturing Plants ◽  
Using Data ◽  
Large Representative ◽  
The U.S ◽  
Case Based

We provide a systematic empirical study of the diffusion and adoption patterns of data-driven decision making (DDD) in the U.S. Using data collected by the Census Bureau for a large representative sample of manufacturing plants, we find that DDD rates nearly tripled (11%-30%) between 2005 and 2010. This rapid diffusion, along with results from a companion paper, are consistent with case-based evidence that DDD tends to be productivity-enhancing. Yet certain plants are significantly more likely to adopt than others. Key correlates of adoption are size, presence of potential complements such as information technology and educated workers, and firm learning.

Using Data-Driven Decision Making

2021 ◽  
pp. 83-99
Author(s):  
Mary Ruth Coleman ◽  
Jennifer Job
Keyword(s):  
Decision Making ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Using Data

scholarly journals Data-driven decision making in early education: Evidence From North Carolina’s Pre-K program

2019 ◽  
Vol 27 ◽  
pp. 18
Author(s):  
Michael Harris Little ◽  
Lora Cohen-Vogel ◽  
James Sadler ◽  
Becca Merrill
Keyword(s):  
Decision Making ◽  
Data Sharing ◽  
Early Education ◽  
Screening Tools ◽  
Data Driven ◽  
Policy And Practice ◽  
Data Driven Decision Making ◽  
Use Of Data ◽  
Depth Interviews ◽  
Shed Light

The purpose of this study is to shed light on the use of data in early education settings—specifically, North Carolina’s Pre-K program. In this mixed-methods study, we draw upon in-depth interviews and survey data to examine (1) the types of data available to educators in Pre-K, (2) the ways in which data are intended to be used, (3) how data are reportedly used, and (4) the facilitators and inhibitors of effective data-driven decision making. Our findings reveal that Pre-K settings are data-rich environments, often with informal data collected through developmental screening tools and formative assessment systems. We find that engagement with and use of these data for instruction is variable. Finally, we find data sharing between grades is inconsistent, but an important factor predicting data sharing is co-location of Pre-K programs within elementary school buildings. We consider our findings in the context of existing academic literature and discuss the implications for policy and practice. 

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