Moneyball and Beyond

Go to Course: https://www.coursera.org/learn/moneyball-and-beyond

Introduction

### Course Review: Moneyball and Beyond on Coursera #### Overview "Moneyball and Beyond" is an engaging and insightful course offered on Coursera that delves into the revolutionary concepts in sports analytics brought to light by the best-selling book "Moneyball." This course is perfect for anyone interested in the intersection of sports, data analytics, and Python programming. It guides learners through the essential principles used in baseball performance statistics, illustrating how data can significantly influence team success. #### Course Structure and Syllabus **Week 1: Introduction to Moneyball and Performance Statistics** The course kicks off with an introduction to the Moneyball story, laying a strong foundation for understanding how performance statistics correlate with team success. You will learn to replicate the analysis that shows the relationship between a team’s winning percentage and metrics like On Base Percentage (OBP) and Slugging Percentage (SLG). This module is well-structured, providing background and context that will make the statistical methods you learn later more accessible. **Week 2: Salaries and Performance Statistics** In the second week, the course shifts its focus to player salaries in relation to their performance. You'll engage with practical data analysis to confirm the Moneyball assertion that OBP was historically undervalued compared to SLG. This module not only reinforces theoretical concepts but also provides real-world data applications, making the analysis feel relevant and impactful. **Week 3: Evolution of Performance Metrics** As you advance into the third week, the course delves deeper into performance metrics, specifically the rewards of OBP and SLG from 1994 to 2015. You will explore how various components of SLG contribute to overall player performance. This module encourages critical thinking, asking students to connect historical data changes to current player evaluations and team strategies. **Week 4: Understanding Run Expectancy** The fourth week introduces the fascinating concept of run expectancy, equipping you with the skills to create a run expectancy matrix using data from the 2018 MLB season. This analytical approach helps learners assess the value of different play types, providing a comprehensive understanding of how decisions made in games can have significant outcomes. **Week 5: Wins Above Replacement (WAR)** Finally, Week 5 culminates in the examination of Wins Above Replacement (WAR) and its implications for player evaluation. This module connects the dots between run values, team win percentages, and salaries, reinforcing the correlation between performance metrics and financial implications in baseball. Here, you’ll gain a stronger grasp of how predictive analytics plays a role in team construction and player valuation. #### Learning Outcomes By the end of the course, students will have: - Developed proficiency in Python programming for data analysis related to baseball statistics. - Gained a solid understanding of key performance metrics and how they impact winning percentages. - Acquired the analytical skills needed to derive insights from professional sports data. - Gained exposure to practical data sets and real-world applications of sports analytics concepts. #### Recommendation I highly recommend "Moneyball and Beyond" for anyone interested in sports analytics, whether you're a data science novice or someone looking to deepen your understanding of the statistical analysis used in professional sports. The blend of theory and hands-on programming in Python is particularly appealing, making complex concepts accessible. Additionally, the course is suitable for sports fans, aspiring data analysts, and professionals in the sports industry seeking to leverage data to enhance decision-making processes. It not only empowers learners with technical skills but also enriches their knowledge of how data-driven strategies are transforming sports management. Overall, this course embodies a unique learning experience that advances your analytical capabilities while showcasing the compelling narrative established by the "Moneyball" phenomenon. Don’t miss the opportunity to learn from industry experts and gain valuable insights into the evolving world of sports analytics!

Syllabus

Week 1

In this module we introduce the Moneyball story and explore the method used to test that story. We begin the process of replicating the moneyball test by establishing the relationship between team winning and and two performance statistics - on base percentage (OBP) and slugging percentage (SLG).

In this module we estimate the relationship between MLB player salaries and their performance statistics, OBP (on base percentage) and SLG (slugging). The results appear to confirm the Moneyball story - OBP was undervalued relative to SLG prior to the publication of Moneyball, while after publication the relative significance is reversed.

This module updates the analysis of Hakes & Sauer and estimates the rewards to OBP and SLG over the period 1994 -2015. In addition it shows how rewards can be related to individual components of SLG: walks, singles, doubles, triples, and home runs.

This module introduces the concept of run expectancy, shows how to derive the run expectancy matrix and the calculation of run values based on an MLB dataset of all events in the 2018 season. Run values are calculated by event type (walks, singles, doubles, etc.) and by player.

This module examines the concept of Wins Above Replacement (WAR) and shows how to calculate WAR based on batting performance. The relationship between play run values team win percentage and player salaries is then explored. Run values are shown to have a high degree of correlation with winning and with salaries. Run values can to a limited extent predict win percentage.