Math behind Moneyball

Go to Course: https://www.coursera.org/learn/mathematics-sport

Introduction

### Course Review: Math Behind Moneyball **Overview:** The "Math Behind Moneyball" course on Coursera offers a fascinating exploration into how probability, mathematics, and statistics shape decision-making in sports, particularly in baseball, football, and basketball. Drawing inspiration from the groundbreaking approaches detailed in Michael Lewis's book "Moneyball," this course empowers participants to leverage data-driven analysis to enhance player performance, strategize effectively, and refine team structures. **Course Content:** The course spans ten modules, each presenting diverse yet interconnected topics aimed at equipping students with both theoretical knowledge and practical Excel skills. Here’s a closer look at the course syllabus: #### Module Breakdown: - **Module 1:** Introduces fundamental concepts like predicting team performance based on scoring metrics and dives into multiple regression analysis applied to baseball hitters. Participants also learn essential Excel functions like VLOOKUP and INDEX. - **Module 2:** Focuses on Excel tools and concepts such as Range Names, Tables, and Conditional Formatting, enabling learners to manipulate and analyze sports data more effectively. - **Module 3:** Covers Monte Carlo simulations, showcasing how statistical modeling can evaluate a baseball team's offense and even contextualize controversies like DEFLATEGATE. - **Module 4:** Discusses key metrics in baseball, including WAR (Wins Above Replacement), analyzing fielding performance, pitching efficiency, and strategic decision-making during games. - **Module 5:** Expands into football analytics, breaking down random variables and regression analysis to understand winning factors, particularly evaluating NFL QBs and unpacking the myth of momentum. - **Module 6:** Introduces game theory in the context of football and soccer, alongside an examination of basketball fundamentals through shot analysis and team win parameters. - **Module 7:** Advances into basketball metrics, discussing concepts like Adjusted Plus-Minus and SportVu data for in-depth game analysis and decision-making. - **Module 8:** Teaches sports ratings, point spreads, and NCAA tournament simulation, giving participants practical insights into filling out brackets. - **Module 9:** Explores NASCAR driver ratings and delves into sports betting concepts, introducing students to the mathematics behind betting odds. - **Module 10:** Concludes with discussions on optimizing bets using Kelly Growth, analyzing sports performance with regression to the mean, and examining daily fantasy sports analytics. - **Final Exam:** A 10-question exam that reinforces learning, requiring students to apply their knowledge through the use of provided Excel files. **Recommendation:** This course is ideal for sports enthusiasts, aspiring analysts, or anyone interested in harnessing the power of mathematics and data in sports contexts. With practical Excel skills being a cornerstone of the course, learners will benefit from integrating statistical analysis into real-world sports scenarios. The course's incremental approach makes it accessible; even those without a strong math background can grasp complex concepts. Overall, the "Math behind Moneyball" course serves as a gateway into the exciting intersection of sports and data analytics, and it is highly recommended for those looking to enhance their understanding of how analytics are transforming athletic performance and decision-making at all levels of play. Whether you're a fan looking to deepen your appreciation of sports strategy or a professional exploring career opportunities in analytics, this course offers valuable insights and skills that extend far beyond the realm of athletics. Engage with the material, tackle real-life scenarios, and immerse yourself in the statistical strategies that shape the games we love. Enroll today and take the first step towards unlocking the math behind the sports!

Syllabus

Before you start...

You will learn how to predict a team’s won loss record from the number of runs, points, or goals scored by a team and its opponents. Then we will introduce you to multiple regression and show how multiple regression is used to evaluate baseball hitters. Excel data tables, VLOOKUP, MATCH, and INDEX functions will be discussed.

You will concentrate on learning important Excel tools including Range Names, Tables, Conditional Formatting, PivotTables, and the family of COUNTIFS, SUMIFS, and AVERAGEIFS functions. You will concentrate on learning important Excel tools including Range Names, Tables, Conditional Formatting, PivotTables, and the family of COUNTIFS, SUMIFS, and AVERAGEIFS functions.

You will learn how Monte Carlo simulation works and how it can be used to evaluate a baseball team’s offense and the famous DEFLATEGATE controversy.

You will learn how to evaluate baseball fielding, baseball pitchers, and evaluate in game baseball decision-making. The math behind WAR (Wins above Replacement) and Park Factors will also be discussed. Modern developments such as infield shifts and pitch framing will also be discussed.

You will learn basic concepts involving random variables (specifically the normal random variable, expected value, variance and standard deviation.) You will learn how regression can be used to analyze what makes NFL teams win and decode the NFL QB rating system. You will also learn that momentum and the “hot hand” is mostly a myth. Finally, you will use Excel text functions and the concept of Expected Points per play to analyze the effectiveness of a football team’s play calling.

You will learn how two-person zero sum game theory sheds light on football play selection and soccer penalty kick strategies. Our discussion of basketball begins with an analysis of NBA shooting, box score based player metrics, and the Four Factor concept which explains what makes basketball teams win.

You will learn about advanced basketball concepts such as Adjusted plus minus, ESPN’s RPM, SportVu data, and NBA in game decision-making.

You will learn how to use game results to rate sports teams and set point spreads. Simulation of the NCAA basketball tournament will aid you in filling out your 2016 bracket. Final 4 is in Houston!

You will learn how to rate NASCAR drivers and get an introduction to sports betting concepts such as the Money line, Props Bets, and evaluation of gambling betting systems.

You will learn how Kelly Growth can optimize your sports betting, how regression to the mean explains the SI cover jinx and how to optimize a daily fantasy sports lineup. We close with a discussion of golf analytics.

Final exam has 10 questions. Please download and open Excel files before taking the exam. You will be referred to Excel files during the exam. Each question is wort 1 point. You need to answer 6 questions or more correctly to pass the exam.