Learn how probability, math, and statistics can be used to help baseball, football and basketball teams improve, player and lineup selection as well as in game strategy.
Overview
Syllabus
- Before you start...
- Module 1
- 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.
- Module 2
- 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.
- Module 3
- 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.
- Module 4
- 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.
- Module 5
- 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.
- Module 6
- 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.
- Module 7
- You will learn about advanced basketball concepts such as Adjusted plus minus, ESPN’s RPM, SportVu data, and NBA in game decision-making.
- Module 8
- 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!
- Module 9
- 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.
- Module 10
- 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
- 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.
Taught by
Wayne Winston
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Reviews
3.3 rating, based on 3 Class Central reviews
4.5 rating at Coursera based on 97 ratings
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This course is very low quality in terms of its engagement with students, compared to the many other excellent course available and that I have done, both on Coursera and edX. Here are my concerns: 1. The course says it is free - you have to go into…
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If you enjoy statistics and sports, this is an excellent course. Most of the assignments were easy to follow and required some analytical skills. What this course lacked was the ability to engage in dialogue with the instructor. Had the instructor been present or even the ability to contact the instructor, I would have rated this 5 stars, plus. Coursera platform is not well thought out and cumbersome to navigate.
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