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ECON30580

Academic Year 2024/2025

Economics of Betting Markets (ECON30580)

Subject:
Economics
College:
Social Sciences & Law
School:
Economics
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Professor Karl Whelan
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Betting markets have grown dramatically in recent years with most bets now placed online via computers or smartphones. This module covers the economics of these markets. We will use economic theories to describe how bettors behave and how different kinds of betting markets work. In particular, we emphasise how betting odds will behave differently depending on the betting market's structure. We will examine research that assesses these theories by looking at evidence on betting odds and the average payoffs to bettors from various types of bets.

After discussing the history of betting markets, we will cover topics such as the use of betting odds to estimate probabilities, the role of risk in decision-making, favourite-longshot bias (the pattern for losses betting on favourites to be lower than losses betting on longshots), the economics of ``exotic'' bets such as accumulators and the behaviour of betting exchanges/prediction markets where people can agree to bilateral bets on the outcomes of non-sports events. We also discuss a set of issues related to repeated gambles, drawing on classic statistical topics such as the Law of Large Numbers, the Central Limit Theorem and the Gambler's Ruin problem. We conclude by discussing the evidence on the social and financial impact of gambling and the case for greater regulation of the betting sector.

Warning: The module does not offer a recipe for success in betting. The lecturer does not bet and does not recommend betting. On average bettors loses money and you will almost certainly be worse off if you spend money on betting.

About this Module

Learning Outcomes:

Upon completion of this module, you will
• Understand the history of betting markets and how they have changed in recent decades.
• Be able to use betting odds on an event to calculate implied probabilities and the bookmaker’s profit margin.
• Know how to use the economics of decision making under uncertainty to model decisions taken to place money in betting markets.
• Understand how different betting market structures generate different outcomes.
• Be knowledgeable about the empirical literature on outcomes in various betting markets.
• Understand why certain kinds of bets have higher average loss rates than others.
• Be able to evaluate arguments for and against "optimal betting" rules such as the Kelly criterion.
• Be able to use classic tools from statistics to understand the outcomes from repeated gambles of various sorts.
• Be aware of the social issues caused by problem gambling and the policy debates about restrictions on betting.

Indicative Module Content:

1. Introduction
2. The past and present of betting markets
3. Basics of betting markets: Odds, win rates, expected payouts and margins
4. Risk-taking and utility in gambling
5. Pari-mutuel betting and the favourite-longshot bias
6. Disagreement and pari-mutuel betting
7. Introduction to fixed-odds betting markets
8. Competitive fixed-odds bookmakers
9. A monopolistic model of fixed-odds betting markets
- Favourite-longshot bias
- Application to European soccer
- Advertising, proposition bets and accumulators
- Biased beliefs from bettors
- Inside information in sports betting
10. What determines bookmakers’ margins?
11. Spread betting and the Asian Handicap market for soccer bets
12. How much to bet? The Kelly criterion
13. Betting exchanges and prediction markets
14. Repeated Gambles
- The gambler's fallacy and the hot hand fallacy.
- The binomial theorem
- The law of large Numbers
- The central limit theorem
- The gambler's ruin.
- The martingale strategy
- Samuelson's fallacy of large numbers
15. Gambling addiction, social impacts and regulation

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

80
Lectures

24
Total

104

Approaches to Teaching and Learning:
The module is based around lectures given by the module co-ordinator. There is no set textbook. Instead, the principle teaching materials are a set of lecture notes provided by the module co-ordinator and a number of research papers.

Lecture slides are made available in advance of live in-person classes which will give students a chance to prepare for lectures and come to class ready to engage and ask questions about material.

On the use of AI: You can use AI if you want to but it won't be a very good way of approaching the assessment.

- The assignment requires you to submit an unique set of answers so generic answers from AI will probably be fails. Also, the content of this module is very specific and I know various AI tools provide incorrect answers to these questions. The assignment is not hard if you just go through the relevant parts of the lecture notes.

- You could try to use AI for preparing answers for the final exam essay questions but, again, you are really just better off consulting the relevant parts of the lecture notes. This is because the lecture notes have been prepared by the person actually grading the exams. I am generally looking for some specific key points to be made and the lecture notes will emphasise these points. The AI tool is unlikely to provide answers that emphasise these same key points. And remember that if you provide a bad answer that look a lot like bad answers that other students have written, this clearly sends a signal to the grader that you did not research this answer yourself in the recommended way.

Requirements, Exclusions and Recommendations
Learning Recommendations:

There are no formal pre-requisites for this module but it is strongly recommended that students have a good understanding of statistics and also that they have an understanding of intermediate microeconomics material on optimisation of utility and decision-making under uncertainty.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): An assignment that covers the basic topics in the economics of betting markets. Week 8 Alternative linear conversion grade scale 40% No
30
 No
Exam (In-person): End of trimester final exam End of trimester
Duration:
2 hr(s)
 Alternative linear conversion grade scale 40% No
70
 No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Summer Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

The module co-ordinator will be available throughout the term to provide feedback in relation to preparing for assessments and in relation to how the assessment has been graded.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 30, 31, 32, 33 Fri 11:00 - 11:50
Spring Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 13:00 - 13:50
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