Rating Systems Explained

Teams in league sports often play balanced schedules. For example, in each season of the English Premier League, each football/soccer team plays each other team in the league exactly twice - once at their home stadium, and once at the opposing stadium. That way, at the end of the season, you can merely look at the record of wins, draws, and losses to determine which teams have done the best, second best, and so on. This works because everyone has had the same opposition, so wins against that same opposition are comparable.

But what would happen if different teams played different opponents, or even different numbers of matches? This is exactly the situation in many eSports, as well as chess; individual players may play different amounts of time against completely different opponents. To compare competitors in such as situation, we can use a rating system.

 

Reflection on an undergrad/grad crosslisted design of experiments course

 

I'm looking to re-examine and improve my teaching since I started working as a professor again for the University of Waterloo, and part of that means writing reflections on courses again. This one is for a 4^th year / graduate level crosslisted course on design and analysis of experiments.

Reading Assignment – Collecting Carefully.

This is a reading assignment for “Episode 28: Collect Carefully” of “The Data Science Ethics Podcast”, available at: https://datascienceethics.com/podcast/collect-carefully/ . My eventual hope is to incorporate it into a course on data ethics and (AI) safety, but that's still a long way from being anything solid. From recent interviews with post secondary institutions, I heard that a lot of schools are looking to incorporate ethics into their stats and data science courses, so I hope this and some of my future posts can contribute to those efforts.

 

Peer review of "Algorithmically deconstructing shot locations as a method for shot quality in hockey"

This is an open peer review I did for the manuscript "Algorithmically deconstructing shot locations as a method for shot quality in hockey" by Devan G. Becker, Douglas G. Woolford and Charmaine B. Dean as submitted to the Journal of Quantitative Assessment in Sport, back in 2020.

You can find the manuscript behind a $42 paywall put up by DeGruyter at https://www.degruyter.com/document/doi/10.1515/jqas-2020-0012/html

 

Sampling, conditional probability, and random number generation

 Part of the motivation behind making the course Statistics and Gambling is to infuse new applicability into introductory or intermediate probability courses. This blog post is a look at how the course is going to cover familiar probability topics with examples in games of chance, and a simulation-based (rather than theory-based) approach.

This post covers basic methods of random number generation (RNG) in R, and applying RNG to demonstrate core concepts in sampling, conditional probability, and conditional distributions. It is meant to be a very surface-level primer on the topics, just enough to give context for the deeper dives into specific games of chance.

The Bottleneck Retirement Plan

I do not have a voluntary retirement plan or a pension. I have the means to put money away specifically for retirement, but I choose not to. Instead, I use an investment strategy that has been described as "the most and least insane thing I've ever heard". Here is that strategy:

 

Wow, what are the odds? (Part 1: American Odds, Decimal Odds, and Implied Probability)

The term "odds" is slippery because it's used to mean different things in different contexts. In layperson terms, "odds" is often used as a synonym for probability. In proper statistical terms, "odds" is a function of probability, but it's not the same as probability. There are also other uses of the term "odds" in gambling contexts which are functions of a parallel concept called "implied probability". In these notes, we're going to look at some common types of odds in statistics and gambling contexts, and some of the calculations to convert between them.

 

How does Polychoric Correlation Work? (aka Ordinal-to-Ordinal correlation)

Let's say you've got data of many paired cases of two ordinal variables, like you might when you ask a large number of people the same two Likert scale questions (e.g. "poor", "fair", "good", "very good", "excellent").

 

What could you learn from the data from those two questions?

Here's a few common approaches:

 

1) Compare the means of each variable by abusing a t-test.

2) Compare the distribution of each variable with a chi-squared goodness-of-fit test.

3) Check for a relationship between responses of each variable with a chi-squared independence test.

4) Estimate the strength of such a relationship with a Spearman correlation.

Lottery tickets, baseball cards, and the coupon collector's problem

A man, a woman, an enby, and 27 elephants walk into a bar. The bartender looks at the group of 30 and asks "What is the probability that 2 or more of you have the same birthday?".

The group proceeds to disregard twins, leap years, and building codes. They talk amongst themselves, leave a little space for the reader to calculate or guess for themselves,

Fantasy Sports Explained

Information on fantasy sports ranges from the overly broad or impossible to apply like personal stories about how fantasy sports changed lives, to extremely narrow or short-lived like opinions on players recent performances. This article is intended to cover some of the middle ground to help you understand the basics of fantasy with a worked example of how you might choose players at the beginning of a league.

 

Borel Dice Edition - Brute Forcing Experiments

 

Borel and Borel: Dice Edition are educational games about probability. I picked up a copy of each because I thought they would be useful in introducing some ideas of probability and gambling without the cultural baggage of better known games.

 

I'm biased because it's my field, but Borel has a lot more play value than most games of its kind. The dice edition, which is much easier to find, and easier to get into and play, has a set of 7 dice (four 6-sided, and one each of a 10-sided, 20-sided and 30-sided die), and a deck of 100 "experiments", like Experiment 001:

 

T1B: Goodhart's Law and Baserunning

When I was about 11 years old, I was good at running, good enough to represent my elementary school as the anchor in a relay at a district track meet. This prompted all the grown-ups in my life to coach me on running. They told me all sorts of tricks about keeping my hands flat and remembering to breathe and to keep in a straight line and to keep from dragging my feet and to start early to get the baton.

The race came and I did all those things – hands, breathing, straight line, no dragging, start early. I forgot, however, to run. I blew a huge lead by running in perfect form, but in slow motion.

What's the lesson here?

Lost Chapter: Writing for your Career

 This is one of the 'lost chapters' of the textbook "Writing for Statistics and Data Science", which was removed because information changes too quickly. This chapter covers data science resumes, describing class projects to businesses, and writing letters of introduction to potential grad supervisors.

Textbook: Writing for Statistics and Data Science

If you are looking for my textbook

Writing for Statistics and Data Science

here it is for free in the Open Educational Resource Commons.

Writing for Statistics and Data Science is given out under the Creative Commons 3.0 - Attribution license. That means you and anyone else has the right to copy it, change it, even sell your version of it, as long as credit for the original continues to be attributed to me. In short, it's open source, have fun. 

There were a few chapters that I didn't include because they were either too niche or too prone to becoming obsolete. I'll be posting them here on the blog, with links being added in this post as those chapters go up. Details after the break.

 

Review of The Theory of Gambling and Statistical Logic

 

There are two reasons why I read Review of The Theory of Gambling and Statistical Logic, Second Edition (2009), by Richard A. Epstein, which dictated which of the text's 440 pages I paid attention to and which I skimmed.

 

First, to learn more of the fundamentals of betting strategy for my current job at Sportlogiq. Second, to get material to include in a possible future Statistics and Gambling course.

Fantasy League Sports Cards

 

The sports card industry (specifically baseball cards) crashed in 1994. Fantasy sports existed as early as the 60's, but really caught public attention around 1995. That timing is not coincident.

In both hobbies, fans get to have  surrogate ownership of players, and the market value of those surrogates goes up or down with the performance of those players. At the casual level, being in a fantasy league is just a more publicly acceptable way to collect and play with cards. At the serious level, fantasy is a more viable, faster way to make a profit with your expertise than cards were.

In short, fantasy is just trading cards for grown ups.

But what if physical cards let you draft players?

 

Soccer-to-Hockey Translation Guide

Hockey and soccer (ice hockey and football) are similar enough from a fan or analytics perspective that if you’re familiar with one, it’s easy to become familiar with another. There could be a whole new world of sport you’re missing out on!

In this article I’ve organized many of the parallels and contrasts between hockey and soccer so that you can watch a few games and of either one and confidently say that "hockey is just like soccer except X instead of Y".

 

The Price of Carbon Absolution

Worrying about everything else is wearing down my sanity. Worrying about climate change feels like a welcome, familiar distraction because the solutions are so linear. We can literally money our way out of this one, but it's a lot of money? How much, exactly, is the personal price of carbon absolution?

Statistics, Gambling, and Games of Chance

This is a proposal for a survey course on statistics that uses gambling extensively in examples. The target audience is senior undergraduates with a non-statistical background, but quantitative students will also find enough novelty to be interested. The goal of the course is not to encourage gambling, but to use it as a vehicle for a broad range of otherwise difficult statistical topics.

Cheating vs Innovation in Sports

Why do some changes in sports end up being considered cheating, and others innovation? Let's look at some historical examples for patterns.

 

In baseball, "the shift" is a strategy in which defensive players deviate, or ‘shift’ from the default locations for their positions to locations closer to where they expect the ball to land. This practice has had a measurable statistical effect on the game; hits other than home runs have become rarer, and the hits that do happen are more often singles compared to seasons before 2010. The shift is simply data-driven strategy and yet the practice is still controversial.

It seems like such an arbitrary thing to call out as unfair