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.