Betting parlays of positively correlated events will generally be profitable.

How does one measure the winner of a debate? If the debate itself is the focus, it’s wholly unsatisfying to just tally up the opinions of the viewers, as that solely reflects the prior beliefs of the audience. Intelligence Squared is an excellent podcast which teaches policy through moderated debate. A resolution is proposed (e.g. “Affirmative Action does more harm than good”), and two teams of two experts argue the issue in a structured setting. Many other programs aim for balance with a halfhearted attempt to pay lip service to the opposing side (without truly believing it), so it’s refreshing to hear passionate and informed argument from both sides of the aisle. If one side cites a misleading statistic, their opponents are ready to actually call them out on it, penalizing the lazy rhetorical tactics that plague discourse in an echo chamber.

This is a brief companion to the post analyzing the methods of assigning a winner to a debate, using the Intelligence Squared dataset. I will briefly outline here how I assemble that dataset, for trasparency.

Part 1 can be found here, and Part 2 here.

The challenge of data exploration frequently lies not in answering any specific question, but in the scope of possible questions which might be asked. Usual incentives promote us to analyze data in defense of a predefined narrative, but it can be just as valuable to construct methods for any user to conduct their own open ended exploration of the dataset. To do this requires interactivity with the user, which presents a substantially different challenge than what we usually face. I wanted to explore how to construct a simple Shiny app, which creates a slick and simple user interface for a given R program.

Brief update to the past post. I knew a few people who wanted to try the extension, so I thought it’d be a fun exercise to go through the process of uploading it to the Chrome store. You can view and add the extension to your own browser here. This is far from a polished state, given that it was meant to be a quick way to explore something new. However, I do find myself using the finished product a surprising amount, so I thought it was worth keeping on the Webstore. And it’s nice to see how easy Google makes it to upload a simple app.

Opening a New Page

The same betting odds are often displayed in disparate ways, depending on the context. In America, normally in terms of a Moneyline, where +200 is shorthand for “if you bet $100, you can win an additional $200”. In other contexts, we generally see the tradition “X/Y” fractional odds. Both of these have clear mathematical meanings, but for inexperienced bettors, they tend to be poor conveyors of intuitive meaning. We can quickly estimate what “+350” implies, but for most people, it takes a mental calculation to do so. The most useful intuitive information about betting odds comes from the “Implied Probability”, which is the probability of the event necessary for you to “break even” on the bet. Betting sites don’t particularly like Implied Probability because it makes the cut they take more obvious (when mutually exclusive and complete events have probabilities that sum up to greater than one), and more intuitively accurate information for bettors can make them realize how daunting it is to actually keep up a positive expected value.

Part 1 can be found here, and a followup discussing the eventual observed World Cup results is found here.

Part 2 can be found here, and a followup discussing the eventual observed World Cup results is found here.

As outlined here, I thought it would be interesting to run a basic analysis of how effective sports power rankings are as a tool for predicting the results of games. Specifically, I will use the reddit.com/r/NBA biweekly community power ranking (for example, this ranking), an aggregate poll that tends to fairly accurately reflect community consensus on what the power ranking should look like. I will focus on the 2015-2016 NBA season, as the power rankings over that season followed consistent timing and format.

In the last post, I provided a rambling introduction to sports power rankings. My goal is to provide a rudimentary analysis for the efficacy of NBA power rankings as a predictive tool (once the data is compiled, the actual analysis can be found here). For this example, we will use the /r/NBA Reddit community power rankings, a poll involving one appointed fan representing each team. These provide a pretty well rounded representative rankings, as aggregate polls tend to be fairly representative, and less prone to shocking choices designed to generate discussion. To compare the accuracy of using power rankings to predict game results, versus using metrics like win/loss record or point differential. To do this, we need two pieces: a dataset of the NBA teams power rankings throughout the 2015-2016 season (we use the /r/NBA reddit community power rankings for this example), the teams’ win/loss record and point differential on those dates, and then the actual observed results of the NBA season. We begin by compiling the power rankings data.

Discussing sports has always appealed to me more than the actual viewing experience, and thus sports “Power Rankings” are a guilty pleasure of mine. They distill the discussion of the state of a league into a simple and clear list, and avoid the wishy washy generalities that plague most sports discussion. For basic background context, some example power rankings might be the weekly ESPN’s weekly NFL power ranking, or reddit.com/r/NBA’s biweekly community power ranking, or Bleacher Report’s power ranking of 14 distinctive NBA hairstyles. Generally, power rankings are roughly intended as a measure of how strong a team is at that moment in time, something with a team’s simple win loss record does a very imprecise job of measuring. Of course, the downside of sports power rankings is that they are poorly defined. Any argument over the ranking of teams must necessarily devolve into an argument over the definition of a “power ranking”.

In the previous post, we cleaned the walking data stored by my iPhone. I was curious how I could display my walking patterns in graphical form. My first goal was to compare my patterns by day of the week.

Overall, these blog posts are intended to be a simple diary for some side projects that I work on in my spare time. They have no specific end goal, I just think it’s valuable to get used to recording and explaining the things that you do. I’m quite novice in most things related to computing, so this is just one person’s learning exploration, and may be riddled with poor form and errors.