Title Sports Betting and R: How R is changing the sports betting world Speaker: Marco Blume Keywords: Sports Betting, Sports Analytics, Vegas, Markets
Webpages -
https://cran.r-project.org/web/packages/odds.converter/index.html -
https://cran.r-project.org/web/packages/pinnacle.API/index.html -
http://pinnacle.com/ Sports Betting markets are one of the purest prediction markets that exist and are yet vastly misunderstood by the public. Many assume that the center of the sports betting world is situated in Las Vegas. However, in the modern era, sports bookmaking is a task that looks a lot like market making in finance with sophisticated algorithmic trading systems running and constantly adjusting prices in real-time as events occur. But, unlike financial markets, sports are governed by a set of physical rules and can usually be measured and understood.
Since the late 90s, Pinnacle has been one of the largest sportsbooks in the world and one of the only sportsbooks who will take wagers from professional bettors (who win in the long term). Similar to card counters in Blackjack, most other sportsbook will ban these winners. At Pinnacle the focus is on modelLing, automation, data science and R is a central piece of the business and a large number of customers use an API to interact with us.
In this talk, we dispel common misconceptions about the sports betting world and show how this is actually a very sexy problem in modelLing and data science and show how we are using R to try to beat Vegas and other sportsbooks every day in a form of data science warfare.
Since the rise of in-play betting markets, an operator must make a prediction in real time on the probability of outcomes for the remainder of an event within a very small margin of error. Customers can compete by building their own models or utilising information that might not be accounted for in the market and expressing their belief through wagering.
Naturally, a customer will generally wager when they believe they have an edge, and then the operator must determine how to change its belief after each piece of new information (wagers, in-game events, etc). This essentially involves predicting how much information is encoded in a wager, which depends partially on the sharpness of each customer, and then determining how to act on that information to maximise profits.
One way to look at this is that we are aggregating, in a smart way, the world’s models, opinions, and information when we come up with a price. This is a powerful concept and is why, for example, political prediction markets are much more accurate than polls or pundits.
For this reason, we are releasing another package to CRAN very soon: We will be releasing a package that has all our odds for the entire MLB season and US Election 2016, which can be combined with the very popular Lahman package to build predictive models and to measure the prediction vs real market data to see how your model would have performed in a real market.
We believe this is a very exciting (and difficult) problem to use for educational purposes. This package can be used in conjunction with two of our existing packages already on CRAN for a few years: odds.converter (to convert between betting market odds types and probabilities) and Pinnacle.API (used to interact with Pinnacle’s real-time odds API in R).
Even if you have no interest in sports or wagering, we believe this is a fascinating problem and our data and tools are perfect for the R community at large to work with, for academic reasons or for hobby.
