Let us go back to a simpler time, when the Astros had yet to win a World Series, the Las Vegas Golden Knights were preparing for the expansion draft, and the hype for The Fate of the Furious was taking over the internet. I am, of course, talking about March 2017.
In my last few posts in this series, I discussed my predictions and conclusions for 2018 MLB end-of-season win totals and playoff odds. However, I decided to step back in this article and examine how this methodology works in greater detail, as well as how it applies to a season where we already know the results, the 2017 season.
A common dream of most baseball aficionados is to visit every ballpark, stepping inside with their own two feet, feeling the history, and seeing every team live. I’m no different; and can already cross off almost two-thirds of the current ballparks off my list (I’m coming for you soon East Coast). After seeing this article on an itinerary that would allow travelers to see every single national park in the 48 contiguous states on a road trip without wasting any time, I wondered how this methodology worked, and how it could be applied to a ballpark journey. As a side note, Randy Olson has also organized the ultimate US road trip and the best cross-Canada journey. I highly recommend him as a follow on Twitter as well!
For those of you that have followed my blog in the past year and a half, one statistical technique that you may have noticed I commonly use is Monte Carlo simulation. While I usually skim over the basics of Monte Carlo simulation to get to the meat of my analysis, I want to take the time in this post to delve into this method a little more deeply, and show by example, the immense power of the Monte Carlo method.
Monte Carlo simulation is a type of probability simulation used by companies to understand the impact of risk and uncertainty in financial, project management, cost, and other forecasting models. It is also a major strategy in decision analytics. One of the drawbacks with trying to predict the future is that you can't know with certainty what the actual value will be...
One business strategy that is finally gaining traction in sports is dynamic pricing. Associated with a poor connotation to users of Uber and Lyft, dynamic pricing, which is also referred to as surge pricing, is a pricing strategy in which businesses set their prices for products or service based on current market demands. This real-time pricing strategy is not a new concept. Travel-related industries such as hotels, rental cars, and airlines have employed this technique for years, as well as more recently in the energy market and sports businesses.
In 2014, Cubs single-game ticket prices at Wrigley Field were set through dynamic pricing, which helped more accurately price tickets for individual games and provides fans with more price options. However, with football season finally back, we decided to look at how a dynamic pricing structure could work for an NFL team. Back in April, the Buffalo Bills announced a new dynamic ticket pricing model for all Bills home games in the 2017 regular season that will adjust ticket prices to better reflect demand throughout the season. With this precedent established, we decided to see how this economic strategy would work for the Houston Texans.
The St. Louis Cardinals are not great this year. They were not great last year either. To the casual baseball fan, the fact that the Cardinals stood pat at the most recent trade deadline, then, is surprising. Why not selloff your best players for prospects to help you win in the future? While many gripe that this organization has been missing a certainty of direction for the last couple years, we wanted to look back at perhaps their largest trade of the last half decade, one that boosted them from greatness to new heights.