 Not pictured, the MLB All-Star Game As the dog days of summer continue to roll on, all thirty baseball teams have finally reached the informal halfway point of the season, and get a well-deserved break as the country focuses on the All-Star Game and Home Run Derby. As such, I decided to see how my end of season forecasts have changed based on all of this new data that is available.
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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...  Joe Buck, perhaps discussing small sample sizes. Credit: Tom Ley, Deadspin The Major League Baseball season has only been in action for around a month, yet some fans have already declared their team’s season as “over”. While this may be true for the Miami Marlins, how can we use statistics to see how a team’s start will affect their overall season? Many pundits simply say a baseball team is on pace to win and lose a certain number of games by simply multiplying a team’s current winning percentage over 162 games.
For example, here’s a silly article by CBS Sports that will be totally irrelevant come October: More than half of MLB teams are on pace to win or lose 100 games in 2018. That article was the inspiration behind this post; if you are going to make outlandish claims, at least use math to support it. 
Author: Keith Allison from Hanover, MD, USA. Source: Wikipedia Commons
On Valentine’s Day 2018, late into the still frigid Hot Stove that is this year's offseason, the Orioles entered into a relationship with Andrew Cashner to the tune of two years for $16 million, much to the chagrin of some of his ex’s (Rangers) fans. Both SBNation’s site of the Texas Rangers, LoneStarBall, and beat writer Evan Grant, recommended a Cashner reunion at one point or another this offseason.
By looking at some of Cashner’s raw statistics from his 2017 campaign, it could be argued, albeit wrongly, that he was one of, if not the best, pitcher on the Ranger’s staff. For the sake of this entire analysis, I will be using data from FanGraphs. There are some data discrepancies, especially with batted ball data and WAR, between FanGraphs and BaseballReference, which is why I am only using FanGraphs data for consistency. |
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