In case you missed the previous installment of Breakdown, we are defining three basic baseball statistics in terms of singles, double, triples, and home runs. Then using those three equations, we will determine a player’s hit distribution for the season! Unfortunately, we looked at four equations, but found only two that were independent. Luckily, thanks in part to you all, we have found our missing link.
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A major mathematical technique used throughout baseball these days is probability. Probability influences baseball more than any other sport since baseball can be broken down into a sequence of one on one battles – the pitcher vs. the batter. Baseball analysts use probability to forecast which teams will make the postseason, to determine the likelihood that a player will hit a home run or bat around a runner, or even what the next pitch could be. To understand these complexities, we must decipher the differences between independent and dependent events. Without further ado, let us dive into a realm where probabilities have been utilized for decades if not centuries, the casino. In celebration of the baseball Hall of Fame elections today, we decided to investigate the distribution of hits necessary for a player to achieve the offensive triple crown and the minimal number of hits needed to accomplish this feat in 2016.
The offensive triple crown goes to the player who leads the league in home runs (HR), batting average (BA), and runs batted in (RBI). However, many baseball analysts, including myself, ignore RBIs as a measure of skill because they depend too strongly on factors outside a player's control. Instead, we will use slugging percentage (SLG). A simple way to describe a statistic in terms of other variables is through a linear equation. As the name suggests, a linear equation is an equation that makes a straight line when it is graphed.
Linear equations are powerful tools because they allow you to see relationships between similar variables. If you have the same amount of independent equations as unknown variables, you can even solve for them! These can be applied to baseball in a plethora of ways because most calculated statistics actually originate from linear equations. I pause here to quickly say that calculated statistics are statistics that involve some sort of arithmetic between multiple variables, such as Batting Average, while counted statistics involve only a singular variable summed over a period of time, such as Home Runs. How can the lessons exposed in Moneyball relate to hotels? Through analytics of course! As a quick recap, Moneyball relates the tale of the Oakland Athletics, who in the early 2000s, used innovative analysis of baseball statistics to make strategic decisions regarding the team. The strategy proved fruitful as the A’s won more games with less money than anyone thought possible. In my opinion, the story in Moneyball bears truth across a myriad of other industry vertical, including the hospitality industry. Here we discuss what hotel executives should do to harness the power of analytics. 
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