Predictive Analytics in Baseball
By Samuel Barnes 10/22/2019
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Baseball is one of the few examples that has improved because of data analytics. All of the attributes that make a great baseball player are measurable, and we have been doing so since professional baseball started. A very popular hobby used to be buying, selling and trading baseball cards amongst children. The advancements in baseball are related to the statistics on these cards. Algorithms are being implemented so that a team can analyze a players future in their organization. We also see a change in the way baseball is played.
Baseball organizations gather statistics on players in order to better understand the way they play. Using these stats, in a strategy known as defensive shift, they are able to position their players uniquely for each batter. Allowing the players to have a better chance at making a play on the ball. Originally this strategy was only used on the top hitters in the league. Now it is used almost universally throughout a teams line-up. Defensive shift has changed the way baseball is played. The Players have had to become more versatile, by being able to hit and play in more places on the field. Baseball used to be played with designated positions in the field. Now they are constantly moving into different places forming the shift. This new strategy has created controversy amongst the fans and old players.
A lot of baseball fans believe that defensive shifting takes the purity out of the game. For over 100 years baseball has been played pretty much the same way. The shift slows down the offense of the other team, bringing their batting average down. Fans believe this takes the excitement out of the game as well as suppresses the big hitters. However, what this actually does is challenge the hitters to be able to hit the ball in all areas of the field. This means that they are capable of hitting the ball in more directions, giving themselves a better chance to get a hit. In the early stages of the shift you could identify a good hitter if the defense would shift when he came up to bat. Now you can tell who a great hitter is if they don’t shift because it shows that they have perfected their ability to hit the ball. This also changes the way teams look for players. Instead of the power hitters they are looking for more well rounded players with the ability to cover the field.
This new definition of a “great player” is chnging how teams recruit new players. Before Data analytics entered baseball, the only way to judge a player was ending a scout to watch them play. Now with data analytics we can take the players statistics from his previous years and use an algorithm to predict his future in baseball, such as how long he will be able to play for and how well he will perform. A baseball player can only play for so long before their performance begins to decline. These algorithms allow teams to notice this and offer contracts that better reflect a players life span in the game. And instead invest in new players who will provide a longer and brighter future for them.
Free agents are examples of elite players who are being payed for more than they can offer. The most recent examples are Manny Machado, who signed a ten year 300 million dollar contract. And Bryce Harper, who signed a 330 million dollar contract over the next thirteen years. These contracts pay over twenty million dolors per year, this is over five times the average contract in the Major Leagues. Because the players are already in the middle of their career, their performance is not guaranteed through the duration of the contract. Data analytics are making these contracts much more rare. Creating a power shift between the teams and free agents. Before the introduction of these algorithms “big name” players were able to run up the price and time needed sign them. This is because teams could not truly identify how their future performance would be or the performance of the other players. Because of this new knowledge teams can better dictate the terms of the contracts.
When the organizations use these algorithms, they are more likely to make better deals which will be a better use of their budgets and will help improve the depth of the team. Data analytics and the use of algorithms has created a healthier relationship between the players and the organizations. Free agents may not be able to extend their careers as long, however they will still receive the pay their performance deserves. And when these algorithms are universally implemented, organizations will be able to pick up the “big name players” and recruit new young talent in a manner that reflects the best interest in the team’s success and longevity.
