Analytics in the NBA

It's no surprise that players in the NBA are paid extraordinarily high amounts of money. In the 2017-2018 season, Lebron James made over $33.3 million and Stephen Curry made over $34.7 million. In a single season, that is a lot of money. As some of the best players in the game, they are making the most money, clearly. But what determines if a player is "good" and how much money they deserve to make?

Past studies have shown that players are paid based off specific stats: points scored, rebounds, assists, blocks, field goal percentage, and fouls. In addition, players are paid higher amounts of money if they have more experience in the league; the minimum salary increases for each year they have been playing.

However, the NBA is evolving. Similar to the documentary we watched in class, teams are focusing much more attention on analytics. Now, coaches and owners can analyze much more data that count towards a player's pay. This includes the 3-point shot and the Hollinger player efficiency rating (PER). The Hollinger player efficiency rating is a per-minute rating that was developed by John Hollinger, an ESPN.com columnist. In his words, "The PER sums up all a player's positive accomplishments, and returns a per-minute rating of a player's performance."

Analytics are not just used to determine an NBA player's pay. Analytics can be used to rest players. Players have to wear monitors during games and practices to measure performance and fatigue. They even have saliva sampled, and teams track a player's diet. All this to help boost performance and decrease the risk of injuries for the players. Another example of analytics in the NBA is in picking players. Data from college and high school play is analyzed and certain types of players can have their performance quantified.


Sources:
http://thesportjournal.org/article/nba-players-pay-and-performance-what-counts/
https://hoopshype.com/2018/09/19/nba-minimum-salary/
https://www.basketball-reference.com/about/per.html
https://onlinedsa.merrimack.edu/nba-analytics-changing-basketball/

Image:
https://www.si.com/nba/2018/10/31/nba-records-warriors-stephen-curry-klay-thompson-kevin-durant

Comments

  1. AnonymousApril 25, 2019 at 10:55 PM

    Nice post Naomi, the recent rise of analytics has created a whole new layer of depth for the sport of basketball. Because player performance can be quantified into a Hollinger player efficiency rating (PER), player statistics have become much more important in today's game, but this can be a double-edged sword. Stats indeed can give us an accurate idea of how good a player is, but there are also many aspects of basketball that simply can't be quantified, such as hustle and off-ball actions. Thus, I think both analytics and the "eye-test" should be used in tandem by teams to access and scout players.

    ReplyDelete
  2. AnonymousApril 26, 2019 at 12:15 AM

    Analytics have completely changed the way we view and understand the NBA, especially because it can completely go against the eye-test and our own reasoning. Russell Westbrook and Steph Curry are prime examples. Westbrook, who is explosive above-the-rim dunker, would be assumed to have a greater field goal percentage than Curry, who is known for his three point shooting. However, the truth is actually quite the opposite. Westbrook is actually a below-average finisher, while Curry is one of the best finishers in the league. While this may have gone unnoticed in previous iterations of the league, the rise of analytics has cases like this to be used as major headlines to promote certain storylines. Furthermore, because analytics can be used to quantify players as merely numbers, this makes it easier to compare players with similar roles simply based on their statistical averages, thus simplifying the process of determing a player's value by finding the ratio of their worth to the team versus the amount they are paid.
    https://www.theatlantic.com/entertainment/archive/2015/06/nba-data-analytics/396776/

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