What is Data Analytics?
Data analytics is a strategic tool for improving strategies and has been used more frequently in recent years. Data is at the heart of the National Basketball Association and its teams, as almost every decision today is based on analytics. The last decade of professional basketball has changed dramatically because of advanced analytics. NBA teams mainly use high-technology analytics in three ways: scouting players, designing winning strategies, and predicting and avoiding player injuries. The NBA relies on employing advanced analytics to improve its strategies to better the NBA odds.
Using these analytical models, the traditional NBA was re-imagined into an entirely different and new game. With the creation of this new NBA game, each move can be predicted and calculated with the goal of optimizing a winning strategy. The technological advancements today, such as velocity-based training, GPS tracking in training and games, and force platform technology, changed NBA odds. So, how are NBA teams employing advanced analytics to improve their strategies?
Let us explain.
How The NBA Uses the Data
1. Collecting data
Collecting previous data is an effective way for the NBA to start making predictions and designing their strategies based on changes in past games and teams. By collecting data from previous years, strategic improvements can be made, taking into account players and their statistics in past games. Sophisticated data collection is a tool that NBA teams employ to improve strategies, and data collection expands into other areas of advanced analytics.
2. Design of winning strategies
By installing cameras in each basketball court, the NBA used technology to collect data on each player’s movements. The NBA can start designing winning strategies by using machine learning models to analyze the data. In the past, only basic statistics could have been collected because of the lack of technology. However, with the advanced analytics we have access to today, the NBA has in-depth data it can use to improve its strategies, from certain ‘tells’ to more player-specific movements. This helps coaches plan defensive and offensive strategies in accordance with the data collected.
3. Prediction of injuries
Advanced analytics can effectively be used in predicting injuries and help athletes minimize their chances of injury. By collecting player data points through elaborated technological means such as sleep monitors, saliva, and wearables, advanced analysis can determine an individual player’s physical capacity and how injury prone they are. By gathering sleep data, for example, the NBA can predict how tired a player is, which increases their chances of getting injured. As such, the NBA can grant resting periods for its key players to avoid future injuries.
NBA teams are employing advanced analytics for scouting purposes. By using player data, the NBA draft can be accordingly altered. It can also help scouting prospects using numerical analytics and traditional scouting methods. Advanced analytics can predict, with a certain level of accuracy, the prospect of a college player as an NBA player. Since a wrong player pick can bring a team back to lower division standards, the ability to scout more effectively is a precious tool for the NBA.
5. Picking players
Picking players for the NBA is an important and consequential job, as entire teams can rely on that pick at any moment during a game. Because of the hefty monetary and team implications of picking a player, scouting methods through advanced analytics come in handy. However, picking players is done in other ways supported by data analytics. Data analytics come in handy when NBA teams are looking through the draft as it provides the player’s playing history and their weaknesses and strengths. Evaluating picks through data gives the teams access to whatever advantage they could gain from having that particular player on the team.
NBA teams also employ advanced analytics to improve their strategies in scoring predictions. By comparing teams’ yearly analytics, scoring predictions can be achieved, although such predictions cannot be perfect. Data can show the reward in taking risks and vice-versa based on past games; for example, a three-point shot reward outweighs the risk. This shows that teams that attempt three-point shots score more points, proving that the risk is outweighed by the reward.