Data is a key component in nearly every industry. Global sports have evolved as well, with teams and managers becoming more open to utilizing data for a competitive advantage 스포츠분석. Billy Beane is one of the most well-known examples of Sports Data Analytics. Billy Beane, the General Manager of the Oakland Athletics used sabermetrics such as on-base percentage to sign players at low prices.
Thanks to increased processing power, Machine Learning principles, and Artificial Intelligence (AI), the marriage of Technology and Sports Data Analytics has opened up new opportunities for teams to assess every detail about players. Wearable sensors and cloud processing power have made it possible to collect data for both current games as well as past ones. Wearable technology is now being developed by some of the largest tech companies in the world to help them evaluate player performance and their metrics. They can also monitor their fitness in great detail.
The sensors and devices are available in a variety of sizes. The sensors can be installed on sports equipment, such as balls and bats, or in shoes. They don’t have to be stuck onto the body of players. The data is transferred in real-time, allowing coaches on the sidelines to make informed decisions and monitor performances. The fault-tolerant architecture ensures that data is consistent and secure. Hevo offers a fully automated and efficient solution for managing data in real-time and having data that is ready for analysis.
Sports Data Analytics is used by the backroom staff more than ever, mainly because it makes it easier to predict key events. It can also be used to make big decisions. This is especially true when a team wants to purchase a high-profile player. Sports Data Analytics is useful for the following purposes:
Sports Data Analytics is becoming more accurate as wearable technology becomes more prevalent in sports. You can see from the graph that a study using Zephyr BioHarness Wearable Technology was conducted to gain more insight into how to predict and avoid injuries by assessing overall mechanical load and BMI.
It is possible to identify players who are at a greater risk of injury. This allows fitness staff to adjust their exertion levels, and enroll them into conditioning programs as needed. To assess the likelihood of injuries, Logarithmic Regression via Binomial Distribution models is commonly used.