Five Realistic Methods For Implementing Sports Analytics

Five Realistic Methods For Implementing Sports Analytics

When Moneyball first hit the general public nearly two decades ago, it chronicled an epoch of the industry as the latest statistical methods were gradually introduced to comprehensively evaluate athletes. A lot of time has passed since then. The latest inventions in cloud computing and artificial intelligence have enabled the use of unstructured data such as player position, movement, field conditions, weather, and the physical condition of players. Below are five advanced analytics and artificial intelligence techniques that are well established in sports that can be applied to organizations from different industries.

1. Simulation

How to share luck or regularity with binary outcomes of situations, such as hitting the ball in the goal at the last second or not? Teams examine more and more data to predict situations.

Doing tens of thousands of experiments was the standard method for predicting results. Today"s cloud computing makes it possible to simulate situations with a much larger number of initial ones in the short term.

2. Computer vision and natural language processing

Computer vision and NLP are needed to quickly process and structure unstructured data such as video, audio, and images to discover patterns. For example, Hawk-eye Innovations provides ball-tracking technology that is used in professional tennis, football, cricket, rugby, and baseball.

3. Optimizing the path of the player and the ball

Given the dynamics of variables such as opponents, player positioning, and weather, how do you calculate the optimal actions for players to take to achieve success? For example, you need to calculate the probability that a particular player will score from a certain place using a certain shot, taking into account the uniforms that he is currently wearing. It also helps you figure out how to find the best path for the defenders as the ball moves.

AI is used to optimize paths based on weather conditions and travel in real-time.

4. Telemetry and data collection

The widespread use of various sensors makes it possible to make predictions and recommendations. The Formula 1 team is already tapping into telemetry data from thousands of sensors that generate a huge amount of information every second to optimize race strategy, such as stopping, changing tires, and overtaking.

5. Physical and professional condition of the players

A team"s investment in an athlete is much larger than the average salary. There have been times when a player has added tens or hundreds of millions to the cost of the franchise. Therefore, sports teams need to increase their investment in data to prevent injury and improve performance. Gone are the days when you could count on sponsorship support. Now, for example, some teams are using wearable IoT devices along with artificial intelligence to get information and assess the risk of injury. Thus, in light of current developments, there will inevitably be a trend towards more relevant use of data.