Can AI Help Us Find The Next Sports Stars?

Performance metrics aim to assess an athlete's current readiness and forecast their future athletic success by measuring their overall athleticism.

The four main metrics of sports performance are physical, social, technical, and psychological metrics. Any sport that demands high performance must have a distinctive balance of these factors, though the ratios required for each sport vary greatly.

These performance measures are determined by a number of ways and the latest of them is AI evaluation.

The most important data points to consider while scouting for athletes should be across the four main metrics of sports performance.

So let's get started.  Do you know what Sports Performance Metrics an athlete needs to play at the next level?

Physical metrics:

1. Strength
2. Power
3. Speed
4. Stamina
5. Anthropometric Data

Scouts can determine strength, power, easily with the use of even primitive metrics and technology. Dash times, bench press repetitions and other physical metrics are easily calculated using mechanical means.

Anthropometric includes height, size and weight. These too are easily measured using simple means that are all easily attainable by everyone.

Social metrics, this is where it gets complicated. This is where the digital world enters the equation, and calculations and algorithms are necessary to present tangible metrics that are easily interpreted as an indicator of success. These include;

1. Social media following
2. Participation level
3. Engagement
4. ?

In order to assess an athlete's social skills, participation, and engagement, we can track and observe social media followings, which include both the number of followers and the types of accounts they follow, as well as the people who interact with their accounts.

Psychological metrics are the trickiest... they are subjective, intangible, invisible while also being the largest difference maker. These factors separate the greats from the great talents, and the combination of these attributes if identified early on in combination with other factors would create an extremely powerful indicator of who we should be placing our bets on.

1. Endurance
2. Pressure management
3. Stress tolerance
4. Resilience
5. Confidence
6. Clutch
7. Work ethic
8. ?

There are different good metrics for different sports and different positions in each sport. You probably need to be tall to increase your chances of successfully scoring, rebounding and block shots in basketball, but it's not the same with other sports and positions, and being tall doesn't guarantee success but does increase your odds.

These metrics should be viewed preliminarily as a guide for future success for athletes. Which now brings the question...

How can this data realistically be collected and measured?

The good news is that all of this data can also be collected by AI in addition to the conventional scouting process.

Numerous human problems have been successfully solved by AI, including the scouting issue. The majority of the data on physical metrics are usually collected with the aid of AI.

Video sequences are used to track and sense human motion using computer vision. Deep learning, binocular positioning principle, big data and so much have been used and by using these methods and tools, algorithms can now be trained to recognize human poses in real time. The identification of human joints is done using key point skeleton models.

The player's movements and body orientation, as well as everything else that happens on the field, are all tracked and recorded.

Additionally, data aggregation and evaluation of players' abilities and potential across a range of game categories is done using machine learning algorithms.

Finding motion patterns and enhancing play have always been challenges for players and coaches alike. These challenges became a fully attainable goal with the aid of AI. In addition to mining enormous amounts of game data, It is now possible to continuously calculate performance metrics created during gameplay and which can be used during scouting.

A deep learning model known as sentiment analysis, which enables computers to identify emotions behind specific content posted by users on social media platforms and also allows the computer to profile an athlete based on some psychological questions set for them, can be used to track the psychological and social metrics.

The best indicator of future success across all sports cannot be a single metric; rather, it must be a combination of all these metrics.

During every training session and game, athletes produce a large amount of data. The majority of this data will be required for an excellent scouting process, and traditional scouting will never be able to gather and process this data as effectively as AI.

The use of big data in sports scouting has become widespread, but it has many flaws because the other components of performance metrics have not always been taken into account. Through the use of the techniques and tools described in this article, a more meticulous scouting procedure can be accomplished.