Tracking A Player’s Performance: 5 Useful AI-based Tools

Sports have evolved rapidly with the application of new technology. In recent years, artificial intelligence (AI) is increasingly becoming a big part of the sports industry. We saw how useful statistical data and analytics were in the 2002 movie Moneyball and almost two decades later, we’re seeing coaches making strategic plays with the help of AI.

AI-based tools have proven to be extraordinarily useful in macro and micromanaging sports teams. While humans can analyze team and individual performance reliably, AI can do so much more by analyzing even the smallest detail. AI can squeeze out valuable data from the fastest, most subtle player movement, something a human will struggle to do. Let’s discuss ways AI has been used to track player performance.

1. Match prediction

Machine learning is a branch of AI and computer science that imitates how humans learn through the use of data and algorithms. In sports, machine learning has been used to predict the outcome of matches. The project IPL Cricket Match Outcome Prediction using AI Techniques by Jerry Davis, Pulkit Malhotra, and Rajesh Goel is a great example of using machine learning for match predictions. The project showed how machine learning could make a forecast in the middle of a match, helping team management determine what adjustments are beneficial and execute them on the fly.

Certainly, the predictions of AI are not 100% accurate, as of now, at least. But their application serves as a catalyst for the evolution of team management. In terms of player performance, machine learning will allow for a closer look at player performance. Knowing what to expect from a player, not only by a hunch but also through data analytics, will give a significant advantage to talent scouts, sports bettors, and owners of sports collectibles such as NFTs.

2. Real-time sports analytics

It’s common practice for sports teams to view a game film, whether they won or lost, to spot things that need to be improved and ones that need to be maintained. Through machine learning, preparing a game film has been revolutionized. Video intelligence companies such as GAMEFACE.AI allow management to skip hours of splicing, tagging, and coding games as it can prepare video footage and analysis in real-time. Services like the one offered by GAMEFACE.AI allow teams to get the edge over the competition by tracking every aspect of the game. It saves time and allows for better decision-making.

3. Coaching

We’ve had a lot of legendary sports coaches throughout history. While AI alone cannot make any coach legendary, it can help them reach new heights. Like how machine learning can predict match outcomes by closely looking at quantitative data, coaches can use AI to develop better training regimes for their players and prepare them to face tougher competition.

4. Improving player performance

Using the power of AI to analyze player performance in greater detail gives way to creating more effective training programs. AI can help players and coaches spot which areas of a player’s game need improvement to tailor their training around these weaknesses.

An iPhone app called HomeCourt allows basketball players to track their shots and analyze the success rate of each attempt. The app, which is free for the first 300 shots, offers players real-time feedback and an automatically-spliced video recording of each shot attempt so they can take a closer look at their form. Being able to do this allows players to develop a better jump shot in a shorter time. HomeCourt’s AI is rudimentary compared to other AI applications, but it allows almost every basketball player to improve their skills like never before.

5. Player recruitment

Talent scouts have long used statistics to evaluate players. But sports teams are now utilizing the capabilities of today’s AI in processing more player data. Since AI can go over data faster than any human can, it has become an undeniably efficient tool in creating in-depth player evaluations. The power of AI has allowed teams to evaluate data that was previously inaccessible.

Apart from helping create player evaluations, AI can also help teams spot talents that would otherwise fly under the radar. AI’s capability to get into the tiniest details allows it to spot potential talent, helping teams recruit better. Companies like Sportlogiq have been developing AI systems that will enable teams to cast a wider net — and potentially find talented athletes they would never detect by traditional means.

On the other hand, AI company SparkCognition has created an AI that lets teams process applications significantly faster. The AI system not only evaluates players’ statistics and their performance via game videos, but also their academic grades, recommendations, and other information relevant to their application.

Final thoughts

AI is sure to take a more prominent role in sports in the coming years. We should expect more advancements in AI technology and for sports to integrate more of it. As always is the case when discussing AI, the issue is the threat it poses to human jobs. Will sports AI advance enough to be capable of replacing human coaches? Today, AI’s role is only to assist players and coaches — to help them be more efficient in what they do.