Unravel Padel Patterns: Enhancing Your Game with AI-Powered Coaching

Transforming player data into winning insights to unlock the performance secrets with Computer Vision and LLMs

7 min read6 days ago

Have you ever noticed how everyone seems to have a friend who plays Padel nowadays? You know, the one who has probably invited you to a game and is still trying to convince you to pick up a racket?

Padel is rapidly gaining popularity, and it’s hard to resist the charm of those stylish courts. With millions of players worldwide, it’s becoming the go-to activity for anyone looking to stay active while having fun — often while trying to figure out how to hit the ball off the walls without looking like a total beginner!

Now, what if we use technology to gain a deeper understanding of all those impressive moves? With the help of Computer Vision and LLMs (Large Language Models), it is possible to uncover valuable insights into player performance and strategy. Let’s look at the stats behind this sport and explore how we can analyze player movements meaningfully.

The Quick Rise of Padel: A Booming Sport

In recent years, Padel has transformed from an unknown sport into a popular pastime. This unique combination of tennis and squash has become a vibrant addition to the sports world. With its straightforward rules and social atmosphere, it’s no surprise that Padel is gaining global traction. Aren’t you convinced yet? Let’s analyze some interesting facts. According to the World Padel Report 2024 from the International Padel Federation:

There are +30 million amateur players and +600k federated players in 130 countries;
There are +63k Padel courts and +19k clubs;
Padel is most popular in Spain, Italy, and Argentina, which are the top three countries for the sport.

🤓 Fun fact: it’s Spain’s second most popular sport, featuring approximately 6 million regular players, outpacing tennis and ranking just behind football.

With Padel becoming such a beloved sport, how can cool technologies like Computer Vision and LLMs help us understand the game better? What can they reveal about improving player skills?

Computer Vision: An Old Technique or a Chance to Innovate?

Computer Vision has been around for a long time, starting with simple image processing. However, the advances in Artificial Intelligence have transformed it into a powerful technique. Picture this: we can now analyze how players move, gathering valuable insights to help players enhance their skills. This combination of traditional techniques and new technology not only deepens our understanding of the game but also makes it more exciting for everyone involved. One of the main tools behind this innovation is OpenCV, an open-source library that helps developers easily handle complex image processing tasks.

As we reflect on these advancements, it’s clear that Computer Vision is a fantastic opportunity to innovate and take Padel to the next level.

But… How can we take this a step further? Let’s look at how LLMs can analyze the processed images, showing us new ways to understand the game and help players improve in Padel.

AI-Powered Virtual Coach: Unlocking Player Insights

Analyzing a Padel match video can be a complex task. With four players showcasing distinct behaviors, court positions, and playing styles, it requires individualized attention across various aspects. Managing hours of match footage, analyzing each one separately, and producing comprehensive reports can be overwhelming. So, what strategies can we employ to efficiently analyze the video and deliver personalized reports for each player and team promptly?

One powerful solution: LLMs! For instance, if you’re looking to analyze and track player movements, how can you achieve that? The process can be surprisingly simple:

Select a short video (~1 min).
Use OpenCV and an object detection model to detect and track the players’ movements.
Create a heatmap for each player, visualizing their activity on the court.
Send the heatmap image to an LLM which can analyze images (GPT-4o mini, for example).

The results were both intriguing and promising! Before continuing, I invite you to watch the video used in this analysis (from 1:24 to 2:21) so you’re ready to follow along and verify the findings for yourself. Think of yourself as the evaluator of this Virtual Assistant Coach!

Heatmap obtained from the video analysis (Padel Court image from here)

The heatmap above represents two pairs of Padel players:

Left-side: Ale Galán (in Blue to Purple tones) and Juan Lebrón (in Red to Yellow tones);
Right-side: Agustín Tapia (in Blue to Purple tones) and Sanyo Gutiérrez (in Red to Yellow tones).

Now… It’s time for magic! I will guide you step-by-step so you can try it for yourself.

First, inform the LLM that it is an expert in Padel game analysis.
Next, attach the heatmap for analysis.
Finally, construct a strong and clear prompt to ensure the LLM can provide the most accurate insights.

Here’s the prompt that was used:

This heatmap illustrates the movements of players from both teams: Team 1 (on the left) and Team 2 (on the right). Please generate a detailed report with all relevant information about each player. Note that blue to pink tones represent Ale Galán (Team 1) and Agustín Tapia (Team 2), while red to yellow tones represent Juan Lebrón (Team 1) and Sanyo Gutiérrez (Team 2). Take your time to conduct a thorough analysis.

And… here are the responses from the LLM:

Area of Coverage

Ale Galán (Team 1) predominantly occupies the left side of the court, particularly near the baseline and mid-court. Shows significant movement toward the net, indicating an aggressive playstyle.
Juan Lebrón (Team 1) covers the right side of the court extensively, with a notable presence in the right-back area. Less engagement on the left side, which may lead to gaps in coverage.
Agustín Tapia (Team 2) is active on both the left and right sides of the court, indicating versatility. Significant presence near the net, suggesting an aggressive playing style.
Sanyo Gutiérrez (Team 2) primarily occupies the right side of the court, with less engagement on the left. Strong presence near the net, indicating an aggressive strategy.

Defensive vs. Offensive Movements

Ale Galán (Team 1) spent more time in offensive positions, particularly during service returns and net play. Quick recovery to baseline after net approaches, indicating good stamina.
Juan Lebrón (Team 1) spent more time in defensive positions, particularly during opponent serves. Quick lateral movements to cover shots, but less aggressive in transitioning to offense.
Agustín Tapia (Team 2) spent more time in offensive positions, particularly during rallies. Quick recovery to defensive positions when necessary.
Sanyo Gutiérrez (Team 2) spent more time in offensive positions, particularly during service returns. Quick lateral movements to cover shots, indicating good agility.

Summary Insights

Team 1 shows a balanced approach with Ale Galán being more aggressive and Juan Lebrón adopting a defensive strategy.
Team 2 demonstrates versatility, with both players actively engaging in net play but with Sanyo Gutiérrez focusing more on offensive strategies.
Areas for improvement include enhancing coverage in less occupied zones, particularly for Juan Lebrón from Team 1 and Sanyo Gutiérrez from Team 2.

Tactical Recommendations

Encourage Sanyo Gutiérrez (Team 2) to engage more in offensive positioning to balance the team’s strategy.
Suggest Team 2 work on improving coverage in the right-back area to avoid gaps during rallies.
Focus on drills that enhance lateral movements and quick transitions to net play for both teams.

Overall, it’s truly impressive how the LLM demonstrates such a deep understanding of the game, offering intricate details and even inferring each player’s unique style of play. Furthermore, it’s able to provide personalized recommendations, tailoring insights to enhance individual performance.

Final Thoughts

This was a straightforward example of how we can combine the power of LLMs with a Computer Vision system.

In a fast-moving sport like Padel, where every player has their style and strategy, technology is changing how we understand and improve the game. From tracking movements with Computer Vision to using LLMs to create personalized feedback, AI has become an amazing assistant, turning complex data into useful insights.

By automating video analysis, it’s possible to save time, produce detailed reports, and provide tailored coaching for each player. This not only boosts individual performance but also takes the game to the next level. As technology keeps advancing, the possibilities are endless, and the future looks exciting for players and coaches alike.

At the end of the day, AI isn’t about replacing human judgment — it’s about enhancing it, giving the tools to push the limits, and helping players reach their full potential.

Try it for free! Go to ChatGPT, upload the image from this article, and start experimenting. Test different prompts and ask for new insights. Keep in mind, that the results may vary from what’s shown here due to the creative nature of LLMs. Feel free to reach out and share with me your findings :)