AI Ball Tracking for Sport Analysis

AI Ball Tracking for Sport Analysis

Pranav Patel
AI in Sport Analysis

Accurate ball tracking has long been a holy grail in sports technology, promising to unlock new insights and improvements in athlete performance. It can be used for various tasks, from gathering valuable data to helping referees make decisions, even for predictive analysis. However, developing reliable ball tracking systems has proven to be a complex and challenging task. However, the recent breakthroughs in AI and computer vision space have introduced new models and methods to enhance ball tracking greatly.

In this article, we'll explore the latest advancements in AI-powered ball tracking, and how they're overcoming these challenges to revolutionize the sports industry.

What is Ball Tracking

Ball tracking in sports is simply tracking the ball as it moves around in the field. Along with this, things like collision detection with the bat or racket, speed analysis, and player interaction with the ball are also measured and tracked. These are valuable data points in the sports analysis industry as for most of the games the “ball” itself is at the core. It might be kicking the ball, putting the ball in a basket, or hitting the ball with a bat or a racquet. Balls are involved in one way or another in most of the popular games.

Ball tracking has become almost a necessary part of most of the games. It just allows the players, regulators, and viewers to extract so much more information from the games. It really enriches the experience and improves the game overall.

Let’s talk about some of the advantages of using Ball tracking in sports.

Ball tracking for Making Better Decisions

Making judgment decisions is one of the most important parts of any sport, with or without balls. Most of them are straightforward like fours and sixes in Cricket, but in some cases, these can become very difficult, like LBW (Leg before Wicket) and No Ball judgment in Cricket. These edge case scenarios are now handled by “3rd Umpires”, These are the umpires sitting in a room with a lot of video feed coming from all over the field.

For the 3rd umpires, ball tracking data is essential. When the field umpires cannot make a decision they refer to the 3rd umpires, which then using ball tracking and other sport analysis techniques, make a decision for them. This requires a lot of work and a lot of data processing in real-time.

Ball Tracking for Improving Player Performance

Ball tracking has been used by players for a long time now to improve their own performance and to understand the flaws in other people’s playing styles too. Bowlers use it all the time to understand how they are handling the ball and where can they improve further to score the most wickets. Golf players use ball tracking with pose estimation to understand how they should hit the ball and at what angle. Doing all this provides players with extremely granular data and very good feedback by the system, with all this, it’s very to improve and catch mistakes. At this point, this is something every player in the industry uses.

Ball tracking for Assisting Coaches

Along with improving individual performances, coaches have started making better teams based upon different metrics gathered through sports analytics systems such as ball speed, spin rate, distance covered while running after hitting the shot, and number of steps taken during the batting stance preparation phase. This allows managers/coaches access detailed insights regarding strengths & weaknesses amongst squad members allowing informed tactical adjustments throughout the season leading ultimately toward success.

Coaches also analyze opponents’ games thoroughly beforehand so they can devise strategies accordingly against certain styles played previously seen footage recorded via cameras capturing entire match proceedings including replays slow motion clips highlighting key moments helps understand tactics employed by both sides resulting in improved overall quality play. This really helps coaches prepare a better team strategy against any other team.

Ball Tracking for Fan Engagement

Ball tracking for fan engagement is rather a new phenomenon where providers has been introducing things like “Ball cam”, a special drone or camera that follows the ball specifically. This is new but something viewers love engaging with. Providers also often show the “ball path” and other important visualizations that keep viewers engaged and informed.

How does Ball tracking work?

There have been many ways Ball tracking is implemented in the actual games. One of them notably being using TrackNet and YOLO Networks. These techniques are often paired to provide a good experience and also work well to this date. But we want to introduce newer better models which can track the ball and other objects even better.

Let’s learn how to build ball tracking pipelines.

Segment Anything Model

Segment Anything Model by Meta is a rather new and recent model. Segment Anything Model or SAM by Meta is a rather straightforward model. It takes in an image and can take in various types of prompts like masks, boxes, points, and even free-form text. Then the both image and the passed prompt is encoded into a much smaller subspace, these embeddings are then passed into a decoder which outputs a final mask that represents the segmented parts of the image. As you can see in the diagram below:

This means that you can pass a normal image like this:

And segment all the players and extract information from it like this:

As you can see the model was able to extract all the details from the image, the players, the pitch, the umpire the hats, helmets, etc. This is very granular data. This data can further be used to analyze a lot of things in the games, and also, track specific players, balls, and whatnot.

Track Anything Model

The Track Anything model is an extension of the Segment Anything Model, integrating the X-Mem architecture with it to allow it to operate over images. Track anything is first used to create a segmentation mask for the object that is desired to be tracked, the mask is then provided to the X-Mem model which is very good in tracking objects over a long-term video.

X-Mem uses the Atkinson-Shiffrin Memory Model which is similar to how human beings process and store information and memory, the same architecture is then used over consistent frames to track an object through the video. Over the years X-mem has evolved into a much better architecture, X-Mem++ being the latest one. All these techniques can be used to track players, balls, and other elements in a game.

Here you can see Stephen Curry being tracked across shot changes over a 2 minute video.

This same pipeline can be used for any game, like soccer, baseball, basketball, cricket, golf and whatnot.

How to use Ball Tracking Data

Once you have the ball tracking data along with the player data, then you can do a lot of things from there to generate a ton of analytics. Things like player interaction with the ball, team interaction, what player is best at what area, where the ball goes most, etc. All these very essential metrics become very easy to extract and track once we have the data ready, let's see how we can work with all this data.

Team Analysis

Once the ball tracking data is in, we can measure how teams or specific members of the team interact with the ball at several different incidents. This is rather important for games like Basketball and Soccer, as different members seem to perform differently given the phase of the game and the area of the field. Important metrics like the Strech Index and Team Synchrony can be extremely useful in these scenarios.

These metrics help understand how much area a team is using and how well. For example, the stretch index of 3 players might be very tight, and that could explain why they have trouble maneuvering the ball over large areas. Whereas, if the other team’s covered area overlaps with our team’s area, we can understand how they are going to interact and study what are some techniques to secure the ball in those scenarios. All this is very valuable to a manager or a coach. You can read more about these metrics here.

Individual Analysis

Individual player analysis is as important as team analysis. Things like the individual path of a player, movement speed of a player, distance from the ball as the game moves on, etc. These are important metrics for specific players. We can also understand how a player is performing in different areas of the field. For example, if a player is not moving much in the defensive area, we can understand that the player is not performing well in that area. This is important for the coach to understand and make decisions on how to improve the player in specific areas or what areas to target them for.

This data can also be used to pair correct players together. 3 players who have a high stretch index together can be paired together to cover a large area of the field. Players who can run faster can be placed closer to the opponent’s side so that they can quickly move back and forth between defense and offense.

Pose Analysis

Ball tracking data can be further paired up with pose estimation to refine the technique of the player in games like cricket and golf. These games where you have to hit the ball with great accuracy and precision can benefit greatly from pose analysis. You can see how the position of the body changes during hitting the ball and whether certain positions result in better performance than others. Pose estimations can also detect injuries early on before they become serious problems down the road.

As you can see in the image, pose estimation in golf can be helpful. Tracking where the ball goes, when hit a certain way, and when the pose is in a certain way. Poses can also be compared with other better players to get an idea of what the player is doing wrong and where the improvement is needed. This same technique can be used for various other things like exercise and posture analysis if needed.

Traditional methods vs Track Anything Model

As mentioned before, traditionally, networks like Tracknet have been used for this application. But even Tracknet has its issues.

Performance Issues

Tracknet, being a single architecture, can make mistakes. Tracknet was mainly developed for tracking shuttle cocks during tennis matches, performance across other domains can be very degraded unless finetuned properly. It is seen that smaller faster moving objects, like a ball in cricket, can be problematic for Tracknet to track. However, techniques that build upon Tracknet like MOTRv2 seem to show much better performance. These are not single network architectures but rather pipelines that use the network for the core tracking task.

SAM, on the other hand, is highly performant in most of the out-of-domain tasks, and when combined with other architectures like X-Mem and X-Mem++, the tracking capabilities are simply SOTA. Similar to Tracknet, pipelines built with SAM might also require some finetuning but performance gains are much greater compared to Tracknet.

Computation Cost and Inference Time

Another big issue with TrackNet and its dependent pipelines is that it is computationally very heavy as it is mostly a single convolution network, mostly. TrackNetV2’s performance is around 31.8 FPS. Whereas the Track Anything Model paired with X-Mem++ can do 39 FPS. And much more if a smaller version of the model is finetuned for a specific use case.

In production, it is often the case that not all the frames are processed, most are clumped together and a Kalman filter is used with it to track the ball in all the frames altogether

Want to Build Ball Tracking Pipelines for Sports?

If you are looking to build ball-tracking pipelines and sports analysis applications, please reach out to us. We have worked with many computer vision pipelines and have integrated them into already existing systems. Reach out to us to build such pipelines or just to chat. Would love to chat!

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