Tag Archives: sports

Hacking Pickleball

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2023/04/hacking-pickleball.html

My latest book, A Hacker’s Mind, has a lot of sports stories. Sports are filled with hacks, as players look for every possible advantage that doesn’t explicitly break the rules. Here’s an example from pickleball, which nicely explains the dilemma between hacking as a subversion and hacking as innovation:

Some might consider these actions cheating, while the acting player would argue that there was no rule that said the action couldn’t be performed. So, how do we address these situations, and close those loopholes? We make new rules that specifically address the loophole action. And the rules book gets longer, and the cycle continues with new loopholes identified, and new rules to prohibit that particular action in the future.

Alternatively, sometimes an action taken as a result of an identified loophole which is not deemed as harmful to the integrity of the game or sportsmanship, becomes part of the game. Ernie Perry found a loophole, and his shot, appropriately named the “Ernie shot,” became part of the game. He realized that by jumping completely over the corner of the NVZ, without breaking any of the NVZ rules, he could volley the ball, making contact closer to the net, usually surprising the opponent, and often winning the rally with an un-returnable shot. He found a loophole, and in this case, it became a very popular and exciting shot to execute and to watch!

I don’t understand pickleball at all, so that explanation doesn’t make a lot of sense to me. (I watched a video explaining the shot; that helped somewhat.) But it looks like an excellent example.

The blog post also links to a 2010 paper that I wish I’d known about when I was writing my book: “Loophole ethics in sports,” by Øyvind Kvalnes and Liv Birgitte Hemmestad:

Abstract: Ethical challenges in sports occur when the practitioners are caught between the will to win and the overall task of staying within the realm of acceptable values and virtues. One way to prepare for these challenges is to formulate comprehensive and specific rules of acceptable conduct. In this paper we will draw attention to one serious problem with such a rule-based approach. It may inadvertently encourage what we will call loophole ethics, an attitude where every action that is not explicitly defined as wrong, will be seen as a viable option. Detailed codes of conduct leave little room for personal judgement, and instead promote a loophole mentality. We argue that loophole ethics can be avoided by operating with only a limited set of general principles, thus leaving more space for personal judgement and wisdom.

Qatar Spyware

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/10/qatar-spyware.html

Everyone visiting Qatar for the World Cup needs to install spyware on their phone.

Everyone travelling to Qatar during the football World Cup will be asked to download two apps called Ehteraz and Hayya.

Briefly, Ehteraz is an covid-19 tracking app, while Hayya is an official World Cup app used to keep track of match tickets and to access the free Metro in Qatar.

In particular, the covid-19 app Ehteraz asks for access to several rights on your mobile., like access to read, delete or change all content on the phone, as well as access to connect to WiFi and Bluetooth, override other apps and prevent the phone from switching off to sleep mode.

The Ehteraz app, which everyone over 18 coming to Qatar must download, also gets a number of other accesses such as an overview of your exact location, the ability to make direct calls via your phone and the ability to disable your screen lock.

The Hayya app does not ask for as much, but also has a number of critical aspects. Among other things, the app asks for access to share your personal information with almost no restrictions. In addition, the Hayya app provides access to determine the phone’s exact location, prevent the device from going into sleep mode, and view the phone’s network connections.

Despite what the article says, I don’t know how mandatory this actually is. I know people who visited Saudi Arabia when that country had a similarly sketchy app requirement. Some of them just didn’t bother downloading the apps, and were never asked about it at the border.

Automatic Cheating Detection in Human Racing

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/09/automatic-cheating-detection-in-human-racing.html

This is a fascinating glimpse of the future of automatic cheating detection in sports:

Maybe you heard about the truly insane false-start controversy in track and field? Devon Allen—a wide receiver for the Philadelphia Eagles—was disqualified from the 110-meter hurdles at the World Athletics Championships a few weeks ago for a false start.

Here’s the problem: You can’t see the false start. Nobody can see the false start. By sight, Allen most definitely does not leave before the gun.

But here’s the thing: World Athletics has determined that it is not possible for someone to push off the block within a tenth of a second of the gun without false starting. They have science that shows it is beyond human capabilities to react that fast. Of course there are those (I’m among them) who would tell you that’s nonsense, that’s pseudoscience, there’s no way that they can limit human capabilities like that. There is science that shows it is humanly impossible to hit a fastball. There was once science that showed human beings could not run a four-minute mile.

Besides, do you know what Devon Allen’s reaction time was? It was 0.99 seconds. One thousandth of a second too fast, according to World Athletics’ science. They’re THAT sure that .01 seconds—and EXACTLY .01 seconds—is the limit of human possibilities that they will disqualify an athlete who has trained his whole life for this moment because he reacted one thousandth of a second faster than they think possible?

We in the computer world are used to this sort of thing. “The computer is always right,” even when it’s obviously wrong. But now computers are leaving the world of keyboards and screens, and this sort of thing will become more pervasive. In sports, computer systems are used to detect when a ball is out of bounds in tennis and other games and when a pitch is a strike in baseball. I’m sure there’s more—are computers detecting first downs in football?—but I’m not enough of a sports person to know them.

China’s Olympics App Is Horribly Insecure

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2022/01/chinas-olympics-app-is-horribly-insecure.html

China is mandating that athletes download and use a health and travel app when they attend the Winter Olympics next month. Citizen Lab examined the app and found it riddled with security holes.

Key Findings:

  • MY2022, an app mandated for use by all attendees of the 2022 Olympic Games in Beijing, has a simple but devastating flaw where encryption protecting users’ voice audio and file transfers can be trivially sidestepped. Health customs forms which transmit passport details, demographic information, and medical and travel history are also vulnerable. Server responses can also be spoofed, allowing an attacker to display fake instructions to users.
  • MY2022 is fairly straightforward about the types of data it collects from users in its public-facing documents. However, as the app collects a range of highly sensitive medical information, it is unclear with whom or which organization(s) it shares this information.
  • MY2022 includes features that allow users to report “politically sensitive” content. The app also includes a censorship keyword list, which, while presently inactive, targets a variety of political topics including domestic issues such as Xinjiang and Tibet as well as references to Chinese government agencies.
  • While the vendor did not respond to our security disclosure, we find that the app’s security deficits may not only violate Google’s Unwanted Software Policy and Apple’s App Store guidelines but also China’s own laws and national standards pertaining to privacy protection, providing potential avenues for future redress.

News article:

It’s not clear whether the security flaws were intentional or not, but the report speculated that proper encryption might interfere with some of China’s ubiquitous online surveillance tools, especially systems that allow local authorities to snoop on phones using public wireless networks or internet cafes. Still, the researchers added that the flaws were probably unintentional, because the government will already be receiving data from the app, so there wouldn’t be a need to intercept the data as it was being transferred.

[…]

The app also included a list of 2,422 political keywords, described within the code as “illegalwords.txt,” that worked as a keyword censorship list, according to Citizen Lab. The researchers said the list appeared to be a latent function that the app’s chat and file transfer function was not actively using.

The US government has already advised athletes to leave their personal phones and laptops home and bring burners.

Building a Data Pipeline for Tracking Sporting Events Using AWS Services

Post Syndicated from Ashwini Rudra original https://aws.amazon.com/blogs/architecture/building-a-data-pipeline-for-tracking-sporting-events-using-aws-services/

In an evolving world that is increasingly connected, data-centric, and fast-paced, the sports industry is no exception. Amazon Web Services (AWS) has been helping customers in the sports industry gain real-time insights through analytics. You can re-invent and reimagine the fan experience by tracking sports actions and activities. In this blog post, we will highlight common architectural and design patterns for building a data pipeline to track sporting events in real time.

The sports industry is largely comprised of two subsegments: participatory and spectator sports. Participatory sports, for example fitness, golf, boating, and skiing, comprise the largest share of the market. Spectator sports, such as teams/clubs/leagues, individual sports, and racing, are expected to be the fastest growing segment. Sports teams/leagues/clubs comprise the largest share of the Spectator sports segment, and is growing most rapidly.

IoT data pipeline architecture overview

Let’s discuss the infrastructure in three parts:

  1. Infrastructure at the arena itself
  2. Processing data using AWS services
  3. Leveraging this analysis using a graphics overlay (this can be especially useful for broadcasters, OTT channels, and arena users)

Data-gathering devices

Radio-frequency identification (RFID) chips or IoT devices can be worn by players or embedded in the playing equipment. These devices emit 20–50 messages per second. These messages are collected and output using JSON. This information may include player coordinate positions, player speed, statistics, health information, or more. To process the game, leagues, coaches, or broadcasters can analyze this data using analytics tools and/or machine learning.

Figure 1. Data pipeline architecture using AWS Services

Figure 1. Data pipeline architecture using AWS Services

Processing data, feature engineering, and model training at AWS

Use serverless services from AWS when possible in order to keep your solution scalable and cost-efficient. This also helps with operational overhead for teams. You can use the Kinesis family of services for stream ingestion and processing. The streaming data from hundreds to thousands of IoT sources (from equipment and clothing) can be fed to Amazon Kinesis Data Streams (KDS). KDS and Amazon Kinesis Data Firehose provide a buffering mechanism for streaming data before it lands on Amazon Simple Storage Service (S3). With Amazon Kinesis Data Analytics, you can process and analyze Kinesis stream data using powerful SQL, Apache Flink, or Beam. Kinesis Data Analytics also supports building applications in SQL, Java, Scala, and Python. With this service, you can quickly author and run powerful SQL code against Amazon Kinesis Streams as your source. This way you can perform time series analytics, feed real-time dashboards, and create real-time metrics. Read more about Amazon Kinesis Data Analytics for SQL Applications.

You might want to transform or enhance the streaming data before it is delivered to Amazon S3. Amazon Kinesis Data Firehose can be used with an AWS Lambda function to do the transformation. Let’s say you have a player prediction timestamp that you want to represent in a different time format to different ML algorithms. Lambda can process and transform this data. Kinesis Data Firehose will deliver the transformed and raw data to the destination (Amazon S3). This can occur after the specific buffering size or when the buffering interval is reached, whichever happens first.

For more complex transformations, AWS Glue can be used. For example, once the data lands in Amazon S3, you can start preparing and aggregating the training dataset using Amazon SageMaker Data Wrangler. As part of the feature engineering process, you can do the following:

  • Transform the data
  • Delete unneeded columns
  • Impute missing values
  • Perform label encoding
  • Use the quick model option to get a sense of which features are adding predictive power as you progress with your data preparation

All the data preparation and feature engineering tasks can be performed from Data Wrangler’s single visual interface.

Once data is prepared in Amazon S3, Amazon SageMaker can be used for model training. In soccer, you can predict a goal percentage based on the player’s position, acceleration, and past performance history.  SageMaker provides several built-in algorithms that can be trained. For real-time predictions, Amazon API Gateway provides an API layer to clients like an OTT, broadcasting service, or a web browser. API Gateway can invoke a Lambda function, with logic to call a SageMaker endpoint and persist the output to the database. This data can be used later on for further analysis or to fine-tune your models.

Figure 2. Deliver real-time prediction using SageMaker

Figure 2. Deliver real-time prediction using Amazon SageMaker

Computer vision-based object detection techniques can be very useful in Sports. These techniques use deep learning algorithms to predict the pass probability, game player face-off, or win prediction. For the sports industry, object detection technology like these are crucial. They obviate the need for sensors. Real-time object identification can be used to:

  • Generate new advanced analytics regarding player and team performance
  • Aid game officials in making correct calls
  • Provide fans an improved and more data-rich viewing experience

Read Football tracking in the NFL with Amazon SageMaker for more information on how to track using broadcast video data. Using SageMaker, you can train object detection models that analyze thousands of images. You can then locate and classify the football itself, and distinguish it from background objects.

Creating a graphics overlay

When you have the ML inference data and video ingestion ready, you may want to represent this data on your broadcasted video. The graphic overlay feature lets you insert an image (a BMP, PNG, or TGA file) at a specified time. It is displayed as a static overlay on the underlying video for a specified duration. The motion graphic overlay feature lets you insert an animation (a MOV or SWF file, or a series of PNG files) on the underlying video. This can be displayed at a specified time for a specified duration.

For example, a player’s motion prediction can be inserted on video during a game, through a RESTful API call of ML inferences. You can use AWS Elemental Live to achieve this. Read about AWS Elemental Live Graphic Overlay at AWS documentation.

Reducing latency

You may want to reduce latency for analytics such as for player health and safety. Use video, data, or machine learning processing at the arena using AWS Outposts. You can also use AWS Wavelength along with 5G infrastructure. For more information, read Catch Important Moments in Sports with 5G and AWS Wavelength.

Summary

In this blog, we’ve highlighted how customers in the sports industry are using AWS to increase the quality of the game, and enhance the sports fan’s experience. The following benefits can be achieved by building a data pipeline for tracking sporting events using AWS services:

  • Amazon Kinesis collects, processes, and analyzes in-game streaming data in real time. This way both teams and fans get timely insights and can react quickly to new information.
  • The serverless nature of this architecture enables a cost-effective, scalable, and operationally efficient environment for customers.
  • Amazon Machine Learning services like Amazon SageMaker can be used to enrich the fan viewing experience. It presents in-game predictions such as who will score next, or which team will win the game.

Visit our AWS Sports Partnerships page for more information on how AWS is changing the game.