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Artificial Intelligence in Sports – Current and Future Applications
The North American sports industry is a cultural and economic staple generating billions of dollars in revenue each year.
We set out in this article to examine the applications of artificial intelligence in professional sports to help business leaders understand current and emerging trends within the industry.
In this article we will explore the following sub-topics in order: Before exploring the present applications, we’ll begin with background facts and a quick summary of the findings of our research on artificial intelligence in professional sports.
Current applications of AI appear to fall into four major categories: Next, we’ll explore the major areas of current AI applications in professional sports: In June 2016, in partnership with Sapien, a custom bot developer (formerly known as JiffyBots), the Sacramento Kings introduced a chatbot called KAI –
The chatbot operates through the Facebook Messenger platform for the purpose of answering fan inquiries including info about franchise history, current team stats, the team roster, franchise history and details about the Golden 1 Center, the home arena of the Sacramento Kings.
While the franchise is keeping details on the number of users and their strategic goals for the chatbot under wraps for now, Kings CTO Ryan Montoya has stated his “commitment to utilizing technology to enhance the fan experience.” In fact, the franchise claims that the Golden 1 Center is the “world’s most technologically advanced and sustainable arena.” According to a 2016 report published by communications firm Avaya,“a digitally connected fan is becoming a [sports] venue’s biggest online influencer.” “Stadium owners and teams that provide more personalized digital experiences through stadium apps, digital offers direct to mobile phones, and game information on digital boards can increase fan engagement and generate new revenue opportunities.” –
“Thunder Bot” can respond to fans’ questions regarding various topics including specifics about Amalie arena (Tampa Bay Lightning’s home arena), tickets to games and general parking information for game day.
In a published case study, Satisfi Labs describes how it worked with an unnamed “leading sports entertainment franchise” to boost awareness and revenue of premium offerings to ticket buyers through a “native client app.” The company claims that over 50 percent of individuals who saw the Satisfi feature actually used it.
A single race car is estimated at $300,000 (not including repair, maintenance or labor costs) and tires are changed every race with a reported price tag of $500 per tire.
Argo AI/Ford Motor Company has used deep learning to develop self-driving cars and is now expanding its application of deep learning to help improve safety measures in the world of auto racing.
Automated Insights claims that this translates to “3,700 quarterly earnings stories – a 12-fold increase over [AP’s] manual efforts.” AP is one of 200 clients using the Wordsmith platform which generates a reported 1.5 billion pieces of content annually.
PIQ, a French sports robotics startup and Everlast joined forces to develop what is described as the “first AI-powered wearable for combat sports.” Crafted using GAIA Intelligence, (machine learning platform for sports analytics) the startup claims that the platform is capable of tracking and analyzing “microscopic variations in boxing movements” to help maximize the efficiency of workouts and training.
The company is attempting an interconnected approach to its offerings which include “connected sneakers,” fitness trackers and a “stride sensor.” The connected sneakers are designed with a stride sensor which can be synced via bluetooth with the company’s app.
We aimed to provide a high-level view of major applications that emerging from the sports industry, giving readers a sense of technologies that either (a) may become mainstream, or (b) are indicative of an important future trend.
– Alan Fern, a computer science professor at Oregon State University While the ROI on developing virtual assistant coaches may not be readily apparent, the focus is on using deep learning to uncover strategic insights that may not have been previously achievable.
For example, a fan could sit with their family during one match and move to a section with a “louder, more energetic supporters’ section.” This option follows a industry-wide trend of enhancing sports fan’s experience with their favorite teams.
in the match by drawing data from players, fans, and more: IBM claims its technology will support a team of research scientists and consultants, to automatically curate game highlights based on game-specific data such as “analysis of crowd noise, player’s’ movements and match data.” The process of organizing and processing video highlights can normally take hours and IBM aims to significantly accelerate the process.
As in the world of heavy industry and manufacturing (where we covered recently), smart companies may be able to gain a continual edge over the competition by gaining more and more granular data on their equipment –
What is AI? Artificial Intelligence Tutorial for Beginners
A machine with the ability to perform cognitive functions such as perceiving, learning, reasoning and solve problems are deemed to hold an artificial intelligence.
In this basic tutorial, you will learn- Nowadays, AI is used in almost all industries, giving a technological edge to all companies integrating AI at scale.
According to McKinsey, AI has the potential to create 600 billions of dollars of value in retail, bring 50 percent more incremental value in banking compared with other analytics techniques.
Concretely, if an organization uses AI for its marketing team, it can automate mundane and repetitive tasks, allowing the sales representative to focus on tasks like relationship building, lead nurturing, etc.
In a nutshell, AI provides a cutting-edge technology to deal with complex data which is impossible to handle by a human being.
The primary purpose of the research project was to tackle 'every aspect of learning or any other feature of intelligence that can in principle be so precisely described, that a machine can be made to simulate it.'
Machine learning is based on the idea that there exist some patterns in the data that were identified and used for future predictions.
AI has broad applications- AI is used in all the industries, from marketing to supply chain, finance, food-processing sector.
Explained by three critical factors for its popularity are: Machine learning is an experimental field, meaning it needs to have data to test new ideas or approaches.
Hardware In the last twenty years, the power of the CPU has exploded, allowing the user to train a small deep-learning model on any laptop.
Besides, big companies use clusters of GPU to train deep learning model with the NVIDIA Tesla K80 because it helps to reduce the data center cost and provide better performances.
Those pictures can be used to train a neural network model to recognize an object on the picture without the need to manually collect and label the data.
company needs exceptionally diverse data sources to be able to find the patterns and learn and in a substantial volume.
Algorithm Hardware is more powerful than ever, data is easily accessible, but one thing that makes the neural network more reliable is the development of more accurate algorithms.
Since 2010, remarkable discoveries have been made to improve the neural network Artificial intelligence uses a progressive learning algorithm to let the data do the programming.
At the beginning of the AI's ages, programmers wrote hard-coded programs, that is, type every logical possibility the machine can face and how to respond.
Everyday Examples of Artificial Intelligence and Machine Learning
With all the excitement and hype about AI that’s “just around the corner”—self-driving cars, instant machine translation, etc.—it can be difficult to see how AI is affecting the lives of regular people from moment to moment. What are examples of artificial intelligence that you’re already using—right now?
You’ve also likely used AI on your way to work, communicating online with friends, searching on the web, and making online purchases. We distinguish between AI and machine learning (ML) throughout this article when appropriate.
According to a 2015 report by the Texas Transportation Institute at Texas A&M University, commute times in the US have been steadily climbing year-over-year, resulting in 42 hours of rush-hour traffic delay per commuter in 2014—more than a full work week per year, with an estimated $160 billion in lost productivity.
driving to a train station, riding the train to the optimal stop, and then walking or using a ride-share service from that stop to the final destination), not to mention the expected and the unexpected: construction;
Engineering Lead for Uber ATC Jeff Schneider discussed in an NPR interview how the company uses ML to predict rider demand to ensure that “surge pricing”(short periods of sharp price increases to decrease rider demand and increase driver supply) will soon no longer be necessary.
Glimpse into the future In the future, AI will shorten your commute even further via self-driving cars that result in up to 90% fewer accidents, more efficient ride sharing to reduce the number of cars on the road by up to 75%, and smart traffic lights that reduce wait times by 40% and overall travel time by 26% in a pilot study.
“filter out messages with the words ‘online pharmacy’ and ‘Nigerian prince’ that come from unknown addresses”) aren’t effective against spam, because spammers can quickly update their messages to work around them.
In a research paper titled, “The Learning Behind Gmail Priority Inbox”, Google outlines its machine learning approach and notes “a huge variation between user preferences for volume of important mail…Thus, we need some manual intervention from users to tune their threshold.
The researchers tested the effectiveness of Priority Inbox on Google employees and found that those with Priority Inbox “spent 6% less time reading email overall, and 13% less time reading unimportant email.” Glimpse into the future Can your inbox reply to emails for you?
Smart reply uses machine learning to automatically suggest three different brief (but customized) responses to answer the email. As of early 2016, 10% of mobile Inbox users’ emails were sent via smart reply.
A brute force search comparing every string of text to every other string of text in a document database will have a high accuracy, but be far too computationally expensive to use in practice. One MIT paper highlights the possibility of using machine learning to optimize this algorithm.
– Credit Decisions Whenever you apply for a loan or credit card, the financial institution must quickly determine whether to accept your application and if so, what specific terms (interest rate, credit line amount, etc.) to offer. FICO uses ML both in developing your FICO score, which most banks use to make credit decisions, and in determining the specific risk assessment for individual customers.
In early 2016, Wealthfront announced it was taking an AI-first approach, promising “an advice engine rooted in artificial intelligence and modern APIs, an engine that we believe will deliver more relevant and personalized advice than ever before.” While there is no data on the long-term performance of robo-advisors (Betterment was founded in 2008, Wealthfront in 2011), they will become the norm for regular people looking to invest their savings.
In a short video highlighting their AI research (below), Facebook discusses the use of artificial neural networks—ML algorithms that mimic the structure of the human brain—to power facial recognition software.
In June 2016, Facebook announced a new AI initiative: DeepText, a text understanding engine that, the company claims “can understand with near-human accuracy the textual content of several thousand posts per second, spanning more than 20 languages.” DeepText is used in Facebook Messenger to detect intent—for instance, by allowing you to hail an Uber from within the app when you message “I need a ride” but not when you say, “I like to ride donkeys.” DeepText is also used for automating the removal of spam, helping popular public figures sort through the millions of comments on their posts to see those most relevant, identify for sale posts automatically and extract relevant information, and identify and surface content in which you might be interested.
– Pinterest Pinterest uses computer vision, an application of AI where computers are taught to “see,” in order to automatically identify objects in images (or “pins”) and then recommend visually similar pins. Other applications of machine learning at Pinterest include spam prevention, search and discovery, ad performance and monetization, and email marketing.
– Instagram Instagram, which Facebook acquired in 2012, uses machine learning to identify the contextual meaning of emoji, which have been steadily replacing slang (for instance, a laughing emoji could replace “lol”).
This may seem like a trivial application of AI, but Instagram has seen a massive increase in emoji use among all demographics, and being able to interpret and analyze it at large scale via this emoji-to-text translation sets the basis for further analysis on how people use Instagram.
A few months later, it opened its messenger platform to developers, allowing anyone to build a chatbot and integrate Wit.ai’s bot training capability to more easily create conversational bots.
–Recommendations You see recommendations for products you’re interested in as “customers who viewed this item also viewed” and “customers who bought this item also bought”, as well as via personalized recommendations on the home page, bottom of item pages, and through email.
While Amazon doesn’t reveal what proportion of its sales come from recommendations, research has shown that recommenders increase sales (in this linked study, by 5.9%, but in other studies recommenders have shown up to a 30% increase in sales) and that a product recommendation carries the same sales weight as a two-star increase in average rating (on a five-star scale).
Square, a credit card processor popular among small businesses, charges 2.75% for card-present transactions, compared to 3.5% + 15 cents for card-absent transactions.
By utilizing AI that can learn your purchasing habits, credit card processors minimize the probability of falsely declining your card while maximizing the probability of preventing somebody else from fraudulently charging it.
We may soon see retailers take it one step further and design your entire experience individually for you. Google already does this with search, even with users who are logged out, so this is well within the realm of possibility for retailers.
however, a month later Amazon’s press release boasted a 9x increase in Echo family sales over the previous year’s holiday sales, suggesting that 5 million sold is a significant underestimate.
For example, casual chess players regularly use AI powered chess engines to analyze their games and practice tactics, and bloggers often use mailing-list services that use ML to optimize reader engagement and open-rates.
- On 15. april 2021
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