- The following depicts where can a A.I. used in sports
- Media management
-fan relationship management
-news and content
-sports betting
-media rights - Post game analysis
-analysis and feedback
-recovery
-injury management - In game activity
-umpiring
-coaching - Selections
-talent identification
-selection of players - Management opertions
-competition management
-venues, ticket and events
-team management
-sponsership
-payments - pre game preparation
-nutrition
-physical fitness
-injury management
-tactical game planning
AI Applications used in sports
Training & coaching
The A.I. applications can be used in training of the players.it can act as the best coaching assistant to the players. Analysis of a game performance and how the coach prepares their team to tackle the opponent team changes with the advancement of technology. A.I. contains much data of information about the training tactics and performances. Artificial intelligence in sports is having a significant impact on both pre-game and in-game strategies. Computer analysis is used to influence line-up decisions before and during games. By comprehending many metrics, including spin, speed, serve placement, and even player posture and motion, artificial sports intelligence can enhance sporting performance further. In this regard, AI supports managers and coaches in making better decisions for various games and important competitions.
Games news coverage
Artificial intelligence is already being leveraged by many media organizations for creating automated news and content. Sports broadcasting utilizes AI for compiling key highlights of the match. In the future, advancements in machine learning will help develop extensive reports as well. Today, many AI and data science companies such as United Robots, Narrative Science, and Automated Insights offer algorithms to media giants. Leading news providers such as Los Angeles Times, ProPublica, Forbes, and Associated Press have adopted this technology.
AI in media can reduce the considerable amount of time required for manually compiling these highlights and developing stories and short summaries. Moreover, the use of AI by Twitter and Facebook in the battle against fake news will continue to gain traction. The use of artificial intelligence in media has expanded well beyond the realms of television into the world of social media. Sports teams are improving their online presence by creating and promoting well-curated content for Instagram, Facebook, Twitter. Social media is turning out to be the best way to remain connected with the fans. The aforementioned platforms even stream livestream highlights after the match to help fans catch-up on the important events.
AI-powered Fan Relationship Management
Sports is a highly lucrative industry and thus attracts huge investments through sponsorships and media rights. The loyalty of fans for their teams is being rewarded by integrating technology such as voice technology and chatbots. Media companies are using apps and websites to disseminate information about important sports events to fans. Moreover, loyalty programs are being offered by media giants for enhancing fan engagement.
Chatbots as example
There have been several applications that are widely being used in this industry.Chatbots. This is an application that is being used to answer questions from the fan. It searches the history of all the games and the history of sports in general. It then stores it on the home page, but the app is linked to Facebook messenger. This means that the fans can connect with the app through Facebook and get relevant information. This app has been appropriate in keeping the history of the sport and enhancing understanding among fans. Over the years now, it has helped the economy of fun to be on a higher level.
The Increasing reliance of broadcasters and organizers on AI for providing a superior viewing experience restates its importance. Therefore, artificial intelligence is quickly picking up pace in fan relationship management. Everything from venues and events to ticketing, AI is revolutionizing the fan experience. For instance, smart ticketing technology using AI offers variable seating options with business associates, friends, or family during a match.
Accuracy in sports
AI is changing our understanding of sports and revolutionizing how we measure and understand the massive amounts of data available. This is because AI technology can process and interpret data in real time with high accuracy, allowing sports athletes and coaches to take advantage of this information to transform the global sports industry. For example, athletes can learn more about their abilities and limitations, whereas coaches can make data-informed decisions about training and performance.
Advanced Diet Plans and Personalized Training
Thanks to machine learning and deep learning algorithms, AI can offer athletes personalized training and diet plans. For instance, an AI diet plan can customize different meal plans based on the player’s current needs and schedule. For example, AI offers specific dietary recommendations based on whether the player has a match the next day or is recovering. One of the popular AI-assisted dietary apps is FoodVisor. It can identify different types of foods through object recognition and report the nutritional breakdown to the users. In addition to AI-assisted dietary apps, athletes can also take advantage of AI-assisted fitness apps. Thanks to computer vision techniques, algorithms can detect human poses in real-time, where they can identify human joints and provide the player with guidance on how to exercise accurately. For example, FitnessAI and AlfaAI are popular AI apps that help athletes train and create a fitness schedule.
Improving Player Performance
AI-powered athlete tracking systems such as wearables can also measure and improve on-field performance thanks to predictive analytics. For example, PrecisionWear is a company that engineers vests worn by athletes and equipped with multiple sensors that measure up to 21 metrics, including heart rate, total stress, and fatigue. Trainers can use these measures to monitor players’ health, develop customized training programs to optimize exercise returns, reduce the risk of injuries, and maximize their strengths.
AI in sports training can also help players improve their performance by playing against artificial intelligence. For example, a company called DeepMind has trained an AI algorithm called AlphaGo, the first computer program to defeat a Go world champion. Go is an Asian board game like chess, where two players take turns placing white or black stones on the board.
. Scouting and Recruitment
Sports teams are adding AI to their scouting and recruitment process to make competition fiercer. Thanks to machine learning algorithms and computer vision in sports, AI tracks every player’s movement and the orientation of their bodies. Then, it evaluates their skills and overall potential to help make the right recruitment decision and build a successful team. The AI utilized in recruitment is similar to machine learning in gaming. Both algorithms can predict team dynamics and chemistry based on individual player statistics and performance. For example, AiSCOUT is an AI-based platform that professional football clubs and organizations use to find and scout players through video recognition technology. The platform analyzes and evaluates players’ performance, such as their technical, physical, cognitive, and psychometric abilities.
AI Score Prediction
Even though AI can’t accurately predict the outcomes of every single match yet, it can get close. Moreover, it can predict future match results much better than humans, thanks to predictive algorithms and computer vision. For instance, Kickoff.ai is an AI/ML platform that predicts the results of football matches.
In addition, through computer vision, AI collects and analyses data based on multiple factors such as the number of passes between teammates, chances created and passes that led to a goal scoring opportunity. Then AI uses that information and data to forecast the result of future matches.
. Live Broadcasting and Advertising
AI is also revolutionizing live broadcasting. This includes the way the audience watches sports and broadcasters monetize sporting events. For instance, when streaming a game, AI systems can automatically choose the right camera angle to display on the viewers’ screens and enhance their viewing experience. In addition, the AI system can automatically generate subtitles for live events in different languages based on the fans’ location and language preferences.
On the other hand, broadcasters can use AI to identify monetization opportunities and present relevant ads based on demographics. Moreover, AI and machine learning in sports can monitor crowd excitement levels during matches and present ads accordingly to influence their purchasing decisions based on the emotions they are experiencing at the time. This is an effective way to help advertisers drive sales.
. Ticketing
Fans struggling to get to sporting events on time are a persistent problem at stadiums. Luckily, AI can resolve this issue. For instance, stadiums provide smart ticketing services. Moreover, Wicket uses biometric analysis and facial authentication to allow fans to enter stadiums without displaying their ticket.
In addition, AI predictive analytic tools can forecast the number of attendances in the game and the time fans might be expected to arrive at the stadium. This information also helps to improve the security and organize merchandise and food according to the number of visitors.
Algorithms used in A.I.
There are algorithms artificial intelligence grouped into broadly three categories such as supervised learning, unsupervised learning and reinforcement learning.
Artificial intelligence algorithms can be broadly classified as
- Classification Algorithms:
Classification algorithms are part of supervised learning. These algorithms are used to divide the subjected variable into different classes and then predict the class for a given input. For example, classification algorithms can be used to classify emails as spam or not. In the classification algorithms artificial intelligence classifies a new category of observations based on the existing data which we can call as training data as well. The program learns from the dataset that is already given. Let’s discuss some of the commonly used classification algorithms.
a) Naive Bayes
Naive Bayes algorithm works on Bayes theorem and takes a probabilistic approach, unlike other classification algorithms. The algorithm has a set of prior probabilities for each class. Once data is fed, the algorithm updates these probabilities to form something known as posterior probability. This comes useful when you need to predict whether the input belongs to a given list of classes or not.
This probabilistic classifier predicts on the basis of probability. The Naive Bayes algorithm that is a probabilistic classifier is used in sentiment analysis, recommendation, spam filtering, etc. It is called as Naive Bayes because it assumes class conditional independence. The attribute value of a given class is independent of the values of other existing attributes.
b) Decision Tree
The decision tree algorithm is more of a flowchart like an algorithm where nodes represent the test on an input attribute and branches represent the outcome of the test.It is a very simple kind of a probabilistic tree that enables to make decisions about some kind of process. This tool assumes a tree like model and its possible consequences.
c) Random Forest
Random forest works like a group of trees. The input data set is subdivided and fed into different decision trees. The average of outputs from all decision trees is considered. Random forests offer a more accurate classifier as compared to Decision tree algorithm. Existence of many decision trees is random forest algorithm in classification. In order to build uncorrelated forest trees it uses the features of bagging randomness while building individual trees. This allows the prediction to be more accurate as compared to the individual tree.Random forests is used in many industries such as healthcare, manufacturing, banking, retail, etc. One of the real-life applications of random forest would be to decide if an email is spam or not spam.
d) Support Vector Machines
SVM is an algorithm that classifies data using a hyperplane, making sure that the distance between the hyperplane and support vectors is maximum.It is a supervised learning algorithm that can be used for either classification and regression problems. One of the example of SVM is Face detection, classification of images, hand writing detection, text and hypertext categorization, etc.
e) K Nearest Neighbors
KNN algorithm uses a bunch of data points segregated into classes to predict the class of a new sample data point. It is called “lazy learning algorithm” as it is relatively short as compared to other algorithms.Some of the applications of KNN is finance, medicine, such as bank customer profiling, credit rating, etc. There are various advantages to using KNN such as easy to implement and understand, also it is very simple and intuitive. - Regression Algorithms
Regression algorithms are a popular algorithm under supervised machine learning algorithms. Regression algorithms can predict the output values based on input data points fed in the learning system. The main application of regression algorithms includes predicting stock market price, predicting weather, etc. The regression algorithms also aids in predicting the output values based on the input features that are fed from the data. There are various types of regression such as linear regression, polynomial regression, etc. The most common algorithms under this section are
a) Linear regression
It is used to measure genuine qualities by considering the consistent variables. It is the simplest of all regression algorithms but can be implemented only in cases of linear relationship or a linearly separable problem. The algorithm draws a straight line between data points called the best-fit line or regression line and is used to predict new values. One of the common examples of linear regression would be medical practice wherein the doctors understand the relationship between the sugar intake and high blood sugar levels.
b) Lasso Regression
Lasso regression algorithm works by obtaining the subset of predictors that minimizes prediction error for a response variable. This is achieved by imposing a constraint on data points and allowing some of them to shrink to zero value. The lasso regression is used to obtain the subset of predictors that helps in minimisng the error in prediction. Lasso puts a condition on the model parameters that make the regression coefficients shrink to zero.
c) Logistic Regression
Logistic regression is mainly used for binary classification. This method allows you to analyse a set of variables and predict a categorical outcome. Its primary applications include predicting customer lifetime value, house values, etc.There are multiple real-life applications of logistic regression such as banking. A credit card company can know if the transaction amount and credit score will lead to fraudulent transaction or not.
d) Multivariate Regression
This algorithm has to be used when there is more than one predictor variable. This algorithm is extensively used in retail sector product recommendation engines, where customers preferred products will depend on multiple factors like brand, quality, price, review etc.
e) Multiple Regression Algorithm
Multiple Regression Algorithm uses a combination of linear regression and non-linear regression algorithms taking multiple explanatory variables as inputs. The main applications include social science research, insurance claim genuineness, behavioural analysis, etc. - Clustering Algorithms
Clustering is the process of segregating and organizing the data points into groups based on similarities within members of the group. This is part of unsupervised learning. The main aim is to group similar items. For example, it can arrange all transactions of fraudulent nature together based on some properties in the transaction. There are various advantages to using clustering algorithms . Some of the example of clustering algorithms would be identifying fake news, marketing, spam filter, etc.
Below are the most common clustering algorithms.
a) K-Means Clustering
It is the simplest unsupervised learning algorithm. The algorithm gathers similar data points together and then binds them together into a cluster. The clustering is done by calculating the centroid of the group of data points and then evaluating the distance of each data point from the centroid of the cluster. Based on the distance, the analysed data point is then assigned to the closest cluster. ‘K’ in K-means stands for the number of clusters the data points are being grouped into. There are various applications to K- means clustering from banking to cybersecurity, search engines, etc.
K-means has various real-life applications such as sentiment analysis, spam detection, etc. It is used where the user has the unlabeled data. Unlabeled data is that type of data which does not have a category or groups.
b) Fuzzy C-means Algorithm:
FCM algorithm works on probability. Each data point is considered to have a probability of belonging to another cluster. Data points don’t have an absolute membership over a particular cluster, and this is why the algorithm is called fuzzy. Fuzzy C- Means is a clustering technique wherein the data set gets grouped into N clusters where each data point in the dataset belongs to every cluster in one way or the other.
c) Expectation-Maximisation (EM) Algorithm
It is based on Gaussian distribution (statistics). Data is pictured into a Gaussian distribution model to solve the problem. After assigning a probability, a point sample is calculated based on expectation and maximization equations. The Expectation-Maximisation (EM) algorithm is used in those places where there is a need to find a local maximum likelihood parameters of a statistical model. It is also used in the places wherein the equations cannot be solved directly.
Uses of A.I. in cricket
Cricket is one of the most popular sports in the world and it keeps adding new dynamics to the game. All these dynamics have been either brought about by increase in competition or use of new tools. AI is one such tool that has completely transformed the game.
Here are some of the ways in which AI is being used in cricket: - Batsense
Most bats that are used in international cricket are powered with a sensor chip and are known as ‘Batsense’. The sensor would generate data for every stroke a batsman plays. It has storage onboard to retain session information, and Bluetooth connectivity to enable real-time data transfer. The sensor measures a variety of things like:
*. Bat-speed when playing a shot,
*. Power & twist of the ball hit from the bat,
*. Quality of the shot. - Bowling Chips
Each ball in modern-day cricket is fitted with sensor chips which are called ‘smart balls’ and provide:
*.Speed of the ball when it leaves the bowlers’ hand, and after it pitches,
*.Length of the ball,
*.Degree of turn and swing/spin. - Third-Umpire
The role of a third-umpire is extremely crucial to a match and they are given many tools to bring out the correct decision when the on-field umpire needs their help. Their Decision Review System or DRS is powered by AI and some examples of it are:
. Snickometer: Used by the bowling team to get a batsman out, the third umpire graphically analyzes sound and video to ascertain whether the batsman has edged the ball or not. This is a very common tool used by the third umpire and is broadcasted during the game on television as well. Nowadays, this is know as ‘ultra edge’.
. Hot Spot: Another technology which is for the bowlers’ assistance to determine whether the batsman nicked the ball. It is an alternative to Snicko. It uses an infra-red imaging system to analyze if the ball struck the bat before reaching the fielder.
. Hawk-Eye: A very popular technology named after its developer Paul Hawkins, Hawk-Eye is not only used in cricket but in several other sports like football, tennis, badminton, etc.This unique technology is used by umpires in LBW (Leg Before Wicket) appeals. It uses ball tracking and displays a path of the ball to the wicket, the location of impact with the batsman’s leg, and whether the ball would hit the stumps or miss them. It can also the bowling length deliveries of a pacer and spin turn of a spinner.
. Match Analysis
AI is also used to predict and analyze certain outcomes, both before and during a match. This machine-learning model helps in analyzing:
- #Venue/Pitch conditions,
- #Predicted score of a team according to run rate,
- #A batsman’s record against spin vs pace,
- #Ideal bowling length of a bowler,
Impact of AI on cricket
AI has completely changed the game of cricket and has made it much more competitive. Apart from the ways listed above, the coaching staff takes the help of AI to measure the team’s performances by analysing player data and statistics.
Conclusion:
Artificial Intelligence in sports has a massive impact on audience engagement, game strategy, and the way games are currently played. Artificial intelligence are widely used in sports nowadays. Given the significant influence that precision technology has had on sports, artificial intelligence in sports has become more and more common in recent years, and it is projected to succeed in this industry.