Competition in sports has, over time, only made better sportspersons, making sporting activities over the world a display of excellence. And because of this, progressively, sports have enjoyed heavy financial investments.
Especially the more competitive sports. The most competitive sports have global awareness such that professionals who excel in these activities, whether players or trainers, become superstars for their efforts.
The rise of machine learning and data analysis in competitive sports is not surprising following the wave of technological advancement that has enveloped the world. What is astonishing is how astronomically they have impacted both sports as an activity and all participants involved. So much is their impact that sporting competitors have endeavored to draw all they can from this science. Well, who can blame them?
As with any aspect of artificial intelligence and computer science, the wonders, machine learning and data analysis that can be wrought in our everyday life are only limited by how far we are willing to explore. The impact of machine learning in sports has caused a demand for more in-depth study and research. And it just keeps giving as asked.
History of scientific investigation in competitive sports
A pre-historical overview of competitive sports would go back more than 15,000 years, according to the Lascaux cave paintings in the southwestern part of France. These paintings showed people sprinting and wrestling. Many of these types of discoveries often remind us that our ancestors were sporting people.
Over the years, different types of sports and their information were passed through either word of mouth or experience. And the development in sports remained static, except for ideas and inventiveness that came to individuals, years, and generations apart.
In the early 2000s, that inventiveness was showcased once again. Billy Beane, then general manager (GM), and Paul DePodesta, former assistant GM of the Oakland Athletics, did a systematic scientific investigation of the inefficiencies in baseball. Only this time, it would change competitive sports forever.
Why? His team was among the lowest ranked in the league and poor. So, looking for a way out, they decided to use that which there was an abundance of: data. They collected information by looking at their team’s performances, players, and opponents.
From there, they developed a novel approach to the game that made their team enjoy a successful run for over 16 seasons, winning six American League West titles (2000; 2002-03; 2006; 2012; 2013). The Oakland Athletics’ achievement with data analytics is well documented in the book Moneyball by Michael Lewis.
How Machine Learning/Data Analytics has impacted competitive sports
Machine learning is a section of artificial intelligence nestled comfortably in computer science. It takes a large volume of data and interprets it; then learns from these data to replicate and improve its necessary actions without pre-programming the machine.
So, data is a raw material that needs to be rightly sourced. The process of teaching the machines to learn needs models supported by algorithms dependent on the raw material available.
There is a lot of data available in competitive sports, the abundance of which was difficult to store or process years ago. But with technological progress, there is now high demand for computer scientists. The market is not stopping for anyone to catch their breath. Programs like the Laurier online master’s in computer science enable scientists to remain relevant and not get left behind.
These online masters add practical skills to excel in algorithm design, data mining, application development, analysis, machine learning and technology entrepreneurship, and cyber-attack and defense. Remaining relevant in computer science requires adding to one’s knowledge and reaching the peak of professional potential.
Sections of competitive sports altered by Machine Learning/Data Analytics
Machine learning/data analytics has a strong influence on competitive sports because the following areas completely depend on it.
Player behavior analysis
Boosting the performance of a sportsperson involves teaching and cementing actions observed during competitions and training periods. Actions that are done when said athlete seems to be in optimum form. This is impossible for humans to observe in real-time since the moment’s emotions can blind them.
Here is where data analytics come in; devices such as highspeed cameras, motion sensors, detectors, GPS trackers, and so many others are used to gather data of all moments. Data collected can then be analyzed, by aggregation, parsing, and turning them into understandable visuals. So each player’s behavior is completely available in tangible form.
The trainers can now look at this processed information and develop a training regimen based on what they now have. When this information is loaded into a machine, it learns from it and discovers or creates a behavioral pattern. An example of machine learning usage is the Kinexon wearable wristband technology. This helps trainers get a direct and complete end-to-end update of an athlete’s performance, health gauge, and position during training or competition.
Since this technology can spot the tiniest differences and patterns that would be lost to the human eye, these behavioral patterns can be further optimized by sports psychologists who, in tandem with trainers, can get each player to stick with or drop behaviors that make or break them.
Game activity is a computation of all match events, everything that is tangible to the eye on the field of play. These match events include the referral of the game and non-players contribution to it; an example is ball boys in soccer returning out-of-play balls to the field of play.
Since this is mainly about what is seen, such as player movements, positions, shots, and others, the major data-collecting devices here will be recording cameras and sensors. All positioned to collect data from every direction, missing nothing. In most cases, data analytics of games would take place during a recess or when the game has ended.
In the case of RSPCT Basketball Technologies, machine learning has utilized the data to produce something truly remarkable. RSPCT Basketball Technologies adapted rifle sharpshooting technology to basketball using an Intel RealSense 3D depth camera device to track and analyze every shot’s trajectory and location. This way, even the fans could see and analyze every shot.
Another example is seen while wsatching the halftime show of soccer games. Soccer pundits now do the video analysis of the previous half, showing each player’s contribution to the team’s overall impact. This is achieved with software that visualizes match events and performs things like analyzing shot trajectory, ball tracking, charting player movements and positions during the match, and highlighting key performance indicators.
From all data collected, machine learning is used to produce a match outcome model using key performance indicators. The trainer uses collated, processed, and visualized data to construct a more fortified front against the competition.
The purpose of machine learning is to simulate and project possibilities with factual data without the accompanying limitations of reality.
In-game activity includes all the data that is gotten from a sporting activity. The statistical analysis of such data is known as sabermetrics. The term sabermetrics is local to baseball but is addressed as in-game analysis in other sports. The analysis of these in-game data will give answers to problems that have held back team performance.
Artificial intelligence deeply analyzes these great data sets collected and aggregated to return very accurate analytics. The amount of data involved is often huge, continuous, and requires real-time processing. Processing is done using machines with high computing power to process collated data into a simpler form for real-time use.
Machine learning technology develops a more progressive game plan after performing the immediate in-game strategy analysis. It can turn a game on its head to favor the user during matches.
Predicting player injuries
Players are assets, and in a business, assets are protected. Before the advent of certain technologies, it was risky to invest in athletes because of injuries. And some sports lost some promising talents to preventable injuries. With machine learning, different algorithms are used to model a machine; the algorithm used is based on the available data type. Convolutional neural network (CNN) is a type of neural network with a collection of neurons. These neurons are arranged in interconnected layers that are fused, complex, and completely joined.
CNNs are responsible for image recognition data that has to do with the basic unit of programmable color. The convolutional neural networks (CNNs) architecture for machine deep learning algorithms is employed in predicting player injuries since it can read patterns from image-based data. It is so accurate it is relied on in medicine, due to its usefulness. It has been adopted by the sports world to minimize the rate at which many athletes get injured from preventable causes like excessive training.
CNN begins as a simple form, becoming more complex as it learns. It is able to spot even the minuscule change in training regimen, technique, form, posture, and performance. Also, since machine learning is capable of modeling, the machine can model the results of various changes in training regimens. And the reaction to any training technique or style can be noted by the trainer until the best fit is gotten for implementation. It is gold for any team doctor or physical therapist.
Data analytics enable many sports organizations to accumulate data such as player strengths and weaknesses. The results of game analysis, in-game activity, and player behavior analysis can contribute to getting the right talents. Having done the data analysis, the raw data don’t lie; after processing, there will be areas that need to be fixed. Often those areas are positions in the team that are vacant or in need of replacement.
Machine learning enables teams to acquire the right fit since they can simulate any player pick to know how the individual will fit in with the structure on the ground before making a decision. With this ability, teams can compare potential players. With machine learning, they can even project the growth curve of said athletes.
Errors in match officiating have been a thorn in the flesh of every sports fan, including players and coaches. And because it is one of the most influential aspects of the game, sport’s governing authorities try to use every available means to ensure that this department is equipped enough to be fair during competitions. Unsurprisingly, the application of artificial intelligence always seems to start with refereeing.
The predictive ability and accuracy of the machine learning technology help the officiating department make the right judgments. Especially when, due to human lapses, the official could not witness the situation first-hand.
There are many examples of refereeing technology, such as goal-line technology in soccer, Hawk-Eye technology in tennis and cricket, and virtual assistant referee in soccer. The perfect error-free refereeing has yet to be achieved, but advancements in technology show that it is within reach.
Many legal sports betting companies have incorporated machine learning technology into their business, with simulated matches even when real-life sporting activities are not ongoing. Users can get information about teams they have never seen in action through data analytics and machine learning. And they can make informed decisions about such teams, getting accurate results.
Future of machine learning in competitive sports
Competitive sports keep growing, and their dependency on processing data only continues to increase. The future of machine learning, artificial intelligence, and data analytics is predictive in that we all know it will outsize all that has happened to date. And it is obvious that those who are shareholders in it will be highly sought after.
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