Josh Finlayson 11/30/2019

Figure 1:

How Artificial Intelligence Can Prevent Sports Injuries and Maximize Athletic

TABLE OF CONTENTS

Exectutive Summary2

Introduction3

Real Life Examples5

AI and Injury Prevention6

How Data Science is Used7

Using Models to Make Outcomes8

Obstacles for Using AI in Sports Injury Detection9

Conclusion10

Informaiton11

Citation11

Executive Summary

The goal for a general manager of a professional sports team is to assemble the most talented roster possible. Once the roster is created each team will battle through adversity throughout the season. Injuries are a part of sports and the teams that can limit injuries are usually the ones playing for the championship at the end of the season. Injuries affect much more than the individual who was injured, it affects the team, league, and region around the team.

With artificial intelligence, data analysts are beginning to take an individual’s traits and morph that data into usable information. This information can be used for injury prevention, recovering from a past injury, or finding ways to maximize players performance. It can be used for injury prevention by finding the correct workload each player needs without overusing them. It aids the recovery process be showing recovery tactics that will benefit the player the most. Finally, it maximizes each players performance by putting each player in the best possible situation for them to succeed.

Some repeating recommendations throughout the articles was:

· Artificial intelligence is in the introduction stage and will only continue to grow

· The prediction systems will learn from their history to make more accurate prediction

· Artificial intelligence prediction systems are on a per team or per league bases

· Sorting players by age, height, weight, sex, and injury history will produce more accurate measurement

After researching this topic, it is evident that there is no concrete way to predict sports injuries. The future is bright and as our knowledge about the matter increases, we will find a consistent way to prevent injuries. In the future it is recommended that the more data collected will only benefit the injury prevention process. Each injury is different for every athlete and the more information we have about the person the better.

This white paper will take an in-depth dive into where A.I. is currently and how experts have used the tools that are available now, and their input on how to use this application in the future.

Introduction

Being a professional athlete is a very dangerous occupation, and every time they practice, train or compete they are at risk of an injury. According the Bureau of Labor Statistics more than 2,000 athletes are injuries per 10,000. In the future the goal is to limit the number of injuries in professional sports. Artificial intelligence is a steppingstone in the right direction to reach this goal. This is becoming a common practice in sports today and will only continue its upward trajectory in the future. Sports injuries can alter much more than the individual who is injured. It can cause a ripple effect that impacts the team, management, city, and other teams in the league.

An injury to a key player for a major sports organization could lead to:

· The team losing local fan support

· Playing fewer effective players leading to more lost games

· The difference between the season being deemed as a success or failure

Kakavas et al. (2019) looked into why teams use simplistic approaches to injury detection; each team has their own way of attempting to discover those hidden variables and metrics that can accurately predict the risk of injury to particular players. Kakavas et al. note that some limitations are extrinsic and intrinsic factors that are in each sport. Extrinsic factors for soccer include design of playing field, air temperature, characteristics of the ball, altitude, and the time of the match. A few intrinsic factors are age, sex and prior injuries. Also, some players define as being injured differently; to evaluate it though the ACWR (Acute Chronic Workload Ratio) the player either has to be injured or not, there is no in between. Also, they cannot predict whether an at-risk player will be put in a situation that will result in an injury.

Some factors that can influence the chance of a player of getting injured include:

· Biological variable

· Type of sport being played

· Amount of practice

· Workload of a given athlete

The purpose of this white paper is to persuade general managers of professional sports teams to hire a data analyst to contrive data and turn it into usable information to help their team stay injury free throughout the season.

Real Life Examples

In 2018-2019 the average price for a Golden State Warriors regular season game cost $322, that team had four all-star caliber players on the roster. So far in the 2019-2020 season the average cost is $225. How could the price decrease by almost one hundred dollars over a few months? Talent; the warriors are currently without their top three plays from last year’s team due to injuries. The price per ticket has dropped by an average of $100 because the team is not as talented. An injury prevention staff could have been hired to warn them about playing star players when not fully recovered from a previous injury.

In an effort to increase performance the National Hockey League has hired data analysts to gain an in-depth perspective on what the mainstream stats do not show (goals, assists, saves, points, blocks). The National Hockey League implemented a way to take a shot for and shot against metric. The data analysts took an individual player and recorded how many shot attempts their team had when they were on the ice and divided it by the amount of shot attempts the opposing team had at even strength (When both teams have five players on the ice). This ratio is called "Corsi" and if a player is above 50%, they are considered a possession driven player and they are benefiting the team. Some general managers are hesitant to take these stats seriously but the ones who are taking these stats into account when forming their team are seeing the instant benefit.

AI and Injury Prevention

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11/30/2019Josh Finlayson

Artificial Intelligence with sports related injuries has recently been used to see what type of training and post-injury practice is best used for a particular player. An example of this is when professional Ice Hockey player Steven Stamkos entered the league at 18, his then coach Berry Melrose stated “[Stamkos] isn’t ready for the NHL” . His trainers suggested that he would sit out games periodically. When he was not playing, he spent the time training to increase body mass. They also hired a nutritionist to have personalized meals that would help him gain muscle mass quickly. This worked effectively for him and since then has been one of the best players in entire National Hockey League. If an athlete can get a personalized training plan that will benefit him, he will be able to perform at peak performance.

Complete Concussion Management completed a survey that showed which sports had the most concussion. (measured in concussions per 1000 Athletes)

· Rugby (4.18/1,000)

· Ice hockey (1.20/1,000)

· Football (0.53/1,000)

· Lacrosse (0.24/1,000)

· Men’s Soccer (0.23/1,000)

· Wrestling (0.17/1,000)

· Basketball (0.13/1,000)

· Softball (0.10/1,000)

· Field Hockey (0.10/1,000)

· Baseball (0.06/1,000)

· Cheerleading (0.07/1,000)

· Volleyball (0.03/1,000)

Figure 2

How Data Science is Used

Claudino et al. (2019) and his research team went through systematic searches that included PubMed, Web of Science online databases and Scopus. These were used when conducting the studies and were used when reporting AI techniques applied to team sports athletes. New findings in data science have recently emerged as a strategical area to gain knowledge in sports science. Data science is more than the combination of statistics and computer science, it requires training on how to combine statistical and computational techniques into a larger framework, and to discuss discipline-specific questions.

Claudino et al examination was much more quantitative then other articles that I read. By using data science, they could take actual numerical information to understand what the data was and where we are currently at in the injury prediction process. While Claudino et al had valuable information, that also came across a few issues.

A few issues that Claudino et al. found was:

· The data that is used was only collected in the past five years

· They used a wide range of participants; they should have split them by demographics

· The sample size had a wide spectrum of athletes, some being professional and some young athletes

Using Models to Make Outcomes

The study conducted by Bittencourt et al. (2019) discussed how challenging injury prediction is and how they created a complex system model for sports injuries.

The authors acknowledged that:

· Determinants can be linked together in a non-linear combination

· A few small changes to the determinants can lead to unexpected consequences

· We have to keep in mind that even when the problem is put into quantitative units the prediction may not be possible.

(Determinants are factors which decisively affect the outcome of the report)

The model Bittencourt et al. created using the unpredictable and unplanned ways complex injuries occur and the easily predicted injuries to make a global pattern. With more trails, the system learns from its own history to form observable regularities that are creating more accurate predictions. Bittencourt et al. stated that they may need to use an alternate model for different individuals to improve prediction capability. They took this idea from economists who consider individual characteristics when looking at strategies on the stock market because people have different ages, income, and job occupation. So, inputting the age, gender, weight, and other physical attributes can help with the prediction method. Most of the work so far has been supervised learning, but by practicing more we can find patterns that can lead to specific outcomes.

The three key outcomes of Bittencourt et al. research included:

· Injuries are a complex emergent phenomenon

· Sports injury prevention relies on the identification of risk profiles

· Sports injuries prediction, and prevention, depend on interest philosophical paradigm and methods of analysis.

Figure 3

Obstacles for Using AI in Sports Injury Detection

One limitation with using AI is that injuries are challenging to predict because injuries are not situational, and everyone is different. Also, I thought that it is challenging to find studies where the authors incorporated quantitative data rather then showing the models they used. Currently some managers are still hesitant to use the technology that is presented to help them down the road. As the data keeps proving to be sufficient more and more teams will implement it.

An issue with using AI to predict injury outcomes is that there is not much consistency across sports when comparing data. Most outputs are made on a per team or per league basis, so it is challenging to find a way to implement one formula to multiple sports.

Claudino et al. (2019) stressed a major issue with injury prevention is that the AI techniques used to predict injury only used data within the past five years. Sports, like anything else are changing rapidly so the data may not be as accurate due to how recent it was discovered. With every passing day more data is collected that will be used in the future to make more accurate predictions.

Conclusion

Artificial Intelligence is approaching in the near future whether we are prepared for it or not. Sports are no exception; AI is growing every day and if we do not start using the tools offered soon general managers or team directors will fall behind. Using AI will only benefit athletes involved, Claudiono et al. stated that “The current state of development in the area proposes a promising future with regard to AI use in team sports. Further evaluation research based on prospective methods is warranted to establish the predictive performance of specific AI techniques and methods.” The researchers agree that AI will be used in the future and will only become more critical as more data is gathered. As stated before AI is still a foreign idea to many, but I believe that as we get more accustomed to using it will be used universally across all sports

Information

For more information, please contact Jlf379@drexel.edu

Works Cited

Text Citation:

Bittencourt (2016, July 21), Complex systems approach for sports injuries: moving from risk factor identification to injury pattern recognition—narrative review and new concept.

Retrieved from https://bjsm.bmj.com/content/50/21/1309

Claudino (2019, July 3) Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review Retrieved from: https://link.springer.com/article/10.1186/s40798-019-0202-3

Kakavasa, (2019, August 19). Artificial intelligence. A tool for sports trauma prediction. Retrieved from https://www.sciencedirect.com/science/article/pii/S0020138319305066.

Tanyarezak (2017, February 15) 91 days of Stamkos: Day 45, train like an NHL player

Retrieved from https://www.rawcharge.com/2017/2/15/14622692/91-days-of-stamkos-day-45-train-like-an-nhl-player

Works Consulted

Fitzgerald (2019, January 1), Today's coaches and trainers are promoting safety, strength, and conditioning to protect players from disabling injuries

Retrieved from https://consumer.healthday.com/encyclopedia/work-and-health-41/occupational-health-news-507/professional-athletes-648171.html

Image Citation

Figure 1: https://www.flickr.com/photos/mikemacmarketing/30188200627

figure 2

https://www.marketreportgazette.com/2019/08/ai-in-sports-market-to-register-huge-cagr-of-32-over-period-2019-2025-leading-players-microsoft-corporation-24-7-ai-inc-active-ai-advanced-micro-devices-amd-inc-aibrian-inc-amazon-inc-an/

figure 3

https://www.lawinsport.com/media/zoo/images/Runner_inside_of_statitics_2cdc7f4a2494b3abfd10180eb29a2cca.jpg