The Growing Trend of AI in Sports

Posted on July 26, 2025 by JimS

By Jim Shimabukuro (assisted by Gemini)
Editor

[Also see AI in Sports: Update Oct. 2025]

Introduction: I interviewed Gemini for this article. -js

JS: Hi, Gemini. A natural fit for AI and chatbots seems to be athletics, and I can’t help but think that athletes, coaches, and front offices alike are using them extensively to crunch the massive amounts of data that define and guide performance in nearly every aspect of a sport, especially in elite pro and college leagues. And what’s good for the top usually filters downward and begins to define success at lower levels such as high school. And where there’s fierce competition, betting is sure to follow, so I’m guessing that AI/chatbots are being used extensively by oddsmakers, bookies, and gamblers too. Am I correct on this? Using the latest 2025 data, gauge the growth of AI in these sectors and provide annotated references for your responses.

Gemini: You are absolutely correct! AI and chatbots are being extensively used across various sectors of athletics, from elite professional and college leagues down to high school levels, and are profoundly impacting sports betting. The growth in these areas is significant and continues to accelerate in 2025.

Here’s a breakdown of the growth and applications, with annotated references:

Growth of AI in Sports

The global AI in sports market is experiencing substantial growth. It was valued at USD 10.82 billion in 2025 and is projected to reach approximately USD 60.78 billion by 2034, expanding at a Compound Annual Growth Rate (CAGR) of 21.14% from 2025 to 2034. North America currently dominates this market, with Europe being the fastest-growing region.

This growth is driven by the increasing demand for data-driven performance analysis, injury prevention, tactical planning, and personalized fan engagement.

AI and Chatbots in Athletics (Elite Pro and College Leagues)

AI and chatbots are integral to modern sports management, coaching, and athlete performance at the highest levels.

  1. Athlete Performance Analysis and Training Optimization:
    • Real-time Tracking: AI-powered systems, often integrated with wearable sensors and smart cameras, meticulously track every nuance of an athlete’s movement (e.g., stride angle, jump shot force). This data is processed at astonishing speed to provide immediate feedback to coaches and trainers.
    • Personalized Training: AI creates tailored training programs by analyzing vast datasets, including heart rate, calorie expenditure, workout intensity, gait, and endurance. This helps athletes fine-tune their performance, push boundaries, and achieve peak physical condition.
    • Game Strategy Optimization: Coaches and analysts use AI-driven predictive analytics to examine historical player statistics, game conditions, and opponent behavior, allowing them to optimize strategies and make on-the-fly tactical adjustments during matches.
    • Virtual Reality (VR) Training: AI-powered VR simulations mimic real game scenarios, allowing athletes to practice decision-making, reaction times, and situational awareness without physical fatigue or risk of injury.
    • References:
  2. Injury Prevention and Recovery:
  3. Talent Identification and Scouting:
    • AI-driven platforms are augmenting traditional scouting methods by evaluating thousands of players globally, analyzing not only physical skills and performance metrics but also consistency, adaptability, and psychological resilience. This helps teams unearth hidden talent and make objective recruitment decisions.
    • An example is AI-powered scouting platforms that leverage computer vision, performance analytics, and crowdsourced video uploads to identify untapped talent, democratizing opportunity by bypassing traditional gatekeepers.
    • References:
  4. Fan Engagement and Broadcasting:
    • AI is revolutionizing how fans experience sports by personalizing content (highlight reels, real-time stats, predictive insights), enhancing live commentary, and providing interactive features through chatbots and recommendation engines.
    • Chatbots can answer fan questions instantly, provide game outcomes, and offer personalized content, making the viewing experience more engaging and immersive.
    • AI also optimizes stadium operations, including security, facial recognition, crowd management, and inventory management for concessions and merchandise.
    • References:

AI and Chatbots in High School Sports

While the adoption might be slower than at the professional and collegiate levels due to resource constraints, the influence of AI is definitely filtering down to high school sports.

  • Training and Performance: High school athletes and coaches are increasingly using AI-enabled simulators for technical drills and game scenarios. Wearable devices, often integrated with AI, are becoming more accessible for tracking performance metrics and early injury detection.
  • Personalized Coaching: Early adopter educators and coaches are developing custom GPTs and AI chatbots trained on specific data sets related to individual students, enabling more personalized learning experiences in sports.
  • Talent Identification: As AI scouting platforms become more sophisticated and accessible (e.g., through smartphone video uploads), they offer the potential to identify hidden talent in underserved areas, including high schools, that might otherwise be overlooked by traditional scouting.
  • References:

AI and Chatbots in Sports Betting

You are very much correct about the pervasive use of AI in sports betting. It’s revolutionizing the industry for oddsmakers, bookies, and gamblers alike.

  1. Real-Time Odds Adjustments:
    • AI-driven systems are capable of adjusting betting odds in real time by continuously processing live game data, betting trends, weather conditions, injuries, and even social media sentiment. Machine learning algorithms can process tens of thousands of data points per second, ensuring highly accurate and responsive odds markets.
  2. Personalized Betting Recommendations:
    • AI analyzes individual user behavior (betting history, preferred sports, spending habits) to offer customized bet suggestions that match a user’s profile, increasing user engagement.
  3. Fraud Detection and Responsible Gambling:
    • AI’s strength in pattern recognition is central to modern fraud detection systems, flagging suspicious activities like repeated bets on statistically unlikely outcomes or sudden changes in betting volume.
    • AI also promotes responsible gambling by monitoring player habits and identifying red flags like chasing losses, potentially sending alerts or triggering self-exclusion.
  4. Enhanced Customer Support:
    • AI-powered chatbots provide 24/7 customer support, answering queries, assisting with transactions, and directing users to human agents or responsible gambling resources when needed. They use natural language processing (NLP) to understand tone and maintain human-like conversations.
  5. Data-Driven Insights for Gamblers:
    • While not explicitly “gamblers using chatbots,” the data and insights provided by AI are leveraged by sophisticated gamblers. AI models can achieve over 60% accuracy in predicting match outcomes by analyzing patterns like formations, key passes, and other in-game metrics, influencing betting markets.

In conclusion, AI and chatbots are not just a natural fit for athletics and betting; they are already deeply embedded and driving significant innovation and growth across all levels of sports, from optimizing performance to enhancing fan experiences and reshaping the landscape of sports wagering. The trend is clearly towards more widespread and sophisticated adoption in the coming years.

JS: Thanks, Gemini. Can you dive deeper into AI/chatbot use among coaches? Does this mean that head coaches in top-tier leagues must have expertise in AI? Or are they simply leaving this aspect of performance in the hands of assistant coaches or other staff with unique titles that reflect this skillset? Are colleges offering courses and majors for this expertise, and are graduates in high demand? Can you identify the colleges, their programs/courses, and notable professors in this emerging field? Are these AI experts on coaching staffs being strongly recruited or hunted and receiving exponentially higher salaries?

Gemini: You’ve hit on a crucial and rapidly evolving aspect of modern sports. The integration of AI and chatbots into coaching is indeed profound, leading to specialized roles and a surge in demand for relevant expertise.1

AI/Chatbot Use Among Coaches in Top-Tier Leagues

Head coaches in top-tier leagues are increasingly relying on AI, but this doesn’t necessarily mean they need to be coding experts. Their role is evolving to interpret and leverage AI-driven insights rather than directly developing the AI. Think of it like a surgeon using advanced medical imaging; they understand the output and how it informs their decisions, but they aren’t the ones who built the MRI machine.

Here’s how coaches are using AI:

  • Game Planning and Strategy: AI can analyze vast amounts of opponent footage and historical data to identify tendencies, weaknesses, and optimal strategies in minutes.2 Instead of reviewing hours of film, a coach can use AI to simulate thousands of scenarios and recommend the best playbook adjustments.
  • Personalized Training and Development: AI creates tailored training programs by analyzing individual athlete data (wearables, biometric feedback, technique analysis from video).3 This allows for highly personalized feedback on mechanics (e.g., a pitcher’s throw, a gymnast’s landing), fatigue levels, and progress, all without needing a massive coaching staff for individual attention.
  • Injury Prevention and Recovery: This is one of AI’s biggest impacts. AI tools analyze movement patterns, workload, and fatigue to predict injuries before they occur, allowing coaches to adjust training or rest at-risk players.4 This proactive approach not only keeps athletes healthier but can also extend their careers.
  • Talent Identification and Scouting: AI platforms can analyze data from countless athletes, identifying potential talent based on a broader range of metrics and patterns than human scouts alone might catch.5
  • Tracking Physical and Mental Performance: AI helps coaches monitor both physical metrics (speed, endurance, heart rate) and mental states (stress, mood via questionnaires), providing a holistic view of an athlete’s well-being and readiness.6
  • References:

Evolution of Coaching Staff Roles

You are absolutely right. Head coaches are increasingly relying on specialized staff for AI. New roles are emerging and becoming crucial parts of coaching staffs:

  • Director of Sports Analytics: This role oversees the entire data analysis operation, from data collection and cleaning to model development and interpretation.
  • Sports Data Scientist: These individuals build and refine the AI models, algorithms, and predictive tools used for performance analysis, injury prediction, and strategy. They have strong backgrounds in machine learning, statistics, and programming (often Python and R).
  • Performance Analyst (with AI specialization): These professionals bridge the gap between the data and the coaching staff, translating complex AI outputs into actionable insights for coaches and athletes. They often work directly with video analysis software integrated with AI.8
  • Sports Technology Integration Specialist: This role focuses on implementing and managing the various AI tools and technologies, ensuring seamless data flow and system functionality.

These specialists work collaboratively with the coaching staff, presenting findings in an accessible way so coaches can make informed decisions. The goal is for AI to be a “coaching co-pilot,” enhancing intuition with data-backed insights.

College Programs and Demand for Graduates

Colleges are indeed stepping up to meet this demand, offering specialized courses and even full degrees in sports analytics, sports technology, and data science with sports applications.10 Graduates with these skill sets are in high demand across professional and collegiate sports.

Here are some examples of institutions and programs (note that specific course titles and faculty may evolve, but these represent the types of offerings):

  • Dedicated Sports Analytics/Science Programs:
    • Lasell University: Master of Science in Sport Management with a concentration in Sport Analytics.11 This program focuses on leveraging data for informed decisions in various aspects of the sports industry, from player performance to fan engagement. Courses may include “Big Data & Stat Analysis in Sport” and “Applied Data Analytics with R.”
    • University of Mississippi: Master of Science in Sports Analytics.12
    • Marquette University: Master of Science in Sport and Exercise Analytics.13
    • Northeastern University: Master of Sport Leadership with a Sports Analytics Concentration.14
    • Northwestern University: Master’s in Sports Administration, Sports Analytics Concentration.15
    • Syracuse University: Sports Analytics Major and Minor.
    • Temple University: Master’s in Sport Business with a Concentration in Sport Analytics.
    • University of North Texas: Advanced Data Analytics with a concentration in Sports, MS.16
    • University of Oklahoma: Master of Science in Sports Data Analytics (hybrid program).17
    • Springfield College: Sports Analytics Minor.
  • Broader Data Science/AI Programs with Sports Applications: Many computer science, data science, and engineering programs at universities will have courses or research opportunities applicable to sports, especially if they have faculty interested in the domain.
    • American University: MS in Sports Analytics and Management (online).18
    • Arizona State University: Online Bachelor of Science in Business Data Analytics (can be applied to sports).19
    • George Mason University: MS in Data Analytics Engineering and Certificate in Data Analytics.20
    • Indiana University–Purdue University Indianapolis (IU Indianapolis): Master of Science in Applied Data Science.21
    • Stevens Institute of Technology: Offers dual-degree programs with a Sports Tech component.22
  • Online/Professional Development Courses:
    • Sports Management Worldwide (SMWW): Offers an “Sports Analytics & AI online course” focusing on SQL, Python, R, and data science for on-field, off-field, and business analytics in sports.23 Ari Kaplan (MLB Analytics Pioneer, Databricks Head of Evangelism) is noted as a mentor for their program, providing real-life examples from MLB, NBA, NFL, F1, NASCAR, EPL, MLS, and NHL.
    • AI Sports Lab: Offers an “AI Sports Course” with modules on “Introduction to Artificial Intelligence in Sports,” “Fundamentals of Machine Learning in Sports,” “Deep Learning in Sports,” and “Generative AI and Strategic Simulations in Sports.”24 Notable faculty include Raúl Peláez (Director of Sports Data at Kynisca, former Head of Sports Technology, Innovation and Analysis at F.C. Barcelona) and Miguel Houghton (Basketball Senior Data Analyst at Skillcorner, former Data Architect at Gemini Sports Analytics).
  • Notable Professors/Experts (who may contribute to academia or industry):
    • Cade Massey (Wharton School, University of Pennsylvania): Explores how analytics are used in injury prevention, scouting, and referee evaluation in professional leagues.
    • Paul Laursen, PhD (Co-Founder, Athletica.ai; Adjunct Professor, AUT University, New Zealand):25 Deep expertise in sports science, particularly in HIIT, and applying AI to personalized training.
    • Andrea Zignoli, PhD (AI Data and Sport Scientist, Athletica.ai; University of Trento): Leads backend logic development for AI coaching platforms, focusing on human movement modeling.
    • Martin Buchheit, PhD (Co-founder, HIIT Science; Product Expert, Athletica.ai; formerly Head of Performance, Paris Saint-Germain Football Club): Extensive scientific publications and practical experience in applying sports science to elite performance.
  • References:

Recruitment and Salaries for AI Experts in Sports

Yes, these AI experts on coaching staffs are absolutely being strongly recruited and “hunted.” The demand for professionals who can leverage data and AI to gain a competitive edge is escalating rapidly.

  • High Demand: The overall AI in sports market is projected to reach over $60 billion by 2034, with a CAGR of over 21% from 2025.26 This growth signifies a massive increase in the need for skilled professionals. Teams and organizations recognize that AI is no longer a luxury but a necessity for optimal performance, injury prevention, and competitive advantage.27
  • Salaries: Salaries for sports analytics and AI professionals are competitive and often higher than traditional sports roles.28
    • As of July 2025, the average annual pay for a Sports Analytics professional in the United States is around $125,326 per year, which translates to about $60.25 an hour.
    • The majority of salaries in this field range from $100,000 (25th percentile) to $149,000 (75th percentile).
    • Top earners (90th percentile) can reach $165,000 annually.
    • For roles requiring high technical knowledge, including machine learning, data science, statistics, and AI for sports analytics, salaries were at least $130,000 at the end of 2023 and have likely increased since.

While these salaries are strong, whether they are “exponentially higher” depends on the comparison. They are certainly significantly higher than many entry-level or even mid-career traditional coaching or administrative roles within sports. The specialized nature of the skillset and the direct impact on team success command premium compensation.

In essence, the sports world is undergoing a significant technological transformation, and AI/data expertise is at its core. This has created a vibrant and highly sought-after career path for those with the right blend of analytical skills and a passion for sports.

JS: Are coaches actually relying on chatbots/AI under game conditions to make on-the-fly lineup (player) adjustments and decisions based on the opponent’s lineup on the field or court? If yes, is this use extensive in the pro and collegiate levels? Can you identify coaching staffs that are doing this now? For example, in the NBA and WNBA?

Gemini: You’re touching upon one of the most exciting and impactful applications of AI in live sports! Yes, coaches are increasingly relying on AI and advanced analytics to make on-the-fly lineup adjustments and strategic decisions during games, and this use is becoming extensive in top-tier professional and collegiate leagues.1

How AI Assists Coaches in Real-Time Game Decisions

The key to AI’s effectiveness in live game situations is its ability to process massive amounts of real-time data far beyond human capacity and at speeds that allow for immediate action. Here’s how it works:

  1. Real-Time Player Tracking: Sophisticated camera systems (like Sony’s Hawk-Eye AI or the NBA’s Second Spectrum) track every player’s movement, the ball’s trajectory, and even limb positions at incredibly high frame rates (e.g., 120 frames per second for Hawk-Eye, 25 times per second for Second Spectrum).2 This creates a 3D point cloud of the entire game.
  2. Instant Data Processing: Edge servers (local processing units) run computer-vision models trained on millions of historical plays.3 These models analyze the real-time data to identify patterns, evaluate player efficiency, track fatigue, and understand tactical formations. Latency is crucial here, with systems aiming for sub-500 millisecond processing times.
  3. Predictive Analytics and Scenario Simulation: AI can then apply predictive models to this real-time data.4 For example:
    • Optimal Lineups: Based on current player performance, opponent’s lineup, energy levels, and historical matchup data, AI can suggest which five-man unit is most effective at that exact moment.5
    • Defensive Matchups: AI can identify which defensive matchups are working or failing against specific opponent players or offensive sets.
    • Fatigue Monitoring: Wearable sensors and optical tracking can feed data into AI models that assess player fatigue and injury risk in real-time, allowing coaches to sub out players before their performance drops or they become more susceptible to injury.
    • Offensive Play Suggestions: In sports like basketball, AI can analyze defensive alignments and suggest offensive plays with the highest probability of success against that specific setup.
  4. Actionable Insights on the Sideline: The AI doesn’t directly control the game, but it feeds these insights to the coaching staff, often via tablets or specialized dashboards.6 The human coaches and their analytics teams then interpret these recommendations and make the final decision. This creates a “hybrid human-AI workflow.”

Extent of Use in Pro and Collegiate Levels (NBA & WNBA)

The use of AI for real-time, in-game decision-making is extensive and growing rapidly at the professional level, particularly in leagues like the NBA.9 The WNBA is also adopting similar technologies.10

  • NBA: The NBA is a leader in AI adoption.11 The league itself has “green-lit a full-arena deployment of Sony’s Hawk-Eye AI camera system” for the 2025 Playoffs.12 This infrastructure provides the raw, precise data needed for real-time analysis. Teams are very secretive about their specific AI models and the exact prompts or outputs they use, considering it a competitive advantage. However, industry experts confirm its widespread use. For example, Daryl Morey, President for Basketball Operations for the Philadelphia 76ers, has publicly stated how AI, especially large language models (LLMs), has become an integral part of their decision-making process, including for game strategies.13
  • WNBA: The WNBA also utilizes similar technology to the NBA, obtaining three-dimensional player and ball-tracking data through its partnership with Genius Sports’ Second Spectrum.14 WNBA coaches and front office leaders have access to analytical tools that include “shot quality, maximum speed and defensive matchup data.” While the pace of adoption might differ slightly, the fundamental tools are available and being utilized.
  • Collegiate Level: While some top-tier college programs with larger budgets are also investing in similar tracking and analytics systems, the extent of real-time AI integration for on-the-fly decisions may not be as universal as in the NBA/WNBA. This is primarily due to the cost of the advanced infrastructure (cameras, processing power, specialized staff) and the sheer volume of data required. However, the insights gained from post-game AI analysis are heavily used to inform future game plans and player development.15

Identifying Coaching Staffs and Teams

While specific teams are often tight-lipped about the exact AI tools and strategies they use (due to competitive reasons), some are frequently cited as early and leading adopters:

Daryl Morey, 76ers
  • NBA Teams:
    • Philadelphia 76ers: Under Daryl Morey’s leadership, the 76ers have long been known for their pioneering use of analytics and advanced metrics in all aspects of basketball operations, including in-game strategy. While specific AI implementations for real-time lineup changes are proprietary, their philosophy strongly supports data-driven decisions.
    • Houston Rockets: Previously under Daryl Morey as well, the Rockets were early proponents of advanced analytics, and their style of play was heavily influenced by data-driven insights.16
    • Golden State Warriors: Known for their innovative approach to training, the Warriors have been reported to use “Physical AI” initiative with AI-powered robots for practice sessions, which speaks to a broader acceptance of AI in their coaching methods.17 Their overall philosophy of optimizing player performance and strategy aligns with heavy AI use.
    • Oklahoma City Thunder: Cited among early adopters, with their current success attributed, in part, to playing “different” due to strategic insights derived from analytics.
    • Orlando Magic: As of October 2024, the Magic were reported to be ingesting “4,200 college and overseas games into a private Snowflake lakehouse, then layered a Python feature factory that computed defensive interaction maps, passing windows created, and movement efficiency metrics,” 18indicating a strong infrastructure for deep analytical insights, which would naturally extend to in-game applications.19

It’s important to understand that it’s often the sports data scientists and performance analysts on these teams who are working directly with the AI models and systems. They then translate the complex outputs into digestible, actionable insights that the head coach and assistant coaches can quickly grasp and apply during timeouts or brief stoppages in play.20

In summary, AI is no longer a futuristic concept but a vital tool being actively deployed in real-time by coaching staffs in elite professional basketball to gain a competitive edge.21

JS: Does the extensive use of AI/chatbots extend to sports such as boxing, MMA, tennis, swimming, and track?

Gemini: Yes, the extensive use of AI and chatbots is absolutely expanding into individual sports like boxing, MMA, tennis, swimming, and track & field, mirroring the trends seen in team sports. While the dynamics of individual performance differ from team strategy, the core benefits of AI—data analysis, performance optimization, injury prevention, and personalized training—are equally, if not more, impactful in these disciplines.

Here’s how AI is being utilized in each of these sports:

Boxing and MMA (Combat Sports)

AI is revolutionizing how fighters train, strategize, and recover.1

  • Performance Analysis:
    • Punch/Strike Metrics: AI-powered computer vision systems and smart sensors embedded in gloves or wraps track punch speed, power, accuracy, frequency, and placement.2 This gives fighters and coaches objective data on their striking technique, identifying strengths and weaknesses.3
    • Movement and Footwork Analysis: AI analyzes footwork patterns, head movement, and defensive maneuvers, providing insights into efficiency and predictability.4
    • Opponent Scouting: AI can analyze vast amounts of fight footage to identify an opponent’s tendencies, preferred combinations, defensive gaps, and fatigue patterns. This helps coaches develop highly specific game plans.
  • Personalized Training: AI creates tailored training programs that adjust in real-time based on a fighter’s performance metrics (speed, power, endurance, heart rate, recovery).5 If a fighter’s endurance is a weak point, the AI can adjust the regimen to focus more on cardiovascular exercises.
  • Injury Prevention and Recovery: Wearable devices and computer vision monitor a fighter’s movements, detecting signs of fatigue, improper technique, or potential overtraining.6 This allows trainers to adjust workloads, recommend rest, and even guide rehabilitation protocols to prevent injuries and optimize recovery.7
  • Virtual Sparring Partners: AI-driven applications like ShadowBox create virtual opponents with varying skill levels and fighting styles, allowing fighters to practice and refine strategies against specific types of opponents without the physical toll of constant live sparring.8
  • Judging and Scoring (Future/Emerging): There’s a strong push, particularly in boxing, for AI to assist judges and referees by providing objective, real-time data on punch accuracy, power, and effectiveness to reduce controversy and enhance fairness.9 Companies like Jabbr.ai and HITAI are developing platforms for this.10
  • References:

Tennis

Tennis is a prime example of an individual sport heavily integrating AI.13

  • Real-Time Performance Analysis: AI-powered computer vision systems (like Hawk-Eye) track ball speed, spin, trajectory, player positioning, shot selection, and rally patterns.14 This provides instant insights into optimal hit points, opponent’s playing patterns, and tactical effectiveness during matches.15
  • Player Training and Development: AI systems provide detailed feedback on swing mechanics, footwork, serve consistency, and shot placement. Virtual reality training tools (e.g., Sense Arena) allow players to simulate real match scenarios and practice against virtual opponents, focusing on mental development and decision-making under pressure.16
  • Injury Prevention and Management: By analyzing biomechanics, workload, and fatigue, AI recommends adjustments to training schedules and racket specifications to prevent common injuries.
  • Scouting and Recruitment: AI-driven tools analyze data from thousands of matches to identify emerging talent globally, assessing not just skill but also adaptability, improvement rate, and psychological resilience.17
  • Automated Umpiring: Electronic Line Calling (ELC) systems using AI (e.g., Hawk-Eye) are now standard in many professional tournaments, replacing human line judges for precise “in” or “out” calls.18
  • References:

Swimming

AI is making significant waves in swimming, particularly in biomechanical analysis.19

  • Technique Analysis: AI-powered video analysis systems (like SenSwim or the DIVE project) track every aspect of a swimmer’s movement—stroke rate, stroke length, body alignment, hand entry, pull-through, kick efficiency, and turn performance—both above and below water, without the need for wearable sensors.20
  • Real-Time Feedback: Coaches can receive immediate, objective feedback on technique, allowing for on-the-spot adjustments during training sessions.21
  • Personalized Training: AI helps design customized training plans based on a swimmer’s unique biomechanics, strengths, and weaknesses, aiming to reduce drag and optimize propulsion.22
  • Injury Prevention: By identifying subtle inefficiencies or imbalances in stroke mechanics, AI can help prevent overuse injuries common in swimming.23
  • Data Collection and Research: Initiatives like DIVE are working to create high-quality, publicly available datasets of swimming performance video to train more robust AI systems and advance the science of aquatic biomechanics.24
  • References:

Track & Field

AI is becoming indispensable for optimizing speed, power, and efficiency in track and field athletes.

  • Biomechanics and Gait Analysis: AI-driven systems using high-speed cameras and motion sensors analyze stride length, stride frequency, ground contact time, joint angles, and body posture during sprints, jumps, and throws. This allows for precise identification of inefficiencies and areas for improvement.
  • Personalized Training Programs: AI creates customized training schedules that adapt to an athlete’s unique physiological responses, recovery needs, and competition schedule, maximizing performance gains while minimizing injury risk.26
  • Injury Prediction and Prevention: By tracking subtle changes in an athlete’s movement patterns or biometric data (from wearables), AI can flag potential fatigue or stress that could lead to injury, prompting coaches to adjust training loads or recommend rest.27
  • Event-Specific Optimization:
    • Sprinters: AI analyzes starting block mechanics, acceleration phases, and top-end speed maintenance.
    • Jumpers: AI evaluates approach mechanics, take-off angles, and flight dynamics.
    • Throwers: AI dissects rotational power, release angles, and kinetic chain efficiency.
  • Talent Identification: AI can process data from large pools of young athletes to identify those with the highest potential based on specific physical and biomechanical markers relevant to different events.28
  • References:

In essence, for individual sports, AI provides an unparalleled level of granular detail and personalized insight that was previously impossible.29 It’s moving these sports from relying heavily on subjective coaching “eyes” to a more objective, data-driven approach, allowing athletes to truly optimize every aspect of their performance and reduce the risk of injury.

JS: In current and recent WNBA games, referees are constantly being criticized for poor officiating. Is AI/chatbots being used by officiating organizations? I’m assuming that they could make a huge difference in improving calls that are made. Games are all-too-often delayed by referees to review existing videos, but these reviews are costly for teams and of mixed value. Do you see a growth in their use by refereeing officials?

Gemini: You’ve pinpointed a major area of frustration for WNBA fans and teams, and indeed, AI and advanced analytics are very much at the forefront of discussions and developments in sports officiating. The goal is to move beyond the current, often slow, and sometimes subjective video review process towards more objective and real-time decision support.

AI/Chatbots in Officiating Organizations: Current and Growing Use

Yes, AI and related technologies are being used by officiating organizations, and their use is absolutely expected to grow. The current criticism in the WNBA (and other leagues) is precisely why there’s a strong push for technological solutions.

Here’s how AI is being applied and its future growth:

  1. Optical Tracking Systems (e.g., Hawk-Eye, Second Spectrum):
    • Current Use: The WNBA, like the NBA, already utilizes sophisticated player and ball tracking systems (such as Genius Sports’ Second Spectrum). These systems capture real-time 3D data of every player and the ball, including movement, speed, and positioning. This data is primarily used by teams for analytics and strategy, but it’s also available to officiating review centers. The NBA has also fully deployed Sony’s Hawk-Eye AI camera system for the 2025 Playoffs, which offers similar capabilities.
    • How it helps officiating: This raw data is the foundation for AI-powered officiating. It provides objective evidence that far surpasses what human eyes or even standard slow-motion replays can offer. For example, it can precisely determine if a player’s foot was on the line for an out-of-bounds call, if a shot left a hand before the shot clock expired, or if goaltending occurred.
    • WNBA’s current infrastructure: The WNBA’s partnership with Second Spectrum means they have the foundational data for more advanced AI officiating. While the league publicly stated in May 2025 that they are using AI for social media monitoring (to combat hate speech) and not directly for in-game officiating, the underlying tracking technology is capable of much more. Commissioner Cathy Engelbert’s comments about referee “humanity” indicate a cautious, rather than dismissive, approach to technology’s role.
  2. Semi-Automated Offside/Line Calling:
    • Current Use: This is most prominently seen in soccer (FIFA’s semi-automated offside technology at the World Cup) and tennis (Hawk-Eye’s automated line calling, which is replacing human line judges in many top tournaments by 2025). These systems use AI to process camera data and make instant, objective decisions on black-and-white calls.
    • Relevance to WNBA: While not yet fully automated for every call, the principles are directly applicable to basketball. Calls like goaltending, shot clock violations, backcourt violations, and even precise out-of-bounds decisions could be automated with high accuracy. The NBA already uses AI to detect goaltending.
  3. AI-Assisted Foul Detection (Emerging):
    • Development Stage: This is the more complex frontier. While easy for AI to track objective movements, subjective calls like fouls (e.g., blocking fouls vs. charging fouls, or subtle contact) require more sophisticated AI that can interpret intent, degree of contact, and impact on play.
    • Research & Prototypes: Researchers and companies are actively developing AI models that use computer vision and pose estimation to identify various basketball violations, including traveling, double dribbling, and even shooting fouls. Ayush Pai, a Georgia Tech student, has notably developed an “AI Basketball Referee” that aims to detect a wider range of violations.
    • Benefits for WNBA: Such systems could provide real-time alerts or recommendations to referees, reducing missed calls and inconsistencies. It could highlight situations where a foul might have occurred that a human official missed due to obstruction or speed of play.
  4. Improving Video Review (VAR/Review Centers):
    • Current Challenge: You’re right – current video reviews are often lengthy, costly, and can be of “mixed value,” especially if the human reviewers still struggle with angles or subjective interpretation.
    • AI’s Role: AI can significantly enhance existing video review processes. Instead of just playing back raw video, AI can:
      • Automatically Isolate Key Moments: Quickly identify contentious plays from multiple camera angles.
      • Apply Filters/Enhancements: Highlight contact points, track specific limbs, or show precise distances.
      • Provide Data Overlays: Display real-time speed, force, or precise player positions relevant to the call.
      • Suggest Rulings: An AI system could, for certain types of calls, present a highly probable ruling based on its analysis, reducing human deliberation time.
    • Goal: The aim is to make reviews faster, more objective, and more consistently accurate, thereby reducing game delays and controversies.

Growth in Use by Officiating Officials

The growth in AI use by officiating organizations is inevitable and will be significant. Several factors are driving this:

  • Fan Demands & Trust: As you noted, constant criticism of officiating erodes fan trust. AI offers a path to greater accuracy and perceived fairness, which is crucial for the integrity of the game.
  • Technological Maturation: AI, computer vision, and real-time processing capabilities have advanced to a point where they are robust enough for live sports environments.
  • Competitive Pressure: As teams use AI for performance, leagues will feel pressure to adopt similar tech for officiating to maintain competitive balance and ensure fair play.
  • Cost vs. Benefit: While initial investment is high, the long-term benefits of reduced controversy, faster game flow, and potentially improved player safety (by identifying dangerous plays more consistently) could outweigh the costs. The potential for automating simpler calls could also allow human referees to focus on more complex, subjective aspects of game management.
  • Hybrid Approach: The consensus among experts isn’t to fully replace human referees with AI robots (at least not yet). Instead, the growth will likely be in hybrid systems where AI acts as a powerful assistant, providing real-time data and insights to human officials, allowing them to make more informed and consistent decisions. This preserves the “human element” of game management (dealing with player emotions, flow, etc.) while leveraging AI for objective rule enforcement.

While specific WNBA officiating organizations or their direct adoption of AI for foul calling are not widely publicized (competitive advantage and ongoing development reasons), the trends in NBA (with Hawk-Eye and Second Spectrum, and commissioner comments on AI), soccer (VAR, semi-automated offside), and tennis (automated line calling) clearly indicate the direction. The WNBA, as a professional league closely aligned with the NBA, will undoubtedly continue to integrate these technologies to address officiating challenges and enhance the game experience.

JS: Thank you, Gemini! Your information and opinions are much appreciated. A hui hou!

Gemini: A hui hou! It was a pleasure assisting you. You’re very welcome! I’m glad I could provide helpful insights. “A hui hou” is a beautiful Hawaiian phrase meaning “until we meet again.” It implies a hope for future encounters and carries a warm, positive sentiment. It’s a lovely way to end our conversation! If you have any more questions about AI in sports or any other topic, don’t hesitate to ask.

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