AI in Sports Judging: What Technology Can and Cannot Do

The evolution of sports judging technology has accelerated significantly over the past decade. What was once an entirely paper-based process -- judges writing scores on cards, runners collecting them, manual calculation of results -- has shifted toward digital infrastructure that improves speed, accuracy, and transparency.

Motion tracking has progressed from basic video replay to sophisticated 3D analysis. Fujitsu's Judging Support System (JSS), deployed in artistic gymnastics since 2019, uses laser sensors and computer vision to generate real-time 3D skeletal models of athletes. The International Skating Union (ISU) has introduced the Replay Operator system, where a designated official uses multi-angle video to assist technical panels in identifying under-rotations and edge calls.

Digital scoring platforms have replaced paper across most major competitions. The International Ski and Snowboard Federation (FIS) uses electronic scoring for freestyle skiing and snowboarding events, while the X Games has tested various scoring technology integrations. These platforms handle everything from score entry to automatic calculation of trimmed means, difficulty multiplications, and weighted formulas.

| Technology | Sport | Status | |---|---|---| | Fujitsu JSS (3D skeletal tracking) | Artistic Gymnastics | Active since 2019 | | ISU Replay Operator system | Figure Skating | Active in ISU events | | Electronic scoring platforms | Freestyle Skiing, Snowboarding | Standard at FIS events | | Video replay for technical panels | Figure Skating, Diving | Standard at major competitions | | Tablet-based score entry | Multiple judged sports | Widely adopted |

Despite widespread discussion about AI replacing judges, the current reality is more nuanced: AI is used for assistance, not replacement. No sport at the Olympic level relies on fully automated AI scoring for official results.

Fujitsu's Judging Support System is the most prominent example of AI assistance in elite sport. The system uses laser sensors and depth cameras to create a 3D skeletal model of gymnasts in real-time, tracking joint positions and body angles throughout routines. It can identify specific elements, measure rotation degrees, and provide objective biomechanical data to supplement judge observations. However, judges still make all final scoring decisions -- the system is explicitly designed as a support tool, not a replacement.

In figure skating, technology assists through the Replay Operator system, where multi-angle video replay helps the technical panel verify element calls such as under-rotations, wrong edges, and level features. This is technology-assisted human judgment, not automated scoring.

Motion capture for training is another growing application. Coaches and athletes use AI-powered analysis to break down technique, compare movements against ideal patterns, and identify areas for improvement. This data can inform judging education but does not directly produce competition scores.

The key distinction is clear: technology currently helps judges see more clearly and calculate more accurately, but the evaluative judgment remains human.

Digital scoring platforms represent the most impactful technology shift in judged sports -- the transition from paper-based judging to electronic scoring systems. While less headline-grabbing than AI, this infrastructure change directly improves the accuracy and transparency of every competition that adopts it.

A modern digital scoring platform like JudgeMate works through several integrated layers:

Score Entry: Judges enter scores on tablets or devices in real-time, eliminating paper forms, illegible handwriting, and manual collection delays. Each score is timestamped and attributed to a specific judge.

Automatic Calculation: The platform applies the sport's scoring formula instantly. This includes:

• Trimmed mean calculation -- dropping highest and lowest scores, averaging the rest
• Difficulty degree multiplication -- multiplying execution scores by difficulty coefficients
• Weighted formulas -- applying category-specific weighting (e.g., air, form, landing in moguls)
• Tie-breaking rules -- applying the correct protocol when scores are equal

Instant Protocol Generation: Detailed scoring breakdowns are available immediately after each run. Athletes, coaches, and spectators can see exactly how the score was constructed -- which judges gave which marks, how the trimmed mean was calculated, and how deductions were applied.

Live Leaderboard Updates: Rankings update in real-time as scores are entered, keeping spectators, broadcast teams, and athletes informed.

The benefits are concrete: elimination of arithmetic errors, faster result delivery, full auditability of every score, and consistency monitoring that can flag outlier judge behavior for review.

While technology excels at measuring objective parameters -- rotation counts, distances, angles, and time -- judged sports contain significant subjective and artistic dimensions that remain beyond the reach of current AI.

Artistic evaluation is the clearest example. In figure skating, the Program Component Scores (PCS) assess skating skills, transitions, performance, composition, and interpretation of music. These qualities involve aesthetic judgment, emotional communication, and stylistic nuance that no algorithm can reliably evaluate. A skater who brings genuine emotional depth to a performance creates something that cameras can capture but algorithms cannot score.

Overall impression scoring in freestyle sports (snowboarding slopestyle, freeskiing halfpipe, skateboarding) similarly requires judges to weigh amplitude, style, flow, variety, and difficulty in a holistic assessment. Two runs might contain identical tricks but feel entirely different in their execution, rhythm, and creativity.

Contextual judgment presents another limitation for automation. Judges observe factors that sensors may not capture: how an athlete adapts to unexpected conditions, the momentum shift of a competition, the difference between a cautious execution and a committed one. A fall recovery that shows athleticism and poise is different from a stumble, even if the biomechanical outcome looks similar.

Intent versus execution is particularly difficult for AI. A gymnast who attempts a harder variation and slightly misses differs fundamentally from one who plays it safe. Human judges can read intention and effort in ways that motion tracking cannot.

The consensus across international federations is clear: AI can assist with measurable, objective parameters (rotation count, distance, height), but human judges remain essential for aesthetic, artistic, and contextual evaluation.

Moving toward greater automation in judging carries real risks that the sports community is actively debating.

Training data bias is a fundamental concern. AI systems learn from historical judging data, which may encode existing biases -- preferences for certain body types, movement styles, or artistic traditions. An automated system trained on biased data would perpetuate and potentially amplify those biases while appearing objective.

The inability to evaluate artistry remains AI's most significant limitation. Current computer vision can measure angles, distances, and timing with precision, but cannot meaningfully assess whether a figure skating performance was emotionally compelling or whether a snowboard run had creative flow. Reducing sports to measurable parameters would fundamentally change their character.

The black box problem directly affects athletes. Under human judging, athletes and coaches can understand the criteria, study the judges, and make strategic decisions. If scoring is driven by opaque algorithms, athletes lose the ability to understand and respond to how they are being evaluated. Transparency is not just a technical requirement -- it is essential for athlete trust and fair competition.

Technical failure is a practical risk. Systems can malfunction, sensors can lose tracking, and software can contain bugs. Competition results must not depend on technology that might fail at critical moments without a reliable fallback.

Loss of the human narrative is a subtler concern. Part of what makes judged sports compelling is the human element of evaluation -- the possibility of disagreement, the weight of experience, the tension between objective measurement and subjective appreciation. Fully automated judging could diminish the sporting drama that engages audiences.

JudgeMate approaches technology in judged sports with a clear philosophy: build digital infrastructure that helps human judges be more accurate and transparent, rather than claiming to replace them.

JudgeMate is a digital scoring platform, not an AI judging system. The distinction matters. The platform does not attempt to evaluate athletic performance automatically. Instead, it provides tools that make human judging more effective:

Real-time electronic scoring replaces paper-based workflows. Judges enter scores on tablets, and the platform handles all calculations instantly -- trimmed means, difficulty multiplications, weighted formulas, and deduction applications. This eliminates the arithmetic errors and delays that plague paper-based competitions.

Detailed scoring protocols provide transparency. Every score breakdown is available immediately: which judges gave which marks, how the calculation was performed, and how the final result was reached. This level of detail supports accountability and helps athletes understand their scores.

Consistency monitoring helps head judges and technical delegates identify potential scoring issues. When a judge's marks consistently deviate from the panel average, the system can flag this for review -- not to override the judge, but to support the quality control processes that federations already use.

Multi-sport flexibility means the same platform adapts to different scoring systems -- from the ISU's base value + GOE approach in figure skating to impression-based scoring in action sports to the turns-air-speed formula in moguls.

The approach is deliberately honest: technology should serve the judges, not replace them. Better tools lead to more accurate scores, faster results, and greater transparency -- outcomes that benefit athletes, audiences, and the integrity of the sport.