AI could standardize targeting penalties in college football with real-time analysis

The context: The recent Texas-Arizona State Peach Bowl quarterfinal game highlighted ongoing concerns about the consistency of targeting calls in college football, particularly after a controversial no-call impacted the game’s outcome.

• A critical hit by Texas DB Michael Taaffe on an Arizona State receiver during a crucial third-down play sparked debate when officials upheld a no-targeting call after review
• The decision forced Arizona State to punt instead of potentially receiving a first down near the Texas 35-yard line
• Texas ultimately won the game in double overtime

Current targeting rules: The NCAA defines targeting as when a player deliberately attacks an opponent with forcible contact beyond legal gameplay, carrying severe penalties including ejection.

• The rule was introduced in 2008 as a 15-yard penalty and enhanced in 2013 to include automatic ejection
• Officials look for specific indicators like launching, crouching followed by upward thrust, and leading with the helmet
• Players who are ejected in the second half must also sit out the first half of their next game

The challenge: Targeting calls remain highly subjective despite clear guidelines in the NCAA rulebook, leading to inconsistent enforcement across games.

• Officials must make real-time judgments about player intent and force of contact
• Different interpretations of the same play can lead to varying outcomes
• Big 12 Commissioner Brett Yormark has called for greater standardization in how targeting is enforced

AI potential: Artificial intelligence could provide a more standardized approach to assessing targeting penalties through advanced video analysis and pattern recognition.

• Deep learning algorithms can analyze player positioning, impact force, and timing
• AI systems can process thousands of video frames without fatigue or bias
• Similar technology has been successfully implemented in other sports, like VAR in soccer and ABS in baseball

Implementation hurdles: Several technical and cultural barriers must be overcome before AI-assisted targeting calls become reality.

• Significant data requirements for training accurate models
• Need for real-time processing capabilities during live games
• High implementation costs across multiple venues
• Potential resistance from traditionalists and stakeholders

Looking ahead: The path to standardized targeting calls through AI requires extensive collaboration and investment, but could significantly reduce controversy in college football.

• Universities, conferences, AI vendors, and broadcast networks would need to work together
• Success could lead to reduced referee subjectivity and more consistent enforcement
• The technology remains a future prospect rather than an immediate solution

Critical perspective: While AI offers promising solutions for standardizing targeting calls, the complexity of football plays and the nuanced nature of player interactions suggest that human oversight will likely remain essential, even in an AI-assisted future.