X’s, O’s, and Data: The Impact of Analytics on College Football
By Nick Lowery
On January 20, 2025, Ohio State defeated Notre Dame in the College Football National Championship. Lead by Coach Ryan Day, they won 34-23, overcoming fan criticism after losing to Michigan previously this season. 20.5 million fans tuned in to watch. Despite all the lead up and excitement most people do not think about the data and analytics that coaches, scouts, and analysts utilize to make the game what it is today. But as a marketing research firm, it was one of the topics that instantly came to our attention.
Analytics are a powerful tool in college football, ranging from player evaluation to game play calling and decision making. According to Sports Technology in “The Future of College Football: Technology and Innovation”, college teams began to use analytics in the early 2000s with basic tools such as data tracking third down conversions, player performance, and red zone efficiency. From 2010 to 2020, teams began to slowly incorporate video analysis software, which is used to recognize opponent tendencies, player effectiveness, and identify patterns in play calling from opponents. From 2020 and still today, coaches track players with wearable technology, GPS system and biometric tracking, to optimize athlete training and performance giving coaches and analysts the ability to track heart rate, top speed, and even fatigue level.
Chart provided by NYC Data Science Academy
With the evolution of NIL and the extent of player investment recruitment is more important now than ever. According to On3 in “Top 10 NIL deals of 2024” the highest NIL earner of the 2024-2025 season is claimed to be Shedeur Sanders, valued at $5.1 million. Coaches use analytics in the form of watching game film and looking at player KPI (key performance indicators) like reaction time, positioning, and decision making under pressure. Science for Sport in “How Can Coaches and Sports Scientist Use Data to Their Advantage”, notes how analytics also plays a role in looking at injury history and the health risks of such valuable players. This includes injury prediction model and biomechanical analysis both looking at how vulnerable a player is to injuries and steps to prevent them. They also report on how sharing data tracked about an athlete is more helpful if shared with them and how coaches use tools like these to assess a player's sleep, nutrition, mental strength, and other keys to high-performance athleticism.
Provided by NYC Data Science Academy
The future of college football looks strong and will surely keep evolving to allow players to be stronger, faster, and healthier athletes. According to Student Sport Union in “The Role of Analytics in College Football Strategy”, some programs are looking to use virtual reality (VR) and augmented reality (AR) to put players in game situations over and over without putting them at risk of injury or near as much fatigue. This can target weaknesses like allowing a quarterback to read defenses or a cornerback to track a wide receiver pattern. Coaches have also begun to use Relative Success Probability (RSP) which is a new way of analyzing player performance using probability. This looks to understand the probability of a player converting a third down or kicking a 50-yard field goal based on previous attempts and how well a player is playing for his relative role.
I am excited to see coaches embrace these tools and watch how they evolve to enhance the game. As both a passionate fan and a marketing researcher, I am always looking to explore and uncover how analytics can help add value to clients. If you believe your business could benefit from qualitative or quantitative analytics, W5 would be more than happy to assist and provide support.
Contact us today to turn your analytical dreams into actionable data.