AI Analytics College Football

AI Analytics Media
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  • Football
  • Updated Semiweekly

AI Analytics College Football goes beyond the scoreboard to find what actually matters. Maya and Trey break down the numbers behind college football, separating signal from noise through matchup analysis, advanced metrics, opponent context and predictive analytics. From quarterback play and roster changes to hidden matchup edges and the games that could reshape the season, AI Analytics College Football looks for what the numbers reveal before everyone else sees it. Production note: This show features AI-generated host voices.

Episodes

  1. 6d ago

    Week 5 Aftermath: What Actually Changed?

    Week 5 is in the books, but not every big score means the same thing. Maya and Trey break down which results actually changed the college football picture, which teams strengthened their case, and which performances deserve a closer look beneath the final score. Then they turn to what matters next, the matchups that could expose real strengths and weaknesses, and the teams whose records may not tell the whole story. Deep numbers. Simple football talk.

  2. Sep 28

    Week 4 Aftermath: What Actually Changed?

    Week 4 is in the books, but the scores only tell part of the story. Maya and Trey break down which results actually changed the college football picture, which teams strengthened their case, and which performances deserve a closer look beneath the final score. Then they turn to the next slate, dig into the matchups that could expose real strengths and weaknesses, and put their predictions on the record. Deep numbers, simple football talk, and no overreaction to one Saturday.

  3. Sep 21

    Week 3 Aftermath: What Actually Changed?

    Week 3 gave us plenty of scores. Maya and Trey are more interested in what actually changed. They break down Kentucky vs. Texas A&M, Ole Miss vs. LSU, Alabama vs. Florida State, Louisville vs. SMU, Georgia vs. Arkansas, and more. Which performances are sustainable? Which teams are moving up? AI Analytics doesn't tell you what happened. It measures what happened, adjusts for who it happened against, and tells you what is most likely to happen next.

  4. Sep 14

    Week 2 Aftermath: What Actually Changed?

    Week 2 gave us results. The harder question is what those results actually changed. Maya and Trey break down the biggest signals from the weekend, including Texas' comeback against Ohio State, Oklahoma State's upset of Oregon, Michigan's response against Oklahoma, and what the underlying numbers say about the teams that moved the most. Which performances were real? Which were noise? And which teams should we be looking at differently after Week 2? AI Analytics goes beyond the scoreboard to measure what happened, adjust for context and opponent quality, and figure out what it means going forward.

  5. Sep 11

    College Football Week 2 Intelligence Report: What Week 1 Actually Told Us

    Week 1 gave us results. Now it's time to figure out which ones actually matter. Maya and Trey break down the signals, noise and misleading takeaways from the opening week of college football, then turn those lessons toward Week 2. They examine quarterback performance, opponent-adjusted results, matchup advantages, teams the public may be misreading and the numbers that could point toward what's coming next. This isn't a recap of everything that happened. It's an attempt to determine what actually changed. Production note: This episode features AI-generated host voices.

  • 5 Episodes

About

AI Analytics College Football goes beyond the scoreboard to find what actually matters. Maya and Trey break down the numbers behind college football, separating signal from noise through matchup analysis, advanced metrics, opponent context and predictive analytics. From quarterback play and roster changes to hidden matchup edges and the games that could reshape the season, AI Analytics College Football looks for what the numbers reveal before everyone else sees it. Production note: This show features AI-generated host voices.

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