🏈 The Perfect FBS College Football Schedule

Welcome to my research project focused on building the Perfect FBS College Football Schedule. This project seeks to rethink the way college football schedules are created by removing strength of schedule bias, conference favoritism, and other unfair advantages that exist in the current system.

Through analysis, iteration, and thoughtful experimentation, I aim to develop a model that delivers a fair and balanced schedule for all FBS teams.

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🎯 Project Goal

The goal is to create a scheduling system that:

• Ensures Fairness: Every team has an equal shot at success.
• Reduces Bias: No team benefits from conference affiliation, strength of schedule perceptions, or geographic favoritism.
• Preserves Tradition: Historic rivalries (e.g., Ohio State vs Michigan) are maintained to enhance the excitement of the season.
• Maximizes Balance: Equally weighted conference and non-conference matchups without repetition of opponents or unfair travel burdens.

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📖 Why This Matters

The current FBS scheduling system suffers from:

• Strength of Schedule Bias: Some teams play tougher opponents, artificially boosting or harming their playoff chances.
• Conference Bias: Power conferences dominate, while Group of Five programs are excluded from fair playoff opportunities.
• Uneven Matchups: Certain teams schedule weak out-of-conference opponents to inflate their win totals.
• Rivalry Dilution: Traditional rivalries are sometimes neglected or overshadowed by conference realignment.

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🔬 Research & Methodology

This project focuses on iterative development and experimentation:

1. Data Collection

Gather detailed information about FBS teams, including:

• Schools: Names, locations, enrollments, and nicknames.
• Current Conferences: Existing alignments (e.g., SEC, Big Ten).
• Former Conferences: Historical affiliations for context in out-of-conference games.
• Rivalries: Traditional matchups that must be prioritized.
• Must Plays: 2nd and 3rd level rivalries that are historically important to the game or the fanbases.

2. Scheduling Rules

• Every team plays 12 games with 3 bye weeks.
• A designated "Rivalry Week" ensures teams play their top rival. May have to make up making 2 Rivalry weeks so people can watch them all.
• Equal weighting for:
  • Conference games (3-5 per team).
  • Out-of-conference games (3-5 per team), prioritizing teams with shared former conferences.
  • Everything else
• Seems obvious, but:
  • No team plays more than one game per week.
  • No team faces the same opponent twice in the regular season.

3. Iterations

• Start with simple schedule generation.
• Incorporate rivalries and former conference relationships.
• Incorporate "must plays"
• Refine to balance travel distances and game diversity.

4. Testing Fairness

Evaluate fairness by analyzing:

• Frequency of matchups across multiple iterations.
• Consistency in opponent strength without bias.
• Travel distance balance between teams.

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⚙️ Progress So Far

1. Data Preparation

• Schools and rivalries have been mapped using real-world FBS data.
• Traditional rivalries (e.g., Alabama-Auburn, Army-Navy) are accounted for.

2. Schedule Generation

• "Sucessfully" generated a 12-game schedule for:
  • 5 Random FBS teams.
  • Specific conferences
  • They weren't pretty, but did they did generate.
• Designed "Rivalry Week" where all teams play their rivals on the same week.

3. Rule Implementation

• Enforced single-game matchups and 3 evenly spaced bye weeks.

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🚧 / 💡 Next Steps / Future Enhancements

• Expand Rules: Introduce travel constraints to ensure fairness in out-of-conference games.

• Include "Must Plays" Outside of Rivalries: Auburn and Georgia are not each others largest rivals, but it's a game that must be played. More examples like this, possibly up to 3.

• Historical Context: Prioritize games with historic conference ties to preserve traditions.

• Historic W-L: Not entirely sure on this one, but maybe some way to identify prestige based on a 3, 5, and 10 year W-L for some better matchups

• Explore travel constraints to ensure geographic fairness.

• Develop a visual tool for schedule visualization and analysis.

• Test models to ensure all teams have an equal probability of making playoffs.

• Consider user-defined constraints (e.g., custom rivalry settings or conference rules)

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🛠️ Tools & Technologies

• Python: Core scheduling logic and data manipulation.
• Pandas: Handling large datasets efficiently.
• Excel: Input/output of team data and schedules.
• ChatGPT: I am weak

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🚀 Follow Along

I'd like to get the first raw data upload by early 2025, at the earliest.