Blue Chip Analytics treats college football analysis as something to be examined rather than protected: we publish the data, assumptions and models behind a result so another analyst can repeat the process and reach the same conclusion, or identify where they disagree. Every study states its sample explicitly - the market-efficiency work runs on 3,616 FBS games from 2021 to 2025 - and distinguishes what the data shows from what we infer and what remains uncertain. For readers and fellow analysts, this page is the standard to hold our research to.
College football analysis should be open to examination, not protected from it.
Our aim is to make the data, assumptions and models behind our research as transparent as possible. A result is more valuable when another analyst can understand how it was produced, repeat the process and reach the same conclusion—or identify where they disagree.
That means our work should be replicable. We will explain the sample, define the measures we use and distinguish clearly between what the data shows, what we infer from it and what remains uncertain.
In practice that is why every piece of research here closes with its sample size, its window, and the caveats that come with them, and why the inputs behind the ratings are set out in our methodology.
Challenges are not merely accepted; they are encouraged. If a result looks wrong, a method feels questionable or an explanation does not convince you, tell us. A frank exchange of ideas is one of the best safeguards against becoming overly attached to a theory.
When we get something wrong we say so and fix it in the open, under our corrections policy.
This matters because college football is noise. Short seasons, uneven schedules, changing rosters, extreme scorelines and a relatively small number of games create enormous variance. That unpredictability is part of what makes the sport unique. It is also what makes analysing it difficult.
The purpose of this research is not to eliminate uncertainty or manufacture definitive answers. It is to separate genuine signals from randomness as carefully and honestly as the available evidence allows—and to remain willing to change our minds when better evidence arrives.
Transparency, replicability and openness to challenge. We explain the sample, define the measures, and distinguish clearly between what the data shows, what we infer from it, and what remains uncertain.
That is the aim. Every piece of research states its sample, its window and its caveats, and the inputs behind the ratings are set out in the methodology, so another analyst can repeat the process and reach the same conclusion - or identify where they disagree.
Tell us. Challenges are not merely accepted, they are encouraged. A frank exchange of ideas is one of the best safeguards against becoming overly attached to a theory, and when we get something wrong we fix it in the open under our corrections policy.
Short seasons, uneven schedules, changing rosters, extreme scorelines and a relatively small number of games create enormous variance. That unpredictability is part of what makes the sport unique, and it is also what makes separating signal from randomness difficult.
College football is noise, and the purpose of this research is not to eliminate uncertainty but to separate genuine signal from randomness as honestly as the available evidence allows.
