Compare betting lines to Power Ratings with our Line Value Calculator, with HFA and customizable adjustments for weather, travel and rest factor effects.
Last updated: July 21, 2026
Games ranked by power-rating gap (closest matchups first).
| Matchup | Date | Line | Power Gap |
|---|---|---|---|
| Hawai'i at Stanford | Aug-29 | Stanford -3.0 | 0.0 |
| NC State at Virginia | Aug-29 | Virginia -3.0 | 1.1 |
| Memphis at UNLV | Aug-30 | UNLV -3.0 | 1.1 |
| Jacksonville State at North Dakota State | Aug-29 | North Dakota State -10.0 | 1.9 |
| Sacramento State at Eastern Michigan | Aug-29 | Eastern Michigan -8.5 | 6.6 |
Take any matchup, select home team, away team, stadium, weather preset, and compute an implied spread from power ratings, home-field advantage, travel distance, and time-zone shift. Enter a market line to see the implied edge in either direction.
Ranks all 138 FBS teams by a margin-of-victory model that adjusts for opponent strength, location, and rest, updated each week as results come in. Use it to see where your team sits relative to the field and whether the market spread reflects the underlying power gap.
Assigns a point value to each FBS stadium based on historical home-team margin data, controlling for roster quality. Some venues are worth more than two possessions; others are effectively neutral - the tool shows you which is which.
Pulls game-time forecasts for every stadium on the schedule and flags games where wind, temperature, or precipitation cross thresholds known to suppress scoring. Powered by WeatherAPI.com data baked in at build time.
Aggregates the model's power-rating output, HFA adjustment, and travel inputs into a single directional read per matchup. Intended as a sanity check alongside your own research, not a replacement for it.
Breaks down how far each road team travels, the body-clock shift from crossing time zones, and how many days of rest each side has before kickoff. Use it to spot scheduling disadvantages the market spread may not fully price in.
Our research principles: transparent data and assumptions, a replicable method, and challenges actively encouraged. College football is noise - the job is separating genuine signal from randomness, honestly.
Everyone prices home field at two and a half points. Ten seasons and 6,954 games say the folklore is broadly right - but the team-by-team rankings are far noisier than they look.
We ranked 131 teams by how badly ratings and bookmakers missed them across five seasons. The list is real history. It is almost useless as prediction - and showing why is more valuable than the list.
Fifty familiar beliefs about college football games, run through the same test: does the effect survive team quality, and is anything left after the line? Most are fiction, several are real but fully priced.
A factor can be completely real and still be worthless, because the line already moved. This is the ledger of what the market charged, what the games delivered, and what was left over.
Home-field advantage is worth roughly 2-3 points on average, but the range across FBS venues is large. We break down what actually drives it - crowd, travel, altitude, and familiarity - and how to apply it to a line.
Blue Chip Analytics is an independent college football analytics site built by Liam Browne. The goal is to help fans and bettors understand the lines and find value. Every number on the site is computed from publicly available data using documented methods. Blue Chip is a transparent resource that serious bettors and analysts can actually audit, and use to improve their own research and decision making.
Read the full methodology or learn more on the author page.
Schedule-by-schedule breakdowns with power ratings, travel load, home-field context, and weather flags are available for all 138 FBS teams on the team pages index.
