About

What is this?
Omni Rankings publishes ratings and projections for each Division I team in men’s basketball, women’s basketball, and football. Wait, it gets even spicier. The ratings and projections are generated by mathematical models gauging the current strength of each team. The abridged version: It’s an orgy of math and college sports.

Matt Olson, Founder of Omni RankingsWho is behind it?
Omni Rankings was founded by Matt Olson, a Minnesota native and Northwestern University alumnus with a professional background in structural engineering and broadcast journalism (the natural career progression into manatee dermatology is pending). Matt has developed sports rating algorithms and mathematical models since 1990.

Which games do the models use to generate the rankings?
The models consider the results of every game between Division I teams in a particular sport for a given season. Results of any game involving a non-Division I team are not included.

Are all of a team’s games given equivalent significance in determining that team’s rating?
The models assign moderately greater weight to the most recent games. This weighting affords the models flexibility (as each team’s performance varies due to improved/diminished level of play, injuries, coaching changes, or other factors) and balance (not allowing the result of a single game to skew the ratings of the teams involved).

Are results from previous seasons used in generating a team’s current rating?
No, each season stands alone. An iterative algorithm uses the results of the first series of each team’s games to generate a “best guess” initial rating for that team. This approach yields a more accurate and less arbitrary comparison of teams at the beginning of a season without relying on previous season results.

Do the models take into account home court or home field advantage?
Yes. In each season and in each sport, the models quantify the benefit of playing at home. The home advantage is indicated on each Division I and conference rankings page and may be added to each home team’s rating when projecting margin of victory between two teams in a hypothetical game.

Does running up the score on an opponent benefit a team’s Macro rating?
The model applies a diminishing returns algorithm to limit the effect of “meaningless” points in no-longer-competitive games.  A similar algorithm tempers the margin of victory in overtime games.

Why are ratings and projections not available for each team’s early season games?
The iterative process described above to establish initial ratings for each team requires four games’ worth of results, a sample size considered sufficient to represent team performance. Due to this approach, Omni Rankings does not publish projections for any game involving a team playing one of its first four games, while each team’s ratings are withheld until that team completes its fourth game.

My team has a better win-loss record than our rivals and defeated them earlier in the season. Why are our rivals currently ranked higher?
The ratings reflect the model’s assessment of each team’s current strength, not a grade of the team’s overall performance for the season. If the rival team has performed better than your team in recent games — based on the ratings criteria, not winning percentage — the rivals may overcome that earlier loss to your team and an inferior win-loss record to achieve a higher current rating.

How are the conference basketball tournament projections generated?
Omni Rankings projects each team’s conference record to establish the seeding, bracket, and game sites (with current Omni Rankings ratings serving as tiebreakers until the conference standings are final). Game projections are made based on current Omni Ranking ratings and the game site until a projected tournament champion is determined. This projected champion is considered the automatic bid qualifier for the respective conference in Omni Rankings’ NCAA tournament field.

How are the top performances, surprise performances, and upset wins determined?
Top performances represent the highest individual game ratings in a particular season, surprise performances indicate the largest exceedance of pre-game ratings projections, and upset wins reflect the greatest pre-game ratings deficits overcome.

If you have additional questions or comments about Omni Rankings or the ratings methodology, feel free to email via comments@omnirankings.com. Thanks for visiting OmniRankings.com, named seventh-finest college sports numerical analysis website by Minutiae Obsession magazine.