Sports rating system
They are often mentioned in discussions about the teams that could or should receive invitations to participate in certain contests, despite not earning the most direct entrance path (such as a league championship).
[1] Computer rating systems can tend toward objectivity, without specific player, team, regional, or style bias.
Ken Massey writes that an advantage of computer rating systems is that they can "objectively track all" 351 college basketball teams, while human polls "have limited value".
The basic idea is to maximize the number of transitive relations in a given data set due to game outcomes.
To address these logical breakdowns, rating systems usually consider other criteria such as the game's score and where the match was held (for example, to assess a home field advantage).
Some academic work is published in forums like the MIT Sloan Sports Analytics Conference, others in traditional statistics, mathematics, psychology, and computer science journals.
This phenomenon is evident in systems that analyze historical college football seasons, such as when the top Ivy League teams of the 1970s, like Dartmouth, were calculated by some rating systems to be comparable with accomplished powerhouse teams of that era such as Nebraska, USC, and Ohio State.
For example, systems may be crafted to provide a perfect retrodictive analysis of the games played to-date, while others are predictive and give more weight to future trends rather than past results.
This results in the potential for misinterpretation of rating system results by people unfamiliar with these goals; for example, a rating system designed to give accurate point spread predictions for gamblers might be ill-suited for use in selecting teams most deserving to play in a championship game or tournament.
The size of the effect changes based on the era of play, game type, season length, sport, even number of time zones crossed.
Other systems record the exact final game score, then judge teams based on margin of victory.
Rating teams based on margin of victory is often criticized as creating an incentive for coaches to run up the score, an "unsportsmanlike" outcome.
ARGH Power Ratings is an example of a system that uses multiple previous years plus a percentage weight of returning players.
And if anything else happened in that game with gambling repercussions – a comeback win, a blown lead, major dysfunction, whatever — I tagged that, too.Pythagorean expectation, or Pythagorean projection, calculates a percentage based on the number of points a team has scored and allowed.
[12] Bill Simmons cites Barnwell's work before week 10 of that season and adds that "any numbers nerd is waving a “REGRESSION!!!!
Researchers like Matt Mills use Markov chains to model college football games, with team strength scores as outcomes.
[16][17] In collegiate American football, the following people's systems were used to choose teams to play in the national championship game.
