Perceived Advantages
In 2006 economists Charles C. Moul and John V. C. Nye used Chessmetrics to determine the "expected" results of games, and wrote:
Ratings in chess that make use of rigorous statistics to produce good estimates of relative player strength are now relatively common, but comparing ratings across different time periods is often complicated by idiosyncratic changes (cf. Elo, 1968 for the pioneering discussion). Sonas uses the same rating formula throughout our sample and updates this rating monthly instead of annually, as is more common. Moreover, retrospective grading allows him to establish rankings that are unbiased estimates of the "true" relative strengths of players.
Jeff Sonas, Chessmetrics' author, repeatedly emphasises the importance of a rating system's ability to "predict" results (during testing the results to be "predicted" are those of past games whose outcomes are known to the tester).
Sonas also claims that Chessmetrics has other advantages over Elo ratings:
- Elo ratings have a built-in bias against top players, in other words a top player's rating will suffer if he / she achieves against a lower-rated player the percentage result that the players' Elo ratings predict.
- Chessmetrics is more responsive to change than Elo ratings, for example Chessmetrics will track more promptly and accurately improvements in the playing strength of a rising star. Sonas regards this as an important part of Chessmetrics's superior ability to predict results.
- Chessmetrics takes account of the empirically-measured difference in results when playing as White or Black, which the Elo system ignores.
Read more about this topic: Chessmetrics
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