In an article published by the ICGA Journal in 2017, Jean-Marc Alliot of the Toulouse Computer Science Research Institute (IRIT) presents a new method, based on a Markovian interpretation of a chess game. Starting with those of Wilhelm Steinitz, all 26,000 games played since then by chess world champions have been processed by a supercomputer using Stockfish (rated between 3310 Elo at the CCRL and 3337 at the SSDF as of 10/2015, but around 3150 under the test condition according to the authors) in 62000 CPU hours, in order to create a probabilistic model for each player. For each position, the model estimates the probability of making a mistake and the magnitude of the mistake by comparing the two best moves calculated at an average of 2 minutes by move (26 plies on the average) with the move actually played, starting from move number 10. These models can then be used to compute the win/draw/lose probability for any given match between two players. These predictions have proven not only to be extremely close to the actual results when players have played concrete games against one another, but to also fare better than those based on Elo scores. The results demonstrate that the level of chess players has been steadily increasing. Magnus Carlsen (in 2013) tops the list, while Vladimir Kramnik (in 1999) is second, Bobby Fischer (in 1971) is third, and Garry Kasparov (in 2001) is fourth.