Win_probability of player i in a tournament with n players E i = 10 Elo i / (10 Elo 1 + 10 Elo 2 +. See Pawn Advantage, Win Percentage, and Elo Expected win_ratio, win_probability (E) Elo Rating Difference (Δ) = Elo_Player1 - Elo_Player2 E = 1 / ( 1 + 10 -Δ/400) Δ = 400 log 10(E / (1 - E)) So, although one would expect with increasing draw rates the win ratio to approach 50%, in fact it is remaining about equal.Īs posted in October 2016, Andreas Strangmüller conducted an experiment with Komodo 9.3, time control doubling matches under Cutechess-cli, playing 3000 games with 1500 opening positions each, without pondering, learning, and tablebases, Intel i5-750 3.5 GHz, 1 Core, 128 MB Hash, see also Kai Laskos' 2013 results with Houdini 3 and Diminishing Returns: The program playing white seems to be more supported by the additional level of strength. This correlation was also shown by Kirill Kryukov, who was analyzing statistics of his test-games. Due to the second reason given, these ratios are very much influenced by the timecontrol, what is also confirmed by the published statistics of the testing orgnisations CCRL and CEGT, showing an increase of the draw rate at longer time controls. These two ratios depend on the strength difference between the competitors, the average strength level, the color and the drawishness of the opening book-line. Win & Draw Ratio win_ratio = score/N draw_ratio = draws/N Score_difference = wins - losses score = wins + draws/2 The score is a representation of the tournament-outcome from the viewpoint of a certain engine. The total number of games played by an engine in a tournament. Common tools, ratios and figures to illustrate a tournament outcome and provide a base for its interpretation.
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