More detailed results on the cost of MultiPV are available. MultiPV Ī value higher than 1 weakens the quality of the best move computed, as resources are used to compute other moves. More detailed results on the cost of too little hash are available. For a system with 8GiB of RAM, one could use 6000 as a reasonable value for the Hash. The value is specified in MiB, and typical consumer hardware will have GiB of RAM. The Hash can be any value, not just powers of two. Set the hash to nearly the maximum amount of memory (RAM) available, leaving some memory free for other tasks. Set it to the maximum - (1 or 2 GiB RAM). More detailed results on the efficiency of threading are available. Consumer hardware typically has at least 4-8 threads, Stockfish supports hundreds of threads. SMT or Hyper-threading is beneficial, so normally the number of threads available is twice the number of cores available. Set the number of threads to the maximum available, possibly leaving 1 or 2 threads free for other tasks. Set it to the maximum - (1 or 2 threads). The following settings are important as well: Threads To get the best possible evaluation or the strongest move for a given position, the key is to let Stockfish analyze long enough, using a recent release (or development version), properly selected for the CPU architecture. ![]() as the engines get stronger, an evaluation of 0.0 will approach the 100% draw limit. These graphs have been generated from a model derived from Fishtest data for Stockfish playing against Stockfish (so an equally strong opponent), at 60+0.6s per game. With bullet games, the draw rate will lower, and against a weak opponent, even a negative score could result in a win. The probability of winning or drawing a game, of course, depends on the opponent and the time control. From these probabilities, one can also obtain the expected match score. The full plots of win, loss, and draw probability are given below. Some GUIs will be able to show the win/draw/loss probabilities directly when the UCI_ShowWDL engine option is set to True. An evaluation of 0.0 means equal chances for a win or a loss, but also nearly 100% chance of a draw. The new normalized evaluation is now linked to the probability of winning, with a 1.0 pawn advantage being a 0.5 (that is 50%) win probability. ![]() However, with engines being so strong, and the NNUE evaluation being much less tied to material value, a new scheme was needed. A value of 1, implied a 1 pawn advantage. The evaluation of a position that results from search has traditionally been measured in pawns or centipawns (1 pawn = 100 centipawns). Interpretation of the Stockfish evaluation ![]()
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