Good work! It looks like you have used the relationship between score and centipawns given by S. Fischer & P. Kannan 's study:Adam Hair wrote:Here is some data from high Elo, longer time control engine matches, using similar criteria Larry used in his material imbalance study:
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qr>=0p7 = 1 queen, 0 up to 2 rooks, 7 pawns B2 vs N2 Total Games Centered Score Centipawns total 9084 58.15 57.1 qr>=0p7 2842 55.15 35.9 r>=0p7 902 58.5 59.6 qr>=0p6 3220 56.2 43.3 r>=0p6 1191 59.5 66.8 qr>=0p5 1568 60.35 73.0 r>=0p5 1125 63.75 98.1 qr>=0p4 559 61.65 82.5 r>=0p4 738 64.3 102.2 qr>=0p3 r>=0p3 403 65.5 111.4 qr>=0p2 r>=0p2 B2N vs BN2 Total Games Centered Score Centipawns total 76553 52.7 18.8 qr>=0p7 46066 52.05 14.3 r>=0p7 6465 55.4 37.7 qr>=0p6 24444 51.95 13.6 r>=0p6 4872 54.75 33.1 qr>=0p5 6157 54.2 29.3 r>=0p5 2407 57.6 53.2 qr>=0p4 1091 55.45 38.0 r>=0p4 927 60.65 75.2 qr>=0p3 r>=0p3 qr>=0p2 r>=0p2 B2 vs BN Total Games Centered Score Centipawns total 46787 54.1 28.6 qr>=0p7 14073 51.6 11.1 r>=0p7 3474 52.4 16.7 qr>=0p6 16737 53.75 26.1 r>=0p6 6622 54.85 33.8 qr>=0p5 8476 55.7 39.8 r>=0p5 5999 58.4 58.9 qr>=0p4 2813 57.95 55.7 r>=0p4 3954 60.7 75.5 qr>=0p3 603 59.45 66.5 r>=0p3 2128 60.9 77.0 qr>=0p2 r>=0p2 1032 59.4 66.1 BN vs N2 Total Games Centered Score Centipawns total 20479 52.4 16.7 qr>=0p7 6953 52.9 20.2 r>=0p7 1464 52.2 15.3 qr>=0p6 7571 52.35 16.3 r>=0p6 2630 51.65 11.5 qr>=0p5 3707 53.45 24.0 r>=0p5 2487 52.25 15.6 qr>=0p4 1215 52.6 18.1 r>=0p4 1717 52.8 19.5 qr>=0p3 r>=0p3 844 52.95 20.5 qr>=0p2 r>=0p2 B vs N Total Games Centered Score Centipawns total 64957 51.45 10.1 qr>=0p7 8201 50.45 3.1 r>=0p7 3384 47.45 -17.7 qr>=0p6 15498 51.15 8.0 r>=0p6 8739 48.85 -8.0 qr>=0p5 13530 52.35 16.3 r>=0p5 12857 50.8 5.6 qr>=0p4 7699 52.35 16.3 r>=0p4 13758 51.75 12.2 qr>=0p3 3266 51.95 13.6 r>=0p3 11546 52.6 18.1 qr>=0p2 1146 51.3 9.0 r>=0p2 8052 52.45 17.0
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Centipawns_i = 400*log[score_i/(1 - score_i)]
Re: OT. revisiting Material Imbalance study.
I am not totally sure, but I think that as he mentioned the logistic function, the equivalent from scores to centipawns is something like this (using his data with the reference point at centipawns = 100):Antonio Torrecillas wrote:...
I crush the likelihood probability with a logistic function, setting the constant for 100 = having an extra pawn.
Here are some results:
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data: CCRL-4040.[379894].pgn + CCRL-404.[731822].pgn + cegtallblitz.pgn P1N0B0R0Q0 -> +50.54 -14.80 =34.66 -> 67.87 -> 100 P-3N1B0R0Q0 -> +37.75 -30.43 =31.83 -> 53.66 -> 19 P-3N0B1R0Q0 -> +45.38 -24.52 =30.10 -> 60.43 -> 56 P0N1B0R0Q0 -> +92.43 - 2.52 = 5.04 -> 94.96 -> 392 P0N0B1R0Q0 -> +94.70 - 1.57 = 3.73 -> 96.57 -> 446 P0N-1B1R0Q0 -> +35.37 -27.84 =36.79 -> 53.76 -> 20 P0N-2B2R0Q0 -> +45.16 -24.29 =30.54 -> 60.43 -> 56 P0N0B-1R1Q0 -> +72.06 -12.55 =15.39 -> 79.75 -> 183 P0N-1B0R1Q0 -> +78.40 - 8.91 =12.69 -> 84.75 -> 229 P0N0B0R1Q0 -> +97.81 - 0.93 = 1.26 -> 98.44 -> 554 P0N0B0R-2Q1 -> +29.01 -30.17 =40.82 -> 49.42 -> -3 P0N0B-1R-1Q1 -> +64.03 -11.22 =24.74 -> 76.41 -> 157 P0N-1B0R-1Q1 -> +68.48 -10.09 =21.43 -> 79.20 -> 178 1 => passed_2 -> + 130178 - 84222 = 71593 1 => passed_2 -> +45.52 -29.45 =25.03 -> 43 2 => passed_3 -> + 150534 - 119345 = 99116 2 => passed_3 -> +40.80 -32.34 =26.86 -> 22 3 => passed_4 -> + 207096 - 153461 = 140791 3 => passed_4 -> +41.31 -30.61 =28.08 -> 28 4 => passed_5 -> + 220429 - 111173 = 126192 4 => passed_5 -> +48.15 -24.28 =27.57 -> 65 5 => passed_6 -> + 194629 - 61672 = 85535 5 => passed_6 -> +56.94 -18.04 =25.02 -> 109 6 => passed_7 -> + 111988 - 29737 = 44441 6 => passed_7 -> +60.15 -15.97 =23.87 -> 126
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Centipaws_i = 100*ln[score_i/(1 - score_i)]/ln(0.6787/0.3213)
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(Adam's centipawns)/(Antonio's centipawns) = 4*log(0.6787/0.3213) ~ 1.2991
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(Adam's score) = 1/[1 + 10^(-centipawns/400)
a = (0.6787/0.3213)^(centipawns/100); (Antonio's score) = a/(1 + a)
Regards from Spain.
Ajedrecista.