Introducing Igel chess engine

[pohl4711]

Your sample size is too small You will need to run around 16k long time control games to get something precise, here is the run from updated regression:

http://chess.grantnet.us/test/9069/

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ELO   | 23.78 +- 3.56 (95%)
SPRT  | 60.0+0.6s Threads=1 Hash=64MB
LLR   | 0.00 (-2.94, 2.94) [0.00, 0.00]
Games | N: 16112 W: 4102 L: 3001 D: 9009

AB-testrun of Igel 2.9.0 finished (7000 games)

https://www.sp-cc.de

(Perhaps you have to clear your browsercache or reload the website)

[voffka]

AB-testrun of Igel 2.9.0 finished (7000 games)

https://www.sp-cc.de

(Perhaps you have to clear your browsercache or reload the website)

Great! Thank you for the test pohl4711!

[voffka]

I finished my regression testing between Igel and top 10 engines, the results are quite interesting. This time I used a short time control (20s and 0.2s increment) with many games to make sure elo estimate is accurate.

In this time control the elo gain of Igel 2.9.0 is around 95 elo over 2.8.0 which is mind blowing:

Igel 2.8.0 BMI2 AVX2 vs top 10 engines in tc=all/20+0.2:

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Rank Name                          Elo     +/-   Games   Score   Draws
   0 Igel 2.8.0 64 BMI2 AVX2      -179       7    6000   26.3%   38.1%
   1 Stockfish 12 64-bit           458      30    1000   93.3%   13.0%
   2 Komodo 14 64-bit              208      17    1000   76.8%   39.7%
   3 Ethereal 12.75 64-bit         173      16    1000   73.0%   42.7%
   4 Fire 7.1 64-bit               130      17    1000   67.8%   41.1%
   5 Xiphos 0.6 64-bit             121      16    1000   66.7%   47.6%
   6 Komodo 14 MCTS 64-bit         103      16    1000   64.3%   44.3%

Igel 2.9.0 BMI2 AVX2 vs top 10 engines in tc=all/20+0.2:

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Rank Name                          Elo     +/-   Games   Score   Draws
   0 Igel 2.9.0 64 BMI2 AVX2       -84       7    6000   38.2%   40.8%
   1 Stockfish 12 64-bit           387      25    1000   90.3%   18.3%
   2 Komodo 14 64-bit              122      16    1000   66.9%   46.4%
   3 Ethereal 12.75 64-bit          83      16    1000   61.8%   46.9%
   4 Fire 7.1 64-bit                21      16    1000   53.0%   46.3%
   5 Xiphos 0.6 64-bit               5      16    1000   50.7%   45.0%
   6 Komodo 14 MCTS 64-bit         -13      16    1000   48.2%   41.6%

I attribute this improvement to the use of the IGN network which was trained on Igel own search and eval.