(1) 'Stockfish and LCZero tied for 1st/2nd in the [TCEC] FRC4 'Final League', a point ahead of KomodoDragon. Stockfish beat LCZero +13-9=28 in the Final.'
(2) 'A [TCEC] note mentioned, "!bookfrc • Final League and the Final will use unbalanced books [...] On the edge between draw and white win." For more info, see TCEC FRC 4, under 'FRC Book Generation'.'
(3) 'After FRC4, the site ran an event called 'S22 - DFRC Sanity Check'. What's DFRC? "!dfrc • Double Fischer random chess: The same as Fischer random chess, except the White and Black starting positions do not mirror each other. Double FRC has 921,600 (960*960) possible starting positions."'
The 'more info' reference in (2) was for TCEC FRC 4 - TCEC wiki (wiki.chessdom.org), where the nuts and bolts of the tournament are explained. I covered the previous event in TCEC C960 FRC3 (March 2021).
One welcome difference between FRC3 and FRC4 was the increased number of competitors in the 'First phase', comprised of four leagues. In FRC3, there were four engines in each league; in FRC4, six engines were planned, although only 23 engines started the event. After FRC3, I analyzed engine runtime data for the first time in a pair of posts:-
- An Engine Iceberg (November 2021) 'TCEC FRC3 was a 50 game match won by KomodoDragon over Stockfish on a final score of +2-1=47.'
- The Engine Iceberg Looms Larger (ditto) 'Final match of the CCC C960 Blitz Championship (October 2021).'
It might be useful to repeat the exercise for FRC4, although I should be clear on objectives for the exercise. What can be learned by looking at only a small, random subset of the 960 possible start positions?
The TCEC wiki page discusses 'unbalanced books'. It's an interesting concept, but perhaps too heavy on the human manipulation: 'following work is done by hand'; 'then (by hand and eye) I choose'; '[sequences] that don't look crazy to me'; 'eliminated lines that looked too drawish or too busted'; 'some looked too artificial, some looked a bit too similar to others'.
Traditional A/B engines have never been particularly good at evaluating the long term consequences of opening decisions. A comprehensive analysis extends well beyond their search horizons. Maybe the AI NNUE engines are better at this, but that hasn't been studied anywhere (that I know of).
A red flag goes up when I see a phrase like 'lines that looked ... too busted'. In the years of writing about and playing chess960, I haven't seen any start positions that were 'too busted'. To the contrary, Black always has resources to counteract White's various initiatives. Perhaps the researcher behind the analysis (Bastiaan) should make available his full analysis showing which positions were eliminated for which reasons.
Another phrase caught my attention: 'not a single position favours Black in my analysis'. This is what one would expect to see in a position between opponents having exactly the same resources, except one gets to move first. Otherwise we would talk about 'first move disadvantage' or 'zugzwang in the start position'.
As for DFRC (FRC squared? chess921.6K?), this is a new area for analysis. It's another example of Gene Milener's idea that I covered in Chess960 Phase Zero (November 2018). A first action might be to examine runtime data from the DFRC games.