Michael Chaly [Fri, 24 Feb 2023 09:09:45 +0000 (12:09 +0300)]
Fix duplicated moves generation in movepicker
in a some of cases movepicker returned some moves more than once which lead
to them being searched more than once. This bug was possible because of how
we use queen promotions - they are generated as a captures but are not
included in position function which checks if move is a capture. Thus if
any refutation (killer or countermove) was a queen promotion it was
searched twice - once as a capture and one as a refutation.
This patch affects various things, namely stats assignments for queen promotions
and other moves if best move is queen promotion,
also some heuristics in search and qsearch.
With this patch every queen promotion is now considered a capture.
After this patch number of found duplicated moves is 0 during normal 13 depth bench run.
Created by retraining the master net with modifications to the previous best dataset:
* Improving T80 oct+nov 2022 endgame lambda accuracy by rescoring with 12-16tb of syzygy 7p tablebases
* Filtering T78 jun+jul+aug 2022 with d6pv2 search to remove positions with bestmove captures or one good move
* Adding T80 sep 2022 data, rescored with 16tb of 7p tablebases, unfiltered
Trained with max-epoch 900, end-lambda 0.7, and early-fen-skipping 28.
Dubslow [Wed, 22 Feb 2023 11:45:43 +0000 (05:45 -0600)]
Late counter bonus: boost underestimated moves
The idea here is very intuitive: since we've just proven that the move is good, then if it previously had poor stats, boost those stats more than otherwise.
Call the recently added hint function for NNUE accumulator update after a failed probcut search.
In this case we already searched at least some captures and tt move which, however, is not sufficient for a cutoff.
So it seems we have a greater chance that the full search will also have no cutoff and hence all moves have to be searched.
pb00067 [Sun, 26 Feb 2023 08:59:35 +0000 (09:59 +0100)]
Use common_parent_position hint also at PVNodes TT hits.
Credits to Stefan Geschwentner (locutus2) showing that the hint
is useful on PvNodes. In contrast to his test,
this version avoids to use the hint when in check.
I believe checking positions aren't good candidates for the hint
because:
- evasion moves are rather few, so a checking pos. has much less childs
than a normal position
- if the king has to move the NNUE eval can't use incremental updates,
so the child nodes have to do a full refresh anyway.
Michael Chaly [Fri, 24 Feb 2023 15:25:24 +0000 (18:25 +0300)]
Search tuning at very long time control
This patch is a result of tuning session of approximately 100k games at 120+1.2.
Biggest changes are in extensions, stat bonus and depth reduction for nodes without a tt move.
Linmiao Xu [Tue, 21 Feb 2023 16:17:59 +0000 (11:17 -0500)]
Reintroduce nnue pawn scaling with lower lazy thresholds
Params found with the nevergrad TBPSA optimizer via nevergrad4sf modified to:
* use SPRT LLR with fishtest STC elo gainer bounds [0, 2] as the objective function
* increase the game batch size after each new optimal point is found
The params were the optimal point after TBPSA iteration 7 and 160 nevergrad evaluations with:
* initial batch size of 96 games per evaluation
* batch size increase of 64 games after each iteration
* a budget of 512 evaluations
* TC: fixed 1.5 million nodes per move, no time limit
nevergrad4sf enables optimizing stockfish params with TBPSA:
https://github.com/vondele/nevergrad4sf
Using pentanomial game results with smaller game batch sizes was inspired by:
Use of SPRT LLR calculated from pentanomial game results as the objective function was an experiment at maximizing the information from game batches to reduce the computational cost for TBPSA to converge on good parameters.
For the exact code used to find the params:
https://github.com/linrock/tuning-fork
This patch introduces `hint_common_parent_position()` to signal that potentially several child nodes will require an NNUE eval. By populating explicitly the accumulator, these subsequent evaluations can be performed more efficiently.
This was based on the observation that calculating the evaluation in an excluded move position yielded a significant Elo gain, even though the evaluation itself was already available (work by pb00067).
Sopel wrote the code to perform just the accumulator update. This PR is based on cleaned up code that
The sdot instruction computes (and accumulates) a signed dot product,
which is quite handy for Stockfish's NNUE code. The instruction is
optional for Armv8.2 and Armv8.3, and mandatory for Armv8.4 and above.
The commit adds a new 'arm-dotprod' architecture with enabled dot
product support. It also enables dot product support for the existing
'apple-silicon' architecture, which is at least Armv8.5.
The following local speed test was performed on an Apple M1 with
ARCH=apple-silicon. I had to remove CPU pinning from the benchmark
script. However, the results were still consistent: Checking both
binaries against themselves reported a speedup of +0.0000 and +0.0005,
respectively.
```
Result of 100 runs
==================
base (...ish.037ef3e1) = 1917997 +/- 7152
test (...fish.dotprod) = 2159682 +/- 9066
diff = +241684 +/- 2923
MinetaS [Mon, 13 Feb 2023 02:54:59 +0000 (11:54 +0900)]
Fix overflow in add_dpbusd_epi32x2
This patch fixes 16bit overflow in *_add_dpbusd_epi32x2 functions,
that can be triggered in rare cases depending on the NNUE weights.
While the code leads to some slowdown on affected architectures
(most notably avx2), the fix is simpler than some of the other
options discussed in
https://github.com/official-stockfish/Stockfish/pull/4394
Created by retraining the master net on a dataset composed of:
* Most of the previous best dataset filtered to remove positions likely having only one good move
* Adding training data from Leela T77 dec2021 rescored with 16tb of 7-piece tablebases
Trained with end lambda 0.7 and max epoch 900. Positions with ply <= 28 were removed from most of the previous best dataset before training began. A new nnue-pytorch trainer param for skipping early plies was used to skip plies <= 24 in the unfiltered and additional Leela T77 parts of the dataset.
The depth6 multipv2 search filtering method is the same as the one used for filtering recent best datasets, with a lower eval difference threshold to remove slightly more positions than before. These parts of the dataset were filtered:
* 96% of T60T70wIsRightFarseerT60T74T75T76.binpack
* 99% of dfrc_n5000.binpack
* T80 oct + nov 2022 data, no positions with castling flags, rescored with ~600gb 7p tablebases
* T79 apr + may 2022 data, rescored with 12tb 7p tablebases
* T60 nov + dec 2021 data, rescored with 12tb 7p tablebases
These parts of the dataset were not filtered. Positions with ply <= 24 were skipped during training:
* T78 aug + sep 2022 data, rescored with 12tb 7p tablebases
* 84% of T77 dec 2021 data, rescored with 16tb 7p tablebases
The code and exact evaluation thresholds used for data filtering can be found at:
https://github.com/linrock/Stockfish/tree/tools-filter-multipv2-eval-diff-t2/src/filter
The exact training data used can be found at:
https://robotmoon.com/nnue-training-data/
Local elo at 25k nodes per move:
nn-epoch859.nnue : 3.5 +/ 1.2
Michael Chaly [Sat, 4 Feb 2023 20:46:44 +0000 (23:46 +0300)]
Cleanup and reorder in qsearch
This patch is a simplification / code normalisation in qsearch.
Adds steps in comments the same way we have in search;
Makes a separate "pruning" stage instead of heuristics randomly being spread over qsearch code;
Reorders pruning heuristics from least taxing ones to more taxing ones;
Removes repeated check for best value not being mated, instead uses 1 check - thus removes some lines of code.
Moves prefetch and move setup after pruning - makes no sense to do them if move will actually get pruned.
pb00067 [Fri, 3 Feb 2023 16:57:19 +0000 (17:57 +0100)]
Improve excluded move logic
PR consists of 2 improvements on nodes with excludeMove:
1. Remove xoring the posKey with make_key(excludedMove)
Since we never call tte->save anymore with excludedMove,
the unique left purpose of the xoring was to avoid a TT hit.
Nevertheless on a normal bench run this produced ~25 false positives
(key collisions)
To avoid that we now forbid early TT cutoff's with excludeMove
Maybe these accesses to TT with xored key caused useless misses
in the CPU caches (L1, L2 ...)
Now doing the probe with the same key as the enclosing search does,
should hit the CPU cache.
2. Don't probe Tablebases with excludedMove.
This can't be tested on fishtest, but it's obvious that
tablebases don't deliver any information about suboptimal moves.
Side note:
Very surprisingly it looks like we cannot use static eval's from
TT since they slightly differ over time due to changing optimism.
Attempts to use static eval's from TT did loose about 13 ELO.
This is something about to investigate.
Michael Chaly [Thu, 2 Feb 2023 10:05:54 +0000 (13:05 +0300)]
Do less SEE pruning in qsearch
Current master prunes all moves with negative SEE values in qsearch.
This patch sets constant negative threshold thus allowing some moves with negative SEE values to be searched.
Value of threshold is completely arbitrary and can be tweaked - also it as function of depth can be tried.
Original idea by author of Alexandria engine.
update the WLD model with about 400M positions extracted from recent LTC games after the net updates.
This ensures that the 50% win rate is again at 1.0 eval.
MinetaS [Thu, 19 Jan 2023 00:49:42 +0000 (09:49 +0900)]
Remove maxNextDepth
This patch allows full PV search to have double extensions as well when
extension == 1 && doDeeperSearch && doEvenDeeperSearch && !doShallowerSearch
is true, which is extremely rare to occur.
Dubslow [Sun, 15 Jan 2023 10:08:33 +0000 (04:08 -0600)]
Remove `previousDepth` in favor of `completedDepth + 2`
Beyond the simplification, this could be considered a bugfix from a certain point of view.
However, the effect is very subtle and essentially impossible for users to notice. 5372f81cc8 added about 2 Elo at LTC, but only for second and later `go` commands; now, with
this patch, the first `go` command will also benefit from that gain. Games under time
controls are unaffected (as per the tests).
disservin [Mon, 23 Jan 2023 18:32:26 +0000 (19:32 +0100)]
Fixed UCI TB win values
This patch results in search values for a TB win/loss to be reported in a way that does not change with normalization, i.e. will be consistent over time.
A value of 200.00 pawns is now reported upon entering a TB won position. Values smaller than 200.00 relate to the distance in plies from the root to the probed position position,
with 1 cp being 1 ply distance.
Michael Chaly [Wed, 25 Jan 2023 05:12:40 +0000 (08:12 +0300)]
Rebalance usage of history heuristics in pruning
This patch has multiple effects:
* history heuristics sum in futility pruning now can't exceed some negative value so futility pruning for moves with negative histories should become slightly less aggressive;
* history heuristics are now used in SEE pruning for quiet moves;
Created by retraining the master net with Leela T78 data from Aug+Sep 2022 added to the previous best dataset. Trained with end lambda 0.7 and started with max epoch 800. All positions with ply <= 28 were skipped:
Around epoch 750, training was manually paused and max epoch increased to 950 before resuming. The additional Leela training data from T78 was prepared in the same way as the previous best dataset.
The exact training data used can be found at:
https://robotmoon.com/nnue-training-data/
While the local elo ratings during this experiment were much lower than in recent master nets, several later epochs had a consistent elo above zero, and this was hypothesized to represent potential strength at slower time controls.
Local elo at 25k nodes per move
leela95-dfrc96-filt-only-T80octnov-T60novdecT78augsepT79aprmay-12tb7p-sk28-lambda7
nn-epoch819.nnue : 0.4 +/- 1.1 (nn-bc24c101ada0.nnue)
nn-epoch799.nnue : 0.3 +/- 1.2
nn-epoch759.nnue : 0.3 +/- 1.1
nn-epoch839.nnue : 0.2 +/- 1.4
Stephen Touset [Mon, 16 Jan 2023 22:25:47 +0000 (14:25 -0800)]
Remove precomputed SquareBB
Bit-shifting is a single instruction, and should be faster than an array lookup
on supported architectures. Besides (ever so slightly) speeding up the
conversion of a square into a bitboard, we may see minor general performance
improvements due to preserving more of the CPU's existing cache.
The old parameterization (https://github.com/official-stockfish/Stockfish/pull/2225/files) has now become quite inaccurate.
This updates the formula based on updated results with master. The formula is based on a fit of the Elo results for games
played between master at various skill levels, and various versions of the Stash engine, which have been ranked at CCRL.
Skill 0 .. 19, now covers CCRL Blitz Elo from 1320 to 3190, approximately.
Indeed, the Elo of stash in this analysis is only to within +- 100 Elo of CCRL,
probably because it depends quite a bit on the opponent pool.
To obtain a skill level for a given Elo number, the above data is fit as a 3rd
degree polynomial Skill(Elo). A quick test confirms the correspondence to the above table:
```
Score of master-elo-2721 vs stash-bot-v21.0: 51 - 16 - 19 [0.703] 86
Elo difference: 150.1 +/- 70.2, LOS: 100.0 %, DrawRatio: 22.1 %
```
Fix asm modifiers in add_dpbusd_epi32x2 implementations
The accumulator should be an earlyclobber because it is written before
all input operands are read. Otherwise, the asm code computes a wrong
result if the accumulator shares a register with one of the other input
operands (which happens if we pass in the same expression for the
accumulator and the operand).
Created by retraining the master net on a dataset composed of:
* The Leela-dfrc_n5000.binpack dataset filtered with depth6 multipv2 search to remove positions with only one good move, in addition to removing positions where either of the two best moves are captures
* The same Leela T80 oct+nov 2022 training data used in recent best datasets
* Additional Leela training data from T60 nov+dec 2021 and T79 apr+may 2022
Trained with end lambda 0.7 and started with max epoch 800. All positions with ply <= 28 were skipped:
Around epoch 780, training was manually paused and max epoch increased to 920 before resuming.
During depth6 multipv2 data filtering, positions were considered to have only one good move if the score of the best move was significantly better than the 2nd best move in a way that changes the outcome of the game:
* the best move leads to a significant advantage while the 2nd best move equalizes or loses
* the best move is about equal while the 2nd best move loses
The modified stockfish branch and exact score thresholds used for filtering are at:
https://github.com/linrock/Stockfish/tree/tools-filter-multipv2-eval-diff/src/filter
About 95% of the Leela portion and 96% of the DFRC portion of the Leela-dfrc_n5000.binpack dataset was filtered. Unfiltered parts of the dataset were left out.
The additional Leela training data from T60 nov+dec 2021 and T79 apr+may 2022 was WDL-rescored with about 12TB of syzygy 7-piece tablebases where the material difference is less than around 6 pawns. Best moves were exported to .plain data files during data conversion with the lc0 rescorer.
The exact training data can be found at:
https://robotmoon.com/nnue-training-data/
Local elo at 25k nodes per move
experiment_leela95-dfrc96-mpv-eval-fonly-T80octnov-T79aprmayT60novdec-12tb7p-sk28-lambda7
run_0/nn-epoch899.nnue : 3.8 +/- 1.6
Removed sprintf() which generated a warning, because of security reasons.
Replace NULL with nullptr
Replace typedef with using
Do not inherit from std::vector. Use composition instead.
optimize mutex-unlocking
Warn if a global function has no previous declaration
If a global function has no previous declaration, either the declaration
is missing in the corresponding header file or the function should be
declared static. Static functions are local to the translation unit,
which allows the compiler to apply some optimizations earlier (when
compiling the translation unit rather than during link-time
optimization).
The commit enables the warning for gcc, clang, and mingw. It also fixes
the reported warnings by declaring the functions static or by adding a
header file (benchmark.h).
mstembera [Wed, 28 Dec 2022 00:44:32 +0000 (16:44 -0800)]
Fix stack initialization
This fixes a bug where on line 278 the Stack::staticEvals are
initialized to 0. However VALUE_NONE is defined to be 32002 so
this is a bug in master. It is probably due to the calculation
of improvement, where staticEval prior to rootPos can be accessed.
This is a later epoch (epoch 859) from the same experiment run that trained yesterday's master net nn-60fa44e376d9.nnue (epoch 779). The experiment was manually paused around epoch 790 and unpaused with max epoch increased to 900 mainly to get more local elo data without letting the GPU idle.
Local elo vs. nn-335a9b2d8a80.nnue at 25k nodes per move:
experiment_leela93-dfrc99-filt-only-T80-oct-nov-skip28
run_0/nn-epoch779.nnue (nn-60fa44e376d9.nnue) : 5.0 +/- 1.2
run_0/nn-epoch859.nnue (nn-a3dc078bafc7.nnue) : 5.6 +/- 1.6
Created by retraining the master net on the previous best dataset with additional filtering. No new data was added.
More of the Leela-dfrc_n5000.binpack part of the dataset was pre-filtered with depth6 multipv2 search to remove bestmove captures. About 93% of the previous Leela/SF data and 99% of the SF dfrc data was filtered. Unfiltered parts of the dataset were left out. The new Leela T80 oct+nov data is the same as before. All early game positions with ply count <= 28 were skipped during training by modifying the training data loader in nnue-pytorch.
Trained in a similar way as recent master nets, with a different nnue-pytorch branch for early ply skipping:
In both modified methods, the variable 'result' is checked to detect
whether the probe operation failed. However, the variable is not
initialized on all paths, so the check might test an uninitialized
value.
A test position (with TB) is given by:
position fen 3K1k2/R7/8/8/8/8/8/R6Q w - - 0 1 moves a1b1 f8g8 b1a1 g8f8 a1b1 f8g8 b1a1
This is now fixed by always initializing the variable.
Created by retraining the master net with a combination of:
the previous best dataset (Leela-dfrc_n5000.binpack), with about half the dataset filtered using depth6 multipv2 search to throw away positions where either of the 2 best moves are captures
Leela T80 Oct and Nov training data rescored with best moves, adding ~9.5 billion positions
Trained effectively the same way as the previous master net:
Local testing at a fixed 25k nodes:
experiments/experiment_leela-dfrc-filtered-T80-oct-nov/training/run_0/nn-epoch779.nnue
localElo: run_0/nn-epoch779.nnue : 4.7 +/- 3.1
The new Leela T80 part of the dataset was prepared by downloading test80 training data from all of Oct 2022 and Nov 2022, rescoring with syzygy 6-piece tablebases and ~600 GB of 7-piece tablebases, saving best moves to exported .plain files, removing all positions with castling flags, then converting to binpacks and using interleave_binpacks.py to merge them together. Scripts used in this data conversion process are available at:
https://github.com/linrock/lc0-data-converter
Filtering binpack data using depth6 multipv2 search was done by modifying transform.cpp in the tools branch:
https://github.com/linrock/Stockfish/tree/tools-filter-multipv2-no-rescore
Links for downloading the training data (total size: 338 GB) are available at:
https://robotmoon.com/nnue-training-data/
MinetaS [Tue, 20 Dec 2022 07:01:05 +0000 (16:01 +0900)]
Fix a dependency bug
Instead of allowing .depend for specific build-related targets, filter
non-build-related targets (i.e. help, clean) so that other targets can
normally execute .depend target.
mstembera [Tue, 13 Dec 2022 07:22:02 +0000 (23:22 -0800)]
Don't reset increaseDepth back to true after it has been set to false
Resetting increaseDepth back to true each time on the very next iteration was not intended so this is a bug fix and a simplification.
See more discussion here #2482 (comment) Thanks to xoto10
Michael Chaly [Sat, 17 Dec 2022 09:48:03 +0000 (12:48 +0300)]
Reintroduce doEvenDeeperSearch
This patch is basically the same as a reverted patch
but now has some guarding against search being stuck - the same
way as we do with double extensions. This should help with
search explosions - albeit slowly but they eventually should be resolved.
Alfredo Menezes [Fri, 9 Dec 2022 15:11:43 +0000 (12:11 -0300)]
Extend all moves at low depth if ttMove is doubly extended
If ttMove is doubly extended, we allow a depth growth of the remaining moves.
The idea is to get a more realistic score comparison, because of the depth
difference. We take some care to avoid this extension for high depths,
in order to avoid the cost, since the search result is supposed
to be more accurate in this case.
Guenther Demetz [Tue, 6 Dec 2022 18:09:33 +0000 (19:09 +0100)]
Correctly output lowerbound/upperbound scores
fixes the lowerbound/upperbound output by avoiding
scores outside the alpha,beta bracket. Since SF search
uses fail-soft we can't simply take the returned value
as score.
FauziAkram [Fri, 2 Dec 2022 15:23:28 +0000 (18:23 +0300)]
doEvenDeeperSearch + tuning
Credit for the main idea of doEvenDeeperSearch goes to Vizvezdenec,
tuning by FauziAkram: Expansion of existing logic of doDeeperSearch -
if value from LMR is really really good do full depth search not
1 ply deeper but rather 2 instead.
ppigazzini [Thu, 8 Dec 2022 17:32:30 +0000 (18:32 +0100)]
Set the right PATH for ARM compiler and build tests in CI
Fix for the GitHub upgrade:
https://github.com/actions/runner-images/issues/5879
that broke our ARM workflows because it changed the value of
the ANDROID_NDK_HOME variable referenced in our PATH.
Today, we have the pleasure to announce Stockfish 15.1.
As usual, downloads will be freely available at stockfishchess.org/download
*Elo gain and competition results*
With this release, version 5 of the NNUE neural net architecture has
been introduced, and the training data has been extended to include
Fischer random chess (FRC) positions. As a result, Elo gains are largest
for FRC, reaching up to 50 Elo for doubly randomized FRC[1] (DFRC).
More importantly, also for standard chess this release progressed and
will win two times more game pairs than it loses[2] against
Stockfish 15. Stockfish continues to win in a dominating way[3] all
chess engine tournaments, including the TCEC Superfinal, Cup, FRC, DFRC,
and Swiss as well as the CCC Bullet, Blitz, and Rapid events.
*New evaluation*
This release also introduces a new convention for the evaluation that
is reported by search. An evaluation of +1 is now no longer tied to the
value of one pawn, but to the likelihood of winning the game. With
a +1 evaluation, Stockfish has now a 50% chance of winning the game
against an equally strong opponent. This convention scales down
evaluations a bit compared to Stockfish 15 and allows for consistent
evaluations in the future.
*ChessBase settlement*
In this release period, the Stockfish team has successfully enforced
its GPL license against ChessBase. This has been an intense process that
included filing a lawsuit[4], a court hearing[5], and finally
negotiating a settlement[6] that established that ChessBase infringed on
the license by not distributing the Stockfish derivatives Fat Fritz 2
and Houdini 6 as free software, and that ensures ChessBase will respect
the Free Software principles in the future. This settlement has been
covered by major chess sites (see e.g. lichess.org[7] and chess.com[8]),
and we are proud that it has been hailed as a ‘historic violation
settlement[9]’ by the Software Freedom Conservancy.
*Thank you*
The Stockfish project builds on a thriving community of enthusiasts
(thanks everybody!) that contribute their expertise, time, and resources
to build a free and open-source chess engine that is robust, widely
available, and very strong. We invite our chess fans to join the
fishtest testing framework and programmers to contribute to the
project[10].
If multiple threads have the same best move,
pick the thread with the largest contribution to the confidence vote.
This thread will later be used to display PV, so this patch is
about user-friendliness and/or least surprises, it non-functional for playing strenght.
Guenther Demetz [Tue, 22 Nov 2022 10:07:18 +0000 (11:07 +0100)]
Correctly output lowerbound/upperbound in threaded searches
fixes the lowerbound/upperbound output by taking the alpha,beta bracket
into account also if a bestThread is selected that is different from the master thread.
Instead of keeping track which bounds where used in the specific search,
in this version we simply store the quality (exact, upperbound,
lowerbound) of the score along with the actual score as information on
rootMove.
VoyagerOne [Tue, 22 Nov 2022 17:07:33 +0000 (20:07 +0300)]
Skip full depth search in LMR depending on depth
dynamically adjust newDepth, and skip full depth search if newDepth doesn't exceed the previous search depth.
This affects the used newDepth for future searches, and influences the stat bonus for the move.
disservin [Mon, 7 Nov 2022 17:15:42 +0000 (18:15 +0100)]
speedup CI
Github Actions allows us to use up to 20 workers.
This way we can launch multiple different checks
at the same time and optimize the overall time
the CI takes a bit.
disservin [Sun, 6 Nov 2022 15:17:17 +0000 (16:17 +0100)]
Change versioning and save binaries as CI artifacts
For development versions of Stockfish, the version will now look like
dev-20221107-dca9a0533
indicating a development version, the date of the last commit,
and the git SHA of that commit. If git is not available,
the fallback is the date of compilation. Releases will continue to be
versioned as before.
Additionally, this PR extends the CI to create binary artifacts,
i.e. pushes to master will automatically build Stockfish and upload
the binaries to github.
Normalizes the internal value as reported by evaluate or search
to the UCI centipawn result used in output. This value is derived from
the win_rate_model() such that Stockfish outputs an advantage of
"100 centipawns" for a position if the engine has a 50% probability to win
from this position in selfplay at fishtest LTC time control.
The reason to introduce this normalization is that our evaluation is, since NNUE,
no longer related to the classical parameter PawnValueEg (=208). This leads to
the current evaluation changing quite a bit from release to release, for example,
the eval needed to have 50% win probability at fishtest LTC (in cp and internal Value):
June 2020 : 113cp (237)
June 2021 : 115cp (240)
April 2022 : 134cp (279)
July 2022 : 167cp (348)
With this patch, a 100cp advantage will have a fixed interpretation,
i.e. a 50% win chance. To keep this value steady, it will be needed to update the win_rate_model()
from time to time, based on fishtest data. This analysis can be performed with
a set of scripts currently available at https://github.com/vondele/WLD_model
projects / stockfish / log