Stockfish Docs

Depth vs. TC

Newer and older results showing the average depth for games at fishtest conditions

New	Old

Elo cost of small Hash

We measure the influence of Hash on the playing strength, using games of SF15.1 at LTC (60+0.6s) and VLTC (240+2.4s) on the UHO book. Hash is varied between 1 and 64 MB and 256MB in powers of two, leading to as average hashfull between 100 and 950 per thousand. The data suggests that keeping the average hashfull below 30% is best to maintain strength.

Raw data for the above graph

Hash	Hashfull	Elo	Elo-err
64	109	0.00	0.00
32	199	-3.80	13.00
16	336	0.70	12.80
8	513	-10.70	11.00
4	689	-21.50	13.30
2	825	-29.50	13.10
1	902	-47.80	8.80

Hash	Hashfull	Elo	Elo-err
256	131	0.00	0.00
128	239	-1.00	7.50
64	397	-0.80	6.60
32	591	-12.10	6.10
16	766	-21.40	7.30
8	865	-32.30	4.20
4	931	-52.40	6.20
2	943	-67.40	5.70
1	947	-95.20	6.60

Elo cost of using MultiPV

MultiPV provides the N best moves and their principal variations. This is a great tool for understanding the options available in a given position. However, this information does not come for free and the cost of computing it reduces the quality of the best move found compared to a search that only needs to find a single line.

MultiPV	Elo	Elo-err
1	0.0	0.0
2	-97.2	2.1
3	-156.7	2.8
4	-199.3	2.9
5	-234.5	2.8

Engine: Stockfish 15.1
Time control: 60s+0.6s
Book: UHO

Elo gain using MultiPV at fixed depth

MultiPV	Elo	Elo-err	Points	Played
1	0.0		13496.5	30614
2	45.7	3.1	15388.0	30697
3	53.9	3.5	15732.5	30722
4	59.5	3.2	15862.5	30479
5	63.7	3.6	16078.5	30604

Time control: 580s+5.8s
Depth: 18

Elo gain using syzygy

TB6 testing for various versions of SF

Consistent measurement of Elo gain (syzygy 6men vs none) for various SF versions:

TB are in RAM (so fast access), TC is 10+0.1s (STC), book UHO_XXL_+0.90_+1.19.epd. No adjudication. The introduction of NNUE (with SF12) is clearly visible. With SF15, there is just 2.7 Elo gain.

Raw data for the above graph

SF	Elo	Elo-err
6	14.5	1.4
7	15.6	1.3
8	15.8	1.3
9	16.5	1.5
10	16.2	1.5
11	15.8	1.5
12	7.2	1.4
13	11.1	1.4
14	7.3	1.4
15	2.7	1.4

Testing depending on number of pieces and TC

Tested at 10+0.1, with all syzygy WDL files on tmpfs (i.e. RAM), testing using none(0), 4, 5, and 6 man TB in a round-robin tournament (SF10dev).

Rank	Name	Elo	+/-	Games	Score	Draws
1	syzygy6	13	2	82591	51.8%	59.5%
2	syzygy5	2	2	82590	50.3%	59.4%
3	syzygy4	-7	2	82591	49.0%	59.3%
4	syzygy0	-7	2	82592	48.9%	59.4%

Tested at 60+0.6, with all syzygy WDL files on tmpfs (i.e. RAM), testing using none(0) against 6 man TB:

Score of syzygy6 vs syzygy0: 4084 - 3298 - 18510 [0.515] 25892 Elo difference: 10.55 +/- 2.25

Threading efficiency and Elo gain.

Efficiency

Here we look at the threading efficiency of the lazySMP parallelization scheme. To focus on the algorithm we play games with a given budget of nodes rather than at a given TC. In principle, lazySMP has excellent scaling of the nps with cores, but practical measurement is influenced by e.g. frequency adjustments, SMT/hyperthreading, and sometimes hardware limitation.

Equivalent nodestime

In these tests, matches are played at a fixed nodes budget (using the nodestime feature of SF), and equivalence in strength between the serial player and the threaded player (for x threads in the graph below) is found by adjusting the number of nodes given to the threaded player (e.g. with 16 threads, the threaded player might need 200% of the nodes of the serial player to match the strength of the serial player). This 'equivalent nodestime' is determined for various number of threads and various nodes budgets (60+0.6Mnodes/game is somewhat similar to our usual LTC at 60+0.6s/game, if we assume 1Mnps).

The interesting observation one can make immediately is that this 'equivalent nodestime' grows with the number of threads, but not too steeply, and further more that the 'equivalent nodestime' decreases with increasing nodes budget. The data shows that with 64 threads, the equivalent nodestime is about 200% for a node budget of 240+2.4Mn, i.e. despite such games being much faster than STC (10+0.1s), efficiency is still around 50%.

The curves are sufficiently smooth to be fitted with a model having 1 parameter that is different between the curves (f(x), parameter a, see caption). A smaller value of a means a higher efficiency.

A fit for the `a` parameter, and extrapolation to long TCs.

The above parameter a from the model, can be fit as a function of nodes budget, this allows for extrapolating the parameter, and to arrive at and estimate for the 'equivalent nodestime' at large TC / nodes budgets:

The fit is again fairly good. Taking a leap of faith, these measurements at up to 240+2.4Mn can be extrapolated to node budgets typical of TCEC or CCC (up to 500Gn). This allows us to predict speedup and/or efficiency.

Speedup	Efficiency

These extrapolations suggest that even at thread counts of >300, at TCEC TCs efficiency could be 80% or higher, provided the nps scales with the number of threads.

Elo results (older)

LTC

Playing 8 threads vs 1 thread at LTC (60+0.6, 8moves_v3.pgn):

Score of t8 vs seq: 476 - 3 - 521  [0.737] 1000
Elo difference: 178.6 +/- 14.0, LOS: 100.0 %, DrawRatio: 52.1 %

Playing 1 thread at 8xLTC (480+4.8) vs (60+0.6) (8moves_v3.pgn):

Score of seq8 vs seq: 561 - 5 - 434  [0.778] 1000
Elo difference: 217.9 +/- 15.8, LOS: 100.0 %, DrawRatio: 43.4 %

Which is roughly 82% efficiency (178/218).

STC

Playing 8 threads vs 1 thread at STC (10+0.1):

Score of threads vs serial: 1606 - 15 - 540  [0.868] 2161
Elo difference: 327.36 +/- 14.59

Playing 8 threads @ 10+0.1 vs 1 thread @ 80+0.8:

Score of threads vs time: 348 - 995 - 2104  [0.406] 3447
Elo difference: -66.00 +/- 7.15

So, 1 -> 8 threads has about 83% scaling efficiency (327 / (327 + 66)) using this test.

Elo from speedups

For small speedups (<~5%) the linear estimate can be used that gives Elo gain as a function of speedup percentage (x) as:

Elo_stc(x) = 2.10 x
Elo_ltc(x) = 1.43 x

To have 50% passing chance at STC<-0.5,1.5>, we need a 0.24% speedup, while at LTC<0.25,1.75> we need 0.70% speedup. A 1% speedup has nearly 85% passing chance at LTC.

Raw data:

tc 10+0.1:
16   32.42  3.06
 8   13.67  3.05
 4    8.99  3.04
 2    3.52  3.05

tc 60+0.6:
16   20.85  2.59
 8   12.20  2.57
 4    4.67  2.57

_Note: Numbers will depend on the precise hardware. The model was verified quite accurately on fishtest see https://github.com/locutus2/Stockfish-old/commit/82958c97214b6d418e5bc95e3bf1961060cd6113#commitcomment-38646654_

Distribution of lengths of games at LTC (60+0.6) on fishtest

In a collection of a few million games, the longest was 902 plies.

Win-Draw-Loss statistics of LTC games on fishtest

The following graph gives information on the Win-Draw-Loss (WDL) statistics, relating them to score and material count. It answers the question 'What fraction of positions that have a given score (and material count) in fishtest LTC, have a Win or a Draw or a Loss ?'.

This model is used when Stockfish provides WDL statistics during analysis with the UCI_ShowWDL option set to True, as well as for the normalization of Stockfish's evaluation that ensures that a score of "100 centipawns" means the engine has a 50% probability to win from this position in selfplay at fishtest LTC time control. For details see the WDL model repo.

Equivalent time odds and normalized game pair Elo

A suitable measure to define the Elo difference between two engines is normalized game pair Elo as defined from the pentanomial statistics by:

python

def normalized_game_pair_elo(row):
    return -100 * np.log10((2 * row['pntl0'] + row['pntl1']) / (2 * row['pntl4'] + row['pntl3']))

It is nearly book independent, and thus a good measure of relative strength of two engines at a given TC. To express more clearly what a given strength difference implies. We use 'equivalent time odds', i.e. the TC factor needed to have equivalent strength, i.e. zero Elo difference in a match between two engines (which is independent of the definition of Elo used).

We see that at STC the equivalent time odds is about 6x for SF14 vs SF17, while at LTC this time odds factor has become 16x.

Raw data for the above graph

=======================================  UHO_Lichess_4852_v1 =======================================
   engine1        tc1    engine2        tc2        elo  pntl0  pntl1  pntl2  pntl3  pntl4    ngp_Elo
      sf17     10+0.1       sf14   10.0+0.1     165.29     10    432   6509  25598   3291     185.24
      sf17     10+0.1       sf14   40.0+0.4      41.74    251   5110  16632  13516    331      40.25
      sf17     10+0.1       sf14   60.0+0.6       7.47    395   7724  17826   9736    159       7.22
      sf17     10+0.1       sf14   80.0+0.8     -17.52    569  10075  17691   7408     97     -16.88
=======================================          noob_3moves =======================================
   engine1        tc1    engine2        tc2        elo  pntl0  pntl1  pntl2  pntl3  pntl4    ngp_Elo
      sf17     10+0.1       sf14   10.0+0.1     108.47      6    610  16073  16012   3139     155.43
      sf17     10+0.1       sf14   40.0+0.4      10.85    100   3004  27471   5089    176      23.00
      sf17     10+0.1       sf14   60.0+0.6      -4.87    165   4048  28312   3257     58     -11.33
      sf17     10+0.1       sf14   80.0+0.8     -15.02    219   4892  28520   2184     25     -37.76
=======================================  UHO_Lichess_4852_v1 =======================================
   engine1        tc1    engine2        tc2        elo  pntl0  pntl1  pntl2  pntl3  pntl4    ngp_Elo
      sf17     60+0.6       sf14   60.0+0.6     163.96      1    194   5269  29060   1316     220.87
      sf17     60+0.6       sf14  240.0+2.4      88.09     25   2021  14134  19482    178      98.13
      sf17     60+0.6       sf14  360.0+3.6      63.06     41   3212  16546  15938    103      69.03
      sf17     60+0.6       sf14  480.0+4.8      46.39     72   4243  17703  13760     62      50.03
=======================================          noob_3moves =======================================
   engine1        tc1    engine2        tc2        elo  pntl0  pntl1  pntl2  pntl3  pntl4    ngp_Elo
      sf17     60+0.6       sf14   60.0+0.6      71.55      0    131  22234  12279   1196     204.92
      sf17     60+0.6       sf14  240.0+2.4      19.23      0    436  31090   4231     83     100.37
      sf17     60+0.6       sf14  360.0+3.6      11.51      3    616  32255   2938     28      68.25
      sf17     60+0.6       sf14  480.0+4.8       7.08      6    716  32949   2149     20      47.81

Elo gain with time odds

New	Old

One year of NNUE speed improvements

Presents nodes per second (nps) measurements for all SF version between the first NNUE commit (SF_NNUE, Aug 2th 2020) and end of July 2021 on a AMD Ryzen 9 3950X compiled with make -j ARCH=x86-64-avx2 profile-build. The last nps reported for a depth 22 search from startpos using NNUE (best over about 20 measurements) is shown in the graph. For reference, the last classical evaluation (SF_classical, July 30 2020) has 2.30 Mnps.

The impact of efficient (incremental) updates (NNUE)

As measured with SF17dev (dev-20230824-4c4cb185), disabling the update_accumulator_incremental() functionality.

Speedup:

Result of  10 runs
==================
base (./stockfish.master       ) =    1287575  +/- 8703
test (./stockfish.patch        ) =     696064  +/- 3451
diff                             =    -591511  +/- 7318

speedup        = -0.4594
P(speedup > 0) =  0.0000

CPU: 16 x AMD Ryzen 9 3950X 16-Core Processor

Which corresponds to -67.55 ± 9.5 Elo on fishtest with the UHO book at LTC.

Round-robin tournament with SF releases, impact of book and time odds

Measured playing games of 5+0.05s, with SF 7 - 15, using the three different books. Each version plays once with the base TC, and once with 20% time odds.

Raw data for the above graph

UHO

SF	Elo	20%-odds	Elo-err	Odds-err
SF7	0.0	38.1	0.0	4.1
SF8	95.8	40.3	4.2	5.9
SF9	142.3	38.8	3.9	5.5
SF10	199.5	38.2	4.0	5.7
SF11	231.2	40.5	4.3	5.7
SF12	405.6	37.5	4.0	5.9
SF13	476.5	28.4	4.2	6.0
SF14	553.4	27.8	4.5	6.3
SF15	627.6	24.5	4.6	6.7

noob

SF	Elo	20%-odds	Elo-err	Odds-err
SF7	0.0	37.8	0.0	3.8
SF8	97.2	39.8	4.3	5.9
SF9	146.8	40.5	3.9	5.9
SF10	211.1	39.3	4.3	6.2
SF11	241.8	43.0	4.4	6.0
SF12	458.4	32.1	4.3	6.2
SF13	536.2	31.9	4.2	6.3
SF14	611.3	29.1	4.5	6.5
SF15	660.9	24.8	4.3	6.2

8moves

SF	Elo	20%-odds	Elo-err	Odds-err
SF7	0.0	33.0	0.0	4.2
SF8	86.7	32.2	4.2	5.8
SF9	126.7	37.3	4.0	5.6
SF10	182.3	33.7	4.3	5.6
SF11	206.5	42.6	4.0	5.4
SF12	380.7	31.6	4.1	5.6
SF13	445.8	25.0	4.0	5.7
SF14	512.4	23.8	4.1	5.9
SF15	554.5	26.4	4.1	5.9

Branching factor of Stockfish

The branching factor ($B_f$) of Stockfish is defined such that $\text{nodes} = B_f^{\text{rootDepth}}$ or equivalently $B_f = \exp\left(\frac{\log(\text{nodes})}{\text{rootDepth}}\right)$. Here, this has been measured with a single search from the starting position.

The trend is the deeper one searches the lower the branching factor, and newer versions of SF have a lower branching factor. A small difference in branching factor leads to very large differences in number of nodes searched. For example, Stockfish 10 needs about 338x more nodes than Stockfish 17 to reach depth 49.

chartbf

Raw data for the above graph

Depth	SF_9	SF_10	SF_11	SF_Classical	SF_12	SF_13	SF_14	SF_14.1	SF_15	SF_15.1	SF_16	SF_16.1	SF_17	SF_17.1
1	20	20	20	20	20	20	20	20	20	20	20	20	20	20
2	47	54	54	56	42	42	48	51	45	66	40	44	48	45
3	152	136	147	150	153	84	181	154	191	120	70	69	76	72
4	495	247	574	479	303	241	630	807	264	144	101	92	97	512
5	1036	1157	782	989	532	572	741	1061	1449	174	131	123	124	609
6	2148	2250	2734	1161	1004	741	2448	1761	3954	1303	489	1815	197	752
7	3836	4481	5780	2808	1680	1169	3488	5459	6269	3126	1560	2096	309	2019
8	6480	7849	9568	10224	4717	1849	5172	6998	11049	5791	2105	2565	1984	3436
9	12958	11846	14134	15801	7299	3894	10602	12053	15347	8541	4500	5901	3415	4748
10	27933	27338	18613	25231	15571	10144	23676	28785	23259	20978	7548	7103	3634	8620
11	89387	61978	37332	36205	32300	29662	44751	34551	40064	29040	13058	22753	8402	15172
12	161734	80917	99541	98957	79712	57232	60151	103152	57767	41207	35836	27955	11157	34187
13	257254	160903	161123	159227	97905	89986	99443	166195	97551	60308	59384	31553	14979	37544
14	292516	263420	235857	188774	135890	114602	140890	226798	152736	97789	73942	47991	47967	50495
15	619466	474032	525778	372631	239380	263818	206290	296769	214622	142447	173344	90454	82930	64450
16	1019034	680143	654750	405644	420597	319442	327876	460525	484091	199887	302305	130482	117953	80751
17	1273498	1065531	1154718	497031	502334	539694	700175	620141	570807	268004	358119	279903	130041	100673
18	2090089	1736290	1333553	602794	888362	605175	1254757	674532	873460	298071	406239	378645	278301	242639
19	3889524	2883012	2466048	1697325	1163966	849949	1461089	1086024	1119774	332202	517102	492584	479846	294310
20	6651739	4186926	3442296	2223954	2233676	1504284	2142551	1731345	1494341	557706	768773	898610	631058	356864
21	8289557	4805680	5139240	2921191	2445813	3168396	2296200	2217575	2170638	753633	872083	1176108	698143	693162
22	10859418	6827891	6158594	4887298	3007878	3898518	3992124	2939401	2709915	1197489	1356583	1504022	942656	753875
23	15123810	11731363	9742620	5254745	4135648	5354837	5163931	7071762	3144247	1586189	2240135	2486833	1239406	901681
24	20808306	17783159	12170495	5804128	5840781	6300391	8203572	9001254	5196382	2305729	3180221	2849481	2442109	1179519
25	29432182	25759382	16577641	9887365	8616999	9226135	9663515	9575262	7230340	3251912	3441646	3582655	2851004	2042808
26	38405658	37355659	29415435	15416329	12367860	13291389	13132115	12540800	10155997	3545947	5785705	5160652	3777377	2915802
27	61348538	65193345	35334801	16892508	14200465	17614230	17260982	14253616	11757619	3840811	7477520	6713587	4182386	4365029
28	91156568	84966056	52360545	22446945	20151711	22949203	23305850	17086667	14952204	4248920	7825201	7315094	4579410	4505986
29	152014843	113526990	56749397	31024928	24336215	27545670	32590861	24537585	16522922	6665787	9338607	8935544	5542564	4841166
30	208471933	134803005	80264413	40636168	41377827	52979471	37897422	29100645	17532106	9416686	11373475	12808942	6276969	6090037
31	240772842	196015388	120969918	43679803	49872575	70001618	43511005	39989064	21326309	12481255	14291027	16399268	7084546	6583727
32	305614124	243327159	176419860	60779582	77092786	83237024	66185509	58476165	35327951	13176152	14508031	19160671	9105481	8718066
33	460341298	525261279	262818230	104196553	99594493	121678252	70265337	74720366	42781773	29047503	21536616	28320792	12975039	8882232
34	627444498	626723336	349662654	147117787	134411590	131502991	117669613	89119274	51973369	33777300	24887993	31733455	17887048	12279026
35	877803214	1117294961	437775571	244005733	167315794	172011884	149278426	110476836	55002290	39953796	32167747	36703106	21077663	13673813
36	990773530	1824606196	547475749	296743922	216932118	226846508	281527921	161111544	66188561	52697231	36605915	45026834	30184087	15440239
37	1741868476	1948381278	625189048	309340804	255329101	286768418	327695873	324326807	89076357	63613297	39166657	48749981	36650844	16953238
38	2363551706	3608706372	1432246160	358255505	378948934	428910293	415235309	419954003	104499345	81300722	43595727	57954972	39310411	18760781
39	3148966217	5554166307	1959631791	418553825	654266100	578750849	624817445	485331551	146707304	109373862	62509514	80617677	40464757	31534030
40	3652327064	10014646570	2986844761	647206461	794186607	736676898	984219044	627202823	181038342	120243575	89602927	105004273	41399955	38626444
41	5224502250	11274140350	4543685536	802616934	1476486355	945454647	1610720260	702641311	327908422	127205662	98940749	145615463	49181743	41710464
42	6127078317	13841000586	5932293513	836227440	1533085761	1746304445	1953800834	1052213918	588646348	138853511	117973493	180607180	67316394	48578868
43	16232620003	17967835068	7783862835	896362311	1962927879	2372473217	2416294925	1204729923	704287921	187144599	143353641	190606039	70823411	65829957
44	17718877503	25273820455	9652527844	1952302791	2099616405	3372924274	3072158370	1970670881	872977208	233082949	156729107	227657402	101871246	91721103
45	25741622196	30584201846	14557217345	2862286068	2534362233	4495023334	4453379221	2145570435	1125148935	286366031	203808982	286695307	139675067	113349617
46	40499236349	40789584966	15067833369	3334440078	5072562082	4943259313	8612561011	3902062051	1928316855	357211612	241777135	443906150	146118621	132959880
47	62111873752	46215215573	19558181065	9230842551	5760986205	10088353213	8788037210	7455854537	2230430028	479309517	286733914	524498432	151232944	174172903
48	86847634536	63800375952	23005194627	10641223658	7116913776	13627712965	12633107125	8190890802	3212535920	658225438	367143915	641079230	158042099	236563617
49	111558931172	120597835455	73124504606	23520860602	21079212074	15273984174	22132085123	9179551077	3418110578	839590469	543884199	887068496	356737000	379142240
50			121532848050		28477080465	33936548877	44066435831	11975030336	5444154366	1093863142	701965402	1119335316	422735554	425769633

Contempt measurements

Older SF (around SF10) had contempt that worked rather well. This data shows the dependence of Elo difference between SFdev of October 2018 and older versions of Stockfish depending on contempt value (The SFdev used is approx. 40Elo above SF9). Upper and lower bounds represent value with maximum error.

Opponent	STC	LTC
7
8
9

Full data with values https://docs.google.com/spreadsheets/d/1R_eopD8_ujlBbt_Q0ygZMvuMsP1sc4UyO3Md4qL1z5M/edit#gid=1878521689

Elo change with respect to TC

Here is the result of some scaling tests with the 2moves book. 40000 games each (STC=10+0.1, LTC=60+0.6)

	SF7 -> SF8	SF8 -> SF9	SF9 -> SF10
Elo STC	95.91 +-2.3	58.28 +-2.3	71.03 +-2.4
Elo LTC	100.40 +-2.1	68.55 +-2.1	65.55 +-2.2

So we see that the common wisdom that increased TC causes elo compression is not always true.

See https://github.com/official-stockfish/Stockfish/issues/1859#issuecomment-449624976

TC dependence of certain terms in search

Discussed here https://github.com/official-stockfish/Stockfish/pull/2401#issuecomment-552768526

Elo contributions from various evaluation terms

See spreadsheet at: https://github.com/official-stockfish/Stockfish/files/3828738/Stockfish.Feature.s.Estimated.Elo.worth.1.xlsx

Note: The estimated elo worth for various features might be outdated, or might get outdated soon.

Depth vs. TC ​

Elo cost of small Hash ​

Elo cost of using MultiPV ​

Elo gain using MultiPV at fixed depth ​

Elo gain using syzygy ​

TB6 testing for various versions of SF ​

Testing depending on number of pieces and TC ​

Threading efficiency and Elo gain. ​

Efficiency ​

Equivalent nodestime ​

A fit for the a parameter, and extrapolation to long TCs. ​

Elo results (older) ​

LTC ​

STC ​

Elo from speedups ​

Distribution of lengths of games at LTC (60+0.6) on fishtest ​

Win-Draw-Loss statistics of LTC games on fishtest ​

Equivalent time odds and normalized game pair Elo ​

Elo gain with time odds ​

One year of NNUE speed improvements ​

The impact of efficient (incremental) updates (NNUE) ​

Round-robin tournament with SF releases, impact of book and time odds ​

UHO ​

noob ​

8moves ​

Branching factor of Stockfish ​

Contempt measurements ​

Elo change with respect to TC ​

TC dependence of certain terms in search ​

Elo contributions from various evaluation terms ​

Depth vs. TC

Elo cost of small Hash

Elo cost of using MultiPV

Elo gain using MultiPV at fixed depth

Elo gain using syzygy

TB6 testing for various versions of SF

Testing depending on number of pieces and TC

Threading efficiency and Elo gain.

Efficiency

Equivalent nodestime

A fit for the `a` parameter, and extrapolation to long TCs.

Elo results (older)

LTC

STC

Elo from speedups

Distribution of lengths of games at LTC (60+0.6) on fishtest

Win-Draw-Loss statistics of LTC games on fishtest

Equivalent time odds and normalized game pair Elo

Elo gain with time odds

One year of NNUE speed improvements

The impact of efficient (incremental) updates (NNUE)

Round-robin tournament with SF releases, impact of book and time odds

UHO

noob

8moves

Branching factor of Stockfish

Contempt measurements

Elo change with respect to TC

TC dependence of certain terms in search

Elo contributions from various evaluation terms