27 April, 2025 Jorge Ruiz Centelles 0 Comments 1 category

Opening for Stockfish

Introduction

In the realm of computer chess, Stockfish stands as one of the most formidable engines ever developed. Its unparalleled search algorithms, combined with the Neural Network Unified Evaluation (NNUE) enhancement, enable it to navigate the vast complexities of chess positions with astonishing precision. However, even the strongest engine’s performance can hinge critically on the opening it encounters. Certain openings lead to dynamic imbalances that favour tactical fireworks, while others settle into quiet manoeuvring that exploits Stockfish’s deep positional understanding.

This article examines which White openings yield the best results for Stockfish, drawing on multiple authoritative sources:

By analysing these data, we will:

  1. Explain the methodology and data sources used to gauge opening performance.
  2. Detail Stockfish’s performance in CCRL 40/15 opening tests, highlighting the top-performing ECO lines for White.
  3. Survey superfinal results in TCEC, illustrating how Stockfish converts with White against elite opposition.
  4. Analyse the factors that make certain openings particularly favourable.
  5. Recommend, in objective order, the White openings Stockfish should employ to maximise its winning chances.

Throughout, we provide win–draw–loss percentages and absolute counts, backed by precise citations, and conclude with a solid, SEO-optimised summary of the best White openings for Stockfish.

Methodology and Data Sources

A rigorous analysis of opening performance for Stockfish requires multiple vantage points. Below, we outline the key data sources, their scope, and how we extract meaningful statistics.

CCRL 40/15 Opening Statistics

The Computer Chess Rating Lists (CCRL) maintain a vast database of engine games played at the 40 moves in 15 minutes time control. As of 25 April 2025, the CCRL 40/15 dataset comprised 2,074,188 games involving 4,130 programs. Aggregate results show:

  • White wins: 634,646 (30.6%)
  • Black wins: 441,505 (21.3%)
  • Draws: 998,037 (48.1%)
  • White score: 54.7% (CCRL).

CCRL classifies each game by its ECO code and provides, for each line:

  1. Number of games.
  2. Draw percentage.
  3. White score (i.e. total points for White ÷ games).

From these, we compute:

  • Win percentage for White (W%) = White score – (Draw % ÷ 2).
  • Loss percentage (L%) = 100% – Draw % – Win %.

Absolute counts (Wins, Draws, Losses) are obtained by multiplying each percentage by the number of games.

Opening Stockfish

Stockfish Testing Framework

To refine its strength, Stockfish employs a continuous testing framework (tests.stockfishchess.org) that runs tens of thousands of short games on each code update. For instance, on 27 April 2025, the long_term_improving_6_r network was tested over 93,568 games at the 10 + 0.1 time control, yielding:

  • Wins: 23,982
  • Losses: 23,990
  • Draws: 45,596

This corresponds to a roughly 25.6% win rate, 25.6% loss rate, and 48.7% draw rate (Stockfish Testing Framework). Although these figures do not break down by opening, they illustrate the baseline balance and robustness of Stockfish’s neural evaluation, which directly influences its handling of opening positions.

NNUE Training (nnue-pytorch)

Stockfish’s NNUE (efficiently updatable neural network) evaluator is trained using the nnue-pytorch framework. This repository provides tools to:

The continual improvement of NNUE nets enhances Stockfish’s opening play, particularly in positions requiring nuanced evaluation of imbalances.

TCEC Superfinals

The Top Chess Engine Championship (TCEC) pits the world’s strongest engines in month-long leagues culminating in a 100-game superfinal. Notable Stockfish superfinal results include:

While these aggregate figures span many openings, they showcase Stockfish’s ability to convert with White under classical time controls.

Stockfish Performance in CCRL 40/15 ECO Report

Stockfish’s opening results vary significantly across different ECO lines. Below, we detail the six highest-scoring White openings (by White score ≥ 60.0% and sample size ≥ 450 games) from the CCRL dataset. For each, we present:

  • ECO code and opening name.
  • Total games.
  • Win/Draw/Loss percentages.
  • Absolute counts (rounded to the nearest whole game).

B03: Alekhine’s Defence

  • Games: 2,982
  • Draw rate: 42.9%
  • White score: 61.7%
  • Win rate: 40.3% (≈1,200 wins)
  • Loss rate: 16.9% (≈502 losses)
  • Draws: 42.9% (≈1,280 draws)

Calculation:
Wins % = 61.7 – (42.9 ÷ 2) = 40.3%;
Losses % = 100 – 42.9 – 40.3 = 16.8% (CCRL).

A59: Benko Gambit, 7.e4

  • Games: 462
  • Draw rate: 46.5%
  • White score: 65.5%
  • Win rate: 42.3% (≈195 wins)
  • Loss rate: 11.2% (≈52 losses)
  • Draws: 46.5% (≈215 draws)

Wins % = 65.5 – (46.5 ÷ 2) = 42.3%;
Losses % = 100 – 46.5 – 42.3 = 11.2% (CCRL).

B16: Caro-Kann, Bronstein-Larsen Variation

  • Games: 937
  • Draw rate: 38.7%
  • White score: 61.3%
  • Win rate: 42.0% (≈393 wins)
  • Loss rate: 19.4% (≈181 losses)
  • Draws: 38.7% (≈363 draws)

Wins % = 61.3 – (38.7 ÷ 2) = 42.0%;
Losses % = 100 – 38.7 – 42.0 = 19.3% (CCRL).

A55: Old Indian Defence, Main Line

  • Games: 883
  • Draw rate: 37.7%
  • White score: 61.2%
  • Win rate: 42.4% (≈374 wins)
  • Loss rate: 20.0% (≈176 losses)
  • Draws: 37.7% (≈333 draws)

Wins % = 61.2 – (37.7 ÷ 2) = 42.4%;
Losses % = 100 – 37.7 – 42.4 = 19.9% (CCRL).

A42: Modern Defence, Averbakh System

  • Games: 2,442
  • Draw rate: 39.5%
  • White score: 61.0%
  • Win rate: 41.3% (≈1,008 wins)
  • Loss rate: 19.3% (≈471 losses)
  • Draws: 39.5% (≈963 draws)

Wins % = 61.0 – (39.5 ÷ 2) = 41.3%;
Losses % = 100 – 39.5 – 41.3 = 19.2% (CCRL).

A52: Budapest Defence, Declined

  • Games: 2,158
  • Draw rate: 49.3%
  • White score: 60.6%
  • Win rate: 36.0% (≈776 wins)
  • Loss rate: 14.7% (≈318 losses)
  • Draws: 49.3% (≈1,064 draws)

Wins % = 60.6 – (49.3 ÷ 2) = 36.0%;
Losses % = 100 – 49.3 – 36.0 = 14.7% (CCRL).

Stockfish Performance in TCEC Tournaments

Stockfish’s prowess extends beyond synthetic CCRL tests and into the crucible of TCEC superfinals, where it faces equally relentless adversaries on classical time controls. Below are select superfinal statistics, illustrating how Stockfish converts with White in a broad opening mix:

While the precise opening breakdown in TCEC is proprietary, these aggregate results confirm Stockfish’s ability to maintain a robust edge when wielding the White pieces under high-stakes conditions.

Analysis: Why Certain Openings Excel

The statistical dominance of particular ECO lines for White against Stockfish hinges on several interrelated factors:

  1. Imbalanced Structures
    • Benko Gambit, 7.e4 (A59): This line sacrifices a queenside pawn to generate dynamic play. Stockfish’s ability to evaluate static weaknesses versus dynamic initiative allows it to extract maximum compensation, resulting in a 65.5% White score in the CCRL database (CCRL).
    • Alekhine’s Defence (B03): The early attack on the central e-pawn leads to asymmetrical pawn chains. Stockfish excels at manoeuvring in such positions, achieving 40.3% wins and only 16.9% losses (CCRL).
  2. Complex Tactical Themes
    • Caro-Kann Bronstein-Larsen (B16) and Old Indian Main Line (A55) both feature rich tactical skirmishes. Stockfish’s deep search tree and NNUE evaluation detect intricate tactics earlier than many opponents, translating into over 42% win rates in CCRL tests (CCRL).
  3. Deep Positional Play
    • Modern Defence, Averbakh (A42) and Budapest Defence Declined (A52) often settle into nuanced positional battles. Stockfish’s granular evaluation of pawn structures and piece activity yields White scores above 60% (CCRL).
  4. Network Evaluation Strength
  5. Opening Book Quality
    • While Stockfish primarily relies on brute-force search, its built-in opening books and synergy with external .epd books (as used in CCRL and TCEC) ensure it reaches favourable book exit positions. The testing framework confirms consistent performance improvements with each network iteration (Stockfish Testing Framework).

In sum, openings that present a blend of imbalance, tactical nuance, and deep strategic themes tend to showcase Stockfish’s engine features most effectively.

Recommended White Openings for Stockfish

Drawing on the above data, here is an objective, ordered list of the best White openings for Stockfish—ranked by White score in CCRL tests and corroborated by TCEC success:

  1. Benko Gambit, 7.e4 (A59) — 65.5% White score (≈195 W–215 D–52 L)
    Dynamic play against the Benko yields the highest conversion rate.
  2. Alekhine’s Defence (B03) — 61.7% White score (≈1,200 W–1,280 D–502 L)
    Imbalanced pawn structure favours Stockfish’s tactical acumen.
  3. Caro-Kann, Bronstein-Larsen Variation (B16) — 61.3% White score (≈393 W–363 D–181 L)
    Complex middlegame tactics suit deep search.
  4. Old Indian Defence, Main Line (A55) — 61.2% White score (≈374 W–333 D–176 L)
    Central tension and piece play lead to rich positions.
  5. Modern Defence, Averbakh System (A42) — 61.0% White score (≈1,008 W–963 D–471 L)
    Subtle positional themes benefit evaluation nets.
  6. Budapest Defence, Declined (A52) — 60.6% White score (≈776 W–1,064 D–318 L)
    Early central control and tactical chances reward White.

By prioritising these lines, Stockfish maximises its practical winning chances, capitalising on both its search depth and NNUE evaluation strengths.

Conclusion

Selecting the right opening can transform the outcome of a chess game, even for an engine as powerful as Stockfish. Through meticulous analysis of CCRL 40/15 ECO statistics, Stockfish’s own network test results, and TCEC superfinal performances, we have identified the six White openings that deliver the highest conversion rates:

  1. Benko Gambit, 7.e4 (A59) — 65.5%
  2. Alekhine’s Defence (B03) — 61.7%
  3. Caro-Kann Bronstein-Larsen (B16) — 61.3%
  4. Old Indian Main Line (A55) — 61.2%
  5. Modern Defence Averbakh (A42) — 61.0%
  6. Budapest Defence Declined (A52) — 60.6%

Each opening leverages aspects of Stockfish’s architecture—tactical depth, positional evaluation, and dynamic imbalance handling—to deliver consistent advantages. While real-world match conditions (book choice, time control, opponent style) will vary, this ordered list provides a solid foundation for opening selection, ensuring that Stockfish begins on the driver’s seat.

Whether you are configuring an engine match, studying computer chess strategies, or simply seeking to understand how a modern AI evaluates openings, these findings offer clear guidance. By combining quantitative CCRL data, qualitative TCEC insights, and technical knowledge of NNUE training, one can appreciate why these openings stand out—and why Stockfish plays them so convincingly.

Bibliography

  1. CCRL 40/15 Opening Statistics: “CCRL 40/15 ECO report by ECO code,” Computer Chess Rating Lists, last updated 25 April 2025. (CCRL)
  2. Stockfish Testing Framework: “Stockfish Testing Framework,” tests.stockfishchess.org/tests, April 2025. (Stockfish Testing Framework)
  3. nnue-pytorch Repository: official-stockfish/nnue-pytorch, GitHub, 2025. (GitHub – official-stockfish/nnue-pytorch: Stockfish NNUE (Chess evaluation) trainer in Pytorch)
  4. TCEC Season 11–13 Superfinals: Haworth & Hernández, ICGA Journal, “TCEC Cup” articles; summary via chessprogramming.org. (TCEC Superfinal Leela-Stockfish continues. Equal after 33 games!)
  5. TCEC Season 18 Superfinal: “TCEC Season 18,” Wikipedia, last edited April 2025. (TCEC Season 18)

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Jorge Ruiz Centelles

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