How Do Chess Engines Evaluate Positions?

A Deep Dive Into the Mechanics of Modern Chess AI

The rise of chess engines has fundamentally transformed the way we study and play the game. Whether it’s Grandmasters preparing for tournaments, club players analyzing their games, or online users seeking improvement, chess engines have become indispensable tools. But how do these engines actually “think”? How do they evaluate whether a position is winning, losing, or balanced?

This article takes a deep look into the mechanics behind chess engine evaluation, from brute-force calculation and evaluation functions to neural networks and probabilistic learning. We’ll also explore how these evaluations are interpreted and applied by players at all levels.

1. The Basics of Position Evaluation

At its core, a chess engine evaluates positions by trying to assign a numerical value that reflects which side is better and by how much.

The Evaluation Scale

Most engines use a centipawn scale, where:

• +1.00 means White is better by the equivalent of a pawn.

• -1.00 means Black is better by a pawn.

• 0.00 indicates a balanced position.

Extreme values (like +10.00) suggest a forced mate or overwhelming material advantage. The higher the number (positive or negative), the greater the engine’s perceived advantage for that side.

2. Two Key Components: Evaluation Function vs. Search

Chess engine evaluation consists of two intertwined components:

a. Search

This refers to the engine’s ability to explore possible future moves and counter-moves, often several millions of positions per second. The most common algorithms include:

• Minimax: Considers all possible moves and assumes the opponent will also play optimally.

• Alpha-Beta Pruning: Cuts off branches of the search tree that are unlikely to influence the final decision, improving efficiency.

• Monte Carlo Tree Search (in some engines): Used in some learning-based engines for probabilistic exploration.

b. Evaluation Function

Once a leaf node (a final position in a search branch) is reached, the evaluation function is applied. This function assigns a score based on the characteristics of that position. It is the engine’s “opinion” of how good a position is.

3. What Factors Do Engines Evaluate?

The evaluation function incorporates a vast number of features, including both tangible and positional elements:

Tangible Factors:

• Material count: The value of pieces on the board (e.g., pawns = 1, knights/bishops = 3, rooks = 5, queens = 9).

• King safety: Exposure to threats, pawn cover, and open files around the king.

• Pawn structure: Doubled pawns, isolated pawns, backward pawns, and passed pawns.

• Piece activity: Rooks on open files, knights on strong outposts, bishops on long diagonals.

• Control of center: Occupation and influence of central squares (d4, e4, d5, e5).

Intangible/Positional Factors:

• Space advantage: More room to maneuver, usually due to advanced pawns.

• Mobility: The number of legal moves available.

• Coordination: How well the pieces work together.

• Threat potential: Imminent threats like forks, pins, or discovered attacks.

• Initiative: Who is making threats and dictating play.

In modern engines, especially those powered by neural networks, many of these are not hardcoded but are learned from millions of games.

4. Material Evaluation: More Than Just Counting

Most engines start by evaluating material—but modern engines go deeper.

For example:

• A pair of bishops might be valued slightly more than a bishop and knight.

• Rook + pawn vs. two minor pieces can be evaluated contextually based on pawn structure and king safety.

• Engines adjust for imbalances that are dynamic in nature (e.g., the advantage of an extra pawn on the queenside might be neutralized by an active knight in the center).

Engines like Stockfish use tapered evaluation, which weights factors differently depending on the game phase (opening, middlegame, endgame).

5. Search Depth and Horizon Effect

One of the engine’s strengths is how deep it can calculate.

• Search depth is typically measured in plies (a half-move). A depth of 20 means the engine is looking 10 moves ahead for both sides.

• The deeper the search, the more accurate the evaluation. However, due to exponential growth in possibilities, engines use heuristics to prioritize important lines.

• Horizon effect occurs when an engine fails to look far enough ahead to see a decisive consequence (e.g., it thinks it’s safe until something bad happens just beyond its search depth).

Engines combat this using quiescence search, which extends analysis in “noisy” positions with lots of captures or checks.

6. Neural Network Engines: A New Era of Evaluation

The traditional engines (like Stockfish up to version 11) used handcrafted evaluation functions. But recent advances introduced neural-network-based engines, most famously:

• AlphaZero (by DeepMind)

• Leela Chess Zero (Lc0)

• NNUE-enhanced Stockfish (from version 12 onwards)

What’s Different?

Instead of relying solely on brute-force evaluation and hand-coded rules, these engines:

• Use deep learning models trained on millions of self-play games.

• Learn non-linear relationships in positions (e.g., how a bishop pair’s strength varies with pawn structure).

• Mimic human intuition by recognizing patterns, not just calculating.

Leela, for instance, will often “understand” a slow-building attack better than traditional engines because its evaluations are more global and pattern-based.

7. Stockfish vs. Lc0: Two Approaches to Evaluation

Practical Difference:

Stockfish may find a winning tactic quickly; Leela may play a slow squeeze over 20 moves that Stockfish misjudges initially.

8. How Do Engines Display Their Evaluation?

Most users see engine evaluations in three ways:

• Score bar (e.g., +1.2 for White, -3.4 for Black)

• Best move suggestions (often with top 3 lines)

• Win/draw/loss probability (in AI-driven models like AlphaZero)

Interpretation:

• +0.2 to +0.5: Slight edge for White

• +0.6 to +1.0: Clear advantage

• +1.0 to +2.0: Winning chances

• +2.0+: Decisive advantage (usually due to material)

9. Limitations of Engine Evaluations

Despite their power, engines have weaknesses:

• Over-reliance on depth: Shallow evaluations can be misleading.

• Non-human playstyles: Moves may be technically best but hard for humans to understand or execute.

• Unexplained evaluations: A position might be +1.5, but without a clear tactical reason visible to a human.

• Inaccuracies in closed positions: Some engines struggle to assess slow maneuvering plans until more plies are reached.

Thus, engine evaluations are best used as tools, not absolute truth.

10. How to Use Engine Evaluations Effectively

For practical players, engine evaluations are most useful when:

• Reviewing blunders and inaccuracies

• Understanding turning points in the game

• Analyzing why a plan failed or succeeded

• Checking alternative move options

But for learning, it’s crucial to ask “why” instead of just copying engine lines. Combine engine output with your own reasoning or a coach’s guidance.

Conclusion: A Window Into Machine Understanding

Chess engine evaluations are more than just numbers—they are snapshots of how artificial intelligence perceives the board. From brute-force calculation to neural-network intuition, the evolution of engine evaluation reflects the broader growth of AI in cognitive tasks.

Whether you’re a Grandmaster preparing for a world championship or a beginner reviewing your first tournament game, understanding how engines evaluate positions allows you to improve strategically, tactically, and psychologically.

Used wisely, chess engines don’t replace human thinking—they enhance it.