Top Scrabble engines can compete with elite players, but the evidence cited here does not show that they reliably beat the best humans across full games. The clearest comparison is narrower: in endgames attributed to World English-Language Scrabble Players Association (WESPA) data, Nigel Richards had a 1.2% cumulative error rate while the top AI had a 4.9% rate. That is an attributed move-error comparison—not a head-to-head win rate. A separate 500-game match ended almost evenly, with Quackle losing 248–252 to grandmaster Kenji Matsumoto.
What does “AI can’t always beat the best” actually mean?
It means strong Scrabble engines are serious opponents, not that humans always win or that computers are weak. The available results measure different things: errors in endgame decisions, wins across a long match, or estimates of performance. None alone establishes that people generally outperform machines in every phase of play.
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In a 2026 article, Andy Boxall attributes endgame figures to WESPA data: Richards’ cumulative error rate was 1.2%, compared with 4.9% for the top AI on those same games. The article does not identify the year of the WESPA data or expose the underlying dataset, so those percentages should be read as an attributed comparison, not an independently verified result or a prediction of full-game outcomes. Boxall’s account of the comparison.
What do the match results show?
Quackle versus Kenji Matsumoto
In a 500-game match, Quackle won 248 games and Matsumoto won 252. César del Solar, a Scrabble software developer, reported the result in 2022. A margin of four games across that sample shows that Quackle could compete closely with a grandmaster; it does not establish that humans are categorically stronger, nor does it settle how other engines or players would perform. Del Solar’s account of the match.
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Reported win-rate estimates are not one controlled comparison
Boxall’s 2026 article reports several estimates: a 75% lifetime win rate for Richards, a 50% average for WESPA players, and approximate win rates of 50–55% for Quackle and about 60% for BestBot. These figures do not share a stated comparison protocol, player pool, or match format, so they should not be lined up as if they came from the same trial.
BestBot developer César del Solar offered further context in 2025: he estimated the strongest engines were near 50% against the best human players, and said 2024 North American champion Mack Meller was winning 40–42% against BestBot in a long-running series at the time of writing. Those are developer-reported estimates, not independent controlled results. Del Solar’s explanation of BestBot.
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Why Scrabble is harder for AI than finding a high-scoring word
An engine can identify legal plays and calculate scores exceptionally well. But choosing the best move often means looking beyond the immediate score: the tiles left on a rack, the shape of the board, what an opponent may hold, and how open or volatile the position will become.
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Del Solar describes BestBot as using Monte Carlo simulations to evaluate early- and midgame choices, alongside tile-leave values. These methods help estimate what may happen next, but decisions still depend on strategic factors that current engines—including BestBot—do not fully capture. In Boxall’s 2026 article, del Solar summarizes the gap this way: “It’s that Scrabble is more than just about finding the top-scoring word. There’s a lot of board shape considerations, inferences, volatility, etc. that current engines, including mine, don’t yet take into account.”
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The game changes when the bag is empty. If both racks are known, the remaining endgame has perfect information, and an engine can search possible continuations exhaustively in principle. Yet complex endings can still exceed an engine’s allotted evaluation time, according to del Solar’s technical account. Perfect information makes the problem more tractable; it does not guarantee that every position is easy to solve in practice.
How to read the numbers without mixing unlike results
| Figure | What it measures | Source and qualification |
|---|---|---|
| 1.2% Richards; 4.9% top AI | Cumulative error rates in the same tracked endgames | Attributed to WESPA data by Boxall in 2026; the data year is not specified and the underlying dataset is not presented in the article. |
| 248–252 across 500 games | Match wins: Quackle 248, Matsumoto 252 | Reported by César del Solar in 2022. |
| 75%; 50%; 50–55%; about 60% | Reported estimates for Richards’ lifetime win rate, WESPA players’ average, Quackle, and BestBot, respectively | Boxall’s 2026 article; the figures are not presented as a single controlled comparison. |
| About 50%; 40–42% | Estimated top-engine performance against top humans; Meller’s reported win rate against BestBot in a long-running series | BestBot developer César del Solar, 2025; developer-reported estimates at the time of publication. |
Error rate and win rate answer different questions. A player can make fewer mistakes in a set of endgames without that percentage translating directly into a full-game win rate; full games also include uncertain draws, changing board positions, and earlier decisions. Comparisons also depend on which engine, players, rules, lexicon, and match format are involved. The cited accounts do not provide one common experiment that controls all those factors.
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Could an engine eventually beat Nigel Richards convincingly?
That remains an open possibility, not a proven outcome or a guaranteed timeline. Del Solar said in Boxall’s 2026 article, “My hope is that we can get something that can beat Nigel Richards, convincingly. It sounds hard, but I think it’s possible. Give us a few more months or years, and we’ll get there.” That is the developer’s hope, not a forecast backed by a demonstrated result.
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