Suffix Statistics
20 Most Common Suffixes, Word Counts, and Average Lengths

Suffix Counts — Complete Top 20 Table

All counts computed using Python str.endswith() on every lowercase word. Minimum word length = suffix_length + 2. Average length is the mean character count of all matching words. Sorted by count descending.

Methodology: str.endswith(suffix.lower()) on all 74,872 words with minimum length filter suffix_length + 2. Fingerprint: 916f47949b41. Generated: 2026-07-27.

Key Finding — The -ED/-ING Near-Tie

The most notable finding in the suffix data is the near-tie between -ED (7,022 words) and -ING (7,007 words). This 15-word difference represents the close parallelism between past participial forms (-ED) and present participial forms (-ING) in English morphology. Most regular verbs form both endings, contributing to both counts. The slight -ED lead reflects the inclusion of -ED adjectives (BEARDED, TALENTED, CROOKED) that have no -ING counterpart.

Together, -ED and -ING account for 14,029 words — approximately 18.7% of the entire database. This concentration in two suffix groups reflects the grammatical centrality of English verbal morphology in the overall vocabulary. Scrabble implications: having I, N, G or E, D available as ending letters opens access to approximately 9.4% of the database each.

Average Length by Suffix — Morphological Patterns

Suffix average lengths reveal morphological layering in English vocabulary. -ATION words average 11.7 characters — the highest of any measured suffix. This reflects -ATION's combination with long Latinate roots: TRANSFORMATION (14), TRANSPORTATION (14), CONGRATULATION (14). -TION words average 11.3 characters, almost exclusively from Latin roots.

At the short end: -ER words average 7.6 characters and -FUL words average 8.2 characters. -ER's short average reflects agent nouns from common short verbs: RUNNER (6), WRITER (6), PLAYER (6), TEACHER (7). -FUL's short average reflects its combination primarily with short noun and adjective roots: HOPEFUL (7), CAREFUL (7), HELPFUL (7), PEACEFUL (8).

The -LY suffix (2,426 words, average 9.8 characters) sits at a medium length because it combines with both short adjectives (KINDLY=6, LONELY=6, SLOWLY=6) and long participial adjectives (COMFORTABLY=11, SURPRISINGLY=12, REMARKABLY=10).

Suffix Productivity and Morphological Derivation — How Suffixes Build English Vocabulary

English uses suffixes to convert words between grammatical categories — a process called derivational morphology. The suffix data reveals which conversions are most productive. -ER (3,391 words) converts verbs to agent nouns: PLAY → PLAYER, WRITE → WRITER, TEACH → TEACHER, READ → READER. -NESS (926 words) converts adjectives to abstract nouns: KIND → KINDNESS, DARK → DARKNESS, HAPPY → HAPPINESS. -TION (1,259 words) converts verbs to abstract process nouns: OPERATE → OPERATION, COLLECT → COLLECTION, DETECT → DETECTION.

The -ATION count (926 words) deserves special attention alongside -TION (1,259 words). ATION is a subset of TION — all -ATION words also end in -TION. But the suffix -ATION specifically converts latinate verbs ending in -ATE: OPERATE → OPERATION, EDUCATE → EDUCATION, COMMUNICATE → COMMUNICATION. The 926 -ATION words represent the most productive Latinate derivational pattern in the database.

For vocabulary researchers, the -LESS/-FUL near-pair is instructive. -LESS (164 words) forms antonyms of -FUL (160 words): HOPEFUL/HOPELESS, CAREFUL/CARELESS, PAINFUL/PAINLESS, USEFUL/USELESS. The near-equal counts (164 vs 160) suggest that most adjective roots that take -FUL also take -LESS. Exceptions: AWFUL has no *AWLESS equivalent; RUTHLESS has no *RUTHFUL equivalent. These asymmetries are vocabulary-level gaps rather than morphological impossibilities.

The -ISM/-IST suffix pair shows similar near-symmetry: -ISM (250 words, doctrines and movements) and -IST (357 words, practitioners). The -IST count exceeds -ISM because some -IST words have no -ISM counterpart — an artist practices art but ARTISM is not a word; a pianist plays piano but PIANISM is a word. The higher -IST count reflects -IST's greater semantic range.

Suffixes interact with each other in suffix stacking: -TION + -AL = -TIONAL (EDUCATIONAL, OPERATIONAL), -TION + -IST = -TIONIST (ABOLITIONIST, PERFECTIONIST), -NESS + nothing (since -NESS typically terminates derivation). Understanding these stacking patterns reveals why the suffix counts in this data do not simply add up to the total word count — many words contain multiple suffixes counted in different rows of the table.

For Scrabble players, the -ATION suffix pattern is valuable because it reliably produces long words from familiar verb roots. Any verb ending in -ATE that a player knows likely has a valid -ATION form: LOCATE → LOCATION, DEFLATE → DEFLATION, INFLATE → INFLATION, MIGRATE → MIGRATION. Knowing the -ATE → -ATION transformation allows generating valid 8-10 letter plays from familiar verb roots without memorising each form separately.

Methodology and Limitations — What This Data Represents

Suffix counts use Python str.endswith(suffix.lower()) on every lowercase word. The minimum length filter (word_length > suffix_length + 1) excludes the suffix itself if it appears as a standalone word. This means -ED excludes the word ED itself; -ER excludes ER itself; -LY excludes the word LY (if it were a word).

The key limitation is that str.endswith() matches letter sequences, not morphemes. -ER in TEACHER is a productive agent noun suffix. -ER in WATER is part of the root. Both count in the -ER total of 3,391. The count represents the number of database words ending with these letters, not the number of words where the suffix is a productive morpheme. Academic morphological analysis would use a morpheme segmenter (such as the Morfessor toolkit or MorphyNet) to distinguish productive suffixes from terminal letter sequences; this data uses the simpler str.endswith() approach for transparency and reproducibility.

Despite this limitation, the data is practically useful for word games because what matters in Scrabble is the letter sequence, not its morphological status. Whether -ER in WATER is a suffix or a root segment, the word counts toward the pool of -ER words a player can form. The tool shows all words ending in the searched suffix regardless of morphological analysis, which is the correct behaviour for a word game tool.

All counts were generated from fingerprint 916f47949b41 on 2026-07-27. The counts are reproducible by running str.endswith() on the wordlist.js file with the documented minimum-length filter. Any discrepancy between a reproduced count and a published count indicates either a database update (check the fingerprint) or an implementation difference (check the minimum-length filter).

Reference Summary — Top 5 Suffixes at a Glance

Quick reference for the five most common suffixes: -ED (7,022 words, 9.4% of database, average 8.3 characters) for past tense and past participial forms; -ING (7,007 words, 9.4%, average 9.2 characters) for present participial and gerund forms; -ER (3,391 words, 4.5%, average 7.6 characters) for agent nouns and comparative adjectives; -LY (2,426 words, 3.2%, average 9.8 characters) for adverbs; -TION (1,259 words, 1.7%, average 11.3 characters) for process and result nouns. All data from fingerprint 916f47949b41, generated 2026-07-27, reproducible from the same wordlist.js database file used by the live suffix word finder tool.

Suffix Tool — Try It Live

The Suffix Word Finder tool allows instant lookup of all database words ending in any suffix. Enter any suffix (ED, ING, TION, NESS, or any other letter sequence) and filter by word length or sort by Scrabble score. All 74,872 words are searched in the browser — no server request, no rate limit. The suffix counts in this research page are static figures from fingerprint 916f47949b41; the live tool always queries the current database and returns complete results.