How it works

What this is

Every r/NBA comment of the 2025-26 season that names a tracked player, read by a language model and tied to one player. Here is one, followed from download to its row in the data. The figures down the side are the season's totals at each step.

1. 01DownloadArctic Shift · r/NBA7,283,478 comments

   Just feels like a smarter player. Poole will probably have higher highs but Ajay feels more consistent

   One line per comment, the whole regular season and playoffs. A Thunder fan comparing two guards. May 12, 2026.

   The raw record

   {
     "id": "oldhosi",
     "body": "Just feels like a smarter player. Poole will probably have higher highs but Ajay feels more consistent",
     "author_flair_text": ":okc-2: Thunder",
     "score": 1,
     "link_id": "t3_1tav0sz"
   }
   + created_utc and the rest of what Reddit stores. The author's name never leaves the pipeline.

   ↳ Deleted, removed and empty bodies drop out first.

2. 02Find namesplayers.yaml · 223 players2,110,485 kept

   Just feels like a smarter player. Poole will probably have higher highs but Ajay feels more consistent

   Every name r/NBA uses for a tracked player is matched against the body. “Ajay” is Ajay Mitchell; “Poole” is Jordan Poole. Short names only match as whole words.

   The names found

   {
     "mentioned_players": [
       "Ajay Mitchell",
       "Jordan Poole"
     ],
     "mentioned_text": [
       "Ajay",
       "Poole"
     ]
   }

   ↳ 5,172,993 comments name no tracked player and stop here. None of them are ever classified.

3. 03Ask the classifierAnthropic Batch API100,000 per batch

   The whole prompt goes out with the comment inside it, to claude-haiku-4-5-20251001, in batches of 100,000. The prompt is below, word for word.

   The request

   {
     "custom_id": "oldhosi",
     "params": {
       "model": "claude-haiku-4-5-20251001",
       "max_tokens": 75,
       "temperature": 0,
       "messages": [
         {
           "role": "user",
           "content": "Classify sentiment toward NBA players.\nSlang: nasty/sick/filthy=positive, washed/brick/fraud/cooked=negative, GOAT=positive.\nA trailing \"/s\" tags the comment as sarcasm.\n\nComment: Just feels like a smarter player. Poole will probably have higher highs but Ajay feels more consistent\n\nRespond ONLY with JSON: {\"s\":\"pos|neg|neu\",\"c\":0.0-1.0,\"p\":\"Player Name\"|null}"
         }
       ]
     }
   }

4. 04The answerclaude-haiku-4-5-202510012,059,495 usable

   {"s":"pos", "c":0.7, "p":"Ajay"}

   That is the entire response. s is the sentiment, c the model's own confidence from 0 to 1, p who it thinks the comment is about.

   ↳ 50,990 requests fail or come back as something that can't be read.

5. 05Pick the playerthe attribution rule1,787,520 counted
   names foundAjay MitchellJordan Poole

   several names, so ask p"Ajay"→ players.yaml →Ajay Mitchell

   flair":okc-2: Thunder"→ teams.yaml →Oklahoma City Thunder

   p comes back as the model wrote it, so it goes through the same name list as the body did. The flair goes through the team list.

   ↳ 271,975 usable comments can't be pinned to one player and count for nobody.

6. 06One rowsentiment.parquet1 row per comment

   One positive for Ajay Mitchell, from a Thunder fan. At c = 0.7 it counts in the rates but is never quoted: a quote needs 0.9.

   The row

   {
     "comment_id": "oldhosi",
     "mentioned_players": [
       "Ajay Mitchell",
       "Jordan Poole"
     ],
     "mentioned_text": [
       "Ajay",
       "Poole"
     ],
     "sentiment": "pos",
     "confidence": 0.7,
     "sentiment_player": "Ajay",
     "attributed_player": "Ajay Mitchell",
     "fan_team": "Oklahoma City Thunder",
     "score": 1
   }
   + body, created_utc, link_id …

What the numbers mean

Why four? One rate tells you one thing. Chet Holmgren and Naz Reid are praised at nearly the same rate, and that is the only thing their numbers share: the comments around that praise split differently, and a different metric is needed to see it.

The positive rate says how much praise a player draws and the negative rate how much criticism; neither says anything about the other. Net sentiment puts the two together, praise minus criticism, one figure for where r/NBA stands on a player overall. Polarization sets the neutral comments aside and asks how much of the conversation took a side at all.

How a comment is read

This r/NBA comment was labeled {sentiment_word}. Is that {sentiment_word} sentiment aimed at an NBA player, or at a team, front office, coach, referees, a decision, or a play? If at a player, name them; otherwise answer null.
The target is the player being praised or criticized - not a player who is merely mentioned, sympathized with, or the subject of someone else's decision.
Never name a player who does not appear in the comment.

Comment: {comment_body}

Respond ONLY with JSON: {"t":"Player Name"|null,"c":0.0-1.0}

Checked by hand

A blind random draw of 1,000 counted comments, each read and labeled under a written rubric without the classifier's answer in view; 509 were scored. The margins are at 95%. A label is a careful read, not the truth: where the two disagree, the page says so, and never says which is right.

Why positive runs low. The prompt never says what counts as positive, so mild approval reads as neutral. The LaMelo Ball comment under Where it slips is the pattern: praise with no praise word in it.

What it cannot say. The check is across every player together. It says nothing about any one player's rate, and nothing about a single comment.

The rules

Receipts. The comments quoted on a player page are the top 10 by score among his positive and negative comments at c ≥ 0.9, up to 500 characters, after the quote check. The check reached 99.9% of the candidates; 77.7% of them were about the player they count for, and only those are shown.

What this is not

• Sentiment is not hate. A box-score criticism and a personal attack both register as negative. The rates measure the tone of the conversation, not hostility.
• Every comment weighs the same. A comment with 15,367 upvotes counts once, the same as one with 1. Viral threads carry no extra weight.
• Flair is a sample. 1,244,249 of the 2,059,495 usable comments carry a team flair. The fanbase views are about those commenters only, and the thinnest fanbase (New Orleans Pelicans) is 8,065 comments.
• Small samples move. Under the minimums above, one viral thread can swing a rate. That is why the minimums exist and why the slider says so.
• One subreddit, one season. r/NBA is not the NBA audience, and 2025-26 is not any other year.

Use the data

Everything the site shows is read from one folder of Parquet files at a public URL. The manifest says what was published and under which rules; the schema says every table's columns, types and what may be null.

The leaderboard, from your terminal (DuckDB; the line selects whole on a click):

duckdb -c "SELECT attributed_player, neg_rate, comment_count FROM 'https://courtsentiment.com/data/season=2025-26/player_overall.parquet' WHERE comment_count >= 5000 ORDER BY neg_rate DESC LIMIT 10"

Versions

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