{"data":[{"all_awardings":[],"archived":false,"associated_award":null,"author":"liortulip","author_created_utc":1390524928,"author_flair_background_color":null,"author_flair_css_class":"ocmaker","author_flair_richtext":[],"author_flair_template_id":"1c7d62a6-099d-11e7-9b3c-0ee50bfd7a4c","author_flair_text":"OC: 3","author_flair_text_color":"dark","author_flair_type":"text","author_fullname":"t2_ey8m1","author_patreon_flair":false,"author_premium":false,"body":"*Disclaimer: I am a software engineer for Kalshi, the prediction market this data was taken from.*\n\nEach Monday for the past four months, traders on Kalshi.com were asked whether there would be more than <threshold> Covid vaccination in the US in the next week. These plots track the accuracy of their initial predictions and how they evolved over the course of week.\n\nIn the first graph, the markets have been split into two groups: Markets where COVID vaccinations did pass the specified threshold are shown in green, and markets where vaccinations did not pass the specified threshold are shown in blue. For each group, the mean and interquartile range of the markets' predicted probabilities is shown as determination time (when the answer is available through government sources) approaches.\n\nThe second graph plots average error over time across all markets. Error as measured using cross entropy loss. An error of 0 would mean a perfect classifier, and an error of 0.35 would mean a random predictor.\n\nProbabilities were inferred from market prices. Kalshi's members trade binary options which pay out $1 if a question is answered correctly, and $0 if it is answered incorrectly. If at a given moment in time, market participants are willing to pay $0.75 for a YES contract, that means they think the answer to the question is \"Yes\" is at least 75%.  You can read more [here](https://kalshi.com/learn/what-are-event-contracts).\n\n**Data Source**: Kalshi's anonymized trade data, available through their public API [here](https://kalshi-public-docs.s3.amazonaws.com/KalshiAPI.html).\n\n**Tools Used**: Figma, Python, Seaborn, Matplotlib\n\nCredit to Osub Lee for arranging this visualization!","can_gild":true,"collapsed":false,"collapsed_because_crowd_control":null,"collapsed_reason":null,"collapsed_reason_code":null,"comment_type":null,"controversiality":0,"created_utc":1636331848,"distinguished":null,"edited":1636333034.0,"gilded":0,"gildings":{},"id":"hjquoj0","is_submitter":true,"link_id":"t3_qp1vo8","locked":false,"name":"t1_hjquoj0","no_follow":false,"parent_id":"t3_qp1vo8","permalink":"/r/dataisbeautiful/comments/qp1vo8/oc_how_prediction_markets_anticipate_us/hjquoj0/","retrieved_on":1646090616,"score":6,"score_hidden":false,"send_replies":true,"stickied":false,"subreddit":"dataisbeautiful","subreddit_id":"t5_2tk95","subreddit_name_prefixed":"r/dataisbeautiful","subreddit_type":"public","top_awarded_type":null,"total_awards_received":0,"treatment_tags":[],"unrepliable_reason":null}],"metadata":{},"error":null}