Let's figure out the expected loss of playing scratchers.

California publishes all of the information we need to determine this at their website http://www.calottery.com/play/scratchers-games/top-prizes-remaining

From there, for each game, we can see:

1. Price of a ticket
2. Value of each prize
3. Odds of winning each prize
4. Number of tickets printed for each prize
5. Number of tickets claimed for each prize

They update the data every day.

With requests, beautifulsoup, and pandas, it's really easy to scrape each page and start analyzing.

import requests
from bs4 import BeautifulSoup as bs
import pandas as pd

sample_game_url = "http://www.calottery.com/LotteryHome/play/scratchers-games/$20-scratchers/100X-the-money-1230"

soup = bs(requests.get(sample_game_url).content)
df = pd.read_html(str(soup.find(class_='draw_games')))[0]
print(df)

Yeeehaaww!

With a little more scripting and munging you can answer some cool questions, like "Of all the games that have sold less than 50% of their tickets, how close are they to matching their expected payout of grand prizes?"

The majority are under!

As of 2017-09-02, here is what the games looked like:

Talk about a huge range of expected values! Look at that Instant Fast Cash!!!

That table was generated with pytablewriter. Super helpful.

Maybe I'll set this up somewhere and have it auto-update each day.