# How can I improve my prediction for the end time of the Reddit button game? There is an April Fools game on Reddit where users with accounts can press a button (once) to reset a 60 second countdown timer:

Obviously this game must end, since there are only a finite number of user accounts (created before April 1, 2015). There is a fair bit of speculation going on as to when this game will end. The source data that can be used to analyse what is going on is in a Google Docs spreadsheet, found here:

I made this into a cloud object so it is easier to handle in the Wolfram Language directly:

data = CloudGet[CloudObject["https://www.wolframcloud.com/objects/user-7053ce31-817f-4643-aec1-eda27051bba6/thebutton"]]


This results a list of pairs {t,c}, where t is the time (in steps of 10 minutes: 10, 20, 30, ...) and c is the total number of button presses for the last 10 minutes. A sample of this list looks like this (elements 300 through 303 of data):

{{2990, 313}, {3000, 329}, {3010, 305}, {3020, 289}}


And here is a ListLogPlot of the full data: It's not totally obvious what the best statistical fit for this data is. A simple linear fit:

FindFit[data, a x + b, {a, b}, x]


gives these parameter estimates:

{a -> -0.450547, b -> 2541.06}


which leads leads to a game end time (when x equals zero) of:

• 5,640 minutes (almost 4 days). Which is obviously wrong, since this would mean the game would have ended two days ago. Similarly, an exponential type fit (b Exp[-a x]) leads to a fit that undercuts most of the most recent data: This is the solution for a/(x^c+b), as suggested by Algohi: I am curious if anyone has good ideas to improve this estimate? I don't think using a higher degree polynomial is going to be particularly helpful. I suspect that underlying all this there is exponential decay and periodic (e.g. daily) fluctuations.

• "No CloudObject found at the given address" ----What do?
– Ivan
Apr 8, 2015 at 0:59
• From what I've been reading on reddit people are keeping their votes to avoid the "game" ending. Now model that. Apr 8, 2015 at 2:01
• Can you check this model: a/(x^c + b) /. FindFit[data, a/(x^c + b), {a, b, c}, x] ? Apr 8, 2015 at 2:31
• @Ivan - I didn't provide the full cloud object url. I think this will work now. Let me know if not... Apr 8, 2015 at 14:11
• Any progress with this? I spent the better part of a day exploring different models - exponential decay is only part of the story, since it alone can't explain the plateau behavior currently underway (A Exp[-B t] + C) would suggest never ending clicks). I also tried modeling total number of clicks as opposed to the click rate, again to no avail. Apr 9, 2015 at 14:00

NonlinearModelFit[data, {b Log[a x] + c, c > 0}, {a, b, c}, x]