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During the weeks after the elections were declared, Ofer Moshaioff, Yoav Ram and Idan Cohen developed a smartphone application ('app') called HAMIDGAM המדגם (). This app allowed users to anonymously vote for their party of choice in the upcoming elections (2015), and their actual vote in the previous elections (2013), and view the projected results of the elections based on the aggregated data from the entire user base.  The application was published for Android devices on the [Android Play Store](https://play.google.com/store/apps/details?id=com.bmi.midgam) on December 29th, 2014 and for iPhone on the [Apple App Store](https://itunes.apple.com/il/app/hmdgm/id956943031?mt=8) on January 26th, 2015. It quickly gained media attention on local radio shows, digital media and newspapers. This media attention contributed to over 6,000 7,000  application downloads by March 2nd 15th  2015. This Our  app differs from traditional surveys polls  in several aspects. In traditional surveys, polls,  media outlets publish forecasts based on a group of  500-1,000 individuals that were chosen by a survey polling  institution at a specific point in time to reflect an unbiased sample of the population. In contrast, our app allows users to view a realtime, online projection forecast  of the election results elections  based on individuals that contribute their choices. votes.  The sample size in our app is ~10-fold. However, in contrast to traditional surveys, the polls, our  app doesn't collect any demographic information information,  such as age, socio-economical status, religion and ethnicity. Therefore, the our  app's sample may be biased and therefore requires statistical standardization. The Our  app does collect information that is unique: first, the app allows users to change their mind at any time and it keep a history of user choices; second, it logs the precise time and ,if allowed by the device, location; third, the app asks users what which party  they voted for  in the previous elections (2013). Our hypothesis is that this information allows to make a good  forecast. In the following, this article  we will describe how the app works, thedifferent  methods we used to standardize the data, and theresults we got, including elections  forecasts and inferences regarding movement between parties and geographical disparity. we got.