• Home
  • About Us
  • Contact Us
  • Privacy Policy
  • Special Offers
Business Intelligence Info
  • Business Intelligence
    • BI News and Info
    • Big Data
    • Mobile and Cloud
    • Self-Service BI
  • CRM
    • CRM News and Info
    • InfusionSoft
    • Microsoft Dynamics CRM
    • NetSuite
    • OnContact
    • Salesforce
    • Workbooks
  • Data Mining
    • Pentaho
    • Sisense
    • Tableau
    • TIBCO Spotfire
  • Data Warehousing
    • DWH News and Info
    • IBM DB2
    • Microsoft SQL Server
    • Oracle
    • Teradata
  • Predictive Analytics
    • FICO
    • KNIME
    • Mathematica
    • Matlab
    • Minitab
    • RapidMiner
    • Revolution
    • SAP
    • SAS/SPSS
  • Humor

Steam Spy switches algorithms to survive Valve’s data shutdown

April 28, 2018   Big Data
 Steam Spy switches algorithms to survive Valve’s data shutdown

The PC gaming market-intelligence resource Steam Spy lives. Its creator, developer Sergey Galyonkin, is shifting the tool to a new algorithm that calculates the sales of PC games on the the Steam distribution platform based on “coincidental data” from around the internet. Galyonkin had to make this change after Valve began hiding its customers’ game libraries by default after years of exposing that information through its API.

In response to that privacy change at the time, Galyonkin said on Twitter that Steam Spy would not have the data it needs to operate. But he is now saying that the site will continue, but he needs some time to dial in his algorithm and integrate it into the site. Galyonkin said he wanted to solve this problem after indie developers from around the world reached out to him.

“I received over two hundred emails and messages from developers telling me how Steam Spy improved their lives,” Galyonkin wrote in a blog post. “There was an indie company from Berlin that managed to secure financing from the government for their niche title because they had the data to prove that this niche is big enough. The title got released and succeeded.”

The need for reliable data to enable smart business decisions is not in question by anyone. The problem, instead, is that the gaming market is so large and dynamic that algorithmic models are often inaccurate.

The good news for Steam Spy is that Galyonkin’s new math does seem capable of making estimations within an acceptable margin of error … some of the time.

“Frostpunk devs just announced that the game sold 250,000 copies and the new algorithm estimated it at 252,000 copies,” reads Galyonkin’s blog. “[But overall, it’s] not very accurate, to be honest.”

A small pool of developers have shared their sales data with Galyonkin. He has the numbers for approximately 70 games. For 90 percent of those, the algorithm was able to calculate their sales within a 10 percent margin of error.

“But I also saw some crazy outliers,” said Galyonkin. “Where the difference between the estimates and the real data could be fivefold.”

Steam Spy will still charge ahead. Galyonkin has closed off most of the site’s features while he makes some changes and improves his system. But he does plan to reopen essential information to the public once he gets everything fixed.

x

For more PC gaming goodness, choose which newsletters you’d like to receive:


Please enter a valid email address

Welcome to the
VB PC Gaming Community!

Check your email weekly for
breaking news, insight, and analysis.

Let’s block ads! (Why?)

Big Data – VentureBeat

algorithms, data, shutdown, Steam, survive, Switches, Valve’s
  • Recent Posts

    • New Customer Experience Needs and Commerce Trends for 2021
    • A data transformation problem in SQL and Scala: Dovetailing declarative solutions
    • George Wallace Joins Laverne Cox For Comedy Titled ‘Clean Slate’
    • How Microsoft Azure DevOps and Dynamics 365 CRM Work Together to Improve Service Responsiveness
    • The Benefits of Dynamics 365 Online Versus On-Premise
  • Categories

  • Archives

    • February 2021
    • January 2021
    • December 2020
    • November 2020
    • October 2020
    • September 2020
    • August 2020
    • July 2020
    • June 2020
    • May 2020
    • April 2020
    • March 2020
    • February 2020
    • January 2020
    • December 2019
    • November 2019
    • October 2019
    • September 2019
    • August 2019
    • July 2019
    • June 2019
    • May 2019
    • April 2019
    • March 2019
    • February 2019
    • January 2019
    • December 2018
    • November 2018
    • October 2018
    • September 2018
    • August 2018
    • July 2018
    • June 2018
    • May 2018
    • April 2018
    • March 2018
    • February 2018
    • January 2018
    • December 2017
    • November 2017
    • October 2017
    • September 2017
    • August 2017
    • July 2017
    • June 2017
    • May 2017
    • April 2017
    • March 2017
    • February 2017
    • January 2017
    • December 2016
    • November 2016
    • October 2016
    • September 2016
    • August 2016
    • July 2016
    • June 2016
    • May 2016
    • April 2016
    • March 2016
    • February 2016
    • January 2016
    • December 2015
    • November 2015
    • October 2015
    • September 2015
    • August 2015
    • July 2015
    • June 2015
    • May 2015
    • April 2015
    • March 2015
    • February 2015
    • January 2015
    • December 2014
    • November 2014
© 2021 Business Intelligence Info
Power BI Training | G Com Solutions Limited