Op

Open Video Game Data: A new approach to evaluating games

Hacker News

Open Video Game Data: A new approach to evaluating games

> Introduction Our idea is to offer an alternative to well-known sites like Metacritic and OpenCritic, but with a different approach. Instead of being a score aggregator, we will be a list aggregator. Metacritic brings together reviews from multiple review sites in one place, providing a final score of 0-100 based on a weighted arithmetic average, where some critics carry more weight than others. An alternative to Metacritic is OpenCritic, where all critics are weighted equally in the final average. However, both still work with numeric scores. > Why relying on scores can be problematic? - Ratings only reflect the state of the game at launch Today, more than ever, games are constantly evolving. It is common to have "patch day one", that is, games released with bugs and incomplete content. However, with time and help from the community, these games can be improved, as was the case with No Man's Sky. When No Man's Sky was released in 2016, its average on Metacritic was just 61, due to the troubled release. However, over the years, the game has evolved significantly with updates, but its Metacritic score remains frozen at 61. Alternative: As the lists are constantly evolving and updating, they more accurately reflect the current quality of the games, tracking their improvements and changes over time. - The average score can be unfair as it is based on the amount of critics Sometimes, the amount of crits heavily influences a game's rating. An example of this is The Legend of Zelda: Ocarina of Time, with an average of 99 on Metacritic, based on 22 critics. While The Legend of Zelda: Breath of the Wild averages a 97, based on 109 critics. Getting a high average based on a large number of critics is extremely difficult, and this can influence the overall perception of a game. Alternative: When a final list is created, all games have an equal chance of appearing in different lists. For example, game A might be included in 3 out of 11 lists, while game B might be mentioned in 5 out of 11 lists. The total amount of lists will always be the same for all games. - Relying on an average can be inaccurate Metacritic converts the different rating scales of review sites into a single percentage-based quantitative scale. However, this conversion can be inaccurate and unfair, as each site uses different rating systems. This approach can result in important information being lost during conversion, affecting the accuracy of the final result. Alternative: With our ranked lists approach, we eliminate the need to convert rating systems, as all lists, regardless of site, follow the same common logic. In all lists, there will always be first place, second place, and so on. > A great alternative: *Open Video Game Data* Our site aims to be just another alternative to note-based sites. Our approach to aggregating lists allows users to have a more comprehensive and up-to-date view of games as these lists are constantly updated by the community. The calculation method is quite simple and transparent. All lists on the site have a maximum size of 15 games. When a game ranks first in a list, it is rewarded with 15 points, while if it ranks last, it only receives 1 point. > Conclusion Open Video Game Data seeks to provide gamers and game enthusiasts with a reliable tool to make informed decisions about which games to play, taking into account critics' opinions and the ongoing evolution of the gaming industry. With the active participation of the community, users can add critic lists and can also create personal lists that are also aggregated, we hope to build an inclusive and reference platform for the gaming community, promoting a more complete and updated analysis about the games that so much we love. Come be part of our community! Create an account and join us to explore the world of playlists. Welcome to Open Video Game Data! Visit us at: https://www.openvideogamedata.com/

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
AppSumoStrong fit for a featured deal · Strong signals: platform, reviews, users · Missing: plus, intuitive, host
54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, video, users · Missing: mobile apps, ios, entrepreneurs
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: user, new, single · Missing: mac, agents, macos
45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, ide, io · Missing: https docs, excited, just released
34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Strong signals: arr, active · Missing: mrr, revenue, profit
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: reward · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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