Game analytics supports game development by providing direct quantitative feedback about player experience. Player retention and monetization have become central business statistics in free-to-play game development. Total playtime and lifetime value in particular are central benchmarks, but many metrics have been used for this purpose. However, game developers often want to perform analytics in a timely manner before all users have churned from the game. This causes data censoring, which makes many metrics biased. In this article, we introduce how the mean cumulative function (MCF) can be used to measure metrics from censored data. Statistical tools based on the MCF allow game developers to determine whether a given change improves a game or whether a game is good enough for public release. The MCF is a general tool that estimates the expected value of a metric for any data set and does not rely on a model for the data. We demonstrate the advantages of this approach on a real in-development free-to-play mobile game Hipster Sheep.

中文翻译：

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使用平均累积函数衡量玩家留存率和货币化

游戏分析通过提供有关玩家体验的直接定量反馈来支持游戏开发。玩家留存率和货币化已成为免费游戏开发的核心业务统计数据。特别是总游戏时间和生命周期价值是核心基准，但为此目的使用了许多指标。然而，游戏开发者通常希望在所有用户离开游戏之前及时执行分析。这会导致数据审查，从而使许多指标存在偏差。在本文中，我们将介绍如何使用平均累积函数 (MCF) 来衡量审查数据的指标。基于 MCF 的统计工具允许游戏开发人员确定给定的更改是否改进了游戏或游戏是否足以公开发布。MCF 是一种通用工具，用于估计任何数据集的度量预期值，并且不依赖于数据模型。我们在真正的开发中的免费手机游戏 Hipster Sheep 上展示了这种方法的优势。