Computer-based automatically generated text are used in various applications (e.g. text summarization, machine translation) and such the machine-generated text significantly helps our social life. However, machine-generated text may produce confusing information sometimes due to errors or inappropriate use of wordings caused by language processing, which could be a critical issue in president elections or in product advertisements. Previous methods for detecting such machinegenerated text typically estimates the text fluency, but, this may not be useful in near future because recently proposed neuralnetwork based natural language generation results in improved wording close to human-crafted one. However, we hypothesize that the habit of human on writing is still more consistent. For instance, the Zipf's law states that the most frequent word in the text written by human approximates twice the second most frequent word, nearly three times the third most frequent word, and so forth. We found that this is not true in the case of machine-generated text. We hence propose a method to identify the machine-generated text based on such the statistics-First, word distributed frequencies are compared with the Zipfian distribution to extract frequency features. Second, complex phrase features are extracted to show that humangenerated text contains more sophisticated phrases than machinegenerated one. Finally, the higher consistency of the humangenerated text is quantified at both the sentence level using phrasal verbs and at the paragraph level based on coreference resolution relationships, which are integrated into consistency features. The combination of the frequency, the complex phrase, and the consistency features is evaluated on a hundred of original English books and a hundred of translated ones from Finnish. The result shows that our method achieves the better performance (accuracy = 98.0% and equal error rate = 2.9%) comparing with a state-of-the-art method using parsing tree feature extraction. An advantage of this method is that this method can be used for large collections of text such as books efficiently. Other evaluation results in two other languages including French and Dutch showed similar results. They demonstrated that the proposed method works consistently in various languages.