Without historical context this analysis is meaningless. Is this one election the exception or the norm?
Why is it meaningless? It is still interesting and important to look for fraud during a single, particular election. Especially, one could argue, in the most recent one, since that presumably means bad-actors are more likely to be active in the next presidental election. I agree that it is interesting to see if voting fraud is common or widespread in primary elections in general, but studying individual events is not useless.
It occurred to me that you may have meant to say that the analysis is not robust without historical comparisons to establish that such anomalies are not just statistical fluctuations? That would something worthwhile to explore. It seemed (from very quickly skimming the pdf) that they did try various statistical tests to ensure their results were robust. But comparison with other datasets would likely provide a good test of the robustness of their findings.
This is a general problem with statistical analysis: all you can do is whittle down the space of possible explanations, you can never "prove" any one of them. I don't mean to nitpick about highly improbable statistical fluctuations. You can always make a "straw man" null hypothesis so shitty that it's trivially rejected (p<0.05 whee) and then claim that this supports your crazy alternative hypothesis. A robust review process should reject this kind of "p-hacking."
Controls are one of the best tools for cutting down the space of potential alternative explanations and avoiding p-hacking (intentional or not). It's not unreasonable to ask for one here. I haven't read deeply enough to know if the existing controls are convincing or not but the opinion "they aren't convincing" isn't silly.
This is a good hypothesis as it stands, and may be valuable as a predictor of fraud. However the conclusion is meaningless without sufficient evidence of predictivity to back it up.
Take historical examples of elections with known fraud and run this analysis to see if it can correctly label the fraud/no-fraud categories. _Then_ after we can agree that it works well, only then will I care about the results of any specific analysis.