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Some claims are too strong and overselling the results, moreover in a number of components I’d strongly recommend to contemplate further related literature. My main level of criticism is the integration of the current outcomes into the existing literature. Please note that PLOS ONE has particular pointers on code sharing for submissions during which author-generated code underpins the findings in the manuscript. The literature on this space is sort of huge (search “NFL betting” in Google Scholar to see what I mean). The common patterns in Determine 1, that home underdogs perform higher relative to the purpose unfold than residence favorites and that outcomes are usually much less excessive than predicted by excessive over/unders, are well-documented within the literature.
The author might additionally describe how the current manuscript is completely different from the aforementioned paper, e.g. through the use of level spreads and by analysing quantiles. Or literature specializing in the consequences of non-optimal decisions of bettors Whereas I definitely see what the manuscript adds to the literature, I wish to see acknowledged that decisions from a bettor’s perspective have been considered within the literature explicitly and implicitly.
While I agree that a high standard deviation indicated frequent blowouts, this has nothing to do with the mean margin of victory which is rather an estimate of residence benefit. As far as I am involved, that is pretty uncommon in the literature and – until there’s a specific cause that I am not aware of – I would quite anticipate the wording “sports betting”. Assuming that a bin finally ends up containing one hundred video games or extra, the paper calculates the 47.sixth and fifty two.4th percentiles of the finish result distribution and compares them to the “consensus” level spread or level complete offered by the casinos.
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- When the sportsbook underestimates the median, the anticipated profit on a unit wager is zero.015±0.0071, zero.076± zero.014, and 0.14± zero.020, for deviations of 1, 2, and three factors, respectively.
- Conventional odd least-squares (OLS) regression yields estimates of the imply of a random variable, conditioned on the predictors.
- The aim of knowledge evaluation isn’t to completely predict the lengthy run.
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