We will start by analyzing the finish times across all years in the dataset for both genders.
We can do the following observations. First, the race wasn't held in 2020 due to the COVID-19 pandemic, so there's no data for that year. Second, the female group is underrepresented. We will focus the rest of the analysis on males.
Finally, we can see that the distribution of the data is bimodal. The explanation is that we have different groups of runners; first, the elite runners, the minority of the field, making it a long tail on the left, usually finishing under twenty hours. Then, the rest of the field is the amateur runners, with the first group trying to run a hundred miles under twenty-four hours (we will refer to them as the sub-24 hour runners). These two groups make the first right-skewed normal distribution. After that, we see the runners that just want to finish within the race limit of thirty hours. This group makes the second right-skewed normal distribution.
We will continue the analysis by exploring how weather factors affect the runner's finishing time.