r/dataisbeautiful • u/rsrgrimm • 2d ago

How did draft position affect fantasy football league performance in 2024? (12-man leagues, snake draft)

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To assess how draft position affected league performance, I looked into over 400 12-man leagues (all snake drafts) and plotted win ratio, normalized points earned (normalized within a given league to account for various scoring and roster settings), and final league ranking for each draft position.

Surprisingly, 1st pick performed worst on average across all metrics.

League data collected from Sleeper API.

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u/ScientistFromSouth OC: 1 2d ago

It appears nothing happened whatsoever. Did you run any statistical tests such as turkey HSD, ANOVA, or something more specific for rank ordering to test this rigorously.

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u/rsrgrimm 2d ago edited 2d ago

Look more closely. There are clear differences between averages. The mean final ranking for 1st pick teams was 6.9 as opposed to 5.7 for 10th pick teams. That is pretty significant for a sample of 400 teams.

Before running statistical tests, the samples and differences are large enough that it is evident that draft position had an effect. After running ANOVA, for the metrics measured the largest p-value was 5e-6, so draft position was significant for each metric.

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u/ScientistFromSouth OC: 1 1d ago

Boxplots aren't appropriate for rank order data. They are designed for continuous data. These plots really don't make sense for variables defined on discrete support.

Second, while this is significant at the population level, for an individual player, I am not sure that this provides me with actionable information. Going 10th somehow gives me a 1.2 rank advantage over going first on average. However, that feels pretty trivial in a 12 person game in a one off instance.

Additionally, the validity of an ANOVA test that checks for nonzero slopes of a linear model fit to continuous data being used on a rank order model with discrete support are highly suspect.

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u/rsrgrimm 1d ago edited 1d ago

That's not true. Box plots are fine for discrete data if the data is ordinal, which this is, and there are a sufficient number of possible values. You could argue there are not enough possible values here (I admit the whiskers are pretty useless), but the means, medians, and IQR are different enough to convey useful information.

I agree that you shouldn't be using this data to guide your draft position (not that many have a choice, though I suppose some leagues allow trading). Though statistically significant, the differences are relatively small and, more importantly, this data only encapsulates a single year.

Additionally, the validity of an ANOVA test that checks for nonzero slopes of a linear model fit to continuous data being used on a rank order model with discrete support are highly suspect.

I don't necessarily agree. There are three metrics being measured here. All can be considered representative of team performance in a league and the ANOVA tests suggest a statistically significant relationship between the predictor and the response. The final league ranking is the only one of the three metrics that is discrete. Plus, I'll remind you that you are the one who asked about an ANOVA test in the first place.