2019 Expectations vs Performance Based on Overall Roster Talent Levels

In 2019, overall talent levels as listed in the Composite 247 ratings were highly correlated with the overall win percentage for the SEC. I ran a more sophisticated model earlier in the year and found a correlation of 51%. At the end of the year, however, a simple linear regression model has shown a very high 82% correlation. (I also looked at expectations offensively and defensively here: https://thefaircatch.com/2020/01/04/2019-sec-offensive-and-defensive-performance-vs-expectations/ )

Here is a look at how SEC teams did relative to their roster talent in 2019:

The Stats:

The yellow highlights the correlation. The green highlights the effect size (see below).

The tables above are out regression outputs. As stated in the caption, the multiple R is the correlation between the variables. The R Square is the percentage of the outcome (win percentage) that is attributed to the overall roster talent level, which is used as a measure of effect size. A 68% is considered a large effect according to convention. Additionally, the Signficance F is less than 0.05, which achieves statistical significance. This means there is less than a 1% chance that the results of the model were due to randomness.



This scatterplot shows each team’s actual win percentage relative to what would be expected given their overall roster talent. Vanderbilt, Mississippi State, Tennessee, Auburn, and Georgia all performed right as expected. Missouri, Kentucky, Florida, and LSU all outperformed model expectations. Arkansas, Ole Miss, South Carolina, Texas A&M, and Alabama all underperformed relative to model expectations.

Ranking Achievement:

The distance above (or below) the line indicates how much a team over or underperformed relative to model expectations. Here is how each team’s numbers worked out:


As we can see, LSU was the highest achiever (duh), winning 100% of their games with the model expecting them to win 78.6%. Arkansas had the worst season relative to model expectations, winning only 16.7% of their games while expected to win 40%.


The model does not take into consideration SOS and is not intended to be predictive, as the margin of error would be about 4 games. It is solely intended to examine the relationship between two variables- talent and winning. The goal here isn’t to build a model that accounts for every worldly possibility, but instead to look back at the season and see who generally overachieved and who didn’t. All that being said, this simple model with one predictor variable actually performs fairly well, though the sample size of one season is way too small to draw any hard conclusions beyond the stated scope.

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