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# Talent: These 10 High School Football Teams Do More with Less

## Taking a look at which high school football teams outperform their talent the most

Hi All,

Welcome to Post #28 of the Goosepoop Newsletter. We are now over **212 subscribers strong**, and we couldn’t be more thankful for this support.

Thanks to all who have subscribed over the last 6 months - **here’s to the 2022 season and knocking down 500 subscribers**!

Today’s post is Part II of our series on talent in high school football. If you haven’t read Part I, I highly suggest you do so before proceeding further. We’re going to build on the analysis we discussed in that post, so if you haven’t read that yet, a lot of what we discuss below won’t make much sense.

In Part I of this series, we established the * relationship* between the presence of ranked recruits on a high school football team and that team’s average wins per season, 2014 - 2021. While there are a number of issues that prevent us from concluding that talent

*more wins (read the post for more detail), we can definitively say that there’s a*

**causes***between the two variables:*

**correlation**

**as a team’s number of ranked recruits increase, so do its wins.** In today’s analysis, we will build on this insight: we will be diving into the individual teams from our dataset, hoping to get a handle on the following question: **which teams outperform the talent on their rosters the most?**

To answer this question, here’s a look at how we’ll approach it:

**Part I Recap**: A brief recap on the insights we discussed in Part I of this series**Lines of Best Fit**: Diving deeper into the correlation that we established in Part I, and how that can be used to determine which teams do more with less**Division 1 Top 10**: Which teams in Division 1 do more with less?**All Divisions Top 10**: Which teams in all divisions do more with less?

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We’re trying to spread the word on using data in high school sports, and every bit of support helps!

### Part 1: Recap

Remember this graph?

This graph shows the relationship we discussed in Part I - there is a positive correlation between the number of ranked recruits a team has and their average wins per season, 2014 - 2021.

As you can see, the relationship between these two variables is not linear - it’s actually logarithmic.

What does that mean? Well, instead of the normal case of a straight (linear) line, where an additional ranked recruit generates a fixed number of additional wins for their team, there is actually a decreasing return to adding an additional ranked recruit: **after a point,****each additional ranked recruit generates fewer and fewer wins for their team.**

**Lines of Best Fit: How to Determine The Teams That Outperform Their Talent**

We can use this logarithmic relationship to assess which teams outperform their talent.

How will we do this? By diving deep into the relationship’s line of best fit. This ‘line of best fit’ is the line that you see on the chart above.

Our line of best fit is summarized by this equation:

What does this equation imply? Well, if talent was the only thing that determined a team’s win totals, you could simply insert a team’s number of ranked recruits into this equation, run the math, and determine the team’s average number of wins across the time period.

Here’s an example, assuming 5 ranked recruits:

* Lucky for us, talent is not the only thing that determines a team’s win totals*: remember, our R-Squared for this line of best fit was 45% - this means that only 45% of the variation in teams’ win totals is explainable by the differences in talent.

The other 55% is driven by a bunch of different variables - coaching, resources, and strength of schedule all combine to push a team’s average win total higher or lower than their talent alone might suggest.

We can see which of these team’s are higher or lower than expected by looking at our graph: **teams with a datapoint above the line of best fit are winning more than their talent alone suggests they should.**

**Division 1: These Teams Outperform The Most**

With this understanding in mind, we can take the final step of our analysis: **a team’s vertical distance from the line of best fit is how many wins per year more they are averaging than their talent alone would suggest**

Below are the 10 Division 1 teams who are outperforming their talent the most:

Said another way, **Brownstown Woodhaven** averages 3.1 more wins per year than their talent set would suggest (1 ranked recruit in the time period 2014 - 2021).

**All Divisions: These Teams Outperform The Most**

How does this look in other divisions? Here’s the top 10 across all divisions:

Not a ton of surprises here - **Pewamo-Westphalia**, who has averaged the state’s highest wins per year over the time period (see our previous post) also has had 0 ranked recruits. Thus, they outperform quite a bit. **Impressive!**

One caveat here is that the other divisions’ data was not as strong as Division 1’s (save for D7). Because of this, you should take the analyses here with a grain of salt.

For a refresher on why other divisions’ data was not as strong, head over to Part I of this series.

That’s all for today’s post guys, thanks for reading!

* Our next post will be the last post in this series* - it will be a bit of an “odds & ends” post, as we’re going to review some of the player level data from our recruiting dataset we’ve been reviewing in the last few newsletters.

In that post, we’ll cover the following topics:

**Which colleges do Michigan’s high school recruits commit to most frequently?****Have Michigan’s high school recruits become more talented over time?****Which position group is most represented in Michigan’s high school recruits?**

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