BEHAVIORAL

ANALYSIS OF ELITE

CFB RECRUITSNicholas Vogt

What’s Ahead

Descriptive Statistics

Data

Conditional Logit Model

Potentially unreasonable assumptions

Results

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Distance to Offers

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54 90 148 244 403 665 1096180829804914

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Distance in Miles

Distance to All Offers

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54 90 148 244 403 665 1096 1808 2980 4914

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Distance to Signed Offers

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Academic Rank & Graduation Rate 5

0

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55 60 65 70 75 80 85 90 95 100

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6-Year Graduation Rate

Academic Rank and Graduation Rates

Out of Pocket Cost Density 6

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Out-of-Pocket Cost

Winning Percentage of Offers

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Winning Percentage

Win % of All Offers

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Win % of Signed Offers

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Stand-Out Schools: Signing Rates

0%

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14%

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20%

Signed Unsigned

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Signed Unsigned

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What’s Important?

Family & Finances

• Distance from home

• Relative cost-of-

attendance

• Personal interests not

captured by our

model.

Academics

• Graduation Rates

• Academic Ranking

• Small Class Sizes

NFL Prep

• Winning Percentage

• Strength of Schedule

• Stadium capacity

• Playing Time

• Historic Success

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Questions Covered Today

To what extent do recruits value cash incentives?

To what extent do recruits value distance from home?

Do recruits value academic rank and graduation rates?

Are recruits more reactive to recent or historic success?

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Where Does The Data Come From?

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Where Do The Data Come From?

Prior literature uses proxies in place of complete data.

But how do we proxy complex interactions holistically? Often, we can’t.

Reliable information is out there; it’s just buried in beneath hundreds

of webpages.

Fortunately, there’s Python.

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Where Do The Data Come From? 13

Web ScrapingData

Wrangling Analysis

Where Do The Data Come From? 14

Recruits

• Distance to Team

• Position, height, weight

• Visit Dates

Coaches

• Career Statistics

• NCAA & NFL

• Bowl Statistics

Teams

• On-the-Field Stats

• Stadium Age/Size

• Rosters

Institutions

• Academic Rank

• Graduation Rates

• Student Population

• Cost-to-Attend

NCAA Sanctions

• Nature of Violation

• Restrictions

4- and 5-star recruits, 2013 - 2015

The Conditional Logit Model

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Conditional Logit Model

What is Conditional Logit? It’s a log-linear random utility model.

𝑈𝑖𝑗 = β1𝑥1 + β2𝑥2 +⋯+ 𝜀

Think logit, but we allow for conditionally dependent alternatives.

Signing with one school forgoes the option to sign with another.

Another Example: Grocery shopping with a limited budget.

Decisions are analyzed in the context of each recruit’s choice set, not

against the entire sample.

Conditional Logit requires that odds remain fixed.

This assumption may be unreasonable in some cases.

For more information, check out Discrete Choice Models.

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Odds Ratios

Teams Michigan Alabama

Probability 0.4 0.6

Odds remain fixed.

In the case of Michigan : Alabama, the odds ratio is 1.5. (1.5 = 0.3 / 0.2)

Teams Michigan Alabama Michigan State

Probability 0.2 0.3 0.5

Probability vs. Odds?

Absolute probability is the likelihood a recruit signs with a university.

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Results

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Results 19

Log-odds preserve sign of

the effect.

For odds ratios, the effect is:

positive if > 1

negative if < 1

** p < 0.01, * p < 0.05

Variable Odds

Ratio

Std.

Err.

Visit 76.69 (0.175) **

Log(distance) 0.962 (0.005) **

Relative Out-of-Pocket Cost 1.010 (0.004) *

Academic Rank 1.010 (0.004) **

6-Year Graduation Rate 1.054 (0.01) **

Total Wins in Last 4 years 1.021 (0.009) *

Historic Win % 1.008 (0.012)

Number of Observations 9326

Pseudo R-Squared 0.5252

Results 20

A recruit’s own preferences

clearly trump all other

measures.

A visiting increases the

likelihood he signs by 7669%

A failing of the Conditional

logit model is that

disproportionately large

contributors have wide

confidence intervals.

95% Confidence Interval

54.39 – 108.1448

** p < 0.01, * p < 0.05

Variable Odds

Ratio

Std.

Err.

Visit 76.69 (0.175) **

Log(distance) 0.962 (0.005) **

Relative Out-of-Pocket Cost 1.010 (0.004) *

Academic Rank 1.010 (0.004) **

6-Year Graduation Rate 1.054 (0.01) **

Total Wins in Last 4 years 1.021 (0.009) *

Historic Win % 1.008 (0.012)

Number of Observations 9326

Pseudo R-Squared 0.5252

Results 21

A 10% increase in distance

reflects a 3.8% decreased

likelihood to sign.

It’s a small effect, but

explains some of the

barriers to recruiting in the

Southeast United States.

A recruit from Atlanta is 26%

less likely to attend MSU

than Alabama, all else

equal.

95% Confidence Interval

0.9533 – 0.9703

** p < 0.01, * p < 0.05

Variable Odds

Ratio

Std.

Err.

Visit 76.69 (0.175) **

Log(distance) 0.962 (0.005) **

Relative Out-of-Pocket Cost 1.010 (0.004) *

Academic Rank 1.010 (0.004) **

6-Year Graduation Rate 1.054 (0.01) **

Total Wins in Last 4 years 1.021 (0.009) *

Historic Win % 1.008 (0.012)

Number of Observations 9326

Pseudo R-Squared 0.5252

Results 22

Recruits were already

considering costs when

signing with universities.

Evidence the concerns over

new scholarship guidelines

may be valid.

σ ≈ $1,400. A 14% increased

likelihood to sign!

95% Confidence Interval

1.001 – 1.018

** p < 0.01, * p < 0.05

Variable Odds

Ratio

Std.

Err.

Visit 76.69 (0.175) **

Log(distance) 0.962 (0.005) **

Relative Out-of-Pocket Cost 1.010 (0.004) *

Academic Rank 1.010 (0.004) **

6-Year Graduation Rate 1.054 (0.01) **

Total Wins in Last 4 years 1.021 (0.009) *

Historic Win % 1.008 (0.012)

Number of Observations 9326

Pseudo R-Squared 0.5252

Results 23

Despite positive correlation,

we find an inverse effect!

Recruits seek schools with

higher graduation rates and

lower academic ranking.

Estimate a 5.4% increased

likelihood with one

percentage point increase

in graduation rate.

Might signal recruits want

the benefit of a college

degree without distractions

from football.

** p < 0.01, * p < 0.05

Variable Odds

Ratio

Std.

Err.

Visit 76.69 (0.175) **

Log(distance) 0.962 (0.005) **

Relative Out-of-Pocket Cost 1.010 (0.004) *

Academic Rank 1.010 (0.004) **

6-Year Graduation Rate 1.054 (0.01) **

Total Wins in Last 4 years 1.021 (0.009) *

Historic Win % 1.008 (0.012)

Number of Observations 9326

Pseudo R-Squared 0.5252

Results 24

Recruits seek winning

programs, but seem to

value current success more

than historic success.

One additional win

corresponds to a 2.1%

increased likelihood.

The effect of historic win % is

likely positive, but we didn’t

have the power to reject

the null hypothesis.

** p < 0.01, * p < 0.05

Variable Odds

Ratio

Std.

Err.

Visit 76.69 (0.175) **

Log(distance) 0.962 (0.005) **

Relative Out-of-Pocket Cost 1.010 (0.004) *

Academic Rank 1.010 (0.004) **

6-Year Graduation Rate 1.054 (0.01) **

Total Wins in Last 4 years 1.021 (0.009) *

Historic Win % 1.008 (0.012)

Number of Observations 9326

Pseudo R-Squared 0.5252

Questions Covered Today

To what extent do recruits value cash incentives?

σ ≈ $1,400. A 14% increased likelihood to sign!

To what extent do recruits value distance from home?

A 10% increase in distance reflects a 3.8% decreased likelihood to sign.

Do recruits value academic rank and graduation rates?

Opposite effects! Estimate a 5.4% increased likelihood with one

percentage point increase in graduation rate.

Are recruits more reactive to recent or historic success?

More reactive to recent success. One additional win corresponds to a 2.1%

increased likelihood.

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Questions?

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