From Canes Insight to Code: Finding College Football’s Portal “Sweet Spot”

I was watching Canes Insight the other day, listening to Steve Kim, when he said something that stuck with me.

Paraphrasing, he wondered out loud if there was a “sweet spot” in the transfer portal era — not too portal-heavy, not too old-school high-school recruiting — but a mix that actually correlates with winning. He also asked the natural follow-up: has anyone already done this math?

That question sent me down a rabbit hole.

Because once you strip away the talking points, the portal debate is really a data problem:

• How much portal is too much?
• How much continuity matters?
• What happens when a team loads up one year… then loses those players the next?

So I built a model. Then I built a tool.

What you see below is the Portal Sweet Spot framework — the same logic powering the interactive widget under this article.

Step 1: Measuring Roster Mix (Portal vs High School)

Let’s start simple.

Let:

• P = number of portal players added
• H = number of high-school recruits signed

There are two useful ways to express roster mix:

Portal Share (PS)

This is the cleanest headline number.$$PS = \frac{P}{P + H}$$PS=P+HP​

• 0.00 → all high-school
• 0.40 → 40% portal
• 1.00 → all portal

This is the number fans intuitively understand.

Portal Ratio (PR)

For the math heads:$$PR = \frac{P}{H}$$PR=HP​

• PR = 1.0 → equal portal and HS
• PR > 1.0 → portal-heavy
• PR < 1.0 → HS-heavy

In the tool, you’ll see both — but Portal Share is what drives most of the analysis pasted.

Step 2: Winning Still Matters (A Lot)

No advanced metric should ignore the obvious.$$Win\ Percentage = \frac{W}{W + L}$$Win Percentage=W+LW​

Everything we do later is anchored to winning, not vibes.

Step 3: The Missing Piece — Retention & Continuity

This is where the portal conversation usually breaks down.

Steve Kim’s point — whether explicitly or implicitly — was this:

Teams can win the portal one year… then get crushed when that class leaves.

So we track portal retention.

Let:

• P₀ = portal players added in Year 0
• S₁ = how many are still on the roster the next year
• S₂, S₃ = still there two and three years later

Retention rates:$$R_1 = \frac{S_1}{P_0}, \quad R_2 = \frac{S_2}{P_0}, \quad R_3 = \frac{S_3}{P_0}$$R1​=P0​S1​​,R2​=P0​S2​​,R3​=P0​S3​​

Then we combine them into a Roster Stability Score (RSS):$$RSS = 0.60R_1 + 0.30R_2 + 0.10R_3$$RSS=0.60R1​+0.30R2​+0.10R3​

Why the weighting?

• Immediate continuity matters most
• Long-term retention still counts, just less

Programs that cycle players every year get exposed here.

Step 4: Penalizing Extremes (The “Sweet Spot” Idea)

The core hypothesis is simple:

Too little portal = slow roster correction
Too much portal = instability

So we define a target portal share — call it PS* (for example, 0.35).

Then we reward balance:$$Balance = 1 – |PS – PS^*|$$Balance=1−∣PS−PS∗∣

The closer you are to the sweet spot, the higher the balance score.

Step 5: The Sweet Spot Score (SSS)

Now we combine everything into one number:$$SSS = (0.50 \times Win\%) + (0.25 \times RSS) + (0.15 \times Balance) + (0.10 \times (1 – Churn))$$SSS=(0.50×Win%)+(0.25×RSS)+(0.15×Balance)+(0.10×(1−Churn))

This score:

• Rewards winning
• Rewards continuity
• Penalizes roster whiplash
• Penalizes extreme portal dependence

It also explains why two teams with similar records can feel very different long-term.

Why This Matters (Especially for Miami)

This model finally lets us test narratives we argue about every offseason:

• Did a big portal year actually sustain success?
• Did losing portal starters show up in next year’s record?
• Who builds reloadable rosters — not just one-year spikes?

For Miami specifically, this helps separate:

• “Portal hype” from portal efficiency
• Volume from value
• Short-term wins from program stability

What the Tool Below Does

The interactive widget below lets you:

• Manually enter team-year data
• Paste or load CSVs (including Python-generated outputs)
• Compare teams across conferences
• Visualize multi-year trends with sparklines
• Rank programs by Sweet Spot Score

No database. No login. Just math.

Final Thought

Steve Kim asked the right question.

This isn’t about being pro-portal or anti-portal.
It’s about how much, how well, and for how long.

That’s the sweet spot.

👇 Use the calculator below, plug in your teams, and see where the data lands.

🧮 Simple Sweet Spot Score (Public Formula)

This is intentionally short + explainable:

PAS = Portal_In / (Portal_In + HS_Signed)

Win% = Wins / (Wins + Losses)

Sweet Spot Score =
(Win% × 100)
− |PAS − 0.40| × 30
− Churn × 15

Where:

• 0.40 = hypothesized portal sweet spot
• Churn = (Portal In + Portal Out) ÷ Total Adds

Score range (rough):

• 80–100 = Elite balance
• 65–79 = Solid / sustainable
• 50–64 = Volatile
• < 50 = Roster risk

I’M building The Full scale Here…https://studio1live.com/college-football-roster-sweetspot/ coming soon , a little scrape code build so it can be automatic …

Check out the simple tool below

https://studio1live.com/college-football-roster-sweetspot