The Ivy League Academic Index calculator and guide.

The origin and purpose of the Academic Index.

The Ivy League Academic Index was created in the 1980s by the Ivy League Council of Presidents to address a specific concern: ensuring that athletic recruiting at Ivy schools did not compromise the academic standards that defined the league's identity. The AI was designed as an internal governance mechanism, not a public-facing admission criterion. Understanding this origin explains why the AI works the way it does today.

Before the AI existed, Ivy League athletic recruiting operated with substantial variation across schools and sports. Some coaches at some schools could recruit athletes whose academic profiles were significantly below the school's general applicant averages. The Council of Presidents grew concerned that this variation, if left unchecked, would erode the academic reputation that gave Ivy athletics its distinctive positioning within college sports. In 1985, the Council formally adopted the Academic Index as a mechanism to constrain athletic recruiting to academic profiles compatible with the general student body.

What the AI was designed to prevent

Two specific outcomes concerned the Council of Presidents. First, the risk that individual Ivy schools would recruit academically weak athletes to gain competitive advantage in specific sports, creating an unofficial arms race that would gradually lower academic standards across the league. Second, the risk that specific athletic teams within schools would develop academic profiles substantially below the school's overall average, creating institutional problems around athlete graduation rates, academic support burdens, and public perception.

The AI addressed both concerns through the same mechanism: requiring that recruited athletes' composite academic profiles fall within one standard deviation of the school's non-athlete profile at multiple levels. The formula produces the composite. The band system enforces the constraint. Coaches operate within team AI budgets that must be balanced across each recruiting class.

How the AI has evolved since 1985

The formula has been modified multiple times since inception. Three changes matter most:

• Class rank to GPA transition. The original formula used class rank as a primary input. As high schools stopped reporting class ranks in increasing numbers through the 1990s and 2000s, the Ivy League transitioned to a GPA-based calculation called the Converted GPA Score (CGS) as the primary academic profile measure.
• SAT Subject Test elimination (2021). The original three-component formula included SAT Subject Tests as one input. When the College Board discontinued Subject Tests in January 2021, the Ivy League modified the formula to redistribute weight to the remaining GPA and SAT components.
• Dual-scale accommodation (post-2020). The pandemic-era test-optional policies produced a lasting change: the AI now operates on two distinct scales. The test-inclusive scale caps at 240, and the test-exclusive scale caps at approximately 221 for applicants who do not submit standardized test scores.

Each modification has preserved the AI's core function while adapting to changes in the underlying admissions landscape. The formula that families encounter in 2026 is substantially different from the formula Hernandez described in 1997, but the purpose remains constant: constraining recruited athlete academic profiles to remain compatible with the general student body.

The Hernandez formula and its history.

The Ivy League Academic Index was a closely-held internal governance mechanism from its 1985 creation through the mid-1990s. The formula was known to admissions officers, athletic directors, and league administrators, but was not disclosed to applicants, families, or the general public. This changed in 1997 with the publication of Michele Hernandez's book A is for Admission, which revealed the formula publicly for the first time.

Who Michele Hernandez is and why the disclosure mattered

Michele Hernandez served as an assistant director of admissions at Dartmouth College from 1993 to 1997. During her four years reading admissions files at Dartmouth, she gained direct working knowledge of how the Academic Index was calculated, how it was used to constrain athletic recruiting, and how it functioned in the actual admissions review process. When she left Dartmouth to become a private admissions consultant, she wrote A is for Admission as a comprehensive guide to the selective admissions process, and the book's disclosure of the AI formula was one of its most consequential revelations.

The disclosure mattered for two reasons. First, it gave families outside the admissions industry access to information that had been available only to insiders, changing the strategic landscape for Ivy League athletic recruiting for the first time. Second, it established the substantive foundation that subsequent public discussion of the AI would build on. Every online AI calculator, every recruiting consultant's explanation of AI mechanics, and every published article about Ivy academic constraints traces back to Hernandez's original 1997 disclosure.

The 1997 formula as Hernandez described it

The formula Hernandez revealed used three components, each converted to an 80-point scale to produce a maximum composite of 240:

• Converted Rank Score (CRS): A conversion of the applicant's class rank in high school to an 80-point scale. Higher rank produced higher CRS. This was the primary academic profile input in the original formula.
• Test Score (SAT or ACT): A conversion of standardized test scores to an 80-point scale. The math and verbal SAT sections were combined; ACT scores were converted using College Board concordance tables.
• SAT Subject Test Average: A conversion of the average of the applicant's best two or three SAT Subject Test scores to an 80-point scale.

These three 80-point components were summed to produce the AI on a 60-240 scale. The floor of approximately 60 reflected the practical minimum below which no component score would fall for a competitive Ivy applicant; the ceiling of 240 reflected perfect scores in all three components.

Three subsequent modifications to the formula

The formula has been modified three substantive times since Hernandez's 1997 disclosure:

The class rank to GPA transition (1990s-2000s). As increasing numbers of high schools stopped reporting class ranks through the 1990s and 2000s, the Ivy League transitioned from the Converted Rank Score to a Converted GPA Score (CGS) as the primary academic profile input. The CGS conversion produces an 80-point score from unweighted GPA using the formula (GPA / 4.0) × 80. This transition was not announced formally but became universally applied as class ranks disappeared from most high school transcripts.

SAT Subject Test elimination (2021). The College Board discontinued SAT Subject Tests in January 2021. The Ivy League had to modify the AI formula to accommodate this change. The formula was restructured to redistribute the weight of the eliminated Subject Test component across the remaining GPA and Test Score components. Under the current formula, both GPA Score and Test Score are weighted at approximately 1.5x to reach the same 240 maximum that the original three-component formula produced.

Dual-scale accommodation (post-2020). The pandemic-era test-optional policies at Ivy schools produced a lasting modification: the AI now operates on two distinct scales. The test-inclusive scale caps at 240 for applicants who submit standardized test scores. The test-exclusive scale caps at approximately 221 for applicants who do not submit test scores. The 19-point gap between the scales reflects the Ivy League's continued emphasis on standardized testing even during periods when test submission was optional.

The current dual-scale formula in depth.

The current Academic Index formula operates on two distinct scales that produce different maximum values based on whether the applicant submits standardized test scores. This section walks through both scales in operational detail, explains why the 19-point gap between them matters strategically, and provides worked examples that illustrate the formula in practice.

The test-inclusive scale (max 240)

The test-inclusive scale applies to applicants who submit SAT or ACT scores. The formula combines two components, each converted to an 80-point scale and weighted at approximately 1.5x to reach the 240 maximum:

Worked example (test-inclusive): A student with a 3.85 unweighted GPA and a 1440 SAT.

• GPA Score = (3.85 / 4.0) × 80 = 77
• Test Score = ((1440 − 400) / 1200) × 80 = 69.3
• Test-inclusive AI = (77 × 1.5) + (69.3 × 1.5) = 115.5 + 104 = 220 (rounded)

This AI of 220 falls into the "strong Ivy recruiting position" band, competitive at all Ivy schools with reasonable athletic profile support.

The test-exclusive scale (max approximately 221)

The test-exclusive scale applies to applicants who do not submit standardized test scores. Without a Test Score component, the AI is calculated from GPA alone using a modified formula that redistributes weight to the GPA component:

Worked example (test-exclusive): A student with a 3.85 unweighted GPA and no submitted test score.

• GPA Score = (3.85 / 4.0) × 80 = 77
• Test-exclusive AI = 77 × 2.75 = 212 (rounded)

Under the test-exclusive scale, the same 3.85 GPA produces a lower AI (212 vs. 220) than under the test-inclusive scale with a 1440 SAT. The difference reflects the value the Ivy League places on standardized test submission.

Why the 19-point gap matters

The maximum test-exclusive AI (approximately 221) is 19 points lower than the maximum test-inclusive AI (240). This gap is deliberate and signals the Ivy League's continued emphasis on standardized testing. Even during test-optional cycles, the AI formula structurally values test submission by capping the achievable AI substantially higher when tests are submitted.

The strategic implication: test-optional applicants who could submit strong test scores are structurally disadvantaged on the AI compared to test-inclusive applicants with identical GPAs and strong tests. The 19-point ceiling reduction functions as a hidden cost of test-optional submission. However, applicants whose test scores would substantially reduce their AI (moderate GPA with weak tests) may still benefit from the test-exclusive scale.

The enhanced Academic Index calculator.

The calculator below implements the current dual-scale formula and provides sport-specific band interpretation, school-by-school comparison against estimated non-athlete AI averages, and strategic recommendations calibrated to the resulting AI band. This calculator is more detailed than the compact calculator in the pillar guide, providing additional inputs and richer output.

The school comparison bars show your student's estimated AI relative to each Ivy school's estimated non-athlete AI average. Bars pointing beyond the marker (student AI above school average) indicate strong recruiting positioning at that school; bars falling short of the marker indicate the student's AI is below the school's average and the coach would need to balance the recruit against higher-AI recruits elsewhere in the class.

How the AI actually works in recruiting.

The AI is not applied to individual applicants in isolation. It operates as a system of team-level and program-level constraints that coaches must balance across each recruiting class. Understanding how the constraint mechanics work explains why the same individual AI can produce very different recruiting outcomes depending on which sport, which school, and which specific recruiting class the athlete would join.

The three levels of AI application

The AI is applied at three distinct levels within each Ivy school:

• Individual applicant level. Each recruit has an individual AI calculated from their academic profile. This is the number families and consultants focus on. It functions as the entry threshold: below the practical minimum of 176, the coach typically cannot support the recruit through admissions regardless of athletic value.
• Team level. Each Ivy sport team must maintain an overall AI average that falls within one standard deviation of the school's non-athlete AI average. This is where the constraint actually operates. Coaches balance high-AI and lower-AI recruits within each recruiting class to maintain the team's required average.
• Athletic department level. The overall recruited athlete population across all sports must also maintain an AI average within the required standard deviation of the non-athlete population. This produces cross-sport pressure: if certain sports run below average, other sports must run above average to compensate.

What "one standard deviation" means practically

The one-standard-deviation constraint is the operative constraint that shapes actual recruiting decisions. If a school's non-athlete AI average is 220 with a standard deviation of 15, the team-level AI averages must fall between 205 and 235. This constraint has three practical implications:

• Coaches at higher-average schools have less flexibility for low-AI recruits. A team at Harvard (estimated non-athlete average approximately 220) must maintain team averages higher than a team at Penn (estimated approximately 210). This gives Penn coaches more room to recruit lower-AI athletes than Harvard coaches have.
• Balancing is a class-level exercise. A coach who wants to recruit a specific athlete at AI 185 cannot do so unless the coach also recruits higher-AI athletes elsewhere in the class to bring the team average back within range. This produces trade-offs: recruiting one lower-AI athlete costs the ability to recruit a second lower-AI athlete.
• The team AI budget affects who else gets recruited. Every recruit spent on a lower-AI athlete reduces the coach's capacity to recruit additional lower-AI athletes in that class. This produces the "one recruit per year at the minimum" pattern at highest-average schools.

The band system in operational terms

Within each team, coaches typically organize recruits into bands based on AI. The bands are informal but well-established in practice:

The band system operates informally: the Ivy League does not publish official band definitions, and each school and each coach may apply slightly different band boundaries. What is consistent is the underlying mechanic: coaches balance recruits across bands to maintain the team-level AI average within the required standard deviation of the school's non-athlete average.

Sport-specific band tolerances.

Different sports operate with different AI band tolerances even within the same Ivy school. Understanding these sport-specific dynamics matters strategically because a student's recruiting profile at one sport may be substantially stronger or weaker than the same profile would be at another sport, even at the same school.

Why sport-specific tolerances vary

Three factors drive sport-specific variation in AI band tolerances:

• Team size and roster limits. Larger teams (football, track) have more roster spots and more capacity to balance AIs across the class. Smaller teams (squash, fencing) have fewer roster spots and less balancing capacity per recruit.
• Historical recruiting patterns. Some sports have historically recruited from feeder programs (prep schools, elite club programs) that produce higher-AI athletes on average. Other sports draw from broader talent pools with more academic variation.
• Sport-specific admissions treatment. Ivy League athletic departments allocate different AI budgets to different sports based on institutional priorities and historical patterns. Football and basketball typically receive different treatment than Olympic sports at most Ivy schools.

Sport categories and typical AI dynamics

The following patterns are widely observed but not officially documented, and vary by school and by cycle. Families should verify sport-specific dynamics with the specific school's coaching staff rather than relying on general patterns:

The strategic implication for families: the same AI produces different recruiting outcomes across different sports at the same school. A recruit with an AI of 195 might be a comfortable middle-band recruit for football at Harvard but a below-band recruit for tennis at the same school. Understanding sport-specific tolerances is essential for realistic assessment of recruiting probability at Ivy programs.

Non-Ivy conferences using AI-like frameworks.

The Academic Index concept was pioneered by the Ivy League but has been adopted in modified forms by several other conferences and schools that share the Ivy League's approach to balancing athletic recruiting with academic standards. Families targeting selective academic schools for athletic recruiting should understand which non-Ivy programs apply similar frameworks, since the underlying dynamics are similar even when the specific formulas differ.

The Patriot League Academic Index

The Patriot League, which includes Bucknell, Colgate, Lafayette, Lehigh, Loyola Maryland, American University, Army West Point, Boston University, and Holy Cross, uses an Academic Index framework substantially similar to the Ivy League's. The Patriot League AI operates as a coach constraint mechanism at each member school, requiring that recruited athletes maintain academic profiles compatible with the school's general student body.

The Patriot League differs from the Ivy League in several ways that affect recruiting dynamics. First, Patriot League schools do offer athletic scholarships in some sports (notably football and basketball), which changes the recruiting economics. Second, the Patriot League's AI thresholds differ from Ivy thresholds and vary substantially across the member schools. Third, Patriot League schools have more autonomy in setting their own AI standards than Ivy schools do within the Council of Presidents framework.

NESCAC academic frameworks

The New England Small College Athletic Conference (NESCAC), which includes Amherst, Bates, Bowdoin, Colby, Connecticut College, Hamilton, Middlebury, Trinity, Tufts, Wesleyan, and Williams, applies academic frameworks that function similarly to the Ivy AI even though the NESCAC does not use the exact "Academic Index" terminology. NESCAC schools are D3, so they do not offer athletic scholarships, but they do provide substantial admissions support for recruited athletes through the coach recommendation process.

The NESCAC framework requires that recruited athletes' academic profiles fall within acceptable ranges of the school's general applicant profile. Each NESCAC school administers its own version of this constraint, with some variation in specifics. Amherst, Williams, and Bowdoin operate with academic thresholds broadly comparable to Harvard, Yale, and Princeton. Other NESCAC schools operate with somewhat more flexibility. The absence of a unified conference formula means families targeting NESCAC schools should investigate school-specific practices directly.

Other schools with AI-like frameworks

Several non-conference schools apply academic recruiting frameworks that function similarly to formal AI systems:

• Stanford operates its own internal academic-athletic constraint framework that limits recruit academic profiles even without formal AI publication. Stanford's overall admission rate (below 4%) and recruit academic expectations are broadly comparable to the highest-average Ivy schools.
• MIT is D3 and does not have athletic scholarships, but recruit academic profiles must meet substantial thresholds because of the school's overall admission standards. The functional constraint operates similarly to a formal AI even without published formula.
• University of Chicago operates similarly to MIT with strong internal academic-athletic constraint despite absence of published AI formula.
• Duke, Northwestern, Vanderbilt, and other academic D1 programs vary in specific practices but generally maintain higher recruit academic profiles than the median D1 program. Formal AI frameworks are not published but functional constraints operate.

The test-optional strategic decision.

The pandemic-era test-optional policies at Ivy League schools produced a lasting strategic question for applicants: whether to submit test scores or apply test-optional. For applicants pursuing athletic recruiting, this decision has additional complexity because the AI operates on two distinct scales. This section provides the framework for making the decision explicitly rather than by default.

Current Ivy League test policies

As of the 2024-25 admissions cycle and beyond, Ivy League schools have taken varied approaches to test policies:

• Test-required schools: Some Ivy schools have returned to requiring standardized test submission for all applicants. Verify current policy at each specific school before applying, since policies have shifted since 2020.
• Test-preferred schools: Some Ivy schools indicate that test scores are strongly preferred even when not strictly required, particularly for competitive admissions.
• Test-optional schools: Some Ivy schools maintain formal test-optional policies, though the practical dynamics of admissions may favor test-submitting applicants even when the policy permits withholding.
• Test-blind is not currently the Ivy League posture: No Ivy school has moved to a test-blind policy where submitted scores would not be considered.

For athletic recruiting specifically, coaches typically encourage recruited athletes to submit test scores because the test-inclusive AI produces higher maxima than the test-exclusive scale. However, applicants whose test scores would substantially reduce their overall AI may benefit from the test-exclusive scale.

The strategic framework for the submit-or-withhold decision

The framework for making the test-optional decision explicit involves three steps:

Step 1: Calculate the AI on both scales. Use the calculator in Section 4 to compute the test-inclusive AI (with the actual test score) and the test-exclusive AI (GPA only). Compare the two numbers directly.

Step 2: Identify the band each AI falls into. The bands (176+, 195+, 215+, 230+) matter more strategically than the specific numerical AI values. If both scales produce AIs in the same band, the strategic difference between submitting and withholding is smaller than if the two scales produce AIs in different bands.

Step 3: Consider school-specific test policy. At test-required schools, the decision is made for you. At test-preferred and test-optional schools, factor in the practical bias toward test-submitting applicants even when policy permits withholding.

Four common scenarios and their strategic conclusions

Applying the framework to common academic profiles produces four typical scenarios:

The pattern is straightforward once made explicit: students with strong GPAs relative to their test scores may benefit from the test-exclusive scale; students with strong tests relative to their GPAs benefit from the test-inclusive scale. The calculation should be done with actual numbers rather than estimated.

Why coaches typically prefer test submission

Ivy League coaches typically encourage recruited athletes to submit test scores for three reasons beyond the raw AI math:

• Test scores add data. Even when the test-exclusive AI is higher than the test-inclusive AI on paper, coaches and admissions officers may perceive test-submitting applicants as providing more complete academic profiles. This effect is difficult to quantify but observed in practice.
• Test-inclusive scale accommodates growth. Applicants who take the SAT or ACT multiple times can superscore, which may raise the test-inclusive AI closer to the test-exclusive AI or above it. Withholding scores forecloses this option.
• Institutional posture is drifting back toward test-preferred. Ivy League schools that had been fully test-optional during the pandemic have generally moved back toward test-preferred or test-required. Withholding scores in this environment carries some risk that policy will shift further toward requiring scores mid-cycle.

Common misconceptions and pitfalls.

Seven recurring misconceptions and pitfalls that families encounter with the Academic Index. These patterns occur often enough that identifying them early prevents predictable strategic errors.

1. Treating the AI minimum as the AI target

The commonly-referenced minimum of 176 is not a target. Coaches at high-average schools may only support one athlete per year at or near the minimum, and only in specific high-priority positions. Families whose students are approaching 176 should not assume the coach can absorb them. Realistic recruiting positioning at the highest-average Ivy schools requires AIs of 200 or higher, with 215+ providing meaningful coach flexibility.

2. Assuming the AI is uniform across Ivy schools

The AI formula is uniform, but the AI band requirements differ by school. Harvard, Yale, and Princeton operate at estimated non-athlete AI averages of approximately 220. Penn and Dartmouth operate at estimated averages of 210-215. Columbia, Brown, and Cornell fall between these ranges. A recruit whose AI is competitive at Penn may not be competitive at Harvard, and vice versa.

3. Focusing on the exact AI number rather than the band

A 218 and a 221 both fall into the "strong Ivy recruiting position" band. The strategic difference between them is minimal. Families sometimes obsess over small AI differences (whether the calculator says 214 vs. 217, for example) when the underlying strategic reality is nearly identical. What matters is the band, not the specific number.

4. Assuming the coach knows exactly what your AI is

Coaches at Ivy programs have access to internal AI calculations that may differ from public formulas in specifics. Coaches also have current-cycle knowledge of their team's specific AI band constraints that varies year to year. Never rely on an online calculator estimate when making significant recruiting decisions. Verify the coach's actual assessment of the student's positioning through direct communication with the coaching staff.

5. Treating the AI as an admission criterion rather than a coach constraint

A strong AI does not produce Ivy admission on its own. The AI functions as a coach constraint that allows coaches to include the athlete in their recruit list. Admission is subsequently determined by the full admissions review process, which considers essays, extracurriculars, recommendations, and the other elements of the application. Recruited athletes with strong AIs still need to submit strong applications.

6. Ignoring the sport-specific dimension

A student's AI produces different recruiting outcomes across different sports at the same school. Football and basketball have different AI band tolerances than tennis and squash. Water polo and volleyball differ from track and swimming. Section 6 covers the sport-specific patterns; families should investigate the actual dynamics of the specific target sport rather than assuming uniform behavior across sports.

7. Missing the difference between the AI and the actual application strength

The AI captures GPA and test scores. It does not capture essay quality, extracurricular substance, recommendation strength, personal characteristics, or the other elements that admissions readers evaluate. Two applicants with identical AIs can have very different admission probabilities depending on how well the rest of their applications perform. AI matters, but it is not the whole picture. Families should invest in the full application quality alongside the AI-relevant academic profile.

When to work with a counselor, FAQ, and next steps.

The Academic Index is one substantive input into Ivy League athletic recruiting, but it operates within a broader strategic environment that families navigate best with informed help. This section addresses when outside counseling adds value, common family questions, and how to proceed from here.

When outside counseling adds value on AI strategy

Not every family navigating Ivy League athletic recruiting needs paid counseling. Families whose situation is straightforward often navigate well with the free tools and information available. Outside counseling typically adds the most value when:

• The student's AI falls near a band boundary. Whether a student's AI is 213 or 216 makes a small mathematical difference but a meaningful strategic difference (falls into "mid-tier" band vs. "strong Ivy recruiting" band). Interpreting the practical implications requires nuanced understanding of coach dynamics.
• The test-optional decision is genuinely ambiguous. When calculation of both scales produces similar AIs, the school-policy and coach-preference variables become determinative. Understanding these variables requires current-cycle knowledge.
• The recruiting timeline is compressed. Junior year families have time to develop the academic profile alongside the recruiting process. Senior year families face immovable deadlines that require strategic prioritization of limited attention.
• The academic profile has complexity. Curriculum rigor variations, GPA calculation nuances (weighted vs. unweighted vs. school-specific), international transcripts, homeschool contexts, and transfer situations all affect AI interpretation in ways online calculators cannot handle.
• The student is targeting multiple sports or schools with different AI dynamics. When strategic decisions require balancing recruiting positioning across multiple targets, coordinating the strategy across sports and schools benefits from experienced guidance.

Frequently asked questions

How accurate is the enhanced calculator on this page?

The calculator implements the widely-referenced formula approximation from Michele Hernandez's 1997 book as modified after SAT Subject Test elimination. The AI number it produces should be treated as a rough estimate rather than a precise measurement. What matters strategically is the band the AI falls into (176+, 200+, 215+, 230+), not the specific number. Coaches at Ivy programs have access to internal calculations that may differ from public formulas.

Can I use this calculator for schools other than the Ivy League?

The formula in this calculator is calibrated to the Ivy League AI. Other conferences that use similar frameworks (Patriot League, some NESCAC schools) may apply modified formulas that produce different numerical outputs. The calculator is a reasonable starting point for estimating academic profile positioning at any highly selective school with academic recruiting constraints, but families should verify school-specific dynamics with the specific institution.

Should my student focus on raising GPA or test scores to improve AI?

The answer depends on the starting profile. Students with strong GPAs but moderate test scores benefit most from test preparation. Students with strong test scores but moderate GPAs benefit most from academic profile improvement (challenging courses, strong senior fall grades, curriculum rigor). The calculator can help families see the marginal AI improvement produced by specific GPA or test score improvements.

Does the AI apply to non-athletes?

The AI is calculated for all Ivy League applicants, not just athletes. For non-athletes, the AI serves primarily as one input into holistic admissions review and does not function as a coach-support constraint. Non-athlete applicants with strong AIs still need to submit strong applications; non-athletes with weaker AIs face substantial competition even with strong applications. The AI matters more for athletic recruiting than for non-athlete admissions.

What if my student's AI is below 176?

Below the 176 practical minimum, Ivy League coaches typically cannot support the athlete through admissions regardless of athletic value. Alternative recruiting paths are more appropriate: strong D1 non-Ivy programs where the AI does not apply (USC, UCLA, Stanford, and many others), competitive D3 programs with more flexibility in academic profiles, or specialized D1 programs where athletic recruitment operates on different criteria. Families in this situation should focus strategic energy on the alternative paths rather than pursuing Ivy programs where the AI constraint makes coach support unlikely.

Related PCG resources

The following PCG resources treat related topics in operational depth:

• The Los Angeles Athletic Recruiting Guide covers the LA-specific recruiting landscape, USC and UCLA local dynamics, the post-House settlement environment, D1 vs. Ivy path comparison, and common LA family pitfalls. This guide focuses specifically on Academic Index mechanics; the LA guide provides the broader recruiting context.
• The Post-House Settlement Recruiting Guide covers the current-cycle NCAA changes including the roster limit contractions, revenue-sharing provisions, NIL Go clearinghouse, and the Written Offer of Athletics Aid replacement of the NLI. Ivy schools do not opt into the House settlement revenue provisions but the surrounding environment affects Ivy recruiting families.
• The Human Advantage in the AI Era guide covers the essay development and voice work that recruited athletes need alongside the AI-relevant academic profile.
• The California College Admissions Guide for 2026-27 covers the broader admissions cycle for California families.
• PCG vs. Crimson, IvyWise, Collegewise, Spark, and Solomon is the structural comparison of the six consulting firms families most commonly consider.