Which Schools Get The Most Out Of Transfers

A statistical study to find the schools that develop their transfers better than anyone else

In the age of unlimited transfer movement, NIL dollars are the biggest factor in convincing a player in the portal to join a school. If your school bids the most money for a player, you have a good chance of landing him.

I want to ask a different question: If you are a transfer considering several different schools with competitive offers, which school will actually help you perform the best?

Sure, getting more money is great, but how a transfer performs at his next destination could have a massive impact on the rest of his career. If a transfer chooses a school where he flops, not only are future NIL opportunities stifled, but he doesn’t develop as much as a basketball player.

Today, I want to analyze which schools get the most out of their incoming transfers. Using all of the player performance data at our disposal at EvanMiya.com, we can easily determine which schools successfully get transfers to produce, and which schools struggle.

First Pass: Transfer Production

To conduct this study, I will only look at transfers from the last five seasons (2019-20 to 2023-24) in their first season after transferring. Given how fluid roster movement is these days, there’s no guarantee that a player will attend a school for more than one year, so only analyzing the initial season after transferring seems fair. I also only considered immediately eligible transfers (sorry Jared Butler and Davion Mitchell) because we rarely deal with guys who sit one year anymore. Finally, I separated out transfers from non-D1 programs and will address those at the end of the article.

To measure transfer production, we will use end-of-season Bayesian Performance Rating from EvanMiya.com. BPR is the best single metric for capturing a college basketball player’s value that he brings while on the court, on a per-possession basis. The metric incorporates a player’s individual efficiency stats and on-court play-by-play impact all in one. It also adjusts a player’s rating to account for the strength of all other players on the court for every possession played. You can find the full historical leaderboards for BPR on the Player Ratings page.

The table below shows which teams in the last five seasons got the most production from each transfer on average:

While helpful, this table doesn’t fully answer the question of “What school should I choose as a transfer?”. The best schools will often have the biggest chance of landing high-profile portalers and thus will consistently have good production from these players, just like any other player on their roster. To put it another way, Hunter Dickinson, the consensus top player in the portal in 2023, would have mostly likely been great at any school he chose. The fact that he played well at Kansas doesn’t necessarily mean that Kansas gets the most out of their transfers. There are plenty of other mid-major schools that extract a ton of value from their transfers but won’t ever sniff the top 25 of the chart above.

Digging Deeper

What we actually want to measure here is how each transfer performs relative to expectations at his new destination. If the top-ranked transfer in the portal ends up being the best-performing transfer, that transfer is simply meeting expectations. However, if the 200th-ranked transfer becomes a borderline All-American, he massively outperformed expectations.

The easiest way to measure these expectations is using the transfer portal ratings from the Portal Rankings page at EvanMiya.com. Essentially, each transfer rating predicts how valuable a transfer will be in his next season on a per-possession basis, regardless of what school he chooses. When the new season begins, a transfer’s projection becomes his preseason BPR, which then updates throughout the season as he performs better or worse than expected.

The scatterplot below shows that the transfer projections do a pretty good job of predicting actual season performance:

Final Analysis: Outperforming Expectations

Since we can compare any player’s end-of-season BPR to his preseason projection, we can actually find out which teams consistently get their transfers to outperform expectations. To quantify this, I created a statistical model¹ that estimates each team’s “Expected Transfer Improvement”, or how much, on average, a transfer is expected to outperform his transfer projection in BPR when he attends that school.

The top 25 programs in Expected Transfer Improvement are below:

To no one’s surprise, Arizona and UConn have extracted more out of their transfers than anyone else in the last 5 years. Arizona has brought in a lot of transfers in the last 5 years (close to 3 per year) and has had great success with many of them, the main standouts being Oumar Ballo from Gonzaga in 2021-22, Caleb Love from UNC in 2023-24, Jaden Bradley from Alabama in 2023-24, and Jemarl Baker from Kentucky in 2019-20. UConn hasn’t taken as many transfers as Arizona, but their incoming players usually outperform expectations by a ton. Cam Spencer from Rutgers in 2023-24, Joey “California” Calcaterra from San Diego in 2022-23, and Tristen Newton from East Carolina in 2022-23 all played vital roles in winning national titles for the Huskies.

Drake is the best mid-major in this chart, as they have done some fantastic development work with transfers: ShanQuan Hemphill from Green Bay and Darnell Brodie from Seton Hall in 2020-21, and Atin Wright (Cal State Northridge) and Kyron Gibson (UT Arlington) in 2023-24. Darian DeVries’s track record with transfers at Drake bodes well for West Virginia as the program hopes to reach new heights under his leadership.

San Diego State also made the top 10, having used transfers Darrion Trammell, Micah Parrish, and Matt Bradley to good effect on their way to a Final Four in 2023. They also had other transfers outperform expectations, KJ Feagin (Santa Clara) and Yanni Wetzell (Vanderbilt) in 2019-20 being the biggest ones.

High Majors

The two tables below show the best and worst high-major programs at getting transfers to outperform expectations.

There are some noteworthy schools at the bottom of this list:

• North Carolina has had some solid transfers (Harrison Ingram, Cormac Ryan, Brady Manek), but has also had a number of transfers perform under expectation (Pete Nance, Justin Pierce, Christian Keeling, Paxson Wojcik, Justin McKoy, and Dawson Garcia)

• Arkansas, known under Eric Musselman as one of the most popular transfer destinations, has taken 21 transfers in five seasons. There have been several transfers who have done well (Justin Smith and Trevon Brazile being the notables), but almost every transfer from 2023 underperformed (Keyon Menifield, El Ellis, Jeremiah Davenport, Tramon Mark).

• Indiana hit it out of the park with the transfer of Xavier Johnson from Pittsburgh in 2021-22, but there have been several major disappointments (Payton Sparks, Anthony Walker, Michael Durr).

Non-Division 1 Transfers

Though nowhere near as important as Division I transfers, some teams have thrived bringing in players from non-D1 schools. Since I haven’t formulated transfer projections for non-D1 players (yet), we will just look at the top 10 schools in average transfer production from non-D1 schools. To qualify for the table, a school must have brought in at least 2 non-D1 players in the last five years.

Colorado State rates as the best team in non-D1 transfer production by far, in part due to the success of Black Hills State transfer Joel Scott, who started all 36 games for the Rams in 2023-24 en route to the tournament, averaging 13 points and 6 rebounds per game.

1. The model is a bayesian regression to allow for shrinkage to the mean so that schools with a small number of transfers are pulled towards the average more than schools with a large number of transfers. Besides a random effect covariate for the specific school, I also included a covariate that measures the difference between the team efficiency of the transfer’s previous school and his new school, both from the past season. The reason for this inclusion is that if a player transfers from a really bad school to a really good school, his BPR will typically jump a little bit in the next season just because he’s playing with so many other good players. This covariate balances this effect out a bit by not penalizing mid majors for not being as good as high majors, basically.