The transfer portal has made movement feel like momentum.
A player leaves one program for another, and the move itself starts to carry meaning. Does the new spot mean more playing time? A better outcome for future prospects? Is the grass greener? One of the main reasons we started this site came from a very simple thought process: what Bill James created for Major League Baseball is wildly difficult to translate to the college games of softball and baseball. One saying we like to give coaches is that a home run off Paul Skenes should count differently than a home run off Paul Blart.
The same idea applies to the portal. What a player does at one school, whether it is a great outcome or an insignificant one, is difficult to translate when the level changes. That is the gradient shift, the move from one level to the next. It is also what makes the portal process so difficult for coaches and players alike. A player has been good at X school, but will they be good at Y school?
Sometimes it translates quite well. You see players jump to Division I from the Division II and III ranks all the time. This year, for example, AJ Swader at Bellarmine is the number 120 hitter in all of Division I by our rankings and is leading the Knights offense with a .377/.469/.705 slash line. Not many people have forgotten how good that Wake Forest offense was with Seaver King. In softball, one of the top hitters in all of DI this year with a 98th % wRC, JT Smith of Northwestern St. came over from DII powerhouse UT Tyler.
Still, while you can absolutely point to examples of players succeeding after jumping levels, what we wanted to provide for players, parents, and coaches was a data driven look at how difficult that jump can be.
Because a transfer is not just a change of uniform. It is a change in environment, competition, role, and margin for error. Some players arrive at a new stop and look even better than they did before. Others find that the version of themselves that worked at one level does not hold up the same way at the next. At the previous school, maybe they were hitting second in the lineup and opposing staffs were game planning around them. What happens when they are now hitting eighth and facing better pitching that is coming right after them? Do the old 2-1 counts become 1-2 counts? Do the mistakes get smaller? The portal creates opportunity, but it also changes the conditions around that opportunity.
That is the real question here. Not whether players move. Not whether those moves matter. But what tends to happen to performance when a player changes levels.
The pattern across the full cohort
Across the full transfer cohort, the pattern is hard to miss. Moving down tends to work. Moving laterally holds much steadier than larger upward jumps. Moving up, especially by multiple levels, usually comes with a real statistical cost.
How we defined a jump
But first, we had to define what a jump is.
There are 30 conferences in Division I baseball and 31 in Division I softball. For baseball, it was mathematically clean to separate those 30 conferences into three buckets. For softball, we used the same approach, with the lowest bucket carrying the one conference remainder. That gave us five levels to review: D3, D2, D1 lower third, D1 middle third, and D1 upper third.
Those conference buckets were built using five year RPI trends. RPI is not especially good at telling you whether one individual team is truly better than another, but in the aggregate it does a fairly useful job of sorting conferences.
So when reviewing players, what we wanted to study was where those movement types worked and where, on average, they did not.
Hitters: the slope gets steeper
Among hitters, moving down one level produced a median wRC gain of +10.6. Down two levels rose to +18.9. Down three levels reached +21.9. Down four levels climbed to +33.2, in a small sample that still points in the same direction as the larger trend. Turn that around and the numbers harden quickly. Up one level brought a median wRC change of -7.4. Up two levels fell to -14.5. Up three levels dropped to -21.0. Up four levels landed at -27.5.
While the picture already appears fairly clear, it jumps out even more in the heatmap below.
Pitchers tell the same story in different numbers
Pitchers tell the same story in different numbers. Since lower is better for FIP and WHIP, downward moves look exactly the way they should if the game has become a little more forgiving. Down one level produced a median FIP change of -0.79. Down two levels improved to -0.84. Down three levels reached -1.67. Going up reversed the picture, with median FIP changes of +0.38, +0.77, and +1.23 across the same progression.
That is the portal tax. The further a player climbs, the more likely it is that some part of their old production comes under pressure.
The data shows that larger upward jumps are often the hardest to sustain statistically. That does not make the move itself a mistake, and it does not say anything definitive about whether a player belongs at the new level. It simply shows that the transition is demanding. Higher levels tighten the margin for error, and even strong performers often see their numbers change when the competition changes with them.
Those are not minor adjustments. Those are category changing results.
Lateral movement holds steadier
One of the more useful findings in the study is that same level movement looks relatively stable. The median change for lateral hitter transfers was +2.8 wRC, and the median change for lateral pitcher transfers was -0.10 FIP. In other words, production was broadly maintained.
That does not mean every transfer works the same way, or that every player improves. But it does suggest that when players stay within the same competitive tier, their prior numbers are much more likely to remain relevant. For coaches trying to answer whether a player's performance can carry to their school, that is one of the clearest signals in the data.
That stability still comes with context. Same level does not mean same fit, same role, or same path to production. A transfer that looks modest on paper can still be a real performance bet once the season begins.
Even the highest profile version of that move behaves that way. D1 Upper to D1 Upper hitters posted a median wRC change of +2.7, with 55 percent improving. D1 Upper to D1 Upper pitchers posted a median FIP change of +0.01, with exactly 50 percent improving.
Going down works
Which makes the clearest finding in the study feel almost too simple: going down works.
Every down level hitter bucket showed positive median movement in both wRC and wRAA. Every down level pitcher bucket improved in both FIP and WHIP. For hitters, the improvement rate moved from 71 percent for down one level to 82 percent for down two, 86 percent for down three, and 92 percent for down four. For pitchers, the improvement rate ranged from 69 percent for down one to 83 percent for down three, reaching 100 percent in the smallest down four group.
Same level, different launch point
One of the most useful cuts in the data looks only at players who stayed within the same level but changed conferences. That helps separate level from context, and it shows quickly that same level does not mean the same launch point.
At the D1 Upper level, the conference splits were not subtle. For hitters, players leaving the SEC saw the biggest median bump at +16.2 wRC, while the Big 12 at +7.1 and the ACC at +6.7 also trended in a positive direction. On the other end, the Missouri Valley Conference was the clear low point at -21.1, with the Big East at -8.8 and Conference USA at -7.0 also finishing below zero.
Pitchers showed the same kind of spread. Since lower FIP is better, the best outcomes came from players leaving the ACC at -0.47, followed by the SEC at -0.31 and the Big Ten at -0.23. The weaker end of that table came from the Colonial Athletic Association at +0.84 and the SoCon at +0.63.
These numbers are not meant to serve as a final ranking of conferences. But they do reinforce the idea that origin matters. A same level transfer is not one thing. Different conferences create different baselines, different demands, and different kinds of adjustment once the player lands somewhere new.
Hitters vs pitchers: the edges
The hitter and pitcher stories also separate a bit once the sample is laid out. Hitters show sharper median movement across the buckets, as if the body of the distribution is quicker to reveal the cost of a jump. Pitchers are less neat. The medians move in the same direction, but the extremes are louder.
One of the biggest FIP improvements in the sample came from a 2024 D1 Middle to 2025 D2 move, where the pitcher improved by -9.12. One of the biggest regressions came from a 2024 D3 to 2025 D1 Lower move, where the pitcher worsened by +7.46. Another major regression followed a 2023 D1 Middle to 2024 D1 Upper move, where a pitcher went from 156.0 innings to 20.0 and saw a +6.92 change in FIP.
That does not change the larger pattern. It sharpens it. Pitchers may not always show the steepest median movement, but their edges can still get loud in a hurry.
What it all means
And maybe that is the fairest way to frame the portal as a whole. It is not random. The directional trends are too strong for that.
Most transfers are not making the dramatic leap anyway. For both hitters and pitchers, the most common directions were lateral, up one level, and down one level. The major jump gets the attention because it is dramatic. The actual market is built much more on reorientation than reinvention.
That is why this matters. The portal can absolutely work. But not every move is asking the same question, and not every level gives the same answer.
The farther the jump, the less likely the old numbers are to come with it.
Caveats & methodology: A transfer event is a player appearing on one team in year N and a different team in year N+1 in rosters.csv, with levels assigned from conferences.division (D-I/II/III) plus conferences.classification (upper/middle/lower) for D-I. Cohort years: 2021–2026. Hitters required 25+ plate appearances in each season; pitchers required 10+ innings pitched in each season — cup-of-coffee appearances whose rate stats would otherwise dominate the tails are excluded. Metrics are wRC and wRAA for hitters (higher is better), FIP and WHIP for pitchers (lower is better). The percentile-delta views in Figures 01, 06, and 08 rank each player against their peers in the (sport, level, year) cohort and report the change in standing, not the change in raw stat. Cohort sizes: 12,327 transfer events with both ends classifiable; 4,147 hitter events and 2,889 pitcher events met the usage thresholds. Figure 06 restricts to same-level cross-conference transfers with at least 20 events per from/to pair.