NCAA Transfer Portal Analysis

by The SII Team

Background

In college athletics, things have changed significantly over the past decade. The most notable change that has greatly altered the landscape has been the creation of the “Transfer Portal” and Name, Image, and Likeness (NIL) deals for athletes. This has led to a surge in student-athletes transferring, where over 3,000 completed the process in Football and Men’s Basketball last season alone. The research provided in this article specifically investigated player performance surrounding transfers. Teams were assigned into tiers based on a series of performance factors, and that tier system was used to assess if a transfer had “moved up” to a more competitive program or “moved down” to a less competitive program. Player data was used from both the season before the transfer and the season after to assess the impact the transfer had on the player and the programs involved. 

Research Questions

For basketball and football, we asked the same six questions for each sport to understand the impact of transferring on team and player performance.

1. What are the player migration patterns across conferences and tiers?
2. What is the relationship between the percentage of roster comprised of transfers and team performance?
3. How do transfer players compare to those who did not transfer?
4. How do players who transferred to a school at the same tier compare to those who did not transfer?
5. For those players who transferred, how does moving up, staying at the same level, or moving down impact performance?
6. For those players who transferred, how much influence does tier movement impact performance? This is similar to #5, but drills down to how far up or down a player transferred.

Methodology

Tier System Creation

To understand the impact of transferring to a “better” or “worse” school on player performance, we developed a methodology to assign each team to one of six tiers that represented the quality of the team.

There is always a fierce debate when deciding how good a collegiate program is. Do you look at the program’s history; should a “Blue Blood” that has not had a winning record in the past five years still be considered at the top of the sport? Does being in a certain conference make you a better program? These questions led us to create our own NCAA Tier Ranking System for NCAA D1 FBS Football and NCAA D1 Men’s Basketball.

We created this system using data from the past 5 years. When creating the tier system, we placed more weight on recent success rather than all-time records. Important factors considered for both tier systems included records, conference finishes, and strength of schedule for each of the past 5 seasons. In addition, for football we considered if the team was in a Power 5 Conference, the number of 10+ win seasons they had, the number of AP (or CFP) Top-25 finishes, and types of bowl game appearances. For basketball, important factors included NCAA tournament appearances and advancement, number of Quad-1 wins, non-conference strength of schedule, KenPom Rating, and Evan Miyakawa’s Bayesian Performance Rating (BPR).   

Using these calculations, each sport was divided into 6 tiers. The lowest tier, which was deemed “Tier 6”, consisted of all non-FBS and non-D1 schools for football and basketball, respectively. The other 5 tiers were calculated based on the quartiles of the data for each sport. Tier 5 was the bottom 25% of the teams based on the data, with each tier moving up in 25% increments. Tier 1 are the teams that are considered outliers for their respective sports. Below is a table showing how many teams are in each tier and some notable teams in each one. 

Table 1: Football and Basketball Tiers

Tier	Quartile Range	Football Count	Notable Football Program in that Tier	Basketball Count	Notable Basketball Program in that Tier
Tier 1	Outliers	10	Georgia, Utah	21	Gonzaga, UCLA
Tier 2	75%-Outliers	24	Texas, Oregon State	73	Wisconsin, Providence
Tier 3	50-75%	37	UCF, Illinois	87	Nebraska, Notre Dame
Tier 4	25-50%	29	Arizona, New Mexico State	93	Georgia, Wake Forest
Tier 5	0-25%	34	Arkansas State, Akron	93	Army, La Salle
Tier 6	Non-FBS/D1	91	Montana State, North Dakota State	269	Adams State, Central Missouri

Data Collection

Football: Data consisted of the list of transfers, which was scraped from 24/7 Sports, and the players’ statistics, which were downloaded from Pro Football Focus. The list of transfers was narrowed down to those with statistics from two seasons, the season before they transferred and the season following the transfer. The transfer window that was considered was between December 2022 and September 2023. In that time 782 players met the requirements of having two seasons of data.  The study then aimed to break down the 782 football players by position so their data could be analyzed. This was a necessary step as each position in football has different key statistics. We analyzed player performance for the quarterback, running back, wide receiver, tight end, offensive line, defensive line, linebacker, and defensive back positions.

Table 2: Transfer Count by Position in Football

Position	Total Transfers	Transfers w/ Data
QB	119	65
RB	110	54
WR	246	135
TE	78	31
OL	241	123
DB	304	148
DL	249	160
LB	169	66

Basketball: Data consisted of the list of transfers, box score player statistics, and advanced player statistics. The list of transfers along with advanced player statistics were downloaded from Evan Miyakawa’s website, while box score statistics for all players were scraped from ESPN. We looked at the data that surrounded the most recently completed transfer portal cycle, meaning the season at the old school was 2022-2023 and the season at the new school was 2023-2024. Once again, the complete transfer list was narrowed down to include only those players who had two seasons of data. This resulted in a list of 898 players out of the complete list of 1,500+ players. The main question that we looked at for basketball, was how the box score stats, such as minutes, points, rebounds, assists, and steals changed per season? We also looked at how their advanced metrics, BPR, OBPR, and DBPR changed as well. 

Findings

Question 1: Talent Migration

To understand how players moved around via the transfer portal we looked at three levels of movement: Power-5/Non-Power-5, tier, and conference.

Football

Power-5/Non-Power-5 Migration Patterns

The broadest level of transfer migration analyzed was the level of the conference, which looked at how players moved to and from Power-5 and non-Power-5 conferences. Almost 60% of the 1,560 players in the transfer portal came from Power-5 schools while only 50% of players that completed a transfer ended up at a Power-5 school. The reason for this is due to a larger number of players transferring out of Power-5 schools (444) than transferring into Power-5 schools (316). Movement like this could ostensibly lead to a smaller gap in talent between the “levels” of college football. Figure 1 exemplifies the fact that the transfer portal is giving non-Power-5 schools greater access to Power-5 talent than ever before.

Figure 1: Football Transfer Migration by Power 5 Affiliation (N = 1,560)

Tier Migration Patterns

The next level of transfer migration that was analyzed was the movement between tiers. Tier 3 and tier 2 have the highest percentage of transfers leaving, while tier 3 and tier 5 have the highest percentage of transfers coming in. Overall, the higher tiers, 1 and 2, see the biggest difference in transfer out percentage and transfer in percentage. This shows that teams that are consistently competing at the highest level are less active in adding players via the portal. A big reason for this is that tier 1 and 2 schools, like Georgia and Texas, have significant advantages in recruiting. This allows these programs multiple pathways to build their teams meaning they don’t have to rely on the transfer portal as heavily. On the contrary, lower-tier teams, like UCF and Arizona, need to take full advantage of this newly found access to premium talent. This isn’t to say that there isn’t value in recruiting for lower-tiered schools, however, in the “win now” culture of today’s college football coaches may not have the luxury of developing homegrown talent over the course of four years like they used to.

Figure 2: Tier Migration Patterns in Football

Conference Migration Patterns

The final level of transfer migration that we analyzed was how players moved between conferences. The Power 5 conferences have the most transfers out, while the Non-Power 5 conferences have more transfers in than out, This once again exemplifies the point that lower-division schools have more direct access to higher levels of talent than ever before. A couple conferences that can be seen taking advantage of the transfer portal are the American Athletic Conference (AAC) and the Sun Belt Conference (SBC). Both conferences had significantly more transfers into the conference than out of the conference, 168 to 90 for the AAC and 136 to 66 for the SBC. More evidence that schools in these conferences are taking advantage of the portal can be seen in the recent success of non-traditional football schools in these conferences. In the past two years alone the AAC has been home to a college football playoff team, Cincinnati 2022, a Cotton Bowl win, Tulane 2023, and multiple teams cracking into the AP/CFP Top 25, Tulane (2023-24), SMU (2023-24). The SBC has seen similar success with multiple teams cracking into the AP/CFP Top 25, JMU (2023-24), and Troy (2022-23) along with major national media attention.

Figure 3: Conference Migration Patterns in Football

Basketball

 Power-5/Non-Power-5 Migration Patterns

Once again looking at the broadest level of transfer migration, we saw a drastic difference in the number of players entering the portal from non-Power-6 schools (77.4%), versus Power-6 schools (22.6%). Unlike football, there was not a significant change in these percentages when looking at where these transfers ended up. As evident in Figure 4, the largest migration group was non-Power-6 to non-Power-6. This shows that most of the movement taking place is happening within lower major schools. This is likely a result of a couple new phenomena that occur on a yearly basis. The first is quick coaching changes. In the “win now” era of college sports coaches at all both Power-6 and non-Power-6 schools are held to high standards early on. This leads to a lot of coaching changes at the end of disappointing seasons. Quick-to-follow coaching changes are the mass exodus of current players and the quick influx of new players via the portal, which often amount to non-Power-6 players shuffling around to other non-Power-6 schools. The second phenomenon that is happening more and more is high-major schools poaching low-major coaches after a successful season. After the 2023-23 season, we saw this happen with Dusty May, who went from FAU to Michigan, and Pat Kelsey, who went from Charleston to Louisville. These coaching moves result in players at the non-Power-6 level looking for new homes, which often happens to be at a different non-Power-6 school, most often the case for those who saw little playing time.

Figure 4: Basketball Migration Patterns (N = 1,490)

Tier Migration Patterns

The tier migration for basketball was much less interesting than it was for football. Below in Figure 5, we see that the percentages for each tier are similar on the left and the right, meaning that the percent of transfers out of each tier is roughly equal to the percent of transfers moving into each tier. The lone exception is seen in tier 6. Tier 6 includes all schools that are not NCAA Division 1, which includes NCAA Division 2 and below, NAIA, junior colleges, and any other non-NCAA Division 1 schools. Because the new school tier 6 bucket is larger than the previous school tier 6 bucket, we know that more players left division 1 schools altogether than came into division 1 from non-division 1 schools.

Figure 5: Basketball Tier Migration Patterns

Conference Migration Patterns

Like the lack of patterns we saw in basketball tier migration patterns, we once again see few trends here. The most interesting thing to note is the dispersion of the power-6 conferences among the conferences with the most transfer activity. In fact, two of the top 3 conferences with the most transfers are non-power-6 conferences, AAC and the Atlantic-10 (A10). This is drastically different than what we saw for football, where the top 5 conferences were the power-5 football conferences. The lowest power-6 basketball conference in terms of transfer activity is the Big 10 (B10), which only had 46 players transfer out and 44 players transfer into the conference.

Figure 6: Conference Migration Patterns

Question 2: Transfer Portal Activity Impact on Winning

For football, we found that, on average, as teams increased the percentage of transfers on their roster their total wins in a season decreased. On the other hand, as teams increased the percentage of transfers on their rosters their win differential from the previous season increased.

In short, having a high percentage of transfers doesn’t lead to overall success, however, it can put a team on the fast track to individual success. So, this begs the question, if you’re a coach that might be on the “hot seat” why wouldn’t you turn to the portal? The answer is you would and as we saw above lower tier schools are doing exactly that.

Figure 7: Wins (top) and Win Differential (bottom) by Percentage of Roster Comprised of Transfers in Football

Similar to football, when looking at average wins grouped by the percentage of transfers comprising a team’s roster in basketball, we saw that increasing the percentage of transfers leads to an increase in average wins. This is evident in Figure 8. However, there is more nuance to this trend. Figure 9, breaks separates Figure 8 into Power-6 and non-Power-6. For non-Power-6 schools, increasing the percentage of transfers on a roster increases the number of wins for a season on average. However, Power-6 schools see the exact opposite trend. Once again, we can see the effects of the transfer portal benefitting low-major schools. These schools are taking advantage of a new recruiting tool that gives them unprecedented access to talented athletes who are looking for a fresh start.

Figure 8: Wins by Roster Transfer Percent in Basketball

Figure 9: Wins by Roster Transfer Percent by Conference Level in Basketball

Question 3: Transfer vs. Non-Transfers

The third question we wanted to investigate was: Regardless of where you come from or where you go, how does transferring in general affect performance compared to staying at the same school?

 Football

In general, all players saw their playing time increase from one season to the next, regardless of their transfer status, which speaks to the value of gaining an extra year of experience. The only three positions that saw transfers increasing their playing time year-over-year compared to non-transfers were offensive line, defensive line, and running backs. All other positions saw transfers get less playing time year-over-year, on average, than their peers who did not transfer. (See Table 3)

Table 3: Football Playing Time Differential from 2022 to 2023 by Position for Transfers and Non-Transfers

Position	Differential Metric	Transfers	Non-Transfers	Difference	%Difference
OL	Snap Count	111.8	89.0	+22.8	+20.4%
DL	Snap Count	62.0	49.5	+12.5	+20.1%
RB	Touches	11.4	9.2	+2.2	+19.2%
QB	Pass Snaps	21.5	27.6	-6.1	-28.4%
TE	Pass Plays	25.0	35.9	-10.9	-43.6%
LB	Snap Count	52.3	78.5	-26.2	-51.0%
CB	Snap Count	46.1	81.7	-35.6	-77.2%
EDGE	Snap Count	38.9	80.1	-41.2	-105.9%
WR	Targets	3.8	8.4	-4.6	-121.1%
S	Snap Count	1.8	76.6	-74.8	-4000%

Running back, defensive backs, and defensive line were the only three positions that saw transfers outperforming non-transfers on key performance metrics when comparing the differential in their 2022 and 2023 stats. For example, transfer running backs increased their yards per game from 2022 to 2023 by 55 yards, while non-transfers only increased 42 yards. (see Table 4)

Table 4: Football Performance Differential from 2022 to 2023 by Position for Transfers and Non-Transfers 

Position	Differential Metric	Transfers	NonTransfers	Difference	%Difference
RB	Yards	55.0	42.0	+13.0	+23.6%
DBs	PFF Defensive Grade	2.7	2.3	+0.4	+14.8%
DL	Tackles+Hurries+Sacks	3.5	3.2	+.3	+8.6%
TE	Yards	37.6	39.5	-1.9	-5%
QB	Passing Grade	1.5	2.7	-1.2	-80%
WR	Yards	32.6	77.1	-44.5	-136%
LB	PFF Defensive Grade	-2.0	1.1	-3.1	-155%
OL	Pass Block Grade	-2.0	4.3	-6.3	-315%

In sum, the winners in the transfer game are running backs and defensive linemen, as the data shows that both positions will increase their playing time and key statistical performance grades compared to those who do not transfer.

Basketball

All basketball players were analyzed together because players produce the same statistics. We begin by looking at all transfers grouped together, regardless of movement type or magnitude, and comparing them to non-transfers. For both non-transfers and transfers their box score and BPR statistics increase. However, the box score statistics increase more for non-transfers and BPR statistics increase more for transfers. Before making any further declarations about this fact we broke down the transfers by the level of transfer.

Table 5: Basketball Performance and Playing Time Differential from 2022 to 2023 by Metric for Transfers and Non-Transfers 

Differential Metric	Transfers	Non-Transfers	Difference	%Difference
BPR	0.658	0.576	+0.082	+12.4%
Rebounds	0.273	0.502	-0.229	-83.8%
Possessions	59.7	154.5	-94.8	-158.8%
Steals	0.036	0.096	-0.06	-166.6%
Minutes	0.926	2.691	-1.765	-190.6%
Points	0.499	1.590	-1.091	-218.6%
Assists	0.082	0.274	-0.192	-234.1%

Question 4: Transferring to Same Tier vs. Non-Transfers

The first part of the deep dive we took after answering Question 3 was to analyze how performance was affected by transferring laterally. An example of this scenario was Caleb Love, who transferred from North Carolina, tier 1, to Arizona, also tier 1.

 Football

Overall, the trend found within football players who transferred to the same level when compared to players who did not transfer was that their stats either declined or increased less than their non-transferring peers. This suggests that for the vast majority of positions, a player is better off, in terms of seeing more playing time and production, if they stay at their current school as opposed to transferring to a school on their current school’s level. However, there were a few exceptions to this trend.

• Wide Receivers: For all stats looked at among wide receivers, which were pass plays, targets, and yards, it was found that transfers that stayed at the same level saw a larger increase in all three stats than the players that did not transfer.
• Defensive Backs: A similar exception was found within the defensive backs for the stats coverage grade, tackles, and QBR against.

Basketball

Non-transfers (N = 1,941) saw a significant increase in possessions played (154.5) from 2023 to 2024 compared to those who transferred to a school at the same tier (51.1). The same trend that was found for the average transfer remains true for transfers that transferred to a school in the same tier as their previous school. This trend was that box score stats increased less than non-transfers and BPR stats increased more than non-transfers.

This suggests that if a player is looking to increase production measured in the stat sheet it is in their best interest to stay put. On the other hand, if they are looking to increase production in the eyes of advanced metrics relocating may be the better option. It should be noted that larger increases in BPR production from year to year are more often seen in players jumping from a low major conference to a high major conference. This is likely due to the fact that high-major teams generally play tougher schedules, which helps boost your BPR more assuming you can maintain box score production against these opponents.

Question 5: Impact of Moving Up or Down on Performance

The second part of the deep dive we took after answering question 3 was to analyze how performance was impacted by moving up or down tiers.

 Football

The overall trend found within football players was that transferring down increases playing time and production while transferring up significantly decreases opportunities and production.

For, example wide receivers (see Figure 10) that moved down level saw almost 73 more pass plays and 130 more receiving yards on average. When compared to the players that moved up, which on average saw 61 fewer pass plays and 167 fewer receiving yards, we see that moving down is the way to go for players looking to boost playing time and production. The same trends holds across all positions except offensive line.

Figure 10: Wide Receiver Performance Metrics Group by Transferring Up, Down, or Staying the Same

One exception to this comes when looking at the pass grade and run block grade difference for offensive linemen. For both players that moved up and moved down, it was found that pass block grades decreased. Furthermore, the only group in which run block grade increased was for those that moved up. However, the offensive lineman position is much more of a group position than an individual person. For example, a team could have four great linemen, but if the fifth guy gets beat the QB will still be hurried, sacked, or forced to make a play on his own. For this reason, we shifted how we looked at offensive linemen transfers. Instead of looking at the traditional tier movement, we calculated the offensive line unit grade per team and looked at how transfers performed when moving to a worse unit compared to a better unit.

When offensive lineman transferred to a better unit, their pass block and run block grades increased. When they transferred to a worse unit, they saw major decreases in both performance metrics.

Therefore, we determined that both players and coaches should not look at the quality of their team as a whole when deciding which offensive linemen to bring in, but rather just the quality of their offensive line.

Basketball

Looking at the impact of moving up or down on performance finally lets us see the overall trend. Like football, moving up leads to a decrease in playing time and production on average, while moving down leads to an increase in both playing time and production.

Figure 11: Basketball Performance Metrics Group by Transferring Up, Down, or Staying the Same

The caveat for basketball is found in the advanced metrics. The advanced metrics, OBPR, DBPR, and BPR show the exact opposite trend; moving up leads to an increase in these metrics, and moving down leads to a decrease in these metrics.

On average, players that move down see 6.68 minutes per game and score 3.44 points per game more than they did the previous season. On the other hand, players that move up see the court 4 minutes per game less and score 2.06 fewer points per game than their previous season. Despite these numbers, the advanced metrics are trending in the opposite direction directions. Players that move down see a small increase in BPR, 0.270, while players moving up see over a full-point increase in total BPR on average. The likely cause of this is that BPR scores a player’s production based on the scenario where they were playing with 4 other average players and against 5 average players. Because players moving up have a better chance of playing against above-average players their production, while slightly less than before, is now valued higher. This allows higher-tiered players to produce less in terms of the box score stats and still achieve higher BPR values.

Question 6: Impact of Tier Movement on Performance

The last question we looked at answering was – how does the magnitude of tier movement impact performance? This section attempts to answer the question, is there a difference in performance when moving up 1 tier versus moving up 3 tiers? The magnitude of tier change is an important aspect of a transfer, especially from a coaching standpoint. With so many players entering the transfer portal, it can be overwhelming for a staff to keep up and even begin to scout players. However, if they know that they want to avoid transfers that would be making a large jump they can eliminate players to scout, effectively narrowing the search for the perfect fit.

 Football

Expanding Question 5 to look at the magnitude of transfers as opposed to grouping all positive and negative tier changes together showed very interesting results. Not only does moving up lead to a decrease in playtime and production, but moving further up increases these effects. For example, defensive linemen begin to see play time and production decrease as they transfer up one level, however, this decrease becomes more significant as they make a bigger tier jump. The opposite. The reverse of this trend happens when transferring down. Moving down one level shows increases while moving down further shows larger increases. See Figure 12.

Figure 12: Offensive Line snap count difference (top) and Defensive Line PFF statistics difference (bottom) grouped by transfer magnitude

Basketball

The same trend that was seen in Question 5 regarding football can be seen in basketball.

For box score statistics there becomes a more drastic decrease as players move further and further up while increasing more as players transfer further and further down. When looking at BPR, players transferring up see a more significant increase in as they transfer higher up and see less of an increase and potentially decrease as they transfer further down.

Figure 13: Box Score (top) and BPR (bottom) Difference by Tier Change Magnitude

Summary

Future research should continue to examine the performance and playing time of athletes in different sports and in specific playing positions. Data on what to expect, on average, when staying put, transferring up, transferring down, or even transferring at the same level is important for athletes to consider as they seek the right fit for the next step in their athletic journey. The data from men’s basketball and football are clear with a handful of nuanced exceptions: players can expect to see decreased playing time and performance when they transfer up and can expect to see more playing time and improved performance metrics when they transfer down, but that the best decision is likely to stay with the current team and not transfer.

Additional Research and Visuals

Interact with the data on our two Transfer Portal Dashboards:

Basketball

Football

View our presentation to the NCAA Research staff.

**Research prepared by graduate sports analytics students at Indiana University Indianapolis. Kael Ecord, Mark Ronad, Chandler Davis, Connor Bryson, Margaret Wade, Rohan Aykapati, Will Emhardt, Chin-yu Wu, and Chandana Yelamancheli