Fans spend the offseason making sense of their teams’ portal additions: which players fit the system, who will fill each role, and how will the team gel? The speculation serves to fill these empty months, but one type of offseason addition will never make sense: simply adding up the points per game for the new roster. For example, defensive-minded Tennessee certainly won’t score 100+ points per game despite its impressive portal haul.

Don’t believe me? The chart below shows points per game for 400+ players who transferred to major-conference teams in the seasons before and after their transfers. Players below the diagonal line averaged fewer points at their new school, and more than 82% of the transfers fall below the line.

The magnitude of the scoring drop depends on the change in competition. Transfers from Power 5 schools averaged 1 fewer point at their new school. Players coming from high mid-majors – which I’m defining as the A-10, American, Mountain West, and Gonzaga – averaged 3.1 fewer points, while low mid-major transfers averaged 6.8 fewer points.

We can decompose the scoring drop into the number of opportunities transfers take at their new school and the efficiency with which they score on those opportunities.

Usage Rate vs Efficiency

With usage rate and true shooting percentage data, we can show why this lazy PPG math is wrong and test the axiom that “there’s only one ball.” A player’s usage rate measures the share of possessions that he took a shot, turned the ball over, or drew a foul. In other words, it measures how often the player was the last one with the ball in his hands. To build some intuition around usage rate:

  • 23% usage ranked in the top 20% of players last season
  • AJ Dybantsa’s 33.7% usage rate led power conference players last season
  • James Harden’s usage rate peaked at 40% (!!) with the Rockets in 2018-19

Using CBB Analytics data since 2021, I’ve examined how usage rate changes year-to-year for more than 400 high-usage players mentioned in the chart above. Unsurprisingly, usage rates typically drop, and the level of competition once again correlates with the size of the change.

The chart shows the players' usage rate before they transferred and the change in their usage rate after they transferred. Most players are below the 0 line, meaning their usage rate decreased. The median decrease was similar for players who came from P5 schools or the high mid-majors, with both groups’ usage dropping by 3.5%, while the median low mid-major transfer dropped more than 7%.

There’s no perfect mapping of usage rate to team role, but rates above 25% tend to be associated with the primary creator and scorer. Anything above 20% is a secondary scorer, while rates in the 15% range signal solid role players.  While these high-usage transfers had the ball less, they were still contributors on their new teams.

Another factor impacting the change in usage is whether the transfer’s new teammates already include a primary scorer. On average, the drop in usage is more than twice as large for players joining a roster that had a 25+% usage player the previous season. There is, in fact, only one ball, and teaming with another high-usage player means someone must give up shots.

How often a player uses a possession is only half of the scoring equation. Fewer shots mean fewer chances to score, but how well can a player capitalize on those opportunities?

Fans might reasonably expect that a high-usage player will have the same efficiency, if not even better efficiency, if their role changes from primary scorer to supporting role player. However, the data shows that changes in efficiency can go either way. Using true shooting percentage as a measure of scoring efficiency, there’s no clear relationship between changes in usage and efficiency.

Power conference transfers tend to score more efficiently, with a median TS% change of +3%. Both high and low mid-major transfers generally shot the same, with a median TS% change of only -1%. However, the range of outcomes is wide. One in five players had a 5% decrease in TS%, while one in five had a 4% increase, which are swings that separate average scorers from elite scorers. Overall, more than half of the transfers saw declines in both usage and scoring efficiency.

2023-24 Belmont Bruins: a case study in transfer hits and misses

Perhaps no team better captures the variance in transfer production than the 2023-24 Belmont Bruins. Ja’Kobi Gillespie, Malik Dia, and Cade Tyson all put up big counting stats on high usage.

As we trace their career arcs in the power conferences, we find a smattering of almost every possible transfer outcome.

Dia started his career at Vanderbilt but transferred to Belmont after playing fewer than 9 mins per game. In his lone season with the Bruins, he put up strong counting stats (16.9 PPG) on huge usage (39%) and solid efficiency (56% TS). After transferring to Ole Miss, his scoring (10.8 PPG), usage (26%), and efficiency (54%) all dipped, but Dia established himself as a productive player in the country’s toughest league. In his second season in Oxford, he increased his scoring (14.5 PPG) and usage (33%), but his efficiency suffered (52%).

Tyson’s sophomore year at Belmont (16.2 PPG on 23% usage and 64% TS) led him to fill North Carolina’s stretch-4 role previously held by Brady Manek and Pete Nance. Tyson failed to fill those shoes, scoring just 2.6 PPG on 51% TS, one of the largest efficiency drops in the dataset. However, Tyson excelled at his next P5 stop, putting up 19.6 PPG on 25% usage and 66% TS at Minnesota.

Similar to Dia, Gillespie’s scoring (14.7 PPG vs 17.2 at Belmont), usage (22% vs 25%), and efficiency (60% vs 66%) all dropped when he jumped to the power conferences, but he was a reliable contributor for Maryland’s Crab 5. At his second P5 stop at Tennessee, Gillepie put up career highs in scoring (18.4 PPG) and usage (27%) but at the expense of efficiency (54% TS).

With just three players, we’ve hit on examples of: freshmen struggling to carve a role at the P5 level; low-major players contributing after making the jump; low-major players flaming out at the P5 level, then rebounding; players decreasing their usage and efficiency; and players increasing their usage but decreasing their efficiency. With such a wide range of outcomes from just three former teammates, it’s clear that forecasting transfer outcomes isn’t so straightforward as simply looking at last season’s PPG.

The data and Belmont example can help us establish some rules of thumb as fans assess their portal additions:

  • Proven P5 production translates the best
  • Usage rate will generally decrease, especially for players teaming with another ball-dominant player
  • It’s a coin flip whether a transfer’s efficiency will drop or not
  • Even with these rules of thumb, the range of outcomes is wide