Testing the “One Ball” Theory of Transfer Usage and Efficiency--Basket Under Review
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
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