We used these pairings to create two roughly balanced lines of five players each to alternate between during games. The alternating lines contained a mix of starters and subs; this allows older players to be leaders of different lineups instead of competing with each other to lead the starting line (or disengaging when they cannot lead). Mixing age and ability levels has been shown to have benefits in general educational settings, where high achieving students can grow more when they are not combined in classes with gifted students who overshadow them [2], and lower achieving students benefit from working alongside more capable peers.  By contrast, combining too many dominant or "star" players in a lineup decreases team cooperation and performance, even in professional NBA teams [3].

Two different versions of these optimized lineups were tested in two different games in the middle of the season. The top rows contain the outcomes for the Aquinas game, where the starting line played in the first quarter and half of the second quarter. The rest of the game consisted of lines 2 and 3 in 2-3 minute rotations. By the time the rotations began, Sacred Heart had a 30 point lead and so the clock followed the "mercy rule" protocol and remained running for the rest of the game (which results in less than half the amount of playing time compared to a standard game clock protocol.) Despite that, lines 2 and 3 performed better than the starting line on several dimensions.  The starting line achieved fewer assists or rebounds than either of the other lines, and had a lower FG%.  In the end, Sacred Heart won 80-21, their highest score of the season, and the game overall had the highest number of Assists, AST/TO, and FG% of the season to date.

In the Ave Maria game, only 8 players were available. In rotating the lines, the coach mixed the composition a bit, resulting in line 3 which does not include the optimal pairings identified in our preliminary analysis. In looking at the performance of the four lines in the game, we see that lines 2 and 4 ( built on synergistic player combos) did as well or better on many dimensions of the game as the starting line, particularly in light of playing relatively fewer minutes. Both lines 2 and 4 achieved the same or more assists with fewer turnovers, resulting in a higher AST/TO than the starting line, also allowing fewer points by the opponent and making a similar number of rebounds (again, in fewer minutes of playing time). Line 3 was relatively weaker by comparison.

Taken together, these two test cases support the potential utility of building player lineups around naturally-occurring player pairings, and could help the team build better team coordination to achieve stronger results together.  Hopefully we will have a chance to trying maintaining a stable lineup composed in a similar manner to see if we could improve even more upon these trends.

References

1. Araújo, D., & Davids, K. (2016). Team Synergies in Sport: Theory and Measures. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.01449

2. Gentry, M. (2021). Total school cluster grouping model: Implementation and practice. In Total School Cluster Grouping and Differentiation (pp. 27–54). Routledge. 

3. Swaab, R. I., Schaerer, M., Anicich, E. M., Ronay, R., & Galinsky, A. D. (2014). The too-much-talent effect: Team interdependence determines when more talent is too much or not enough. Psychological Science, 25(8), 1581–1591. https://doi.org/10.1177/0956797614537280

4. Edmondson, A. C., Bohmer, R. M., & Pisano, G. (2001). Disrupted routines: Effects of team learning on new technology adaptation. Administrative Science Quarterly, 46, 685–716.

5. NTSB. (1994). A review of flight-crew involved major accidents of U.S. air carriers, 1978 through 1990.

6. Malakis, S., & Kontogiannis, T. (2023). Team adaptation and safety in aviation. Safety Science, 158, 105985. https://doi.org/10.1016/j.ssci.2022.105985

7. Guo, T., Cui, Y., Min, W., Zhang, W., Mi, J., & Shen, Y. (2022). Exploring the relationship between basketball rotation and competitive performance using substitution network analysis. Journal of Sports Sciences, 40(24), 2704–2713. https://doi.org/10.1080/02640414.2023.2189216