The chart above shows the shooting percentage (i.e. FG) and rebounding (also as a percentage of total rebound opportunities vs. opponent) in relation to the point differentials in Sacred Heart's most competitive matchups this season, with the two losses shown in red. We see that FG% and Reb% were virtually the same in some games the team won as they were in others they lost. (This is also true in other less competitive matchups where SH was even more dominant in terms of point differential).
A stronger predictor of point differential seems to be Assist-to-Turnover Ratio (AST/TO) which tracks almost perfectly with point differential (see graph above). And looking more closely at the pattern of Assists and Turnovers that go into the calculation of AST/TO separately (see graph below), the impact seems to be driven by variation in Assists, which clearly distinguishes wins from losses.
The game against St. Louise in the AMA tournament was one played without a key player; even though the SH FG% was only 34% (even lower than in the losses to Chartiers Valley - 40% - or to St. Louise at the Blessed Sacrament Tournament - 39%) the team won that game; Assists were likely a major contributor to the win.
Even though the association of Assists and margin of victory is clear in most of the cases displayed, we still see some examples where SH turnovers are quite (Holy Cross-2 stands out) yet SH still won, arguably because over 60% of their buckets had Assists. The game against St. Louise in the AMA tournament was an even narrower victory, where 93% of makes had Assists. In addition, the proportion of our points that have an Assist is significantly negatively related to the opponent's FG% -- the more our team cooperates to get buckets, the worse the other team does. Or, more concerningly, it may be that the better our opponent is doing, the less we seem to cooperate.
The second concept that coaches can leverage to increase team assists is to keep lineup composition as stable as possible. There is ample evidence of the value of team stability for learning and coordination in many environments. In medicine, surgical teams that maintain stable membership learn new procedures more quickly and experience lower patient mortality [4]. In aviation, flight crews are much more likely to be involved in safety incidents when they are newly formed than when they have been working together for a few days or more [5,6]. Both surgery and aviation are considered highly scripted environments, where participants are extensively trained in the nearly exhaustive list of possible events and courses of action they should pursue in response. And yet, team members' experience working together, even for a short time, significantly increases the quality of their coordination in executing these scripted actions.
Sports competitions include the additional element of an opponent who is very actively interfering with the team's execution of their plans. Such an environment makes team adaptation even more important, and thus creating lines that maximize cooperation is essential. Even in elite teams in the NBA, research demonstrates that substituting players in stable subgroups from game to game leads to fewer turnovers, more assists, and a higher standing for the team in the playoffs [7]. When we further consider a context in which players are continuing to learn at a rapid pace, this underscores the potential value of stable team composition, which greatly accelerates individual and team learning.
The only factor that has a larger impact on point differential than number of Assists is the total number of Sacred Heart players scoring in a game. Across the 30 games played so far this season, the number of players scoring ranges from 4 to 8. When more players score we see a variety of other strengths emerge in the team's game:
FG% - when we involve more players in scoring, it's often because we are moving the ball around more and getting it to the guy with the better shot. So the number of scorers is highly correlated with Assists (.45), FG% (.65) and score differential (.73) making it the strongest predictor of the margin of victory.
Number of scorers has a similar impact on reducing the points the opponent scores -- in addition to our rebounding percentages and Assists, having more players scoring has a substantial negative impact on the opponent's shooting percentage, particularly in the final quarters of the game.
Having more players involved in scoring is the strongest predictor of the number of steals in a game -- suggesting that there is broader engagement in the game across the team.
Having more scorers involved does not necessarily mean everyone is equally involved in scoring, in fact there appears to be additional benefit to more involvement along with clear offensive leadership, where scoring is somewhat more concentrated in a subset of players. In testing our alternative lineups to improve teamwork during games, as described above, we also mixed starters and bench players which had the additional benefit of clarifying leadership on the floor. Having clear leadership in the offense increases the number of assists and the FG% of the team overall. Offensive leadership also has the single largest impact on Sacred Heart's scoring in the final quarter of the game -- a reason why coaches should think carefully about whether putting all of the starters in during the final minutes of close games is the best strategy. It is important that scoring is not so concentrated on a single player that it is too predictable for the defense, however, as that results in significantly more turnovers and negates the other potential benefits of offensive leadership.
Performance statistics can be a useful tool for basketball coaches to identify areas to strengthen individual player and team performance. A review of the performance of the Sacred Heart Boys Varsity Basketball team this season indicates that an area for improvement, as they head into the post-season, is increasing Assists, and incorporating regular use of different player lines, mixing players and starters, seems like a promising approach. Established offensive plays can be helpful, but extant research demonstrates that building line-ups around groups of players with a track record of effective cooperation during competition will enhance the chances of future success. Once a coach identifies an effective line composition, trying to keep groups intact will further enhance the quality of their coordination and performance.
Araújo, D., & Davids, K. (2016). Team Synergies in Sport: Theory and Measures. Frontiers in Psychology, 7. https://doi.org/10.3389/fpsyg.2016.01449
Gentry, M. (2021). Total school cluster grouping model: Implementation and practice. In Total School Cluster Grouping and Differentiation (pp. 27–54). Routledge.
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
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.
NTSB. (1994). A review of flight-crew involved major accidents of U.S. air carriers, 1978 through 1990.
Malakis, S., & Kontogiannis, T. (2023). Team adaptation and safety in aviation. Safety Science, 158, 105985. https://doi.org/10.1016/j.ssci.2022.105985
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
Click on the link for each game in the table below to see the page with shot charts. Discussion of key trends available on this page