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UID:pretalx-2026-BP8D7Q@conferences.acspri.org.au
DTSTART;TZID=AEST:20261125T120000
DTEND;TZID=AEST:20261125T121500
DESCRIPTION:Basketball depends on players working together. Statistics such
  as points\, assists\, shooting efficiency\, rebounds\, and turnovers tell
  us what players and teams produced\, but they do not show who worked with
  whom. Two teams can record the same number of assists through different p
 atterns of interaction. One may repeatedly use a few player combinations\,
  while another spreads assisted scoring across many pairs. Examining these
  relationships may help us understand aspects of team play that aggregate 
 statistics leave out.\nThis study asks how assist-scorer interactions are 
 organised within professional basketball teams and how these patterns rela
 te to game performance. It also examines whether the findings change when 
 assisted scoring is measured as a total count or relative to the points a 
 team scores. This exploratory study reports initial findings from the 2025
 –26 Australian National Basketball League season as the first stage of a
  planned multi-season analysis.\nPlay-by-play data were used to construct 
 351 complete team-game networks from 10 teams across 176 matches. Each net
 work represents one team in one game. Players involved in recorded assist-
 scorer interactions are the nodes. An assist from Player A leading to a ma
 de score by Player B creates a directed connection from A to B\, weighted 
 by how often that interaction occurred. These networks capture recorded as
 sisted scoring rather than all passing or teamwork.\nWe measured interacti
 on volume\, directed density\, reciprocity\, and the number of players inv
 olved. Density describes the proportion of possible directed connections t
 hat occurred among involved players\; reciprocity describes whether player
 s assisted one another in both directions. Linear regression modelled poin
 t differential\, and logistic regression modelled win/loss. Both models in
 cluded the network measures\, team and opponent as categorical fixed effec
 ts\, and home/away status as a covariate. Match-clustered robust standard 
 errors accounted for dependence between opposing observations from each ma
 tch.\nPreliminary analyses found that greater interaction volume was assoc
 iated with a higher point differential and higher odds of winning. This po
 sitive association remained when all matches involving each team were excl
 uded in turn. However\, every interaction already involves a made score\, 
 so this finding alone does not establish a performance advantage associate
 d with a particular network structure.\nStructural findings were less cons
 istent. Density was negatively associated with winning when the model incl
 uded total interactions\, but positively associated when the total was rep
 laced by interactions per 100 team points. This interaction rate showed no
  clear association in simpler adjusted models and became negative when den
 sity and player involvement were included together. Previous-game network 
 measures also showed no clear associations with next-game outcomes.\nThese
  findings highlight the need to distinguish how much assisted scoring occu
 rs from how it is organised across teammates. Networks describe player-pai
 r relationships that team totals omit\, but the current analyses do not id
 entify a consistently successful pattern of play. Following abstract submi
 ssion\, the analysis will be extended to additional seasons to assess whet
 her the observed relationships remain consistent.
DTSTAMP:20261013T070013Z
LOCATION:MacCallum Room
SUMMARY:Beyond Assist Counts: Interaction Structure and Team Performance in
  Professional Basketball - Syahmi
URL:https://conferences.acspri.org.au/2026/talk/BP8D7Q/
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