Basketball is an intermittent, anaerobic-dominant, team sport that is played by athletes across a range of levels (Scanlan et al., 2012; Tessitore et al., 2006). Several studies have reported on the key physical and physiological characteristics of basketball athletes (Hoare, 2000; Koklu et al., 2011). While these characteristics contribute to individual performance, the combination of all individual performances in a coherent manner ultimately results in team success (Gomez et al., 2009). Subsequently, team performance indicators during matches may provide a holistic foundation for coaches in the development of training and match strategies to enhance success.
Basketball match success has been associated with a range of team performance indicators including successful 3-point (Ibanez et al., 2009; Jukic et al., 2000; Lorenzo et al., 2010), 2-point (Ibanez et al., 2009; Jukic et al., 2000; Lorenzo et al., 2010) and 1-point (free-throw) (Jukic et al., 2000; Sampaio et al., 2006; Trninic et al., 2002) shots, 'defensive rebounds' (Gomez et al., 2008; Ibanez et al., 2009; Trninic et al., 2002), 'fouls' (Sampaio et al., 2006), 'turnovers' (Lorenzo et al., 2010) and 'assists' (Gomez et al., 2008; Ibanez et al., 2009; Trninic et al., 2002). Gomez and colleagues (2008) reported that 'defensive rebounds' and 'assists' discriminated all wins and losses during the 2004-2005 Spanish Men's Professional League. Other studies focussing on European basketball matches have also reported the importance of 'defensive rebounds', 'assists' and 'field-goal percentage' for team match success (Jukic et al., 2000; Trninic et al., 2002). During short-term, junior tournaments, a range of performance indicators including '2-point field-goal' accuracy, 'defensive rebounds', 'turnovers' and 'assists' were acknowledged as discriminatory for wins or losses (Ibanez et al., 2009; Lorenzo et al., 2010). Despite this quantity of work, the contribution of similar team performance indicators to match success during the men's basketball tournament at the Olympic Games has yet to be examined. Unlike season long competitions, players within teams for the Olympics have a limited opportunity to interact regularly with identification of these team performance indicators expected to provide vital direction to coaches in the design of training programs and match strategies to enhance match success likelihood.
To identify performance indicators explanatory of a predetermined response (e.g. match outcome), sports performance analysts have become increasingly proficient with the use of machine learning techniques (Gomez et al., 2015a; Gomez et al., 2015b; Robertson et al., 2015). One of the benefits of machine learning techniques is its capability to resolve meaningful, non-linear interactions within multivariate datasets in contrast to traditional linear techniques (Morgan et al., 2013; Robertson et al., 2015). Accordingly, machine learning may assist coaches with the identification of flexible targets or performance indicator combinations that enhance the likelihood of team success. Classification trees have been shown to be an effective, machine learning technique to explain match outcome in elite Australian Football (AF) (Robertson et al., 2015) and rugby league (Woods et al., In press), as well as explaining the effectiveness of ball screens and inside passes in basketball (Courel-Ibanez et al., 2016; Gomez et al., 2015a). However, such an approach has yet to be employed for team performance indicator combinations and match outcome during an elite basketball tournament.
The aims of the current study were: 1) to examine the relationship between team performance indicator characteristics and match outcome during the men's basketball tournament at the Olympic Games; 2) to compare the utility of linear and non-linear statistical approaches in the resolution of this relationship. Given the findings of others (Courel-Ibanez et al., 2016; Gomez et al., 2015a; Robertson et al., 2015), it was hypothesised that: 1) distinctive performance indicator combinations would be explanatory of match outcome; and 2) the classification accuracy of both statistical approaches would yield similarity, however, the non-linear approach would offer greater practical utility given the non-linear interactions between team performance indicators.
This study was a retrospective analysis of publically available data from the official Olympic websites with all procedures considered in line with local, institutional, ethical approval. All matches (n = 156) undertaken within the men's basketball tournament at the 2004 (Athens, n = 42), 2008 (Beijing, n = 38), 2012 (London, n = 38) and 2016 (Rio de Janeiro, n = 38) Olympic Games were examined. The team performance indicators for each match were downloaded and collated (Table 1) and a priori classified according to match outcome (win/loss). All team performance indicators were normalised for the number of ball possessions by each team per match, as previously described (Gomez et al., 2013; Lorenzo et al., 2010; Sampaio and Janeira, 2003). Briefly, ball possessions were calculated using the following equation: field-goal attempts - 'offensive rebounds' + 'turnovers' - (0.4 x free-throw attempts) (Oliver, 2004). Each match provided two datasets or observations (one per team) with a total of 312 observations (84 from 2004, 76 from 2008, 76 from 2012 and 76 from 2016) examined in the current study.
Descriptive statistics (mean [+ or -] SD) were calculated for each team performance indicator relative to match outcome (win/loss). All analyses and visualisations were conducted using R (version 3.2.2, Vienna, Austria). Prior to hypothesis testing, a Spearman's correlation matrix was built to assess the level of...