Schatz (2005) points out this deficiency in football statistics and Franks et al. Defensive contributions are under-represented in many sports by traditionally available data. More recent advances in this methodology describe the use of penalized regression models to construct an adjusted plus/minus that controls for the other players on the court and other contextual factors ( Gramacy et al., 2013, Macdonald, 2012).įocusing on net points accumulated during a player’s time in the game gives this metric the potential to combine various measured skills as well as capture certain intangible qualities that a player provides such as defensive ability. (2007) briefly describes its history in professional basketball and how it entered the official record for the National Hockey League as early as the 1967–68 season (2016a). In its basic form, plus/minus is the points scored by a player’s team minus the points scored by the opposing team when that player is in the game. The plus/minus has persisted as a tool for player evaluation since the late 1960’s in ice hockey and has more recently become popular in basketball. Because of this, the author approached team staff to see about using these data to construct the plus/minus metric in the hopes of gaining useful player insights. One author, who served as a keyer, noticed that not every action taken by players is recorded (e.g., block retained by blocker’s team) and not every recorded action translated into a traditional statistic (e.g., failed dig). These data are compiled into summary statistics for the coaches, media, and record keeping. The role of the keyer on the game day stat crew for a college volleyball team is to enter the live call of the statistician into the computer software program Statcrew. Although the presented methods and results were derived from a collaboration with a single team, the data source and methodology can be extended to multiple teams. The recovery is in the form of a posterior distribution of player presence, which can then be used to not only calculate the plus/minus metric but also quantify the uncertainty of the metric due to the incomplete information. This paper introduces methodology to recover court presence information from standard play-by-play data. Unfortunately, the primary collection tool (Statcrew) for National Collegiate Athletic Association (NCAA) Women’s Volleyball, does not record the movement of the Libero, resulting in incomplete court presence information for a large percentage of plays. The advantages of the plus/minus lie in the limited amount of information needed for its calculation (e.g., court presence and scoring) combined with its ability to fuse together both measured and unmeasured contributions. Historically, volleyball players have been evaluated through a series of single skill metrics (e.g., number of aces per set and hitting percentage). This work describes a collaboration with a single collegiate volleyball team to leverage existing data to examine the potential of the plus/minus metric for player evaluation.
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