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We introduce a novel type of basketball shot chart, a true shot chart, that incorporates total points, accounting for both field goals and free throws, corresponding to each shot taken. We then create a shot selection measure called shot selection efficiency that summarizes the efficiency of a team’s shot chart. Lastly, we validate the measure using fixed effects regression analysis and determine whether shot selection is priced efficiently in the NBA player labor market.

We utilize fixed effects regression analysis to validate a measure and also to determine managerial payroll efficiency in the NBA.

We find that shot selection efficiency is a source of wins not fully explained by team payroll. That is, it is not fully priced into the player labor market.

Teams are in need of the shot chart summary measure provided, as they are not efficiently pricing players in the player labor market with respect to shot selection.

This research introduces a novel shot chart type for basketball as well as a novel shot chart summary measure. It also validates the measure and establishes an NBA player labor market inefficiency related to the measure.

In golf, strokes gained (SG) unify different shot types under a single system, including off the tee (SG: OTT), approach (SG: APP), around the green (SG: ARG) and putting (SG: PUTT). Despite SG having equal value across types, each shot type requires different skills and physical attributes. This paper analyzes how professional golfers leverage these diverse shot types to achieve wins and maximize their earnings.

This paper explores shot type consistency, demonstrating the superior consistency of the long game compared to the short game through regression and correlation analyses. These analyses are conducted season-to-season, tournament-to-tournament and round-to-round using seven seasons of professional golf data. Additionally, we examine the types of shots that contribute to improvements within the field.

Through analyzing average gains across evenly distributed groups and employing a regression model, we find that SG: APP is the most important type of shot, followed by SG: PUTT, SG: OTT and finally SG: ARG. Additionally, when considering both empirical earnings data and a regression model, we find that SG: PUTT becomes less important. Top players primarily win their earnings through SG: APP, followed by SG: OTT, SG: PUTT and finally SG: ARG shots. Next, we examine where strokes are gained at different levels of earnings and find that SG: APP makes up most of the SG increases across the field, followed by SG: PUTT, then SG: OTT and SG: ARG.

In this paper, we extend previous research by analyzing the entire professional golf association field rather than focusing solely on amateurs or small sections of the professional field; we also conduct our analysis using SG instead of traditional stats.