Player Complementarities in the Production of Wins
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Journal of Sports Analytics 5 (2019) 247–279 247 DOI 10.3233/JSA-190248 IOS Press Uncovering the sources of team synergy: Player complementarities in the production of wins Scott A. Bravea,∗, R. Andrew Buttersb and Kevin A. Robertsc aFederal Reserve Bank of Chicago, Economic Research, IL, Chicago bBusiness Economics and Public Policy, Kelley School of Business, Indiana University, IN, Bloomington cDepartment of Economics, Duke University, NC, Durham Abstract. Measuring the indirect effects on team performance from teammate interactions, or team synergy, has proven to be an elusive concept in sports analytics. In order to shed further light on this topic, we apply advanced statistical techniques designed to capture player complementarities, or “network” relationships, between teammates on Major League Baseball teams. Using wins-above-replacement metrics (WAR) over the 1998-2016 seasons and spatial factor models embodying the strength of teammates’ on-the-field interactions, we show that roughly 40 percent of the unexplained variation in team performance by WAR can be explained by team synergy. By building a set of novel individual player metrics which control for a player’s effect on his teammates, we are then able to develop some “rules-of-thumb” for team synergy that can be used to guide roster construction. Keywords: Complementarity, network analysis, wins-above-replacement, spatial factor model 1. Introduction Here, we provide a formal definition based on the complementarity of player skill sets in the produc- A whole that is greater than the sum of its parts: tion of team wins via the interconnectedness of This aphorism is often used in sports to rationalize Major League Baseball (MLB) teammates’ on-the- how a team made up of seemingly inferior play- field interactions. ers manages to outperform a superstar-laden team Evaluating the degree to which a variety of inputs that looks unbeatable on paper. The movie Mira- – in our case, different position players on a sports cle features a scene in which U.S. men’s Olympic team – are complementary or substitutable in pro- hockey team head coach Herb Brooks, facing skepti- duction (e.g. of team wins) is a topic that economists cism about his chosen roster, tells his assistant coach, have wrestled with for just under a century (Hicks “I’m not looking for the best players, Craig. I’m (1932) and Robinson (1933)). We appeal to this looking for the right ones.” (Guggenheim, 2004). tradition and apply advanced statistical techniques At its heart, this statement captures the dilemma designed to capture “network” relationships in order faced by every professional sports executive in con- to measure the degree of team synergy through structing a team roster. What defines the “right fit” nonlinear effects in the on-the-field performance of players, however, can often be very subjective. interactions of teammates that are characteristic of player complementarities. Finding a large degree of complementarities across players on the same ∗Corresponding author: Scott A. Brave, Federal Reserve Bank of Chicago, Economic Research, 230 S. LaSalle St. Chicago, IL MLB team provides scope for the hypothesis that 60604, USA. Tel.: 312 322 5784. E-mail: [email protected]. these synergies play a fundamental role in team suc- 2215-020X/19/$35.00 © 2019 – IOS Press and the authors. All rights reserved This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0). 248 S.A. Brave et al. / Uncovering the sources of team synergy: Player complementarities in the production of wins cess in baseball. Similarly, finding individual players ers were perfectly substitutable along this dimension, whose presence routinely complements their team- the residuals for each team would sum to zero. How- mates allows for the identification of its sources. ever, this is not the case, with roughly 20 percent of The basis for our analysis is the correlations across the variation in wins across teams left unexplained teammates in their wins-above-replacement (WAR) according to our productivity residuals. metrics and their relationship to team wins. WAR From this unexplained variation in the win-loss calculations, like those made by FanGraphs and ledger of MLB teams, we then isolate the element Baseball-Reference, provide a comprehensive mea- of team wins arising from teammate interactions sure of individual player performance. While these as opposed to potential mismeasurement in WAR measures often differ in some key assumptions, cen- stemming from other contextual factors. To measure tral to each is the belief that a player’s actions should the strength of teammate interactions, we take into be judged regardless of the game situations in which account several dimensions of teammates’ on-the- they occur. By making context-neutral evaluations, field relationships, weighting more heavily pairings: WAR has the advantage of judging players solely 1) that play more often (taking into account both past on the aspects of team outcomes over which they and present playing time), and 2) that are character- have direct control (Cameron, 2017). A consequence ized by the network relationships that exist between of this assumption is that while WAR measures a hitters in a team’s lineup and defensive positions. player’s performance by how many wins a player is For instance, the correlations of the residuals of hit- expected to contribute to his team above replacement ters who bat in adjacent positions at the top of the level, it will not necessarily correspond to the actual lineup are given more weight than the residual cor- contribution he made to team wins. relations of hitters who bat at the top vs. the bottom This discrepancy between WAR and team wins has of the lineup. Similarly, the pitcher-catcher defen- been at the center of critiques of its value as a statisti- sive relationship is given more weight than any other cal tool for player evaluation. An oft-cited example is defensive pairing on the field in terms of measuring whether a player performed relatively well or poorly residual correlations across teammates. in so-called “high-leverage” situations (James, 2017). Measuring teammate interactions in this way lends WAR may arguably lead to misleading judgments of itself to the use of a spatial factor model to decompose a player’s ex-post value to his team in such confound- our player productivity residuals into two separate ing circumstances where a mismatch between team unobserved components capturing elements of player wins and aggregate team WAR can result. In fact, complementarities. The first component identifies other researchers have used this mismatch to con- what we call character players, or players who pos- struct measures of “clutchness” which aim to back itively influence their teammates regardless of the out the context-relevant portion of team wins (Sul- team that they play for; while the second compo- livan, 2015). Similarly, aggregate team WAR may nent accounts for the role that a team’s management also fail to reflect a player’s full contribution to his has on team performance to isolate what we call team depending on the manner in which his interac- team players. This second component also makes tions with teammates aids in the production of team it possible to capture a team’s historical ability wins. In this paper, we find that the mismatch between to consistently turn individual player talents into team wins and team WAR serves as a valuable empir- extraordinary team outcomes, allowing for a relative ical regularity for understanding the nature of team ranking of MLB teams that can be used to measure synergies in baseball. organizations on the dimension of what we refer to We begin our analysis by using the WAR metrics as organizational culture. produced by FanGraphs and Baseball-Reference to Our methodology also has a natural connection construct player productivity residuals for the 1998- to network statistics that allows us to construct 2016 seasons. These residuals reflect the difference refinements of WAR which isolate a player’s own between the expected and actual number of team wins contribution to team wins irrespective of his team- that can be attributed to each player in a given season. mates, WAR−, and his contribution adjusted for When aggregated across teammates, they measure the his effect on his teammates, WAR+. Using WAR−, difference between a team’s actual win count and its we demonstrate that roughly 40% of the unex- expected wins based solely on individual player per- plained variation in team wins by WAR is explained formances. If WAR was a comprehensive measure by player complementarities. We refer to this total of each players’ contribution to team wins and play- network effect of a team’s players as tcWAR,or S.A. Brave et al. / Uncovering the sources of team synergy: Player complementarities in the production of wins 249 team complementarity WAR, and provide examples in roster construction, we next construct age-position of over- and under-achieving teams in recent seasons. profiles for pcWAR conditional on the dynamics dis- Similarly, using WAR+, we show that WAR tends to cussed above and player and team characteristics. overvalue the contribution of low impact players and For example, we show that the conventional wis- undervalue the contributions of high impact players to dom that older players make for good teammates has team performance. A player’s net impact on his team- support empirically, but the rate of development of mates, i.e. WAR+ − WAR, is then what we refer to team synergy-related skills varies by position. Our as his pcWAR,orplayer complementarity WAR. Our profiles also allow for the estimation of a player’s analysis of pcWAR confirms the conventional wis- Intangibles, defined by whether or not their pcWAR dom that star players tend to make their teammates exceeds or falls short of their profile.