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Do Long-time Team-mates Lead to Better Team Performance? A Social Network Analysis of Data from Major League Baseball

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Abstract

Objectives

To estimate the effects of team-mate shared experience on overall team performance as well as to determine whether concentration of time together among subgroups of players and/or focal players enhances team performance.

Methods

Social network analysis (SNA) was used to model 30 active Major League Baseball teams from 2006 to 2015 with years of experience together connecting players resulting in 300 individual team networks. Social network metrics of network density, network centralization, and average weighted degree were computed and analyzed with team attributes by generalized least squares regression to predict wins, and team rank. Logistic regression was used to predict binary outcomes of world series and division wins.

Results

Network density was negatively associated with team rank (β = − 0.115, p = .05), while average weighted degree was positively associated with team rank (β = 0.147, p = .01). On average, each extra year of shared player time per team was associated with 14.86% higher probability of winning a division title (B = 2.69, exp(B) = 14.86, p = .05). Each extra shared year of infield membership among team-mates predicted 2.4% lower odds of winning the world series (B = −0.024, exp(B) = 0.976, p = .01), and each extra shared year between outfield players predicted 2.9% lower probability of winning a team’s division (B = −0.029. exp(B) = 0.972, p = .05).

Conclusions

Prolonged shared time between players is beneficial when it is spread evenly among all players of the team, whereas having few focal players who have been on a team together for many years is a disadvantage to overall performance.

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Data Availability

The data that support the findings of this study are available from Sean Lahman’s Baseball Archive at http://www.seanlahman.com/baseball-archive/statistics.

Notes

  1. In a network where links are undirected, in other words, links between players are reciprocal, there are a total of n possible number between n players. Network density is the sum of all k ties between players team (wk) divided by the potential number of ties between players, n.

    \({\text{Density }} = \frac{{{\sum }w_{k} }}{n}.\)

  2. Centralization is the sum of differences between the most central node and all other nodes divided by the sum of the maximum possible difference. For a network with n nodes, nodes v1,…, vn , maximum degree centrality cmax, and degree centrality of vertex vi given by c(vi), network centralization is given by

    \(\frac{{{\sum }{\hbox{cmax} } - c(v_{i} )}}{{\hbox{max} {\sum }{\hbox{cmax} } - c(v_{i} )}}.\)

  3. For a network with n nodes, sum of all weights of edges adjacent to node i, wi , Average weighted degree = \(\frac{{{\sum }_{i} w_{i} }}{n(n - 1)}.\)

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Authors and Affiliations

  1. Claremont Graduate University, Claremont, CA, USA

    Danielle Jarvie

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  1. Danielle Jarvie
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Correspondence to Danielle Jarvie.

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Danielle Jarvie declares that she has no conflict of interest.

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No financial support was received for the conduct of this study or preparation of this manuscript.

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No ethics committee approval was required for the use of the data.

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Jarvie, D. Do Long-time Team-mates Lead to Better Team Performance? A Social Network Analysis of Data from Major League Baseball. Sports Med 48, 2659–2669 (2018). https://doi.org/10.1007/s40279-018-0970-9

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  • DOI: https://doi.org/10.1007/s40279-018-0970-9

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