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Using social networks to improve team transition prediction in professional sports.

ABSTRACT: We examine whether social data can be used to predict how members of Major League Baseball (MLB) and members of the National Basketball Association (NBA) transition between teams during their career. We find that incorporating social data into various machine learning algorithms substantially improves the algorithms' ability to correctly determine these transitions in the NBA but only marginally in MLB. We also measure the extent to which player performance and team fitness data can be used to predict transitions between teams. This data, however, only slightly improves our predictions for players for both basketball and baseball players. We also consider whether social, performance, and team fitness data can be used to infer past transitions. Here we find that social data significantly improves our inference accuracy in both the NBA and MLB but player performance and team fitness data again does little to improve this score.

SUBMITTER: Evans EJ

PROVIDER: S-EPMC9231699 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Using social networks to improve team transition prediction in professional sports.

Evans Emily J EJ Jones Rebecca R Leung Joseph J Webb Benjamin Z BZ

PloS one 20220624 6

We examine whether social data can be used to predict how members of Major League Baseball (MLB) and members of the National Basketball Association (NBA) transition between teams during their career. We find that incorporating social data into various machine learning algorithms substantially improves the algorithms' ability to correctly determine these transitions in the NBA but only marginally in MLB. We also measure the extent to which player performance and team fitness data can be used to p ...[more]

PMID: 35749376

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Project description:Purpose To quantify the social media utilization of professional sports team physicians on popular platforms and analyze differences between physician users and physician non-users for smaller major professional sports: Major League Soccer (MLS), Major League Lacrosse (MLL), Major League Rugby (MLR), Winter Olympics (WO) and Women’s National Basketball Association (WNBA). Methods Physicians for the MLS, MLL, MLR, WO, and WNBA were identified and characterized based on training background, practice setting, years of experience, and geographic location. Social media presence on Facebook, Twitter, LinkedIn, Instagram, and ResearchGate were determined. Differences between social media users and non-users were analyzed via chi-squared tests for nonparametric variables. Secondary analysis consisted of univariate logistic regression to identify associated factors. Results 86 team physicians were identified. 73.3% of physicians had at least one social media profile. 80.2% of physicians were orthopedic surgeons. Specifically, 22.1% had a professional Facebook page, 24.4% had a professional Twitter page, 58.1% had a LinkedIn profile, 25.6% a ResearchGate profile, and 9.3% an Instagram account. All physicians with a social media presence were fellowship-trained. Conclusions Seventy-three percent of team physicians in the MLS, MLL, MLR, WO, or WNBA have social media presence, with over half using LinkedIn. Fellowship-trained physicians were significantly more likely to use social media, and 100% of physicians with social media presence were fellowship trained. MLS and WO team physicians were significantly more likely to use LinkedIn (P = .02). MLS team physicians were significantly more likely to use social media overall (P = .004). No other metric significantly impacted social media presence. Clinical Relevance The influence of social media is vast. It is important to explore the extent that sports team physicians utilize social media and how this use may influence patient care.

Project description:Reconstructive allografts using Vascularized Composite Allotransplantation (VCA) are providing individuals living with upper limb loss and facial disfigurement with new opportunities for a sensate, esthetically acceptable, and functional alternative to current treatment strategies. Important research attention is being paid to how best to assess and screen candidates for VCA, measure optimal patient outcomes, and support patient adherence to lifelong behaviors and medical regimens. Far less attention, however, has been dedicated to the team science required for these complex VCA teams to form, prepare, and provide the highest quality clinical and psychosocial care to those receiving VCA. VCA teams are unique in that they require specialized team members whose scope of practice may not otherwise overlap. The team also needs to constantly negotiate balancing patient safety with multiple risks throughout the transplant process. This study aimed to elucidate the team science needed for this highly innovative and complex area of medicine. Using in-depth qualitative interviews with 14 VCA team members and observations at team meetings, we found that careful consideration of team composition, team structure, and organizational commitment (e.g., local culture and team values; investment of resources) influences team performance and patient outcomes, but that to be efficient and truly effective, teams need to commit to developing processes that foster collaboration. These processes are action-oriented (e.g., communication, leadership), strategic (e.g., planning, training) and interpersonal (e.g., conflict management, trust building). Dedication and commitment to team science allows teams to manage conflict under stress and exercise ways to leverage strengths to provide optimal performance or patient psychosocial and clinical outcomes. This study can provide insight into quality improvement efforts for VCA teams and guidance for other transplant programs that wish to consider expansion into VCA.

Project description:Note: Data Use Restrictions: <ul> <li>The data must only be used for genetic research projects solely in the area of health-related social-network research.</li> </ul> This study compiles detailed information on family and social ties linked to participants in the offspring cohort of the Framingham Heart Study. The investigators took computerized information from self-report data used by the FHS for over 30 years to facilitate health exam scheduling. These tracking sheets asked participants to identify people close to them, which were assumed by the investigators to indicate a social tie. Individuals are listed as "egos" (if the ties are from their perspective) and/or "alters" (if they are linked as a friend or family members to one of the other participants). All egos were FHS offspring participants while alters could be FHS participants in any cohort or non-participants. This information was combined with internal FHS pedigree data on family ties to list family member alters (as well as the nature of their relationship). The resultant dataset therefore includes each ego-alter tie, the nature of their relationship and the start and end dates for their ties for each of seven waves for the offspring study. Due to privacy concerns, all exam dates for individuals are listed relative to the initial FHS exam date. The study investigators chose a random, confidential date, to which all initial exam dates are linked (by number of months from random date to initial exam date). With this information, researchers can have access to the temporal relationships among participants' exam dates while remaining unaware of the actual exam dates for individual participants. During each clinic exam cycle, the participants undergo a detailed examination including physical examination, medical history, laboratory testing, and electrocardiogram. Over the years, other tests (that may not be performed at every exam cycle) have included pulmonary function, lifestyle, physical function, cognitive function questionnaires, and various noninvasive cardiovascular tests including echocardiograms. This study involves phenotypic data from these exams including (when available) basic body measurements (height, weight, blood pressure), laboratory values (blood sugar levels, LDL levels), information on smoking and alcohol use, and tests of depression and cognitive functioning. Important links to apply for individual-level data <ol> <li><a href="http://dbgap.ncbi.nlm.nih.gov/aa/wga.cgi?view_pdf&stacc=phs000153.v3.p3" target="_blank">Data Use Certification Requirements (DUC)</a></li> <li><a href="GetPdf.cgi?id=phd001183.1" target="_blank">Instructions to Request Authorized Access</a></li> <li><a href="http://view.ncbi.nlm.nih.gov/dbgap-controlled" target="_blank">Apply here for controlled access to individual level data</a></li> <li><a href="GetPdf.cgi?id=phd000317" target="_blank">Participant Protection Policy FAQ</a></li> </ol> In 1948, researchers recruited men and women from the town of Framingham, Massachusetts, beginning the first round of extensive physical examinations and lifestyle interviews that would later be analyzed for common patterns related to CVD development. Initially, the Framingham Heart Study enrolled 5,209 men and women from the Framingham area who were between the ages of 28 and 62 years. Beginning in 1971, the Framingham Heart Study enrolled 5,124 men and women, who were either offspring of the original cohort or spouses of those offspring. In 2002, 4,095 third generation participants (men and women) were enrolled.

Dataset Information

Using social networks to improve team transition prediction in professional sports.

Publications

Using social networks to improve team transition prediction in professional sports.

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