Research Papers

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Every year, the MIT Sloan Sports Analytics Conference Research Paper Competition brings exciting and innovative insight and changes to the way we analyze sports. With submissions on topics ranging from the spelling bee to rugby, basketball, and more, we represent the largest forum for groundbreaking research in sports. The Research Paper Competition is an incredible opportunity to reach a diverse audience while still contributing to the advancement of analytics in sports.

Previous year’s top papers were featured on top media outlets through the world, and captured the attention of representatives from numerous professional sports teams.

For full rules of the competition, see the rules page.

We are pleased to announce the Research Papers and Posters selected for SSAC 2017:


Major League Baseball teams are turning to analytics in an attempt to gain a number of small advantages that, in the composite, may result in significantly altering the odds of winning in their favor.  This paper addresses two related areas surrounding the evolving strategies of baseball in the wake of the sports analytics movement, and, in particular, the potential of unconventional uses of relief and starting pitchers. The first area addresses the home-field advantage and proposes a strategy for starters of visiting teams that can be used to remove roughly one half of the home team’s first-inning advantage. A byproduct of this analysis is a set of proper adjustments that must be made to calculate the true home-field advantage, which is roughly 0.429 runs/game, rather than the 0.133 runs/game suggested by the scoring data. The second area addresses pitching performance degradation for both starters and relievers and tackles the age-old question of when to remove a pitcher from a game. The strikes-to-balls ratio is tracked as a function of pitch count to identify trigger points that appear to act as thresholds in pitcher degradation. This comparative analysis of inter- and intra-pitcher performance uses data that can be easily measured during a game, and could better inform and support managers in real-time decision making for pitcher changes. Finally, this work concludes with a summary of additional pitching strategies and a list of potential bullpen tactics for future research.
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2017 Research Paper Posters

The 2017 Research Paper posters selected for the Conference are listed below.
This paper develops a novel statistical method to detect abnormal performances in Major League Baseball. The career trajectory of each player’s yearly home run total is modeled as a dynamic process that randomly steps through a sequence of ability classes as the player ages. Performance levels associated with the ability classes are also modeled as dynamic processes that evolve with age. The resulting switching Dynamic Generalized Linear Model (sDGLM) models each player’s career trajectory by borrowing information over time across a player’s career and locally in time across all professional players under study. Potential structural breaks from the ability trajectory are indexed by a dynamically evolving binary status variable that flags unusually large changes to ability. We develop an efficient Markov chain Monte Carlo algorithm for Bayesian parameter estimation by augmenting a forward filtering backward sampling (FFBS) algorithm commonly used in dynamic linear models with a novel Polya-Gamma parameter expansion technique. We validate the model’s ability to detect abnormal performances by examining the career trajectories of several known PED users and by predicting home run totals for the 2006 season. The method is capable of identifying Alex Rodriguez, Barry Bonds and Mark McGwire as players whose performance increased abnormally, and the predictive performance is competitive with a Bayesian method developed by Jensen et al. (2009) and two other widely utilized forecasting systems.
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  • Chris Glynn
  • Surya Tokdar