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Articles 1 - 5 of 5
Full-Text Articles in Statistics and Probability
Analytics And Baseball's New Generation, John Roche
Analytics And Baseball's New Generation, John Roche
Essential Studies UNDergraduate Showcase
Major League Baseball has been a catalyst for making decisions in sports and competition from a purely mathematical viewpoint. We have seen teams utilize unique on-field player alignments and roster-building strategies based on statistical observations and applications of math. This project examines the advantages Sabermetrics and analytics present within the sport. Untapped statistical categories that could further the success of teams in the future is also briefly discussed.
Analyzing Baseball Data With R, Claudia Sison
Comparing Baseball Players Using Expected Runs In Shiny, Spencer Rodrigues
Comparing Baseball Players Using Expected Runs In Shiny, Spencer Rodrigues
Statistics
No abstract provided.
A Comprehensive Analysis Of Team Streakiness In Major League Baseball: 1962-2016, Paul H. Kvam, Zezhong Chen
A Comprehensive Analysis Of Team Streakiness In Major League Baseball: 1962-2016, Paul H. Kvam, Zezhong Chen
Department of Math & Statistics Faculty Publications
A baseball team would be considered “streaky” if its record exhibits an unusually high number of consecutive wins or losses, compared to what might be expected if the team’s performance does not really depend on whether or not they won their previous game. If an average team in Major League Baseball (i.e., with a record of 81-81) is not streaky, we assume its win probability would be stable at around 50% for most games, outside of peculiar details of day-to-day outcomes, such as whether the game is home or away, who is the starting pitcher, and so on.
In this …
Quantifying The Effect Of The Shift In Major League Baseball, Christopher John Hawke Jr.
Quantifying The Effect Of The Shift In Major League Baseball, Christopher John Hawke Jr.
Senior Projects Spring 2017
Baseball is a very strategic and abstract game, but the baseball world is strangely obsessed with statistics. Modern mainstream statisticians often study offensive data, such as batting average or on-base percentage, in order to evaluate player performance. However, this project observes the game from the opposite perspective: the defensive side of the game. In hopes of analyzing the game from a more concrete perspective, countless mathemeticians - most famously, Bill James - have developed numerous statistical models based on real life data of Major League Baseball (MLB) players. Large numbers of metrics go into these models, but what this project …