1 / 6

Analyzing Baserunner Scoring Percentage (BSP): Insights from Play-by-Play Data

This study investigates Baserunner Scoring Percentage (BSP) using extensive play-by-play data. We analyze the teams with the highest BSP and explore the influence of strategic plays such as sacrifice bunts and stolen bases on scoring efficacy. Employing linear and logistic regression models, we assess BSP's predictive power for runs scored and wins. Our data acquisition involves web scraping and parsing using R and Java. The findings highlight significant correlations between BSP and team performance over 16 years, with specific insights into trends for various teams.

derek-mccormick
Download Presentation

Analyzing Baserunner Scoring Percentage (BSP): Insights from Play-by-Play Data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Baserunner Scoring Percentage (BSP):an analysis using play-by-play data William Knapp and Dr. Jason A. Osborne, Dept. of Mathematics and Dept. of Statistics, N.C. State University

  2. Introduction • What is BSP? • Which teams have the best BSP? • Effect of sacrifice bunts and stolen bases • BSP as a predictor for runs scored and wins • Methodology • Linear and logistic regression • Correlation

  3. Data Acquisition • Web-scraping and parsing with R and Java • R functions: readLines() and regexp() • Java: sort into arrays and change certain variables to binary

  4. Analysis: Sacrifice and SB effects • Multiple Logistic Regression model • , , and are effects for SB attempt, SAC Bunt attempt, and outs. is a team effect.

  5. Analysis: MLR on Runs and Wins • +++ • Coefficients of determination (), with

  6. BSP vs Winning Percentage • Scatter of all 30 teams over all 16 years • Individual teams to show trends • Houston needs some work

More Related