Modeling Strikeout Rate between minor league levels

Modeling Strikeout Rate between minor league levels

In this post I’ll go over my results for predicting strikeout rates between minor league levels. This article will cover the following:

Data

Data Wrangling

Graphs and Correlation

Model and Evaluation

Data

This time around I’ve change my approach up so I can do some cross-validation. The article will cover data from 2004-2015 but I’ll be training my model on data from 2004-2013 and evaluating it using the 2014-2015 data. The data itself consists of 39,349 data points and came from Baseball Reference . The data points represent minor league data from Short Season(SS-A) to AAA ball. I end up removing the SS-A data because currently I’m only modeling data between the full season leagues(A-AAA). Also, players data points were only included if they had >=200 plate appearances.

Data Wrangling

In order to model the data between minor league levels I need to do some data wrangling to get the dataframe in the format I need. The original data has each players season as a different entry.

ramosoriginaldata
Snippet from original dataframe. Each entry represents a year and minor league level the stats are for.

In order for me to graph and get correlation values between minor league levels I need all this data on one row with the stats for each level represented by a column. Below you can see a snippet of the dataframe I use for my analysis:

ramoscorrelationsnippet
Snippet of correlation dataframe.

Notice how in the dataframe above all the stats I need for each level have been merged into one row.

Graphs and Correlation

regressionlineformilbdatausedformodel
Graphs showing the scatter plot and regression lines for the levels of minor league data I modeled.

As you can see from the graphs above a positive linear relationship exists for strike out rate between the minor league levels(A to A+, A+ to AA, AA to AAA) I’ve analyzed. Here are the correlation values for each level:

  • A to A+ :  0.7532319
  • A+ to AA : 0.7717004
  • AA to AAA : 0.7666475

From the numbers above and graphs you can see a ‘strong’ positive correlation exists for the strikeout rate between levels.

Model and Evaluation

The models for the regression line in the graphs above are:

  • A to A+ : A+ SO Rate = .7598*(A SO Rate) + .04591
  • A+ to AA: AA SO Rate = .83204*(A+ SO Rate) + .03608
  • AA to AAA: AAA SO Rate = .80664*(AA SO Rate) + .04147

The ‘Doing Data Science‘ book suggests using R-squared, p-values, and cross-validation to validate linear models. For this article I’ll be using R-squared and cross-validation:

  • A to A+: .5674
  • A+ to AA: .5955
  • AA to AAA: .5877

To do cross validation I’m going to use the data  from 2014-2015. This dataset consists of  of 8198 points. I performed the same steps I described above in the data wrangling section and that bought the dataframe I do my analysis on down to 427 points. The correlation numbers remained strong per level:

  • A to A+: 0.7366793
  • A+ to AA: 0.729288
  • AA to AAA: 0.7794951

Here is a graph showing the regression line against the 2014-2015 data:

crossvalidationmultigraph

To tell how often I’m correct or not I once again used the classification provided by fangraphs in this chart:

fangraphsbbrate
Picture retrieved from http://www.fangraphs.com/library/offense/rate-stats/

This time using the average difference between classifications of K% and got that to be .0291667. So if my model is more than ~.03 off the actual error rate then I say it’s wrong for that data point. Here are my results for each level:

A to A+:

  • Incorrect: 48
  • Correct: 66
  • Percentage Correct: 57.89%

A+ to AA:

  • Incorrect:78
  • Correct: 93
  • Percentage Correct: 54.39%

AA to AAA:

  • Incorrect: 52
  • Correct: 74
  • Percentage Correct: 58.73

 

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