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Category: R

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…

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Modeling Walk Rate between minor league levels

Modeling Walk Rate between minor league levels

After reading through Projecting X by Mike Podhorzer I decided to try and predict some rate statistics between minor league levels. Mike states in his book “Projecting rates makes it dramatically easier to adjust a forecast if necessary.”; therefore if a player is injured or will only have a certain number of plate appearances that year I can still attempt to project performance. The first rate statistic I’m going to attempt project is Walk Rate between minor league levels. This…

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Correlation between Salary Cap and Winning?

Correlation between Salary Cap and Winning?

After doing my initial blog looking at how much each team is spending per position group. I wanted to take a look to see if there was any correlation between how much teams are spending on a position group and winning. To do this I needed to merge the cap data from spotrac  and season summary data from pro-football-reference . I merged these datasets over the last 5 years but it’d be interesting to try and find data since the salary cap was…

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Where is your favorite NFL teams cap space going…Part2(AFC)

Where is your favorite NFL teams cap space going…Part2(AFC)

Part 2 takes a look at what teams in the AFC are doing with their cap space. AFC East AFC North AFC South AFC West AFC East Biggest thing standing out to me below is Miami is spending a lot of money on their DL, 2 times more than league average and almost 30% of their cap space. Buffalo Bills Miami Dolphins New England Patriots New York Jets NFL DB 29,479,496 27,616,499 17,018,794 38,402,025 26,240,056 DL 33,279,980 43,671,818 26,648,167 23,376,055…

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Enriching Datasets with R

Enriching Datasets with R

If you have a simple data set and have some additional statistics you’d like to add to that dataset you can easily do that with R. Going to add fip, woba, wrc, and wraa to a couple of baseball datasets as an example of this. To calculate FIP I first needed the following R functions: [code language=”r”] #Calculate FIP Constant fip_constant_calc <- function(pitching_data){ #FIP Constant = lgERA – (((13*lgHR)+(3*(lgBB+lgHBP))-(2*lgK))/lgIP) era = sum(pitching_data["ER"])/sum(pitching_data["IP"]) * 9 lgHR = sum(pitching_data["HR"]) lgBB = sum(pitching_data["BB"])…

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Learning wOBA

Learning wOBA

As I continue to learn R and go down the road of becoming a data scientist. I need to learn how to use and compute advanced statistics. The first advanced analytic I’m going to learn how to compute is weighted on-base average(wOBA). Weighted on-base average combines all the parts of a players offensive game and gives them all appropriate weights for their impact on the game. For example, a HR is given more weight than a BB or a Single…

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Exploratory Data Analysis of Nationals Minor League Pitching Stats

Exploratory Data Analysis of Nationals Minor League Pitching Stats

After attending SSAC this year I decided one of the skills I need to pick up is R. Well after finally finishing Grad School I finally have time. Best way for me to learn is to actually get some data I’m interested in. Daily I look up Nationals minor league statistics to see how the upcoming Nationals are doing. So minor league data made a lot of sense for me to collect and doing Exploratory Data Analysis(seeing the data) is…

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