Data Products: wOBA for Predicting Player Offensive Value in Major League Baseball

In Major League Baseball it is often difficult to assign value to individual players, or compare players due to the vast differences in skillsets between the types of offensive production they contribute. To solve this, wOBA was invented for to solve this problem, comparing all players by a single offensive statistic.

My application, allows one to enter in values of any offensive player, and determine how good they are on a sliding scale.

Application is available at: https://gjonmartin.shinyapps.io/wOBAPredictor/

Weighted On-Base Average (wOBA) is a rate statistic which attempts to credit a hitter for the value of each outcome (single, double, etc.) rather than treating all his hits or times on base equally.

• Created by Tom Tango, and used in “The Book”, to measure a hitters overall offense.
• Conceptually, it is simple, all hits are not created equal and therefore Batting Average (BA) is a poor statistical measure.

Formula

• (BB * 0.69 + HBP * .72 + S *.89 + D * 1.28 + T1 * 1.64 + HR * 2.14)/PA

BB = Walks HBP = Hit by Pitchs S = Singles D = Doubles T1 = Triples HR = Home Runs PA = Plate Appearances

In 2015, Bryce Harper was by far the best player in MLB (Major League Baseball) with a wOBA of .461. The next 4 closest were Joey Votto (.427), Corey Seager (.421), Paul Goldschmidt (.418), and Mike Trout (.415).

playerdata <- read.csv("~/GitHub/wOBAPredictor/2015 player data.csv")
mean(playerdata$wOBA)

[1] 0.3113933

by examining the Player Data from 2015, we can see that the mean player was just below average with a wOBA of .311

OPS is often discussed as a better measure because it provides positional context to the OBA statistic, we can often use both in tandem to truly defiine a hitters ability and value.