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Georgia Tech Baseball: Advanced Stats and Sabermetrics 2017 - An Introduction

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Let’s take a more analytical look at how the Jackets are performing on the diamond this year.

MattGrimes

What are Sabermetrics?

I’ll let SABR describe the overall concept far better than I can, but for our purposes, sabermetrics will attempt to paint a better picture of Georgia Tech’s performances than traditional box score statistics.

The metrics I’ve chosen to follow this season aim to accomplish three goals:

  1. Focus on outcomes that have the most affect on win probability
  2. Isolate the individual player’s performance from the rest of the team’s performance, thus increasing the “signal to noise ratio” of the information gleaned from the stats
  3. Predict whether or not a player’s traditional statistics will regress(Either positively or negatively)

Many of these stats will be normalized based on league averages. For our purposes, that league will be the ACC. League normalization was developed for the majors, where the teams always play each other. With out-of-conference play, this isn’t always the case in college, so strength of schedule will skew the numbers a bit. Additionally, sample size will be a problem. Don’t read too much into the stats until conference play gets going.

This is going to be a bit of a long first article as I explain the stats and provide links that show how they are calculated, but future articles will be much more bearable.

Pitching

WHIP

Walks and Hits per Inning Pitched is not really known as a sabermetric statistic, but is an important stat for pitchers. It is a simple measure of how many base runners a pitcher is allowing, which is a measure of how efficient he is.

FIP

More detailed explanation here

Fielding Independent Pitching has become one of the most frequently used sabermetrics for pitchers, and it is very good at predicting a pitcher’s future performance. The statistic separates the pitcher from the defense behind him by focusing only on the outcomes over which the defense has no control. That is, Home Runs, Walks, and Strikeouts.

FIP is scaled so that the league average ERA matches the league average FIP. A player with a FIP higher than his ERA is benefiting from his defense, and vice versa. ERA often regresses towards FIP over time, so this metric is a good indicator of future performance.

Batting

ISO

Isolated Power is the difference between a player’s slugging percentage and batting average. It measures a player’s propensity for hitting extra base hits.

OPS

On Base Percentage Plus Slugging is just that. It’s a simple formula, but it captures the essence of what makes a hitter valuable. Getting on base, and driving the ball a long way.

wOBA

More Detailed Explanation here.

Weighted On Base Average is a statistic that aims to capture how valuable a hitter is to his team. It’s much like OPS, but the values of each possible PA outcome are weighted to reflect how likely they are to produce runs. It does not, however, normalize against league stats.

wOBA is on the same scale as OBP. If a players wOBA would be good as an OBP, it’s a good wOBA.

wRC+

More detailed explanation here

wOBA is actually used to calculate Weighted Runs Created+, through a couple intermediate stats. wRC+, like wOBA, attempts to show the value a hitter brings to a team, based on the results of that player’s PAs.

wRC+ is normalized by league runs scored, so 100 is an average player in the league. It does not, however, normalize for position played. An 85 wRC+ is pretty good for a shortstop, while 100 is bad for a first baseman. Different positions come with different requirements at the plate.

I won’t be reading into the stats too much in this installment since it’s early and meant to be an introduction, but I plan on providing some analysis in future installments.

The following stats are through the weekend, and do not contain any stats from this week’s midweek games.

Pitching Stats So Far

Pitchers17.csv

Starting Pitcher ERA BA Against WHIP FIP BB% K%
Starting Pitcher ERA BA Against WHIP FIP BB% K%
Jonathan Hughes 1.29 0.167 1.29 4.60 16% 23%
Ben Parr 1.59 0.320 2.12 4.56 11% 7%
Xzavion Curry 2.7 0.308 1.50 2.80 7% 23%
Keyton Gibson 12.15 0.259 1.61 10.21 10% 16%
Starting Pitchers 2017

Hitting Stats So Far

Batters17.csv

Player BA K% BB% ISO OPS wOBA wRC+
Player BA K% BB% ISO OPS wOBA wRC+
Trevor Craport 0.407 9% 15% 0.297 1.204 0.501 159
Kel Johnson 0.333 16% 3% 0.500 1.177 0.489 153
Joey Bart 0.400 16% 3% 0.267 1.105 0.477 148
Wade Bailey 0.414 6% 12% 0.172 1.057 0.450 135
Chase Murray 0.391 17% 4% 0.174 0.982 0.429 124
Ryan Peurifoy 0.333 8% 4% 0.292 0.985 0.420 120
Austin Wilhite 0.381 17% 9% 0.048 0.864 0.386 104
Brant Stallings 0.190 17% 4% 0.143 0.594 0.269 47
Batters with at least 20 ABs