# USING STANDARD DEVIATIONS TO ANALYZE THE HOMER-HAPPY START OF THE SEASON

Every baseball fan and fantasy player knows it: homers are up, and steals are on the decline. Last year, MLB teams averaged 1.15 HR per game. This year, that number is up to 1.35 and the summer heat will likely make that number rise even further.

In 2018, MLB teams on average stole 82 bags over the course of the season, or 0.51 bases per game. That number is at 0.47 so far this year, continuing a downward trend.

## Average Team Steals Per Game

 2014 0.57 2015 0.52 2016 0.52 2017 0.52 2018 0.51 2019 0.47*

*As of June 17, 2019

To figure out how to adjust to the new norm, we can examine season-to-date averages and standard deviations. I took the top 200 highest performing players based on player rater and calculated the averages and standard deviations.

While the number of hitters rostered at any one time in a typical 12-team roto is probably closer to 150, stretching the pool to 200 allows for some of the underperforming rostered players to be taken into account as well. For example, despite his 16 steals, Jose Ramirez does not rank in the top 150 hitters yet he has been sitting in most fantasy owners’ starting lineups for a good chunk of the year. Also, while about 150 hitters are rostered at one time, it is certainly not always the 150 best players. Anyway, to the averages:

## Average Production Per Player (as of June 17, 2019)

 HR 10.9 R 35.1 RBI 34.1 SB 3.6 AVG 0.275

These numbers probably look a little higher than expected because, again, these are the mean totals of the top 200 batters to this point of the year, not the mean totals of all players. For the fun of it, I tried to find the player who most closely embodied these averages and came up with Ryan Braun who has 11 HR, 29 R, 37 RBI, 4 steals, and a .266 average (Honorable mentions: JT Realmuto, Alex Gordon, and Nomar Mazara).

Now that we know the averages, we can look into standard deviations. You don’t need to pull out your STAT 200 book from undergrad to understand why standard deviations can be valuable to fantasy performance. They give us a cleaner way to look at categories with varying average values. Once we know what the standard deviation for each category is, we can compare across categories.

## Standard Deviations (As of June 17, 2019)

 HR 4.8 R 10 RBI 10.2 SB 4.1 AVG 0.031

While the standard deviations for home runs (4.8) and stolen bases (4.1) are similar, the average for home runs is obviously much higher. For home runs, one standard deviation above the mean for top 200 players is 15.7 (10.9 + 4.8). One standard deviation above the mean for stolen bases is 7.7 (3.6 + 4.1). Let’s look at two players, George Springer and Austin Meadows, who match up with these HR and SB numbers pretty closely.

 Name Team HR HR+ R R+ RBI RBI+ SB SB+ AVG AVG+ Z-score George Springer HOU 17 1.28 41 0.59 43 0.87 4 0.10 0.308 1.08 3.91 Austin Meadows TB 12 0.24 33 -0.21 38 0.38 8 1.08 0.314 1.27 2.76

The + categories (e.g., HR+) show how many standard deviations above or below the mean Springer and Meadows have been on the season-to-date. The z-score is sum of all 5 categories. While Springer has been more valuable across the five categories, this is a decent example of comparing two fairly similar profiles to date, who differ in HR and SB, the two stats going in opposite directions in the MLB.

If their runs, RBI, and AVG were hypothetically equal, their value would be almost identical. Springer is just about one standard deviation better than Meadows in HR, and Meadows is likewise one SD better for steals.

Targets based on standard deviation and Z-score

Whit Merrifield: I have heard some whispers of Whit Merrifield owners being disappointed, but they shouldn’t be. He is a five category contributor and has completely justified his third round  draft position.

Rafael Devers: Because he has contributed in all five categories, Devers is a top 20 hitter in fantasy. His mid-800s OPS doesn’t make him a top 20 hitter in real life, but this is fantasy!

Avisail Garcia: You might not have noticed, but Avisail Garcia is a top 65 hitter due to being at or above average in all five categories. His seven steals are a pleasant surprise, matching his career high already. His StatCast page shows a lot of red (which is good), but they do note a big drop in xWOBA (expected wOBA) over the past few weeks.

Ramon Laureano: If you were able to weather Laureano’s horrid start (61 wRC+ on May 7), he has rewarded you with excellent production since then (128 wRC+, 8 HR, 5 steals).

A few notes:

• Since steals are far more rare to begin with, a single player (like Adalberto Mondesi) could be 5 or 6 standard deviations above the mean. That would be equivalent to a player having about 35 HR right now.
• The mean average of 0.275 sticks out as being higher than I expected. And one SD is about 30 points (0.031). This means guys like Mitch Haniger (0.220) and Yasiel Puig (0.229), who are generally decent AVG guys, have been about 1.5 SD worse than the mean. For comparison, this is as detrimental to your team as a player with 3 or 4 HR.

In this article, we got a baseline from what has already happened in the new landscape with a bouncier, “juiced” ball. In part 2 of this article, we will explore rest of season projections and compare players who vary in 2 distinct categories using standard deviation.