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The Effect of Batting Order on R and RBI Production

The Effect of Batting Order on R and RBI Production

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If an average hitter is bumped from the sixth spot in the batting order to the two-hole, how much of a bump in performance can we expect?

I’ve written a little about this before. Mostly just suggesting that this is something to keep in mind when you’re looking for hidden value. And I always had in the back of my mind that when I finally got around to downloading all the retrosheet game logs for each season AND learned SQL that I could figure out exactly how much of a benefit this would represent.

Then my five-year old daughter starts playing soccer and is bringing homework back from kindergarten, my sister and twin sister-in-laws all decide to get married in a two-year period, work gets in the way… and before I know it those plans of teaching myself how to process game logs are out the window!

Thankfully though, I stumbled upon the league splits page for the 2014 season at Baseball-Reference.com.  And it has the batting order splits already calculated for me!

Charts and Table Data

I have taken the 2014 data from Baseball Reference tweaked it some.  You will first see a series of charts depicting the batting order splits for 2014.  Then after the charts you will see tables showing the MLB, AL-only, and NL-only data.

I’ve added calculations for Plate Appearances per Game, Runs per Plate Appearance, and RBI per Plate Appearance.

These measures are all important inputs when I’m projecting a player’s performance (side note, if you are interested in projecting stats here is the approach I use).  Knowing (or estimating) where a player will bat in the order affects the number of times they’ll come to the plate during the season.  That spot in the order also affects their run scoring and run driving productivity.  You’re more likely to score batting in front of the 3- and 4-hitter than you are batting seventh.

Plate Appearances

The graph below shows that for every spot a player drops in the lineup, they can expect to see about 0.10 or 0.11 fewer plate appearances per game.  Over the course of a 162 game season that is about 16 plate appearances.  Fall from second in the order to 7th, you’re looking at 80 less plate appearances.

Notice that there’s really not much of a difference between the AL and NL in terms of plate appearances for any spot in the lineup.

PLATE_APPEARANCES_PER_GAME (more…)

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Analyzing the Last Five Years of Rotisserie Baseball Drafts

Analyzing the Last Five Years of Rotisserie Baseball Drafts

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How many of the top hitters and pitchers at the end of the year were actually drafted? How many of the top hitters and pitchers were not drafted and were picked up during the season?  Were hitters or pitchers drafted more accurately?  What is the dollar value earned by the players that were picked up during the season?  Is there a position of hitter that’s more reliable than other positions?

Have you ever asked yourself draft analysis questions like these?

What follows is a five year analysis (with colorful graphs and an enormous Excel file!) of how accurately our projections in the preseason depict what has actually happened at the end of the season. How well we drafted.  What positions yield the best returns.  What positions offer the most free loot.  And more.

Assumptions You Should Know

A number of the graphs depend on dollar value earnings for the “top 168” projected hitters or “top 108” projected pitchers.  The dollar values are calculated using the approach documented in “Using Standings Gain Points to Rank and Value Fantasy Baseball Players” assuming a 12-team league, $260 team budget, 14 hitters (C, C, 1B, 2B, SS, 3B, CI, MI, OF, OF, OF, OF, OF, UTIL), 9 pitchers, and a 70%-30% hitter-to-pitcher allocation.  That’s a total of 168 hitters and 108 pitchers.

These top projected players in the preseason were determined using Steamer’s preseason projections for that season (I downloaded the historical projections here).

I suppose using ADP results or expert rankings from the given year might give a better picture of the players that were actually drafted, but then you get into the question of what’s good ADP data, where to get it, what experts to use, league differences, lineup differences, etc.

To Be Clear…  The Goal of this Study

The goal of this is not to measure the accuracy of particular experts.  It is to determine which positions can we draft and get the most return on our investment.  To some extent this is a review of Steamer’s accuracy, but that’s also not my intent.  It’s my understanding (tell me if I’m wrong) that there are not significant differences between the top projections systems.  So whether we were looking at PECOTA, Steamer, or Marcel projections, we would see similar results.

How Much of a Return Do We Get For Drafting HItters vs. Pitchers?

People have long been telling us to, “Load up on hitters early in the draft”.

“Don’t overspend on pitching.”

“Wait on pitching until most teams already have one.”

I’ve always heard these things.  They sounded right.  But I can’t say I’ve ever seen the data to support it.

In looking at the chart below it is very clear that we are much better at identifying the top hitters than the top pitchers.  The top 168 hitters in the preseason provide about 70% of the dollars earned at the end of the season.  For pitchers, it’s more in the neighborhood of 40%.

With results like that it’s very easy to see why the hitter-pitcher split is not 50-50.

Hitters are safer investments than pitchers.  We’ve always been told this, but now you can see it.  And things have not changed in the new era of pitching that we’ve been seeing the last few years.  If anything, the gap seems to have widened.

Hitter-Pitcher-Draft-Returns-With-Losses
In a draft and hold environment, the return on investment for drafting hitters fluctuates between 65% and 80%. The return on pitchers is much lower, falling roughly between 30% and 50%.

(more…)

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