Beyond the Box Score: An SB Nation Community

Navigation: Jump to content areas:


Sports blogs for fans, by fans.
New Blog: RSL Soapbox for Real Salt Lake Fans!

Foolish Consistencies

No matter what anybody tells you, baseball is a game that is based in numbers.  The only thing that separates the performance analysis community from mainstream analysis is which numbers we look at.  If you're James Click, you're poring over VORP reports and WXRL spreadsheets.  If you're the average fan, you're looking at AVG and HR.  And if you're John Kruk, you're still paying attention to pitcher wins and losses (has Randy Johnson gotten to 30 wins yet?).

The only problem baseball has with numbers is that there are too many of them.  In order to get a better handle on things, analysts often talk in terms of gross averages; sometimes it is the only way to illustrate a point without burying oneself in information.

And yet, as baseball fans, we often talk about consistency and balance, as if we recognize, even if dimly, that there is some sort of value in predictability.  This manifests itself in a lot of ways, but there's one idea in particular that I am going investigate today.

Let me ask you a question: would you rather have an offense of one premiere offensive player and a bunch of average hitters or a deep offense of above average hitters?

I'll be more specific.  You can choose between these two lineups:


  STAR AND SCRUBS       EVEN STEVENS
1 Scrub   270/333/433   Steven  277/340/452
2 Scrub   270/333/433   Steven  277/340/452
3 Star    333/400/600   Steven  277/340/452
4 Scrub   270/333/433   Steven  277/340/452
5 Scrub   270/333/433   Steven  277/340/452
6 Scrub   270/333/433   Steven  277/340/452
7 Scrub   270/333/433   Steven  277/340/452
8 Scrub   270/333/433   Steven  277/340/452
9 Scrub   270/333/433   Steven  277/340/452

Team averages:  277/340/452

(Stats are AVG/OBP/SLG)

Star and Scrubs has Vladimir Guerrero or Manny Ramirez batting third in a lineup filled with otherwise league-average hitters (league average defined as 2004 AL).  Even Stevens hitters are all exactly the same - no superstars, but all solid.  The mean AVG, OBP, and SLG are the same for each team.  Which team will score more runs?  Which team will win more games?

Which offense would you want?

To answer this question, I've employed my lineup simulator, described in this article (by the way, my program needs a cool name, so please leave suggestions in the comments).  In a nutshell, it is a decent predictor of offensive output that does not take into account secondary effects such as baserunning, opposing pitcher, matchups, and "clutchness."  I'm not totally sure, but based on what I've seen, my program tends to underestimate offense.

I used the program to simulate 50,000 games and distilled the results into several forms.  Let's see the what my computer spit out:


STAR AND SCRUBS: 5.043 runs per game average
EVEN STEVENTS:   5.042 runs per game average

For reference, a team of all scrubs scored an average of 4.713 runs per game

Hmmmm...not surprising.  Two teams with exactly the same AVG, OBP, and SLG will, on average, score the same number of runs.  I can hear Marc Normandin now: "You said you would go beyond averages.  You're off the team!"  Don't fire me just yet, Marc.  Let's look at the run scoring distribution for these two teams:

(I was naughty and forgot to label the axes.  The x-axis is the number of runs scored in a game and the y-axis is the frequency  of that event.)

You can see that the Star and Scrubs team has almost the exact same run distribution as the Even Stevens team.  It would appear that having an elite offensive player does not change the distribution of run-scoring in games.  This surprises me, as I would have guessed that a premier offensive player having a good game can help you pile up runs very quickly and avoid shutouts.  But the balanced attack is just as effective at putting up a crooked number or avoiding a shutout.  The Even Stevens are as consistent as the Star and Scrubs: both score the same number of runs on average, and both distribute their run scoring in the same way.

What does it all mean?  For one thing, it is important to remember that a team's run scoring distribution can effect their winning percentage.  In the 2004 AL:


Runs Scored    Winning Pct
0               .000
1               .080
2               .190
3               .332
4               .477
5               .607
6               .694
7               .739
8               .831
9               .904
10              .913
11              .965
12              .980+

That is, a team that scored exactly seven runs in a game won almost 74% of the time in the 2004 AL, averaged over all pitching staffs.  Two teams can score the same number of runs on average, but have different records based on their particular run distributions.  When I originally started this study, I figured that one type of team would have a different run distribution than the other and that it would result in a different winning percentage.  That appears not to be the case.

Talking heads will often tell you that an offense needs to be deep in order to be consistent.  I used to agree - now I am not so sure that I do.  Maybe, just maybe, using gross averages to describe team offense does not hide as much information as we thought.

(I am in no way claiming that run distributions are not important - I have a lot of upcoming work on the topic.  I am saying that lineup construction may not have a huge effect on run distribution.)

It also makes me wonder whether I've been too hard on the Anaheim Angels.  I've said several times that having only one offensive threat - Vladimir Guerrero - would hurt their offense.  And yet for all my tooth-gnashing, they score nearly as much as a team without an offensive standout such as the Oakland Athletics.  (I apologize for using the Angels and A's as examples so often - it's just that I'm a fan of one of the teams, and for two teams that have such disparate philosophies, they sure have similar records).

From a risk-management point of view, having the deeper offensive team is a better way to construct an offense; in the event of an injury to your star, you're out of luck.  Salaries also seem to increase non-linearly with offensive output, and the money saved by getting out from under a Manny Ramirez contract and replacing him with a couple of Grady Sizemores and Coco Crisps may allow you to upgrade that pesky middle relief or the back end of the rotation.  I don't think Theo Epstein is nuts to attempt to dump Manny Ramirez, despite Manny's cartoonish offensive stats.  In fact, if the Red Sox ever did dump Manny's contract, I think they could be quite dangerous with that free money (if spent correctly).

Expect more from me on run distributions, especially as the season ends and league data is made available through Retrosheet.

0 recs  |  Comment 5 comments

Story-email Email Printer Print

Comments

Display:

The fact that the run distributions are so...
similar really is rather surprising. Out of curiosity, does the positioning of the "star player" in the lineup have much if any effect on the run distribution?

by Richard Wade on Sep 22, 2005 1:10 PM EDT reply actions   0 recs

Good question
Something to look into next time.  Another thing I want to look at is whether incrementally adding stronger players has a linear increase with run-scoring (and game-winning).

by salb918 on Sep 22, 2005 1:26 PM EDT up reply actions   0 recs

Just checked it out
The answer is "no" or "very little", according to my simulations.  Batting the Star in different positions:

Position  Avg Runs  Wins, weighted by run-distribution
1         5.06      84.0
2         5.05      83.9
3         5.03      83.6
4         5.07      83.9
5         5.04      84.0
6         5.04      83.6
7         5.04      83.6
8         5.04      83.6
9         5.04      83.7

The deltas are probably within the error of my simulations, but if you want a take-home lesson, it's to bat your star in the first five positions.

by salb918 on Sep 22, 2005 5:17 PM EDT up reply actions   0 recs

Lineup Construction Baffles Me
There. I said it. It stumps the hell outta me. I hate Dusty Baker because he puts low OBP guys in front of D Lee for half the year, but then the statistics say it doesn't make much difference. But it HAS to make a difference....doesn't it? Are we sure this is all modeled right? Has anyone applied models to real lineups and seen how closely the predicted Runs Scored or Runs Created or any of that matches the actual values? Run distributions, do the models match the actual?

I dunno. I'm not convinced by the numbers on lineup construction. I've read all the work done and it seems to make sense, and yet, doesn't. Lineup construction doesn't make a difference.....really? Are we sure?

by cephyn on Sep 22, 2005 1:37 PM EDT reply actions   0 recs

Next week
I will post the actual run distributions and simulated run distributions of a couple of 2004 teams and we'll see if they match.

It baffles me, too - how can it not matter?  I'll also move the star player around in the lineup and add/subtract scrubs and stars to see what happens.

by salb918 on Sep 22, 2005 2:00 PM EDT up reply actions   0 recs

Comments For This Post Are Closed


User Tools

We use numbers and stuff.
Community Guidelines
Why be a member?
Start posting on Beyond the Box Score »

Join SB Nation and dive into communities focused on all your favorite teams.

FanPosts

Community blog posts and discussion.

Recent FanPosts

Leopold_butter_scotch_southpark_small
Using the TVC
Small
Determining Batted Ball Rates using Pitch Type and Location
Small
a new xBABIP calculator
Img587561916661595
Top 15 high school MLB draft prospects
Small
PZR-based Win Values 2001-2006
Small
The "30 parks on a budget" challenge
Sunflower_small
World Series Simulation, Game #6
Small
JT20 Dynasty League
E52205a2_small
New Look
Sth70021_small
Exploring Hit f/x, Albeit Badly

+ New FanPost All FanPosts >

FanShots

Quick hits of video, photos, quotes, chats, links and lists that you find around the web.

Recent FanShots

Primer on BaseRuns
Cool Baseball Infographics
ESPN's Jerry Crasnick on defensive metrics
I’m also a follower, since Brian Bannister’s on our team, of sabermetric st...
Top Ten Baseball-Reference.com's Sponsorships
Primer on Linear Weights
JC Bradbury on "Hot Stove Myths"
Everyone Should Learn to Throw a Cutter
Criminals of WAR
Ten statisticians you should know about

+ New FanShot All FanShots >

BtB on Twitter

Main Feed: @BtBScore

Tommy B: @tommy_bennett
Sky: @BtB_Sky
Dan: @dturkenk
Harry: @harrypav
Jinaz: @jinazreds
Jack: @jh_moore
Erik: @Erik_Manning
Tommy R: @trancel
Justin: @justinbopp

Subscribe to BtB via Email

Enter your email address:

Delivered by FeedBurner

BtB Goes Social


Managers

Nando_small R.J. Anderson

Limes_125_small Sky Kalkman

E52205a2_small Tommy Bennett

Editors

Face_small Harry Pavlidis

Rawlings_baseball_bigger_small Dan Turkenkopf

770insig_small Jeff Zimmerman (TucsonRoyal)

Aviles_small Justin Bopp

Authors

Banny_small erik

Raysring1_small Tommy Rancel

Jinaz-reds-avatar_small JinAZ

Jmlogo_small Jack Moore

1753738656_110919ebe9_o_small vivaelpujols

1_small Graham

Baseball_small Mike Rogers

Redcap_small SFiercex4

Small Patrick Clark

Walter_album_small Walter Fulbright