Tag Archives: science

Moneyball 2: Nerds In Paradise


Last year’s edition of Moneyball introduced four sabermetric-style statistics and identified the wifflers with the best and worst numbers for each. The SLW’s Office of Wiffle Science has raised the bar yet again this year with 9 new metrics (4 offensive, 3 pitching, and 2 which apply to both). In the words of SLW Commissioner James R. Hixson, who was recently named the 32nd greatest Wiffleball Commissioner by the NWLA, “You can not stop progress. Also, the Chief Scientist is a genius.”

As a reminder, all of this information and more, including awards and records, can be found by exploring the SLW stats site.

SPD

This should settle the “race around the bases” debate for good. Speed Score attempts to measure speed using a combination of doubles, triples and runs scored other than by home runs. The minimum and maximum scores are 0 and 10.

Statistics show Endsley would lose the race around the bases against Greg Presson.

Statistics show Endsley would lose the race around the bases against Greg Presson.

SecA

Secondary Average is a natural complement to Batting Average; Batting Average tells you how often a batter gets a hit while SecA tells you how often a batter gains extra bases and walks. A high frequency of walks and/or extra base hits will result in a high SecA.

Sip at bat at

wOBA

Weighted On-base Average measures overall offensive value by giving increasing weights to singles, doubles, triples, and home runs. wOBA accounts for the probabilities that each type of hit will eventually result in a run scored.

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OBI%

Others Batted In Percentage is a measure of a hitter’s performance with runners on base; it is the percentage of runners on base that the hitter caused to score. Even with the obvious risk of being punished with an immediate lifetime ban, the Chief Scientist believes this is almost entirely due to luck.

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Go/Fo

Groundball outs divided by flyball outs. The higher the number, the more often a pitcher/batter induces/hits balls on the ground. For a pitcher, an extreme Go/Fo (in either direction) tends to be a good sign. For a batter, it is a bad sign. The reason for this is that if you induce or hit a lot of line drives (i.e., make solid contact), your Go/Fo would tend to be toward the average.

Pitcher

Batter

  • Highest: Dave Cain (5.50)
  • SLW Average: 1.06
  • Lowest: Sip (0.44)

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HR/F

Percentage of fly balls that end up as home runs. An extreme HR/F, for a pitcher especially, may indicate some amount of luck. A pitcher with a high HR/F was probably unlucky, while a low HR/F probably indicates some amount of good luck. For a batter, it is the opposite.

Pitcher

Batter

  • Highest/lucky: Dave Cain (71%)
  • SLW Average: 31%
  • Lowest/unlucky: Sip (6%)

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LOB%

Left On Base Percentage is the percentage of baserunners allowed by a pitcher that were not allowed to score (i.e., percentage of runners stranded). LOB% is another statistic that may be susceptible to luck, especially over the short-term. Over the long-term however, good pitchers will tend to have high LOB%.

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FIP

Fielding Independent Pitching measures how well a pitcher pitched independent of his team’s fielding by taking into account home run, walk, and strikeout rates and ignoring balls hit in play (singles, double, triples, groundouts, and flyouts).

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xFIP

This is the best way to evaluate a pitcher’s performance and is an improvement over FIP. Expected Fielding Independent Pitching is the same as FIP except, instead of using raw home run rate, the league-average HR/F is used to estimate how many home runs the pitcher should have allowed. This helps to account for the fact that some part of home runs given up is purely luck.

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Wiffleball 19 Stats Now Available


Comprehensive stats from the 19th Annual BJA SLW Classic in 2012 and many other updates to the Stats are now available.

A few new player-level stats have been added including: G/F, LOB%, HR/F, SPD, SecA, wOBA, and OBI%. Explanations for each can be found in any of the legends on any of the stat pages.

Also, new this year: batting splits. Splits can be found on the All-Time Splits page and also on individual wiffler pages.

2012 Awards based on these stats will be handed out in the near future.

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New Scorecards for 2012 Classic


The SLW Front Office is excited to announce a new scorecard format for the 19th annual tournament.

Last year, being the first where detailed individual stats were recorded, a simple “tally sheet” format was used. This extremely simple format ended up being, in practice, difficult and error-prone. It required duplicate entry of most events. For example, a strikeout had to be recorded in two places: once for the pitcher and once for the batter. There was no situational representation of the field which resulted in difficulty in keeping track of ghost and pinch runners. Runs scored were mis-attributed, and walks and RBIs were missed. Overall, it was just too hard.

This year, we went back to the drawing board to come up with a radically different format (inspired by the Reisner Scorekeeping method) to address last year’s issues.

Scorecard Details

The new scorecard lets the Official Scorer focus on what is happening on the field by postponing the busy work and statistical decision making until post-game.

All the magic happens in the Event Box.

A single Event Box represents a single Plate Appearance. Each time a batter comes to the plate:

  1. Write down jersey numbers indicating who is on first, second, and third base.
  2. Wait for the batter to either reach base or make an out.
  3. Write the event abbreviation, indicating what happened, in the rectangular box at the bottom of the Event Box.
  4. Circle the jersey numbers of everyone that scored on the play.
  5. If any outs were made, write the out number/s in the middle of the diamond.

That is it. That is 99% of what the scorer has to do. Just do those 5 things, one of which is “wait for something to happen”. No duplicate entry. No backtracking to previous Event Boxes.

Here is an example Event Box where #17 scored from third base on a sacrifice fly which resulted in the first out of the inning.

#17 scored from third base on a sacrifice fly

The 17 is circled, indicating that the runner scored. The event abbreviation is F, for Flyout. The 1 in the middle of the diamond indicates that the Flyout was the first out made in the inning.

No Errors

There is another major change this year which will make the Official Scorer’s job much easier: there are no errors. This has a number of positive consequences from the scorer’s perspective.

There are no “reached on error” situations. If you hit the ball you are credited for a hit equaling the base you reached safely. If you hit the ball and reach third base because of two throwing errors, you still get a triple.

There are no unearned runs; every run scored is earned. If a runner you pitched to reaches base in any way and he later scores, you will be charged with an earned run.

There are only eight possible event abbreviations: BB, 1B, 2B, 3B, HR, K, F (Flyout), G (Groundout).

Everything is an RBI. A batter is credited with an RBI for every run that scores during his plate appearance.

Full Scorecard Example

Each scorecard contains the Event Boxes for a single team and the pitching information for the opposing team.

Here is an example scorecard for a three-inning game.

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Moneyball


Billy Beane, move over. The Chief Scientist is at his Chief Scientisty and introducing the first sabermetric SLW stats. Check out the stat
page
for the following four new metrics.

Pitcher SLGA – Slugging percentage Against gives the slugging percentage that hitters facing a pitcher accumulate (Total Bases divided by At Bats). A higher SLGA indicates a pitcher prone to the long ball (that is, home runs, not the terrible medical condition rampant throughout many third world countries).
Best: Chris Shoemaker (.618)
Worst: Geoff Hixson (1.014)

Pitcher BABIP – Batting Average on Balls In Play is most useful as an indicator of the pitcher’s defense; it is not very useful for determine the quality of the pitcher. It is the batting average that hitters facing a pitcher have, only counting balls hit in the field of play. A high BABIP is a sign of a pitcher with poor defensive help.
Best: Matt Endsley [Kill Yourself with Jaime Hixson and Luke Kirby] (.250)
Worst: Geoff Hixson [Cinderella Men with Dave Cain and James Morton] (.396)

Batter ISO – Isolated Power attempts to measure a hitter’s raw power at the plate. It is similar to Slugging Percentage, except that singles don’t help your ISO and home runs are three times as important as doubles or triples. A contact hitter will tend to have a low ISO, while a power hitter will tend to have a high ISO.
Best: Spence Hasler (.811)
Worst: Sip (.088)

Batter BABIP – A hitter’s BABIP is an indication of his speed and tendency to “hit it where they ain’t”.
Best: Seph Lietz (.480)
Worst: Derek Mayfield (.083)

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Wiffleball 18 Comprehensive Stats Now Available


Seph Lietz has provided the most thorough look ever for a SLW event.

Here are two links of note:
Due to the scope and breadth of the data generated by the 2011 SLW Stat Crew and Mr. Lietz, a podcast will be scheduled to discuss featuring newly inducted HOFer Seph to provide reactions to the statistical analysis sometime this fall.

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Evening Out The Playing Field


by Seph Lietz

The Problem

If looking at the 2010 SLW championship team triggered a strong sense of déjà vu, there’s a good reason. None of the 3 wifflers were being crowned for the first time. In fact, just 9 of the 27 wifflers in the statistical era have combined to take the 15 championship slots in that time.

9 out of 27 have won a championship from 2006-2010

Let me repeat that. Over the last five years, only 9 people have been on a championship team; 18 have not. There are some notable names on that list of 18, including Hall of Famers Jaime Hixson, Geoff Hixson, Dave Cain, and Matt Warnes.

The Cause

It turns out that there have been two reliable indicators when it comes to the success of SLW teams: team size and handedness.

4-man teams underperform; 2-lefty and 1-lefty teams dominate

4-man teams perform significantly worse than 3-man teams. Teams with 1 or 2 left-handed hitters perform significantly better than all-righty teams.

Dave Cain, Greg Presson, and Jason Morgan have all been unfortunate enough to each have been on two four-wiffler teams in the last five years. None of them have won a championship in that time. Only five regulars have escaped the four-wiffler curse recently: Shane Gentry, Spence Hasler, Jed Pope, Kevin Walsh, and Jeff Cain. They have nine titles between them.

A Solution

I’d like to introduce a new method for determining teams which uses the useful data we’ve gathered over the last 5 years. Statistically Guided Team Selection would automatically place people on teams based on their past performance. The goal is to make all the teams as even as possible. As an example, take last year’s field + Kevin Walsh and feed it into SGTS and you get the following 6 teams, in no particular order.

  • Gentry, Mayfield, Walsh
  • Hasler, Sip, J Cain
  • Lietz, Presson, C Shoemaker
  • Pope, Warnes, Morgan
  • Endsley, J Hixson, Kirby
  • Morton, D Cain, G Hixson

What do you think? Is there one team that jumps out as the favorite/worst? Should we try this out this year? Do I need to get a life?

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