Tag: baseball

Reporting

Put-in-Play Percentage: A "Great Metric" for Youth Baseball?

Posted on by Eric Peterson

BB PitchingMy posts have gotten pretty sporadic (…again, sadly), and I’ll once again play the “lotta’ stuff goin’ on” card. Fortunately, it’s mostly fun stuff, but it does mean I’ve got a couple of posts written in my head that haven’t yet gotten digitized and up on the interweb. This post is one of them.

As I wrote about in my last post, I’ve recently rolled out the first version of a youth baseball scoring system that includes both a scoresheet for at-the-game scoring, as well as a companion spreadsheet that will automatically generate a number of individual and team statistics using the data from the scoresheets. The whole system came about because I’ve been scoring for my 10-year-old’s baseball team, and I was looking for a way to efficiently generate basic baseball statistics for the players and the team over the course of the season.

The Birth of a New Baseball Statistic

After sending the coach updated stats after a couple of games mid-season, he posed this question:

Do we have any offensive stats on putting the ball in play? I’m curious to know which, if any, of the kids are connecting with the ball better than their hit stats would suggest.  That way I can work with them on power hitting.

How could I resist? I mulled the question over for a bit and then came up with a statistic I dubbed the “Put-in-Play Percentage,” or PIP. The formula is pretty simple:

Put-In-Play Percentage Formula

Now, of all the sports that track player stats, baseball is at the top of the list: sabermetrics is a term coined solely to describe the practice of studying baseball statistics,  Moneyball was a best-selling book, and major league baseball itself is fundamentally evolving to increase teams’ focus on statistics (including some pretty crazy ones — I’ve written about that before). So, how on earth could I be coming up with a new metric (and a simple one at that) that could have any value?

The answer: because this metric is specifically geared towards youth baseball.

More on that in a bit.

Blog Reader Timesaver Quiz

Question: In baseball, if a batter hits the ball, it gets fielded by the second baseman, and he throws the ball to first base and gets the batter out, did the batter get a hit?

If you answered, “Of course not!” then skip to the next section in this post. Otherwise, read on.

One of the quirks of baseball — and there are many adults as well as 10-year-olds on my son’s team who don’t understand this — is that a hit is only a hit if:

  1. The player actually reaches first base safely, and
  2. He doesn’t reach first base because a player on the other team screwed up (an error)

“Batting average” — one of the most sacred baseball statistics — is, basically, seeing what percentage of the time the player gets a hit (there’s more to it than that — if the player is walked, gets hit by a pitch, or sacrifices, the play doesn’t factor into the batting average equation…but this isn’t a post to define the ins and outs of batting average).

PIP vs. Batting Average

Batting average is a useful statistic, even with young players. But, as my son’s coach’s question alluded to, at this age, there are fundamentally two types of batters when it comes to a low batting average:

  • Players who struggle to make the split-second decision as to whether a ball is hittable or not — they strike out a lot because they pretty much just guess at when to swing
  • Players who pick good pitches to swing at…but who still lack some of the fundamental mechanics and timing of a good baseball swing — they’ll strike out some, but they’ll also hit a lot of soft grounders just because they don’t make good contact

(Side note: I’m actually one of the rare breed of people who fall into BOTH categories. That’s why I sit behind home plate and score the game…)

What the coach was looking for was some objective evidence to try to differentiate between these two types of players so that he could work with them differently. Just from observation, he knew a handful of players that fell heavily into one category or the other, but the question was whether I could provide quantitative evidence to confirm his observations and help him identify other players on the team who were more on the cusp.

And, that’s what the metric does. Excluding walks, hit by pitches, and sacrifices (just as a batting average calculation does), this statistic credits a player for, basically, not striking out.

But Is It a Great Metric?

Due to one of those “lotta’ things goin’ on” projects I referenced at the beginning of this post, I had an occasion to revisit one of my favorite Avinash Kaushik posts last week, in which he listed four attributes of a great metric. How does PIP stand up to them? Let’s see!

Attribute Summary (Mine) How Does PIP Do?
Uncomplex The metric needs to be easily understandable — what it is and how it works PIP works pretty well here. While it requires some basic understanding of baseball statistics — and that PIP is a derivation of batting average (as is on-base percentage, for that matter) — it is simply calculated and easy to explain
Relevant The metric needs to be tailored to the specific strategy and objectives they are serving This is actually why PIP isn’t a major league baseball stat — the coach’s primary objective in youth baseball is (or should be) to teach the players the fundamentals of the game (and to enjoy the game); at the professional level, the coach’s primary objective is to win as many games as possible. PIP is geared towards youth player skill development.
Timely Metrics need to be provided in a timely fashion so decision-makers can make timely decisions The metric is simple to calculate and can be updated immediately after a game. It takes me ~10 minutes to enter the data from my scorecard into my spreadsheet and generate updated statistics to send to the coach
“Instantly Useful” The metric must be able to be quickly understood so that insights can be found as soon as it is looked at PIP met this criteria — because it met the three criteria above, the coach was able to put the information to use at the very next practice.

I’d call it a good metric on that front!

But…Did It Really Work?

As it turned out, over the course of the next two games after I first provided the coach with PIP data, 9 of the 11 players improved their season batting average. Clearly, PIP can’t entirely claim credit for that. The two teams we played were on the weaker end of the spectrum, and balls just seemed to drop a little better for us. But, I like to think it helped!

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Reporting

Perfect Game / Pretty Good Youth Baseball Scoring System

Posted on by Eric Peterson

I’ve had this post half-written for a few days, but it became more timely last night when Mark Buehrle pitched a perfect game for the Chicago White Sox, so it just became a “finish it up over lunch” priority. I’ve got my own little baseball-related accomplishment that I added to my site earlier this week — it seemed worth a blog post to formally announce it.

I discovered baseball relatively late in life (as in — not as a kid), and there’s been something of a perfect storm that’s made that happen:

  • I had several friends who were interested in Texas Longhorn baseball, and I got hooked on going to their games when I lived in Austin
  • My career evolved towards business data, and there are a lot of parallels between business metrics and baseball statistics — I’ve written on that in the past (and have a new post soon to come on the subject)
  • My oldest son really, really enjoys baseball, and I’m worthless as an assistant coach due to my lack of eye-hand coordination and my lack of “coaching kids” skill; so, the best way I have to be a parent contributor is to be the team scorer…which is really what led to this post

My son’s coach for the past two seasons ia an ex-college baseball player and current IT executive, so he has a deep understanding of the game and how/why stats can help identify players’ strengths and weaknesses. In other words…he’s become something of an enabler of my enthusiasm. 🙂

Partly for fun (okay…mostly for fun), I developed a spreadsheet last season that could take my box scores for each game and generate individual and team stats. In between last season and this season, I developed a scoresheet that would integrate well with that spreadsheet. One key aspect is that the scoresheet was designed specifically for youth baseball — typically, there are more frequent defensive position changes and, up through a certain age, all players on the team bat, rather than just the nine players who are playing in the field. This is the case for both Little League and Pony baseball.

I’ve now added a permanent page on this site with the whole scoring system. It works great for me and for the age group/league in which my son plays. I’m hoping to get some other users of the system who can provide feedback to make it more robust. Check it out!

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Analysis, Reporting

VORP, EqA, FIP and Pure "Data" as the Answer

Posted on by Eric Peterson

I’ve written about baseball before, and I’ll do it again. My local paper, The Columbus Dispatch, had a Sunday Sports cover page two weekends ago titled Going Deeper – Baseball traditionalists make way for a new kind of statistician, one who looks beyond batting averages and homers and praises players’ EqA and VORP. The article caught my eye for several reasons:

  • Lance Berkman was pictured embedded in the article — hey, I’ll always be a Texan no matter where I live, and “The Big Puma” has been one of the real feel-good stories for the Astros for the past few years (I’ll overlook that he played his college ball at Rice, the non-conference arch nemesis of my beloved Longhorns)
  • The graphic above the article featured five stats…of which I only recognized one (OPS)
  • The article is written around the Cleveland Indians, who have one of the worst records in major league baseball this year

With my wife and kids out of town, I got to head to the local bagel shop and actually read beyond the front page of the paper, and the article was interesting. The kicker remains that the article leads off by talking about two members of the Indians front office: Eric Wedge is a traditional, up-through-the-ranks-as-a-player baseball guy; Keith Woolner has two degrees from MIT, a master’s degree from Stanford, and a ton of experience working for software companies. The article treats these two men as the ying and yang of modern baseball, pointing out that both men have experience and knowledge that’s useful to their boss, Indians GM Mark Shapiro.

The problem? The Indians stink this year.

Nonetheless, there’s a great quote in the article from Wedge:

“What I think people get in trouble with is when they go all feel or all numbers. You have to put it all together and look at everything, then make your best decision. You can’t have an ego about it.”

The same holds true in business — if your strategy is simply “analyze the data,” you don’t really have a strategy. You’ve got to use your experience, your assessment of where your market is heading as the world changes, some real clarity about what you are and are not good at, an understanding of your competitors (who they are and where they’re stronger than you are), and then lay out your strategy. And stick to it. The data? It’s important! Use it while exploring different strategies to test a hypotheis here and there, and even to model different scenarios and how things will play out depending on different assumptions about the future. But, don’t sit back and wait for the data to set your strategy for you.

Once you’ve set your strategy, you need to break that down into the tactics that you are going to employ. And the success/failure of those tactics need to be measured so that you are continuosly improving your operations. But don’t get caught up in thinking that the data is the start, the middle, and the end. If it was, we’d all just go out and buy SAS and let the numbers set our course.

So, what about the goofy acronyms in the title of this post? Well:

  • VORP (Value Over Replacement Player) — a statistic that looks to compare how much more a player is worth than a base-level, attainable big leaguer playing the same position (Berkman had the highest VORP at the time of the article)
  • EqA (Equivalent Average) — think of this as Batting Average 2.0, but it takes into account different leagues and ballparks to try to make the measure as equitable as possible
  • FIP (Fielding Independent Pitching) — this is sort of ERA 2.0, but it tries to assess everything that a pitcher is solely responsible for, rather than simply earned runs

The fact is, these are good metrics, even if they start to bend the “it has to be easy to understand” rule. In baseball, there have been a lot of people looking at a lot of data over a long period. My guess is that there were many fans and professionals who realized the shortcomings of batting average and ERA, and it was only a matter of time before someone started tuning these metrics and looking for new ones to fill in the gaps.

At the end of the day, the Indians stink. And it’s a game. And there are countless variables at play that will never be fully captured and analyzable (the same holds true in business). Mark Shapiro will continue to have to make countless decisions based on his instincts, with data as merely one important input. Maybe they won’t stink next season.

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Analytics Strategy, Reporting

Baseball Stats and BI Musings Part II: Data Quality

Posted on by Eric Peterson

In Part I, I took a run at assessing a couple of the most popular baseball statics to see how they measured up as well-formed performance metrics. The other thought that has been running through my mind as I’ve been scoring my son’s baseball games has to do with data management and data quality.

Scoring a baseball game requires a couple of things:

  • Making judgment calls as to what actually happened
  • Capturing the right information on screwy plays where a lot of stuff happens (this happens a lot more in 9-year-old baseball games than it does in college or professional games)

The first item is one of the reasons why college and professional games have an “official scorekeeper.” There are some plays that are clearly fielding errors…but there are some that require a subjective assessment. And, even if there is clearly an error, it’s sometimes subjective as to whether it was a bad throw or a bad catch.

And, things can get a little complicated. For instance, if you look at this picture closely, you’ll be able to tell that my son is churning his 9-year-old legs as fast as he can (admittedly in pants that would fit most 12-year-olds) as he runs towards first base. And, yet, the catcher is standing right at home plate with the baseball, looking like he’s about to make a throw. What’s going on is either totally obvious to you — meaning you played baseball or have followed it with a decent level of interest — or it seems very bizarre. My son had just struck out. The rule in baseball is that, if a player strikes out AND the catcher drops the ball AND EITHER first base is unoccupied OR there are already two outs in the inning, the catcher needs to retrieve the ball and either tag the batter or throw the ball down to first base so the first baseman can tag first base. This is what’s called a “strike him out, throw him out.” You don’t see it very often in the major leagues or college, because catchers don’t drop that many balls. You see it quite a bit when the players are nine and ten years old.

Either way, my son had an official at bat with a strikeout, even if he made it to first base safely (if, for instance, the catcher overthrew first base). If that had happened (in this case, it didn’t), I would have needed to record a strikeout as well as an error on the catcher.

Sound complicated? It is, and it isn’t. Baseball has other semi-obscure rules — if a baserunner passes another baserunner, he is out. I didn’t learn that rule until I saw it happen to Baylor in the College World Series several years ago. So, scoring a baseball game correctly requires:

  • Paying close attention to every play throughout the game
  • Knowing the rules well
  • Knowing how to quickly and accurately record both “normal” plays and oddball plays
  • Being able to make the subjective calls quickly and effectively

I’ve never actually tried to verify this, but I am fairly certain that, if you take three run-of-the-mill scorekeepers and have them score the same game and then compare their results, you will get three slightly different versions of what happened. Yet, we view baseball stats and box scores as being completely black-and-white.

I worked with a data management guru at National Instruments who had a Mark Twain quote in her e-mail signature that said something to the tune of: “A man with one watch always knows what time it is. A man with two watches is never sure.” (I’ve tried to look up the exact wording and confirm that this indeed originated with Mark Twain in the past, and I didn’t have much luck.) This is an excellent point, and it applies to both baseball and business.

If we see a number that appears to be precise — 73 pitches, 10,327 visits to a web site, 2,342 leads — we equate precision with accuracy. It doesn’t cross our mind that a scorekeeper might have inadvertently clicked his pitch counter when the pitcher actually made a throw over to first base to try to pick off a runner. We ignore the fact that all data capture methods when it comes to web analytics are inherently noisy. We forget that sometimes our lead management processes break down and load a duplicate lead or miss a lead. We assume that the data that gets entered into our systems by humans gets entered by a robot rather than by a human — no judgment calls, no mental lapses. And that is simply not reality.

None of this is to say that we should throw out the data. At the end of the day, the ERAs that I calculate for the pitchers on my son’s team are going to be pretty close to the ERAs that another scorekeeper would calculate. Close enough. But, it’s easy to get caught up first in assuming that precise numbers are perfectly accurate, and, then, when something happens where you see a discrepancy, focussing on trying to get the “right” number rather than asking, “Is the difference material?”

The moral? Well…baseball is a great sport!

Oh, wait. There’s more. Don’t rely too much on your data. Don’t expect it to be perfect. Don’t focus on making it perfect. Make sure it’s “good enough” and go from there.

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Reporting

Baseball Stats and BI Musings Part I: Good Metrics?

Posted on by Eric Peterson

It’s late spring, and my 9-year-old’s baseball season is getting rolling. Due to my gross lack of eye-hand coordination, I volunteered to do the scoring for the team.

There are two basic reasons to score a baseball game:

  • Capture enough information on a single page (two pages, actually) that would allow you to entirely recreate the game, play by play, after the fact
  • Capture information required to compile game/season statistics for individual players — things like batting average on offense, fielding effectiveness on defense, and ERA for pitchers (also technically a defensive thing)

This means you need to capture a lot of information. Every pitch typically gets recorded in some fashion, and any time a batter finishes at the plate (through a hit, a walk, hit by a pitch, etc.) requires recording additional information. The more detailed the information, the more fun statistics you can pull from the data. But, generally, it’s good to capture a bit more data than you expect to use. For instance, with the system I’m using now, I actually catch the sequence of pitches for any batter: ball, then strike, then strike, then ball, then hit, for instance. That detail, in theory, would allow me to report how a batter fares when he is “behind in the count” (more strikes than balls) vs. “ahead in the count” (more balls than strikes). I’m not going there at all at this point.

At my son’s age, we really just want to make sure we get the final score right. But, the statistics are awfully alluring, so I’ve been logging the information in a spreadsheet so I can do some crunching and see what it tells me. We’re only four games in, and I’m no baseball sophisticate, so I started with the two most popular stats in baseball: earned run average (ERA) and batting average. I regularly mount my “a metric that isn’t tied to a clear objective is not a good metric” soapbox, and it turns out ERA is a pretty great metric. A pitcher’s objective is pretty clear: allow as few runs to score as possible. But, you can’t simply look at the total runs scored on a pitcher for two reasons:

  1. A great pitcher who has an infield that regularly flubs plays is going to have more runs scored on him than a similar pitcher who has Derek Jeter and Alex Rodriguez shagging grounders
  2. The more innings a pitcher pitches, the more runs he’s going to have scored on him

The “earned run” part of the ERA addresses the first issue by trying to isolate how many runs would have been scored if the other 8 players on the field played perfectly. The “average” part of ERA addresses the second issue by normalizing the metric to a 9-inning average (or a 6-inning average in my son’s case, as their games are only 6 innings long).

What about setting a target? The Gospel According to Gilligan clearly states “Thou shalt not consider a metric worthy if it does not have a preset target.” In the majors, an ERA below 3.00 is considered to be pretty darn good. It’s a “benchmark” of sorts. Or, the other way to look at the metric is to say the target is a 0.00, which is unattainable, but a worthy stretch goal.

So, what about batting average? This seems pretty simple. The batting average is the percent of a player’s at bats where he gets a hit. It’s actually represented as a 3-place fraction rather than a percentage (a .347 batting average means the player gets a hit on 34.7% of his at bats). The stat has been around as long as ERA and has long been considered the metric that is the single best measure of a player’s offensive output. There are a couple of problems with the metric, though. First off, what is a batter’s primary objective? Ultimately, it’s to score runs…but there are too many other factors at play to use that as metric. And, as it turns out, it’s not to get hits as much as it is to get on base. And hits are only one way of doing that. When you peel back the batting average calculation a bit, you find that a walk is not considered an official at bat, so it doesn’t go into the numerator or the denominator of the equation. The reasoning is that the batter got on base because the pitcher screwed up. That’s giving the pitcher a bit too much credit, as a batter who has “plate discipline” is a batter that doesn’t swing at balls — he gets more walks, and when he swings, he’s more likely to be swinging at a hittable ball. (Sacrifices also don’t count as an at bat, but I’m okay with that, as the batter’s objective in that case is to move the baserunner(s) up, so he’s not really trying to get on base himself. A fielder’s choice where the hitter winds up on base doesn’t count as a hit, which makes sense. And, if a batter puts a ball in play and then reaches base on an error, that’s still not considered a hit, because that was more a defensive goof than an offensive success, so it goes into the denominator as an at bat but not in the numerator as a hit. Oh…MAN…can I digress on this subject…!)

Whether it’s true or not, or whether it’s a gross oversimplification, Billy Beane, the general manager of the Oakland A’s, gets credited with this epiphany. The story of how Billy used data to go against baseball’s conventional wisdom to make the Oakland A’s a consistent contender despite their minuscule payroll (by MLB standards) is the basic premise of Moneyball: The Art of Winning an Unfair Game. One of the metrics that Billy and his number crunching assistant started focussing on was on-base percentage (OBP), which includes walks in the numerator and denominator of the calculation. OBP gets a lot closer to a batter’s objective than batting average does. And, Beane started picking up college players who walked a lot but didn’t have a great batting average. And it worked.

Theo Epstein, the general manager of the Boston Red Sox, followed in Beane’s footsteps (he actually worked for Beane for all of 12 hours during Beane’s one-day stint as GM of the Red Sox). And the Red Sox finally won another World Series.

So, as I’ve started tallying the stats for my son’s team, I’ve calculated both batting average and OBP, and, lo’ and behold, we’ve got a couple of kids who are in the lowest third of the team based on batting average…but move up considerably when it comes to OBP. None of this is to be shared with the kids — at this point, they’re having a good time, they’re trying hard, and they’re learning to support each other, so introducing a hierarchy of “who’s better” is wildly counter-productive.

In the end, I’ve violated my core tenet — I’m looking at metrics that are not, in the end, actionable at all! But I’m having fun, and it’s got me thinking about data in some new ways. This post was about metrics. I’ll explore data quality in the next post. Stay tuned!

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