A Free GIPS Webinar!

I will host a webinar on Monday, September 17, at 11:00 AM EST, titled "The Ten Most Common Mistakes GIPS Compliant Firms Make." This is a FREE webinar, but space is limited, so please contact us soon (either by calling 732-873-5700 or emailing Patrick Fowler). GIPS(R) (Global Investment Performance Standards) has become a regular part of our lives, and we thought this would be a good session; we hope you agree!

The Spaulding Group hosts monthly webinars, and they are typically free only to our verification clients and members of the Performance Measurement Forum. Others are charged a modest fee. But this webinar will be free to all, but you must register quickly, as there are a limited number of slots.

In addition to covering these 10 most common mistakes, we will open it up to your questions, which you can submit in advance, or during the webinar. The presentation should last about an hour, and we'll have an additional hour for questions.

Many of our clients make these monthly events "lunch-and-learn" sessions for themselves, their staff, and their colleagues. You can have as many folks on the call with you as you'd like.

If you're attending the GIPS Annual Conference in Boston, please stop by The Spaulding Group's booth to learn about our services and for a chance to win an Apple iPad!

Beware of outliers: lessons from education, sports, and movies

The headline in today's Home News Tribune (a central New Jersey newspaper) is titled "Probe: Teachers enabled cheating." It points out how last year Woodbridge administrators and teachers were celebrating because New Jersey state's School Report Card reported impressive results for several township elementary schools in 2010. For example, Avenel Street Elementary, where 94 % of its third-graders scored "high marks." This was quite a feat, since only 37 % of students statewide earned such scores. As it turned out, this was a case of teachers cheating.

Last week we learned that famed bicyclist Lance Armstrong was being stripped of his medals because of cheating.

I am often reminded of Charlie Sheen's portrayal of a broker in the movie, Wall Street, who suddenly began racking up extraordinary impressive numbers. His success was largely attributed to insider trading.

These are all examples of outliers. And while we can expect to see outliers, there are times when they are just a bit too exceptional. To have a school with 94% of its students achieve "high marks" when the norm is only 37% is just a bit too good; to win the Tour de France a string of seven times is just too good without the help of something illicit.

Americans will recall a few years back when in any given baseball season we'd find several batters hitting more than 50 or 60 home runs, something that used to be quite rare had become the norm. Some observers suggested that the balls were "juiced"; it turned out that the players were. Now that steroids are the exception in baseball, the numbers have dropped to the level they had historically been.

While not all distributions are normal, we still are sensitive (or should be) to events that are just too good. In one of his books (I can't recall offhand which), Harvey Mackay speaks of things just being a bit too good; and when this happens, be careful.

Too often when exceptional events occur, we are slow to think that something improper has occurred; perhaps we are caught up in the moment, or really want to believe that someone can be that good. Who wants to be accused of being a "doubting Thomas" or a "party pooper," raining on someone else's parade?

A candidate for a "poster child" for exceptional behavior in investing would be Bernie Madoff:  his string of above average returns should have caused concerns, from his clients and the regulators, but because everyone liked Bernie, hardly anyone questioned such performance.

In the late 1960s, my older brother, who died more than 20 years ago, came home from Army basic training, and showed me a photo of the woman he had just married (a complete surprise to all of us, since we didn't even know he had a girlfriend). I knew there was a problem the moment I saw her picture: she was just too good looking for my brother. Sounds like a nasty remark for a brother to make, right? You have to understand that Bill was a bit awkard and clumsy, and wasn't particuarly good looking himself. He had a difficult time attracting girls when we were growing up. And so, for him to find someone so attractive just didn't seem right. As it turned out, this was an older woman who had been married before, to soldiers who had gone off to Vietnam (where my brother was expected to go, but didn't), never to return, and so she'd collect the life insurance money. A sad story, yes? I won't bother to share any further details about what transpired, but only to say that this was an outlier and one that I, at a fairly early age, was able to detect.

In our GIPS(R) (Global Investment Performance Standards) verifications, we look for outliers, as these are often cases of errors that crept in. And while outliers are often and perhaps usually fine, there are times when they aren't. Consequently, we should be sensitive when they occur, just in case ...

Money- versus time-weighitng: let me be very clear

It's election time here in the United Staes, so it's appropriate to cite a few of our presidents. America's 37th president (Richard Nixon) is often recalled for his "let me make this perfectly clear" line. Our 42nd president (Bill Clinton) didn't use the term "clear," though it was implied when he denied "having sexual relations with that woman." And our current president (Barack Obama) frequently uses the term "make it clear" in his speeches. Sometimes, when trying to make something "clear," we end up making it muddier; obfuscating rather than elucidating, so to speak.

Over the recent past I have attempted to make the differences between time- and money-weighting clear, and to some degree have met with success, but not to the extent I would like.

I recently received the following note from a long time friend and colleague:
 
"I am working with some financial institutions and they use XIRR to measure the return of even stock and mutual funds. I am trying to convince them to adopt GIPS standard and use Modified Dietz and Time Weighted Rate of Return to calculate rates of return. Since I have never used XIRR, I wanted to see if there was anything you might have or be able to share to help me convince this client - ideally a white paper comparing the two methods and which one is most appropriate."

First, the XIRR is a form of the internal rate of return, and therefore it's a money-weighted method. Second, and more important, we have to understand the reason behind their use of performance / returns. Is it (a) to tell clients how THEY did, or (b) tell them how their MANAGERS did? This is critical in order to decide the way to proceed.

If the answer is "to tell our clients how they performed," XIRR is perfectly fine.

If, however, it's "to tell our clients how those who manage their money have done," then TWRR is probably the right formula, unless their managers control the cash flows, in which case XIRR is the way to proceed.

More and more folks are seeing the wisdom behind this. And while I may sound like a broken record, I (along with several of my colleagues who feel the same way) believe it's important to keep pressing this point. Disagree or, have other thoughts? Please chime  in!

Just because the return or risk formula seems to make sense doesn't mean it's valid

The Spaulding Group offers Operational Reviews and Software Certifications, both of which expose us to formulas that firms put into use. Sometimes, they are variations of formulas that have been around for years; but occasionally, they're brand new; ones the clients developed themselves. Over the years we have encountered a variety of "home grown" methods, which are usually invalid.

An Abbott & Costello routine serves as a great example of just because you can make it look like it makes sense, doesn't mean it does.

The scene: they're in the Navy, and Costello is a baker; he explains that he made 28 donuts for the officers; there are seven officers and he has just enough so that each will get 13 donuts (a "baker's dozen").

You read it right: 28 donuts will be enough so that each of the seven officers will get 13 donuts. How can this be? Well let's see (and I'll do my best to explain, though the video is better):

1) Division: 28 ÷ 7 = 13. How? Seven cannot go into two, so we put that aside. Seven goes into eight once right? And so we divide the seven into eight and have one left over. We now bring over the two, giving us 21. Seven goes into 21 three times, so our answer is 13.

2) Multiplication: 7 x 13 = 28. How? Multiply seven times 3 and we get 21. Seven times one is seven. Add seven to 21 and we get 28!

 
3) Addition: 13 + 13 + 13 + 13 + 13 + 13 + 13 = 28. Begin by adding the 3s: 3, 6, 9, 12, 15, 18, 21; and then add the ones (22, 23, 24, 25, 26, 27, 28).

Clearly, this creative arithmetic is wrong; however, there is no doubt that many who, when presented with it, would be inclined to think that it makes sense and that Costello must be correct.

If you think this is silly, you should see some of the methods that have been given to us to measure returns!

Hopefully you'll agree that this was a "fun" way to end the week. Care to see "the boys" in action? You can, on YouTube! While perhaps not as famous as their "who's on first" routine, it's still enjoyable.

p.s., Ma & Pa Kettle do a similar trick showing how five times 14 equals 28! This adds even more credibility to this creative math.

"Technology Training Can Be A Boon To Productivity"



This post's headline comes from a book by the late Chet Holmes. It's doubtful, very doubtful, that anyone would disagree with such a statement. Our firm provides several training courses, but nothing that deals specifically with technology ... well, this isn't completely true.

We did host a webinar on "Excel Tips & Tricks," which also made its way into both The Spaulding Group's Performance Measurement Forum and Performance Measurement, Attribution & Risk (PMAR) conference. No one walked away from these sessions saying "I knew all that" or "how can I benefit from this?"

And now we're hosting a webinar on the web, titled "Internet Tips & Tricks" (see a pattern?). The host is a professor at Pace University: Dr. Vasant Bhat. I had Professor Bhat for a doctoral course, and found him to be quite gifted when it came to navigating the web and using tools that most folks are unaware of. The webinar will be this coming Monday, August 27, 2012 at 11:00 am (EST).

I invited him to host this session a few months back, and he was kind enough to agree. Our verification clients and forum members can participate at no cost; for all others, a nominal fee is charged. If you want to learn more, please contact Patrick Fowler or just call our office (732-873-5700).

This program is a great way to train your team; the timing might work as a "lunch & learn" session. If you sign up and decide it wasn't beneficial, we'll refund your investment. We have a limited number of slots, so register soon!

Smoothing and geometric attribution

Carl Bacon, CIPM, when asked to contrast geometric and arithmetic attribution, will no doubt point out the chief advantages geometric offers:

• Proportional: the active return is a ratio, not a difference, as we find with arithmetic.
• Convertible: the active return is independent of the base currency; the geometric active return will be the same whether it's expressed in dollars, euros, pounds, yen, etc.
• Compoundable: the geometric active return multiplies across time; no "smoothing factor" is required, as it is with arithmetic.

Carl is, of course, correct in all of these points; however, he fails to explain that while a smoothing factor isn't needed "across time," as we "link" our single period effects, one is needed "within time." That is, we need a smoothing factor for every single time period.

In his '05 FAJ article* on geometric attribution, Jose Menchero, PhD, CFA discusses the need for a smoothing factor. He points out how one can arrive at a "pure" geometric equivalent of an arithmetic model, but that this will not result in an approach that will fully reconcile the effects to the excess return: a smoothing factor of some sort is necessary.

Jose offers a rather healthy formula to "smooth out" the residual. Contrast this with Carl, who assigns the residual entirely to the selection effect. And so while Jose ensures that the residual is assigned across all effects in some appropriate or proportionate fashion, Carl is content with it residing entirely with selection. It is not my purpose here to justify one over the other: Carl's is clearly much simpler to work with than Jose's, and perhaps the differences are immaterial.

My point is merely to explain, in as clear a fashion as possible, that just like arithmetic, smoothing is needed. It's just that arithmetic attribution has no residual for the single period, but will across periods; while geometric has a residual for single periods, but once they're smoothed out, won't have one across time. It's the attribution equivalent of you can pay me now, or you can pay me later, but surely you will pay (i.e., you must smooth out a residual at some point).

Space does not permit the elaboration necessary to give this topic the attention it needs, so please consider this merely an introduction to what will follow in this month's newsletter.

*Menchero, Jose, 2005, Optimized Geometric Attribution, Financial Analysts Journal 61.

Personal rates of return ... what are they, really?

I am home this week, working on my doctoral dissertation proposal, and need references to cite for a "personal rate of return."

And so, like any good researcher, I began with a "Google search," and found the "Finance guy's" blog, which has a post on this subject.  He references an earlier one, where he advocates using the Original Dietz, across the full year, rather than the XIRR. While it's true that it can provide roughly the same result, the industry has pretty much abandoned this mid-point method. Okay, if your readers are truly unsophisticated investors, with limited math skills, perhaps this is okay, but I would couch such a formula with an explanation that its accuracy is not very good.

In the more recent post he has the following:

The personal rate of return you get from a financial service provider like Fidelity or Schwab is usually a Time Weighted Rate of Return. If you want a Dollar Weighted Rate of Return, you will have to do it yourself.

A "time-weighted rate of return" as a "personal" rate of return? What is personal about a time-weighted return? Ten people are invested in the same fund, but contribute different amounts during the year. What will their "personal rate of return" be under time-weighting? The same as the fund's performance, because by definition, we've eliminated the impact of the cash flows.

He cites another website, dailyVest, whose post on this subject states

Time-weighted rates of return can be calculated on a daily basis (one method known as Daily Valuation) or on a slightly less accurate monthly basis (known as Modified Dietz) where inflows/outflows are averaged for the month. This time-weighted methodology used for calculation of personal rate of return provides a truer measurement of how investments are performing.

No, no, no! A "personal rate of return" has to be "personal," does it not? And how do we get this? By taking the flows into consideration.

Perhaps we have a bit of Clinton-like speech here (as in, "it depends on what you mean by the word, is"), at least in dailyVest's case, because one might ask what "This" means, in "This time-weighted methodology." If they're referring to Modified Dietz, then they get partial credit. However, by linking the intraperiod Modified Dietz-derived returns, we achieve an approximation to a time-weighted return, which is not a money-weighted method.

The site "All Financial Matters" gets it right, because this author cites the XIRR as the formula to use. In his post he provides an example of an investor putting money into the Vanguard S&P 500 Index Fund (VFNIX), and states:

Some simple math will tell us that VFNIX returned 6.8% (not including dividends) from 1/31 – 12/31 ((111.64 – 104.54) ÷ 104.54). However, the real question is: how did the portfolio perform for you? Or, what was your personal rate of return?

Given that the investor made contributions during the year, he correctly references the XIRR (i.e., a money-weighted method) to obtain the personal rate of return.

Speaking of Vanguard, they have a brief video that describes the personal rate of return.

Time-weighting offers nothing personal to the investor.We need a money-weighted formula for that, be it (an unlinked) Modified Dietz, as a proxy for the true, exact, money-weighted return, or the internal rate of return. If there are no flows, then the TWRR equals the MWRR.

There is nothing "personal" about time-weighted methods. If you want a "personal rate of return," use an MWRR formula.

The use and misues of statistics, part II

It was just yesterday that I commented on a WSJ article that spoke of the potential misuse of statistics. And today, in this very paper, on the top of page B1 we read the headline "BMW's 'Demo' Sales Boost Results."

The article speaks of how BMW reported a sale of "21,297 of its flagship-brand cars in the U.S. in July," and these "numbers are not as straightforward as they appear," because "Hundreds of BMWs counted as sold in July remain in showroom inventories and are still advertised for sale as new cars."

It seems that BMW offered their dealers an incentive to acquire cars on July 31 and receive a $7,000 discount, provided they be reported as "sold."

Where does it end?

But our industry sees this sort of thing too, does it not? The incentive to present information in just the right light. This is why standards, such as GIPS(R) (Global Investment Performance Standards) and UAPS (Universal Advisor Performance Standards) are critically important. While flexibility is offered, their remain fundamental rules which must be adhered to.

The use and misuse of statistics

In today's WSJ, William McGurn has an article on John James Cowperthwaite ("Go for Bust, Mr. Romney"), which provides some background into Hong Kong's phenomenal success (he cites, for example, the rise in per capita income relative to Great Britain: it was 28% of GB's in 1960, and 137% in 1996). He credits Sir John with much of their success.

He also quotes Cowperthwaite, who "forbade officials from keeping numbers even for such things as gross domestic product":

"If I let them keep statistics...

they can only misuse them."

Perhaps Sir John was also a fan of Mark Twain, who famously said that there were three types of lies:

• lies,
• damned lies, and
• statistics.

Sadly, we all know about the misuse of statistics in the performance and risk measurement profession. Numbers are too often thrown around in improper fashions, sometimes accidentally, but too often intentionally. We've seen everything from completely made up numbers (e.g., Bernie Madoff, who is only one of many), to numbers that were concocted in a way that meets no industry standard or practice that could be considered "best." At times numbers are used to impress, even when they have no relevance to what is being sold.

And so, should we all adopt Sir John's rule of not showing statistics? There have been suggestions that this would be appropriate. For example, not many years back, as I recall, a UK regulator questioned the appropriateness of reporting returns, if "past performance is no indication of future results."

The U.S. witnessed the appearance of misleading performance presentations in the mid 1980s, when for the first time we saw institutional asset managers advertise their past performance. Many of us believe this served as a catalyst for the FAF (Financial Analysts Federation) Performance Presentation Standards, which led to the AIMR-PPS(R), and then to GIPS(R) (Global Investment Performance Standards).

And this, of course, is the answer to those who question the use of statistics: rules. GIPS applies to much of the investment market, and new UAPS (Universal Advisor Performance Standards) will apply to other segments. Those who receive statistics should ensure that the provider adheres to the appropriate rules. Of course, their claim of compliance (and sadly, even sometimes when they've been verified) is no guarantee, as a few have still managed to get away with fraud. But, this is at least a start.

And so, with all due respect to Sir John and Mark Twain, statistics have value and should be reported. The recipient just needs to ensure they understand what's being shown and ideally that it conforms to industry standards of practice.

Another victory for money-weighting!

The United States Governmental Accounting Standards Board (GASB) recently introduced new provisions that call for the reporting of money-weighting rates of return, using the internal rate of return (IRR).

The new provision, Statement No. 67 of the Governmental Accounting Standards Board, titled Financial Reporting for Pension Plans, includes the following details.

On page 12, paragraph 30.b.(4) you'll find

And then on page 17:

The document also includes a glossary, with the following definition:

I had been speaking with someone from GASB a few months back, answering his questions as well as offering my views on money-weighting. Obviously, I'm thrilled that they opted to go with this measure, as opposed to time-weighting, which was their original plan.

If you're involved with or support a government pension fund, you'll want to ensure you adhere to these new rules. And, if you're not a government fund, it's still worthwhile to become familiar with them, because money-weighting is catching on!

Note: The Governmental Accounting Standards Board (GASB) is the independent organization that establishes and improves standards of accounting and financial reporting for U.S. state and local governments. Established in 1984 by agreement of the Financial Accounting Foundation (FAF) and 10 national associations of state and local government officials, the GASB is recognized by governments, the accounting industry, and the capital markets as the official source of generally accepted accounting principles (GAAP) for state and local governments.

Internet Tips & Tricks

This month's Spaulding Group webinar will be on Monday, August 27, from 11:00 AM to 12:30 PM EST, and it will be a bit different. Titled Internet Tips & Tricks, it will provide you with some interesting insights into better ways to improve your Internet experience!

The webinar instructor is Vasant Bhat, PhD. I had Professor Bhat in one of my doctoral classes at Pace University, and he demonstrated a tremendous familiarity with neat ways to use the web. He graciously accepted our invitation to host this program, and we're confident you'll enjoy it.

He will explain better ways to explore the Internet, including how to find important data and information critical to you and your firm. You will be introduced to cool tips and tricks that can significantly enhance your web experience and gain insights into ways to better utilize the tremendous resources of the Internet.

Space is limited, so reserve your Webinar seat today!

Note: the webinar is free for Performance Measurement Forum members and TSG verification clients. All others will pay a nominal site fee, meaning you can have as many folks on the call as you would like.

GIPS Survey 2012

The Spaulding Group has announced that it is conducting a survey on the Global Investment Performance Standards (GIPS(R)). This marks the eighth time we've conducted a survey on the presentation standards, starting with the AIMR-PPS in 1993. This makes our firm the leader in gathering information on these vitally important standards.

The survey's length has been trimmed, to (a) make it quicker to go through (it should take you only five minutes or so to complete), (b) allows us to focus on the most important issues, and (c) should result in even more participants!

And speaking of participants, all will receive a complimentary copy of the results! So please join in today!

Oh, and before I forget, I want to acknowledge the firms who are sponsoring this research effort:

Should this be a required UAPS or GIPS disclosure?

I try to exercise every morning, and when I do, I often watch the news.

This morning there was an advertisement from CancerCenter.com, that included the following disclaimer:

No case is typical.

You should not expect to experience these results.

It occurred to me that such a statement might be an appropriate inclusion in any presentation of past performance results, be they in compliance with the Universal Advisor Performance Standards (UAPS), the Global Investment Performance Standards (GIPS(R)), or simply reporting that complies with no standard to a prospective client.

It's a fact, is it not? There are no "typical" cases. We have averages, but often even the averages don't represent any account in particular, but rather represent the blending of accounts.

What do you think? Should this be a required statement? Would it add to the presentation or better serve the prospect?

A multi-currency return puzzle for you to solve (part 2)

Yesterday, I introduced a case of going from a USD to a GBP return (via an FX rate conversion), then taking the underlying USD assets and converting them to GBP, deriving the return that way, and converting (via the FX converter) to USD [quite a mouthful]. Recall that the GBP and USD results were different, depending on how they were derived. Thus, the problem: what's going on?

Simple answer: Modified Dietz.

Recall that Modified Dietz (MD) is an approximation formula; that is, its result approximates the true, exact TWRR (time-weighted rate of return). We began with a USD portfolio where there was neither a gain nor a loss for the month; even though there were cash flows, our MD return equaled zero, which is what we would have gotten with the exact method. Therefore, the FX conversion worked perfectly fine.

When we converted the USD values (beginning and ending value, along with the cash flows) to GBP, however, we had a problem. This was because we were now dealing with a non-zero (approximate) return, and MD is sensitive to the size, timing, and frequency of cash flows. Plus, you may have noticed that one of the flows was more than 17% (i.e., "large"), which should result in a revaluation, at least on that date.

For our client I recalculated the GBP return, but this time revalued the portfolio daily. This graphic summarizes all the details (you can click on it to make it larger, for easier viewing):

The spreadsheet is repeated here:

And as you can see, by calculating our GBP return on GBP values, and revaluing when flows arise, we tie out to the earlier derived return of -0.0188 percent.

Well, that was fun for me ... I always enjoy solving puzzles. Hope you found it of interest. Feel free to chime in with your thoughts.

p.s., I mentioned yesterday that Sarah Ringle introduced the FX return conversion formula to me. She cautioned that this is a quick conversion methodology, and that the standard approach is to convert the underlying valuations to the alternate currency, in order to capture the correct unrealized and realized gain/loss including the derivative estimated amounts.

A multi-currency return puzzle for you to solve (part 1)

A Spaulding Group client posed the following situation to me, which I hope you find of interest:

They calculated the return on a portfolio in US Dollars (USD). They next converted the return to Pound Sterling (GBP). Everything worked correctly; i.e., the returns make sense and are accurate.

They next converted the portfolio's assets from USD to GBP. They measured the return on the GBP-based portfolio and got a different return than they got when they simply converted the USD portfolio's return using an FX conversion formula, which I'll show you shortly. And, as expected, when they converted the GBP return they just calculated to USD, it didn't match the earlier return. The following graphic summarizes what occurred and our expectations:

Here are the details behind what is occurring, which only makes this problem a bit more challenging.

As you can see from this table, we began with $23 million, took out $4 million on the first, another $1 million on the 28th, and ended the month with $18 million. Simple arithmetic reveals that we made nothing and lost nothing. Therefore, our return of 0.00% is accurate. This activity occurred in March 2012 (a 31-day month), and the weighting reflects it. Modified Dietz was used to derive the return.

We next want to convert the return to GBP. This can be done by using a fairly simple formula:

With this in hand, along with the following FX Rates:

We find that the GBP return equals the FX return for the period (-0.0188%). This makes sense, since the USD return is zero, the only thing that would affect our return would be the conversion from USD to GBP, which is reflected in the FX rate change, from the beginning to the end of the month.

Now, we want to convert our USD values (both the starting and ending values, as well as our cash flows) to GBP. We use the FX rates that correspond to the date of each value, and get the following:

The table shows the return we get when we use Modified Dietz. And it's obviously not what we got earlier: it's materially different.

At this point it's probably not a surprise for you to learn that when we convert the GBP return back to USD (using our FX conversion formula) we get something other than 0.00%; we get 0.0626 percent, which gain is a materially different value.

What's wrong? Does it not make sense that we should have a closed system? That going from USD to GBP, then from GBP to USD, we should have returns that all tie out?

I invite you to check the math: you'll see that it's all correct. And so, what is happening? If  you can figure it out, send me an email. I will reveal the solution tomorrow (at least the one I have!).

p.s., I thank Sarah Ringle of Alliance Bernstein for introducing me to the FX conversion formula more than a decade ago, when I was doing a consulting assignment for Alliance Capital.

Should you have a "performance bible"?

Yesterday's WSJ had an article by Brian Costa titled "The Rookie and His Pitching Bible," about "the [New York] Met's most promising rookie," Matt Harvey, who relies on a "pitching bible" he created and maintains with much care and attention. "[Harvey] records every mechanical adjustment he makes, even if only temporary, as a reference for the future. When he pitches well, he notes what he did right. When he pitches poorly, he types in a summary of his mistakes, be they mechanical or mental."

In the world of investment performance we refer to such analysis as "attribution," identifying and classifying what worked, and what did not. And while we don't typically refer to our attribution reports as our "performance bible," perhaps such diligence, across time, wouldn't be such a bad idea.

If we look over the past month of July and evaluate what worked and what didn't, that's great. But, how does that compare to June, May, or April? Or last July? Is there a pattern? Have skills shifted? Do certain decisions work better at some times, but not at others?

A temporal evaluation, such as what Harvey does, builds upon the single period evaluation, to provide a macro view, which no doubt offers much benefit to the pitcher, and would for the performance manager and his/her team, I would think.

Your thoughts?