A couple of weeks ago, we looked in some detail at Forbes’ franchise valuations. Every year since 1990 one of Forbes’ financial journalists, some guy called Michael Ozanian (or his lackey), has pored over financial statements and spoken to various contacts to try to determine how much each major league ball club is worth. Mr Ozanian started publishing his results in a magazine called Financial World. That went bust in 1997 so Forbes took up the mantle and a new table is published every April or so.
Nearly every article that touches on the value of ball clubs invokes the Forbes data; part 1 being a case in point. After all, it is the only publicly available source of franchise values and because it is published annually we can look at the year to year capital growth of franchises.
A pretty important question then is how much stock can we put in Forbes data? In other words, how accurate is it?
Forbes and Valuation
First we need to know how Forbes computes its valuations. Not surprisingly neither Forbes nor Mr. Ozanian gives too much away. However, a quick hunt around the web yields one or two hints. In an on-line interview that Mr. Ozanian gave in 2003 he said,
“Teams are valued on a multiple of revenues that takes into account historical transactions as well as the team’s current stadium situation”.
Well, that’s all cleared up, right? Well, no, so what does that all mean?
What Ozanian is saying is that the primary axis of valuation is team revenue, which includes things such as ticket sales, TV money, sponsorship, revenue sharing, concessions, parking and myriad other schemes that franchises use to wheedle money from their fans. Then Ozanian says that historical transactions are accounted for. It is unclear what this means but is likely an adjustment based on recent sale prices of MLB teams. It effectively acts as an anchor point for valuations and sets the bar for the multiple calculation. For instance, if someone has just paid $450m to buy the Dodgers then guess what? That is how much they are worth and that data point should be used when calculating values for that and similar franchises.
The final element is the stadium deal. This is because how the stadium is financed can have a huge impact on either a team’s debt level (interest payments) or tax rate, both of which impact value. New stadiums also allow franchises to generate extra revenue which will nudge valuations up.
Mix all those factors together, do some fancy jiggery-pokery in Excel to produce a revenue multiplier … multiply by revenue … and bingo, Forbes has a valuation.
Later in the same article Ozanian goes on to report that Major League Baseball franchises are typically valued at somewhere between 2-3x revenues. Take a look at the revenue/value multiple for all major league clubs using the 2005 data:
Team Revenue Value Value/Revenue Yankees 277 1026 3.70 Red Sox 206 617 3.00 Mets 195 604 3.10 Dodgers 189 482 2.55 Cubs 179 448 2.50 Nationals (Expos) 145 440 3.03 Cardinals 165 429 2.60 Mariners 179 428 2.39 Phillies 176 424 2.41 Astros 173 416 2.40 Giants 171 410 2.40 Braves 172 405 2.35 Angels 167 368 2.20 Orioles 156 359 2.30 Padres 158 354 2.24 Rangers 153 353 2.31 Indians 150 352 2.35 White Sox 157 315 2.01 Diamondbacks 145 305 2.10 Rockies 145 298 2.06 Tigers 146 292 2.00 Blue Jays 136 286 2.10 Reds 137 274 2.00 Pirates 125 250 2.00 Royals 117 239 2.04 Brewers 131 235 1.79 Athletics 134 234 1.75 Marlins 119 226 1.90 Twins 114 216 1.89 Devil Rays 116 209 1.80
The data seem to fit with what Ozanian says. Most teams are within the expected multiple range with the Yankees leading the pack at a 4x multiple. Also, there are a few small-market teams, such as the Devil Rays and the Twins, which slip below 2x.
One thing that is interesting is that there is a strong relationship between revenue multiple and total value. The R squared here is 0.89. Why is this?
It likely has little to do with a team’s stadium situation, which is one of the factors that Forbes purports to take into account. Why should this benefit larger teams? The only reason for one team to have a higher multiple than another is because there is an expectation that the team with the higher multiple has greater earning potential. Perhaps Forbes believes that larger market teams will grow faster, or may get richer TV deals? Because we don’t know the precise calculation we can’t be sure. All we know is that based on this analysis Forbes expects disparity between the haves and have-nots to widen (assuming all teams grow at the same rate).
Forbes also provides profit and debt data alongside revenue and value. Do these have any significant impact on valuation? Let’s run a quick regression to find out.
Parameter t Stat P-value Intercept -5.32 0.00 Revenue 13.48 0.00 Profit -2.00 0.06 Debt -0.19 0.85 NB: Correlinearity between revenue and profit is relatively small
Interestingly at a 5% significance level we must dismiss profit and debt. Only revenue is significant. This jives with what we learned about Forbes’ methodology but is perhaps a little surprising. Although you wouldn’t expect debt to impact value unless it caused crippling interest payments you might expect profit to be important. Why is that?
Imagine I set up a company that sold you dollar bills for a dollar. Suppose I sell a gazillion dollars, how much would you pay for my company? The answer, of course, is nothing. It costs me a dollar to buy a dollar bill (more if you include my overheads) so my business is worthless despite generating gigantic revenues. Profit is more important than revenue when determining value. Actually, to be technically correct, (if you remember from part 1) it is the present value of all future cash flows that drives valuation—something we’ll expand on in part 3.
The correlation between profit and value gives an R squared of 0.48. Despite not showing any significance in the regression the simple correlation seems reasonable. However, the Yankees skew the data. Take them out and the R squared drops to a more anaemic 0.16, hardly evidence of a convincing relationship; oh, and the presence of the Yankees causes the original R to be negative.
Okay, we have delved a little in to the inner workings of the Forbes’ valuation methodology, but we haven’t answered the question we posed at the start of this article: How accurate is it?
There is only one way to find out and that is to compare the Forbes data with real life MLB sales. This data is quite hard to come by because most franchises are owned by private companies or individuals who aren’t obliged to disclose deal size. However, MLB is big business and generates a ton of publicity so the media usually coalesce around a figure.
One thing to bear in mind is that market forces don’t always dictate the valuation. For example, the pending sale of the Atlanta Braves from Time Warner to Liberty Media involves a complex tax wheeze that will probably result in Liberty overpaying to the tune of over $50-100m.
Without further ado here is a list of recent deals along with the Forbes value in the year of the transaction.
Team Year Forbes Value Actual Value Variance Braves 1993 96 173 0.45 Royals 2000 138 96 0.44 Angels 2003 241 184 0.31 Red Sox 2002 488 700 0.30 Athletics 2005 234 180 0.30 Padres 1995 67 94 0.29 Mets 2002 498 391 0.27 Orioles 1993 129 173 0.25 Cardinals 1995 112 150 0.25 Mariners 1992 81 106 0.24 Pirates 1996 71 92 0.23 Tigers 1992 97 82 0.18 Astros 1992 87 102.7 0.15 Dodgers 2004 424 371 0.14 Marlins 2002 136 158 0.14 Indians 1999 364 323 0.13 Rangers 1998 281 250 0.12 Brewers 2005 235 223 0.05 Giants 1992 103 100 0.03 Reds 2005 274 270 0.01 Nationals 2006 N/A 450
Running down these numbers the purchase prices aren’t as close to the Forbes data as you may have thought. The variance between the purchase price and the Forbes valuation averaged 20%, which on an typical value of $300M is some $60M—that is a lot. Some of the valuations were spectacularly incorrect. In 2002, John Hendy and his consortium, bought the Red Sox for $700m. This is what Forbes thought before and after the deal.
Boston Red Sox Year 2000 2001 2002 2003 2004 2005 Value $m 339 426 488 533 563 617
Forbes reckoned that Hendry overpaid by almost $300M, or 40%. Even in later years the Forbes data do not catch up with the 2002 purchase price. How can that be? The Red Sox auction was, in the murky world of sporting transactions, transparent and open. If someone is prepared to pony up 700 big ones, well, surely that is how much the franchise is worth? Also given that the values of franchises have universally risen since 2002 what would the Red Sox be worth now: surely more than $700m. A billion, perhaps?
Perhaps Forbes is conservative? No, not true. Take a look at another large market team, the LA Angels, after their purchase by Arturo Moreno a year later. Moreno bought the team for a little over $180 million, but according to Forbes the team was worth some $60 million more. Again, here are the Forbes data before and after the transaction.
Los Angeles Angels Year 2000 2001 2002 2003 2004 2005 Value $m 198 195 225 241 294 368
Did Forbes adjust their numbers down following the purchase? No. They kept on rising. Although there is little denying that Arturo made a very astute purchase there has to be doubt as to whether the club is now worth double what was paid for it in less than two years.
What are we to make of all this? Should we trust the Forbes data, or must we discount it all?
Let’s not be too hasty. It is worth digging a little deeper into the accuracy of Forbes’ data before we pass judgement on the methodology. We know that Forbes bases most of its analysis on revenue data so the obvious question is: Is this revenue data correct?
Because all clubs are privately owned they are not compelled to publish audited financials in the US. This makes verifying the accuracy of the Forbes data difficult. However, we do have some sources at our disposal to help.
In 2004, as part of a local task force, the Metropolitan Milwaukee Association of Commerce (MMAC) obtained access to audited (by KPMG) financial statements for the Brewers from 1994 to 2003. Thanks to the power of the Internet (and specifically SABR’s Business of Baseball committee) we have access to these statements. Let’s compare the audited financials to the Forbes numbers for the Brew Crew.
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 KPMG 32.9 33.8 46.7 52.8 73.4** 65.4 67.7 110 104.4 115.9 Forbes 26 29.5 41.6 46.9 55.5 60.8 69.6 108 * 102 * In 2002 Forbes did not publish a revenue figre ** Audited data included $10m of other revenue that is unexplained in the financial statements
Not too bad, I suppose. For most years Forbes is within 10-15% of KPMG’s numbers. However, once manipulated into a valuation this can account for $45m on a $300m valuation, which is significant.
However, we need to sound a note of caution as revenue numbers can still be fudged. Financial data for baseball clubs is notoriously unreliable as it is very easy to hide revenue and profit in various financial vehicles. This allows owners to plead poverty when actually they are making a lot of money off the books. For instance, Sports Economist Andrew Zimblast found that Marlins owner Wayne Huizenga attributed $38 million of local revenue to the stadium rather than the team. This resulted in a purported $34 million loss for the Marlins in their World Series-winning season.
Also teams like the Braves have hidden a fistful of revenue in TV stations, away from the prying eyes of the MLBPA. There are plenty of other ways that revenue can be disguised. Canny owners can outsource services to their own companies at unrealistically low prices. Food is a classic example where you can load revenue on to your caterers and keep it off the clubs’ income statement. Importantly, in this case of the Brewers, the MMAC didn’t find too many revenue or expense related shenanigans, which gives the comparison credibility. At the very least there is little reason to believe that the Forbes data are any more accurate than the audited numbers are.
Are there any other sources we can check? There are. One is the Cleveland Indians. In 1998, the Tribe listed on the stock exchange, so under SEC rules were obliged to publish an annual report. Again, look at the comparison between reported and Forbes revenue.
Revenue $m 1998 1997 1996 Indians 144.5 140 114.2 Forbes 149.7 134 95.4
Okay, again not a perfect match but the data are close (apart from the first year). There is actually one final and controversial source. In 2001, MLB released high level financials for all clubs in an attempt to convince the world that clubs were losing money hand over fist. Take a look at MLB versus Forbes for 2001.
Team MLB Forbes Variance Angels 92 103.0 11% Astros 125 125.0 0% Athletics 75 90.0 16% Blue Jays 78 91.0 14% Braves 147 160.0 8% Brewers 113 108.0 -5% Cardinals 132 123.0 -8% Cubs 130 131.0 1% Devil Rays 81 92.0 12% Diamondbacks 125 127.0 1% Dodgers 144 143.0 0% Expos 34 63 46% Giants 170 142.0 -20% Indians 162 150.0 -8% Mariners 202 166.0 -22% Marlins 61 81.0 25% Mets 183 169.0 -8% Orioles 128 133.0 4% Padres 80 92.0 13% Phillies 82 94.0 13% Pirates 109 108.0 -1% Rangers 135 134.0 -1% Red Sox 177 152.0 -16% Reds 71 87.0 19% Rockies 132 129.0 -2% Royals 64 85.0 25% Tigers 107 114.0 6% Twins 56 75 25% White Sox 112 101.0 -11% Yankees 242 215.0 -13%
We need to take a lot of these numbers a truckful of salt because the purpose of MLB releasing the data was to show how impoverished the teams were. That there is quite a lot commonality between the data-sets is good but there are a few disturbing discrepancies, particularly among the small market teams. Look at the Expos for instance. MLB pegged them at only $34 million revenue while Forbes had them double that.
What to make of all this?
It is difficult to say who is right but the fact there there are differences between Forbes and other, audited financial statements is a worry. Not that Forbes is wrong— we just don’t know. The only way to find out is to have unfettered access to the books of a baseball club and see for ourselves.
In sum, Forbes seems to do an okay job with its revenue estimates. In all fairness this shouldn’t be too difficult as revenue numbers are easy to sense check. All you need to know is attendance, a bit about the TV deal and the stadium situation. Unfortunately this doesn’t help us to explain away the difference between Forbes’ valuations and the recorded deal sizes. I think we need to park that in Donald Rumsfeld’s unknown unknowns box.
While we’re at it let’s have a quick look at how the Forbes data tallies on expenses as, if accurate, it will help us greatly later on. There are a couple of problems. First, there is a lot more scope for fudging these numbers, and second, it is much harder to get reliable data. To start with there is less obligation on clubs to release cost data, and also there is a lot more accounting leeway. Take a look at how Forbes stacks up against the audited data for the Brewers and Indians on expenses.
Expenses $m Year Brewers Forbes Year Indians Forbes 1994 -15.8 -12 1996 7.8 -6 1995 -12 1 1997 8.2 0.2 1996 -8.3 6.6 1998 12.7 -6.4 1997 -8.7 -4.8 1998 -2.2 -8.8 1999 -22.3 -14.4 2000 2 -1.6 2001 6.7 18.8 2002 30.4 -6.1 2003 2.2 5.1
Wow! Completely different. I won’t bother showing you the chasmic difference between the MLB and Forbes data for 2001. Given that the MLB data were designed to show the ill-health of team finances they are assuredly biased. There could be legitimate and auditable reasons for the discrepancies between Forbes’ data and that of the Indians and Brewers, but without understanding Forbes’ methodology I’m inclined to place more trust in the audited numbers.
Here lies a big problem. Forbes calculates values based on adjusted revenue multiples. Forbes’ data are okay but by no means perfect—the fact that the Forbes valuations don’t tie with sale prices tell us so!
Also we’ll learn in part 3 that revenue multiples aren’t a great valuation tool. The best way to determine value is to look at cash flows, specifically the present value of future cash flows. To do this we need to use profit but without accurate expense data it is impossible to do.
Placing full trust in the Forbes data is difficult. The numbers don’t match reality, the methodology is in question and the data it does have are disputable.
So, can we do any better ourselves? In part 3 I want to take a look at different valuation methodologies and work through an example using the most robust. Bring your calculators folks.
One more thing: Given that spring training is approaching and there are a ton of articles I need to do before then the next installment of this series; unfortunately won’t be much until April.