Category Archives: Arturo

Jeremy Lin and the Ghost of NBA Draft’s Past

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“Science is the belief in the ignorance of experts.”
― Richard P. Feynman

Yes, before you ask, as is contractually required by any and all bloggers I will be talking about the unlikely Jeremy Lin. Now, I know we touched on this yesterday but our goal today is different. My take will be different. You see rather than waxing poetic about the unbelievable and unpredictable nature of basketball or focusing on how no one could have seen this coming, I’m going to focus on how we kind of did.

Because when faced with a supposedly unsolvable problem, we brought the science and science once again beat the experts.

The problem I’m alluding to is evaluating talent in the NBA draft. Anyone who knows me knows I love to write about the draft. For those who don’t, hello you must be new here. Just in case, let me illustrate that by throwing some links up for your viewing pleasure.

This lead to a lengthy draft strategy segment in my guide to running an NBA franchise (Build me a winner rev.2).

The key takeaway was that talent was that I needed to build an effective draft model to predict player performance based on publicly available data. I built two (go here for the model build parts 1 & part 2 ). In very general terms the models use the available data to predict future performance for each player coming into the draft from college. Based on that prediction a ranking is done and a draft recommendation is generated.

Now this model is a work in process, I build it then publish it then go back at some future point to review to see if it worked. I will make corrections as needed over time.

One of the key ideas is having a public build to allow for peer review and answer the skeptics.

For the purposes of this discussion for example I will focus on the last 2010 build (see here) because at the request of some of our loyal readers I had included the best undrafted rookies. Can you guess who was number one?

Do you want to answer for the class?

Mr. Lin actually was the number tenth overall ranked prospect on our draft board and easily the best undrafted. The model had him slightly below the draft treshold. Given this and a few other similar data points, I moved the treshold slightly down  to .090 WP48 for Model #1 and .060 WP48 for model #2. You will see the results of that in the numbers that follow.

Why should you care exactly?

It’ll make more sense if I just give you the full story:

That’s every drafted player coming from the NCAA’s from 1995 thru 2010 who’s played at least 400 minutes in the NBA (2010 shows additional players who haven’t played those minutes yet). It shows the player’s draft year, where he was picked, the model predictions and the player actuals for his first 4 years and his career. For 2010 for example, we can see both the Knicks starting guards in the top 10 but this could simply be coincidence. Did the models actually do anything?

A simple test is to look at correlation between the place the player was picked, where the models suggested picking him and actual rank by draft in terms of production. Draft order vs production shows minimal correlation with an R-square of about 5%. It jumps to 25% for the predicted production rank.

A more complex and interesting test is to look at:

  • The probability of landing a better than average player (>.090 WP48)
  • The probability of landing a good player (>.150 WP48)

If I do this for all picks by the Models as well as  all draft picks and Model picks taken after the top 5  picks I get:

The models perform as well or better than the majority of lottery picks.  The only real difference is superstar talent at the number one pick (which isn’t really an every year affair).

So to review, using publicly available data we built a model that picks draft winners at a 75% rate which is better in general than having the #1 pick in the draft and big winners at a 40% rate which is better than everything but the #1 pick.

Science!

-Arturo

P.S. How about one more bonus table?

 

 

Arturos’ Awesome Primer: Everything you need to know about the 2011-2012 NBA Season

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“If you thought that science was certain – well, that is just an error on your part.”
— Richard P. Feynman

Well, it’s been an interesting season so far. Teams bubbling up. Teams crashing down. As always, it’s human nature to rush off and make dramatic pronouncements particularly when you want to tell a good story.

Reality is a good deal more complicated than that.

The  law of large numbers (LLN) describes the result of performing the same experiment a large number of times. It’s a simple enough theorem, the average of results obtained from a large sample (or number of trials) will get closer and closer to the real value of something the larger the sample. Conversely, the error (or more accurately  the possibility of it) gets larger and larger the smaller the sample . What does this mean?

Let’s not get ahead of ourselves. Rushing to judgement based on a small sample is premature. A larger sample size is called for before we can make any definitive conclusions.

We can however use what we know to maximize what we can actually learn from the current sample. And that is precisely what I’ve been doing with my time.

We are going to have some fun today.

Because, today is the day when we I put it together all that I’ve learned about point margins, the homecourt advantage, strength of schedule and give you Team Rankings.

Let’s start with recapping what we know.

Warning: Science ahead!

Point Margin (The Win Cheat Sheet and Point Margin Produced Rev 1.1 (originally seen Here))

I’ve previously shown that on a game to game basis Wins produced correlates at a 99.8% with point margin (Point Margin for a game = 0.0377 + 15.5 Wins Produced for that game) and for the season a 95% correlation has been shown repeatedly (the difference is down to blowouts).The gist of it is that Wins Produced for a team correlates to a teams average point margin which correlates strongly with games won.

Do some additional maths and you can come up with some nice and nifty equations:

Expected Avg Point Margin for Team (season) = 31*(Wins Produced (team for the season) -41 )/82

Wins Produced (team for the season) = (Expected Avg Point Margin for Team (season)*82)/31 +41

Team Win % = Team WP48= (Avg Point Margin for Team (season))/31 +.500

Wins Produced (team for the season) = (Expected Avg Point Margin for Team (season)*82)/31 +41

And

+1 Points per game = 2.645 wins over .500 (43.645 wins)

+3.1 Points per game = 10% increase in Winning %

+10 Points per game= 26.45 wins over .500 (67.45 wins)

+1 WP = +.378 Points per game

+10 WP = +3.78 Points per game

And for Players:

Point Margin Produced per 48= (WP48-.099)* 31.1

Homecourt Advantage (The Unfair Advantage (originally seen Here))

The basic equation goes something like this:

Probability of Home team winning a game (Win %)

= (Projected Wins Home Team-Projected Wins Road Team)/82 +.606

=Win %: (Proj. Home Team Win% – Proj. Road Team Win%) +Homecourt Advantage(.606)

This is the simple equation I came up with for the home team winning a single game. The base assumption being that based on the data set (all regular season games from 1999 thru 2008 ) the home team wins 60.6% of time) and this was good and worked fairly well. As I got older and wiser (or at least more creaky), I then decided to add some more factors in:

  • Add in the effect of rest days and back to backs.
  • Add in the effect of altitude

I did some maths and figured the homecourt advantage in each scenario over playing at a neutral site. For this post I went even further and figured out the value of that advantage in points (using the handy-dandy equations in the previous section):

In summary, both, altitude and rest days affect the Homecourt advantage (HCA) and they interact with one another. Average HCA is at 59.9%. Altitude is directly proportional to HCA. Rest days are a little stranger. Altitude directly interacts with rest. Denver and Utah kill teams at home if they have a rest edge but they get killed themselves if the other team is coming in with at least a two day rest edge.

It just so happens that this kinda adds up. Apply that to a regular season played by identical clones and you get:

So if I assume all teams are equal, Utah and Denver both get a 10% boost in winning percentage when they play at home. This is good for four extra wins a season versus the average.

Strength of Schedule

Simple logic here. The Washington Wizards (hi Ted!!) are not the Miami Heat, or even the Chicago Bulls, or the Sixers for that matter. Wait, I’m getting a little ahead of my self.

All wins are not created equal. Opponents matter. We will account for that. A typical NBA schedule (I used 2010 here) confers Home court as follows over the course of a whole season:

So Utah and Denver get a four point edge over the Lakers,Clippers,Mavs, Rockets,Wizards, Warriors and Celts. This advantage goes away for the most part in the playoffs.

Some of those playoff losses make a wee bit more sense

The Rankings as of 1/11/2012

So, Point Margin, check, Homecourt check, Strength of schedule check. Are we missing anything?

Of course, the game data. God Bless Basketball Reference.

Now let’s put this all together an make a ranking. I will wok out the following numbers:

  • Point Margin per Game: Pts scored by team -Pts scored by opponent divided by games played
  • Home court Point Margin per Game: Point Margin per game due to the schedule and homecourt advantage.
  • Adjusted Point Margin per Game: Point Margin per Game -Home court Point Margin per Game. Schedule independent point margin (neutral site at sea level)
  • Adjusted Opponent Point Margin: The average Point Margin per Game of a teams opponents.
  • Real Point Margin (RPM): Point Margin per Game -Home court Point Margin per Game +Adjusted Opponent Point Margin. Expected Point Margin at a neutral site against perfectly average opposition. This is the Number I use to rank.
  • Neutral Site Win % : RPM/31 + .500

This is meant as a measure of just how strong each team projects based on the data of the season to date . We still need to account for injuries and incorporate what we know of player historical performance. We will address this in a, say it with me, future post.

A few notes:

  • The Sixers look totally legit by any definition. They in fact form a tight group of three (Philly, Chicago and Miami) at the top that must be considered the favorites for the title at this point.
  • Atlanta is the only eastern team in the 2nd tier with Portland, the Lakers, Clippers, Nuggets and Thunder in a logjam out west. Denver, with their unfair advantage, has a stellar shot at the #1 seed out west (barring acts of god or George Karl).
  • The bottom of the East is putrid. Memphis, the second worse team in the West would be the 7th in the East.
  • Some playoff teams from 2011 that look cooked: Hornets, Grizzlies and my beloved Celts.

And before we go, let’s attempt to add the effect of schedule back in to the equation:

As always, the schedule breaks greatly in favor of the Nuggets who look good for the #1 seed in the West. Keep in mind I’m using older schedules for this. Special accounting will have to be done for this season.

In the future, of course. The very uncertain future.

A future where this just might be a critical playoff matchup.

-Arturo

The 2011-2012 NBA Wins Produced Cheat Sheet

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I was born not knowing and have had only a little time to change that here and there.

Richard Feynman, Letter to Armando Garcia J, December 11, 1985

The New Kings

At the end of last season, I was arrogant. I was at the top of my game. I felt I had an answer or a model for every possible scenario the NBA could throw at me.

Then everything changed.

First, we didn’t know if we would have a season.Then we didn’t know where Chris Paul and Dwight Howard would end up. We still don’t know who’s going to be playing where exactly. Never mind the fact that the schedule for the season is so strange that Vegas refused to post over/unders for wins for the season.

But I wasn’t worried, All I had to do was adapt all my existing models slightly to account for the variation right?

Yeah, about that…

One of the projects that we had been working on is on improving the Wins Produced model . The Lockout gave us a great opportunity to do so and we did. This is a very good thing.

Except that it means that the world changed. Up is not quite down but it’s not exactly up anymore. The value and contribution of each player has changed and it’s going to take a while to adjust to the new reality.

It’s easy for anyone to be confused.

Luckily, I’m here to help you with that and I’ve been in the lab working on the problem. To start, I decided to come up with a nice little cheat sheet for the New Wins Produced model. It features every veteran currently on an active roster and it’s sorted by team and by the players Wins Produced for the 2010-2011 season. It has basic information for each player, totals for the last five years, each players average year and the numbers for the 2010-11 season.

Hopefully, that will help everyone involved.

-Arturo

The Bottom Line on the NBA Finances

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Arturo Galletti is the Co-editor and Director of Analytics for the Wages of Wins Network. He is an Electrical Engineer with General Electric on the lovely isle of Puerto Rico, where he keeps his production lines running by day and night (and weekends) and works on sport analysis with his free time.

Damn it all,  the angel of Stern has struck, throwing gasoline on a fire, and so the season hangs by a thread. We really are so close to where we need to be to make it happen. I was not a happy camper, but I thought to myself: what else is a mathematically inclined and bored NBA fan left with? What is left for me but to wait?

Thankfully, inspiration struck (like a podcast you might say). What if I took a stab at laying out the finances of every team? Would that level the playing field enough to get this deal to happen? Then again, I might just make everything worse.

Let’s start with the gate & concessions. For that I needed a few pieces of information:

  • Attendance numbers for the 2010-11 NBA Season
  • Information on the average amount of money spent on concessions.  That study in particular comes up with something called:
    • The Fan Cost Index™ which comprises the prices of:
      • four (4) average-price tickets
      • two (2) small draft beers
      • four (4) small soft drinks
      • four (4) regular-size hot dogs
      • parking for one (1) car
      • two (2) game programs
      • two (2) least-expensive, adult-size adjustable caps.

If I take the attendance figures (divided by four) and multiply them by the The Fan Cost Index™ I get:

The top five teams in the league (Lakers, Knicks, Bulls,  Heat, and Celtics) make three times as much money at the gate as the bottom two teams (Grizzlies and Pacers).  Thirteen teams make twice as much as the bottom two. A smart person might even ask: why only one team in New York (right Mikhail?). This is what the media refers to when they talk about small versus large markets, and this is before we get to the TV money (national and local) and all the other incomes.

Now comes the first tricky bit. You see, while the national tv money is easy to determine, the local tv money and the division of other income is harder to come by. Of course, like any good engineer, I figured out a good workaround:

That is the pricing for NBA tickets from the secondary ticket markets. This is publicly available data (The internet is a wonderful thing). Using that data as a guideline and what I know about the NBA’s finances I came up with this:


That is my estimate for each team’s Net Operating Income (NOI), or Basketball Related Income (BRI) as it’s become known, as well as team valuations. A couple of quick notes here:

  • The share of the national TV contract is not quite split equally amongst the 3o teams. All 26 non-ABA teams get 1/30th of the money. The owners of the Spirits of St. Louis get a 1/28 th share (go here for full detail) and each former ABA team gets a 1/30th share minus a fourth of that Spirits share.
  • I estimated Team valuations at 2.8 times the NOI (BRI) for each team. I am not including other assets such as stadia. For a fuller estimate go here. You’ll note that I am not that far off.

With the sharing from the national TV contract included, the previously noted disparity is somewhat reduced (the Lakers and Knicks only make 2.5 times as much money as the Pacers and T-Wolves, not 3 times :-)), but it’s not quite enough. The problem is that the gate and concessions for the larger market teams are on par with the total income for the small market teams. There can be no real parity until there is actual revenue sharing of the gate at least (the NFL has a 60/40 Home/Road split). A good source for more info on this is here.

That takes care of direct incomes, so let’s get to the bone of contention. Let’s talk player salaries. Again, I needed some sources of information:

  • All salary data is from here. Thanks to ShamSports. I’ll do something fun with it later.
  • Salary Cap/Luxury cap information from the inimitable Larry Coon.

Remember how everyone talks about the 57% Share of BRI for the Players?

That’s correct for the entire league. It’s incorrect for individual teams, and therein lies the problem. Twelve teams have player salaries at less than 57% of their BRI (including the Knicks, Bulls and Heat, yes those Heat, at less than 40% ). The other eighteen are not so lucky.  There are in fact four teams at 80% or greater.

Let’s add in expenses and get to what the league is claiming as their bottom line (backed by their tax returns). (Note: I used $266 as the loss number since the closest I could find for a number was about $300 million).

Those are the league’s claims — near as I can figure it — in technicolor. Add in playoff revenues and about 19 of those teams claimed losses on their tax returns.

There are two very important things missing:

  • The Tax Break: the tax break in question is the Roster Depreciation Allowance (RDA -see here). To put it very simply, the RDA allows you to claim the value of your franchise as a loss in your books over a period of 15 years and in essence save 35% of that amount on your tax returns (this is known as the 15/100 Rule of Thumb [see here for more detail]). You can claim that loss on whatever schedule you like. You want to claim 90% in Year 5? Go right ahead. No loss claim in Year 11? Good for you. For this estimate, I’m assuming 1/15 th of the value of each franchise is available to be claimed and the value has to be multiplied by 1.35 (1 for the loss, .35 for the tax break).
  • The increase in franchise value: The average value of an NBA franchise has increased 78% since 2000 (see page 26 of this report). I’m going to use 4% as my value increase number to be nice.

When I add those in we get:

That looks closer to the truth. I know that not every team is claiming the RDA, but that’s not the player’s fault.  The bottom five teams come out as losers for their owners on the bottom line. For the most part this is is a function of location (7 of the bottom 10 are in my list of franchises in overextended markets), which again is not the player’s fault.

Tomorrow we’ll get into how these numbers will look with the various deals being thrown around during the lockout. I hope you can wait that long!

-Arturo

Disclaimer: I do not have access to the NBA’s books. Everything in this article is put together from public statements or logical inferences. I do not claim to have this perfectly right. However, I do feel like I am in the ballpark. The sources I used are listed, please update me if there is a better location to get my data.

Could Your City Give a Sports Team a Good Home?

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Arturo Galletti is the Co-editor and Director of Analytics for the Wages of Wins Network. He is an Electrical Engineer with General Electric in the lovely isle of Puerto Rico, where he keeps his production lines running by day and night (and weekends) and works on sport analysis with his free time.

“In the middle of difficulty lies opportunity.”

-Albert Einstein

Can your city Support a new Professional Sports Team?

Let’s start with the graphical answer.

What inspired this particular graph? A few posts back, fellow WOW writer Devin Dignam identified 6 cities that  are very difficult markets for their NBA teams. I liked this post a great deal. So much so, that I decided to extend it and flip it around. Rather than ask who shouldn’t have a team, I decided to ask who should.

In my previous post on the state of the lockout, I pointed out that  NBA bemoans its economic situation and shrinking profits but their approach at solving the problem is fairly narrow.

The profit equation for any business is simple : Profit = Revenue-Costs. The NBA is focusing largely on cost-cutting (and mostly on the player side) and are leaving a humongous opportunity on the table to increase revenues.

It all comes back to a simple question: Which cities can best support an NBA team? Or for that matter, any professional (MLB,NFL,NBA or MLS) sports team ?

If the NBA can properly answer this question, they can go from worrying about shrinking profits and contraction to talking about record revenues and expansion.

Let’s see if we can help them out (while answering the broader question).

Before we get to the good stuff, I need to get some explanations of terms and links out of the way:

  • The work done in this piece draws a lot from the fantastic work by The Business Journals site in their OnNumbers section. Particularly their pieces on Sports Capacity , Overextended Sports Markets, Viable NFL, MLB and NBA markets  as well as the link to Metropolitan Areas Total Personal Income for 2010.
  • The data for this piece is a drawn from the US Department of Commerce (for US markets), OnNumbers estimates for Canadian Markets, and the Goverment of Puerto Rico.
  • “Pro teams”: the number of NFL, MLB, NBA, NHL, and MLS teams in a city. The cost of adequately supporting a franchise was estimated by OnNumbers  to be:
    • $85.4 billion for MLB
    • $37.6 billion for the NHL
    • $36.7 billion for the NFL
    • $34.2 billion for the NBA
    • $15.4 billion for MLS.
  • “Total personal income(TPI)”: the sum of all money earned by all residents of an area in a given year. Using team revenue data and average ticket prices one can calculate amount of TPI needed to adequately support a team in each north american professional sports league.
  • “Available personal income(API)”: simply TPI less the cost it takes to support the city’s pro teams. If API is positive, it means that you are good to go for a franchise. If API is negative, then you really need to figure out where you are going to move your team. Only teams in the NFL, MLB, NHL, NBA and MLS are counted for this calculation.

All that nice data is here as a google doc .

Let’s start with the negatives first. Which markets cannot support the teams they have?


There are 19 cities that qualify as overextended. The table ranks each city in terms of the gap in income to support the franchise as a percent of the Total income for the metro Area. To me, this is an important distinction because it allows to identifies markets like Indianapolis, Charlotte, San Francisco, Detroit and Salt Lake (15 thru 19) that could very well be able to support the current franchise load if they achieve some measure of economic growth in the next few years.

Milwaukee comes out on top of my rankings because I am assigning them a 70% share in supporting the Green Bay Packers.  The cities in the top three (Milwaukee, Cleveland and Denver) reveal an interesting problem: as it decreases in popularity, Major League Baseball is going to find it increasingly difficult  to support the current franchise load. Eight of the ten teams in the top ten field MLB franchises and would clear their gap by losing those teams.

For the non MLB cities on the list:

  • Buffalo is already in an NFL timeshare with Toronto.
  • New Orleans had the NBA repossess their team.
  • Winnepeg gets the benefit of the doubt in hockey-mad Canada.
  • Nashville is a victim of NHL overexpansion and can be addresed by a quick relocation

The NBA has 11 teams on that list. They break out as follows:

  • Victims of a bad economy: Pacers, Bobcats,Warriors,Pistons, Jazz.
  • Cities with a johnny-come-lately MLB expansion problem: Denver, Phoenix.
  • Candidates for relocation: Bucks, Cavs, Hornets and Timberwolves.

The NBA needs to be thinking about finding markets for these four teams. Let’s identify the candidates.

There are 37 markets in the US and Canada that could support a new or an additional NBA Team. New York and LA lead the list but already carry two franchises each, so let’s leave them as backup options. Of all the candidates with an NBA team already, I am only going to consider Chicago. That gives us:


That’s 23 markets available for expansion (including Puerto Rico yay!) plus a second Chicago team as number 24. The most logical candidates to me would be: Chicago, Riverside-San Bernardino-Ontario (the Inland Empire), Bridgeport-Stamford-Norwalk or a general Connecticut team, Vegas, and Montreal as fallback option. So one option could be for the NBA could “encourage” their owners to say:

  • Move the Bucks to Chicago
  • Move the Cavs to Conneticut
  • Move the Hornets to Vegas
  • Move the T-Wolves to the Inland Empire

Every single one of those owners would see their franchise value significantly accrue  from such a move. Each one would be a trade up in terms of market size and income available. An additional plus is that 3 of those markets would be NBA only and new markets for the league.

Plus I can see the jerseys now for the Empire team:

Those are not the balls you're looking for

Sometimes, these things just write themselves.

-Arturo