Harvard Law School 2012 Sports Law Symposium

As a fellow sports geek and the winner of the 2012 Sloan Sports “Fan’s Choice” award for most interesting research paper (going to try and change my middle name to that), I want to go on record endorsing this event. Friend of the site, Miles Wiley, will be organizing it on March 23 and the even promises to include such luminaries as David Fehr (former outside counsel to the NFLPA and NBAPA) Mike Zarren (a fellow stat geek and the assistant GM to your Boston Celtics) and discussion topics will range from the NFL, NBA, and MLB CBAs as well as the twin specters of concussion and PEDs that haunt modern American sports.

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Sloan Presentation – Effort vs. Concentration: The Asymmetric Impact of Pressure on NBA Preformance

I am very happy to announce that Justin and I have been invited back to the Sloan Sports Conference to present more of our analysis on the behavior and performance of NBA players. We encourage all of our loyal readers to head over to the ESPN poll and give us a vote as your favorite paper (we are at the bottom of the list). Last year we presented a general investigation into the shot selection of NBA players Allocative and Dynamic Efficiency in NBA Decision Making. I had a great time presenting at the conference and meeting my fellow researchers and basketball nerds last year am very excited to be returning this year.

This time around we decided to narrow our focus on one particularly interesting aspect of the NBA game, the clutch. It is received wisdom in NBA circles that the game of basketball is fundamentally different when it matters most. With everything on the line, defenses clamp down and previously free flowing offenses resort to less efficient isolation plays. Reputations of star players are made and broken in these moments and the titles of “clutch” and “choker” get thrown around based on a small sample of brilliant or embarrassing play.  As basketball fans, we too are drawn into the the same thrilling story lines of “stone cold killers” and shrinking giants. But, as economists, we are well aware of the inherent biases of human perception and wonder if there is a legitimate statistical case to be made about how players shrink from pressure or rise to the occasion when the stakes are high.

In our paper this year, Effort vs. Concentration: The Asymmetric Impact of Pressure on NBA Performance, we develop a continuous measure of the “pressure” or importance of any particular moment of a basketball game and see how player performance in two straightforward tasks (free throws and offensive rebounding) is effected by our metric.

We won’t bore you with the statistical methodology here, but the basic finding is that players feel the effects of pressure much more stridently when they are at home.  Perhaps motivated by a sympathetic audience that they don’t want to disappoint, home teams are spurred to greater heights of motivation and secure progressively higher fractions of available offensive rebounds as the pressure mounts.  However, the added motivation has a deleterious effect on their performance at the foul line.  Here, too much “self-focus” leads to a slight, but strongly statistically significant, decline in performance.  By contrast, the away team sees no decline in preformance at the foul line and actually shoots significantly better than the home team in high lerverage situations.  This, in spite of  occasionally epic efforts by home fans to distract them.

The “home choke” phenomenon we discuss is not obviously unique to basketball free throws and has also been argued to effect preformance in golf and soccer penalty kicks, but analysis in both these cases is encumbered by smaller sample sizes and messier assumptions

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Winner Takes All: A Basketball Prize Fight

The owners believe they are important.  They are betting that the NBA team is the brand that fans really care about and that the players have no other options to play compelling, profitable basketball.  With the lockout looming large and the fate of the NBA season in the balance, it is time to put their wager to the test.

We need a Basketball Prize Fight.  Kobe Bryant and Lebron James will pick 8-man squads and face off in a nationally televised 7-game series — the winning team takes home the grand prize of $80 million.  Kobe and Lebron choose their squads playground style, in a nationally televised draft event.  First pick is awarded by a coin flip and the draft order proceeds in a 1-2-2 fashion until each team has 8 players.  Prior to the draft Kobe and Lebron could each submit a list of 20 players of interest who’s eligibility for the contest could be confirmed.  A coaching staff could be chosen (or not) at the captains’ discretion, and the teams would have a few weeks to practice.

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Why Better Teams Lose

Matt once told me that “the biggest fallacy in sports is that the better team will win.”  This is an interesting statement, especially in light of the unexpected results from the early rounds of the NBA Playoffs this year.  Matt’s statement can be understood in two ways.

First, variance is king.  The idea that the winner of a 7-game playoff series is better than the loser either ignores or significantly discounts the role of variance in the outcome of that series.  The Lakers won it all last year, but do you remember how close the Finals were?  When a few inches could have changed the outcome does it really make sense to say the Lakers were somehow better than the Celtics based on the outcome?  If you think the Lakers were actually better, would you feel differently if the ball had bounced the Celtics way and the Lakers had lost?

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A Collective Bargaining Puzzle

A Collective Bargaining Puzzle

With the NFL in the midst of a labor standoff and a similar dispute looming in the NBA, an interesting question recently occurred to us: why don’t the Owners offer to double the League minimum salary in return for a hard cap on max contracts at a substantial discount to what the highest paid players are currently making? Since there are only a few players earning huge contracts and the vast majority of players earn the minimum salary or slightly more, the Owners could easily set the new minimum and maximum figures in a way that would save them a lot of money and would be beneficial to the majority of the players.

For example, consider a hypothetical league with 100 players. 60 players are making $10, and 40 players are making $100. The players are making $4,600 collectively. If an agreement was reached where the minimum salary was doubled but there was a hard max salary cap at $50, you’d have 60 players making $20 and 40 players making $50. The players are now making $3,200 collectively. The owners are saving $1,400 (30%) and the majority of players (the 60 low earners) just got their salaries doubled. The owners would all vote for this plan, as would the 60 low-earning players. So as long as the players’ union has a majority voting rule we would expect to see this bargain agreed upon. You could take this to the extreme and have the new agreement stipulate a single $11 contract price for all players. The majority of players would still be better off and the Owners would save even more money.

So why don’t we see an agreement like this coming forth?

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Building Bayesian Plus-Minus (Part 1): APM and SPM estimates 2007-11

Adjusted Plus-Minus is the only well-known statistical method of evaluating players that even attempts to capture aspects of player value that are not associated with the accumulation of traditional box-score statistics. It does this by attempting to quantify the actual impact that the presence of each player on the court has on the average number of points a team gets and gives up. And unlike unadjusted +/-, it does so via a regression analysis which controls for all the other teammates and opponents on the court by estimating the following equation.

Points = \alpha Home + \sum_{i \in Off} \beta_{i,O} + \sum_{i \in Def} \beta_{i,D} + \epsilon

where Points represents the number of points scored on any given possession and Off and Def represent the set of players on offense and defense during that possession. Thus, \beta_{i,O} and \beta_{i,D} represent the average impact that each player i‘s presence on the court has on his team’s offensive and defensive performance

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Everything you need to know about the NFL Lockout

Basics of the Dispute

In 1956 the NFL players formed a union: the National Football League Players Association (NFLPA).  In 1968 the NFLPA entered into its first collective bargaining agreement (CBA) with the owners of all the NFL teams, and there has been a CBA (in various iterations) between the NFLPA and the League Owners ever since.  The NFL Owners, in collectively bargaining with the NFLPA, are essentially acting as a single firm, though this actually makes them a cartel since they are independently owned franchises.

A CBA, generally, is an agreement between an employer and a union of employees that specifies contract terms between the employer and each of the union employees.  Using the leverage of their union, employees negotiate with the employer and attempt to extract the best possible terms for the union employees.  These terms are then codified in a CBA that binds the employer and the union members.  A standard CBA will have a variety of industry specific provisions in addition to general things like employee wages, benefits, and insurance.

The most recent NFL CBA was agreed upon by the NFLPA and the Owners in 1993, it was amended in 1998, and again in 2006.  The CBA was scheduled to expire in 2012, but in 2008 the owners opted out, choosing to let it expire at the end of the 2010 season.  This CBA had five key provisions: 1) minimum player salaries, 2) draft rules, 3) free agency rules, 4) waiver rules, and 5) the all-important Salary Cap.

The real conflict between the Owners and the players is all about one thing: revenue sharing

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Parity in the NBA

In 2007, Kevin Garnett, Paul Pierce, and Ray Allen teamed up in Boston and Pau Gasol arrived in L.A. where he joined Kobe Bryant.  In the three years since that blockbuster offseason, the Celtics and the Lakers have combined for all three NBA Championships, and only one other team has even appeared in the finals (the Magic in 2009).  While 3 seasons is a pretty small sample size, I don’t think anyone will dispute that when the best players in the league get together on the same teams, those teams are likely to be successful.  Then came “the Decision,” thrusting this question to the forefront of NBA discourse: is this really good for the League?

A common criticism is that when star players cluster together on a few major market teams it weakens competition which is bad for the NBA.  To evaluate this criticism empirically, you need some sense of how competitive the League is today and how the current level of parity in the league stacks up historically.  This is pretty easy to look at.  We took the win/loss records of all the teams for the past 40 NBA seasons and simply looked at the standard deviation of the number of games won during each season.  This is basically just the average deviation from the mean, which is 41 games.  The higher the standard deviation in a given season, the more uneven the teams were in that season, or, equivalently, a higher standard deviation corresponds with lower League parity.  Here are our results…

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Small N Decisions and the Data Deluge

I was recently asked in a podcast, “what is the next generation in statistics?” My answer is it will all be about N.

In statistics, N is the sample size. The whole “advanced stats” debate in basketball and other sports seems focused not on N, but on which stats best quantify player value. This is important, don’t get me wrong, but it’s more important from a contracting perspective than for actively making coaching decisions. The reason is that nearly all of these advanced stats need a large sample size, large N, to function properly. In Matt and I’s paper that he has discussed heavily on this blog, the same is true. For line-ups that have less than say 4,000 possessions together, we are wary to make any strong claims about efficiency. Now show me the coach that can wait 4,000 possessions to make a decision and I’ll show you a guy who has a job at FIU.
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Marginal Shots and Usage (Skill) Curves

If you have been reading some of our other posts (this is post 3 in our history :) ), you know that our entire analysis of optimal shot selection hinges on our ability to infer a particular player’s “marginal shot” in each period of the shot clock. This is not trivial. It is really easy to measure a player’s average efficiency, but players do not tell us which of their shots they just barely decided to shoot so that we can tell them if those were good or bad shots.

Our approach to this difficulty is to infer the value of these “marginal” scoring opportunities by comparing player performance at different points in the shot clock. The actual technique we use is called Maximum Likelihood and is quite complicated because it has to deal optimally with the limited amount of data we have. However, the intuition behind its performance is pretty straightforward.

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Posted in Basketball, Uncategorized | 12 Comments